{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "editable": true }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import datetime as dt\n", "\n", "pd.set_option('display.max_rows', 16)\n", "\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "plt.rcParams['figure.figsize'] = (16.0, 9.0)\n", "import seaborn as sns\n", "\n", "import statsmodels.api as sm\n", "from sklearn.linear_model import LinearRegression\n", "\n", "import gc" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "plt.rcParams['figure.figsize'] = (16.0, 9.0)" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "# 数据处理" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "## 财务数据" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "财务数据处理的难点在于“报表数据所处的时间”、“报表报告的时间”、“报表修改时间”带来的复杂性。两种处理方式比较合理：\n", "1. 预留充足的时间以便在使用报表数据的时间点上，报表数据是可用的（但不一定是最新的）\n", "2. 无论在哪个时间点上使用报表数据，都只用最新的数据(point-in-time)" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "## 交易数据" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "### 停牌" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "- 停牌在某些时候是可以不处理的，比如计算动量的时候，停牌之后的价格和停牌前的价格计算收益率，可以作为动量的一种衡量\n", "- 但在有的时候，停牌不处理可能会有问题。\n", " - 比如计算beta，市场收益率每个交易日都是有的，但个股停牌的时候没有，此时如果设为0，直接回归会有大的偏差\n", " - 另外比如计算波动率，如果设为0，也有问题\n", " - 从收益率的角度看，如果我们关注点是月收益率，也应当去掉，因为停牌的股票无法交易，也无法调仓\n", "- 我们把停牌超过一个月的观测值删去" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "# Data" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "editable": true }, "outputs": [], "source": [ "START = '2007-01-01'\n", "END = '2022-03-31'" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "editable": true }, "outputs": [], "source": [ "# Security Id\n", "stk_info = DataAPI.SecIDGet(assetClass=\"E\",pandas=\"1\")\n", "cond1 = (stk_info['exchangeCD'] == 'XSHE') | (stk_info['exchangeCD'] == 'XSHG')\n", "cond2 = (stk_info['listStatusCD'] == 'L') | (stk_info['listStatusCD'] == 'DE')\n", "stk_info = stk_info[cond1 & cond2].copy()\n", "stk_id = stk_info['secID']" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDtickersecShortNamecnSpellexchangeCDassetClasslistStatusCDlistDatetransCurrCDISINpartyIDdelistDate
0000001.XSHE000001平安银行PAYHXSHEEL1991-04-03CNYCNE0000000402.0NaN
1000002.XSHE000002万科AWKAXSHEEL1991-01-29CNYCNE0000000T23.0NaN
2000003.XSHE000003PT金田APTJTAXSHEEDE1991-07-03CNYCNE1000031Y54.02002-06-14
3000004.XSHE000004国华网安GHWAXSHEEL1991-01-14CNYCNE0000000Y25.0NaN
4000005.XSHE000005ST星源STXYXSHEEL1990-12-10CNYCNE0000001L76.0NaN
5000006.XSHE000006深振业ASZYAXSHEEL1992-04-27CNYCNE0000001647.0NaN
6000007.XSHE000007*ST全新*STQXXSHEEL1992-04-13CNYCNE0000000P08.0NaN
7000008.XSHE000008神州高铁SZGTXSHEEL1992-05-07CNYCNE0000001C69.0NaN
.......................................
24126900950.XSHG900950新城B股XCBGXSHGEDE1997-10-16USDCNE000000TH11429.02015-11-23
24127900951.XSHG900951退市大化TSDHXSHGEDE1997-10-21USDCNE000000TJ71430.02020-08-27
24128900952.XSHG900952锦港B股JGBGXSHGEL1998-05-19USDCNE000000W88763.0NaN
24129900953.XSHG900953凯马BKMBXSHGEL1998-06-24USDCNE000000WP81431.0NaN
24130900955.XSHG900955*ST海创B*STHCBXSHGEL1999-01-18USDCNE000000YC21063.0NaN
24131900956.XSHG900956东贝B股DBBGXSHGEDE1999-07-15USDCNE000000ZS51432.02020-11-23
24132900957.XSHG900957凌云B股LYBGXSHGEL2000-07-28USDCNE0000013W91433.0NaN
28065DY600018.XSHGDY600018上港集箱SGJXXSHGEDE2000-07-19CNYNaN618.02006-10-20
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4923 rows × 12 columns

\n", "
" ], "text/plain": [ " secID ticker secShortName cnSpell exchangeCD assetClass \\\n", "0 000001.XSHE 000001 平安银行 PAYH XSHE E \n", "1 000002.XSHE 000002 万科A WKA XSHE E \n", "2 000003.XSHE 000003 PT金田A PTJTA XSHE E \n", "3 000004.XSHE 000004 国华网安 GHWA XSHE E \n", "4 000005.XSHE 000005 ST星源 STXY XSHE E \n", "5 000006.XSHE 000006 深振业A SZYA XSHE E \n", "6 000007.XSHE 000007 *ST全新 *STQX XSHE E \n", "7 000008.XSHE 000008 神州高铁 SZGT XSHE E \n", "... ... ... ... ... ... ... \n", "24126 900950.XSHG 900950 新城B股 XCBG XSHG E \n", "24127 900951.XSHG 900951 退市大化 TSDH XSHG E \n", "24128 900952.XSHG 900952 锦港B股 JGBG XSHG E \n", "24129 900953.XSHG 900953 凯马B KMB XSHG E \n", "24130 900955.XSHG 900955 *ST海创B *STHCB XSHG E \n", "24131 900956.XSHG 900956 东贝B股 DBBG XSHG E \n", "24132 900957.XSHG 900957 凌云B股 LYBG XSHG E \n", "28065 DY600018.XSHG DY600018 上港集箱 SGJX XSHG E \n", "\n", " listStatusCD listDate transCurrCD ISIN partyID delistDate \n", "0 L 1991-04-03 CNY CNE000000040 2.0 NaN \n", "1 L 1991-01-29 CNY CNE0000000T2 3.0 NaN \n", "2 DE 1991-07-03 CNY CNE1000031Y5 4.0 2002-06-14 \n", "3 L 1991-01-14 CNY CNE0000000Y2 5.0 NaN \n", "4 L 1990-12-10 CNY CNE0000001L7 6.0 NaN \n", "5 L 1992-04-27 CNY CNE000000164 7.0 NaN \n", "6 L 1992-04-13 CNY CNE0000000P0 8.0 NaN \n", "7 L 1992-05-07 CNY CNE0000001C6 9.0 NaN \n", "... ... ... ... ... ... ... \n", "24126 DE 1997-10-16 USD CNE000000TH1 1429.0 2015-11-23 \n", "24127 DE 1997-10-21 USD CNE000000TJ7 1430.0 2020-08-27 \n", "24128 L 1998-05-19 USD CNE000000W88 763.0 NaN \n", "24129 L 1998-06-24 USD CNE000000WP8 1431.0 NaN \n", "24130 L 1999-01-18 USD CNE000000YC2 1063.0 NaN \n", "24131 DE 1999-07-15 USD CNE000000ZS5 1432.0 2020-11-23 \n", "24132 L 2000-07-28 USD CNE0000013W9 1433.0 NaN \n", "28065 DE 2000-07-19 CNY NaN 618.0 2006-10-20 \n", "\n", "[4923 rows x 12 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_info" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "## ST" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "editable": true }, "outputs": [], "source": [ "st_df = DataAPI.SecSTGet(beginDate=START,endDate=END,secID=stk_id,field=['secID','tradeDate','STflg'],pandas=\"1\")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 513065 entries, 0 to 513064\n", "Data columns (total 3 columns):\n", "secID 513065 non-null object\n", "tradeDate 513065 non-null object\n", "STflg 513065 non-null object\n", "dtypes: object(3)\n", "memory usage: 11.7+ MB\n" ] } ], "source": [ "st_df.info()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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secIDtradeDateSTflg
0000001.XSHE2007-01-04S
1000001.XSHE2007-01-05S
2000001.XSHE2007-01-08S
3000001.XSHE2007-01-09S
4000001.XSHE2007-01-10S
5000001.XSHE2007-01-11S
6000001.XSHE2007-01-12S
7000001.XSHE2007-01-15S
............
513057900955.XSHG2022-03-04*ST
513058900955.XSHG2022-03-07*ST
513059900955.XSHG2022-03-08*ST
513060900955.XSHG2022-03-09*ST
513061900955.XSHG2022-03-10*ST
513062900955.XSHG2022-03-11*ST
513063900955.XSHG2022-03-14*ST
513064900955.XSHG2022-03-15*ST
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513065 rows × 3 columns

\n", "
" ], "text/plain": [ " secID tradeDate STflg\n", "0 000001.XSHE 2007-01-04 S\n", "1 000001.XSHE 2007-01-05 S\n", "2 000001.XSHE 2007-01-08 S\n", "3 000001.XSHE 2007-01-09 S\n", "4 000001.XSHE 2007-01-10 S\n", "5 000001.XSHE 2007-01-11 S\n", "6 000001.XSHE 2007-01-12 S\n", "7 000001.XSHE 2007-01-15 S\n", "... ... ... ...\n", "513057 900955.XSHG 2022-03-04 *ST\n", "513058 900955.XSHG 2022-03-07 *ST\n", "513059 900955.XSHG 2022-03-08 *ST\n", "513060 900955.XSHG 2022-03-09 *ST\n", "513061 900955.XSHG 2022-03-10 *ST\n", "513062 900955.XSHG 2022-03-11 *ST\n", "513063 900955.XSHG 2022-03-14 *ST\n", "513064 900955.XSHG 2022-03-15 *ST\n", "\n", "[513065 rows x 3 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "st_df" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "st_df['tradeDate'] = pd.to_datetime(st_df['tradeDate'],format=\"%Y-%m-%d\")" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "## Book value" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Book/Market ratio, 简称BM，也即价值因子，反映了公司的账面价值和市值的比值。Fama French (1993) 发现估值低(BM高)的股票和高的相比，预期收益为正。\n", "\n", "BM ratio Fama-French(1993) 原文的构造方法：\n", "- 每年的12月底的 book equity\n", "- 每年12月最后一个交易日的mktcap\n", "- 上述二者相除，得到 BM ratio\n", "- 这个 BM ratio 作为下一年6月至下下一年5月的 portfolio 的 sorting variable" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "处理思路：\n", "- 优矿的数据有发布日期，数据日期\n", "- 这里book value比较简单，只取年报数据，也就是“数据日期”都是12月\n", "- 取发布日期最晚，也就是最新的（也许年报和1季报中数据不同，或者年报发布后马上有更改），但不晚于次年6月" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "editable": true }, "outputs": [], "source": [ "# fundmen_df = DataAPI.FdmtBSGet(secID=stk_id,reportType=\"A\",beginDate=START,endDate=END,publishDateEnd=u\"\",publishDateBegin=u\"\",endDateRep=\"\",beginDateRep=\"\",beginYear=\"\",endYear=\"\",fiscalPeriod=\"\",field=[\"secID\",\"publishDate\",\"endDate\",\"endDateRep\",\"actPubtime\",\"fiscalPeriod\",\"TShEquity\",\"TEquityAttrP\",\"minorityInt\"],pandas=\"1\")\n", "\n", "# fundmen_df.to_pickle('./data/fundmen_df.pkl')" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "editable": true }, "outputs": [], "source": [ "fundmen_df = pd.read_pickle('./data/fundmen_df.pkl')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDpublishDateendDateendDateRepactPubtimefiscalPeriodTShEquityTEquityAttrPminorityInt
0000001.XSHE2021-10-212020-12-312021-09-302021-10-20 17:39:15123.641310e+113.641310e+11NaN
1000001.XSHE2021-08-202020-12-312021-06-302021-08-19 17:20:35123.641310e+113.641310e+11NaN
2000001.XSHE2021-04-212020-12-312021-03-312021-04-20 17:54:36123.641310e+113.641310e+11NaN
3000001.XSHE2021-02-022020-12-312020-12-312021-02-01 18:58:35123.641310e+113.641310e+11NaN
4000001.XSHE2021-02-022019-12-312020-12-312021-02-01 18:58:35123.129830e+113.129830e+11NaN
5000001.XSHE2020-10-222019-12-312020-09-302020-10-21 19:21:43123.129830e+113.129830e+11NaN
6000001.XSHE2020-08-282019-12-312020-06-302020-08-27 17:50:41123.129830e+113.129830e+11NaN
7000001.XSHE2020-04-212019-12-312020-03-312020-04-20 18:42:38123.129830e+113.129830e+11NaN
..............................
246098900957.XSHG2009-08-012008-12-312009-06-302009-07-31 18:00:00124.902596e+084.369354e+0853324231.94
246099900957.XSHG2009-04-182008-12-312009-03-312009-04-17 18:00:00124.902596e+084.369354e+0853324231.94
246100900957.XSHG2009-03-262008-12-312008-12-312009-03-25 18:00:00124.902596e+084.369354e+0853324231.94
246101900957.XSHG2009-03-262007-12-312008-12-312009-03-25 18:00:00124.363166e+083.769447e+0859371874.07
246102900957.XSHG2008-10-242007-12-312008-09-302008-10-23 18:00:00124.363166e+083.769447e+0859371874.07
246103900957.XSHG2008-08-252007-12-312008-06-302008-08-24 18:00:00124.363166e+083.769447e+0859371874.07
246104900957.XSHG2008-04-242007-12-312008-03-312008-04-23 18:00:00124.363166e+083.769447e+0859371874.07
246105900957.XSHG2008-04-082007-12-312007-12-312008-04-07 18:00:00124.363166e+083.769447e+0859371874.07
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246106 rows × 9 columns

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135182300720.XSHE2020-04-242019-12-314.783596e+084.783596e+08NaN420202019
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" ], "text/plain": [ " secID publishDate endDate TShEquity TEquityAttrP \\\n", "135178 300720.XSHE 2021-04-27 2019-12-31 4.783596e+08 4.783596e+08 \n", "135179 300720.XSHE 2020-10-30 2019-12-31 4.783596e+08 4.783596e+08 \n", "135180 300720.XSHE 2020-08-28 2019-12-31 4.783596e+08 4.783596e+08 \n", "135181 300720.XSHE 2020-04-24 2019-12-31 4.783596e+08 4.783596e+08 \n", "135182 300720.XSHE 2020-04-24 2019-12-31 4.783596e+08 4.783596e+08 \n", "\n", " minorityInt pub_month pub_year data_year \n", "135178 NaN 4 2021 2019 \n", "135179 NaN 10 2020 2019 \n", "135180 NaN 8 2020 2019 \n", "135181 NaN 4 2020 2019 \n", "135182 NaN 4 2020 2019 " ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fundmen_df[(fundmen_df['secID']=='300720.XSHE') & (fundmen_df['endDate']=='2019-12-31')]" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "editable": true }, "outputs": [ { "data": { "text/plain": [ "0 1\n", "1 1\n", "2 1\n", "3 1\n", "4 2\n", "5 1\n", "6 1\n", "7 1\n", " ..\n", "246098 1\n", "246099 1\n", "246100 1\n", "246101 2\n", "246102 1\n", "246103 1\n", "246104 1\n", "246105 1\n", "Length: 246106, dtype: int64" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fundmen_df['pub_year'] - fundmen_df['data_year'] " ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "editable": true }, "outputs": [], "source": [ "# 每年6月底计算时，只能看到publishDate在6月之前的数值。\n", "# 取 endDate 相同时，publishDate 最晚（但小于等于6）的那个数值\n", "# 同时pub_year - data_year 不能大于1\n", "fundmen_df['pub_month'] = fundmen_df['publishDate'].dt.month\n", "fundmen_df['pub_year'] = fundmen_df['publishDate'].dt.year\n", "fundmen_df['data_year'] = fundmen_df['endDate'].dt.year\n", "fundmen_df = fundmen_df[fundmen_df['pub_year'] - fundmen_df['data_year'] == 1]\n", "fundmen_df = fundmen_df[fundmen_df['pub_month'] <= 6]\n", "fundmen_df.sort_values(['secID','endDate','publishDate'],inplace=True)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDpublishDateendDateTShEquityTEquityAttrPminorityIntpub_monthpub_yeardata_year
135181300720.XSHE2020-04-242019-12-314.783596e+084.783596e+08NaN420202019
135182300720.XSHE2020-04-242019-12-314.783596e+084.783596e+08NaN420202019
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" ], "text/plain": [ " secID publishDate endDate TShEquity TEquityAttrP \\\n", "135181 300720.XSHE 2020-04-24 2019-12-31 4.783596e+08 4.783596e+08 \n", "135182 300720.XSHE 2020-04-24 2019-12-31 4.783596e+08 4.783596e+08 \n", "\n", " minorityInt pub_month pub_year data_year \n", "135181 NaN 4 2020 2019 \n", "135182 NaN 4 2020 2019 " ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fundmen_df[(fundmen_df['secID']=='300720.XSHE') & (fundmen_df['endDate']=='2019-12-31')]" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "editable": true }, "outputs": [], "source": [ "fundmen_df = fundmen_df.groupby(['secID','endDate'],as_index=False).first()" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "editable": true }, "outputs": [], "source": [ "fundmen_df['bm_date'] = fundmen_df['endDate'].dt.to_period('M')" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDendDatepublishDateTShEquityTEquityAttrPminorityIntpub_monthpub_yeardata_yearbm_date
0000001.XSHE2007-12-312008-03-201.300606e+101.300606e+10NaN3200820072007-12
1000001.XSHE2008-12-312009-03-201.640079e+101.640079e+10NaN3200920082008-12
2000001.XSHE2009-12-312010-03-122.046961e+102.046961e+10NaN3201020092009-12
3000001.XSHE2010-12-312011-02-253.351288e+103.351288e+10NaN2201120102010-12
4000001.XSHE2011-12-312012-03-097.538058e+107.331084e+102.069747e+093201220112011-12
5000001.XSHE2012-12-312013-03-088.479878e+108.479878e+10NaN3201320122012-12
6000001.XSHE2013-12-312014-03-071.120810e+111.120810e+11NaN3201420132013-12
7000001.XSHE2014-12-312015-03-131.309490e+111.309490e+11NaN3201520142014-12
.................................
42725900957.XSHG2013-12-312014-03-193.890554e+083.877593e+081.296075e+063201420132013-12
42726900957.XSHG2014-12-312015-04-104.072107e+083.940309e+081.317981e+074201520142014-12
42727900957.XSHG2015-12-312016-03-304.106786e+083.973929e+081.328570e+073201620152015-12
42728900957.XSHG2016-12-312017-03-253.938268e+083.930721e+087.546643e+053201720162016-12
42729900957.XSHG2017-12-312018-04-104.238426e+084.231040e+087.386715e+054201820172017-12
42730900957.XSHG2018-12-312019-03-304.515278e+084.508051e+087.226781e+053201920182018-12
42731900957.XSHG2019-12-312020-04-254.768689e+084.761021e+087.667705e+054202020192019-12
42732900957.XSHG2020-12-312021-04-094.987276e+084.979110e+088.165551e+054202120202020-12
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42733 rows × 10 columns

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" ], "text/plain": [ " secID endDate publishDate TShEquity TEquityAttrP \\\n", "0 000001.XSHE 2007-12-31 2008-03-20 1.300606e+10 1.300606e+10 \n", "1 000001.XSHE 2008-12-31 2009-03-20 1.640079e+10 1.640079e+10 \n", "2 000001.XSHE 2009-12-31 2010-03-12 2.046961e+10 2.046961e+10 \n", "3 000001.XSHE 2010-12-31 2011-02-25 3.351288e+10 3.351288e+10 \n", "4 000001.XSHE 2011-12-31 2012-03-09 7.538058e+10 7.331084e+10 \n", "5 000001.XSHE 2012-12-31 2013-03-08 8.479878e+10 8.479878e+10 \n", "6 000001.XSHE 2013-12-31 2014-03-07 1.120810e+11 1.120810e+11 \n", "7 000001.XSHE 2014-12-31 2015-03-13 1.309490e+11 1.309490e+11 \n", "... ... ... ... ... ... \n", "42725 900957.XSHG 2013-12-31 2014-03-19 3.890554e+08 3.877593e+08 \n", "42726 900957.XSHG 2014-12-31 2015-04-10 4.072107e+08 3.940309e+08 \n", "42727 900957.XSHG 2015-12-31 2016-03-30 4.106786e+08 3.973929e+08 \n", "42728 900957.XSHG 2016-12-31 2017-03-25 3.938268e+08 3.930721e+08 \n", "42729 900957.XSHG 2017-12-31 2018-04-10 4.238426e+08 4.231040e+08 \n", "42730 900957.XSHG 2018-12-31 2019-03-30 4.515278e+08 4.508051e+08 \n", "42731 900957.XSHG 2019-12-31 2020-04-25 4.768689e+08 4.761021e+08 \n", "42732 900957.XSHG 2020-12-31 2021-04-09 4.987276e+08 4.979110e+08 \n", "\n", " minorityInt pub_month pub_year data_year bm_date \n", "0 NaN 3 2008 2007 2007-12 \n", "1 NaN 3 2009 2008 2008-12 \n", "2 NaN 3 2010 2009 2009-12 \n", "3 NaN 2 2011 2010 2010-12 \n", "4 2.069747e+09 3 2012 2011 2011-12 \n", "5 NaN 3 2013 2012 2012-12 \n", "6 NaN 3 2014 2013 2013-12 \n", "7 NaN 3 2015 2014 2014-12 \n", "... ... ... ... ... ... \n", "42725 1.296075e+06 3 2014 2013 2013-12 \n", "42726 1.317981e+07 4 2015 2014 2014-12 \n", "42727 1.328570e+07 3 2016 2015 2015-12 \n", "42728 7.546643e+05 3 2017 2016 2016-12 \n", "42729 7.386715e+05 4 2018 2017 2017-12 \n", "42730 7.226781e+05 3 2019 2018 2018-12 \n", "42731 7.667705e+05 4 2020 2019 2019-12 \n", "42732 8.165551e+05 4 2021 2020 2020-12 \n", "\n", "[42733 rows x 10 columns]" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fundmen_df" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "editable": true }, "outputs": [], "source": [ "fundmen_df.fillna(0,inplace=True)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "editable": true }, "outputs": [], "source": [ "fundmen_df['book'] = fundmen_df['TShEquity'] - fundmen_df['minorityInt']" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "editable": true }, "outputs": [], "source": [ "fundmen_df = fundmen_df[fundmen_df['book'] > 0]" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDendDatepublishDateTShEquityTEquityAttrPminorityIntpub_monthpub_yeardata_yearbm_datebook
0000001.XSHE2007-12-312008-03-201.300606e+101.300606e+100.000000e+003200820072007-121.300606e+10
1000001.XSHE2008-12-312009-03-201.640079e+101.640079e+100.000000e+003200920082008-121.640079e+10
2000001.XSHE2009-12-312010-03-122.046961e+102.046961e+100.000000e+003201020092009-122.046961e+10
3000001.XSHE2010-12-312011-02-253.351288e+103.351288e+100.000000e+002201120102010-123.351288e+10
4000001.XSHE2011-12-312012-03-097.538058e+107.331084e+102.069747e+093201220112011-127.331084e+10
5000001.XSHE2012-12-312013-03-088.479878e+108.479878e+100.000000e+003201320122012-128.479878e+10
6000001.XSHE2013-12-312014-03-071.120810e+111.120810e+110.000000e+003201420132013-121.120810e+11
7000001.XSHE2014-12-312015-03-131.309490e+111.309490e+110.000000e+003201520142014-121.309490e+11
....................................
42725900957.XSHG2013-12-312014-03-193.890554e+083.877593e+081.296075e+063201420132013-123.877593e+08
42726900957.XSHG2014-12-312015-04-104.072107e+083.940309e+081.317981e+074201520142014-123.940309e+08
42727900957.XSHG2015-12-312016-03-304.106786e+083.973929e+081.328570e+073201620152015-123.973929e+08
42728900957.XSHG2016-12-312017-03-253.938268e+083.930721e+087.546643e+053201720162016-123.930721e+08
42729900957.XSHG2017-12-312018-04-104.238426e+084.231040e+087.386715e+054201820172017-124.231040e+08
42730900957.XSHG2018-12-312019-03-304.515278e+084.508051e+087.226781e+053201920182018-124.508051e+08
42731900957.XSHG2019-12-312020-04-254.768689e+084.761021e+087.667705e+054202020192019-124.761021e+08
42732900957.XSHG2020-12-312021-04-094.987276e+084.979110e+088.165551e+054202120202020-124.979110e+08
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41820 rows × 11 columns

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" ], "text/plain": [ " secID endDate publishDate TShEquity TEquityAttrP \\\n", "0 000001.XSHE 2007-12-31 2008-03-20 1.300606e+10 1.300606e+10 \n", "1 000001.XSHE 2008-12-31 2009-03-20 1.640079e+10 1.640079e+10 \n", "2 000001.XSHE 2009-12-31 2010-03-12 2.046961e+10 2.046961e+10 \n", "3 000001.XSHE 2010-12-31 2011-02-25 3.351288e+10 3.351288e+10 \n", "4 000001.XSHE 2011-12-31 2012-03-09 7.538058e+10 7.331084e+10 \n", "5 000001.XSHE 2012-12-31 2013-03-08 8.479878e+10 8.479878e+10 \n", "6 000001.XSHE 2013-12-31 2014-03-07 1.120810e+11 1.120810e+11 \n", "7 000001.XSHE 2014-12-31 2015-03-13 1.309490e+11 1.309490e+11 \n", "... ... ... ... ... ... \n", "42725 900957.XSHG 2013-12-31 2014-03-19 3.890554e+08 3.877593e+08 \n", "42726 900957.XSHG 2014-12-31 2015-04-10 4.072107e+08 3.940309e+08 \n", "42727 900957.XSHG 2015-12-31 2016-03-30 4.106786e+08 3.973929e+08 \n", "42728 900957.XSHG 2016-12-31 2017-03-25 3.938268e+08 3.930721e+08 \n", "42729 900957.XSHG 2017-12-31 2018-04-10 4.238426e+08 4.231040e+08 \n", "42730 900957.XSHG 2018-12-31 2019-03-30 4.515278e+08 4.508051e+08 \n", "42731 900957.XSHG 2019-12-31 2020-04-25 4.768689e+08 4.761021e+08 \n", "42732 900957.XSHG 2020-12-31 2021-04-09 4.987276e+08 4.979110e+08 \n", "\n", " minorityInt pub_month pub_year data_year bm_date book \n", "0 0.000000e+00 3 2008 2007 2007-12 1.300606e+10 \n", "1 0.000000e+00 3 2009 2008 2008-12 1.640079e+10 \n", "2 0.000000e+00 3 2010 2009 2009-12 2.046961e+10 \n", "3 0.000000e+00 2 2011 2010 2010-12 3.351288e+10 \n", "4 2.069747e+09 3 2012 2011 2011-12 7.331084e+10 \n", "5 0.000000e+00 3 2013 2012 2012-12 8.479878e+10 \n", "6 0.000000e+00 3 2014 2013 2013-12 1.120810e+11 \n", "7 0.000000e+00 3 2015 2014 2014-12 1.309490e+11 \n", "... ... ... ... ... ... ... \n", "42725 1.296075e+06 3 2014 2013 2013-12 3.877593e+08 \n", "42726 1.317981e+07 4 2015 2014 2014-12 3.940309e+08 \n", "42727 1.328570e+07 3 2016 2015 2015-12 3.973929e+08 \n", "42728 7.546643e+05 3 2017 2016 2016-12 3.930721e+08 \n", "42729 7.386715e+05 4 2018 2017 2017-12 4.231040e+08 \n", "42730 7.226781e+05 3 2019 2018 2018-12 4.508051e+08 \n", "42731 7.667705e+05 4 2020 2019 2019-12 4.761021e+08 \n", "42732 8.165551e+05 4 2021 2020 2020-12 4.979110e+08 \n", "\n", "[41820 rows x 11 columns]" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fundmen_df" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "editable": true }, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.allclose(fundmen_df['book'],fundmen_df['TEquityAttrP'])" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDendDatepublishDateTShEquityTEquityAttrPminorityIntpub_monthpub_yeardata_yearbm_datebook
756000063.XSHE2016-12-312017-03-244.088509e+102.640115e+105.162612e+093201720162016-123.572248e+10
757000063.XSHE2017-12-312018-03-164.538015e+103.164688e+104.411945e+093201820172017-124.096820e+10
758000063.XSHE2018-12-312019-03-283.296068e+102.289758e+103.810735e+093201920182018-122.914994e+10
759000063.XSHE2019-12-312020-03-283.795430e+102.882687e+102.875066e+093202020192019-123.507923e+10
16070002807.XSHE2016-12-312017-03-159.012908e+098.751954e+092.609530e+083201720162016-128.751955e+09
26208600145.XSHG2018-12-312019-05-096.331054e+086.331054e+08-2.490678e+065201920182018-126.355961e+08
40583603950.XSHG2019-12-312020-04-141.113810e+091.090588e+092.322100e+074202020192019-121.090589e+09
41195688122.XSHG2010-12-312011-02-284.866031e+084.866031e+08-1.193497e+042201120102010-124.866150e+08
\n", "
" ], "text/plain": [ " secID endDate publishDate TShEquity TEquityAttrP \\\n", "756 000063.XSHE 2016-12-31 2017-03-24 4.088509e+10 2.640115e+10 \n", "757 000063.XSHE 2017-12-31 2018-03-16 4.538015e+10 3.164688e+10 \n", "758 000063.XSHE 2018-12-31 2019-03-28 3.296068e+10 2.289758e+10 \n", "759 000063.XSHE 2019-12-31 2020-03-28 3.795430e+10 2.882687e+10 \n", "16070 002807.XSHE 2016-12-31 2017-03-15 9.012908e+09 8.751954e+09 \n", "26208 600145.XSHG 2018-12-31 2019-05-09 6.331054e+08 6.331054e+08 \n", "40583 603950.XSHG 2019-12-31 2020-04-14 1.113810e+09 1.090588e+09 \n", "41195 688122.XSHG 2010-12-31 2011-02-28 4.866031e+08 4.866031e+08 \n", "\n", " minorityInt pub_month pub_year data_year bm_date book \n", "756 5.162612e+09 3 2017 2016 2016-12 3.572248e+10 \n", "757 4.411945e+09 3 2018 2017 2017-12 4.096820e+10 \n", "758 3.810735e+09 3 2019 2018 2018-12 2.914994e+10 \n", "759 2.875066e+09 3 2020 2019 2019-12 3.507923e+10 \n", "16070 2.609530e+08 3 2017 2016 2016-12 8.751955e+09 \n", "26208 -2.490678e+06 5 2019 2018 2018-12 6.355961e+08 \n", "40583 2.322100e+07 4 2020 2019 2019-12 1.090589e+09 \n", "41195 -1.193497e+04 2 2011 2010 2010-12 4.866150e+08 " ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fundmen_df[fundmen_df['book']-fundmen_df['TEquityAttrP'] > 10]" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "## Risk free rate" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "editable": true }, "outputs": [], "source": [ "rf = pd.read_csv(\"rf-monthly.csv\").drop([\"Unnamed: 4\", \"年份_Year\", \"月份_Month\"],axis=1)\n", "rf.columns = ['Date', 'rf']\n", "rf['Date'] = pd.to_datetime(rf[\"Date\"])\n", "rf['Date'] = rf['Date'].dt.to_period('M')\n", "rf.rename(columns={'Date':'ym'},inplace=True)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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ymrf
01989-020.006300
11989-030.006300
21989-040.006300
31989-050.006300
41989-060.006300
51989-070.006300
61989-080.006300
71989-090.006300
.........
3892021-070.002015
3902021-080.001969
3912021-090.001980
3922021-100.002027
3932021-110.002055
3942021-120.002079
3952022-010.002083
3962022-020.002083
\n", "

397 rows × 2 columns

\n", "
" ], "text/plain": [ " ym rf\n", "0 1989-02 0.006300\n", "1 1989-03 0.006300\n", "2 1989-04 0.006300\n", "3 1989-05 0.006300\n", "4 1989-06 0.006300\n", "5 1989-07 0.006300\n", "6 1989-08 0.006300\n", "7 1989-09 0.006300\n", ".. ... ...\n", "389 2021-07 0.002015\n", "390 2021-08 0.001969\n", "391 2021-09 0.001980\n", "392 2021-10 0.002027\n", "393 2021-11 0.002055\n", "394 2021-12 0.002079\n", "395 2022-01 0.002083\n", "396 2022-02 0.002083\n", "\n", "[397 rows x 2 columns]" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rf" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "## Beta" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "editable": true }, "outputs": [], "source": [ "beta_df = pd.read_pickle('./data/beta_df.pkl')\n", "\n", "beta_df\n", "\n", "beta_df['tradeDate'] = pd.to_datetime(beta_df['tradeDate'], format=\"%Y-%m-%d\")\n", "\n", "beta_df['ym'] = beta_df['tradeDate'].dt.to_period('M')\n", "\n", "beta_df[['Beta60','Beta120','Beta252']] = beta_df[['Beta60','Beta120','Beta252']].apply(pd.to_numeric)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "editable": true }, "outputs": [], "source": [ "# # Winsorization\n", "# up_q = 0.99999\n", "# lower_q = 0.00001\n", "# beta_df['Beta60_winsor'] = beta_df['Beta60'].clip(lower=beta_df['Beta60'].quantile(lower_q),upper=beta_df['Beta60'].quantile(up_q))\n", "# beta_df['Beta120_winsor'] = beta_df['Beta120'].clip(lower=beta_df['Beta120'].quantile(lower_q),upper=beta_df['Beta120'].quantile(up_q))" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "### Monthly beta" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDymbeta
0000001.XSHE2007-010.7949
1000001.XSHE2007-020.7880
2000001.XSHE2007-030.8512
3000001.XSHE2007-040.8642
4000001.XSHE2007-050.7715
5000001.XSHE2007-060.4614
6000001.XSHE2007-070.6423
7000001.XSHE2007-080.7722
............
501234689009.XSHG2021-081.0727
501235689009.XSHG2021-091.0100
501236689009.XSHG2021-100.8570
501237689009.XSHG2021-110.7546
501238689009.XSHG2021-120.5898
501239689009.XSHG2022-010.5326
501240689009.XSHG2022-020.5294
501241689009.XSHG2022-030.5710
\n", "

501242 rows × 3 columns

\n", "
" ], "text/plain": [ " secID ym beta\n", "0 000001.XSHE 2007-01 0.7949\n", "1 000001.XSHE 2007-02 0.7880\n", "2 000001.XSHE 2007-03 0.8512\n", "3 000001.XSHE 2007-04 0.8642\n", "4 000001.XSHE 2007-05 0.7715\n", "5 000001.XSHE 2007-06 0.4614\n", "6 000001.XSHE 2007-07 0.6423\n", "7 000001.XSHE 2007-08 0.7722\n", "... ... ... ...\n", "501234 689009.XSHG 2021-08 1.0727\n", "501235 689009.XSHG 2021-09 1.0100\n", "501236 689009.XSHG 2021-10 0.8570\n", "501237 689009.XSHG 2021-11 0.7546\n", "501238 689009.XSHG 2021-12 0.5898\n", "501239 689009.XSHG 2022-01 0.5326\n", "501240 689009.XSHG 2022-02 0.5294\n", "501241 689009.XSHG 2022-03 0.5710\n", "\n", "[501242 rows x 3 columns]" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "beta_m_df = beta_df.groupby(['secID','ym'],as_index=False)['Beta252'].last()\n", "\n", "beta_m_df.rename(columns={'Beta252':'beta'},inplace=True)\n", "\n", "beta_m_df" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "## Trading data" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "editable": true }, "outputs": [], "source": [ "# stk_df = DataAPI.MktEqudAdjAfGet(secID=stk_id,beginDate=START,endDate=END,isOpen=1,\n", "# field=[\"secID\",\"tradeDate\",\n", "# \"closePrice\",\n", "# \"negMarketValue\"],pandas=\"1\")\n", "\n", "# stk_df.to_pickle('./data/stk_df.pkl')" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "editable": true }, "outputs": [], "source": [ "stk_df = pd.read_pickle('./data/stk_df.pkl')\n", "stk_df['tradeDate'] = pd.to_datetime(stk_df['tradeDate'], format='%Y-%m-%d')\n", "stk_df['ym'] = stk_df['tradeDate'].dt.to_period('M')\n", "stk_df.sort_values(['secID','tradeDate'],inplace=True)" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "### Exclude ST" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "editable": true }, "outputs": [ { "data": { "text/plain": [ "(9934190, 8)" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_df.dropna().shape" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "editable": true }, "outputs": [ { "data": { "text/plain": [ "(9934190, 8)" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_df.shape" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "editable": true }, "outputs": [], "source": [ "stk_df = pd.merge(stk_df, st_df, on=['secID','tradeDate'],how='left')" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "editable": true }, "outputs": [], "source": [ "stk_df = stk_df[stk_df['STflg'].isna()].copy()" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "editable": true }, "outputs": [], "source": [ "stk_df.drop('STflg',axis=1,inplace=True)" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(9535739, 8)" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_df.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Monthly trading df" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [], "source": [ "stk_df_m = stk_df.groupby(['secID','ym'],as_index=False).last()" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "#### Fill na months" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "editable": true }, "outputs": [], "source": [ "def fill_missing(df, full_dates, id_col='secID', date_col='ym'):\n", " \"\"\"\n", " This function fills the missing dates for stocks.\n", " Parameters:\n", " df: The dataframe. Could be a sub-dataframe created by \"groupby\".\n", " The dataframe must be sorted on the \"date_col\".\n", " full_dates: the unique dates covering all securities in the full dataframe. \n", " Need to be sorted.\n", " id_col: the security id.\n", " date_col: the dates column for the security\n", " Returns:\n", " A dataframe with the missing dates filled with NA.\n", " \"\"\"\n", " one_stk_id = df[id_col].unique()\n", " date_start = np.where(full_dates == df[date_col].min())[0][0] \n", " date_end = np.where(full_dates == df[date_col].max())[0][0]\n", " dates = full_dates[date_start:date_end+1]\n", " idx = pd.MultiIndex.from_product([one_stk_id,dates],\n", " names=(id_col,date_col))\n", " df = df.set_index([id_col,date_col]).reindex(idx).reset_index()\n", " return df" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "editable": true }, "outputs": [], "source": [ "full_dates = np.sort(stk_df['ym'].unique())" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 34.6 s, sys: 200 ms, total: 34.8 s\n", "Wall time: 34.8 s\n" ] } ], "source": [ "%%time\n", "stk_df_m = stk_df_m.groupby('secID').apply(fill_missing, full_dates=full_dates)" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "editable": true }, "outputs": [], "source": [ "stk_df_m.reset_index(drop=True, inplace=True)" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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secIDymtradeDatepreClosePriceclosePricenegMarketValueturnoverValueturnoverRate
0000001.XSHE2007-062007-06-29953.780870.8704.266117e+101.410758e+090.0316
1000001.XSHE2007-072007-07-311082.2591146.4985.616330e+101.479466e+090.0270
2000001.XSHE2007-082007-08-311193.0161202.5105.890714e+106.552881e+080.0112
3000001.XSHE2007-092007-09-281228.1421265.1676.197651e+101.408136e+090.0228
4000001.XSHE2007-102007-10-311427.1891520.5427.448652e+101.440425e+090.0200
5000001.XSHE2007-112007-11-301172.4471141.7515.593078e+105.452159e+080.0096
6000001.XSHE2007-122007-12-281234.1551221.4976.574629e+101.019671e+090.0154
7000001.XSHE2008-012008-01-311074.3471053.7785.850212e+105.328429e+080.0089
...........................
507459900957.XSHG2021-082021-08-310.6260.6121.116880e+083.033640e+050.0027
507460900957.XSHG2021-092021-09-300.6550.6671.218080e+082.086830e+050.0017
507461900957.XSHG2021-102021-10-290.6360.6401.168400e+086.162200e+040.0005
507462900957.XSHG2021-112021-11-300.6230.6141.120560e+081.161060e+050.0010
507463900957.XSHG2021-122021-12-310.6350.6361.161040e+081.059960e+050.0009
507464900957.XSHG2022-012022-01-280.6170.6221.135280e+081.319240e+050.0012
507465900957.XSHG2022-022022-02-280.6160.6151.122400e+089.851400e+040.0009
507466900957.XSHG2022-032022-03-140.6060.5941.083760e+081.005700e+050.0009
\n", "

507467 rows × 8 columns

\n", "
" ], "text/plain": [ " secID ym tradeDate preClosePrice closePrice \\\n", "0 000001.XSHE 2007-06 2007-06-29 953.780 870.870 \n", "1 000001.XSHE 2007-07 2007-07-31 1082.259 1146.498 \n", "2 000001.XSHE 2007-08 2007-08-31 1193.016 1202.510 \n", "3 000001.XSHE 2007-09 2007-09-28 1228.142 1265.167 \n", "4 000001.XSHE 2007-10 2007-10-31 1427.189 1520.542 \n", "5 000001.XSHE 2007-11 2007-11-30 1172.447 1141.751 \n", "6 000001.XSHE 2007-12 2007-12-28 1234.155 1221.497 \n", "7 000001.XSHE 2008-01 2008-01-31 1074.347 1053.778 \n", "... ... ... ... ... ... \n", "507459 900957.XSHG 2021-08 2021-08-31 0.626 0.612 \n", "507460 900957.XSHG 2021-09 2021-09-30 0.655 0.667 \n", "507461 900957.XSHG 2021-10 2021-10-29 0.636 0.640 \n", "507462 900957.XSHG 2021-11 2021-11-30 0.623 0.614 \n", "507463 900957.XSHG 2021-12 2021-12-31 0.635 0.636 \n", "507464 900957.XSHG 2022-01 2022-01-28 0.617 0.622 \n", "507465 900957.XSHG 2022-02 2022-02-28 0.616 0.615 \n", "507466 900957.XSHG 2022-03 2022-03-14 0.606 0.594 \n", "\n", " negMarketValue turnoverValue turnoverRate \n", "0 4.266117e+10 1.410758e+09 0.0316 \n", "1 5.616330e+10 1.479466e+09 0.0270 \n", "2 5.890714e+10 6.552881e+08 0.0112 \n", "3 6.197651e+10 1.408136e+09 0.0228 \n", "4 7.448652e+10 1.440425e+09 0.0200 \n", "5 5.593078e+10 5.452159e+08 0.0096 \n", "6 6.574629e+10 1.019671e+09 0.0154 \n", "7 5.850212e+10 5.328429e+08 0.0089 \n", "... ... ... ... \n", "507459 1.116880e+08 3.033640e+05 0.0027 \n", "507460 1.218080e+08 2.086830e+05 0.0017 \n", "507461 1.168400e+08 6.162200e+04 0.0005 \n", "507462 1.120560e+08 1.161060e+05 0.0010 \n", "507463 1.161040e+08 1.059960e+05 0.0009 \n", "507464 1.135280e+08 1.319240e+05 0.0012 \n", "507465 1.122400e+08 9.851400e+04 0.0009 \n", "507466 1.083760e+08 1.005700e+05 0.0009 \n", "\n", "[507467 rows x 8 columns]" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_df_m" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 507467 entries, 0 to 507466\n", "Data columns (total 8 columns):\n", "secID 507467 non-null object\n", "ym 507467 non-null period[M]\n", "tradeDate 484453 non-null datetime64[ns]\n", "preClosePrice 484453 non-null float64\n", "closePrice 484453 non-null float64\n", "negMarketValue 484453 non-null float64\n", "turnoverValue 484453 non-null float64\n", "turnoverRate 484453 non-null float64\n", "dtypes: datetime64[ns](1), float64(5), object(1), period[M](1)\n", "memory usage: 31.0+ MB\n" ] } ], "source": [ "stk_df_m.info()" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "editable": true }, "outputs": [], "source": [ "stk_df_m.drop('tradeDate',axis=1,inplace=True)" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDympreClosePriceturnoverValueturnoverRateretmkt_capmkt_cap_date
0000001.XSHE2007-06953.7801.410758e+090.0316NaNNaNNaT
1000001.XSHE2007-071082.2591.479466e+090.02700.3164974.266117e+102007-06
2000001.XSHE2007-081193.0166.552881e+080.01120.0488555.616330e+102007-07
3000001.XSHE2007-091228.1421.408136e+090.02280.0521055.890714e+102007-08
4000001.XSHE2007-101427.1891.440425e+090.02000.2018516.197651e+102007-09
5000001.XSHE2007-111172.4475.452159e+080.0096-0.2491167.448652e+102007-10
6000001.XSHE2007-121234.1551.019671e+090.01540.0698455.593078e+102007-11
7000001.XSHE2008-011074.3475.328429e+080.0089-0.1373066.574629e+102007-12
...........................
507459900957.XSHG2021-080.6263.033640e+050.0027-0.0584621.186800e+082021-07
507460900957.XSHG2021-090.6552.086830e+050.00170.0898691.116880e+082021-08
507461900957.XSHG2021-100.6366.162200e+040.0005-0.0404801.218080e+082021-09
507462900957.XSHG2021-110.6231.161060e+050.0010-0.0406251.168400e+082021-10
507463900957.XSHG2021-120.6351.059960e+050.00090.0358311.120560e+082021-11
507464900957.XSHG2022-010.6171.319240e+050.0012-0.0220131.161040e+082021-12
507465900957.XSHG2022-020.6169.851400e+040.0009-0.0112541.135280e+082022-01
507466900957.XSHG2022-030.6061.005700e+050.0009-0.0341461.122400e+082022-02
\n", "

507467 rows × 8 columns

\n", "
" ], "text/plain": [ " secID ym preClosePrice turnoverValue turnoverRate \\\n", "0 000001.XSHE 2007-06 953.780 1.410758e+09 0.0316 \n", "1 000001.XSHE 2007-07 1082.259 1.479466e+09 0.0270 \n", "2 000001.XSHE 2007-08 1193.016 6.552881e+08 0.0112 \n", "3 000001.XSHE 2007-09 1228.142 1.408136e+09 0.0228 \n", "4 000001.XSHE 2007-10 1427.189 1.440425e+09 0.0200 \n", "5 000001.XSHE 2007-11 1172.447 5.452159e+08 0.0096 \n", "6 000001.XSHE 2007-12 1234.155 1.019671e+09 0.0154 \n", "7 000001.XSHE 2008-01 1074.347 5.328429e+08 0.0089 \n", "... ... ... ... ... ... \n", "507459 900957.XSHG 2021-08 0.626 3.033640e+05 0.0027 \n", "507460 900957.XSHG 2021-09 0.655 2.086830e+05 0.0017 \n", "507461 900957.XSHG 2021-10 0.636 6.162200e+04 0.0005 \n", "507462 900957.XSHG 2021-11 0.623 1.161060e+05 0.0010 \n", "507463 900957.XSHG 2021-12 0.635 1.059960e+05 0.0009 \n", "507464 900957.XSHG 2022-01 0.617 1.319240e+05 0.0012 \n", "507465 900957.XSHG 2022-02 0.616 9.851400e+04 0.0009 \n", "507466 900957.XSHG 2022-03 0.606 1.005700e+05 0.0009 \n", "\n", " ret mkt_cap mkt_cap_date \n", "0 NaN NaN NaT \n", "1 0.316497 4.266117e+10 2007-06 \n", "2 0.048855 5.616330e+10 2007-07 \n", "3 0.052105 5.890714e+10 2007-08 \n", "4 0.201851 6.197651e+10 2007-09 \n", "5 -0.249116 7.448652e+10 2007-10 \n", "6 0.069845 5.593078e+10 2007-11 \n", "7 -0.137306 6.574629e+10 2007-12 \n", "... ... ... ... \n", "507459 -0.058462 1.186800e+08 2021-07 \n", "507460 0.089869 1.116880e+08 2021-08 \n", "507461 -0.040480 1.218080e+08 2021-09 \n", "507462 -0.040625 1.168400e+08 2021-10 \n", "507463 0.035831 1.120560e+08 2021-11 \n", "507464 -0.022013 1.161040e+08 2021-12 \n", "507465 -0.011254 1.135280e+08 2022-01 \n", "507466 -0.034146 1.122400e+08 2022-02 \n", "\n", "[507467 rows x 8 columns]" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_df_m['ret'] = stk_df_m.groupby('secID')['closePrice'].apply(lambda x: x / x.shift() - 1)\n", "\n", "# # Use last month's market cap for sorting\n", "stk_df_m['mkt_cap'] = stk_df_m.groupby('secID')['negMarketValue'].shift()\n", "stk_df_m['mkt_cap_date'] = stk_df_m.groupby('secID')['ym'].shift()\n", "\n", "stk_df_m.drop(['closePrice','negMarketValue'],axis=1,inplace=True)\n", "\n", "stk_df_m" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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secIDympreClosePriceturnoverValueturnoverRateretmkt_capmkt_cap_date
507284900957.XSHG2007-010.4301380447.00.0173NaNNaNNaT
507285900957.XSHG2007-020.5513891729.00.03620.44868776544000.02007-01
507286900957.XSHG2007-030.554598544.00.0058-0.077430110768000.02007-02
507287900957.XSHG2007-040.5974572009.00.03960.171429102304000.02007-03
507288900957.XSHG2007-051.05112847356.00.07350.442073119784000.02007-04
507289900957.XSHG2007-060.6461701711.00.0146-0.319239172776000.02007-05
507290900957.XSHG2007-070.8373577441.00.02290.354037117576000.02007-06
507291900957.XSHG2007-080.8462759917.00.0180-0.047018159160000.02007-07
...........................
507459900957.XSHG2021-080.626303364.00.0027-0.058462118680000.02021-07
507460900957.XSHG2021-090.655208683.00.00170.089869111688000.02021-08
507461900957.XSHG2021-100.63661622.00.0005-0.040480121808000.02021-09
507462900957.XSHG2021-110.623116106.00.0010-0.040625116840000.02021-10
507463900957.XSHG2021-120.635105996.00.00090.035831112056000.02021-11
507464900957.XSHG2022-010.617131924.00.0012-0.022013116104000.02021-12
507465900957.XSHG2022-020.61698514.00.0009-0.011254113528000.02022-01
507466900957.XSHG2022-030.606100570.00.0009-0.034146112240000.02022-02
\n", "

183 rows × 8 columns

\n", "
" ], "text/plain": [ " secID ym preClosePrice turnoverValue turnoverRate \\\n", "507284 900957.XSHG 2007-01 0.430 1380447.0 0.0173 \n", "507285 900957.XSHG 2007-02 0.551 3891729.0 0.0362 \n", "507286 900957.XSHG 2007-03 0.554 598544.0 0.0058 \n", "507287 900957.XSHG 2007-04 0.597 4572009.0 0.0396 \n", "507288 900957.XSHG 2007-05 1.051 12847356.0 0.0735 \n", "507289 900957.XSHG 2007-06 0.646 1701711.0 0.0146 \n", "507290 900957.XSHG 2007-07 0.837 3577441.0 0.0229 \n", "507291 900957.XSHG 2007-08 0.846 2759917.0 0.0180 \n", "... ... ... ... ... ... \n", "507459 900957.XSHG 2021-08 0.626 303364.0 0.0027 \n", "507460 900957.XSHG 2021-09 0.655 208683.0 0.0017 \n", "507461 900957.XSHG 2021-10 0.636 61622.0 0.0005 \n", "507462 900957.XSHG 2021-11 0.623 116106.0 0.0010 \n", "507463 900957.XSHG 2021-12 0.635 105996.0 0.0009 \n", "507464 900957.XSHG 2022-01 0.617 131924.0 0.0012 \n", "507465 900957.XSHG 2022-02 0.616 98514.0 0.0009 \n", "507466 900957.XSHG 2022-03 0.606 100570.0 0.0009 \n", "\n", " ret mkt_cap mkt_cap_date \n", "507284 NaN NaN NaT \n", "507285 0.448687 76544000.0 2007-01 \n", "507286 -0.077430 110768000.0 2007-02 \n", "507287 0.171429 102304000.0 2007-03 \n", "507288 0.442073 119784000.0 2007-04 \n", "507289 -0.319239 172776000.0 2007-05 \n", "507290 0.354037 117576000.0 2007-06 \n", "507291 -0.047018 159160000.0 2007-07 \n", "... ... ... ... \n", "507459 -0.058462 118680000.0 2021-07 \n", "507460 0.089869 111688000.0 2021-08 \n", "507461 -0.040480 121808000.0 2021-09 \n", "507462 -0.040625 116840000.0 2021-10 \n", "507463 0.035831 112056000.0 2021-11 \n", "507464 -0.022013 116104000.0 2021-12 \n", "507465 -0.011254 113528000.0 2022-01 \n", "507466 -0.034146 112240000.0 2022-02 \n", "\n", "[183 rows x 8 columns]" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_df_m[stk_df_m['secID']=='900957.XSHG']" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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secIDympreClosePriceturnoverValueturnoverRateretmkt_capmkt_cap_date
0000001.XSHE2007-06953.7801.410758e+090.0316NaNNaNNaT
37000001.XSHE2010-07NaNNaNNaNNaN5.437499e+102010-06
38000001.XSHE2010-08NaNNaNNaNNaNNaN2010-07
39000001.XSHE2010-09670.8993.472608e+080.0069NaNNaN2010-08
178000002.XSHE2007-01701.3441.400388e+090.0269NaNNaNNaT
286000002.XSHE2016-01NaNNaNNaNNaN2.374641e+112015-12
287000002.XSHE2016-02NaNNaNNaNNaNNaN2016-01
288000002.XSHE2016-03NaNNaNNaNNaNNaN2016-02
...........................
507061900955.XSHG2015-09NaNNaNNaNNaNNaN2015-08
507062900955.XSHG2015-10NaNNaNNaNNaNNaN2015-09
507063900955.XSHG2015-112.4082.345999e+060.0101NaNNaN2015-10
507117900956.XSHG2007-010.6298.597120e+050.0123NaNNaNNaT
507228900956.XSHG2016-04NaNNaNNaNNaN2.051600e+082016-03
507229900956.XSHG2016-05NaNNaNNaNNaNNaN2016-04
507230900956.XSHG2016-062.2621.470422e+060.0061NaNNaN2016-05
507284900957.XSHG2007-010.4301.380447e+060.0173NaNNaNNaT
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31895 rows × 8 columns

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" ], "text/plain": [ " secID ym preClosePrice turnoverValue turnoverRate ret \\\n", "0 000001.XSHE 2007-06 953.780 1.410758e+09 0.0316 NaN \n", "37 000001.XSHE 2010-07 NaN NaN NaN NaN \n", "38 000001.XSHE 2010-08 NaN NaN NaN NaN \n", "39 000001.XSHE 2010-09 670.899 3.472608e+08 0.0069 NaN \n", "178 000002.XSHE 2007-01 701.344 1.400388e+09 0.0269 NaN \n", "286 000002.XSHE 2016-01 NaN NaN NaN NaN \n", "287 000002.XSHE 2016-02 NaN NaN NaN NaN \n", "288 000002.XSHE 2016-03 NaN NaN NaN NaN \n", "... ... ... ... ... ... ... \n", "507061 900955.XSHG 2015-09 NaN NaN NaN NaN \n", "507062 900955.XSHG 2015-10 NaN NaN NaN NaN \n", "507063 900955.XSHG 2015-11 2.408 2.345999e+06 0.0101 NaN \n", "507117 900956.XSHG 2007-01 0.629 8.597120e+05 0.0123 NaN \n", "507228 900956.XSHG 2016-04 NaN NaN NaN NaN \n", "507229 900956.XSHG 2016-05 NaN NaN NaN NaN \n", "507230 900956.XSHG 2016-06 2.262 1.470422e+06 0.0061 NaN \n", "507284 900957.XSHG 2007-01 0.430 1.380447e+06 0.0173 NaN \n", "\n", " mkt_cap mkt_cap_date \n", "0 NaN NaT \n", "37 5.437499e+10 2010-06 \n", "38 NaN 2010-07 \n", "39 NaN 2010-08 \n", "178 NaN NaT \n", "286 2.374641e+11 2015-12 \n", "287 NaN 2016-01 \n", "288 NaN 2016-02 \n", "... ... ... \n", "507061 NaN 2015-08 \n", "507062 NaN 2015-09 \n", "507063 NaN 2015-10 \n", "507117 NaN NaT \n", "507228 2.051600e+08 2016-03 \n", "507229 NaN 2016-04 \n", "507230 NaN 2016-05 \n", "507284 NaN NaT \n", "\n", "[31895 rows x 8 columns]" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_df_m[stk_df_m['ret'].isna()]" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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secIDympreClosePriceturnoverValueturnoverRateretmkt_capmkt_cap_date
0000001.XSHE2007-06953.7801.410758e+090.0316NaNNaNNaT
38000001.XSHE2010-08NaNNaNNaNNaNNaN2010-07
39000001.XSHE2010-09670.8993.472608e+080.0069NaNNaN2010-08
178000002.XSHE2007-01701.3441.400388e+090.0269NaNNaNNaT
287000002.XSHE2016-02NaNNaNNaNNaNNaN2016-01
288000002.XSHE2016-03NaNNaNNaNNaNNaN2016-02
289000002.XSHE2016-04NaNNaNNaNNaNNaN2016-03
290000002.XSHE2016-05NaNNaNNaNNaNNaN2016-04
...........................
507060900955.XSHG2015-08NaNNaNNaNNaNNaN2015-07
507061900955.XSHG2015-09NaNNaNNaNNaNNaN2015-08
507062900955.XSHG2015-10NaNNaNNaNNaNNaN2015-09
507063900955.XSHG2015-112.4082.345999e+060.0101NaNNaN2015-10
507117900956.XSHG2007-010.6298.597120e+050.0123NaNNaNNaT
507229900956.XSHG2016-05NaNNaNNaNNaNNaN2016-04
507230900956.XSHG2016-062.2621.470422e+060.0061NaNNaN2016-05
507284900957.XSHG2007-010.4301.380447e+060.0173NaNNaNNaT
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27867 rows × 8 columns

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" ], "text/plain": [ " secID ym preClosePrice turnoverValue turnoverRate ret \\\n", "0 000001.XSHE 2007-06 953.780 1.410758e+09 0.0316 NaN \n", "38 000001.XSHE 2010-08 NaN NaN NaN NaN \n", "39 000001.XSHE 2010-09 670.899 3.472608e+08 0.0069 NaN \n", "178 000002.XSHE 2007-01 701.344 1.400388e+09 0.0269 NaN \n", "287 000002.XSHE 2016-02 NaN NaN NaN NaN \n", "288 000002.XSHE 2016-03 NaN NaN NaN NaN \n", "289 000002.XSHE 2016-04 NaN NaN NaN NaN \n", "290 000002.XSHE 2016-05 NaN NaN NaN NaN \n", "... ... ... ... ... ... ... \n", "507060 900955.XSHG 2015-08 NaN NaN NaN NaN \n", "507061 900955.XSHG 2015-09 NaN NaN NaN NaN \n", "507062 900955.XSHG 2015-10 NaN NaN NaN NaN \n", "507063 900955.XSHG 2015-11 2.408 2.345999e+06 0.0101 NaN \n", "507117 900956.XSHG 2007-01 0.629 8.597120e+05 0.0123 NaN \n", "507229 900956.XSHG 2016-05 NaN NaN NaN NaN \n", "507230 900956.XSHG 2016-06 2.262 1.470422e+06 0.0061 NaN \n", "507284 900957.XSHG 2007-01 0.430 1.380447e+06 0.0173 NaN \n", "\n", " mkt_cap mkt_cap_date \n", "0 NaN NaT \n", "38 NaN 2010-07 \n", "39 NaN 2010-08 \n", "178 NaN NaT \n", "287 NaN 2016-01 \n", "288 NaN 2016-02 \n", "289 NaN 2016-03 \n", "290 NaN 2016-04 \n", "... ... ... \n", "507060 NaN 2015-07 \n", "507061 NaN 2015-08 \n", "507062 NaN 2015-09 \n", "507063 NaN 2015-10 \n", "507117 NaN NaT \n", "507229 NaN 2016-04 \n", "507230 NaN 2016-05 \n", "507284 NaN NaT \n", "\n", "[27867 rows x 8 columns]" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_df_m[stk_df_m['mkt_cap'].isna()]" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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secIDtradeDatepreClosePriceclosePricenegMarketValueturnoverValueturnoverRateym
769000001.XSHE2010-05-04848.320825.2145.848229e+105.744136e+080.00982010-05
770000001.XSHE2010-05-05825.214821.0885.818987e+101.283693e+090.02222010-05
771000001.XSHE2010-05-06821.088759.1975.380370e+101.296218e+090.02352010-05
772000001.XSHE2010-05-07759.197753.4205.339433e+107.521859e+080.01412010-05
773000001.XSHE2010-05-10753.420760.4355.389143e+107.109740e+080.01322010-05
774000001.XSHE2010-05-11760.435720.8245.108428e+101.017122e+090.01922010-05
775000001.XSHE2010-05-12720.824747.2315.295571e+109.312513e+080.01782010-05
776000001.XSHE2010-05-13747.231770.7505.462245e+101.049526e+090.01942010-05
...........................
781000001.XSHE2010-05-20732.790724.1255.131821e+103.871683e+080.00752010-05
782000001.XSHE2010-05-21724.125735.2665.210772e+103.867383e+080.00762010-05
783000001.XSHE2010-05-24735.266758.7845.377446e+105.826194e+080.01092010-05
784000001.XSHE2010-05-25758.784734.0285.201999e+104.418726e+080.00842010-05
785000001.XSHE2010-05-26734.028738.5675.234165e+103.083689e+080.00592010-05
786000001.XSHE2010-05-27738.567745.5815.283874e+104.967021e+080.00952010-05
787000001.XSHE2010-05-28745.581745.9945.286799e+104.616894e+080.00872010-05
788000001.XSHE2010-05-31745.994722.4755.120124e+104.806838e+080.00922010-05
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20 rows × 8 columns

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" ], "text/plain": [ " secID tradeDate preClosePrice closePrice negMarketValue \\\n", "769 000001.XSHE 2010-05-04 848.320 825.214 5.848229e+10 \n", "770 000001.XSHE 2010-05-05 825.214 821.088 5.818987e+10 \n", "771 000001.XSHE 2010-05-06 821.088 759.197 5.380370e+10 \n", "772 000001.XSHE 2010-05-07 759.197 753.420 5.339433e+10 \n", "773 000001.XSHE 2010-05-10 753.420 760.435 5.389143e+10 \n", "774 000001.XSHE 2010-05-11 760.435 720.824 5.108428e+10 \n", "775 000001.XSHE 2010-05-12 720.824 747.231 5.295571e+10 \n", "776 000001.XSHE 2010-05-13 747.231 770.750 5.462245e+10 \n", ".. ... ... ... ... ... \n", "781 000001.XSHE 2010-05-20 732.790 724.125 5.131821e+10 \n", "782 000001.XSHE 2010-05-21 724.125 735.266 5.210772e+10 \n", "783 000001.XSHE 2010-05-24 735.266 758.784 5.377446e+10 \n", "784 000001.XSHE 2010-05-25 758.784 734.028 5.201999e+10 \n", "785 000001.XSHE 2010-05-26 734.028 738.567 5.234165e+10 \n", "786 000001.XSHE 2010-05-27 738.567 745.581 5.283874e+10 \n", "787 000001.XSHE 2010-05-28 745.581 745.994 5.286799e+10 \n", "788 000001.XSHE 2010-05-31 745.994 722.475 5.120124e+10 \n", "\n", " turnoverValue turnoverRate ym \n", "769 5.744136e+08 0.0098 2010-05 \n", "770 1.283693e+09 0.0222 2010-05 \n", "771 1.296218e+09 0.0235 2010-05 \n", "772 7.521859e+08 0.0141 2010-05 \n", "773 7.109740e+08 0.0132 2010-05 \n", "774 1.017122e+09 0.0192 2010-05 \n", "775 9.312513e+08 0.0178 2010-05 \n", "776 1.049526e+09 0.0194 2010-05 \n", ".. ... ... ... \n", "781 3.871683e+08 0.0075 2010-05 \n", "782 3.867383e+08 0.0076 2010-05 \n", "783 5.826194e+08 0.0109 2010-05 \n", "784 4.418726e+08 0.0084 2010-05 \n", "785 3.083689e+08 0.0059 2010-05 \n", "786 4.967021e+08 0.0095 2010-05 \n", "787 4.616894e+08 0.0087 2010-05 \n", "788 4.806838e+08 0.0092 2010-05 \n", "\n", "[20 rows x 8 columns]" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_df[(stk_df['secID']=='000001.XSHE')&(stk_df['ym']=='2010-05')]" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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secIDtradeDatepreClosePriceclosePricenegMarketValueturnoverValueturnoverRateym
806000001.XSHE2010-09-02722.475750.5325.648664e+102.984709e+090.05262010-09
807000001.XSHE2010-09-03750.532732.7905.515133e+101.110874e+090.02012010-09
808000001.XSHE2010-09-06732.790750.9455.651769e+101.106419e+090.01962010-09
809000001.XSHE2010-09-07750.945744.3435.602083e+107.112746e+080.01272010-09
810000001.XSHE2010-09-08744.343728.2515.480974e+108.656646e+080.01582010-09
811000001.XSHE2010-09-09728.251710.5095.347443e+108.169379e+080.01512010-09
812000001.XSHE2010-09-10710.509709.6845.341232e+104.325192e+080.00812010-09
813000001.XSHE2010-09-13709.684705.1455.307073e+108.567676e+080.01622010-09
...........................
815000001.XSHE2010-09-15708.859698.1315.254282e+104.438044e+080.00832010-09
816000001.XSHE2010-09-16698.131686.1665.164226e+104.783042e+080.00922010-09
817000001.XSHE2010-09-17686.166686.9915.170437e+103.698354e+080.00712010-09
818000001.XSHE2010-09-20686.991689.0545.185964e+103.304790e+080.00642010-09
819000001.XSHE2010-09-21689.054685.3405.158016e+102.232819e+080.00432010-09
820000001.XSHE2010-09-27685.340688.6415.182859e+103.124533e+080.00612010-09
821000001.XSHE2010-09-28688.641670.8995.049328e+104.048172e+080.00792010-09
822000001.XSHE2010-09-29670.899669.2495.036906e+103.472608e+080.00692010-09
\n", "

17 rows × 8 columns

\n", "
" ], "text/plain": [ " secID tradeDate preClosePrice closePrice negMarketValue \\\n", "806 000001.XSHE 2010-09-02 722.475 750.532 5.648664e+10 \n", "807 000001.XSHE 2010-09-03 750.532 732.790 5.515133e+10 \n", "808 000001.XSHE 2010-09-06 732.790 750.945 5.651769e+10 \n", "809 000001.XSHE 2010-09-07 750.945 744.343 5.602083e+10 \n", "810 000001.XSHE 2010-09-08 744.343 728.251 5.480974e+10 \n", "811 000001.XSHE 2010-09-09 728.251 710.509 5.347443e+10 \n", "812 000001.XSHE 2010-09-10 710.509 709.684 5.341232e+10 \n", "813 000001.XSHE 2010-09-13 709.684 705.145 5.307073e+10 \n", ".. ... ... ... ... ... \n", "815 000001.XSHE 2010-09-15 708.859 698.131 5.254282e+10 \n", "816 000001.XSHE 2010-09-16 698.131 686.166 5.164226e+10 \n", "817 000001.XSHE 2010-09-17 686.166 686.991 5.170437e+10 \n", "818 000001.XSHE 2010-09-20 686.991 689.054 5.185964e+10 \n", "819 000001.XSHE 2010-09-21 689.054 685.340 5.158016e+10 \n", "820 000001.XSHE 2010-09-27 685.340 688.641 5.182859e+10 \n", "821 000001.XSHE 2010-09-28 688.641 670.899 5.049328e+10 \n", "822 000001.XSHE 2010-09-29 670.899 669.249 5.036906e+10 \n", "\n", " turnoverValue turnoverRate ym \n", "806 2.984709e+09 0.0526 2010-09 \n", "807 1.110874e+09 0.0201 2010-09 \n", "808 1.106419e+09 0.0196 2010-09 \n", "809 7.112746e+08 0.0127 2010-09 \n", "810 8.656646e+08 0.0158 2010-09 \n", "811 8.169379e+08 0.0151 2010-09 \n", "812 4.325192e+08 0.0081 2010-09 \n", "813 8.567676e+08 0.0162 2010-09 \n", ".. ... ... ... \n", "815 4.438044e+08 0.0083 2010-09 \n", "816 4.783042e+08 0.0092 2010-09 \n", "817 3.698354e+08 0.0071 2010-09 \n", "818 3.304790e+08 0.0064 2010-09 \n", "819 2.232819e+08 0.0043 2010-09 \n", "820 3.124533e+08 0.0061 2010-09 \n", "821 4.048172e+08 0.0079 2010-09 \n", "822 3.472608e+08 0.0069 2010-09 \n", "\n", "[17 rows x 8 columns]" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_df[(stk_df['secID']=='000001.XSHE')&(stk_df['ym']>='2010-07')&(stk_df['ym']<='2010-09')]" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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secIDympreClosePriceturnoverValueturnoverRateretmkt_capmkt_cap_date
35000001.XSHE2010-05745.9944.806838e+080.0092-0.1483466.011979e+102010-04
36000001.XSHE2010-06762.9105.491348e+080.00990.0000005.120124e+102010-05
37000001.XSHE2010-07NaNNaNNaNNaN5.437499e+102010-06
38000001.XSHE2010-08NaNNaNNaNNaNNaN2010-07
39000001.XSHE2010-09670.8993.472608e+080.0069NaNNaN2010-08
40000001.XSHE2010-10774.4636.635094e+080.01150.1350195.036906e+102010-09
41000001.XSHE2010-11689.8794.642922e+080.0091-0.1097235.716982e+102010-10
42000001.XSHE2010-12646.5553.783178e+080.0078-0.0366075.089697e+102010-11
...........................
170000001.XSHE2021-082277.5632.072007e+090.00610.0062183.432878e+112021-07
171000001.XSHE2021-092332.8321.424676e+090.00410.0073033.454224e+112021-08
172000001.XSHE2021-102534.6251.052760e+090.00280.0875633.479452e+112021-09
173000001.XSHE2021-112250.5721.280385e+090.0038-0.1056413.784122e+112021-10
174000001.XSHE2021-122161.8862.899617e+090.0090-0.0550463.384364e+112021-11
175000001.XSHE2022-012095.0502.695470e+090.0086-0.0394423.198068e+112021-12
176000001.XSHE2022-022043.6381.137154e+090.0037-0.0050533.071931e+112022-01
177000001.XSHE2022-031915.1071.637278e+090.0058-0.0800003.056406e+112022-02
\n", "

143 rows × 8 columns

\n", "
" ], "text/plain": [ " secID ym preClosePrice turnoverValue turnoverRate \\\n", "35 000001.XSHE 2010-05 745.994 4.806838e+08 0.0092 \n", "36 000001.XSHE 2010-06 762.910 5.491348e+08 0.0099 \n", "37 000001.XSHE 2010-07 NaN NaN NaN \n", "38 000001.XSHE 2010-08 NaN NaN NaN \n", "39 000001.XSHE 2010-09 670.899 3.472608e+08 0.0069 \n", "40 000001.XSHE 2010-10 774.463 6.635094e+08 0.0115 \n", "41 000001.XSHE 2010-11 689.879 4.642922e+08 0.0091 \n", "42 000001.XSHE 2010-12 646.555 3.783178e+08 0.0078 \n", ".. ... ... ... ... ... \n", "170 000001.XSHE 2021-08 2277.563 2.072007e+09 0.0061 \n", "171 000001.XSHE 2021-09 2332.832 1.424676e+09 0.0041 \n", "172 000001.XSHE 2021-10 2534.625 1.052760e+09 0.0028 \n", "173 000001.XSHE 2021-11 2250.572 1.280385e+09 0.0038 \n", "174 000001.XSHE 2021-12 2161.886 2.899617e+09 0.0090 \n", "175 000001.XSHE 2022-01 2095.050 2.695470e+09 0.0086 \n", "176 000001.XSHE 2022-02 2043.638 1.137154e+09 0.0037 \n", "177 000001.XSHE 2022-03 1915.107 1.637278e+09 0.0058 \n", "\n", " ret mkt_cap mkt_cap_date \n", "35 -0.148346 6.011979e+10 2010-04 \n", "36 0.000000 5.120124e+10 2010-05 \n", "37 NaN 5.437499e+10 2010-06 \n", "38 NaN NaN 2010-07 \n", "39 NaN NaN 2010-08 \n", "40 0.135019 5.036906e+10 2010-09 \n", "41 -0.109723 5.716982e+10 2010-10 \n", "42 -0.036607 5.089697e+10 2010-11 \n", ".. ... ... ... \n", "170 0.006218 3.432878e+11 2021-07 \n", "171 0.007303 3.454224e+11 2021-08 \n", "172 0.087563 3.479452e+11 2021-09 \n", "173 -0.105641 3.784122e+11 2021-10 \n", "174 -0.055046 3.384364e+11 2021-11 \n", "175 -0.039442 3.198068e+11 2021-12 \n", "176 -0.005053 3.071931e+11 2022-01 \n", "177 -0.080000 3.056406e+11 2022-02 \n", "\n", "[143 rows x 8 columns]" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_df_m[(stk_df_m['secID']=='000001.XSHE')&(stk_df_m['ym']>='2010-05')]" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "editable": true }, "outputs": [], "source": [ "stk_df_m.dropna(inplace=True)" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDympreClosePriceturnoverValueturnoverRateretmkt_capmkt_cap_date
1000001.XSHE2007-071082.2591.479466e+090.02700.3164974.266117e+102007-06
2000001.XSHE2007-081193.0166.552881e+080.01120.0488555.616330e+102007-07
3000001.XSHE2007-091228.1421.408136e+090.02280.0521055.890714e+102007-08
4000001.XSHE2007-101427.1891.440425e+090.02000.2018516.197651e+102007-09
5000001.XSHE2007-111172.4475.452159e+080.0096-0.2491167.448652e+102007-10
6000001.XSHE2007-121234.1551.019671e+090.01540.0698455.593078e+102007-11
7000001.XSHE2008-011074.3475.328429e+080.0089-0.1373066.574629e+102007-12
8000001.XSHE2008-021037.9562.267900e+080.0039-0.0045045.850212e+102008-01
...........................
507459900957.XSHG2021-080.6263.033640e+050.0027-0.0584621.186800e+082021-07
507460900957.XSHG2021-090.6552.086830e+050.00170.0898691.116880e+082021-08
507461900957.XSHG2021-100.6366.162200e+040.0005-0.0404801.218080e+082021-09
507462900957.XSHG2021-110.6231.161060e+050.0010-0.0406251.168400e+082021-10
507463900957.XSHG2021-120.6351.059960e+050.00090.0358311.120560e+082021-11
507464900957.XSHG2022-010.6171.319240e+050.0012-0.0220131.161040e+082021-12
507465900957.XSHG2022-020.6169.851400e+040.0009-0.0112541.135280e+082022-01
507466900957.XSHG2022-030.6061.005700e+050.0009-0.0341461.122400e+082022-02
\n", "

475572 rows × 8 columns

\n", "
" ], "text/plain": [ " secID ym preClosePrice turnoverValue turnoverRate \\\n", "1 000001.XSHE 2007-07 1082.259 1.479466e+09 0.0270 \n", "2 000001.XSHE 2007-08 1193.016 6.552881e+08 0.0112 \n", "3 000001.XSHE 2007-09 1228.142 1.408136e+09 0.0228 \n", "4 000001.XSHE 2007-10 1427.189 1.440425e+09 0.0200 \n", "5 000001.XSHE 2007-11 1172.447 5.452159e+08 0.0096 \n", "6 000001.XSHE 2007-12 1234.155 1.019671e+09 0.0154 \n", "7 000001.XSHE 2008-01 1074.347 5.328429e+08 0.0089 \n", "8 000001.XSHE 2008-02 1037.956 2.267900e+08 0.0039 \n", "... ... ... ... ... ... \n", "507459 900957.XSHG 2021-08 0.626 3.033640e+05 0.0027 \n", "507460 900957.XSHG 2021-09 0.655 2.086830e+05 0.0017 \n", "507461 900957.XSHG 2021-10 0.636 6.162200e+04 0.0005 \n", "507462 900957.XSHG 2021-11 0.623 1.161060e+05 0.0010 \n", "507463 900957.XSHG 2021-12 0.635 1.059960e+05 0.0009 \n", "507464 900957.XSHG 2022-01 0.617 1.319240e+05 0.0012 \n", "507465 900957.XSHG 2022-02 0.616 9.851400e+04 0.0009 \n", "507466 900957.XSHG 2022-03 0.606 1.005700e+05 0.0009 \n", "\n", " ret mkt_cap mkt_cap_date \n", "1 0.316497 4.266117e+10 2007-06 \n", "2 0.048855 5.616330e+10 2007-07 \n", "3 0.052105 5.890714e+10 2007-08 \n", "4 0.201851 6.197651e+10 2007-09 \n", "5 -0.249116 7.448652e+10 2007-10 \n", "6 0.069845 5.593078e+10 2007-11 \n", "7 -0.137306 6.574629e+10 2007-12 \n", "8 -0.004504 5.850212e+10 2008-01 \n", "... ... ... ... \n", "507459 -0.058462 1.186800e+08 2021-07 \n", "507460 0.089869 1.116880e+08 2021-08 \n", "507461 -0.040480 1.218080e+08 2021-09 \n", "507462 -0.040625 1.168400e+08 2021-10 \n", "507463 0.035831 1.120560e+08 2021-11 \n", "507464 -0.022013 1.161040e+08 2021-12 \n", "507465 -0.011254 1.135280e+08 2022-01 \n", "507466 -0.034146 1.122400e+08 2022-02 \n", "\n", "[475572 rows x 8 columns]" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_df_m" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "### Merge Book and Market Cap data" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDendDatepublishDateTShEquityTEquityAttrPminorityIntpub_monthpub_yeardata_yearbm_datebook
0000001.XSHE2007-12-312008-03-201.300606e+101.300606e+100.000000e+003200820072007-121.300606e+10
1000001.XSHE2008-12-312009-03-201.640079e+101.640079e+100.000000e+003200920082008-121.640079e+10
2000001.XSHE2009-12-312010-03-122.046961e+102.046961e+100.000000e+003201020092009-122.046961e+10
3000001.XSHE2010-12-312011-02-253.351288e+103.351288e+100.000000e+002201120102010-123.351288e+10
4000001.XSHE2011-12-312012-03-097.538058e+107.331084e+102.069747e+093201220112011-127.331084e+10
5000001.XSHE2012-12-312013-03-088.479878e+108.479878e+100.000000e+003201320122012-128.479878e+10
6000001.XSHE2013-12-312014-03-071.120810e+111.120810e+110.000000e+003201420132013-121.120810e+11
7000001.XSHE2014-12-312015-03-131.309490e+111.309490e+110.000000e+003201520142014-121.309490e+11
....................................
42725900957.XSHG2013-12-312014-03-193.890554e+083.877593e+081.296075e+063201420132013-123.877593e+08
42726900957.XSHG2014-12-312015-04-104.072107e+083.940309e+081.317981e+074201520142014-123.940309e+08
42727900957.XSHG2015-12-312016-03-304.106786e+083.973929e+081.328570e+073201620152015-123.973929e+08
42728900957.XSHG2016-12-312017-03-253.938268e+083.930721e+087.546643e+053201720162016-123.930721e+08
42729900957.XSHG2017-12-312018-04-104.238426e+084.231040e+087.386715e+054201820172017-124.231040e+08
42730900957.XSHG2018-12-312019-03-304.515278e+084.508051e+087.226781e+053201920182018-124.508051e+08
42731900957.XSHG2019-12-312020-04-254.768689e+084.761021e+087.667705e+054202020192019-124.761021e+08
42732900957.XSHG2020-12-312021-04-094.987276e+084.979110e+088.165551e+054202120202020-124.979110e+08
\n", "

41820 rows × 11 columns

\n", "
" ], "text/plain": [ " secID endDate publishDate TShEquity TEquityAttrP \\\n", "0 000001.XSHE 2007-12-31 2008-03-20 1.300606e+10 1.300606e+10 \n", "1 000001.XSHE 2008-12-31 2009-03-20 1.640079e+10 1.640079e+10 \n", "2 000001.XSHE 2009-12-31 2010-03-12 2.046961e+10 2.046961e+10 \n", "3 000001.XSHE 2010-12-31 2011-02-25 3.351288e+10 3.351288e+10 \n", "4 000001.XSHE 2011-12-31 2012-03-09 7.538058e+10 7.331084e+10 \n", "5 000001.XSHE 2012-12-31 2013-03-08 8.479878e+10 8.479878e+10 \n", "6 000001.XSHE 2013-12-31 2014-03-07 1.120810e+11 1.120810e+11 \n", "7 000001.XSHE 2014-12-31 2015-03-13 1.309490e+11 1.309490e+11 \n", "... ... ... ... ... ... \n", "42725 900957.XSHG 2013-12-31 2014-03-19 3.890554e+08 3.877593e+08 \n", "42726 900957.XSHG 2014-12-31 2015-04-10 4.072107e+08 3.940309e+08 \n", "42727 900957.XSHG 2015-12-31 2016-03-30 4.106786e+08 3.973929e+08 \n", "42728 900957.XSHG 2016-12-31 2017-03-25 3.938268e+08 3.930721e+08 \n", "42729 900957.XSHG 2017-12-31 2018-04-10 4.238426e+08 4.231040e+08 \n", "42730 900957.XSHG 2018-12-31 2019-03-30 4.515278e+08 4.508051e+08 \n", "42731 900957.XSHG 2019-12-31 2020-04-25 4.768689e+08 4.761021e+08 \n", "42732 900957.XSHG 2020-12-31 2021-04-09 4.987276e+08 4.979110e+08 \n", "\n", " minorityInt pub_month pub_year data_year bm_date book \n", "0 0.000000e+00 3 2008 2007 2007-12 1.300606e+10 \n", "1 0.000000e+00 3 2009 2008 2008-12 1.640079e+10 \n", "2 0.000000e+00 3 2010 2009 2009-12 2.046961e+10 \n", "3 0.000000e+00 2 2011 2010 2010-12 3.351288e+10 \n", "4 2.069747e+09 3 2012 2011 2011-12 7.331084e+10 \n", "5 0.000000e+00 3 2013 2012 2012-12 8.479878e+10 \n", "6 0.000000e+00 3 2014 2013 2013-12 1.120810e+11 \n", "7 0.000000e+00 3 2015 2014 2014-12 1.309490e+11 \n", "... ... ... ... ... ... ... \n", "42725 1.296075e+06 3 2014 2013 2013-12 3.877593e+08 \n", "42726 1.317981e+07 4 2015 2014 2014-12 3.940309e+08 \n", "42727 1.328570e+07 3 2016 2015 2015-12 3.973929e+08 \n", "42728 7.546643e+05 3 2017 2016 2016-12 3.930721e+08 \n", "42729 7.386715e+05 4 2018 2017 2017-12 4.231040e+08 \n", "42730 7.226781e+05 3 2019 2018 2018-12 4.508051e+08 \n", "42731 7.667705e+05 4 2020 2019 2019-12 4.761021e+08 \n", "42732 8.165551e+05 4 2021 2020 2020-12 4.979110e+08 \n", "\n", "[41820 rows x 11 columns]" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fundmen_df" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDympreClosePriceturnoverValueturnoverRateretmkt_capmkt_cap_date
1000001.XSHE2007-071082.2591.479466e+090.02700.3164974.266117e+102007-06
2000001.XSHE2007-081193.0166.552881e+080.01120.0488555.616330e+102007-07
3000001.XSHE2007-091228.1421.408136e+090.02280.0521055.890714e+102007-08
4000001.XSHE2007-101427.1891.440425e+090.02000.2018516.197651e+102007-09
5000001.XSHE2007-111172.4475.452159e+080.0096-0.2491167.448652e+102007-10
6000001.XSHE2007-121234.1551.019671e+090.01540.0698455.593078e+102007-11
7000001.XSHE2008-011074.3475.328429e+080.0089-0.1373066.574629e+102007-12
8000001.XSHE2008-021037.9562.267900e+080.0039-0.0045045.850212e+102008-01
...........................
507459900957.XSHG2021-080.6263.033640e+050.0027-0.0584621.186800e+082021-07
507460900957.XSHG2021-090.6552.086830e+050.00170.0898691.116880e+082021-08
507461900957.XSHG2021-100.6366.162200e+040.0005-0.0404801.218080e+082021-09
507462900957.XSHG2021-110.6231.161060e+050.0010-0.0406251.168400e+082021-10
507463900957.XSHG2021-120.6351.059960e+050.00090.0358311.120560e+082021-11
507464900957.XSHG2022-010.6171.319240e+050.0012-0.0220131.161040e+082021-12
507465900957.XSHG2022-020.6169.851400e+040.0009-0.0112541.135280e+082022-01
507466900957.XSHG2022-030.6061.005700e+050.0009-0.0341461.122400e+082022-02
\n", "

475572 rows × 8 columns

\n", "
" ], "text/plain": [ " secID ym preClosePrice turnoverValue turnoverRate \\\n", "1 000001.XSHE 2007-07 1082.259 1.479466e+09 0.0270 \n", "2 000001.XSHE 2007-08 1193.016 6.552881e+08 0.0112 \n", "3 000001.XSHE 2007-09 1228.142 1.408136e+09 0.0228 \n", "4 000001.XSHE 2007-10 1427.189 1.440425e+09 0.0200 \n", "5 000001.XSHE 2007-11 1172.447 5.452159e+08 0.0096 \n", "6 000001.XSHE 2007-12 1234.155 1.019671e+09 0.0154 \n", "7 000001.XSHE 2008-01 1074.347 5.328429e+08 0.0089 \n", "8 000001.XSHE 2008-02 1037.956 2.267900e+08 0.0039 \n", "... ... ... ... ... ... \n", "507459 900957.XSHG 2021-08 0.626 3.033640e+05 0.0027 \n", "507460 900957.XSHG 2021-09 0.655 2.086830e+05 0.0017 \n", "507461 900957.XSHG 2021-10 0.636 6.162200e+04 0.0005 \n", "507462 900957.XSHG 2021-11 0.623 1.161060e+05 0.0010 \n", "507463 900957.XSHG 2021-12 0.635 1.059960e+05 0.0009 \n", "507464 900957.XSHG 2022-01 0.617 1.319240e+05 0.0012 \n", "507465 900957.XSHG 2022-02 0.616 9.851400e+04 0.0009 \n", "507466 900957.XSHG 2022-03 0.606 1.005700e+05 0.0009 \n", "\n", " ret mkt_cap mkt_cap_date \n", "1 0.316497 4.266117e+10 2007-06 \n", "2 0.048855 5.616330e+10 2007-07 \n", "3 0.052105 5.890714e+10 2007-08 \n", "4 0.201851 6.197651e+10 2007-09 \n", "5 -0.249116 7.448652e+10 2007-10 \n", "6 0.069845 5.593078e+10 2007-11 \n", "7 -0.137306 6.574629e+10 2007-12 \n", "8 -0.004504 5.850212e+10 2008-01 \n", "... ... ... ... \n", "507459 -0.058462 1.186800e+08 2021-07 \n", "507460 0.089869 1.116880e+08 2021-08 \n", "507461 -0.040480 1.218080e+08 2021-09 \n", "507462 -0.040625 1.168400e+08 2021-10 \n", "507463 0.035831 1.120560e+08 2021-11 \n", "507464 -0.022013 1.161040e+08 2021-12 \n", "507465 -0.011254 1.135280e+08 2022-01 \n", "507466 -0.034146 1.122400e+08 2022-02 \n", "\n", "[475572 rows x 8 columns]" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_df_m" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "editable": true }, "outputs": [], "source": [ "bm_df = pd.merge(stk_df_m[['secID','mkt_cap','mkt_cap_date']], fundmen_df[['secID','book','bm_date']],\n", " left_on=['secID','mkt_cap_date'],right_on=['secID','bm_date'])\n", "bm_df['bm'] = bm_df['book'] / bm_df['mkt_cap']\n", "bm_df.drop(['mkt_cap_date','mkt_cap','book'],axis=1,inplace=True)" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDbm_datebm
0000001.XSHE2007-120.197822
1000001.XSHE2008-120.622601
2000001.XSHE2009-120.287250
3000001.XSHE2010-120.683467
4000001.XSHE2011-121.514294
5000001.XSHE2012-121.704572
6000001.XSHE2013-121.640895
7000001.XSHE2014-120.840421
............
36100900957.XSHG2013-123.483285
36101900957.XSHG2014-123.482069
36102900957.XSHG2015-121.465227
36103900957.XSHG2016-121.893849
36104900957.XSHG2017-122.373042
36105900957.XSHG2018-123.977318
36106900957.XSHG2019-124.653798
36107900957.XSHG2020-125.379798
\n", "

36108 rows × 3 columns

\n", "
" ], "text/plain": [ " secID bm_date bm\n", "0 000001.XSHE 2007-12 0.197822\n", "1 000001.XSHE 2008-12 0.622601\n", "2 000001.XSHE 2009-12 0.287250\n", "3 000001.XSHE 2010-12 0.683467\n", "4 000001.XSHE 2011-12 1.514294\n", "5 000001.XSHE 2012-12 1.704572\n", "6 000001.XSHE 2013-12 1.640895\n", "7 000001.XSHE 2014-12 0.840421\n", "... ... ... ...\n", "36100 900957.XSHG 2013-12 3.483285\n", "36101 900957.XSHG 2014-12 3.482069\n", "36102 900957.XSHG 2015-12 1.465227\n", "36103 900957.XSHG 2016-12 1.893849\n", "36104 900957.XSHG 2017-12 2.373042\n", "36105 900957.XSHG 2018-12 3.977318\n", "36106 900957.XSHG 2019-12 4.653798\n", "36107 900957.XSHG 2020-12 5.379798\n", "\n", "[36108 rows x 3 columns]" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bm_df" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "## Merge data" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "### Merge rf, ret, mktcap, beta" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDympreClosePriceturnoverValueturnoverRateretmkt_capmkt_cap_daterfexret
0000001.XSHE2007-071082.2591.479466e+090.02700.3164974.266117e+102007-060.0026200.313877
1000001.XSHE2007-081193.0166.552881e+080.01120.0488555.616330e+102007-070.0026820.046173
2000001.XSHE2007-091228.1421.408136e+090.02280.0521055.890714e+102007-080.0029340.049171
3000001.XSHE2007-101427.1891.440425e+090.02000.2018516.197651e+102007-090.0032500.198601
4000001.XSHE2007-111172.4475.452159e+080.0096-0.2491167.448652e+102007-100.003545-0.252661
5000001.XSHE2007-121234.1551.019671e+090.01540.0698455.593078e+102007-110.0036430.066202
6000001.XSHE2008-011074.3475.328429e+080.0089-0.1373066.574629e+102007-120.003731-0.141037
7000001.XSHE2008-021037.9562.267900e+080.0039-0.0045045.850212e+102008-010.003753-0.008257
.................................
471013900957.XSHG2021-070.6511.151750e+050.00100.0172141.166560e+082021-060.0020150.015199
471014900957.XSHG2021-080.6263.033640e+050.0027-0.0584621.186800e+082021-070.001969-0.060431
471015900957.XSHG2021-090.6552.086830e+050.00170.0898691.116880e+082021-080.0019800.087889
471016900957.XSHG2021-100.6366.162200e+040.0005-0.0404801.218080e+082021-090.002027-0.042507
471017900957.XSHG2021-110.6231.161060e+050.0010-0.0406251.168400e+082021-100.002055-0.042680
471018900957.XSHG2021-120.6351.059960e+050.00090.0358311.120560e+082021-110.0020790.033752
471019900957.XSHG2022-010.6171.319240e+050.0012-0.0220131.161040e+082021-120.002083-0.024096
471020900957.XSHG2022-020.6169.851400e+040.0009-0.0112541.135280e+082022-010.002083-0.013337
\n", "

471021 rows × 10 columns

\n", "
" ], "text/plain": [ " secID ym preClosePrice turnoverValue turnoverRate \\\n", "0 000001.XSHE 2007-07 1082.259 1.479466e+09 0.0270 \n", "1 000001.XSHE 2007-08 1193.016 6.552881e+08 0.0112 \n", "2 000001.XSHE 2007-09 1228.142 1.408136e+09 0.0228 \n", "3 000001.XSHE 2007-10 1427.189 1.440425e+09 0.0200 \n", "4 000001.XSHE 2007-11 1172.447 5.452159e+08 0.0096 \n", "5 000001.XSHE 2007-12 1234.155 1.019671e+09 0.0154 \n", "6 000001.XSHE 2008-01 1074.347 5.328429e+08 0.0089 \n", "7 000001.XSHE 2008-02 1037.956 2.267900e+08 0.0039 \n", "... ... ... ... ... ... \n", "471013 900957.XSHG 2021-07 0.651 1.151750e+05 0.0010 \n", "471014 900957.XSHG 2021-08 0.626 3.033640e+05 0.0027 \n", "471015 900957.XSHG 2021-09 0.655 2.086830e+05 0.0017 \n", "471016 900957.XSHG 2021-10 0.636 6.162200e+04 0.0005 \n", "471017 900957.XSHG 2021-11 0.623 1.161060e+05 0.0010 \n", "471018 900957.XSHG 2021-12 0.635 1.059960e+05 0.0009 \n", "471019 900957.XSHG 2022-01 0.617 1.319240e+05 0.0012 \n", "471020 900957.XSHG 2022-02 0.616 9.851400e+04 0.0009 \n", "\n", " ret mkt_cap mkt_cap_date rf exret \n", "0 0.316497 4.266117e+10 2007-06 0.002620 0.313877 \n", "1 0.048855 5.616330e+10 2007-07 0.002682 0.046173 \n", "2 0.052105 5.890714e+10 2007-08 0.002934 0.049171 \n", "3 0.201851 6.197651e+10 2007-09 0.003250 0.198601 \n", "4 -0.249116 7.448652e+10 2007-10 0.003545 -0.252661 \n", "5 0.069845 5.593078e+10 2007-11 0.003643 0.066202 \n", "6 -0.137306 6.574629e+10 2007-12 0.003731 -0.141037 \n", "7 -0.004504 5.850212e+10 2008-01 0.003753 -0.008257 \n", "... ... ... ... ... ... \n", "471013 0.017214 1.166560e+08 2021-06 0.002015 0.015199 \n", "471014 -0.058462 1.186800e+08 2021-07 0.001969 -0.060431 \n", "471015 0.089869 1.116880e+08 2021-08 0.001980 0.087889 \n", "471016 -0.040480 1.218080e+08 2021-09 0.002027 -0.042507 \n", "471017 -0.040625 1.168400e+08 2021-10 0.002055 -0.042680 \n", "471018 0.035831 1.120560e+08 2021-11 0.002079 0.033752 \n", "471019 -0.022013 1.161040e+08 2021-12 0.002083 -0.024096 \n", "471020 -0.011254 1.135280e+08 2022-01 0.002083 -0.013337 \n", "\n", "[471021 rows x 10 columns]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ret_df = pd.merge(stk_df_m, rf, on='ym')\n", "\n", "ret_df['exret'] = ret_df['ret'] - ret_df['rf']\n", "\n", "ret_df.sort_values(['secID','ym'],inplace=True)\n", "\n", "ret_df.reset_index(drop=True,inplace=True)\n", "\n", "display(ret_df)" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDym_xpreClosePriceturnoverValueturnoverRateretmkt_capmkt_cap_daterfexretym_ybeta
0000001.XSHE2007-071082.2591.479466e+090.02700.3164974.266117e+102007-060.0026200.3138772007-060.4614
1000001.XSHE2007-081193.0166.552881e+080.01120.0488555.616330e+102007-070.0026820.0461732007-070.6423
2000001.XSHE2007-091228.1421.408136e+090.02280.0521055.890714e+102007-080.0029340.0491712007-080.7722
3000001.XSHE2007-101427.1891.440425e+090.02000.2018516.197651e+102007-090.0032500.1986012007-090.7596
4000001.XSHE2007-111172.4475.452159e+080.0096-0.2491167.448652e+102007-100.003545-0.2526612007-100.7988
5000001.XSHE2007-121234.1551.019671e+090.01540.0698455.593078e+102007-110.0036430.0662022007-110.9560
6000001.XSHE2008-011074.3475.328429e+080.0089-0.1373066.574629e+102007-120.003731-0.1410372007-120.9468
7000001.XSHE2008-021037.9562.267900e+080.0039-0.0045045.850212e+102008-010.003753-0.0082572008-010.9654
.......................................
454242689009.XSHG2021-0768.0409.605356e+070.0238-0.1964295.154186e+092021-060.002015-0.1984442021-061.3761
454243689009.XSHG2021-0874.6601.290943e+080.02820.0964914.141757e+092021-070.0019690.0945222021-071.0975
454244689009.XSHG2021-0980.7009.036183e+070.01830.0786674.541392e+092021-080.0019800.0766872021-081.0727
454245689009.XSHG2021-1064.0008.724025e+070.0031-0.2089004.898648e+092021-090.002027-0.2109272021-091.0100
454246689009.XSHG2021-1160.5109.325415e+070.0035-0.0493752.835168e+102021-100.002055-0.0514302021-100.8570
454247689009.XSHG2021-1268.2401.443655e+080.00470.1517092.695182e+102021-110.0020790.1496302021-110.7546
454248689009.XSHG2022-0160.6006.300221e+070.0024-0.1321543.104066e+102021-120.002083-0.1342372021-120.5898
454249689009.XSHG2022-0256.7107.259036e+070.0029-0.0618322.693853e+102022-010.002083-0.0639152022-010.5326
\n", "

454250 rows × 12 columns

\n", "
" ], "text/plain": [ " secID ym_x preClosePrice turnoverValue turnoverRate \\\n", "0 000001.XSHE 2007-07 1082.259 1.479466e+09 0.0270 \n", "1 000001.XSHE 2007-08 1193.016 6.552881e+08 0.0112 \n", "2 000001.XSHE 2007-09 1228.142 1.408136e+09 0.0228 \n", "3 000001.XSHE 2007-10 1427.189 1.440425e+09 0.0200 \n", "4 000001.XSHE 2007-11 1172.447 5.452159e+08 0.0096 \n", "5 000001.XSHE 2007-12 1234.155 1.019671e+09 0.0154 \n", "6 000001.XSHE 2008-01 1074.347 5.328429e+08 0.0089 \n", "7 000001.XSHE 2008-02 1037.956 2.267900e+08 0.0039 \n", "... ... ... ... ... ... \n", "454242 689009.XSHG 2021-07 68.040 9.605356e+07 0.0238 \n", "454243 689009.XSHG 2021-08 74.660 1.290943e+08 0.0282 \n", "454244 689009.XSHG 2021-09 80.700 9.036183e+07 0.0183 \n", "454245 689009.XSHG 2021-10 64.000 8.724025e+07 0.0031 \n", "454246 689009.XSHG 2021-11 60.510 9.325415e+07 0.0035 \n", "454247 689009.XSHG 2021-12 68.240 1.443655e+08 0.0047 \n", "454248 689009.XSHG 2022-01 60.600 6.300221e+07 0.0024 \n", "454249 689009.XSHG 2022-02 56.710 7.259036e+07 0.0029 \n", "\n", " ret mkt_cap mkt_cap_date rf exret ym_y \\\n", "0 0.316497 4.266117e+10 2007-06 0.002620 0.313877 2007-06 \n", "1 0.048855 5.616330e+10 2007-07 0.002682 0.046173 2007-07 \n", "2 0.052105 5.890714e+10 2007-08 0.002934 0.049171 2007-08 \n", "3 0.201851 6.197651e+10 2007-09 0.003250 0.198601 2007-09 \n", "4 -0.249116 7.448652e+10 2007-10 0.003545 -0.252661 2007-10 \n", "5 0.069845 5.593078e+10 2007-11 0.003643 0.066202 2007-11 \n", "6 -0.137306 6.574629e+10 2007-12 0.003731 -0.141037 2007-12 \n", "7 -0.004504 5.850212e+10 2008-01 0.003753 -0.008257 2008-01 \n", "... ... ... ... ... ... ... \n", "454242 -0.196429 5.154186e+09 2021-06 0.002015 -0.198444 2021-06 \n", "454243 0.096491 4.141757e+09 2021-07 0.001969 0.094522 2021-07 \n", "454244 0.078667 4.541392e+09 2021-08 0.001980 0.076687 2021-08 \n", "454245 -0.208900 4.898648e+09 2021-09 0.002027 -0.210927 2021-09 \n", "454246 -0.049375 2.835168e+10 2021-10 0.002055 -0.051430 2021-10 \n", "454247 0.151709 2.695182e+10 2021-11 0.002079 0.149630 2021-11 \n", "454248 -0.132154 3.104066e+10 2021-12 0.002083 -0.134237 2021-12 \n", "454249 -0.061832 2.693853e+10 2022-01 0.002083 -0.063915 2022-01 \n", "\n", " beta \n", "0 0.4614 \n", "1 0.6423 \n", "2 0.7722 \n", "3 0.7596 \n", "4 0.7988 \n", "5 0.9560 \n", "6 0.9468 \n", "7 0.9654 \n", "... ... \n", "454242 1.3761 \n", "454243 1.0975 \n", "454244 1.0727 \n", "454245 1.0100 \n", "454246 0.8570 \n", "454247 0.7546 \n", "454248 0.5898 \n", "454249 0.5326 \n", "\n", "[454250 rows x 12 columns]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Use last month's beta for grouping\n", "ret_df = pd.merge(ret_df,beta_m_df,left_on=['secID','mkt_cap_date'],right_on=['secID','ym'])\n", "display(ret_df)" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "editable": true }, "outputs": [], "source": [ "ret_df.drop(['ym_y'],axis=1,inplace=True)" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "editable": true }, "outputs": [], "source": [ "ret_df.rename(columns={'ym_x':'ret_date',\n", " 'mkt_cap_date':'mktcap_beta_date'},inplace=True)" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDret_datepreClosePriceturnoverValueturnoverRateretmkt_capmktcap_beta_daterfexretbeta
0000001.XSHE2007-071082.2591.479466e+090.02700.3164974.266117e+102007-060.0026200.3138770.4614
1000001.XSHE2007-081193.0166.552881e+080.01120.0488555.616330e+102007-070.0026820.0461730.6423
2000001.XSHE2007-091228.1421.408136e+090.02280.0521055.890714e+102007-080.0029340.0491710.7722
3000001.XSHE2007-101427.1891.440425e+090.02000.2018516.197651e+102007-090.0032500.1986010.7596
4000001.XSHE2007-111172.4475.452159e+080.0096-0.2491167.448652e+102007-100.003545-0.2526610.7988
5000001.XSHE2007-121234.1551.019671e+090.01540.0698455.593078e+102007-110.0036430.0662020.9560
6000001.XSHE2008-011074.3475.328429e+080.0089-0.1373066.574629e+102007-120.003731-0.1410370.9468
7000001.XSHE2008-021037.9562.267900e+080.0039-0.0045045.850212e+102008-010.003753-0.0082570.9654
....................................
454242689009.XSHG2021-0768.0409.605356e+070.0238-0.1964295.154186e+092021-060.002015-0.1984441.3761
454243689009.XSHG2021-0874.6601.290943e+080.02820.0964914.141757e+092021-070.0019690.0945221.0975
454244689009.XSHG2021-0980.7009.036183e+070.01830.0786674.541392e+092021-080.0019800.0766871.0727
454245689009.XSHG2021-1064.0008.724025e+070.0031-0.2089004.898648e+092021-090.002027-0.2109271.0100
454246689009.XSHG2021-1160.5109.325415e+070.0035-0.0493752.835168e+102021-100.002055-0.0514300.8570
454247689009.XSHG2021-1268.2401.443655e+080.00470.1517092.695182e+102021-110.0020790.1496300.7546
454248689009.XSHG2022-0160.6006.300221e+070.0024-0.1321543.104066e+102021-120.002083-0.1342370.5898
454249689009.XSHG2022-0256.7107.259036e+070.0029-0.0618322.693853e+102022-010.002083-0.0639150.5326
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454250 rows × 11 columns

\n", "
" ], "text/plain": [ " secID ret_date preClosePrice turnoverValue turnoverRate \\\n", "0 000001.XSHE 2007-07 1082.259 1.479466e+09 0.0270 \n", "1 000001.XSHE 2007-08 1193.016 6.552881e+08 0.0112 \n", "2 000001.XSHE 2007-09 1228.142 1.408136e+09 0.0228 \n", "3 000001.XSHE 2007-10 1427.189 1.440425e+09 0.0200 \n", "4 000001.XSHE 2007-11 1172.447 5.452159e+08 0.0096 \n", "5 000001.XSHE 2007-12 1234.155 1.019671e+09 0.0154 \n", "6 000001.XSHE 2008-01 1074.347 5.328429e+08 0.0089 \n", "7 000001.XSHE 2008-02 1037.956 2.267900e+08 0.0039 \n", "... ... ... ... ... ... \n", "454242 689009.XSHG 2021-07 68.040 9.605356e+07 0.0238 \n", "454243 689009.XSHG 2021-08 74.660 1.290943e+08 0.0282 \n", "454244 689009.XSHG 2021-09 80.700 9.036183e+07 0.0183 \n", "454245 689009.XSHG 2021-10 64.000 8.724025e+07 0.0031 \n", "454246 689009.XSHG 2021-11 60.510 9.325415e+07 0.0035 \n", "454247 689009.XSHG 2021-12 68.240 1.443655e+08 0.0047 \n", "454248 689009.XSHG 2022-01 60.600 6.300221e+07 0.0024 \n", "454249 689009.XSHG 2022-02 56.710 7.259036e+07 0.0029 \n", "\n", " ret mkt_cap mktcap_beta_date rf exret beta \n", "0 0.316497 4.266117e+10 2007-06 0.002620 0.313877 0.4614 \n", "1 0.048855 5.616330e+10 2007-07 0.002682 0.046173 0.6423 \n", "2 0.052105 5.890714e+10 2007-08 0.002934 0.049171 0.7722 \n", "3 0.201851 6.197651e+10 2007-09 0.003250 0.198601 0.7596 \n", "4 -0.249116 7.448652e+10 2007-10 0.003545 -0.252661 0.7988 \n", "5 0.069845 5.593078e+10 2007-11 0.003643 0.066202 0.9560 \n", "6 -0.137306 6.574629e+10 2007-12 0.003731 -0.141037 0.9468 \n", "7 -0.004504 5.850212e+10 2008-01 0.003753 -0.008257 0.9654 \n", "... ... ... ... ... ... ... \n", "454242 -0.196429 5.154186e+09 2021-06 0.002015 -0.198444 1.3761 \n", "454243 0.096491 4.141757e+09 2021-07 0.001969 0.094522 1.0975 \n", "454244 0.078667 4.541392e+09 2021-08 0.001980 0.076687 1.0727 \n", "454245 -0.208900 4.898648e+09 2021-09 0.002027 -0.210927 1.0100 \n", "454246 -0.049375 2.835168e+10 2021-10 0.002055 -0.051430 0.8570 \n", "454247 0.151709 2.695182e+10 2021-11 0.002079 0.149630 0.7546 \n", "454248 -0.132154 3.104066e+10 2021-12 0.002083 -0.134237 0.5898 \n", "454249 -0.061832 2.693853e+10 2022-01 0.002083 -0.063915 0.5326 \n", "\n", "[454250 rows x 11 columns]" ] }, "execution_count": 73, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ret_df" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "editable": true }, "outputs": [], "source": [ "ret_df = ret_df[['secID','ret_date','ret','rf','exret','mktcap_beta_date','mkt_cap','beta']]" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDret_dateretrfexretmktcap_beta_datemkt_capbeta
0000001.XSHE2007-070.3164970.0026200.3138772007-064.266117e+100.4614
1000001.XSHE2007-080.0488550.0026820.0461732007-075.616330e+100.6423
2000001.XSHE2007-090.0521050.0029340.0491712007-085.890714e+100.7722
3000001.XSHE2007-100.2018510.0032500.1986012007-096.197651e+100.7596
4000001.XSHE2007-11-0.2491160.003545-0.2526612007-107.448652e+100.7988
5000001.XSHE2007-120.0698450.0036430.0662022007-115.593078e+100.9560
6000001.XSHE2008-01-0.1373060.003731-0.1410372007-126.574629e+100.9468
7000001.XSHE2008-02-0.0045040.003753-0.0082572008-015.850212e+100.9654
...........................
454242689009.XSHG2021-07-0.1964290.002015-0.1984442021-065.154186e+091.3761
454243689009.XSHG2021-080.0964910.0019690.0945222021-074.141757e+091.0975
454244689009.XSHG2021-090.0786670.0019800.0766872021-084.541392e+091.0727
454245689009.XSHG2021-10-0.2089000.002027-0.2109272021-094.898648e+091.0100
454246689009.XSHG2021-11-0.0493750.002055-0.0514302021-102.835168e+100.8570
454247689009.XSHG2021-120.1517090.0020790.1496302021-112.695182e+100.7546
454248689009.XSHG2022-01-0.1321540.002083-0.1342372021-123.104066e+100.5898
454249689009.XSHG2022-02-0.0618320.002083-0.0639152022-012.693853e+100.5326
\n", "

454250 rows × 8 columns

\n", "
" ], "text/plain": [ " secID ret_date ret rf exret mktcap_beta_date \\\n", "0 000001.XSHE 2007-07 0.316497 0.002620 0.313877 2007-06 \n", "1 000001.XSHE 2007-08 0.048855 0.002682 0.046173 2007-07 \n", "2 000001.XSHE 2007-09 0.052105 0.002934 0.049171 2007-08 \n", "3 000001.XSHE 2007-10 0.201851 0.003250 0.198601 2007-09 \n", "4 000001.XSHE 2007-11 -0.249116 0.003545 -0.252661 2007-10 \n", "5 000001.XSHE 2007-12 0.069845 0.003643 0.066202 2007-11 \n", "6 000001.XSHE 2008-01 -0.137306 0.003731 -0.141037 2007-12 \n", "7 000001.XSHE 2008-02 -0.004504 0.003753 -0.008257 2008-01 \n", "... ... ... ... ... ... ... \n", "454242 689009.XSHG 2021-07 -0.196429 0.002015 -0.198444 2021-06 \n", "454243 689009.XSHG 2021-08 0.096491 0.001969 0.094522 2021-07 \n", "454244 689009.XSHG 2021-09 0.078667 0.001980 0.076687 2021-08 \n", "454245 689009.XSHG 2021-10 -0.208900 0.002027 -0.210927 2021-09 \n", "454246 689009.XSHG 2021-11 -0.049375 0.002055 -0.051430 2021-10 \n", "454247 689009.XSHG 2021-12 0.151709 0.002079 0.149630 2021-11 \n", "454248 689009.XSHG 2022-01 -0.132154 0.002083 -0.134237 2021-12 \n", "454249 689009.XSHG 2022-02 -0.061832 0.002083 -0.063915 2022-01 \n", "\n", " mkt_cap beta \n", "0 4.266117e+10 0.4614 \n", "1 5.616330e+10 0.6423 \n", "2 5.890714e+10 0.7722 \n", "3 6.197651e+10 0.7596 \n", "4 7.448652e+10 0.7988 \n", "5 5.593078e+10 0.9560 \n", "6 6.574629e+10 0.9468 \n", "7 5.850212e+10 0.9654 \n", "... ... ... \n", "454242 5.154186e+09 1.3761 \n", "454243 4.141757e+09 1.0975 \n", "454244 4.541392e+09 1.0727 \n", "454245 4.898648e+09 1.0100 \n", "454246 2.835168e+10 0.8570 \n", "454247 2.695182e+10 0.7546 \n", "454248 3.104066e+10 0.5898 \n", "454249 2.693853e+10 0.5326 \n", "\n", "[454250 rows x 8 columns]" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ret_df" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "### Merge all data with bm" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "调整return月份对应的bm月份\n", "\n", "例：2007:12月的bm分组，对应的是 2008:07 -- 2009:06 的return\n", "\n", "调整步骤：\n", "1. ret_year - 1, and set this variable as bm_date\n", "2. if ret_month is in [1,2,3,4,5,6], ret_year - 1 again\n", "3. convert bm_date to year-Dec format" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "editable": true }, "outputs": [], "source": [ "ret_df['year'] = ret_df['ret_date'].dt.year\n", "ret_df['month'] = ret_df['ret_date'].dt.month\n", "ret_df['bm_date'] = ret_df['year'] - 1\n", "idx = ret_df['month'].isin([1,2,3,4,5,6])\n", "ret_df.loc[idx,'bm_date'] = ret_df.loc[idx,'bm_date'] - 1" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDret_dateretrfexretmktcap_beta_datemkt_capbetayearmonthbm_date
0000001.XSHE2007-070.3164970.0026200.3138772007-064.266117e+100.4614200772006
1000001.XSHE2007-080.0488550.0026820.0461732007-075.616330e+100.6423200782006
2000001.XSHE2007-090.0521050.0029340.0491712007-085.890714e+100.7722200792006
3000001.XSHE2007-100.2018510.0032500.1986012007-096.197651e+100.75962007102006
4000001.XSHE2007-11-0.2491160.003545-0.2526612007-107.448652e+100.79882007112006
5000001.XSHE2007-120.0698450.0036430.0662022007-115.593078e+100.95602007122006
6000001.XSHE2008-01-0.1373060.003731-0.1410372007-126.574629e+100.9468200812006
7000001.XSHE2008-02-0.0045040.003753-0.0082572008-015.850212e+100.9654200822006
....................................
454242689009.XSHG2021-07-0.1964290.002015-0.1984442021-065.154186e+091.3761202172020
454243689009.XSHG2021-080.0964910.0019690.0945222021-074.141757e+091.0975202182020
454244689009.XSHG2021-090.0786670.0019800.0766872021-084.541392e+091.0727202192020
454245689009.XSHG2021-10-0.2089000.002027-0.2109272021-094.898648e+091.01002021102020
454246689009.XSHG2021-11-0.0493750.002055-0.0514302021-102.835168e+100.85702021112020
454247689009.XSHG2021-120.1517090.0020790.1496302021-112.695182e+100.75462021122020
454248689009.XSHG2022-01-0.1321540.002083-0.1342372021-123.104066e+100.5898202212020
454249689009.XSHG2022-02-0.0618320.002083-0.0639152022-012.693853e+100.5326202222020
\n", "

454250 rows × 11 columns

\n", "
" ], "text/plain": [ " secID ret_date ret rf exret mktcap_beta_date \\\n", "0 000001.XSHE 2007-07 0.316497 0.002620 0.313877 2007-06 \n", "1 000001.XSHE 2007-08 0.048855 0.002682 0.046173 2007-07 \n", "2 000001.XSHE 2007-09 0.052105 0.002934 0.049171 2007-08 \n", "3 000001.XSHE 2007-10 0.201851 0.003250 0.198601 2007-09 \n", "4 000001.XSHE 2007-11 -0.249116 0.003545 -0.252661 2007-10 \n", "5 000001.XSHE 2007-12 0.069845 0.003643 0.066202 2007-11 \n", "6 000001.XSHE 2008-01 -0.137306 0.003731 -0.141037 2007-12 \n", "7 000001.XSHE 2008-02 -0.004504 0.003753 -0.008257 2008-01 \n", "... ... ... ... ... ... ... \n", "454242 689009.XSHG 2021-07 -0.196429 0.002015 -0.198444 2021-06 \n", "454243 689009.XSHG 2021-08 0.096491 0.001969 0.094522 2021-07 \n", "454244 689009.XSHG 2021-09 0.078667 0.001980 0.076687 2021-08 \n", "454245 689009.XSHG 2021-10 -0.208900 0.002027 -0.210927 2021-09 \n", "454246 689009.XSHG 2021-11 -0.049375 0.002055 -0.051430 2021-10 \n", "454247 689009.XSHG 2021-12 0.151709 0.002079 0.149630 2021-11 \n", "454248 689009.XSHG 2022-01 -0.132154 0.002083 -0.134237 2021-12 \n", "454249 689009.XSHG 2022-02 -0.061832 0.002083 -0.063915 2022-01 \n", "\n", " mkt_cap beta year month bm_date \n", "0 4.266117e+10 0.4614 2007 7 2006 \n", "1 5.616330e+10 0.6423 2007 8 2006 \n", "2 5.890714e+10 0.7722 2007 9 2006 \n", "3 6.197651e+10 0.7596 2007 10 2006 \n", "4 7.448652e+10 0.7988 2007 11 2006 \n", "5 5.593078e+10 0.9560 2007 12 2006 \n", "6 6.574629e+10 0.9468 2008 1 2006 \n", "7 5.850212e+10 0.9654 2008 2 2006 \n", "... ... ... ... ... ... \n", "454242 5.154186e+09 1.3761 2021 7 2020 \n", "454243 4.141757e+09 1.0975 2021 8 2020 \n", "454244 4.541392e+09 1.0727 2021 9 2020 \n", "454245 4.898648e+09 1.0100 2021 10 2020 \n", "454246 2.835168e+10 0.8570 2021 11 2020 \n", "454247 2.695182e+10 0.7546 2021 12 2020 \n", "454248 3.104066e+10 0.5898 2022 1 2020 \n", "454249 2.693853e+10 0.5326 2022 2 2020 \n", "\n", "[454250 rows x 11 columns]" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ret_df" ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "editable": true }, "outputs": [ { "data": { "text/plain": [ "0 2006-12-31\n", "1 2006-12-31\n", "2 2006-12-31\n", "3 2006-12-31\n", "4 2006-12-31\n", "5 2006-12-31\n", "6 2006-12-31\n", "7 2006-12-31\n", " ... \n", "454242 2020-12-31\n", "454243 2020-12-31\n", "454244 2020-12-31\n", "454245 2020-12-31\n", "454246 2020-12-31\n", "454247 2020-12-31\n", "454248 2020-12-31\n", "454249 2020-12-31\n", "Name: bm_date, Length: 454250, dtype: datetime64[ns]" ] }, "execution_count": 78, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.to_datetime(ret_df['bm_date'].astype('str'),format='%Y') + pd.tseries.offsets.YearEnd()" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "editable": true }, "outputs": [], "source": [ "ret_df['year'] = ret_df['ret_date'].dt.year\n", "ret_df['month'] = ret_df['ret_date'].dt.month\n", "ret_df['bm_date'] = ret_df['year'] - 1\n", "idx = ret_df['month'].isin([1,2,3,4,5,6])\n", "ret_df.loc[idx,'bm_date'] = ret_df.loc[idx,'bm_date'] - 1\n", "\n", "ret_df['bm_date'] = pd.to_datetime(ret_df['bm_date'].astype('str'),format='%Y') + pd.tseries.offsets.YearEnd()\n", "\n", "ret_df['bm_date'] = ret_df['bm_date'].dt.to_period('M')\n", "\n", "ret_df.drop(['month','year'], axis=1, inplace=True)" ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDret_dateretrfexretmktcap_beta_datemkt_capbetabm_date
0000001.XSHE2007-070.3164970.0026200.3138772007-064.266117e+100.46142006-12
1000001.XSHE2007-080.0488550.0026820.0461732007-075.616330e+100.64232006-12
2000001.XSHE2007-090.0521050.0029340.0491712007-085.890714e+100.77222006-12
3000001.XSHE2007-100.2018510.0032500.1986012007-096.197651e+100.75962006-12
4000001.XSHE2007-11-0.2491160.003545-0.2526612007-107.448652e+100.79882006-12
5000001.XSHE2007-120.0698450.0036430.0662022007-115.593078e+100.95602006-12
6000001.XSHE2008-01-0.1373060.003731-0.1410372007-126.574629e+100.94682006-12
7000001.XSHE2008-02-0.0045040.003753-0.0082572008-015.850212e+100.96542006-12
..............................
454242689009.XSHG2021-07-0.1964290.002015-0.1984442021-065.154186e+091.37612020-12
454243689009.XSHG2021-080.0964910.0019690.0945222021-074.141757e+091.09752020-12
454244689009.XSHG2021-090.0786670.0019800.0766872021-084.541392e+091.07272020-12
454245689009.XSHG2021-10-0.2089000.002027-0.2109272021-094.898648e+091.01002020-12
454246689009.XSHG2021-11-0.0493750.002055-0.0514302021-102.835168e+100.85702020-12
454247689009.XSHG2021-120.1517090.0020790.1496302021-112.695182e+100.75462020-12
454248689009.XSHG2022-01-0.1321540.002083-0.1342372021-123.104066e+100.58982020-12
454249689009.XSHG2022-02-0.0618320.002083-0.0639152022-012.693853e+100.53262020-12
\n", "

454250 rows × 9 columns

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" ], "text/plain": [ " secID ret_date ret rf exret mktcap_beta_date \\\n", "0 000001.XSHE 2007-07 0.316497 0.002620 0.313877 2007-06 \n", "1 000001.XSHE 2007-08 0.048855 0.002682 0.046173 2007-07 \n", "2 000001.XSHE 2007-09 0.052105 0.002934 0.049171 2007-08 \n", "3 000001.XSHE 2007-10 0.201851 0.003250 0.198601 2007-09 \n", "4 000001.XSHE 2007-11 -0.249116 0.003545 -0.252661 2007-10 \n", "5 000001.XSHE 2007-12 0.069845 0.003643 0.066202 2007-11 \n", "6 000001.XSHE 2008-01 -0.137306 0.003731 -0.141037 2007-12 \n", "7 000001.XSHE 2008-02 -0.004504 0.003753 -0.008257 2008-01 \n", "... ... ... ... ... ... ... \n", "454242 689009.XSHG 2021-07 -0.196429 0.002015 -0.198444 2021-06 \n", "454243 689009.XSHG 2021-08 0.096491 0.001969 0.094522 2021-07 \n", "454244 689009.XSHG 2021-09 0.078667 0.001980 0.076687 2021-08 \n", "454245 689009.XSHG 2021-10 -0.208900 0.002027 -0.210927 2021-09 \n", "454246 689009.XSHG 2021-11 -0.049375 0.002055 -0.051430 2021-10 \n", "454247 689009.XSHG 2021-12 0.151709 0.002079 0.149630 2021-11 \n", "454248 689009.XSHG 2022-01 -0.132154 0.002083 -0.134237 2021-12 \n", "454249 689009.XSHG 2022-02 -0.061832 0.002083 -0.063915 2022-01 \n", "\n", " mkt_cap beta bm_date \n", "0 4.266117e+10 0.4614 2006-12 \n", "1 5.616330e+10 0.6423 2006-12 \n", "2 5.890714e+10 0.7722 2006-12 \n", "3 6.197651e+10 0.7596 2006-12 \n", "4 7.448652e+10 0.7988 2006-12 \n", "5 5.593078e+10 0.9560 2006-12 \n", "6 6.574629e+10 0.9468 2006-12 \n", "7 5.850212e+10 0.9654 2006-12 \n", "... ... ... ... \n", "454242 5.154186e+09 1.3761 2020-12 \n", "454243 4.141757e+09 1.0975 2020-12 \n", "454244 4.541392e+09 1.0727 2020-12 \n", "454245 4.898648e+09 1.0100 2020-12 \n", "454246 2.835168e+10 0.8570 2020-12 \n", "454247 2.695182e+10 0.7546 2020-12 \n", "454248 3.104066e+10 0.5898 2020-12 \n", "454249 2.693853e+10 0.5326 2020-12 \n", "\n", "[454250 rows x 9 columns]" ] }, "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ret_df" ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDbm_datebm
0000001.XSHE2007-120.197822
1000001.XSHE2008-120.622601
2000001.XSHE2009-120.287250
3000001.XSHE2010-120.683467
4000001.XSHE2011-121.514294
5000001.XSHE2012-121.704572
6000001.XSHE2013-121.640895
7000001.XSHE2014-120.840421
............
36100900957.XSHG2013-123.483285
36101900957.XSHG2014-123.482069
36102900957.XSHG2015-121.465227
36103900957.XSHG2016-121.893849
36104900957.XSHG2017-122.373042
36105900957.XSHG2018-123.977318
36106900957.XSHG2019-124.653798
36107900957.XSHG2020-125.379798
\n", "

36108 rows × 3 columns

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" ], "text/plain": [ " secID bm_date bm\n", "0 000001.XSHE 2007-12 0.197822\n", "1 000001.XSHE 2008-12 0.622601\n", "2 000001.XSHE 2009-12 0.287250\n", "3 000001.XSHE 2010-12 0.683467\n", "4 000001.XSHE 2011-12 1.514294\n", "5 000001.XSHE 2012-12 1.704572\n", "6 000001.XSHE 2013-12 1.640895\n", "7 000001.XSHE 2014-12 0.840421\n", "... ... ... ...\n", "36100 900957.XSHG 2013-12 3.483285\n", "36101 900957.XSHG 2014-12 3.482069\n", "36102 900957.XSHG 2015-12 1.465227\n", "36103 900957.XSHG 2016-12 1.893849\n", "36104 900957.XSHG 2017-12 2.373042\n", "36105 900957.XSHG 2018-12 3.977318\n", "36106 900957.XSHG 2019-12 4.653798\n", "36107 900957.XSHG 2020-12 5.379798\n", "\n", "[36108 rows x 3 columns]" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bm_df" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "editable": true }, "outputs": [], "source": [ "ret_df = pd.merge(ret_df,bm_df,on=['secID','bm_date'])" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDret_dateretrfexretmktcap_beta_datemkt_capbetabm_datebm
0000001.XSHE2008-070.0760470.0036820.0723652008-064.140495e+101.06722007-120.197822
1000001.XSHE2008-08-0.0288460.003604-0.0324502008-074.455369e+101.09662007-120.197822
2000001.XSHE2008-09-0.2579220.003591-0.2615132008-084.326849e+101.03862007-120.197822
3000001.XSHE2008-10-0.2719590.003522-0.2754812008-093.210865e+101.11842007-120.197822
4000001.XSHE2008-110.0740750.0030630.0710122008-102.330715e+101.19912007-120.197822
5000001.XSHE2008-120.0522790.0019080.0503712008-112.503361e+101.21922007-120.197822
6000001.XSHE2009-010.2304460.0012560.2291902008-122.634237e+101.22062007-120.197822
7000001.XSHE2009-020.1855670.0010880.1844792009-013.241281e+101.25142007-120.197822
.................................
382776689009.XSHG2021-07-0.1964290.002015-0.1984442021-065.154186e+091.37612020-120.747016
382777689009.XSHG2021-080.0964910.0019690.0945222021-074.141757e+091.09752020-120.747016
382778689009.XSHG2021-090.0786670.0019800.0766872021-084.541392e+091.07272020-120.747016
382779689009.XSHG2021-10-0.2089000.002027-0.2109272021-094.898648e+091.01002020-120.747016
382780689009.XSHG2021-11-0.0493750.002055-0.0514302021-102.835168e+100.85702020-120.747016
382781689009.XSHG2021-120.1517090.0020790.1496302021-112.695182e+100.75462020-120.747016
382782689009.XSHG2022-01-0.1321540.002083-0.1342372021-123.104066e+100.58982020-120.747016
382783689009.XSHG2022-02-0.0618320.002083-0.0639152022-012.693853e+100.53262020-120.747016
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382784 rows × 10 columns

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" ], "text/plain": [ " secID ret_date ret rf exret mktcap_beta_date \\\n", "0 000001.XSHE 2008-07 0.076047 0.003682 0.072365 2008-06 \n", "1 000001.XSHE 2008-08 -0.028846 0.003604 -0.032450 2008-07 \n", "2 000001.XSHE 2008-09 -0.257922 0.003591 -0.261513 2008-08 \n", "3 000001.XSHE 2008-10 -0.271959 0.003522 -0.275481 2008-09 \n", "4 000001.XSHE 2008-11 0.074075 0.003063 0.071012 2008-10 \n", "5 000001.XSHE 2008-12 0.052279 0.001908 0.050371 2008-11 \n", "6 000001.XSHE 2009-01 0.230446 0.001256 0.229190 2008-12 \n", "7 000001.XSHE 2009-02 0.185567 0.001088 0.184479 2009-01 \n", "... ... ... ... ... ... ... \n", "382776 689009.XSHG 2021-07 -0.196429 0.002015 -0.198444 2021-06 \n", "382777 689009.XSHG 2021-08 0.096491 0.001969 0.094522 2021-07 \n", "382778 689009.XSHG 2021-09 0.078667 0.001980 0.076687 2021-08 \n", "382779 689009.XSHG 2021-10 -0.208900 0.002027 -0.210927 2021-09 \n", "382780 689009.XSHG 2021-11 -0.049375 0.002055 -0.051430 2021-10 \n", "382781 689009.XSHG 2021-12 0.151709 0.002079 0.149630 2021-11 \n", "382782 689009.XSHG 2022-01 -0.132154 0.002083 -0.134237 2021-12 \n", "382783 689009.XSHG 2022-02 -0.061832 0.002083 -0.063915 2022-01 \n", "\n", " mkt_cap beta bm_date bm \n", "0 4.140495e+10 1.0672 2007-12 0.197822 \n", "1 4.455369e+10 1.0966 2007-12 0.197822 \n", "2 4.326849e+10 1.0386 2007-12 0.197822 \n", "3 3.210865e+10 1.1184 2007-12 0.197822 \n", "4 2.330715e+10 1.1991 2007-12 0.197822 \n", "5 2.503361e+10 1.2192 2007-12 0.197822 \n", "6 2.634237e+10 1.2206 2007-12 0.197822 \n", "7 3.241281e+10 1.2514 2007-12 0.197822 \n", "... ... ... ... ... \n", "382776 5.154186e+09 1.3761 2020-12 0.747016 \n", "382777 4.141757e+09 1.0975 2020-12 0.747016 \n", "382778 4.541392e+09 1.0727 2020-12 0.747016 \n", "382779 4.898648e+09 1.0100 2020-12 0.747016 \n", "382780 2.835168e+10 0.8570 2020-12 0.747016 \n", "382781 2.695182e+10 0.7546 2020-12 0.747016 \n", "382782 3.104066e+10 0.5898 2020-12 0.747016 \n", "382783 2.693853e+10 0.5326 2020-12 0.747016 \n", "\n", "[382784 rows x 10 columns]" ] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ret_df" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "editable": true }, "outputs": [ { "data": { "text/plain": [ "29" ] }, "execution_count": 84, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gc.collect()" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "# Sorting on BM" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "editable": true }, "outputs": [], "source": [ "q = dict()\n", "keys = ['q'+str(i) for i in range(1, 10)]\n", "values = np.arange(0.1, 1.0, 0.1)\n", "q.update(zip(keys,values))" ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "editable": true }, "outputs": [], "source": [ "quantile_df = pd.DataFrame()\n", "for key, value in q.items():\n", " quantile_df[key] = ret_df.groupby(['bm_date'])['bm'].quantile(value)" ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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q1q2q3q4q5q6q7q8q9
bm_date
2007-120.1650450.2170200.2706200.3214110.3795830.4459260.5421910.6426730.832079
2008-120.4084410.5314450.6430240.7595320.9027001.0707961.2668461.5511352.000275
2009-120.1574140.2098460.2548560.3083230.3640620.4150630.5182360.6898590.926290
2010-120.1474940.2114730.2666590.3277240.4107570.5227870.6844300.9272961.275246
2011-120.2497180.3518510.4526780.5591680.6957570.8603201.1048951.4400331.943926
2012-120.2686630.3713700.4733460.5768390.6993340.8622321.0785621.3895331.850029
2013-120.2125230.3063820.3949280.4743190.5686060.6712450.8039961.0138911.361762
2014-120.1846450.2570600.3158390.3794420.4404500.5102830.6069890.7293920.930937
2015-120.1189970.1691880.2110700.2570900.3026810.3596550.4253220.5279730.733326
2016-120.1697270.2337480.2917520.3469740.4029480.4728790.5520140.6742950.901669
2017-120.2268730.3118500.3966280.4711090.5550720.6456800.7546700.8892811.149012
2018-120.3168980.4389670.5457320.6457000.7633180.8680091.0284601.2240731.564529
2019-120.2348590.3387530.4307160.5231360.6156290.7271100.8759811.0750731.392477
2020-120.1891140.2847970.3688750.4559400.5549560.6641320.8121821.0110781.379245
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" ], "text/plain": [ " q1 q2 q3 q4 q5 q6 q7 \\\n", "bm_date \n", "2007-12 0.165045 0.217020 0.270620 0.321411 0.379583 0.445926 0.542191 \n", "2008-12 0.408441 0.531445 0.643024 0.759532 0.902700 1.070796 1.266846 \n", "2009-12 0.157414 0.209846 0.254856 0.308323 0.364062 0.415063 0.518236 \n", "2010-12 0.147494 0.211473 0.266659 0.327724 0.410757 0.522787 0.684430 \n", "2011-12 0.249718 0.351851 0.452678 0.559168 0.695757 0.860320 1.104895 \n", "2012-12 0.268663 0.371370 0.473346 0.576839 0.699334 0.862232 1.078562 \n", "2013-12 0.212523 0.306382 0.394928 0.474319 0.568606 0.671245 0.803996 \n", "2014-12 0.184645 0.257060 0.315839 0.379442 0.440450 0.510283 0.606989 \n", "2015-12 0.118997 0.169188 0.211070 0.257090 0.302681 0.359655 0.425322 \n", "2016-12 0.169727 0.233748 0.291752 0.346974 0.402948 0.472879 0.552014 \n", "2017-12 0.226873 0.311850 0.396628 0.471109 0.555072 0.645680 0.754670 \n", "2018-12 0.316898 0.438967 0.545732 0.645700 0.763318 0.868009 1.028460 \n", "2019-12 0.234859 0.338753 0.430716 0.523136 0.615629 0.727110 0.875981 \n", "2020-12 0.189114 0.284797 0.368875 0.455940 0.554956 0.664132 0.812182 \n", "\n", " q8 q9 \n", "bm_date \n", "2007-12 0.642673 0.832079 \n", "2008-12 1.551135 2.000275 \n", "2009-12 0.689859 0.926290 \n", "2010-12 0.927296 1.275246 \n", "2011-12 1.440033 1.943926 \n", "2012-12 1.389533 1.850029 \n", "2013-12 1.013891 1.361762 \n", "2014-12 0.729392 0.930937 \n", "2015-12 0.527973 0.733326 \n", "2016-12 0.674295 0.901669 \n", "2017-12 0.889281 1.149012 \n", "2018-12 1.224073 1.564529 \n", "2019-12 1.075073 1.392477 \n", "2020-12 1.011078 1.379245 " ] }, "execution_count": 87, "metadata": {}, "output_type": "execute_result" } ], "source": [ "quantile_df" ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "editable": true }, "outputs": [], "source": [ "ret_df_q = pd.merge(ret_df, quantile_df, on='bm_date')" ] }, { "cell_type": "code", "execution_count": 89, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDret_dateretrfexretmktcap_beta_datemkt_capbetabm_datebmq1q2q3q4q5q6q7q8q9
0000001.XSHE2008-070.0760470.0036820.0723652008-064.140495e+101.06722007-120.1978220.1650450.2170200.2706200.3214110.3795830.4459260.5421910.6426730.832079
1000001.XSHE2008-08-0.0288460.003604-0.0324502008-074.455369e+101.09662007-120.1978220.1650450.2170200.2706200.3214110.3795830.4459260.5421910.6426730.832079
2000001.XSHE2008-09-0.2579220.003591-0.2615132008-084.326849e+101.03862007-120.1978220.1650450.2170200.2706200.3214110.3795830.4459260.5421910.6426730.832079
3000001.XSHE2008-10-0.2719590.003522-0.2754812008-093.210865e+101.11842007-120.1978220.1650450.2170200.2706200.3214110.3795830.4459260.5421910.6426730.832079
4000001.XSHE2008-110.0740750.0030630.0710122008-102.330715e+101.19912007-120.1978220.1650450.2170200.2706200.3214110.3795830.4459260.5421910.6426730.832079
5000001.XSHE2008-120.0522790.0019080.0503712008-112.503361e+101.21922007-120.1978220.1650450.2170200.2706200.3214110.3795830.4459260.5421910.6426730.832079
6000001.XSHE2009-010.2304460.0012560.2291902008-122.634237e+101.22062007-120.1978220.1650450.2170200.2706200.3214110.3795830.4459260.5421910.6426730.832079
7000001.XSHE2009-020.1855670.0010880.1844792009-013.241281e+101.25142007-120.1978220.1650450.2170200.2706200.3214110.3795830.4459260.5421910.6426730.832079
............................................................
382776689009.XSHG2021-07-0.1964290.002015-0.1984442021-065.154186e+091.37612020-120.7470160.1891140.2847970.3688750.4559400.5549560.6641320.8121821.0110781.379245
382777689009.XSHG2021-080.0964910.0019690.0945222021-074.141757e+091.09752020-120.7470160.1891140.2847970.3688750.4559400.5549560.6641320.8121821.0110781.379245
382778689009.XSHG2021-090.0786670.0019800.0766872021-084.541392e+091.07272020-120.7470160.1891140.2847970.3688750.4559400.5549560.6641320.8121821.0110781.379245
382779689009.XSHG2021-10-0.2089000.002027-0.2109272021-094.898648e+091.01002020-120.7470160.1891140.2847970.3688750.4559400.5549560.6641320.8121821.0110781.379245
382780689009.XSHG2021-11-0.0493750.002055-0.0514302021-102.835168e+100.85702020-120.7470160.1891140.2847970.3688750.4559400.5549560.6641320.8121821.0110781.379245
382781689009.XSHG2021-120.1517090.0020790.1496302021-112.695182e+100.75462020-120.7470160.1891140.2847970.3688750.4559400.5549560.6641320.8121821.0110781.379245
382782689009.XSHG2022-01-0.1321540.002083-0.1342372021-123.104066e+100.58982020-120.7470160.1891140.2847970.3688750.4559400.5549560.6641320.8121821.0110781.379245
382783689009.XSHG2022-02-0.0618320.002083-0.0639152022-012.693853e+100.53262020-120.7470160.1891140.2847970.3688750.4559400.5549560.6641320.8121821.0110781.379245
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382784 rows × 19 columns

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" ], "text/plain": [ " secID ret_date ret rf exret mktcap_beta_date \\\n", "0 000001.XSHE 2008-07 0.076047 0.003682 0.072365 2008-06 \n", "1 000001.XSHE 2008-08 -0.028846 0.003604 -0.032450 2008-07 \n", "2 000001.XSHE 2008-09 -0.257922 0.003591 -0.261513 2008-08 \n", "3 000001.XSHE 2008-10 -0.271959 0.003522 -0.275481 2008-09 \n", "4 000001.XSHE 2008-11 0.074075 0.003063 0.071012 2008-10 \n", "5 000001.XSHE 2008-12 0.052279 0.001908 0.050371 2008-11 \n", "6 000001.XSHE 2009-01 0.230446 0.001256 0.229190 2008-12 \n", "7 000001.XSHE 2009-02 0.185567 0.001088 0.184479 2009-01 \n", "... ... ... ... ... ... ... \n", "382776 689009.XSHG 2021-07 -0.196429 0.002015 -0.198444 2021-06 \n", "382777 689009.XSHG 2021-08 0.096491 0.001969 0.094522 2021-07 \n", "382778 689009.XSHG 2021-09 0.078667 0.001980 0.076687 2021-08 \n", "382779 689009.XSHG 2021-10 -0.208900 0.002027 -0.210927 2021-09 \n", "382780 689009.XSHG 2021-11 -0.049375 0.002055 -0.051430 2021-10 \n", "382781 689009.XSHG 2021-12 0.151709 0.002079 0.149630 2021-11 \n", "382782 689009.XSHG 2022-01 -0.132154 0.002083 -0.134237 2021-12 \n", "382783 689009.XSHG 2022-02 -0.061832 0.002083 -0.063915 2022-01 \n", "\n", " mkt_cap beta bm_date bm q1 q2 q3 \\\n", "0 4.140495e+10 1.0672 2007-12 0.197822 0.165045 0.217020 0.270620 \n", "1 4.455369e+10 1.0966 2007-12 0.197822 0.165045 0.217020 0.270620 \n", "2 4.326849e+10 1.0386 2007-12 0.197822 0.165045 0.217020 0.270620 \n", "3 3.210865e+10 1.1184 2007-12 0.197822 0.165045 0.217020 0.270620 \n", "4 2.330715e+10 1.1991 2007-12 0.197822 0.165045 0.217020 0.270620 \n", "5 2.503361e+10 1.2192 2007-12 0.197822 0.165045 0.217020 0.270620 \n", "6 2.634237e+10 1.2206 2007-12 0.197822 0.165045 0.217020 0.270620 \n", "7 3.241281e+10 1.2514 2007-12 0.197822 0.165045 0.217020 0.270620 \n", "... ... ... ... ... ... ... ... \n", "382776 5.154186e+09 1.3761 2020-12 0.747016 0.189114 0.284797 0.368875 \n", "382777 4.141757e+09 1.0975 2020-12 0.747016 0.189114 0.284797 0.368875 \n", "382778 4.541392e+09 1.0727 2020-12 0.747016 0.189114 0.284797 0.368875 \n", "382779 4.898648e+09 1.0100 2020-12 0.747016 0.189114 0.284797 0.368875 \n", "382780 2.835168e+10 0.8570 2020-12 0.747016 0.189114 0.284797 0.368875 \n", "382781 2.695182e+10 0.7546 2020-12 0.747016 0.189114 0.284797 0.368875 \n", "382782 3.104066e+10 0.5898 2020-12 0.747016 0.189114 0.284797 0.368875 \n", "382783 2.693853e+10 0.5326 2020-12 0.747016 0.189114 0.284797 0.368875 \n", "\n", " q4 q5 q6 q7 q8 q9 \n", "0 0.321411 0.379583 0.445926 0.542191 0.642673 0.832079 \n", "1 0.321411 0.379583 0.445926 0.542191 0.642673 0.832079 \n", "2 0.321411 0.379583 0.445926 0.542191 0.642673 0.832079 \n", "3 0.321411 0.379583 0.445926 0.542191 0.642673 0.832079 \n", "4 0.321411 0.379583 0.445926 0.542191 0.642673 0.832079 \n", "5 0.321411 0.379583 0.445926 0.542191 0.642673 0.832079 \n", "6 0.321411 0.379583 0.445926 0.542191 0.642673 0.832079 \n", "7 0.321411 0.379583 0.445926 0.542191 0.642673 0.832079 \n", "... ... ... ... ... ... ... \n", "382776 0.455940 0.554956 0.664132 0.812182 1.011078 1.379245 \n", "382777 0.455940 0.554956 0.664132 0.812182 1.011078 1.379245 \n", "382778 0.455940 0.554956 0.664132 0.812182 1.011078 1.379245 \n", "382779 0.455940 0.554956 0.664132 0.812182 1.011078 1.379245 \n", "382780 0.455940 0.554956 0.664132 0.812182 1.011078 1.379245 \n", "382781 0.455940 0.554956 0.664132 0.812182 1.011078 1.379245 \n", "382782 0.455940 0.554956 0.664132 0.812182 1.011078 1.379245 \n", "382783 0.455940 0.554956 0.664132 0.812182 1.011078 1.379245 \n", "\n", "[382784 rows x 19 columns]" ] }, "execution_count": 89, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ret_df_q" ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "editable": true }, "outputs": [], "source": [ "portfolios = dict()\n", "drop_cols = [col for col in ret_df_q.columns if col[0]=='q']\n", "\n", "portfolios['p1'] = ret_df_q.loc[ret_df_q['bm'] <= ret_df_q['q1']].copy().drop(drop_cols, axis=1)\n", "for i in range(2,10):\n", " idx = (ret_df_q[f'q{i-1}'] <= ret_df_q['bm']) & (ret_df_q['bm'] <= ret_df_q[f'q{i}'])\n", " portfolios[f'p{i}'] = ret_df_q.loc[idx].copy().drop(drop_cols, axis=1)\n", "portfolios['p10'] = ret_df_q.loc[ret_df_q['bm'] >= ret_df_q['q9']].copy().drop(drop_cols, axis=1)" ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDret_dateretrfexretmktcap_beta_datemkt_capbetabm_datebm
0000001.XSHE2008-070.0760470.0036820.0723652008-064.140495e+101.06722007-120.197822
1000001.XSHE2008-08-0.0288460.003604-0.0324502008-074.455369e+101.09662007-120.197822
2000001.XSHE2008-09-0.2579220.003591-0.2615132008-084.326849e+101.03862007-120.197822
3000001.XSHE2008-10-0.2719590.003522-0.2754812008-093.210865e+101.11842007-120.197822
4000001.XSHE2008-110.0740750.0030630.0710122008-102.330715e+101.19912007-120.197822
5000001.XSHE2008-120.0522790.0019080.0503712008-112.503361e+101.21922007-120.197822
6000001.XSHE2009-010.2304460.0012560.2291902008-122.634237e+101.22062007-120.197822
7000001.XSHE2009-020.1855670.0010880.1844792009-013.241281e+101.25142007-120.197822
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382408688520.XSHG2021-070.2572980.0020150.2552832021-063.529510e+090.80562020-120.259524
382409688520.XSHG2021-08-0.1721380.001969-0.1741072021-074.437647e+090.92632020-120.259524
382410688520.XSHG2021-09-0.0856870.001980-0.0876672021-083.662040e+091.02922020-120.259524
382411688520.XSHG2021-10-0.0361750.002027-0.0382022021-093.348252e+091.01192020-120.259524
382412688520.XSHG2021-110.3823530.0020550.3802982021-103.227129e+090.97442020-120.259524
382413688520.XSHG2021-12-0.0719280.002079-0.0740072021-114.461031e+090.84762020-120.259524
382414688520.XSHG2022-01-0.0929000.002083-0.0949832021-124.140160e+090.73652020-120.259524
382415688520.XSHG2022-02-0.0132030.002083-0.0152862022-013.755539e+090.58982020-120.259524
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38410 rows × 10 columns

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" ], "text/plain": [ " secID ret_date ret rf exret mktcap_beta_date \\\n", "0 000001.XSHE 2008-07 0.076047 0.003682 0.072365 2008-06 \n", "1 000001.XSHE 2008-08 -0.028846 0.003604 -0.032450 2008-07 \n", "2 000001.XSHE 2008-09 -0.257922 0.003591 -0.261513 2008-08 \n", "3 000001.XSHE 2008-10 -0.271959 0.003522 -0.275481 2008-09 \n", "4 000001.XSHE 2008-11 0.074075 0.003063 0.071012 2008-10 \n", "5 000001.XSHE 2008-12 0.052279 0.001908 0.050371 2008-11 \n", "6 000001.XSHE 2009-01 0.230446 0.001256 0.229190 2008-12 \n", "7 000001.XSHE 2009-02 0.185567 0.001088 0.184479 2009-01 \n", "... ... ... ... ... ... ... \n", "382408 688520.XSHG 2021-07 0.257298 0.002015 0.255283 2021-06 \n", "382409 688520.XSHG 2021-08 -0.172138 0.001969 -0.174107 2021-07 \n", "382410 688520.XSHG 2021-09 -0.085687 0.001980 -0.087667 2021-08 \n", "382411 688520.XSHG 2021-10 -0.036175 0.002027 -0.038202 2021-09 \n", "382412 688520.XSHG 2021-11 0.382353 0.002055 0.380298 2021-10 \n", "382413 688520.XSHG 2021-12 -0.071928 0.002079 -0.074007 2021-11 \n", "382414 688520.XSHG 2022-01 -0.092900 0.002083 -0.094983 2021-12 \n", "382415 688520.XSHG 2022-02 -0.013203 0.002083 -0.015286 2022-01 \n", "\n", " mkt_cap beta bm_date bm \n", "0 4.140495e+10 1.0672 2007-12 0.197822 \n", "1 4.455369e+10 1.0966 2007-12 0.197822 \n", "2 4.326849e+10 1.0386 2007-12 0.197822 \n", "3 3.210865e+10 1.1184 2007-12 0.197822 \n", "4 2.330715e+10 1.1991 2007-12 0.197822 \n", "5 2.503361e+10 1.2192 2007-12 0.197822 \n", "6 2.634237e+10 1.2206 2007-12 0.197822 \n", "7 3.241281e+10 1.2514 2007-12 0.197822 \n", "... ... ... ... ... \n", "382408 3.529510e+09 0.8056 2020-12 0.259524 \n", "382409 4.437647e+09 0.9263 2020-12 0.259524 \n", "382410 3.662040e+09 1.0292 2020-12 0.259524 \n", "382411 3.348252e+09 1.0119 2020-12 0.259524 \n", "382412 3.227129e+09 0.9744 2020-12 0.259524 \n", "382413 4.461031e+09 0.8476 2020-12 0.259524 \n", "382414 4.140160e+09 0.7365 2020-12 0.259524 \n", "382415 3.755539e+09 0.5898 2020-12 0.259524 \n", "\n", "[38410 rows x 10 columns]" ] }, "execution_count": 91, "metadata": {}, "output_type": "execute_result" } ], "source": [ "portfolios['p2']" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "## return by portfolios" ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.007686995138266914\n", "0.008927101401431603\n", "0.010954134717599519\n", "0.01078954531210895\n", "0.01145279919378316\n", "0.012666096075972211\n", "0.012696783212448546\n", "0.012959540565159642\n", "0.012194139695067626\n", "0.009949855657226508\n" ] } ], "source": [ "for k in portfolios.keys():\n", " print(portfolios[k].groupby(['ret_date'])['exret'].mean().mean())" ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "editable": true }, "outputs": [], "source": [ "portfolios_crs_mean = dict()\n", "for k in portfolios.keys():\n", " portfolios_crs_mean[k] = portfolios[k].groupby(['ret_date'])['exret'].mean()" ] }, { "cell_type": "code", "execution_count": 94, "metadata": { "editable": true }, "outputs": [ { "data": { "text/plain": [ "ret_date\n", "2008-07 0.069335\n", "2008-08 -0.255672\n", "2008-09 -0.075177\n", "2008-10 -0.277521\n", "2008-11 0.209310\n", "2008-12 0.069710\n", "2009-01 0.159645\n", "2009-02 0.082529\n", " ... \n", "2021-07 -0.012262\n", "2021-08 -0.000565\n", "2021-09 -0.015866\n", "2021-10 0.006525\n", "2021-11 0.071225\n", "2021-12 0.007837\n", "2022-01 -0.124356\n", "2022-02 0.031726\n", "Freq: M, Name: exret, Length: 164, dtype: float64" ] }, "execution_count": 94, "metadata": {}, "output_type": "execute_result" } ], "source": [ "portfolios_crs_mean['p1']" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "### More robust by adjusting Newey-West Errors" ] }, { "cell_type": "code", "execution_count": 95, "metadata": { "editable": true }, "outputs": [], "source": [ "mean_values = {}\n", "t_values = {}\n", "for k in portfolios_crs_mean.keys():\n", " y = portfolios_crs_mean[k]\n", " const = np.full(shape=len(y),fill_value=1)\n", " reg = sm.OLS(y, const).fit().get_robustcov_results(cov_type='HAC', maxlags=6)\n", " mean_values[k] = reg.params[0]\n", " t_values[k] = reg.tvalues[0]\n", "# Portfolio 10-1\n", "y = portfolios_crs_mean['p10'] - portfolios_crs_mean['p1']\n", "const = np.full(shape=len(y), fill_value=1)\n", "reg = sm.OLS(y, const).fit().get_robustcov_results(cov_type='HAC', maxlags=6)\n", "mean_values['p10-p1'] = reg.params[0]\n", "t_values['p10-p1'] = reg.tvalues[0]" ] }, { "cell_type": "code", "execution_count": 96, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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p1p2p3p4p5p6p7p8p9p10p10-p1
mean0.0076870.0089270.0109540.010790.0114530.0126660.0126970.0129600.0121940.0099500.002263
t-value1.1179511.3031001.6205171.594471.6933401.7679961.7909001.8943461.7462481.4874510.774473
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" ], "text/plain": [ " p1 p2 p3 p4 p5 p6 p7 \\\n", "mean 0.007687 0.008927 0.010954 0.01079 0.011453 0.012666 0.012697 \n", "t-value 1.117951 1.303100 1.620517 1.59447 1.693340 1.767996 1.790900 \n", "\n", " p8 p9 p10 p10-p1 \n", "mean 0.012960 0.012194 0.009950 0.002263 \n", "t-value 1.894346 1.746248 1.487451 0.774473 " ] }, "execution_count": 96, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.DataFrame([mean_values.values(),t_values.values()],index=['mean','t-value'],\n", " columns=mean_values.keys())" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "#### 既然一年调一次仓，年收益率呢？" ] }, { "cell_type": "code", "execution_count": 97, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDret_dateretrfexretmktcap_beta_datemkt_capbetabm_datebm
370000060.XSHE2008-07-0.0064250.003682-0.0101072008-069.793096e+091.41882007-120.161566
371000060.XSHE2008-08-0.2412010.003604-0.2448052008-079.729630e+091.40212007-120.161566
372000060.XSHE2008-09-0.0132500.003591-0.0168412008-087.382785e+091.39252007-120.161566
373000060.XSHE2008-10-0.3806330.003522-0.3841552008-097.285000e+091.31342007-120.161566
374000060.XSHE2008-110.1455180.0030630.1424552008-104.512090e+091.31972007-120.161566
375000060.XSHE2008-120.0540540.0019080.0521462008-115.511400e+091.27902007-120.161566
376000060.XSHE2009-010.3525550.0012560.3512992008-125.809067e+091.28432007-120.161566
377000060.XSHE2009-020.0540330.0010880.0529452009-017.857136e+091.35172007-120.161566
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382112688333.XSHG2021-070.0602920.0020150.0582772021-068.468294e+090.91352020-120.180625
382113688333.XSHG2021-080.1375920.0019690.1356232021-079.171062e+090.55922020-120.180625
382114688333.XSHG2021-09-0.0705260.001980-0.0725062021-081.043292e+100.60452020-120.180625
382115688333.XSHG2021-100.0246380.0020270.0226112021-099.697107e+090.62122020-120.180625
382116688333.XSHG2021-110.0367260.0020550.0346712021-109.936050e+090.69942020-120.180625
382117688333.XSHG2021-12-0.0424810.002079-0.0445602021-111.030095e+100.65702020-120.180625
382118688333.XSHG2022-01-0.2497650.002083-0.2518482021-121.003187e+100.66612020-120.180625
382119688333.XSHG2022-020.1445510.0020830.1424682022-017.526260e+090.83112020-120.180625
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38331 rows × 10 columns

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" ], "text/plain": [ " secID ret_date ret rf exret mktcap_beta_date \\\n", "370 000060.XSHE 2008-07 -0.006425 0.003682 -0.010107 2008-06 \n", "371 000060.XSHE 2008-08 -0.241201 0.003604 -0.244805 2008-07 \n", "372 000060.XSHE 2008-09 -0.013250 0.003591 -0.016841 2008-08 \n", "373 000060.XSHE 2008-10 -0.380633 0.003522 -0.384155 2008-09 \n", "374 000060.XSHE 2008-11 0.145518 0.003063 0.142455 2008-10 \n", "375 000060.XSHE 2008-12 0.054054 0.001908 0.052146 2008-11 \n", "376 000060.XSHE 2009-01 0.352555 0.001256 0.351299 2008-12 \n", "377 000060.XSHE 2009-02 0.054033 0.001088 0.052945 2009-01 \n", "... ... ... ... ... ... ... \n", "382112 688333.XSHG 2021-07 0.060292 0.002015 0.058277 2021-06 \n", "382113 688333.XSHG 2021-08 0.137592 0.001969 0.135623 2021-07 \n", "382114 688333.XSHG 2021-09 -0.070526 0.001980 -0.072506 2021-08 \n", "382115 688333.XSHG 2021-10 0.024638 0.002027 0.022611 2021-09 \n", "382116 688333.XSHG 2021-11 0.036726 0.002055 0.034671 2021-10 \n", "382117 688333.XSHG 2021-12 -0.042481 0.002079 -0.044560 2021-11 \n", "382118 688333.XSHG 2022-01 -0.249765 0.002083 -0.251848 2021-12 \n", "382119 688333.XSHG 2022-02 0.144551 0.002083 0.142468 2022-01 \n", "\n", " mkt_cap beta bm_date bm \n", "370 9.793096e+09 1.4188 2007-12 0.161566 \n", "371 9.729630e+09 1.4021 2007-12 0.161566 \n", "372 7.382785e+09 1.3925 2007-12 0.161566 \n", "373 7.285000e+09 1.3134 2007-12 0.161566 \n", "374 4.512090e+09 1.3197 2007-12 0.161566 \n", "375 5.511400e+09 1.2790 2007-12 0.161566 \n", "376 5.809067e+09 1.2843 2007-12 0.161566 \n", "377 7.857136e+09 1.3517 2007-12 0.161566 \n", "... ... ... ... ... \n", "382112 8.468294e+09 0.9135 2020-12 0.180625 \n", "382113 9.171062e+09 0.5592 2020-12 0.180625 \n", "382114 1.043292e+10 0.6045 2020-12 0.180625 \n", "382115 9.697107e+09 0.6212 2020-12 0.180625 \n", "382116 9.936050e+09 0.6994 2020-12 0.180625 \n", "382117 1.030095e+10 0.6570 2020-12 0.180625 \n", "382118 1.003187e+10 0.6661 2020-12 0.180625 \n", "382119 7.526260e+09 0.8311 2020-12 0.180625 \n", "\n", "[38331 rows x 10 columns]" ] }, "execution_count": 97, "metadata": {}, "output_type": "execute_result" } ], "source": [ "portfolios['p1']" ] }, { "cell_type": "code", "execution_count": 98, "metadata": {}, "outputs": [], "source": [ "portfolios[k]['1+ret'] = portfolios[k]['ret']+1\n", "portfolios[k]['1+rf'] = portfolios[k]['rf']+1" ] }, { "cell_type": "code", "execution_count": 99, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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secIDret_dateretrfexretmktcap_beta_datemkt_capbetabm_datebm1+ret1+rf
48000016.XSHE2008-070.0773250.0036820.0736432008-062.249663e+091.02762007-121.4256491.0773251.003682
49000016.XSHE2008-08-0.1856350.003604-0.1892392008-072.423636e+091.00782007-121.4256490.8143651.003604
50000016.XSHE2008-09-0.0243260.003591-0.0279172008-081.973704e+091.08772007-121.4256490.9756741.003591
51000016.XSHE2008-10-0.1152670.003522-0.1187892008-091.925711e+091.07452007-121.4256490.8847331.003522
52000016.XSHE2008-110.1514190.0030630.1483562008-101.703744e+091.01762007-121.4256491.1514191.003063
53000016.XSHE2008-12-0.0367120.001908-0.0386202008-111.961706e+091.06402007-121.4256490.9632881.001908
54000016.XSHE2009-010.0857350.0012560.0844792008-121.889712e+091.05352007-121.4256491.0857351.001256
55000016.XSHE2009-020.2660600.0010880.2649722009-012.051688e+091.03062007-121.4256491.2660601.001088
.......................................
382768688981.XSHG2021-070.0464250.0020150.0444102021-066.773327e+100.70172020-121.6501171.0464251.002015
382769688981.XSHG2021-08-0.1391250.001969-0.1410942021-071.210375e+110.06752020-121.6501170.8608751.001969
382770688981.XSHG2021-09-0.0087990.001980-0.0107792021-081.041981e+110.07722020-121.6501170.9912011.001980
382771688981.XSHG2021-10-0.0028990.002027-0.0049262021-091.032813e+110.09842020-121.6501170.9971011.002027
382772688981.XSHG2021-11-0.0141720.002055-0.0162272021-101.029819e+110.16282020-121.6501170.9858281.002055
382773688981.XSHG2021-12-0.0234060.002079-0.0254852021-111.015225e+110.11512020-121.6501170.9765941.002079
382774688981.XSHG2022-01-0.0711450.002083-0.0732282021-129.914631e+100.17932020-121.6501170.9288551.002083
382775688981.XSHG2022-020.0306790.0020830.0285962022-019.209250e+100.33032020-121.6501171.0306791.002083
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38351 rows × 12 columns

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" ], "text/plain": [ " secID ret_date ret rf exret mktcap_beta_date \\\n", "48 000016.XSHE 2008-07 0.077325 0.003682 0.073643 2008-06 \n", "49 000016.XSHE 2008-08 -0.185635 0.003604 -0.189239 2008-07 \n", "50 000016.XSHE 2008-09 -0.024326 0.003591 -0.027917 2008-08 \n", "51 000016.XSHE 2008-10 -0.115267 0.003522 -0.118789 2008-09 \n", "52 000016.XSHE 2008-11 0.151419 0.003063 0.148356 2008-10 \n", "53 000016.XSHE 2008-12 -0.036712 0.001908 -0.038620 2008-11 \n", "54 000016.XSHE 2009-01 0.085735 0.001256 0.084479 2008-12 \n", "55 000016.XSHE 2009-02 0.266060 0.001088 0.264972 2009-01 \n", "... ... ... ... ... ... ... \n", "382768 688981.XSHG 2021-07 0.046425 0.002015 0.044410 2021-06 \n", "382769 688981.XSHG 2021-08 -0.139125 0.001969 -0.141094 2021-07 \n", "382770 688981.XSHG 2021-09 -0.008799 0.001980 -0.010779 2021-08 \n", "382771 688981.XSHG 2021-10 -0.002899 0.002027 -0.004926 2021-09 \n", "382772 688981.XSHG 2021-11 -0.014172 0.002055 -0.016227 2021-10 \n", "382773 688981.XSHG 2021-12 -0.023406 0.002079 -0.025485 2021-11 \n", "382774 688981.XSHG 2022-01 -0.071145 0.002083 -0.073228 2021-12 \n", "382775 688981.XSHG 2022-02 0.030679 0.002083 0.028596 2022-01 \n", "\n", " mkt_cap beta bm_date bm 1+ret 1+rf \n", "48 2.249663e+09 1.0276 2007-12 1.425649 1.077325 1.003682 \n", "49 2.423636e+09 1.0078 2007-12 1.425649 0.814365 1.003604 \n", "50 1.973704e+09 1.0877 2007-12 1.425649 0.975674 1.003591 \n", "51 1.925711e+09 1.0745 2007-12 1.425649 0.884733 1.003522 \n", "52 1.703744e+09 1.0176 2007-12 1.425649 1.151419 1.003063 \n", "53 1.961706e+09 1.0640 2007-12 1.425649 0.963288 1.001908 \n", "54 1.889712e+09 1.0535 2007-12 1.425649 1.085735 1.001256 \n", "55 2.051688e+09 1.0306 2007-12 1.425649 1.266060 1.001088 \n", "... ... ... ... ... ... ... \n", "382768 6.773327e+10 0.7017 2020-12 1.650117 1.046425 1.002015 \n", "382769 1.210375e+11 0.0675 2020-12 1.650117 0.860875 1.001969 \n", "382770 1.041981e+11 0.0772 2020-12 1.650117 0.991201 1.001980 \n", "382771 1.032813e+11 0.0984 2020-12 1.650117 0.997101 1.002027 \n", "382772 1.029819e+11 0.1628 2020-12 1.650117 0.985828 1.002055 \n", "382773 1.015225e+11 0.1151 2020-12 1.650117 0.976594 1.002079 \n", "382774 9.914631e+10 0.1793 2020-12 1.650117 0.928855 1.002083 \n", "382775 9.209250e+10 0.3303 2020-12 1.650117 1.030679 1.002083 \n", "\n", "[38351 rows x 12 columns]" ] }, "execution_count": 99, "metadata": {}, "output_type": "execute_result" } ], "source": [ "portfolios[k]" ] }, { "cell_type": "code", "execution_count": 100, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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secIDbm_date1+ret
0000001.XSHE2013-121.776255
1000001.XSHE2015-121.079310
2000001.XSHE2016-120.982198
3000011.XSHE2014-120.904518
4000011.XSHE2015-121.508421
5000011.XSHE2018-121.744011
6000016.XSHE2007-121.311983
7000016.XSHE2010-120.849273
............
3392688590.XSHG2020-121.080757
3393688596.XSHG2020-121.234372
3394688599.XSHG2020-122.726291
3395688600.XSHG2020-121.062760
3396688658.XSHG2020-121.111634
3397688678.XSHG2020-121.136709
3398688679.XSHG2020-120.967354
3399688981.XSHG2020-120.820608
\n", "

3400 rows × 3 columns

\n", "
" ], "text/plain": [ " secID bm_date 1+ret\n", "0 000001.XSHE 2013-12 1.776255\n", "1 000001.XSHE 2015-12 1.079310\n", "2 000001.XSHE 2016-12 0.982198\n", "3 000011.XSHE 2014-12 0.904518\n", "4 000011.XSHE 2015-12 1.508421\n", "5 000011.XSHE 2018-12 1.744011\n", "6 000016.XSHE 2007-12 1.311983\n", "7 000016.XSHE 2010-12 0.849273\n", "... ... ... ...\n", "3392 688590.XSHG 2020-12 1.080757\n", "3393 688596.XSHG 2020-12 1.234372\n", "3394 688599.XSHG 2020-12 2.726291\n", "3395 688600.XSHG 2020-12 1.062760\n", "3396 688658.XSHG 2020-12 1.111634\n", "3397 688678.XSHG 2020-12 1.136709\n", "3398 688679.XSHG 2020-12 0.967354\n", "3399 688981.XSHG 2020-12 0.820608\n", "\n", "[3400 rows x 3 columns]" ] }, "execution_count": 100, "metadata": {}, "output_type": "execute_result" } ], "source": [ "portfolios[k].groupby(['secID','bm_date'],as_index=False)['1+ret'].prod()" ] }, { "cell_type": "code", "execution_count": 101, "metadata": { "editable": true }, "outputs": [], "source": [ "pf_year_ret = {}\n", "for k in portfolios.keys():\n", " portfolios[k]['1+ret'] = portfolios[k]['ret']+1\n", " portfolios[k]['1+rf'] = portfolios[k]['rf']+1\n", " pf_year_ret[k] = portfolios[k].groupby(['secID','bm_date'],as_index=False)['1+ret'].prod()\n", " pf_year_ret[k]['1+rf'] = portfolios[k].groupby(['secID','bm_date'],as_index=False)['1+rf'].prod()['1+rf']\n", " pf_year_ret[k]['ret'] = pf_year_ret[k]['1+ret'] - 1\n", " pf_year_ret[k]['rf'] = pf_year_ret[k]['1+rf'] - 1\n", " pf_year_ret[k]['exret'] = pf_year_ret[k]['ret'] - pf_year_ret[k]['rf']" ] }, { "cell_type": "code", "execution_count": 102, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDbm_date1+ret1+rfretrfexret
0000004.XSHE2011-121.1806201.0398900.1806200.0398900.140730
1000004.XSHE2012-121.3880071.0520170.3880070.0520170.335990
2000004.XSHE2013-122.7737811.0452961.7737810.0452961.728485
3000004.XSHE2014-120.9566221.023163-0.0433780.023163-0.066541
4000004.XSHE2015-120.9610231.017679-0.0389770.017679-0.056656
5000004.XSHE2016-120.7641291.045471-0.2358710.045471-0.281341
6000004.XSHE2017-121.0826531.0301150.0826530.0301150.052539
7000004.XSHE2018-121.4062241.0246730.4062240.0246730.381552
........................
3546688088.XSHG2020-120.6502251.016408-0.3497750.016408-0.366182
3547688099.XSHG2020-121.1722811.0164080.1722810.0164080.155874
3548688111.XSHG2020-120.5400701.016408-0.4599300.016408-0.476337
3549688116.XSHG2020-121.2779621.0164080.2779620.0164080.261554
3550688122.XSHG2020-121.3246511.0164080.3246510.0164080.308243
3551688166.XSHG2020-120.6969551.016408-0.3030450.016408-0.319452
3552688321.XSHG2020-120.6520891.016408-0.3479110.016408-0.364318
3553688333.XSHG2020-120.9791831.016408-0.0208170.016408-0.037224
\n", "

3554 rows × 7 columns

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" ], "text/plain": [ " secID bm_date 1+ret 1+rf ret rf exret\n", "0 000004.XSHE 2011-12 1.180620 1.039890 0.180620 0.039890 0.140730\n", "1 000004.XSHE 2012-12 1.388007 1.052017 0.388007 0.052017 0.335990\n", "2 000004.XSHE 2013-12 2.773781 1.045296 1.773781 0.045296 1.728485\n", "3 000004.XSHE 2014-12 0.956622 1.023163 -0.043378 0.023163 -0.066541\n", "4 000004.XSHE 2015-12 0.961023 1.017679 -0.038977 0.017679 -0.056656\n", "5 000004.XSHE 2016-12 0.764129 1.045471 -0.235871 0.045471 -0.281341\n", "6 000004.XSHE 2017-12 1.082653 1.030115 0.082653 0.030115 0.052539\n", "7 000004.XSHE 2018-12 1.406224 1.024673 0.406224 0.024673 0.381552\n", "... ... ... ... ... ... ... ...\n", "3546 688088.XSHG 2020-12 0.650225 1.016408 -0.349775 0.016408 -0.366182\n", "3547 688099.XSHG 2020-12 1.172281 1.016408 0.172281 0.016408 0.155874\n", "3548 688111.XSHG 2020-12 0.540070 1.016408 -0.459930 0.016408 -0.476337\n", "3549 688116.XSHG 2020-12 1.277962 1.016408 0.277962 0.016408 0.261554\n", "3550 688122.XSHG 2020-12 1.324651 1.016408 0.324651 0.016408 0.308243\n", "3551 688166.XSHG 2020-12 0.696955 1.016408 -0.303045 0.016408 -0.319452\n", "3552 688321.XSHG 2020-12 0.652089 1.016408 -0.347911 0.016408 -0.364318\n", "3553 688333.XSHG 2020-12 0.979183 1.016408 -0.020817 0.016408 -0.037224\n", "\n", "[3554 rows x 7 columns]" ] }, "execution_count": 102, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pf_year_ret['p1']" ] }, { "cell_type": "code", "execution_count": 103, "metadata": { "editable": true }, "outputs": [], "source": [ "portfolios_crs_mean = dict()\n", "for k in pf_year_ret.keys():\n", " portfolios_crs_mean[k] = pf_year_ret[k].groupby(['bm_date'])['exret'].mean()" ] }, { "cell_type": "code", "execution_count": 104, "metadata": { "editable": true }, "outputs": [], "source": [ "mean_values = {}\n", "t_values = {}\n", "for k in portfolios_crs_mean.keys():\n", " y = portfolios_crs_mean[k]\n", " const = np.full(shape=len(y),fill_value=1)\n", " reg = sm.OLS(y, const).fit().get_robustcov_results(cov_type='HAC', maxlags=6)\n", " mean_values[k] = reg.params[0]\n", " t_values[k] = reg.tvalues[0]\n", "# Portfolio 10-1\n", "y = portfolios_crs_mean['p10'] - portfolios_crs_mean['p1']\n", "const = np.full(shape=len(y), fill_value=1)\n", "reg = sm.OLS(y, const).fit().get_robustcov_results(cov_type='HAC', maxlags=6)\n", "mean_values['p10-p1'] = reg.params[0]\n", "t_values['p10-p1'] = reg.tvalues[0]" ] }, { "cell_type": "code", "execution_count": 105, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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p1p2p3p4p5p6p7p8p9p10p10-p1
mean0.0725040.0873010.1164030.1184860.1263470.1493520.1493370.1549010.1470700.1236290.051125
t-value1.5790321.9409712.4752182.4647792.8932192.4517202.6079072.5248532.3221551.9891361.113239
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" ], "text/plain": [ " p1 p2 p3 p4 p5 p6 p7 \\\n", "mean 0.072504 0.087301 0.116403 0.118486 0.126347 0.149352 0.149337 \n", "t-value 1.579032 1.940971 2.475218 2.464779 2.893219 2.451720 2.607907 \n", "\n", " p8 p9 p10 p10-p1 \n", "mean 0.154901 0.147070 0.123629 0.051125 \n", "t-value 2.524853 2.322155 1.989136 1.113239 " ] }, "execution_count": 105, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.DataFrame([mean_values.values(),t_values.values()],index=['mean','t-value'],\n", " columns=mean_values.keys())" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "## Portfolio characteristics other than return" ] }, { "cell_type": "code", "execution_count": 106, "metadata": { "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.15136861815956384\n", "0.26222822667489554\n", "0.34157884267477\n", "0.4185023407240559\n", "0.5011804215768781\n", "0.5970134405487352\n", "0.7171628816047202\n", "0.8809630272323847\n", "1.124573873890512\n", "2.1555032408601416\n" ] } ], "source": [ "# average beta in each portfolio\n", "for key in portfolios.keys():\n", " print(portfolios[key].groupby('bm_date')['bm'].mean().mean()) " ] }, { "cell_type": "code", "execution_count": 107, "metadata": { "editable": true }, "outputs": [ { "data": { "text/plain": [ "bm_date\n", "2007-12 131\n", "2008-12 143\n", "2009-12 154\n", "2010-12 187\n", "2011-12 220\n", "2012-12 235\n", "2013-12 244\n", "2014-12 251\n", "2015-12 270\n", "2016-12 299\n", "2017-12 325\n", "2018-12 345\n", "2019-12 364\n", "2020-12 386\n", "Freq: M, Name: secID, dtype: int64" ] }, "execution_count": 107, "metadata": {}, "output_type": "execute_result" } ], "source": [ "portfolios['p1'].groupby('bm_date')['secID'].nunique()" ] }, { "cell_type": "code", "execution_count": 108, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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p1p2p3p4p5p6p7p8p9p10
bm_date
2007-12131130129129129131128129128129
2008-12143140143143142141141142141140
2009-12154150151150151150148152148149
2010-12187184184187185183183182183182
2011-12220216215213213211212211210210
2012-12235238232233230232234235229230
2013-12244236234238232234231227227225
2014-12251234234230235234234228232225
2015-12270256258254255249249243250242
2016-12299281280280282274275274272272
2017-12325322323320322318319319319316
2018-12345344341340342341341341341341
2019-12364360359354354355354355353353
2020-12386386387385385388386386386386
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" ], "text/plain": [ " p1 p2 p3 p4 p5 p6 p7 p8 p9 p10\n", "bm_date \n", "2007-12 131 130 129 129 129 131 128 129 128 129\n", "2008-12 143 140 143 143 142 141 141 142 141 140\n", "2009-12 154 150 151 150 151 150 148 152 148 149\n", "2010-12 187 184 184 187 185 183 183 182 183 182\n", "2011-12 220 216 215 213 213 211 212 211 210 210\n", "2012-12 235 238 232 233 230 232 234 235 229 230\n", "2013-12 244 236 234 238 232 234 231 227 227 225\n", "2014-12 251 234 234 230 235 234 234 228 232 225\n", "2015-12 270 256 258 254 255 249 249 243 250 242\n", "2016-12 299 281 280 280 282 274 275 274 272 272\n", "2017-12 325 322 323 320 322 318 319 319 319 316\n", "2018-12 345 344 341 340 342 341 341 341 341 341\n", "2019-12 364 360 359 354 354 355 354 355 353 353\n", "2020-12 386 386 387 385 385 388 386 386 386 386" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 108, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "pf_n_stks = pd.DataFrame()\n", "for key, value in portfolios.items():\n", " pf_n_stks[key] = portfolios[key].groupby('bm_date')['secID'].nunique()\n", "\n", "display(pf_n_stks)\n", "\n", "pf_n_stks.plot()" ] }, { "cell_type": "code", "execution_count": 109, "metadata": { "editable": true }, "outputs": [ { "data": { "text/plain": [ "bm_date\n", "2007-12 5.706619e+09\n", "2008-12 8.227056e+09\n", "2009-12 1.075207e+10\n", "2010-12 1.005489e+10\n", "2011-12 8.741191e+09\n", "2012-12 8.194154e+09\n", "2013-12 1.103450e+10\n", "2014-12 1.218596e+10\n", "2015-12 9.465394e+09\n", "2016-12 8.392424e+09\n", "2017-12 1.834939e+10\n", "2018-12 2.199718e+10\n", "2019-12 4.071181e+10\n", "2020-12 5.329208e+10\n", "Freq: M, Name: mkt_cap, dtype: float64" ] }, "execution_count": 109, "metadata": {}, "output_type": "execute_result" } ], "source": [ "portfolios['p1'].groupby('bm_date')['mkt_cap'].mean()" ] }, { "cell_type": "code", "execution_count": 110, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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p1p2p3p4p5p6p7p8p9p10
bm_date
2007-125.706619e+094.911335e+092.866614e+093.460573e+093.851105e+092.821432e+092.637720e+092.310642e+091.689852e+099.742372e+09
2008-128.227056e+099.172598e+096.741681e+096.799605e+097.385338e+095.752056e+095.960823e+095.320634e+097.748789e+092.501994e+10
2009-121.075207e+108.386681e+097.244166e+098.660869e+099.323876e+091.332688e+101.392993e+101.196730e+104.355061e+092.371436e+10
2010-121.005489e+106.828459e+095.520862e+095.784589e+095.764629e+092.088759e+101.088951e+101.564439e+106.728626e+097.476185e+09
2011-128.741191e+095.438506e+095.641143e+098.411833e+091.336214e+101.451525e+101.386424e+107.415260e+093.982197e+094.369501e+09
2012-128.194154e+097.163417e+095.999376e+096.460852e+098.990263e+091.401125e+101.402987e+104.871112e+096.351771e+097.127844e+09
2013-121.103450e+101.079584e+108.969574e+099.696426e+098.587579e+097.489063e+091.499810e+101.838815e+102.525351e+102.198157e+10
2014-121.218596e+101.393161e+101.272978e+101.130383e+101.299155e+101.077122e+101.059753e+102.056093e+101.710194e+103.281785e+10
2015-129.465394e+099.605355e+091.032111e+101.034439e+109.517314e+091.077515e+101.031216e+109.019756e+091.745588e+105.153994e+10
2016-128.392424e+091.401615e+108.367526e+091.028523e+101.015263e+109.388335e+091.014632e+101.207779e+101.800722e+105.309341e+10
2017-121.834939e+109.165983e+097.452655e+098.748445e+099.213470e+099.836744e+097.602803e+091.089099e+101.242159e+102.637575e+10
2018-122.199718e+101.315502e+108.359079e+098.477248e+091.030374e+109.061505e+091.226731e+101.466307e+101.118460e+102.363212e+10
2019-124.071181e+101.919050e+101.226861e+108.960314e+091.354804e+101.325247e+101.566415e+101.099631e+101.183646e+102.546294e+10
2020-125.329208e+101.657290e+101.361160e+101.068867e+108.485962e+091.220237e+101.378971e+101.108452e+101.151842e+102.683413e+10
\n", "
" ], "text/plain": [ " p1 p2 p3 p4 p5 \\\n", "bm_date \n", "2007-12 5.706619e+09 4.911335e+09 2.866614e+09 3.460573e+09 3.851105e+09 \n", "2008-12 8.227056e+09 9.172598e+09 6.741681e+09 6.799605e+09 7.385338e+09 \n", "2009-12 1.075207e+10 8.386681e+09 7.244166e+09 8.660869e+09 9.323876e+09 \n", "2010-12 1.005489e+10 6.828459e+09 5.520862e+09 5.784589e+09 5.764629e+09 \n", "2011-12 8.741191e+09 5.438506e+09 5.641143e+09 8.411833e+09 1.336214e+10 \n", "2012-12 8.194154e+09 7.163417e+09 5.999376e+09 6.460852e+09 8.990263e+09 \n", "2013-12 1.103450e+10 1.079584e+10 8.969574e+09 9.696426e+09 8.587579e+09 \n", "2014-12 1.218596e+10 1.393161e+10 1.272978e+10 1.130383e+10 1.299155e+10 \n", "2015-12 9.465394e+09 9.605355e+09 1.032111e+10 1.034439e+10 9.517314e+09 \n", "2016-12 8.392424e+09 1.401615e+10 8.367526e+09 1.028523e+10 1.015263e+10 \n", "2017-12 1.834939e+10 9.165983e+09 7.452655e+09 8.748445e+09 9.213470e+09 \n", "2018-12 2.199718e+10 1.315502e+10 8.359079e+09 8.477248e+09 1.030374e+10 \n", "2019-12 4.071181e+10 1.919050e+10 1.226861e+10 8.960314e+09 1.354804e+10 \n", "2020-12 5.329208e+10 1.657290e+10 1.361160e+10 1.068867e+10 8.485962e+09 \n", "\n", " p6 p7 p8 p9 p10 \n", "bm_date \n", "2007-12 2.821432e+09 2.637720e+09 2.310642e+09 1.689852e+09 9.742372e+09 \n", "2008-12 5.752056e+09 5.960823e+09 5.320634e+09 7.748789e+09 2.501994e+10 \n", "2009-12 1.332688e+10 1.392993e+10 1.196730e+10 4.355061e+09 2.371436e+10 \n", "2010-12 2.088759e+10 1.088951e+10 1.564439e+10 6.728626e+09 7.476185e+09 \n", "2011-12 1.451525e+10 1.386424e+10 7.415260e+09 3.982197e+09 4.369501e+09 \n", "2012-12 1.401125e+10 1.402987e+10 4.871112e+09 6.351771e+09 7.127844e+09 \n", "2013-12 7.489063e+09 1.499810e+10 1.838815e+10 2.525351e+10 2.198157e+10 \n", "2014-12 1.077122e+10 1.059753e+10 2.056093e+10 1.710194e+10 3.281785e+10 \n", "2015-12 1.077515e+10 1.031216e+10 9.019756e+09 1.745588e+10 5.153994e+10 \n", "2016-12 9.388335e+09 1.014632e+10 1.207779e+10 1.800722e+10 5.309341e+10 \n", "2017-12 9.836744e+09 7.602803e+09 1.089099e+10 1.242159e+10 2.637575e+10 \n", "2018-12 9.061505e+09 1.226731e+10 1.466307e+10 1.118460e+10 2.363212e+10 \n", "2019-12 1.325247e+10 1.566415e+10 1.099631e+10 1.183646e+10 2.546294e+10 \n", "2020-12 1.220237e+10 1.378971e+10 1.108452e+10 1.151842e+10 2.683413e+10 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 110, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "pf_mktcap = pd.DataFrame()\n", "for key, value in portfolios.items():\n", " pf_mktcap[key] = portfolios[key].groupby('bm_date')['mkt_cap'].mean()\n", "\n", "display(pf_mktcap)\n", "\n", "pf_mktcap.plot()" ] }, { "cell_type": "code", "execution_count": 111, "metadata": { "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.6221765578692882\n", "1.0595311136471859\n", "0.8292412377865788\n", "0.8434491366325018\n", "0.9391259982795449\n", "1.1006523055900357\n", "1.1192155284723835\n", "1.1086489090168263\n", "1.1116850291435363\n", "2.422770766411697\n" ] } ], "source": [ "pf_mktcap = pf_mktcap / 1e10\n", "for i in range(10):\n", " print(pf_mktcap.mean()[i])" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "## BM 1年调仓单排结论" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "排序方法：t年7月至t+1年6月，按照t-1年12月的BM排序，考察区间内每月平均收益率以及区间年平均收益率\n", "\n", "结论：\n", "- 月、年平均收益率呈现微弱递增，但p10的收益率较差。年收益的显著性比较强。\n", "- 最大BM组（也即估值最低组）的market cap起伏很大\n", "- 最小BM组（也即估值最高组）的market cap在样本后期显著增大\n", "- BM的效应可能和market cap有关系" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "## BM Point-in-Time" ] }, { "cell_type": "code", "execution_count": 112, "metadata": { "editable": true }, "outputs": [], "source": [ "del portfolios, portfolios_crs_mean" ] }, { "cell_type": "code", "execution_count": 113, "metadata": { "editable": true }, "outputs": [], "source": [ "# fundmen_df = DataAPI.FdmtBSGet(secID=stk_id,beginDate=START,endDate=END,publishDateEnd=u\"\",publishDateBegin=u\"\",endDateRep=\"\",beginDateRep=\"\",beginYear=\"\",endYear=\"\",fiscalPeriod=\"\",field=[\"secID\",\"publishDate\",\"endDate\",\"endDateRep\",\"actPubtime\",\"fiscalPeriod\",\"TShEquity\",\"TEquityAttrP\",\"minorityInt\"],pandas=\"1\")\n", "\n", "# fundmen_df.to_pickle('./data/fundmen_df_pit.pkl')" ] }, { "cell_type": "code", "execution_count": 114, "metadata": { "editable": true }, "outputs": [], "source": [ "fundmen_df = pd.read_pickle('./data/fundmen_df_pit.pkl')" ] }, { "cell_type": "code", "execution_count": 115, "metadata": { "editable": true }, "outputs": [], "source": [ "fundmen_df[['publishDate','endDate']] = fundmen_df[['publishDate','endDate']].apply(pd.to_datetime)" ] }, { "cell_type": "code", "execution_count": 116, "metadata": { "editable": true }, "outputs": [], "source": [ "fundmen_df.sort_values(['secID','publishDate','endDate'],inplace=True)" ] }, { "cell_type": "code", "execution_count": 117, "metadata": { "editable": true }, "outputs": [], "source": [ "fundmen_df = fundmen_df.groupby(['secID','publishDate'],as_index=False).last()" ] }, { "cell_type": "code", "execution_count": 118, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDpublishDateendDateendDateRepactPubtimefiscalPeriodTShEquityTEquityAttrPminorityInt
0000001.XSHE2007-04-262007-03-312007-03-312007-04-25 18:00:0037.106094e+097.106094e+09NaN
1000001.XSHE2007-08-162007-06-302007-06-302007-08-15 18:00:0067.698478e+097.698478e+09NaN
2000001.XSHE2007-10-232007-09-302007-09-302007-10-22 18:00:0098.363553e+098.363553e+09NaN
3000001.XSHE2008-03-202007-12-312007-12-312008-03-19 18:00:00121.300606e+101.300606e+10NaN
4000001.XSHE2008-04-242008-03-312008-03-312008-04-23 18:00:0031.404138e+101.404138e+10NaN
5000001.XSHE2008-08-212008-06-302008-06-302008-08-20 18:00:0061.694330e+101.694330e+10NaN
6000001.XSHE2008-10-242008-09-302008-09-302008-10-23 18:00:0091.837466e+101.837466e+10NaN
7000001.XSHE2009-03-202008-12-312008-12-312009-03-19 18:00:00121.640079e+101.640079e+10NaN
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176643900957.XSHG2020-04-252019-12-312019-12-312020-04-24 16:29:07124.768689e+084.761021e+08766770.50
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176647900957.XSHG2021-04-092020-12-312020-12-312021-04-08 18:13:16124.987276e+084.979110e+08816555.06
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176651 rows × 9 columns

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" ], "text/plain": [ " secID publishDate endDate endDateRep actPubtime \\\n", "0 000001.XSHE 2007-04-26 2007-03-31 2007-03-31 2007-04-25 18:00:00 \n", "1 000001.XSHE 2007-08-16 2007-06-30 2007-06-30 2007-08-15 18:00:00 \n", "2 000001.XSHE 2007-10-23 2007-09-30 2007-09-30 2007-10-22 18:00:00 \n", "3 000001.XSHE 2008-03-20 2007-12-31 2007-12-31 2008-03-19 18:00:00 \n", "4 000001.XSHE 2008-04-24 2008-03-31 2008-03-31 2008-04-23 18:00:00 \n", "5 000001.XSHE 2008-08-21 2008-06-30 2008-06-30 2008-08-20 18:00:00 \n", "6 000001.XSHE 2008-10-24 2008-09-30 2008-09-30 2008-10-23 18:00:00 \n", "7 000001.XSHE 2009-03-20 2008-12-31 2008-12-31 2009-03-19 18:00:00 \n", "... ... ... ... ... ... \n", "176643 900957.XSHG 2020-04-25 2019-12-31 2019-12-31 2020-04-24 16:29:07 \n", "176644 900957.XSHG 2020-04-29 2020-03-31 2020-03-31 2020-04-28 16:20:18 \n", "176645 900957.XSHG 2020-08-12 2020-06-30 2020-06-30 2020-08-11 15:34:09 \n", "176646 900957.XSHG 2020-10-29 2020-09-30 2020-09-30 2020-10-28 15:44:36 \n", "176647 900957.XSHG 2021-04-09 2020-12-31 2020-12-31 2021-04-08 18:13:16 \n", "176648 900957.XSHG 2021-04-27 2021-03-31 2021-03-31 2021-04-26 16:30:45 \n", "176649 900957.XSHG 2021-08-12 2021-06-30 2021-06-30 2021-08-11 16:03:10 \n", "176650 900957.XSHG 2021-10-29 2021-09-30 2021-09-30 2021-10-28 15:35:42 \n", "\n", " fiscalPeriod TShEquity TEquityAttrP minorityInt \n", "0 3 7.106094e+09 7.106094e+09 NaN \n", "1 6 7.698478e+09 7.698478e+09 NaN \n", "2 9 8.363553e+09 8.363553e+09 NaN \n", "3 12 1.300606e+10 1.300606e+10 NaN \n", "4 3 1.404138e+10 1.404138e+10 NaN \n", "5 6 1.694330e+10 1.694330e+10 NaN \n", "6 9 1.837466e+10 1.837466e+10 NaN \n", "7 12 1.640079e+10 1.640079e+10 NaN \n", "... ... ... ... ... \n", "176643 12 4.768689e+08 4.761021e+08 766770.50 \n", "176644 3 4.790251e+08 4.782593e+08 765868.18 \n", "176645 6 4.879662e+08 4.871592e+08 807021.03 \n", "176646 9 4.936938e+08 4.928884e+08 805424.48 \n", "176647 12 4.987276e+08 4.979110e+08 816555.06 \n", "176648 3 5.070935e+08 5.062701e+08 823373.23 \n", "176649 6 5.136414e+08 5.128208e+08 820511.29 \n", "176650 9 5.197039e+08 5.188844e+08 819528.99 \n", "\n", "[176651 rows x 9 columns]" ] }, "execution_count": 118, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fundmen_df" ] }, { "cell_type": "code", "execution_count": 119, "metadata": { "editable": true }, "outputs": [], "source": [ "fundmen_df = fundmen_df[(fundmen_df['secID'].str.endswith('XSHE')) | (fundmen_df['secID'].str.endswith('XSHG'))]" ] }, { "cell_type": "code", "execution_count": 120, "metadata": { "editable": true }, "outputs": [], "source": [ "# # minorityInt 有时报告，有时不报告。空值时，假设就是上一次报告的值\n", "# # fundmen_df['minorityInt'] = fundmen_df.groupby('secID')['minorityInt'].fillna(method='ffill')\n", "# # 第一轮填完空值为有效数值后，剩下的空值再用0填充。\n", "# fundmen_df['minorityInt'].fillna(0,inplace=True)" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "- 假设是上一次报告的值可能出现误差，因为股权变动了（注意ffill的方法）\n", "- 直接用TEquityAttrP" ] }, { "cell_type": "code", "execution_count": 121, "metadata": { "editable": true }, "outputs": [], "source": [ "# fundmen_df['book'] = fundmen_df['TShEquity'] - fundmen_df['minorityInt']\n", "fundmen_df['book'] = fundmen_df['TEquityAttrP']" ] }, { "cell_type": "code", "execution_count": 122, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDpublishDateendDateendDateRepactPubtimefiscalPeriodTShEquityTEquityAttrPminorityIntbook
0000001.XSHE2007-04-262007-03-312007-03-312007-04-25 18:00:0037.106094e+097.106094e+09NaN7.106094e+09
1000001.XSHE2007-08-162007-06-302007-06-302007-08-15 18:00:0067.698478e+097.698478e+09NaN7.698478e+09
2000001.XSHE2007-10-232007-09-302007-09-302007-10-22 18:00:0098.363553e+098.363553e+09NaN8.363553e+09
3000001.XSHE2008-03-202007-12-312007-12-312008-03-19 18:00:00121.300606e+101.300606e+10NaN1.300606e+10
4000001.XSHE2008-04-242008-03-312008-03-312008-04-23 18:00:0031.404138e+101.404138e+10NaN1.404138e+10
5000001.XSHE2008-08-212008-06-302008-06-302008-08-20 18:00:0061.694330e+101.694330e+10NaN1.694330e+10
6000001.XSHE2008-10-242008-09-302008-09-302008-10-23 18:00:0091.837466e+101.837466e+10NaN1.837466e+10
7000001.XSHE2009-03-202008-12-312008-12-312009-03-19 18:00:00121.640079e+101.640079e+10NaN1.640079e+10
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176590 rows × 10 columns

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" ], "text/plain": [ " secID publishDate endDate endDateRep actPubtime \\\n", "0 000001.XSHE 2007-04-26 2007-03-31 2007-03-31 2007-04-25 18:00:00 \n", "1 000001.XSHE 2007-08-16 2007-06-30 2007-06-30 2007-08-15 18:00:00 \n", "2 000001.XSHE 2007-10-23 2007-09-30 2007-09-30 2007-10-22 18:00:00 \n", "3 000001.XSHE 2008-03-20 2007-12-31 2007-12-31 2008-03-19 18:00:00 \n", "4 000001.XSHE 2008-04-24 2008-03-31 2008-03-31 2008-04-23 18:00:00 \n", "5 000001.XSHE 2008-08-21 2008-06-30 2008-06-30 2008-08-20 18:00:00 \n", "6 000001.XSHE 2008-10-24 2008-09-30 2008-09-30 2008-10-23 18:00:00 \n", "7 000001.XSHE 2009-03-20 2008-12-31 2008-12-31 2009-03-19 18:00:00 \n", "... ... ... ... ... ... \n", "176643 900957.XSHG 2020-04-25 2019-12-31 2019-12-31 2020-04-24 16:29:07 \n", "176644 900957.XSHG 2020-04-29 2020-03-31 2020-03-31 2020-04-28 16:20:18 \n", "176645 900957.XSHG 2020-08-12 2020-06-30 2020-06-30 2020-08-11 15:34:09 \n", "176646 900957.XSHG 2020-10-29 2020-09-30 2020-09-30 2020-10-28 15:44:36 \n", "176647 900957.XSHG 2021-04-09 2020-12-31 2020-12-31 2021-04-08 18:13:16 \n", "176648 900957.XSHG 2021-04-27 2021-03-31 2021-03-31 2021-04-26 16:30:45 \n", "176649 900957.XSHG 2021-08-12 2021-06-30 2021-06-30 2021-08-11 16:03:10 \n", "176650 900957.XSHG 2021-10-29 2021-09-30 2021-09-30 2021-10-28 15:35:42 \n", "\n", " fiscalPeriod TShEquity TEquityAttrP minorityInt book \n", "0 3 7.106094e+09 7.106094e+09 NaN 7.106094e+09 \n", "1 6 7.698478e+09 7.698478e+09 NaN 7.698478e+09 \n", "2 9 8.363553e+09 8.363553e+09 NaN 8.363553e+09 \n", "3 12 1.300606e+10 1.300606e+10 NaN 1.300606e+10 \n", "4 3 1.404138e+10 1.404138e+10 NaN 1.404138e+10 \n", "5 6 1.694330e+10 1.694330e+10 NaN 1.694330e+10 \n", "6 9 1.837466e+10 1.837466e+10 NaN 1.837466e+10 \n", "7 12 1.640079e+10 1.640079e+10 NaN 1.640079e+10 \n", "... ... ... ... ... ... \n", "176643 12 4.768689e+08 4.761021e+08 766770.50 4.761021e+08 \n", "176644 3 4.790251e+08 4.782593e+08 765868.18 4.782593e+08 \n", "176645 6 4.879662e+08 4.871592e+08 807021.03 4.871592e+08 \n", "176646 9 4.936938e+08 4.928884e+08 805424.48 4.928884e+08 \n", "176647 12 4.987276e+08 4.979110e+08 816555.06 4.979110e+08 \n", "176648 3 5.070935e+08 5.062701e+08 823373.23 5.062701e+08 \n", "176649 6 5.136414e+08 5.128208e+08 820511.29 5.128208e+08 \n", "176650 9 5.197039e+08 5.188844e+08 819528.99 5.188844e+08 \n", "\n", "[176590 rows x 10 columns]" ] }, "execution_count": 122, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fundmen_df" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "- ~~考虑到报表公布时间可能在当天收市以后，以及报表解读可能需要时间，把publishDate往后加1~~\n", "- publishDate和 tradeDate merge, \n", "- publishDate可能是非交易日，所以merge时要outer，左右表格都不丢观测值。\n", "- 接着要把tradeDate为空值的设置为publishDate，便于排序，方便下面填充\n", "- 然后按照secID和tradeDate sort，因为publishDate非交易日的被放到merge表格的最后了。\n", "- 接着 book 空值由上面第一个非空值填充（当时已知的最新的book value）\n", "- 再把 na 都丢弃即可" ] }, { "cell_type": "code", "execution_count": 123, "metadata": { "editable": true }, "outputs": [], "source": [ "# fundmen_df['publishDate+1'] = fundmen_df['publishDate'] + dt.timedelta(days=1)" ] }, { "cell_type": "code", "execution_count": 124, "metadata": { "editable": true }, "outputs": [], "source": [ "stk_fundmen_df = pd.merge(stk_df, fundmen_df[['secID','publishDate','endDate','book']], \n", " left_on=['secID','tradeDate'], right_on=['secID','publishDate'],\n", " how='outer')" ] }, { "cell_type": "code", "execution_count": 125, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDtradeDatepreClosePriceclosePricenegMarketValueturnoverValueturnoverRateympublishDateendDatebook
0000001.XSHE2007-06-20824.193987.0074.835036e+104.182345e+090.08402007-06NaTNaTNaN
1000001.XSHE2007-06-21987.0071085.7405.318694e+102.285485e+090.04402007-06NaTNaTNaN
2000001.XSHE2007-06-221085.7401120.2335.487665e+102.761567e+090.05102007-06NaTNaTNaN
3000001.XSHE2007-06-251120.2331113.9045.456661e+102.324186e+090.04262007-06NaTNaTNaN
4000001.XSHE2007-06-271113.9041019.6024.994705e+102.446556e+090.04892007-06NaTNaTNaN
5000001.XSHE2007-06-281019.602953.7804.672266e+101.617434e+090.03362007-06NaTNaTNaN
6000001.XSHE2007-06-29953.780870.8704.266117e+101.410758e+090.03162007-06NaTNaTNaN
7000001.XSHE2007-07-02870.870867.0734.247515e+108.756147e+080.02092007-07NaTNaTNaN
....................................
9592666900957.XSHGNaTNaNNaNNaNNaNNaNNaT2015-04-252015-03-313.938849e+08
9592667900957.XSHGNaTNaNNaNNaNNaNNaNNaT2015-08-152015-06-303.952715e+08
9592668900957.XSHGNaTNaNNaNNaNNaNNaNNaT2016-04-232016-03-314.005150e+08
9592669900957.XSHGNaTNaNNaNNaNNaNNaNNaT2016-08-062016-06-303.906354e+08
9592670900957.XSHGNaTNaNNaNNaNNaNNaNNaT2017-03-252016-12-313.930721e+08
9592671900957.XSHGNaTNaNNaNNaNNaNNaNNaT2019-03-302018-12-314.508051e+08
9592672900957.XSHGNaTNaNNaNNaNNaNNaNNaT2019-08-102019-06-304.618426e+08
9592673900957.XSHGNaTNaNNaNNaNNaNNaNNaT2020-04-252019-12-314.761021e+08
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9592674 rows × 11 columns

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" ], "text/plain": [ " secID tradeDate preClosePrice closePrice negMarketValue \\\n", "0 000001.XSHE 2007-06-20 824.193 987.007 4.835036e+10 \n", "1 000001.XSHE 2007-06-21 987.007 1085.740 5.318694e+10 \n", "2 000001.XSHE 2007-06-22 1085.740 1120.233 5.487665e+10 \n", "3 000001.XSHE 2007-06-25 1120.233 1113.904 5.456661e+10 \n", "4 000001.XSHE 2007-06-27 1113.904 1019.602 4.994705e+10 \n", "5 000001.XSHE 2007-06-28 1019.602 953.780 4.672266e+10 \n", "6 000001.XSHE 2007-06-29 953.780 870.870 4.266117e+10 \n", "7 000001.XSHE 2007-07-02 870.870 867.073 4.247515e+10 \n", "... ... ... ... ... ... \n", "9592666 900957.XSHG NaT NaN NaN NaN \n", "9592667 900957.XSHG NaT NaN NaN NaN \n", "9592668 900957.XSHG NaT NaN NaN NaN \n", "9592669 900957.XSHG NaT NaN NaN NaN \n", "9592670 900957.XSHG NaT NaN NaN NaN \n", "9592671 900957.XSHG NaT NaN NaN NaN \n", "9592672 900957.XSHG NaT NaN NaN NaN \n", "9592673 900957.XSHG NaT NaN NaN NaN \n", "\n", " turnoverValue turnoverRate ym publishDate endDate \\\n", "0 4.182345e+09 0.0840 2007-06 NaT NaT \n", "1 2.285485e+09 0.0440 2007-06 NaT NaT \n", "2 2.761567e+09 0.0510 2007-06 NaT NaT \n", "3 2.324186e+09 0.0426 2007-06 NaT NaT \n", "4 2.446556e+09 0.0489 2007-06 NaT NaT \n", "5 1.617434e+09 0.0336 2007-06 NaT NaT \n", "6 1.410758e+09 0.0316 2007-06 NaT NaT \n", "7 8.756147e+08 0.0209 2007-07 NaT NaT \n", "... ... ... ... ... ... \n", "9592666 NaN NaN NaT 2015-04-25 2015-03-31 \n", "9592667 NaN NaN NaT 2015-08-15 2015-06-30 \n", "9592668 NaN NaN NaT 2016-04-23 2016-03-31 \n", "9592669 NaN NaN NaT 2016-08-06 2016-06-30 \n", "9592670 NaN NaN NaT 2017-03-25 2016-12-31 \n", "9592671 NaN NaN NaT 2019-03-30 2018-12-31 \n", "9592672 NaN NaN NaT 2019-08-10 2019-06-30 \n", "9592673 NaN NaN NaT 2020-04-25 2019-12-31 \n", "\n", " book \n", "0 NaN \n", "1 NaN \n", "2 NaN \n", "3 NaN \n", "4 NaN \n", "5 NaN \n", "6 NaN \n", "7 NaN \n", "... ... \n", "9592666 3.938849e+08 \n", "9592667 3.952715e+08 \n", "9592668 4.005150e+08 \n", "9592669 3.906354e+08 \n", "9592670 3.930721e+08 \n", "9592671 4.508051e+08 \n", "9592672 4.618426e+08 \n", "9592673 4.761021e+08 \n", "\n", "[9592674 rows x 11 columns]" ] }, "execution_count": 125, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_fundmen_df" ] }, { "cell_type": "code", "execution_count": 126, "metadata": {}, "outputs": [], "source": [ "idx = stk_fundmen_df.loc[stk_fundmen_df['tradeDate'].isna()].index" ] }, { "cell_type": "code", "execution_count": 127, "metadata": { "editable": true }, "outputs": [], "source": [ "stk_fundmen_df.loc[stk_fundmen_df['tradeDate'].isna(),'tradeDate'] = stk_fundmen_df.loc[stk_fundmen_df['tradeDate'].isna(),'publishDate']" ] }, { "cell_type": "code", "execution_count": 128, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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secIDtradeDatepreClosePriceclosePricenegMarketValueturnoverValueturnoverRateympublishDateendDatebook
9535739000001.XSHE2007-04-26NaNNaNNaNNaNNaNNaT2007-04-262007-03-317.106094e+09
9535740000001.XSHE2010-08-25NaNNaNNaNNaNNaNNaT2010-08-252010-06-303.042111e+10
9535741000001.XSHE2017-04-22NaNNaNNaNNaNNaNNaT2017-04-222017-03-312.077390e+11
9535742000001.XSHE2017-10-21NaNNaNNaNNaNNaNNaT2017-10-212017-09-302.181110e+11
9535743000002.XSHE2007-08-28NaNNaNNaNNaNNaNNaT2007-08-282007-06-301.581408e+10
9535744000002.XSHE2010-08-10NaNNaNNaNNaNNaNNaT2010-08-102010-06-303.977295e+10
9535745000002.XSHE2016-03-14NaNNaNNaNNaNNaNNaT2016-03-142015-12-311.001835e+11
9535746000002.XSHE2016-04-28NaNNaNNaNNaNNaNNaT2016-04-282016-03-311.006367e+11
....................................
9592666900957.XSHG2015-04-25NaNNaNNaNNaNNaNNaT2015-04-252015-03-313.938849e+08
9592667900957.XSHG2015-08-15NaNNaNNaNNaNNaNNaT2015-08-152015-06-303.952715e+08
9592668900957.XSHG2016-04-23NaNNaNNaNNaNNaNNaT2016-04-232016-03-314.005150e+08
9592669900957.XSHG2016-08-06NaNNaNNaNNaNNaNNaT2016-08-062016-06-303.906354e+08
9592670900957.XSHG2017-03-25NaNNaNNaNNaNNaNNaT2017-03-252016-12-313.930721e+08
9592671900957.XSHG2019-03-30NaNNaNNaNNaNNaNNaT2019-03-302018-12-314.508051e+08
9592672900957.XSHG2019-08-10NaNNaNNaNNaNNaNNaT2019-08-102019-06-304.618426e+08
9592673900957.XSHG2020-04-25NaNNaNNaNNaNNaNNaT2020-04-252019-12-314.761021e+08
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56935 rows × 11 columns

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" ], "text/plain": [ " secID tradeDate preClosePrice closePrice negMarketValue \\\n", "9535739 000001.XSHE 2007-04-26 NaN NaN NaN \n", "9535740 000001.XSHE 2010-08-25 NaN NaN NaN \n", "9535741 000001.XSHE 2017-04-22 NaN NaN NaN \n", "9535742 000001.XSHE 2017-10-21 NaN NaN NaN \n", "9535743 000002.XSHE 2007-08-28 NaN NaN NaN \n", "9535744 000002.XSHE 2010-08-10 NaN NaN NaN \n", "9535745 000002.XSHE 2016-03-14 NaN NaN NaN \n", "9535746 000002.XSHE 2016-04-28 NaN NaN NaN \n", "... ... ... ... ... ... \n", "9592666 900957.XSHG 2015-04-25 NaN NaN NaN \n", "9592667 900957.XSHG 2015-08-15 NaN NaN NaN \n", "9592668 900957.XSHG 2016-04-23 NaN NaN NaN \n", "9592669 900957.XSHG 2016-08-06 NaN NaN NaN \n", "9592670 900957.XSHG 2017-03-25 NaN NaN NaN \n", "9592671 900957.XSHG 2019-03-30 NaN NaN NaN \n", "9592672 900957.XSHG 2019-08-10 NaN NaN NaN \n", "9592673 900957.XSHG 2020-04-25 NaN NaN NaN \n", "\n", " turnoverValue turnoverRate ym publishDate endDate book \n", "9535739 NaN NaN NaT 2007-04-26 2007-03-31 7.106094e+09 \n", "9535740 NaN NaN NaT 2010-08-25 2010-06-30 3.042111e+10 \n", "9535741 NaN NaN NaT 2017-04-22 2017-03-31 2.077390e+11 \n", "9535742 NaN NaN NaT 2017-10-21 2017-09-30 2.181110e+11 \n", "9535743 NaN NaN NaT 2007-08-28 2007-06-30 1.581408e+10 \n", "9535744 NaN NaN NaT 2010-08-10 2010-06-30 3.977295e+10 \n", "9535745 NaN NaN NaT 2016-03-14 2015-12-31 1.001835e+11 \n", "9535746 NaN NaN NaT 2016-04-28 2016-03-31 1.006367e+11 \n", "... ... ... ... ... ... ... \n", "9592666 NaN NaN NaT 2015-04-25 2015-03-31 3.938849e+08 \n", "9592667 NaN NaN NaT 2015-08-15 2015-06-30 3.952715e+08 \n", "9592668 NaN NaN NaT 2016-04-23 2016-03-31 4.005150e+08 \n", "9592669 NaN NaN NaT 2016-08-06 2016-06-30 3.906354e+08 \n", "9592670 NaN NaN NaT 2017-03-25 2016-12-31 3.930721e+08 \n", "9592671 NaN NaN NaT 2019-03-30 2018-12-31 4.508051e+08 \n", "9592672 NaN NaN NaT 2019-08-10 2019-06-30 4.618426e+08 \n", "9592673 NaN NaN NaT 2020-04-25 2019-12-31 4.761021e+08 \n", "\n", "[56935 rows x 11 columns]" ] }, "execution_count": 128, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_fundmen_df.loc[idx]" ] }, { "cell_type": "code", "execution_count": 129, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDtradeDatepreClosePriceclosePricenegMarketValueturnoverValueturnoverRateympublishDateendDatebook
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7000001.XSHE2007-07-02870.870867.0734.247515e+108.756147e+080.02092007-07NaTNaTNaN
....................................
9592666900957.XSHG2015-04-25NaNNaNNaNNaNNaNNaT2015-04-252015-03-313.938849e+08
9592667900957.XSHG2015-08-15NaNNaNNaNNaNNaNNaT2015-08-152015-06-303.952715e+08
9592668900957.XSHG2016-04-23NaNNaNNaNNaNNaNNaT2016-04-232016-03-314.005150e+08
9592669900957.XSHG2016-08-06NaNNaNNaNNaNNaNNaT2016-08-062016-06-303.906354e+08
9592670900957.XSHG2017-03-25NaNNaNNaNNaNNaNNaT2017-03-252016-12-313.930721e+08
9592671900957.XSHG2019-03-30NaNNaNNaNNaNNaNNaT2019-03-302018-12-314.508051e+08
9592672900957.XSHG2019-08-10NaNNaNNaNNaNNaNNaT2019-08-102019-06-304.618426e+08
9592673900957.XSHG2020-04-25NaNNaNNaNNaNNaNNaT2020-04-252019-12-314.761021e+08
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9592674 rows × 11 columns

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6000001.XSHE2007-06-29953.780870.8704.266117e+101.410758e+090.03162007-06NaTNaTNaN
....................................
9535731900957.XSHG2022-03-030.6100.6141.120560e+084.576100e+040.00042022-03NaTNaTNaN
9535732900957.XSHG2022-03-040.6140.6111.115040e+083.987800e+040.00042022-03NaTNaTNaN
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9535737900957.XSHG2022-03-110.6050.6061.105840e+087.993400e+040.00072022-03NaTNaTNaN
9535738900957.XSHG2022-03-140.6060.5941.083760e+081.005700e+050.00092022-03NaTNaTNaN
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9592674 rows × 11 columns

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secIDtradeDatepreClosePriceclosePricenegMarketValueturnoverValueturnoverRateympublishDateendDatebook
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0000001.XSHE2007-06-20824.193987.0074.835036e+104.182345e+090.08402007-06NaTNaTNaN
1000001.XSHE2007-06-21987.0071085.7405.318694e+102.285485e+090.04402007-06NaTNaTNaN
2000001.XSHE2007-06-221085.7401120.2335.487665e+102.761567e+090.05102007-06NaTNaTNaN
3000001.XSHE2007-06-251120.2331113.9045.456661e+102.324186e+090.04262007-06NaTNaTNaN
4000001.XSHE2007-06-271113.9041019.6024.994705e+102.446556e+090.04892007-06NaTNaTNaN
5000001.XSHE2007-06-281019.602953.7804.672266e+101.617434e+090.03362007-06NaTNaTNaN
6000001.XSHE2007-06-29953.780870.8704.266117e+101.410758e+090.03162007-06NaTNaTNaN
....................................
3490000001.XSHE2022-03-032015.3612019.2173.048644e+119.081716e+080.00302022-03NaTNaTNaN
3491000001.XSHE2022-03-042019.2171970.3752.974902e+111.523794e+090.00512022-03NaTNaTNaN
3492000001.XSHE2022-03-071970.3751891.9712.856527e+111.655960e+090.00572022-03NaTNaTNaN
3493000001.XSHE2022-03-081891.9711839.2742.776963e+111.463146e+090.00522022-03NaTNaTNaN
3494000001.XSHE2022-03-091839.2741778.8642.685756e+112.492043e+090.00932022-03NaTNaTNaN
3495000001.XSHE2022-03-101778.8641872.6922.827385e+113.269868e+090.01162022-03NaTNaTNaN
3496000001.XSHE2022-03-111872.6921915.1072.891423e+112.187481e+090.00772022-03NaTNaTNaN
3497000001.XSHE2022-03-141915.1071862.4092.811860e+111.637278e+090.00582022-03NaTNaTNaN
\n", "

3502 rows × 11 columns

\n", "
" ], "text/plain": [ " secID tradeDate preClosePrice closePrice negMarketValue \\\n", "9535739 000001.XSHE 2007-04-26 NaN NaN NaN \n", "0 000001.XSHE 2007-06-20 824.193 987.007 4.835036e+10 \n", "1 000001.XSHE 2007-06-21 987.007 1085.740 5.318694e+10 \n", "2 000001.XSHE 2007-06-22 1085.740 1120.233 5.487665e+10 \n", "3 000001.XSHE 2007-06-25 1120.233 1113.904 5.456661e+10 \n", "4 000001.XSHE 2007-06-27 1113.904 1019.602 4.994705e+10 \n", "5 000001.XSHE 2007-06-28 1019.602 953.780 4.672266e+10 \n", "6 000001.XSHE 2007-06-29 953.780 870.870 4.266117e+10 \n", "... ... ... ... ... ... \n", "3490 000001.XSHE 2022-03-03 2015.361 2019.217 3.048644e+11 \n", "3491 000001.XSHE 2022-03-04 2019.217 1970.375 2.974902e+11 \n", "3492 000001.XSHE 2022-03-07 1970.375 1891.971 2.856527e+11 \n", "3493 000001.XSHE 2022-03-08 1891.971 1839.274 2.776963e+11 \n", "3494 000001.XSHE 2022-03-09 1839.274 1778.864 2.685756e+11 \n", "3495 000001.XSHE 2022-03-10 1778.864 1872.692 2.827385e+11 \n", "3496 000001.XSHE 2022-03-11 1872.692 1915.107 2.891423e+11 \n", "3497 000001.XSHE 2022-03-14 1915.107 1862.409 2.811860e+11 \n", "\n", " turnoverValue turnoverRate ym publishDate endDate \\\n", "9535739 NaN NaN NaT 2007-04-26 2007-03-31 \n", "0 4.182345e+09 0.0840 2007-06 NaT NaT \n", "1 2.285485e+09 0.0440 2007-06 NaT NaT \n", "2 2.761567e+09 0.0510 2007-06 NaT NaT \n", "3 2.324186e+09 0.0426 2007-06 NaT NaT \n", "4 2.446556e+09 0.0489 2007-06 NaT NaT \n", "5 1.617434e+09 0.0336 2007-06 NaT NaT \n", "6 1.410758e+09 0.0316 2007-06 NaT NaT \n", "... ... ... ... ... ... \n", "3490 9.081716e+08 0.0030 2022-03 NaT NaT \n", "3491 1.523794e+09 0.0051 2022-03 NaT NaT \n", "3492 1.655960e+09 0.0057 2022-03 NaT NaT \n", "3493 1.463146e+09 0.0052 2022-03 NaT NaT \n", "3494 2.492043e+09 0.0093 2022-03 NaT NaT \n", "3495 3.269868e+09 0.0116 2022-03 NaT NaT \n", "3496 2.187481e+09 0.0077 2022-03 NaT NaT \n", "3497 1.637278e+09 0.0058 2022-03 NaT NaT \n", "\n", " book \n", "9535739 7.106094e+09 \n", "0 NaN \n", "1 NaN \n", "2 NaN \n", "3 NaN \n", "4 NaN \n", "5 NaN \n", "6 NaN \n", "... ... \n", "3490 NaN \n", "3491 NaN \n", "3492 NaN \n", "3493 NaN \n", "3494 NaN \n", "3495 NaN \n", "3496 NaN \n", "3497 NaN \n", "\n", "[3502 rows x 11 columns]" ] }, "execution_count": 132, "metadata": {}, "output_type": "execute_result" } ], "source": [ "temp = stk_fundmen_df[stk_fundmen_df['secID']=='000001.XSHE'].copy()\n", "temp" ] }, { "cell_type": "code", "execution_count": 133, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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secIDtradeDatepreClosePriceclosePricenegMarketValueturnoverValueturnoverRateympublishDateendDatebook
9535739000001.XSHE2007-04-26NaNNaNNaNNaNNaNNaT2007-04-262007-03-317.106094e+09
39000001.XSHE2007-08-161202.5101147.1315.619431e+101.417128e+090.02492007-082007-08-162007-06-307.698478e+09
82000001.XSHE2007-10-231259.7871300.6096.371272e+106.932570e+080.01102007-102007-10-232007-09-308.363553e+09
181000001.XSHE2008-03-20855.048917.7055.094780e+101.326191e+090.02732008-032008-03-202007-12-311.300606e+10
205000001.XSHE2008-04-24790.808869.9214.829500e+101.348041e+090.02822008-042008-04-242008-03-311.404138e+10
286000001.XSHE2008-08-21659.798639.5454.328991e+101.703450e+080.00392008-082008-08-212008-06-301.694330e+10
325000001.XSHE2008-10-24396.828380.6892.576831e+103.505822e+080.01342008-102008-10-242008-09-301.837466e+10
421000001.XSHE2009-03-20651.507632.5274.268801e+109.747196e+080.02292009-032009-03-202008-12-311.640079e+10
....................................
2992000001.XSHE2020-02-141836.9531884.6012.916685e+112.253906e+090.00782020-022020-02-142019-12-313.129830e+11
3038000001.XSHE2020-04-211628.8071686.4862.610074e+112.861879e+090.01092020-042020-04-212020-03-313.523550e+11
3126000001.XSHE2020-08-281844.0521929.4962.936090e+113.599036e+090.01242020-082020-08-282020-06-303.513970e+11
3159000001.XSHE2020-10-222284.0232239.3893.407650e+113.342069e+090.00972020-102020-10-222020-09-303.587710e+11
3231000001.XSHE2021-02-023130.8082968.8484.517660e+115.679180e+090.01252021-022021-02-022020-12-313.641310e+11
3281000001.XSHE2021-04-212766.0792934.4154.465264e+113.644578e+090.00822021-042021-04-212021-03-313.726170e+11
3364000001.XSHE2021-08-202614.3142496.0663.768598e+113.119153e+090.00832021-082021-08-202021-06-303.771930e+11
3401000001.XSHE2021-10-212472.9302570.6133.881151e+113.198266e+090.00842021-102021-10-212021-09-303.888580e+11
\n", "

59 rows × 11 columns

\n", "
" ], "text/plain": [ " secID tradeDate preClosePrice closePrice negMarketValue \\\n", "9535739 000001.XSHE 2007-04-26 NaN NaN NaN \n", "39 000001.XSHE 2007-08-16 1202.510 1147.131 5.619431e+10 \n", "82 000001.XSHE 2007-10-23 1259.787 1300.609 6.371272e+10 \n", "181 000001.XSHE 2008-03-20 855.048 917.705 5.094780e+10 \n", "205 000001.XSHE 2008-04-24 790.808 869.921 4.829500e+10 \n", "286 000001.XSHE 2008-08-21 659.798 639.545 4.328991e+10 \n", "325 000001.XSHE 2008-10-24 396.828 380.689 2.576831e+10 \n", "421 000001.XSHE 2009-03-20 651.507 632.527 4.268801e+10 \n", "... ... ... ... ... ... \n", "2992 000001.XSHE 2020-02-14 1836.953 1884.601 2.916685e+11 \n", "3038 000001.XSHE 2020-04-21 1628.807 1686.486 2.610074e+11 \n", "3126 000001.XSHE 2020-08-28 1844.052 1929.496 2.936090e+11 \n", "3159 000001.XSHE 2020-10-22 2284.023 2239.389 3.407650e+11 \n", "3231 000001.XSHE 2021-02-02 3130.808 2968.848 4.517660e+11 \n", "3281 000001.XSHE 2021-04-21 2766.079 2934.415 4.465264e+11 \n", "3364 000001.XSHE 2021-08-20 2614.314 2496.066 3.768598e+11 \n", "3401 000001.XSHE 2021-10-21 2472.930 2570.613 3.881151e+11 \n", "\n", " turnoverValue turnoverRate ym publishDate endDate \\\n", "9535739 NaN NaN NaT 2007-04-26 2007-03-31 \n", "39 1.417128e+09 0.0249 2007-08 2007-08-16 2007-06-30 \n", "82 6.932570e+08 0.0110 2007-10 2007-10-23 2007-09-30 \n", "181 1.326191e+09 0.0273 2008-03 2008-03-20 2007-12-31 \n", "205 1.348041e+09 0.0282 2008-04 2008-04-24 2008-03-31 \n", "286 1.703450e+08 0.0039 2008-08 2008-08-21 2008-06-30 \n", "325 3.505822e+08 0.0134 2008-10 2008-10-24 2008-09-30 \n", "421 9.747196e+08 0.0229 2009-03 2009-03-20 2008-12-31 \n", "... ... ... ... ... ... \n", "2992 2.253906e+09 0.0078 2020-02 2020-02-14 2019-12-31 \n", "3038 2.861879e+09 0.0109 2020-04 2020-04-21 2020-03-31 \n", "3126 3.599036e+09 0.0124 2020-08 2020-08-28 2020-06-30 \n", "3159 3.342069e+09 0.0097 2020-10 2020-10-22 2020-09-30 \n", "3231 5.679180e+09 0.0125 2021-02 2021-02-02 2020-12-31 \n", "3281 3.644578e+09 0.0082 2021-04 2021-04-21 2021-03-31 \n", "3364 3.119153e+09 0.0083 2021-08 2021-08-20 2021-06-30 \n", "3401 3.198266e+09 0.0084 2021-10 2021-10-21 2021-09-30 \n", "\n", " book \n", "9535739 7.106094e+09 \n", "39 7.698478e+09 \n", "82 8.363553e+09 \n", "181 1.300606e+10 \n", "205 1.404138e+10 \n", "286 1.694330e+10 \n", "325 1.837466e+10 \n", "421 1.640079e+10 \n", "... ... \n", "2992 3.129830e+11 \n", "3038 3.523550e+11 \n", "3126 3.513970e+11 \n", "3159 3.587710e+11 \n", "3231 3.641310e+11 \n", "3281 3.726170e+11 \n", "3364 3.771930e+11 \n", "3401 3.888580e+11 \n", "\n", "[59 rows x 11 columns]" ] }, "execution_count": 133, "metadata": {}, "output_type": "execute_result" } ], "source": [ "temp[~temp['book'].isna()]" ] }, { "cell_type": "code", "execution_count": 134, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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publishDateendDatebook
95357392007-04-262007-03-317.106094e+09
02007-04-262007-03-317.106094e+09
12007-04-262007-03-317.106094e+09
22007-04-262007-03-317.106094e+09
32007-04-262007-03-317.106094e+09
42007-04-262007-03-317.106094e+09
52007-04-262007-03-317.106094e+09
62007-04-262007-03-317.106094e+09
............
95357312021-10-292021-09-305.188844e+08
95357322021-10-292021-09-305.188844e+08
95357332021-10-292021-09-305.188844e+08
95357342021-10-292021-09-305.188844e+08
95357352021-10-292021-09-305.188844e+08
95357362021-10-292021-09-305.188844e+08
95357372021-10-292021-09-305.188844e+08
95357382021-10-292021-09-305.188844e+08
\n", "

9592674 rows × 3 columns

\n", "
" ], "text/plain": [ " publishDate endDate book\n", "9535739 2007-04-26 2007-03-31 7.106094e+09\n", "0 2007-04-26 2007-03-31 7.106094e+09\n", "1 2007-04-26 2007-03-31 7.106094e+09\n", "2 2007-04-26 2007-03-31 7.106094e+09\n", "3 2007-04-26 2007-03-31 7.106094e+09\n", "4 2007-04-26 2007-03-31 7.106094e+09\n", "5 2007-04-26 2007-03-31 7.106094e+09\n", "6 2007-04-26 2007-03-31 7.106094e+09\n", "... ... ... ...\n", "9535731 2021-10-29 2021-09-30 5.188844e+08\n", "9535732 2021-10-29 2021-09-30 5.188844e+08\n", "9535733 2021-10-29 2021-09-30 5.188844e+08\n", "9535734 2021-10-29 2021-09-30 5.188844e+08\n", "9535735 2021-10-29 2021-09-30 5.188844e+08\n", "9535736 2021-10-29 2021-09-30 5.188844e+08\n", "9535737 2021-10-29 2021-09-30 5.188844e+08\n", "9535738 2021-10-29 2021-09-30 5.188844e+08\n", "\n", "[9592674 rows x 3 columns]" ] }, "execution_count": 134, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_fundmen_df[['secID','publishDate','endDate','book']].groupby('secID').fillna(method='ffill')" ] }, { "cell_type": "code", "execution_count": 135, "metadata": { "editable": true }, "outputs": [], "source": [ "stk_fundmen_df.loc[:,['publishDate','endDate','book']] = stk_fundmen_df[['secID','publishDate','endDate','book']].groupby('secID').fillna(method='ffill')" ] }, { "cell_type": "code", "execution_count": 136, "metadata": { "editable": true }, "outputs": [], "source": [ "## 查看数据\n", "idx = pd.IndexSlice\n", "stk_fundmen_df.set_index(['secID','tradeDate'],inplace=True)\n", "pd.options.display.max_rows = 20" ] }, { "cell_type": "code", "execution_count": 137, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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preClosePriceclosePricenegMarketValueturnoverValueturnoverRateympublishDateendDatebook
secIDtradeDate
000001.XSHE2010-03-02926.303953.5356.757628e+101.599220e+090.02342010-032009-10-292009-09-301.908844e+10
2010-03-03953.535961.3746.813186e+106.554447e+080.00972010-032009-10-292009-09-301.908844e+10
2010-03-04961.374953.1226.754704e+108.773898e+080.01292010-032009-10-292009-09-301.908844e+10
2010-03-05953.122960.1376.804414e+106.811939e+080.01002010-032009-10-292009-09-301.908844e+10
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9592674 rows × 11 columns

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" ], "text/plain": [ " secID tradeDate preClosePrice closePrice negMarketValue \\\n", "0 000001.XSHE 2007-04-26 NaN NaN NaN \n", "1 000001.XSHE 2007-06-20 824.193 987.007 4.835036e+10 \n", "2 000001.XSHE 2007-06-21 987.007 1085.740 5.318694e+10 \n", "3 000001.XSHE 2007-06-22 1085.740 1120.233 5.487665e+10 \n", "4 000001.XSHE 2007-06-25 1120.233 1113.904 5.456661e+10 \n", "5 000001.XSHE 2007-06-27 1113.904 1019.602 4.994705e+10 \n", "6 000001.XSHE 2007-06-28 1019.602 953.780 4.672266e+10 \n", "7 000001.XSHE 2007-06-29 953.780 870.870 4.266117e+10 \n", "8 000001.XSHE 2007-07-02 870.870 867.073 4.247515e+10 \n", "9 000001.XSHE 2007-07-03 867.073 861.693 4.221161e+10 \n", "... ... ... ... ... ... \n", "9592664 900957.XSHG 2022-03-01 0.615 0.616 1.124240e+08 \n", "9592665 900957.XSHG 2022-03-02 0.616 0.610 1.113200e+08 \n", "9592666 900957.XSHG 2022-03-03 0.610 0.614 1.120560e+08 \n", "9592667 900957.XSHG 2022-03-04 0.614 0.611 1.115040e+08 \n", "9592668 900957.XSHG 2022-03-07 0.611 0.605 1.104000e+08 \n", "9592669 900957.XSHG 2022-03-08 0.605 0.604 1.102160e+08 \n", "9592670 900957.XSHG 2022-03-09 0.604 0.600 1.094800e+08 \n", "9592671 900957.XSHG 2022-03-10 0.600 0.605 1.104000e+08 \n", "9592672 900957.XSHG 2022-03-11 0.605 0.606 1.105840e+08 \n", "9592673 900957.XSHG 2022-03-14 0.606 0.594 1.083760e+08 \n", "\n", " turnoverValue turnoverRate ym publishDate endDate \\\n", "0 NaN NaN NaT 2007-04-26 2007-03-31 \n", "1 4.182345e+09 0.0840 2007-06 2007-04-26 2007-03-31 \n", "2 2.285485e+09 0.0440 2007-06 2007-04-26 2007-03-31 \n", "3 2.761567e+09 0.0510 2007-06 2007-04-26 2007-03-31 \n", "4 2.324186e+09 0.0426 2007-06 2007-04-26 2007-03-31 \n", "5 2.446556e+09 0.0489 2007-06 2007-04-26 2007-03-31 \n", "6 1.617434e+09 0.0336 2007-06 2007-04-26 2007-03-31 \n", "7 1.410758e+09 0.0316 2007-06 2007-04-26 2007-03-31 \n", "8 8.756147e+08 0.0209 2007-07 2007-04-26 2007-03-31 \n", "9 6.936451e+08 0.0163 2007-07 2007-04-26 2007-03-31 \n", "... ... ... ... ... ... \n", "9592664 1.150660e+05 0.0010 2022-03 2021-10-29 2021-09-30 \n", "9592665 1.003980e+05 0.0009 2022-03 2021-10-29 2021-09-30 \n", "9592666 4.576100e+04 0.0004 2022-03 2021-10-29 2021-09-30 \n", "9592667 3.987800e+04 0.0004 2022-03 2021-10-29 2021-09-30 \n", "9592668 1.825430e+05 0.0016 2022-03 2021-10-29 2021-09-30 \n", "9592669 1.612110e+05 0.0015 2022-03 2021-10-29 2021-09-30 \n", "9592670 1.285010e+05 0.0012 2022-03 2021-10-29 2021-09-30 \n", "9592671 7.845200e+04 0.0007 2022-03 2021-10-29 2021-09-30 \n", "9592672 7.993400e+04 0.0007 2022-03 2021-10-29 2021-09-30 \n", "9592673 1.005700e+05 0.0009 2022-03 2021-10-29 2021-09-30 \n", "\n", " book \n", "0 7.106094e+09 \n", "1 7.106094e+09 \n", "2 7.106094e+09 \n", "3 7.106094e+09 \n", "4 7.106094e+09 \n", "5 7.106094e+09 \n", "6 7.106094e+09 \n", "7 7.106094e+09 \n", "8 7.106094e+09 \n", "9 7.106094e+09 \n", "... ... \n", "9592664 5.188844e+08 \n", "9592665 5.188844e+08 \n", "9592666 5.188844e+08 \n", "9592667 5.188844e+08 \n", "9592668 5.188844e+08 \n", "9592669 5.188844e+08 \n", "9592670 5.188844e+08 \n", "9592671 5.188844e+08 \n", "9592672 5.188844e+08 \n", "9592673 5.188844e+08 \n", "\n", "[9592674 rows x 11 columns]" ] }, "execution_count": 141, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_fundmen_df" ] }, { "cell_type": "code", "execution_count": 142, "metadata": { "editable": true }, "outputs": [], "source": [ "stk_df_m = stk_fundmen_df.groupby(['secID','ym'],as_index=False).last()\n", "\n", "stk_df_m['ret'] = stk_df_m.groupby('secID')['closePrice'].apply(lambda x: x / x.shift() - 1)" ] }, { "cell_type": "code", "execution_count": 143, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDymtradeDatepreClosePriceclosePricenegMarketValueturnoverValueturnoverRatepublishDateendDatebookret
0000001.XSHE2007-062007-06-29953.780870.8704.266117e+101.410758e+090.03162007-04-262007-03-317.106094e+09NaN
1000001.XSHE2007-072007-07-311082.2591146.4985.616330e+101.479466e+090.02702007-04-262007-03-317.106094e+090.316497
2000001.XSHE2007-082007-08-311193.0161202.5105.890714e+106.552881e+080.01122007-08-162007-06-307.698478e+090.048855
3000001.XSHE2007-092007-09-281228.1421265.1676.197651e+101.408136e+090.02282007-08-162007-06-307.698478e+090.052105
4000001.XSHE2007-102007-10-311427.1891520.5427.448652e+101.440425e+090.02002007-10-232007-09-308.363553e+090.201851
5000001.XSHE2007-112007-11-301172.4471141.7515.593078e+105.452159e+080.00962007-10-232007-09-308.363553e+09-0.249116
6000001.XSHE2007-122007-12-281234.1551221.4976.574629e+101.019671e+090.01542007-10-232007-09-308.363553e+090.069845
7000001.XSHE2008-012008-01-311074.3471053.7785.850212e+105.328429e+080.00892007-10-232007-09-308.363553e+09-0.137306
8000001.XSHE2008-022008-02-291037.9561049.0325.823860e+102.267900e+080.00392007-10-232007-09-308.363553e+09-0.004504
9000001.XSHE2008-032008-03-31918.971892.3894.954234e+106.155862e+080.01232008-03-202007-12-311.300606e+10-0.149321
.......................................
484443900957.XSHG2021-062021-06-300.6330.6391.166560e+081.610070e+050.00142021-04-272021-03-315.062701e+080.027331
484444900957.XSHG2021-072021-07-300.6510.6501.186800e+081.151750e+050.00102021-04-272021-03-315.062701e+080.017214
484445900957.XSHG2021-082021-08-310.6260.6121.116880e+083.033640e+050.00272021-08-122021-06-305.128208e+08-0.058462
484446900957.XSHG2021-092021-09-300.6550.6671.218080e+082.086830e+050.00172021-08-122021-06-305.128208e+080.089869
484447900957.XSHG2021-102021-10-290.6360.6401.168400e+086.162200e+040.00052021-10-292021-09-305.188844e+08-0.040480
484448900957.XSHG2021-112021-11-300.6230.6141.120560e+081.161060e+050.00102021-10-292021-09-305.188844e+08-0.040625
484449900957.XSHG2021-122021-12-310.6350.6361.161040e+081.059960e+050.00092021-10-292021-09-305.188844e+080.035831
484450900957.XSHG2022-012022-01-280.6170.6221.135280e+081.319240e+050.00122021-10-292021-09-305.188844e+08-0.022013
484451900957.XSHG2022-022022-02-280.6160.6151.122400e+089.851400e+040.00092021-10-292021-09-305.188844e+08-0.011254
484452900957.XSHG2022-032022-03-140.6060.5941.083760e+081.005700e+050.00092021-10-292021-09-305.188844e+08-0.034146
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484453 rows × 12 columns

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" ], "text/plain": [ " secID ym tradeDate preClosePrice closePrice \\\n", "0 000001.XSHE 2007-06 2007-06-29 953.780 870.870 \n", "1 000001.XSHE 2007-07 2007-07-31 1082.259 1146.498 \n", "2 000001.XSHE 2007-08 2007-08-31 1193.016 1202.510 \n", "3 000001.XSHE 2007-09 2007-09-28 1228.142 1265.167 \n", "4 000001.XSHE 2007-10 2007-10-31 1427.189 1520.542 \n", "5 000001.XSHE 2007-11 2007-11-30 1172.447 1141.751 \n", "6 000001.XSHE 2007-12 2007-12-28 1234.155 1221.497 \n", "7 000001.XSHE 2008-01 2008-01-31 1074.347 1053.778 \n", "8 000001.XSHE 2008-02 2008-02-29 1037.956 1049.032 \n", "9 000001.XSHE 2008-03 2008-03-31 918.971 892.389 \n", "... ... ... ... ... ... \n", "484443 900957.XSHG 2021-06 2021-06-30 0.633 0.639 \n", "484444 900957.XSHG 2021-07 2021-07-30 0.651 0.650 \n", "484445 900957.XSHG 2021-08 2021-08-31 0.626 0.612 \n", "484446 900957.XSHG 2021-09 2021-09-30 0.655 0.667 \n", "484447 900957.XSHG 2021-10 2021-10-29 0.636 0.640 \n", "484448 900957.XSHG 2021-11 2021-11-30 0.623 0.614 \n", "484449 900957.XSHG 2021-12 2021-12-31 0.635 0.636 \n", "484450 900957.XSHG 2022-01 2022-01-28 0.617 0.622 \n", "484451 900957.XSHG 2022-02 2022-02-28 0.616 0.615 \n", "484452 900957.XSHG 2022-03 2022-03-14 0.606 0.594 \n", "\n", " negMarketValue turnoverValue turnoverRate publishDate endDate \\\n", "0 4.266117e+10 1.410758e+09 0.0316 2007-04-26 2007-03-31 \n", "1 5.616330e+10 1.479466e+09 0.0270 2007-04-26 2007-03-31 \n", "2 5.890714e+10 6.552881e+08 0.0112 2007-08-16 2007-06-30 \n", "3 6.197651e+10 1.408136e+09 0.0228 2007-08-16 2007-06-30 \n", "4 7.448652e+10 1.440425e+09 0.0200 2007-10-23 2007-09-30 \n", "5 5.593078e+10 5.452159e+08 0.0096 2007-10-23 2007-09-30 \n", "6 6.574629e+10 1.019671e+09 0.0154 2007-10-23 2007-09-30 \n", "7 5.850212e+10 5.328429e+08 0.0089 2007-10-23 2007-09-30 \n", "8 5.823860e+10 2.267900e+08 0.0039 2007-10-23 2007-09-30 \n", "9 4.954234e+10 6.155862e+08 0.0123 2008-03-20 2007-12-31 \n", "... ... ... ... ... ... \n", "484443 1.166560e+08 1.610070e+05 0.0014 2021-04-27 2021-03-31 \n", "484444 1.186800e+08 1.151750e+05 0.0010 2021-04-27 2021-03-31 \n", "484445 1.116880e+08 3.033640e+05 0.0027 2021-08-12 2021-06-30 \n", "484446 1.218080e+08 2.086830e+05 0.0017 2021-08-12 2021-06-30 \n", "484447 1.168400e+08 6.162200e+04 0.0005 2021-10-29 2021-09-30 \n", "484448 1.120560e+08 1.161060e+05 0.0010 2021-10-29 2021-09-30 \n", "484449 1.161040e+08 1.059960e+05 0.0009 2021-10-29 2021-09-30 \n", "484450 1.135280e+08 1.319240e+05 0.0012 2021-10-29 2021-09-30 \n", "484451 1.122400e+08 9.851400e+04 0.0009 2021-10-29 2021-09-30 \n", "484452 1.083760e+08 1.005700e+05 0.0009 2021-10-29 2021-09-30 \n", "\n", " book ret \n", "0 7.106094e+09 NaN \n", "1 7.106094e+09 0.316497 \n", "2 7.698478e+09 0.048855 \n", "3 7.698478e+09 0.052105 \n", "4 8.363553e+09 0.201851 \n", "5 8.363553e+09 -0.249116 \n", "6 8.363553e+09 0.069845 \n", "7 8.363553e+09 -0.137306 \n", "8 8.363553e+09 -0.004504 \n", "9 1.300606e+10 -0.149321 \n", "... ... ... \n", "484443 5.062701e+08 0.027331 \n", "484444 5.062701e+08 0.017214 \n", "484445 5.128208e+08 -0.058462 \n", "484446 5.128208e+08 0.089869 \n", "484447 5.188844e+08 -0.040480 \n", "484448 5.188844e+08 -0.040625 \n", "484449 5.188844e+08 0.035831 \n", "484450 5.188844e+08 -0.022013 \n", "484451 5.188844e+08 -0.011254 \n", "484452 5.188844e+08 -0.034146 \n", "\n", "[484453 rows x 12 columns]" ] }, "execution_count": 143, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_df_m" ] }, { "cell_type": "code", "execution_count": 144, "metadata": {}, "outputs": [], "source": [ "stk_df_m['ret'] = stk_df_m.groupby(['secID'])['ret'].shift(-1)\n", "stk_df_m['ret_date'] = stk_df_m.groupby('secID')['ym'].shift(-1)" ] }, { "cell_type": "code", "execution_count": 145, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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secIDymtradeDatepreClosePriceclosePricenegMarketValueturnoverValueturnoverRatepublishDateendDatebookretret_dateym_diff
36000001.XSHE2010-062010-06-29762.910722.4755.437499e+105.491348e+080.00992010-04-292010-03-312.210983e+10-0.0736722010-093
175000001.XSHE2022-032022-03-141915.1071862.4092.811860e+111.637278e+090.00582021-10-212021-09-303.888580e+11NaNNaT9223372036854775182
283000002.XSHE2015-122015-12-182588.8162847.5812.374641e+115.288951e+090.02302015-10-282015-09-308.892608e+10-0.2690352016-077
352000002.XSHE2022-032022-03-142477.3542394.0321.563554e+111.852414e+090.01162021-10-292021-09-302.276229e+11NaNNaT9223372036854775182
410000004.XSHE2016-032016-03-23228.801251.6943.055487e+092.016834e+080.06802015-10-312015-09-308.273100e+070.0171082016-096
417000004.XSHE2017-032017-03-31252.788246.0222.986693e+094.408517e+070.01462016-10-292016-09-309.622171e+07-0.2874992017-063
475000004.XSHE2022-032022-03-14133.330132.0322.247511e+098.951660e+070.03912021-10-272021-09-301.490531e+09NaNNaT9223372036854775182
510000005.XSHE2011-042011-04-2937.43737.9293.527048e+091.632527e+070.00472010-10-292010-09-306.752503e+080.0285012012-0412
543000005.XSHE2014-122014-12-2639.30440.2873.746346e+091.534932e+080.04112014-10-292014-09-305.929574e+082.8585402015-055
615000005.XSHE2021-042021-04-2922.01021.8142.348641e+091.046951e+070.00442020-10-292020-09-301.622737e+09NaNNaT9223372036854775193
.............................................
483607900951.XSHG2020-082020-08-200.0700.0696.900000e+064.458600e+040.00672020-04-252020-03-31-2.115164e+08NaNNaT9223372036854775201
483790900952.XSHG2022-032022-03-140.6060.5815.726140e+072.598340e+050.00452021-10-292021-09-306.583094e+09NaNNaT9223372036854775182
483806900953.XSHG2008-042008-04-290.4300.4301.000800e+085.857590e+050.00582007-10-202007-09-307.413644e+080.5883722009-0715
483959900953.XSHG2022-032022-03-140.3420.3397.896000e+071.131410e+050.00142021-10-302021-09-307.089197e+08NaNNaT9223372036854775182
484035900955.XSHG2013-042013-04-261.2751.2511.188000e+081.802100e+050.00152012-10-312012-09-301.800223e+09-0.1942452014-0412
484050900955.XSHG2015-062015-06-013.2173.4673.293400e+083.140139e+060.00972015-04-252015-03-311.671412e+09-0.2665132015-115
484104900955.XSHG2020-042020-04-290.5630.5074.818000e+072.699610e+050.00542019-10-312019-09-301.332464e+09NaNNaT9223372036854775205
484215900956.XSHG2016-032016-03-141.8511.9132.051600e+083.082141e+060.01492015-10-292015-09-309.870053e+080.1704132016-063
484269900956.XSHG2020-112020-11-043.5693.4243.530500e+088.700989e+060.02412020-10-292020-09-301.430181e+09NaNNaT9223372036854775198
484452900957.XSHG2022-032022-03-140.6060.5941.083760e+081.005700e+050.00092021-10-292021-09-305.188844e+08NaNNaT9223372036854775182
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8881 rows × 14 columns

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" ], "text/plain": [ " secID ym tradeDate preClosePrice closePrice \\\n", "36 000001.XSHE 2010-06 2010-06-29 762.910 722.475 \n", "175 000001.XSHE 2022-03 2022-03-14 1915.107 1862.409 \n", "283 000002.XSHE 2015-12 2015-12-18 2588.816 2847.581 \n", "352 000002.XSHE 2022-03 2022-03-14 2477.354 2394.032 \n", "410 000004.XSHE 2016-03 2016-03-23 228.801 251.694 \n", "417 000004.XSHE 2017-03 2017-03-31 252.788 246.022 \n", "475 000004.XSHE 2022-03 2022-03-14 133.330 132.032 \n", "510 000005.XSHE 2011-04 2011-04-29 37.437 37.929 \n", "543 000005.XSHE 2014-12 2014-12-26 39.304 40.287 \n", "615 000005.XSHE 2021-04 2021-04-29 22.010 21.814 \n", "... ... ... ... ... ... \n", "483607 900951.XSHG 2020-08 2020-08-20 0.070 0.069 \n", "483790 900952.XSHG 2022-03 2022-03-14 0.606 0.581 \n", "483806 900953.XSHG 2008-04 2008-04-29 0.430 0.430 \n", "483959 900953.XSHG 2022-03 2022-03-14 0.342 0.339 \n", "484035 900955.XSHG 2013-04 2013-04-26 1.275 1.251 \n", "484050 900955.XSHG 2015-06 2015-06-01 3.217 3.467 \n", "484104 900955.XSHG 2020-04 2020-04-29 0.563 0.507 \n", "484215 900956.XSHG 2016-03 2016-03-14 1.851 1.913 \n", "484269 900956.XSHG 2020-11 2020-11-04 3.569 3.424 \n", "484452 900957.XSHG 2022-03 2022-03-14 0.606 0.594 \n", "\n", " negMarketValue turnoverValue turnoverRate publishDate endDate \\\n", "36 5.437499e+10 5.491348e+08 0.0099 2010-04-29 2010-03-31 \n", "175 2.811860e+11 1.637278e+09 0.0058 2021-10-21 2021-09-30 \n", "283 2.374641e+11 5.288951e+09 0.0230 2015-10-28 2015-09-30 \n", "352 1.563554e+11 1.852414e+09 0.0116 2021-10-29 2021-09-30 \n", "410 3.055487e+09 2.016834e+08 0.0680 2015-10-31 2015-09-30 \n", "417 2.986693e+09 4.408517e+07 0.0146 2016-10-29 2016-09-30 \n", "475 2.247511e+09 8.951660e+07 0.0391 2021-10-27 2021-09-30 \n", "510 3.527048e+09 1.632527e+07 0.0047 2010-10-29 2010-09-30 \n", "543 3.746346e+09 1.534932e+08 0.0411 2014-10-29 2014-09-30 \n", "615 2.348641e+09 1.046951e+07 0.0044 2020-10-29 2020-09-30 \n", "... ... ... ... ... ... \n", "483607 6.900000e+06 4.458600e+04 0.0067 2020-04-25 2020-03-31 \n", "483790 5.726140e+07 2.598340e+05 0.0045 2021-10-29 2021-09-30 \n", "483806 1.000800e+08 5.857590e+05 0.0058 2007-10-20 2007-09-30 \n", "483959 7.896000e+07 1.131410e+05 0.0014 2021-10-30 2021-09-30 \n", "484035 1.188000e+08 1.802100e+05 0.0015 2012-10-31 2012-09-30 \n", "484050 3.293400e+08 3.140139e+06 0.0097 2015-04-25 2015-03-31 \n", "484104 4.818000e+07 2.699610e+05 0.0054 2019-10-31 2019-09-30 \n", "484215 2.051600e+08 3.082141e+06 0.0149 2015-10-29 2015-09-30 \n", "484269 3.530500e+08 8.700989e+06 0.0241 2020-10-29 2020-09-30 \n", "484452 1.083760e+08 1.005700e+05 0.0009 2021-10-29 2021-09-30 \n", "\n", " book ret ret_date ym_diff \n", "36 2.210983e+10 -0.073672 2010-09 3 \n", "175 3.888580e+11 NaN NaT 9223372036854775182 \n", "283 8.892608e+10 -0.269035 2016-07 7 \n", "352 2.276229e+11 NaN NaT 9223372036854775182 \n", "410 8.273100e+07 0.017108 2016-09 6 \n", "417 9.622171e+07 -0.287499 2017-06 3 \n", "475 1.490531e+09 NaN NaT 9223372036854775182 \n", "510 6.752503e+08 0.028501 2012-04 12 \n", "543 5.929574e+08 2.858540 2015-05 5 \n", "615 1.622737e+09 NaN NaT 9223372036854775193 \n", "... ... ... ... ... \n", "483607 -2.115164e+08 NaN NaT 9223372036854775201 \n", "483790 6.583094e+09 NaN NaT 9223372036854775182 \n", "483806 7.413644e+08 0.588372 2009-07 15 \n", "483959 7.089197e+08 NaN NaT 9223372036854775182 \n", "484035 1.800223e+09 -0.194245 2014-04 12 \n", "484050 1.671412e+09 -0.266513 2015-11 5 \n", "484104 1.332464e+09 NaN NaT 9223372036854775205 \n", "484215 9.870053e+08 0.170413 2016-06 3 \n", "484269 1.430181e+09 NaN NaT 9223372036854775198 \n", "484452 5.188844e+08 NaN NaT 9223372036854775182 \n", "\n", "[8881 rows x 14 columns]" ] }, "execution_count": 145, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_df_m['ym_diff'] = stk_df_m['ret_date'].astype(int) - stk_df_m['ym'].astype(int)\n", "stk_df_m.loc[stk_df_m['ym_diff'] != 1]" ] }, { "cell_type": "code", "execution_count": 146, "metadata": { "editable": true }, "outputs": [], "source": [ "# 停牌删去\n", "stk_df_m['ym_diff'] = stk_df_m['ret_date'].astype(int) - stk_df_m['ym'].astype(int)\n", "stk_df_m.loc[stk_df_m['ym_diff'] != 1,'ret'] = np.nan" ] }, { "cell_type": "code", "execution_count": 147, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDymtradeDatepreClosePriceclosePricenegMarketValueturnoverValueturnoverRatepublishDateendDatebookretret_dateym_diff
467939605116.XSHG2020-092020-09-3017.49016.5206.773200e+08249301778.00.37362020-09-082019-12-319.772832e+080.0611382020-101
467940605116.XSHG2020-102020-10-3019.04017.5307.187300e+08109638314.00.14742020-10-292020-09-301.368126e+090.0450662020-111
467941605116.XSHG2020-112020-11-3018.35018.3207.511200e+0839324380.00.05222020-10-292020-09-301.368126e+09-0.1337342020-121
467942605116.XSHG2020-122020-12-3115.75015.8706.506700e+0835867084.00.05492020-10-292020-09-301.368126e+09-0.1090112021-011
467943605116.XSHG2021-012021-01-2915.71014.1405.797400e+0850687282.00.08482020-10-292020-09-301.368126e+09-0.0339462021-021
467944605116.XSHG2021-022021-02-2613.65013.6605.600600e+0811489080.00.02052020-10-292020-09-301.368126e+090.1098102021-031
467945605116.XSHG2021-032021-03-3115.20015.1606.215600e+0832556596.00.05222020-10-292020-09-301.368126e+090.1464382021-041
467946605116.XSHG2021-042021-04-3017.20017.3807.125800e+08168984435.00.24022021-04-292021-03-311.417570e+09-0.1484462021-051
467947605116.XSHG2021-052021-05-3114.43014.8006.068000e+0819157242.00.03192021-04-292021-03-311.417570e+09-0.0449322021-061
467948605116.XSHG2021-062021-06-3014.18514.1355.748200e+089807849.00.01712021-04-292021-03-311.417570e+09-0.0085602021-071
467949605116.XSHG2021-072021-07-3014.18514.0145.699000e+088463181.00.01492021-04-292021-03-311.417570e+09-0.0151282021-081
467950605116.XSHG2021-082021-08-3113.76213.8025.612900e+089275307.00.01662021-08-312021-06-301.422180e+09-0.0255762021-091
467951605116.XSHG2021-092021-09-3013.10613.4491.031598e+096464580.00.00632021-08-312021-06-301.422180e+09-0.0787422021-101
467952605116.XSHG2021-102021-10-2912.30012.3909.504004e+082981573.00.00312021-08-312021-06-301.422180e+090.7518972021-111
467953605116.XSHG2021-112021-11-3019.73021.7061.664941e+09454379548.00.29222021-10-302021-09-301.471599e+090.0227592021-121
467954605116.XSHG2021-122021-12-3120.18422.2001.702833e+09144668088.00.08682021-10-302021-09-301.471599e+09-0.0345052022-011
467955605116.XSHG2022-012022-01-2819.48821.4341.644061e+0947306575.00.02892021-10-302021-09-301.471599e+090.0267802022-021
467956605116.XSHG2022-022022-02-2821.97822.0081.688140e+0970790127.00.04212021-10-302021-09-301.471599e+090.1818882022-031
467957605116.XSHG2022-032022-03-1423.64226.0111.995145e+09587248355.00.29922021-10-302021-09-301.471599e+09NaNNaT9223372036854775182
\n", "
" ], "text/plain": [ " secID ym tradeDate preClosePrice closePrice \\\n", "467939 605116.XSHG 2020-09 2020-09-30 17.490 16.520 \n", "467940 605116.XSHG 2020-10 2020-10-30 19.040 17.530 \n", "467941 605116.XSHG 2020-11 2020-11-30 18.350 18.320 \n", "467942 605116.XSHG 2020-12 2020-12-31 15.750 15.870 \n", "467943 605116.XSHG 2021-01 2021-01-29 15.710 14.140 \n", "467944 605116.XSHG 2021-02 2021-02-26 13.650 13.660 \n", "467945 605116.XSHG 2021-03 2021-03-31 15.200 15.160 \n", "467946 605116.XSHG 2021-04 2021-04-30 17.200 17.380 \n", "467947 605116.XSHG 2021-05 2021-05-31 14.430 14.800 \n", "467948 605116.XSHG 2021-06 2021-06-30 14.185 14.135 \n", "467949 605116.XSHG 2021-07 2021-07-30 14.185 14.014 \n", "467950 605116.XSHG 2021-08 2021-08-31 13.762 13.802 \n", "467951 605116.XSHG 2021-09 2021-09-30 13.106 13.449 \n", "467952 605116.XSHG 2021-10 2021-10-29 12.300 12.390 \n", "467953 605116.XSHG 2021-11 2021-11-30 19.730 21.706 \n", "467954 605116.XSHG 2021-12 2021-12-31 20.184 22.200 \n", "467955 605116.XSHG 2022-01 2022-01-28 19.488 21.434 \n", "467956 605116.XSHG 2022-02 2022-02-28 21.978 22.008 \n", "467957 605116.XSHG 2022-03 2022-03-14 23.642 26.011 \n", "\n", " negMarketValue turnoverValue turnoverRate publishDate endDate \\\n", "467939 6.773200e+08 249301778.0 0.3736 2020-09-08 2019-12-31 \n", "467940 7.187300e+08 109638314.0 0.1474 2020-10-29 2020-09-30 \n", "467941 7.511200e+08 39324380.0 0.0522 2020-10-29 2020-09-30 \n", "467942 6.506700e+08 35867084.0 0.0549 2020-10-29 2020-09-30 \n", "467943 5.797400e+08 50687282.0 0.0848 2020-10-29 2020-09-30 \n", "467944 5.600600e+08 11489080.0 0.0205 2020-10-29 2020-09-30 \n", "467945 6.215600e+08 32556596.0 0.0522 2020-10-29 2020-09-30 \n", "467946 7.125800e+08 168984435.0 0.2402 2021-04-29 2021-03-31 \n", "467947 6.068000e+08 19157242.0 0.0319 2021-04-29 2021-03-31 \n", "467948 5.748200e+08 9807849.0 0.0171 2021-04-29 2021-03-31 \n", "467949 5.699000e+08 8463181.0 0.0149 2021-04-29 2021-03-31 \n", "467950 5.612900e+08 9275307.0 0.0166 2021-08-31 2021-06-30 \n", "467951 1.031598e+09 6464580.0 0.0063 2021-08-31 2021-06-30 \n", "467952 9.504004e+08 2981573.0 0.0031 2021-08-31 2021-06-30 \n", "467953 1.664941e+09 454379548.0 0.2922 2021-10-30 2021-09-30 \n", "467954 1.702833e+09 144668088.0 0.0868 2021-10-30 2021-09-30 \n", "467955 1.644061e+09 47306575.0 0.0289 2021-10-30 2021-09-30 \n", "467956 1.688140e+09 70790127.0 0.0421 2021-10-30 2021-09-30 \n", "467957 1.995145e+09 587248355.0 0.2992 2021-10-30 2021-09-30 \n", "\n", " book ret ret_date ym_diff \n", "467939 9.772832e+08 0.061138 2020-10 1 \n", "467940 1.368126e+09 0.045066 2020-11 1 \n", "467941 1.368126e+09 -0.133734 2020-12 1 \n", "467942 1.368126e+09 -0.109011 2021-01 1 \n", "467943 1.368126e+09 -0.033946 2021-02 1 \n", "467944 1.368126e+09 0.109810 2021-03 1 \n", "467945 1.368126e+09 0.146438 2021-04 1 \n", "467946 1.417570e+09 -0.148446 2021-05 1 \n", "467947 1.417570e+09 -0.044932 2021-06 1 \n", "467948 1.417570e+09 -0.008560 2021-07 1 \n", "467949 1.417570e+09 -0.015128 2021-08 1 \n", "467950 1.422180e+09 -0.025576 2021-09 1 \n", "467951 1.422180e+09 -0.078742 2021-10 1 \n", "467952 1.422180e+09 0.751897 2021-11 1 \n", "467953 1.471599e+09 0.022759 2021-12 1 \n", "467954 1.471599e+09 -0.034505 2022-01 1 \n", "467955 1.471599e+09 0.026780 2022-02 1 \n", "467956 1.471599e+09 0.181888 2022-03 1 \n", "467957 1.471599e+09 NaN NaT 9223372036854775182 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 查看数据\n", "temp = stk_df_m['secID'].unique()\n", "display(stk_df_m[stk_df_m['secID'] == np.random.choice(temp,1)[0]])" ] }, { "cell_type": "code", "execution_count": 148, "metadata": {}, "outputs": [], "source": [ "del temp" ] }, { "cell_type": "code", "execution_count": 149, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDmktcap_book_datepreClosePricemktcapturnoverValueturnoverRatebookretret_date
0000001.XSHE2007-06953.7804.266117e+101.410758e+090.03167.106094e+090.3164972007-07
1000001.XSHE2007-071082.2595.616330e+101.479466e+090.02707.106094e+090.0488552007-08
2000001.XSHE2007-081193.0165.890714e+106.552881e+080.01127.698478e+090.0521052007-09
3000001.XSHE2007-091228.1426.197651e+101.408136e+090.02287.698478e+090.2018512007-10
4000001.XSHE2007-101427.1897.448652e+101.440425e+090.02008.363553e+09-0.2491162007-11
5000001.XSHE2007-111172.4475.593078e+105.452159e+080.00968.363553e+090.0698452007-12
6000001.XSHE2007-121234.1556.574629e+101.019671e+090.01548.363553e+09-0.1373062008-01
7000001.XSHE2008-011074.3475.850212e+105.328429e+080.00898.363553e+09-0.0045042008-02
8000001.XSHE2008-021037.9565.823860e+102.267900e+080.00398.363553e+09-0.1493212008-03
9000001.XSHE2008-03918.9714.954234e+106.155862e+080.01231.300606e+100.0503552008-04
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484442900957.XSHG2021-050.6201.135280e+081.458800e+050.00135.062701e+080.0273312021-06
484443900957.XSHG2021-060.6331.166560e+081.610070e+050.00145.062701e+080.0172142021-07
484444900957.XSHG2021-070.6511.186800e+081.151750e+050.00105.062701e+08-0.0584622021-08
484445900957.XSHG2021-080.6261.116880e+083.033640e+050.00275.128208e+080.0898692021-09
484446900957.XSHG2021-090.6551.218080e+082.086830e+050.00175.128208e+08-0.0404802021-10
484447900957.XSHG2021-100.6361.168400e+086.162200e+040.00055.188844e+08-0.0406252021-11
484448900957.XSHG2021-110.6231.120560e+081.161060e+050.00105.188844e+080.0358312021-12
484449900957.XSHG2021-120.6351.161040e+081.059960e+050.00095.188844e+08-0.0220132022-01
484450900957.XSHG2022-010.6171.135280e+081.319240e+050.00125.188844e+08-0.0112542022-02
484451900957.XSHG2022-020.6161.122400e+089.851400e+040.00095.188844e+08-0.0341462022-03
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471605 rows × 9 columns

\n", "
" ], "text/plain": [ " secID mktcap_book_date preClosePrice mktcap \\\n", "0 000001.XSHE 2007-06 953.780 4.266117e+10 \n", "1 000001.XSHE 2007-07 1082.259 5.616330e+10 \n", "2 000001.XSHE 2007-08 1193.016 5.890714e+10 \n", "3 000001.XSHE 2007-09 1228.142 6.197651e+10 \n", "4 000001.XSHE 2007-10 1427.189 7.448652e+10 \n", "5 000001.XSHE 2007-11 1172.447 5.593078e+10 \n", "6 000001.XSHE 2007-12 1234.155 6.574629e+10 \n", "7 000001.XSHE 2008-01 1074.347 5.850212e+10 \n", "8 000001.XSHE 2008-02 1037.956 5.823860e+10 \n", "9 000001.XSHE 2008-03 918.971 4.954234e+10 \n", "... ... ... ... ... \n", "484442 900957.XSHG 2021-05 0.620 1.135280e+08 \n", "484443 900957.XSHG 2021-06 0.633 1.166560e+08 \n", "484444 900957.XSHG 2021-07 0.651 1.186800e+08 \n", "484445 900957.XSHG 2021-08 0.626 1.116880e+08 \n", "484446 900957.XSHG 2021-09 0.655 1.218080e+08 \n", "484447 900957.XSHG 2021-10 0.636 1.168400e+08 \n", "484448 900957.XSHG 2021-11 0.623 1.120560e+08 \n", "484449 900957.XSHG 2021-12 0.635 1.161040e+08 \n", "484450 900957.XSHG 2022-01 0.617 1.135280e+08 \n", "484451 900957.XSHG 2022-02 0.616 1.122400e+08 \n", "\n", " turnoverValue turnoverRate book ret ret_date \n", "0 1.410758e+09 0.0316 7.106094e+09 0.316497 2007-07 \n", "1 1.479466e+09 0.0270 7.106094e+09 0.048855 2007-08 \n", "2 6.552881e+08 0.0112 7.698478e+09 0.052105 2007-09 \n", "3 1.408136e+09 0.0228 7.698478e+09 0.201851 2007-10 \n", "4 1.440425e+09 0.0200 8.363553e+09 -0.249116 2007-11 \n", "5 5.452159e+08 0.0096 8.363553e+09 0.069845 2007-12 \n", "6 1.019671e+09 0.0154 8.363553e+09 -0.137306 2008-01 \n", "7 5.328429e+08 0.0089 8.363553e+09 -0.004504 2008-02 \n", "8 2.267900e+08 0.0039 8.363553e+09 -0.149321 2008-03 \n", "9 6.155862e+08 0.0123 1.300606e+10 0.050355 2008-04 \n", "... ... ... ... ... ... \n", "484442 1.458800e+05 0.0013 5.062701e+08 0.027331 2021-06 \n", "484443 1.610070e+05 0.0014 5.062701e+08 0.017214 2021-07 \n", "484444 1.151750e+05 0.0010 5.062701e+08 -0.058462 2021-08 \n", "484445 3.033640e+05 0.0027 5.128208e+08 0.089869 2021-09 \n", "484446 2.086830e+05 0.0017 5.128208e+08 -0.040480 2021-10 \n", "484447 6.162200e+04 0.0005 5.188844e+08 -0.040625 2021-11 \n", "484448 1.161060e+05 0.0010 5.188844e+08 0.035831 2021-12 \n", "484449 1.059960e+05 0.0009 5.188844e+08 -0.022013 2022-01 \n", "484450 1.319240e+05 0.0012 5.188844e+08 -0.011254 2022-02 \n", "484451 9.851400e+04 0.0009 5.188844e+08 -0.034146 2022-03 \n", "\n", "[471605 rows x 9 columns]" ] }, "execution_count": 149, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_df_m.drop(['tradeDate','closePrice','publishDate','endDate', 'ym_diff'],axis=1,inplace=True)\n", "\n", "stk_df_m.rename(columns={'ym':'mktcap_book_date','negMarketValue':'mktcap'},inplace=True)\n", "\n", "stk_df_m.dropna(inplace=True)\n", "\n", "stk_df_m" ] }, { "cell_type": "code", "execution_count": 150, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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secIDmktcap_book_datepreClosePricemktcapturnoverValueturnoverRatebookretret_date
34000001.XSHE2010-04832.2286.011979e+10662829180.00.01112.210983e+10-0.1483462010-05
35000001.XSHE2010-05745.9945.120124e+10480683776.00.00922.210983e+100.0000002010-06
37000001.XSHE2010-09670.8995.036906e+10347260768.00.00693.042111e+100.1350192010-10
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" ], "text/plain": [ " secID mktcap_book_date preClosePrice mktcap turnoverValue \\\n", "34 000001.XSHE 2010-04 832.228 6.011979e+10 662829180.0 \n", "35 000001.XSHE 2010-05 745.994 5.120124e+10 480683776.0 \n", "37 000001.XSHE 2010-09 670.899 5.036906e+10 347260768.0 \n", "\n", " turnoverRate book ret ret_date \n", "34 0.0111 2.210983e+10 -0.148346 2010-05 \n", "35 0.0092 2.210983e+10 0.000000 2010-06 \n", "37 0.0069 3.042111e+10 0.135019 2010-10 " ] }, "execution_count": 150, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_df_m[(stk_df_m['secID']=='000001.XSHE') & (stk_df_m['ret_date']>='2010-05') &(stk_df_m['ret_date']<='2010-10')]" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "### Merge" ] }, { "cell_type": "code", "execution_count": 151, "metadata": { "editable": true }, "outputs": [], "source": [ "ret_df = pd.merge(stk_df_m, rf, left_on='ret_date',right_on='ym')\n", "ret_df.drop('ym',axis=1,inplace=True)\n", "ret_df['exret'] = ret_df['ret'] - ret_df['rf']\n", "ret_df.sort_values(['secID','ret_date'],inplace=True)\n", "ret_df.reset_index(drop=True,inplace=True)\n", "# Use last month's beta for grouping\n", "ret_df = pd.merge(ret_df,beta_m_df,left_on=['secID','mktcap_book_date'],right_on=['secID','ym'])\n", "# display(ret_df)\n", "ret_df.drop(['ym','rf','ret'],axis=1,inplace=True)\n", "ret_df.rename(columns={'mktcap_book_date':'grouping_date'},inplace=True)\n", "ret_df['bm'] = ret_df['book'] / ret_df['mktcap']\n", "ret_df['size'] = np.log(ret_df['mktcap'])\n", "ret_df = ret_df[['secID','grouping_date','size','mktcap','bm',\n", " 'beta','ret_date','exret']]" ] }, { "cell_type": "code", "execution_count": 152, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDgrouping_datesizemktcapbmbetaret_dateexret
0000001.XSHE2007-0624.4765554.266117e+100.1665710.46142007-070.313877
1000001.XSHE2007-0724.7515295.616330e+100.1265260.64232007-080.046173
2000001.XSHE2007-0824.7992285.890714e+100.1306880.77222007-090.049171
3000001.XSHE2007-0924.8500216.197651e+100.1242160.75962007-100.198601
4000001.XSHE2007-1025.0338847.448652e+100.1122830.79882007-11-0.252661
5000001.XSHE2007-1124.7473815.593078e+100.1495340.95602007-120.066202
6000001.XSHE2007-1224.9090696.574629e+100.1272100.94682008-01-0.141037
7000001.XSHE2008-0124.7923295.850212e+100.1429620.96542008-02-0.008257
8000001.XSHE2008-0224.7878145.823860e+100.1436081.02922008-03-0.153072
9000001.XSHE2008-0324.6260934.954234e+100.2625241.02382008-040.046610
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450568689009.XSHG2021-0422.2284694.505069e+090.8313191.57262021-05-0.016887
450569689009.XSHG2021-0522.2135744.438462e+090.8437951.46962021-060.159215
450570689009.XSHG2021-0622.3630755.154186e+090.7266231.37612021-07-0.198444
450571689009.XSHG2021-0722.1443864.141757e+090.9042421.09752021-080.094522
450572689009.XSHG2021-0822.2364994.541392e+090.8855981.07272021-090.076687
450573689009.XSHG2021-0922.3122254.898648e+090.8210121.01002021-10-0.210927
450574689009.XSHG2021-1024.0679522.835168e+100.1475150.85702021-11-0.051430
450575689009.XSHG2021-1124.0173172.695182e+100.1551770.75462021-120.149630
450576689009.XSHG2021-1224.1585643.104066e+100.1347370.58982022-01-0.134237
450577689009.XSHG2022-0124.0168232.693853e+100.1552540.53262022-02-0.063915
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450578 rows × 8 columns

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" ], "text/plain": [ " secID grouping_date size mktcap bm beta \\\n", "0 000001.XSHE 2007-06 24.476555 4.266117e+10 0.166571 0.4614 \n", "1 000001.XSHE 2007-07 24.751529 5.616330e+10 0.126526 0.6423 \n", "2 000001.XSHE 2007-08 24.799228 5.890714e+10 0.130688 0.7722 \n", "3 000001.XSHE 2007-09 24.850021 6.197651e+10 0.124216 0.7596 \n", "4 000001.XSHE 2007-10 25.033884 7.448652e+10 0.112283 0.7988 \n", "5 000001.XSHE 2007-11 24.747381 5.593078e+10 0.149534 0.9560 \n", "6 000001.XSHE 2007-12 24.909069 6.574629e+10 0.127210 0.9468 \n", "7 000001.XSHE 2008-01 24.792329 5.850212e+10 0.142962 0.9654 \n", "8 000001.XSHE 2008-02 24.787814 5.823860e+10 0.143608 1.0292 \n", "9 000001.XSHE 2008-03 24.626093 4.954234e+10 0.262524 1.0238 \n", "... ... ... ... ... ... ... \n", "450568 689009.XSHG 2021-04 22.228469 4.505069e+09 0.831319 1.5726 \n", "450569 689009.XSHG 2021-05 22.213574 4.438462e+09 0.843795 1.4696 \n", "450570 689009.XSHG 2021-06 22.363075 5.154186e+09 0.726623 1.3761 \n", "450571 689009.XSHG 2021-07 22.144386 4.141757e+09 0.904242 1.0975 \n", "450572 689009.XSHG 2021-08 22.236499 4.541392e+09 0.885598 1.0727 \n", "450573 689009.XSHG 2021-09 22.312225 4.898648e+09 0.821012 1.0100 \n", "450574 689009.XSHG 2021-10 24.067952 2.835168e+10 0.147515 0.8570 \n", "450575 689009.XSHG 2021-11 24.017317 2.695182e+10 0.155177 0.7546 \n", "450576 689009.XSHG 2021-12 24.158564 3.104066e+10 0.134737 0.5898 \n", "450577 689009.XSHG 2022-01 24.016823 2.693853e+10 0.155254 0.5326 \n", "\n", " ret_date exret \n", "0 2007-07 0.313877 \n", "1 2007-08 0.046173 \n", "2 2007-09 0.049171 \n", "3 2007-10 0.198601 \n", "4 2007-11 -0.252661 \n", "5 2007-12 0.066202 \n", "6 2008-01 -0.141037 \n", "7 2008-02 -0.008257 \n", "8 2008-03 -0.153072 \n", "9 2008-04 0.046610 \n", "... ... ... \n", "450568 2021-05 -0.016887 \n", "450569 2021-06 0.159215 \n", "450570 2021-07 -0.198444 \n", "450571 2021-08 0.094522 \n", "450572 2021-09 0.076687 \n", "450573 2021-10 -0.210927 \n", "450574 2021-11 -0.051430 \n", "450575 2021-12 0.149630 \n", "450576 2022-01 -0.134237 \n", "450577 2022-02 -0.063915 \n", "\n", "[450578 rows x 8 columns]" ] }, "execution_count": 152, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ret_df" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "## Sorting on BM poin-in-time" ] }, { "cell_type": "code", "execution_count": 153, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
p1p2p3p4p5p6p7p8p9p10p10-p1
mean0.0032810.0058310.0081520.0080770.0107770.0109730.0125990.0132900.0143350.0159310.012650
t-value0.4576590.8289011.1172071.0857141.4550111.4831721.6762211.7592811.8301061.9193763.089765
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" ], "text/plain": [ " p1 p2 p3 p4 p5 p6 p7 \\\n", "mean 0.003281 0.005831 0.008152 0.008077 0.010777 0.010973 0.012599 \n", "t-value 0.457659 0.828901 1.117207 1.085714 1.455011 1.483172 1.676221 \n", "\n", " p8 p9 p10 p10-p1 \n", "mean 0.013290 0.014335 0.015931 0.012650 \n", "t-value 1.759281 1.830106 1.919376 3.089765 " ] }, "execution_count": 153, "metadata": {}, "output_type": "execute_result" } ], "source": [ "q = dict()\n", "keys = ['q'+str(i) for i in range(1, 10)]\n", "values = np.arange(0.1, 1.0, 0.1)\n", "q.update(zip(keys,values))\n", "\n", "quantile_df = pd.DataFrame()\n", "for key, value in q.items():\n", " quantile_df[key] = ret_df.groupby(['grouping_date'])['bm'].quantile(value)\n", "\n", "ret_df_q = pd.merge(ret_df, quantile_df, on='grouping_date')\n", "\n", "portfolios = dict()\n", "drop_cols = [col for col in ret_df_q.columns if col[0]=='q']\n", "\n", "portfolios['p1'] = ret_df_q.loc[ret_df_q['bm'] <= ret_df_q['q1']].copy().drop(drop_cols, axis=1)\n", "for i in range(2,10):\n", " idx = (ret_df_q[f'q{i-1}'] <= ret_df_q['bm']) & (ret_df_q['bm'] <= ret_df_q[f'q{i}'])\n", " portfolios[f'p{i}'] = ret_df_q.loc[idx].copy().drop(drop_cols, axis=1)\n", "portfolios['p10'] = ret_df_q.loc[ret_df_q['bm'] >= ret_df_q['q9']].copy().drop(drop_cols, axis=1)\n", "\n", "portfolios_crs_mean = dict()\n", "for k in portfolios.keys():\n", " portfolios_crs_mean[k] = portfolios[k].groupby(['ret_date'])['exret'].mean()\n", "\n", "mean_values = {}\n", "t_values = {}\n", "for k in portfolios_crs_mean.keys():\n", " y = portfolios_crs_mean[k]\n", " const = np.full(shape=len(y),fill_value=1)\n", " reg = sm.OLS(y, const).fit().get_robustcov_results(cov_type='HAC', maxlags=6)\n", " mean_values[k] = reg.params[0]\n", " t_values[k] = reg.tvalues[0]\n", "# Portfolio 10-1\n", "y = portfolios_crs_mean['p10'] - portfolios_crs_mean['p1']\n", "const = np.full(shape=len(y), fill_value=1)\n", "reg = sm.OLS(y, const).fit().get_robustcov_results(cov_type='HAC', maxlags=6)\n", "mean_values['p10-p1'] = reg.params[0]\n", "t_values['p10-p1'] = reg.tvalues[0]\n", "\n", "pd.DataFrame([mean_values.values(),t_values.values()],index=['mean','t-value'],\n", " columns=mean_values.keys())" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "结论：\n", "\n", "- 用最新的BM更新portfolio可以带来收益率的递增，但每个portfolio本身的收益率并不显著为正\n", "- p10和p1的差距是显著为正的" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "### Sorting on BM with data from Uqer" ] }, { "cell_type": "code", "execution_count": 154, "metadata": { "editable": true }, "outputs": [], "source": [ "# %%time\n", "# begin_ = 2007\n", "# yesterday = dt.datetime.today() - dt.timedelta(days=1)\n", "# yesterday.strftime('%Y%m%d')\n", "# pb_df = DataAPI.MktStockFactorsDateRangeProGet(secID=stk_id,\n", "# beginDate=f'{begin_}0101',\n", "# endDate=yesterday,\n", "# field=['secID','tradeDate','PB'],pandas=\"1\")" ] }, { "cell_type": "code", "execution_count": 155, "metadata": { "editable": true }, "outputs": [], "source": [ "# # # 从优矿下载 PB，时间较长。由于优矿的限制，每次下载3年的数据\n", "\n", "# pb = {}\n", "# begin_ = 2007\n", "# end_ = 2010\n", "# i = 0\n", "# while end_ <= 2022:\n", "# if end_ == 2022:\n", "# yesterday = dt.datetime.today() - dt.timedelta(days=1)\n", "# yesterday.strftime('%Y%m%d')\n", "# pb[i] = DataAPI.MktStockFactorsDateRangeProGet(secID=stk_id,\n", "# beginDate=f'{begin_}0101',\n", "# endDate=yesterday,\n", "# field=['secID','tradeDate','PB'],pandas=\"1\")\n", "# else:\n", "# pb[i] = DataAPI.MktStockFactorsDateRangeProGet(secID=stk_id,\n", "# beginDate=f'{begin_}0101',\n", "# endDate=f'{end_}1231',\n", "# field=['secID','tradeDate','PB'],pandas=\"1\")\n", "# begin_ = end_ + 1\n", "# end_ = begin_ + 3\n", "# i = i+1\n", " \n", "# for i in range(4):\n", "# pb_df = pd.DataFrame(np.vstack([_df for _df in pb.values()]),columns=['secID','tradeDate','PB'])\n", " \n", "# pb_df.to_pickle('./data/pb_df.pkl')" ] }, { "cell_type": "code", "execution_count": 156, "metadata": { "editable": true }, "outputs": [], "source": [ "pb_df = pd.read_pickle('./data/pb_df.pkl')" ] }, { "cell_type": "code", "execution_count": 157, "metadata": { "editable": true }, "outputs": [], "source": [ "pb_df['tradeDate'] = pd.to_datetime(pb_df['tradeDate'])\n", "\n", "pb_df['PB'] = pd.to_numeric(pb_df['PB'])\n", "\n", "pb_df['grouping_date'] = pb_df['tradeDate'].dt.to_period('M')\n", "\n", "pb_df = pb_df.groupby(['secID','grouping_date'],as_index=False).last()\n", "\n", "pb_df['bm_uqer'] = 1 / pb_df['PB']\n", "\n", "ret_df = pd.merge(ret_df,pb_df[['secID','grouping_date','bm_uqer']],on=['secID','grouping_date'])\n", "\n", "del pb_df" ] }, { "cell_type": "code", "execution_count": 158, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDgrouping_datesizemktcapbmbetaret_dateexretbm_uqer
0000001.XSHE2007-0624.4765554.266117e+100.1665710.46142007-070.3138770.123739
1000001.XSHE2007-0724.7515295.616330e+100.1265260.64232007-080.0461730.093992
2000001.XSHE2007-0824.7992285.890714e+100.1306880.77222007-090.0491710.097085
3000001.XSHE2007-0924.8500216.197651e+100.1242160.75962007-100.1986010.092276
4000001.XSHE2007-1025.0338847.448652e+100.1122830.79882007-11-0.2526610.083411
5000001.XSHE2007-1124.7473815.593078e+100.1495340.95602007-120.0662020.111084
6000001.XSHE2007-1224.9090696.574629e+100.1272100.94682008-01-0.1410370.094476
7000001.XSHE2008-0124.7923295.850212e+100.1429620.96542008-02-0.0082570.109513
8000001.XSHE2008-0224.7878145.823860e+100.1436081.02922008-03-0.1530720.110009
9000001.XSHE2008-0324.6260934.954234e+100.2625241.02382008-040.0466100.201102
..............................
450568689009.XSHG2021-0422.2284694.505069e+090.8313191.57262021-05-0.0168870.071493
450569689009.XSHG2021-0522.2135744.438462e+090.8437951.46962021-060.1592150.072566
450570689009.XSHG2021-0622.3630755.154186e+090.7266231.37612021-07-0.1984440.062490
450571689009.XSHG2021-0722.1443864.141757e+090.9042421.09752021-080.0945220.077765
450572689009.XSHG2021-0822.2364994.541392e+090.8855981.07272021-090.0766870.076097
450573689009.XSHG2021-0922.3122254.898648e+090.8210121.01002021-10-0.2109270.070547
450574689009.XSHG2021-1024.0679522.835168e+100.1475150.85702021-11-0.0514300.092734
450575689009.XSHG2021-1124.0173172.695182e+100.1551770.75462021-120.1496300.097551
450576689009.XSHG2021-1224.1585643.104066e+100.1347370.58982022-01-0.1342370.084318
450577689009.XSHG2022-0124.0168232.693853e+100.1552540.53262022-02-0.0639150.097158
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450578 rows × 9 columns

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" ], "text/plain": [ " secID grouping_date size mktcap bm beta \\\n", "0 000001.XSHE 2007-06 24.476555 4.266117e+10 0.166571 0.4614 \n", "1 000001.XSHE 2007-07 24.751529 5.616330e+10 0.126526 0.6423 \n", "2 000001.XSHE 2007-08 24.799228 5.890714e+10 0.130688 0.7722 \n", "3 000001.XSHE 2007-09 24.850021 6.197651e+10 0.124216 0.7596 \n", "4 000001.XSHE 2007-10 25.033884 7.448652e+10 0.112283 0.7988 \n", "5 000001.XSHE 2007-11 24.747381 5.593078e+10 0.149534 0.9560 \n", "6 000001.XSHE 2007-12 24.909069 6.574629e+10 0.127210 0.9468 \n", "7 000001.XSHE 2008-01 24.792329 5.850212e+10 0.142962 0.9654 \n", "8 000001.XSHE 2008-02 24.787814 5.823860e+10 0.143608 1.0292 \n", "9 000001.XSHE 2008-03 24.626093 4.954234e+10 0.262524 1.0238 \n", "... ... ... ... ... ... ... \n", "450568 689009.XSHG 2021-04 22.228469 4.505069e+09 0.831319 1.5726 \n", "450569 689009.XSHG 2021-05 22.213574 4.438462e+09 0.843795 1.4696 \n", "450570 689009.XSHG 2021-06 22.363075 5.154186e+09 0.726623 1.3761 \n", "450571 689009.XSHG 2021-07 22.144386 4.141757e+09 0.904242 1.0975 \n", "450572 689009.XSHG 2021-08 22.236499 4.541392e+09 0.885598 1.0727 \n", "450573 689009.XSHG 2021-09 22.312225 4.898648e+09 0.821012 1.0100 \n", "450574 689009.XSHG 2021-10 24.067952 2.835168e+10 0.147515 0.8570 \n", "450575 689009.XSHG 2021-11 24.017317 2.695182e+10 0.155177 0.7546 \n", "450576 689009.XSHG 2021-12 24.158564 3.104066e+10 0.134737 0.5898 \n", "450577 689009.XSHG 2022-01 24.016823 2.693853e+10 0.155254 0.5326 \n", "\n", " ret_date exret bm_uqer \n", "0 2007-07 0.313877 0.123739 \n", "1 2007-08 0.046173 0.093992 \n", "2 2007-09 0.049171 0.097085 \n", "3 2007-10 0.198601 0.092276 \n", "4 2007-11 -0.252661 0.083411 \n", "5 2007-12 0.066202 0.111084 \n", "6 2008-01 -0.141037 0.094476 \n", "7 2008-02 -0.008257 0.109513 \n", "8 2008-03 -0.153072 0.110009 \n", "9 2008-04 0.046610 0.201102 \n", "... ... ... ... \n", "450568 2021-05 -0.016887 0.071493 \n", "450569 2021-06 0.159215 0.072566 \n", "450570 2021-07 -0.198444 0.062490 \n", "450571 2021-08 0.094522 0.077765 \n", "450572 2021-09 0.076687 0.076097 \n", "450573 2021-10 -0.210927 0.070547 \n", "450574 2021-11 -0.051430 0.092734 \n", "450575 2021-12 0.149630 0.097551 \n", "450576 2022-01 -0.134237 0.084318 \n", "450577 2022-02 -0.063915 0.097158 \n", "\n", "[450578 rows x 9 columns]" ] }, "execution_count": 158, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ret_df" ] }, { "cell_type": "code", "execution_count": 161, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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grouping_datebmbm_uqer
4057302008-080.5744000.118841
4057312008-090.5612710.113771
4057322008-101.8805320.381185
4057332008-111.2712940.322123
4057342008-121.2182010.308661
4057352009-011.2210340.309387
4057362009-021.1852020.300300
4057372009-031.1932830.302352
4057382009-041.2287730.311342
4057392009-051.1780190.298481
............
4058802021-040.9951560.843597
4058812021-050.9819970.832432
4058822021-060.9771520.828295
4058832021-071.0138370.859402
4058842021-080.8638890.732332
4058852021-090.9390100.795988
4058862021-100.9816920.832154
4058872021-110.9818780.832362
4058882021-120.9818780.832362
4058892022-011.0274290.870928
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160 rows × 3 columns

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" ], "text/plain": [ " grouping_date bm bm_uqer\n", "405730 2008-08 0.574400 0.118841\n", "405731 2008-09 0.561271 0.113771\n", "405732 2008-10 1.880532 0.381185\n", "405733 2008-11 1.271294 0.322123\n", "405734 2008-12 1.218201 0.308661\n", "405735 2009-01 1.221034 0.309387\n", "405736 2009-02 1.185202 0.300300\n", "405737 2009-03 1.193283 0.302352\n", "405738 2009-04 1.228773 0.311342\n", "405739 2009-05 1.178019 0.298481\n", "... ... ... ...\n", "405880 2021-04 0.995156 0.843597\n", "405881 2021-05 0.981997 0.832432\n", "405882 2021-06 0.977152 0.828295\n", "405883 2021-07 1.013837 0.859402\n", "405884 2021-08 0.863889 0.732332\n", "405885 2021-09 0.939010 0.795988\n", "405886 2021-10 0.981692 0.832154\n", "405887 2021-11 0.981878 0.832362\n", "405888 2021-12 0.981878 0.832362\n", "405889 2022-01 1.027429 0.870928\n", "\n", "[160 rows x 3 columns]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 161, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Rp857qTnv57Is6x7LsjZalrUxPz9/3M8asVLqpn0XAAAAAOxs0tt3Lcuywj8+KynXGOOUdEZSXvglBQpVSyclEBx6T6kUCqW07wIAAACAfcUUSo0x3zXGXGeMmWuMSQk/dqGkM5ZlBSQ9Keld4Ze/V9JfJnNIy7LkP+9KGElKJZQCAAAAgK3FWildLClH0nxJu40xr0v6iaSPh5//saRrjTHHJTVYlvXnyRwyEAx1Aw9v32WmFAAAAADszBXtCy3LunfQ17cMemrNCK/tkTTpa2Ai/JFQOkL7LjOlAAAAAGBfk54pnQ7+USqltO8CAAAAgL3ZIpQGAqFQev5MqdftpH0XAAAAAGzMFqHUHwwFT7eTSikAAAAAzCQ2CaWRSulIi44IpQAAAABgV7YKpSPNlPqDlnwBWngBAAAAwI5sEUojM6Wu82ZKU9xOSaJaCgAAAAA2ZYtQ6gvPlA6/EiZ0fK6FAQAAAAB7skUoDYw6UxqqlPaxgRcAAAAAbMkWoTQyM0r7LgAAAADMLLYIpYExFh1JtO8CAAAAgF3ZIpQOXAkzbKY0UimlfRcAAAAA7MgeoTS8fdc9rH2XRUcAAAAAYGf2CKXh7bujLTpiphQAAAAA7MkWoXRgpnTU9l1CKQAAAADYkS1CaaR9d9iiIw+hFAAAAADszB6hdGD77nkzpa7Qz1l0BAAAAAD2ZItQGhhlpjRSKWXREQAAAADYky1CqS+yfff8mVIX7bsAAAAAYGe2CKWRRUfnV0odDiOP00GlFAAAAABsyhahNDJT6nYOP26K26E+ZkoBAAAAwJbsEUoDI8+USqFrYWjfBQAAAAB7skcoDY58JYwUWnZE+y4AAAAA2JMtQmlkptQ1Uvuui0opAAAAANiVLUKpb6z2XY9TPcyUAgAAAIAt2SKUBsZo301xOaiUAgAAAIBN2SKUDsyUOll0BAAAAAAziT1CaSBSKR1+3FRCKQAAAADYli1CaSAYmhkdoXtXKW6HepkpBQAAAABbskUo9QUtuZ1GxnAlDAAAAADMJLYIpYGgNeLmXUnyciUMAAAAANiWLUKpP2CNOE8qsegIAAAAAOzMHqE0GBxx864UWnTkC1jyB5grBQAAAAC7sUkotUa8o1QKLTqSpF4/oRQAAAAA7MYWoTQQGH2mNNXjlCRaeAEAAADAhmwRSn3B4OgzpS5CKQAAAADYlS1CaSBojTpTmkKlFAAAAABsyxah1D/GlTAprvBMqY+ZUgAAAACwG3uE0kBQ7jGuhJGkHiqlAAAAAGA7tgilgTEqpSw6AgAAAAD7skUo9QctuUebKQ0vOurpJ5QCAAAAgN3YI5SOeSUM95QCAAAAgF3ZI5SOcSWMlythAAAAAMC2bBFKx7wSxk0oBQAAAAC7skUo9Y3ZvksoBQAAAAC7skUoDQQtuca5p7Snn5lSAAAAALAbW4RSf9CSyznyUV1Oh9xOo14/lVIAAAAAsBt7hNJAcNRKqRS6Fob2XQAAAACwH1uE0kBw9JlSSUrxEEoBAAAAwI5sEUp9waDco7TvSlKK26FeHzOlAAAAAGA3tgilgTG270qh9t2efiqlAAAAAGA3tgil/jG270pSVqpbtW0903giAAAAAEA82CeUOkcPpddfUKQ3atq0/3TbNJ4KAAAAADBZUYVSY0yOMWaHMeYb5z0+xxhzvzHmsDFmtzFmdfjxTxhjqo0xe8N/OSdzyND23dGPevOGhUp1O3XfzqrJfAwAAAAAYJqNG0qNMW5JT0iqGOHpfkm/siyrXNJdkr4RfjxX0j9YlrUu/NekBj7H2747J82t96yfr4f31upsV/9kPgoAAAAAMI3GDaWWZfkk3ShpxwjP9ViW9Xj4p1WS5oa/zpHUFKczyjdO+64k/dVli9XnD+r3e6rj9bEAAAAAgCkWVfuuZVkNUbzsZklPhr92S/oXY8wBY8x3jTFjJ8pxBMZZdCRJ5UVZuqQ0R7/adVKBoDWZjwMAAAAATJO4LDoyxqyX9B5JPw4/9HeWZV0gaaOk1QoF1vO/55PGmD3GmD2NjY2jvrdlWeH23fGP+leXlajmbI+ePRxNhgYAAAAAJNqkQ6kxJk/SvZI+aFlWjyRZlmWFf+yR9IikVed/n2VZ91iWtdGyrI35+fmjvr8/XPV0j1MplaS3ripUUVaKfrmzaqK/DAAAAABAAsQUSsMtudcZY1ySHpD0vy3LOjDo+XnhHx2Srpa0N9YDRlpxnePMlEqS2+nQbZcs0o6jTTre2BnrRwIAAAAApkmsldLFCi0zulnSZknfGnT9S7akfzDG7Jf0uqRTkh6O9YCRSul4M6URt168SG6n0a92noz1IwEAAAAA08QV7Qsty7p30Ne3DHrq/hFefuckzjSEPxCUpDHvKR0sP9Ord66Zp/9+tUb/621lyvBG/UuMSp8/oO/++Yhu2bRQKwoz4/reAAAAADDbxGXR0VQaqJRG0b4b8ZHLStTR59cfXz8d9/P8x/ZK/dcLJ/TPjx2K+3vPZC8ea9Jf/+IV9fuDiT4KAAAAgCQS3zLiFAgMtO9Gn5/XL8zW2gVzdN9LVSovylTN2W7VtPSo+my3as72aO2CbH3p+vIJn+VYQ6fufvaYstPceq6iUccaOrSsgGrpePr8AX35j2/qZHO3jjV0alVxVqKPBAAAACBJJH2l1DfQvht9pdQYo49sLtHRhk7d/B879fnf7dP3nq7Qs0caVdfeq/947ri2H5nYtTHBoKUvP/imUj1O/f5Tm+VxOfSzF6om9B6z1S9fqtLJ5m5JUkV9R4JPAwAAACCZ2KZS6pxAKJWk966fL4/LoawUlxbmpGl+dqpS3E71+QN6xw926KsP79dTn7tKqR5nVO/3+z3VeqWqRd+5aY1WFGbqfevn68HXavTFt5UpJ90z4V/XSA7WtssfDGrN/DkyZuxfr2VZ474mGTR19ulH247piuV52lXZrCOEUgAAAACDJH2lNJaZUikUYt91YbG2lBVoaX6GUtyh8Ol1OfWt965RdUuPfvjM0ajeq7GjT996/JAuKc3RLRsXSpI++pZS9fmD+u3L8dvy+4n79uhdP35R13zvOf1w21GdClcXI1q7+/XQ66d1529f09p/fEpfeejNuH32VPn+0xXq8QX09RtXa0leho4SSgEAAAAMkvSVUn9g4jOl47lkSa5u2bhA//l8pd69rljlRWPPON716EH1+oL61vvWDFQnVxRm6orlebpv50l98sql8rgmd75A0FJde682L8lV0LL0/acr9P2nK7Rh8VxduiRHu0+c1Z6TLQpaUl6GR26nQ/tPt0/qM6fa4bp2PfDKKX1kc4mWFWRoeWGG9tW0JvpYAAAAAJKIDSqloZnSibbvjud/X79SWaluffnBNxUMV2NH8uzhBj2yr1af3bpMS/Mzhjz3sbeUqqGjT4++UTvp85zt7lcgaOltqwv1u09t1otfulp///YydfT6dPezx9XR59dnty7THz9zmV758rW6akW+mrv6Jv25U8WyLH3z0YPKTHHrc9culySVFWaquqVH3f3+BJ8OAAAAQLKwTaXUPcH23fHMTffoK+9cqb/7/T799pVT+vCli4e9prvfr688tF/LCjL06S1Lhj1/1Yp8LSvI0M9eOKH3rp8/qRnPps5QwMzPTJEkzc9O1We2LNMdVy1Vd39A6efdt5qb7lFzZ3/MnzfVth1q0IvHmvWNG1cpOy00c7s8fK/r0fpOXbgwO5HHAwAAAJAkbFApjW3RUTTeu36+Ll+Wq+/8+bAa2nsHHm/r8emBV07pg//5sk639uj/vG+NvK7hC5GMMfro5aU6UNuul0+0TOosjR2hUJqXMXRpkjFmWCCVpNwMr7r7A0lZdez3B/XPjx/S0vx03TYo7JcVhUIpG3gBAAAARCR9KI3lntJoGWP0T+9Zoz5/UN945ICePFCnO379qjb901/0pQffVEevT99+3xptKskZ9T3ed9F8zU1z62cvnJjUWc5VSr1RvT43HF6TsVr6q10ndaKpS1955yq5nef+uS3KSZPX5SCUAgAAABhgg/bd8D2lcW7fjSjNS9ffbF2m7z1docffrFNehke3XbpI710/P6qrWVLcTt12yWLdvf2Yqpq6VJKXHtM5BiqlUYbS/IzQ65o6+7QwJy2mz5wK/f6gfvTMUV2xPE9byvKHPOd0GC0ryNCR+s4EnQ4AAABAskn+UDpQKZ26Ozk/ddVSOZ1GK+dl6YpleXI5J1aV/cjmxfq/zx/XvS9V6RvvWh3TGZo6++V1OZQ5QqvuSJK1Urq7qkWt3T59ZHPJiIG+rDBTOyubE3AyAAAAAMnINu27UzFTGuFxOfSZLcu0taxgwoFUkgqyUnTD2mL9YU/1wHknqrGjT3kZ3qiXJeUOqpQmk22HGuRxOXT5stwRn19emKkzbb1q6/FN88kAAAAAJKOkD6W+cPuuO4awOJ02leSoqz+g2taemL6/qbMv6nlSKbR9V5Kau5KrUvrskQZdtjRXaZ6RK75lRaFrdY41MFcKAAAAwAahdDoqpfFQGp4lrWruiun7GzsmFkpT3E5leF1JVSmtbOzUiaYuXV1eMOprlheENvAeqWOuFAAAAIANQul0zJTGw0AobYo9lOZlRB9KpdD1Mck0U/rM4QZJ0tay0UPp/OxUpXucbOAFAAAAIMkWoTSyfTe5j1qY5VWq26nKGEKpPxBUS3f/hCqlUmiuNJkqpdsONaisMHPMbcAOh9HywswxQ+mp5m597oHXk/IO1qliWZaOEtQBAAAwCyV30pPkD9ijUmqMUUleekyV0pauflmWlB/eqBut3PTkqZS29/q0u6pFW8do3Y1YUZgxZii9b2eVHtpbq1dPno3jCZPbXw416Lp/f16nY5xJBgAAAOwq6UOpXWZKJak0L01Vzd0T/r7GcLUzlkppc1dyVEp3VDTJH7R0zcpoQmmmmjr71TxClTcYtPT4m2ckSQdq2+N+zmR1oqlTliWdTbLFVQAAAMBUS/pQ6ovMlDqTP5SW5KbrVEv3wMbgaDV2hMLZRGdK8zM8aunqj/kamnjadrhe2WlurV+YPe5rVxSGlh1V1A9fdvR69VnVtvVKml2htK4t9Hugzz+x3zsAAACA3SV9KA2EA57LkfRHVWleugJBSzVnJ9aC2RRuwY2lUhq0pLPdia2uBYKWnjvSqKtW5Ec1+1tWFAqlR0e4FubRN87I43LosqW5OlDbFvezJqv69lAQ7/MHEnwSAAAAYHolfdLz26hSGusG3lgrpbnhGdREz5Xuq2lVc1f/mFfBDFaQ6dWcVLeO1A0NpZHW3S0r8nVxaY5ONHXNmmVHdQOhlEopAAAAZhf7hFIbzJSWhEPpRDfwNnX2Kc3jVLrXNaHvy00PhdiRZjOn0zOHGuR0GF21Ij+q1xtjRlx29Oqps6pv79M7187T6uI5sizp0JnZsZG2Ltyy3E8oBQAAwCyT9KHUTouOctM9ykxxxVQpnWjrriTlZ4YqpU0JXo6z7XCDNiyeq+y06LcHryjMVEV9pyzr3Dzso/tq5XU5dM3KQq0qzpIkHZwFLbzBoKWGDiqlAAAAmJ2SPpRGroRx22Cm1Bij0rx0VTVPPJROtHVXOlcpbepIXKX0TFuPDp1pj7p1N2JFYabaenxqCJ89ELT0+P46bS0rUIbXpeI5KcpOc+vgmZm/7Kilu1++8O/zPh8zpQAAAJhdkj7p+YNBGSM5bFAplUJzpZWNE2/fzY8hlM5JdcvpMAm9FuaZww2SpGtiCKWSBlp4d1e1qLGjTzdcOE9SKOCvLs6aFRt4I627EpVSAAAAzD42CKWWLeZJI0py01Xb1qPeCVS8Gjv7lJcZfetrhMNhlJPuSeiio2cONWhhTqqWFWRM6PtWFIZeH1l29NgbZ5TidgypuK4unqPDdR0TvmLHbiKbdyVCKQAAAGaf5A+lgaAtroOJKM1Ll2VJ1S3dUb2+3x9Ua7dP+RkpMX1ebrpn4EqZ6dbrC+jF4026uqxAxkzsDw5yM7zKy/DoaH2n/IGgnth/RteUFyrNc27Z06p5Wer3BydcebabuiGhlPZdAAAAzC5Jn/bsViktneAG3kjrbSyVUil0t2mi2nd3Hm9Wry+oq1cWxvT9KwozdaS+Q6+caLuCDngAACAASURBVFFTZ7/euXbekOdXh5cdzfT7Suvb+xT5Ld7no1IKAACA2SXpQ2kgaMlpgztKI0omeFdpU0eoyhnLTKkUqZQmJpTuONokr8uhS0pzYvr+FYWZOlrfoUfeOKM0j1Nby4bOpS7Jz1CK2zHj50rr23qVl+GVx+lQ/wxvVQYAAADOl/Sh1BewbNW+OyfVrZx0T9QbeBs7Q62bsVwJI4XaYBM1U7qvplVr5s9RitsZ0/evKMxUV39Af3y9RtesLFSqZ+j7OB1GZUVZOjjDQ2lde6+K5qTI63JQKQUAAMCsk/RpLxAM2qp9V5rYBt7G8JUosVwJI0m5GR519wfU3e+P6ftj5QsEtf90my5cmB3ze5QVhZYd9fqCeueaeSO+JrSBt23IfaYzTX17rwqzUuR1O5gpBQAAwKyT9KHUH7TktFkoLcmN/q7SyJKiWCulkTA73dXSivoO9fmDWrtgTszvsawgdC1MusepLWX5I75mdXGW2nv9qjnbE/PnJLu69l4VZaXI63KyfRcAAACzTvKH0oAlt41mSiWpNC9N9e196uobv3rZ2NGnTK8r5hbYvIzQgqTpnivdVx1aPrRuEpXSOaluLclP1zvWzBv1179qXmTZ0cxs4e31BdTa7TvXvksoBQAAwCzjGv8liRWwYaW0NC/UllrV3KXVxWNXEhs7+2KukkpSbnpiKqX7qluVnebWopy0Sb3Pg3dcNmYgLy/KksNIB8+06+0XFE3qs5JR5I7SwqwUeVwO9U3gflsAAABgJkj+SmnQXveUSlJJXiioVTWNf1dpY0dfzPOkUmimVNK0Xwuzr6ZVaxdkT/h+0vNlp3nGDKWpHqeW5mfo4Ay9FqauLRRKQ+27VEoBAAAw+yR92vMHLLls1r5bkhu+FiaKudKmSVZKI4G2aRorpd39flXUd2jdJOZJJ2JVcdaMbd+tG6iUesMzpVRKAQAAMLskfygNWrbbvpvudakwyxvVBt5QpdQT82eluJ3K8LqmdaZ0/+l2BS1NavPuRKwuztKZtl61dCXm6pupNNC+Oye0fbefSikAAABmmaQPpXacKZWi28Db6wuoo9c/qUqpFGrhnc6Z0jdqWiVJaxdMVygNVWRn4n2ldW19SvM4lel10b4LAABgU7sqm/V8RWOij2FbSR9KfYGgXM6kP+YwpXnpqmoaO5RGqpuTDqXpnmmdKd1b3ar52amTPne0zm3gnXlzpfXh62CMMVwJAwAAYEOWZemL/2+f7nr0YKKPYltJn/YCNmzflUKhtLmrX209vlFf09gRCpKTWXQkSbkZ3mmtlO6radWFC6dnnlSS5qZ7VDwnRQfPzMBKaXuvCrNSJClcKWWmFAAAwE7eqGlTdUuPTrV0Kxi0En0cW0r6UOq3a/tuXnjZ0RjV0shyoslWHPMyvNM2U9rS1a/qlp5pa92NWFU8Z0YuO6pv71XRnFAoDV0JQ6UUAADATh5784wkqd8fHFhiiYmxQSgNym3T9l1p7A288aqU5mV41NLVr8A0/MnMvvA86YXTHEpXF2epsrFTPf0zp5JoWZYa2vvOq5QSSgEAAOzCsiw99sYZZae5JUV3+waGS/q05w/Ys1K6KCdNxmjMDbyR6mbuJLbvSqGZ0qAltXZPfQvvvupWGSOtmabrYCJWFWcpaEmH62ZOtbSlq1/9gaCKskJ/KOF1cyUMAACAnbx2qlWnW3v00ctLJUmnmrsTfCJ7SvpQGghactvsnlIpdFVL8ZzUcSulc1Ld8rqck/qs3HCltXkarkx5o6ZNy/IzlOF1TflnDba6OLLsaOaE0kh7R6R91+sKXQljWcwiAAAA2MFjb5yRx+nQRzYvlttpVEUojUnSh9LQTGnSH3NES/LTdWLMmdK+uGywjbT/NnVM7VypZVnaV906bfeTDjY/O1VzUt166mC9Trf2TPvnT4WBO0oHte8GrdDveQAAACS3YNDS42+e0ZUr8pWd5tHCnDSdaqF9NxZJn/b8waAtt+9KobtKTzR1jVr5auzoU94kW3clDbxH0xRXSmvO9qi5qz8hodQYo5s3LNDzFY26/NvP6P0/fUn37ayatgVPU6GuLXT2c5XSUMWcuVIAAIDkt+fkWdW19+rGC+dJkhbnpKmqiUppLJI/lAbseSWMFNrA29HrV8soYTFUKU2Z9OcMtO9OcUB7oyZ0T+i6aV5yFPGVG1bp+S9u1RffVqaOXr++9vABXfKtbfr4L3ers8+fkDNNRl17r4yR8jMiM6Whfx37fMyVAgAAJJo/EBxzkehjb9TK63LompWFkqTFuek62Tx6QQqjS/5QGrTksuFMqSQtCW/gHa2Ft7GjbyCQTEZ2qltOh5nyu0r31bTK43SorChzSj9nLIty0/TZrcv05Oev1JOfu1If2bxYfznUoGcPNyTsTLGqb+tVXoZXrvB2aU/4RyqlAAAAiXW4rl1bv7ddt/3XLvWP8P9mgaClx/fX6erygoFdKyW5aerqD0zLnhe76R2n6JL0oTRg03tKpXN3lY4USrv7/erqDygvc/Ltuw6HUU66R81dU1sp3VvdqlXFWfK4kuO3TVlRpr50fbmcDqMjdR2JPs6E1bX3qijrXKV8oFJKKAUAAEiYZw7X66afvKSOXr92VbborkcPDHvNKyda1NjRp3eunTfw2OLc0P/7n+RamGH+7emKMZ9PjnQxBl8gKJdNFx0tmJsqt9OMuDG2qSP0JyjxqJRKoWthGjui/1OZM209etePX9B/v1oT1esDQUv7T7dpXQLmScfidTm1ND/dllfF1Lf3Diw5kgbPlNK+CwAAMN0sy9J/7ajUx3+5RyV56Xrif16hT121RL/edUoPvHJqyGsffaNWqW6nri4vGHhscW6aJDFXep7mzj7dt/PkmK9J+rQXCNp3ptTtdOia8kI9sq92WNm/sTO0eTUvDtt3pdAG3olUSn+47ajeqGnTF/6wT//x3PFxe9+PNXSquz+gtdN8P2k0yoqydNiuldI55/75e12RmVIqpQAAANOp3x/Ul//4pv7psUO6blWR/vDpzZo3J1V//7ZyXbE8T197+IBeO3VWUmjW9M/763TNygKlec5dk7hgbpocRjrZQigd7GcvnFDvOEWXpA+l/qAlp01nSiXp1osXqrmrX08frB/yeGO8K6UZnqhnSquauvT7PTX64MWLdMPaefr2E4f1zUcPKTjGIPe+6lZJSsjm3fGUF2Wq5myPOnp9iT7KEC1d/Xr0jdoRn+v1BdTa7RvavhuulPYHCKUAAADTpbPPr7/6+Su6/5VqfXbrUv3ktosGwqbTYfSjD65X0ZwUffpXr6qhvVe7KlvU3NWvG9YWD3kfj8uh4uzUpGvffeyNMwnrKmzt7tcvX6oa9vfqfFGFUmNMjjFmhzHmGyM89zFjzFFjzH5jzIZBr99ujDlujPlJLL+ACH8gKLdN23cl6Yrl+ZqfnaoHdg8t+TeGN+XG455SScpN90a9ffff/1Iht9Po829drh/eul63X16in794Qp/73d4RB7ml0JKjzBSXSsO98smkPLx4qaI+uaql3/jTAd3529d1cIT27fPvKJUGb98llAIAAEyXX+08qZ2Vzfr+LRfqi28rl+O8Ls3sNI/u+cgGdfT69elfv6oHX69RusepLWX5w96rJDddVc3JUyl9/dRZ3Xn/a7r72eMJ+fyfv3BCXf0B/c3Vy8Z83bhpzxjjlvSEpGHTqcaYVElfkrRe0gcl3R1+6k5Jf7Isa6mkQmPM9RM7fkgwaCloybaLjqTQ2W/ZuFA7jjbp1KDfoE0dfTJGykmf/KIjScrL9KirP6Ce/rFL40fqOvTwvlr91WUlKshMkcNh9LUbVun/e3u5/rSvVh+9d+TrVfbVtGrtgjnD/iVNBuXzsiRJh84kTyg9WNuuP+0LVUmf2H9m2PN1baFQGrmjVBrUvstMKQAAQFR6+gO67t+e0692VsX8Hg/vPa2LFmXrfRctGPU15UVZ+tebL9Rrp1r14Gun9dZVhUpxO4e9bnFumk4lSaXUHwjqKw/tl2VJJ5o6p/3z23p8+sWLVXrHmiKtKBz79o5xQ6llWT5JN0raMcLTl0h6xbKsTsuy3pSUZ4xJk3S1pEfCr3lI0pYJnH9AIDznaNeZ0ohbNi2Qw0i/23OuWtrY2ae5aR65nfGpAuelhyquTeNUS7//9BFleFz69JVLBx4zxuiOLUv13fev1c7KZl36rW3a+E9PD/lr/+l2rU3Q/aTjKZ6ToswUV1Jt4P3Xp44oK8WlCxfM0WNvnhk2s1vfEfrnNFL7Ltt3AQAAovPA7lOqqO/Uv//l6LjFmZFU1HfocF2H3r1u/rivfefaebpjS+j/oUd7/eLcNJ3t9qmtJ/FjZb/adVIHatu1JC9dJxqn//7Ue1+sUkefX3duXT7ua13jvkKSZVkNxowYDAskNQ36eaOkwvMeb5BUFM3nnM8fCIfSOAW3RJk3J1Vbywr0hz01+vy1K+RyOtQUpztKI3IzQhXX5q5+LcxJG/E1b9a06ckD9frctcs1d4QK7c0bF2p+dqoee3N4Zc/lMPrQxYvidt54MsaovCgzaTbw7q5q0TOHG/T3by9Tptelrz58QEcbOof8CVF9uFJaOKhS6qFSCgAAELU+f0D3PF+p+dmpOt3ao/tfOaWPvqV0Qu/xp721chjpHWvmjf9iSV+8rkzvXT9/1Mpf5FqYU83dWpPABaEN7b363lMVumJ5nt66qlBfe/iAGjr6hoyOTaWOXp9+9kKl3rqqUKuKs8Z9fVShdAwjxW0zwuPDEq0x5pOSPilJixaNHHb8wVDFyO6VUkm69eJF2nbfHj1zuEHXrS5SY2df3OZJJSk3HHDHmiv916eOaG6aWx8b41/Wy5bl6bJleXE713QpK8rUw3trZVmWRvkDlGlhWZb+5c+HVZDp1e2Xlaqj16ev/emAnnizbsh/vOrae5XmcSrTe+5fQbbvAgAARO/B107rTFuv7vvoxfrxs8d0z/OVuu3SRQPdZ+OxLEsP7zuty5flRf3/5Q6HGbMVtSQcSquau+ISSh/ee1o/f7FKn7tmubYOun5mPN987JD6A0F9890XqOZsjySpsrFr2kLpfTtPqr3Xr7+9evwqqTT57btnJA1OMPkKVUsHP16gULV0CMuy7rEsa6NlWRvz84cPCUuh62Ake8+URmwty1dhllcP7K6WJDV29CkvIz7zpJIG3mu09t3dVS16rqJRn75qqTJT3HH73GRRXpSljl6/asMVyETZfqRRu6vO6m+uWa5Uj1MFWSnatDhn2FxpXXuvirJShgToczOlhFIAAICx+ANB/XT7ca1dMEdXLM/TnVuXqa69Vw++djrq93i9ulXVLT1Rte5Ga1G4YzFeG3h/8/Ip7atu1e337tbH7t2tqqbx3/eFo016ZF+t7rhqqUry0lWaHwrKJ6L43njo7PPrP3dU6urygqiDeUyh1BjzXWPMdZJekbTeGJNhjLlQUqNlWR2SnpT0rvDL3yvpL7F8ji/cvuu28ZUwES6nQzdvWKjtRxpU29qjpnhXSgdmSodfC2NZlr775BHlZ3r1kc0lcfvMZBLZwHskgS28waClf3nyiBbnpunWTQsHHr9+TZEO13XoeOO5AfP6tl4VZA395+8ND8uPtgEZAAAAIY++cUanWrr12a3LZIzRFcvztHbBHP10+3H5o7xe7097a+VxOfS21YVxO1eqx6nCLK9OxmEDb3uvT6+dPKtPXFGqL7+jXLsqm3Xdvz2v7/z5sLpGWEwqhVqav/bwfi3OTRuYf52XlaIUt0OVjdOz7OjXu06qtds37sbdwWJt310sKceyrH5jzFckvS4pKOnW8PM/lvSgMeYzkp62LOvPsXzIuUqpvWdKIz6waaF+/Owx/eLFE+r1BZUXx5nSVI9T6R7niHeV7jjapFdOtOiud69Wqie6dga7WREOpYfOdOjq8vj9h2UiHnmjVofOtOsHt64bssDq7RcU6R8fOag/76/TZ7eG/uWsa+/VxsVzh3w/23cBAADGFwxauvvZY1pRmKG3rgz9f58xRnduXaZP/upVPfrGGb1n/djVT38gqEffOKNrygvi3kW4ODc9LqH0pWPN8gctXbuyUJcsydV71s3Xd/58RD/dflwPvlajmy5aoHULs7VuUbYKMkNtufc8V6nKpi7de/umge3ADodRSW56zJXSth6ffrjtqLr7A5IsWZZCf0W+1rmfy5KeOdKgK1fka/2iueO99YCoQ6llWfcO+vqWQV8/KOnB817bIymma2AGm0kzpZK0MCdNVyzP0693hbbwxrNSKoXmSpu7hrbvdvT69I0/HdD87FR9YFD1bqbJSnFrfnZqwjbw+gJBff/pCpUXZerG8y4HnjcnVRctytbjb57RZ7cuk2VZamjvG7LkSAr9PncY2ncBAADG8tTBeh1t6NQPbl035LrCa1cWqqwwU3c/e0zvurB4zKsMd1Y2q6mzT+9eVzzqa2K1OCdNz1U0Tvp9nqtoVKbXpYvChYyCrBR975YLdduli/SdJw7rnucr5Q8X8eZnp2rdwmz95VC93rGmSFvKhs6fLslP1+EYr0/8ybPH9LMXTigvwytjQsuCQj+aQT8Pf22k3HSPvnhd2YQ+Y7KLjqbUue27MyOUStKtmxZpx9HQYuJ4VkpD7+cZMlMaDFr6wu/36WRLt3778UuiHvq2q5XzEreB9/d7qnWyuVs//+uNI/4H8B1r5umfHjukk81dyvC61B8IDrkORgr9y+x1OQmlAAAAo7AsSz9+9qhKctN0w3mFAIfD6DNbl+p/PrBXTx2s09svGH2j7sN7a5XpdQ0Lb/FQkpeuP7xao+5+v9I8scUty7L03JEGXb4sb9gVkhctmqvffWqzen0B7T/dpr3VrXq9ulV7T7Uq3evSV29YNez9SvPS9dSBevkCwQldSVnf3qt7X6rS+y6ar+/fsi6mX0s0krov1j+DFh1FvHVVoXLD17FMSaV0UPvuT587rqcO1usf3rFSlyzJjetnJaOyokxVNnZNe/trIGjpR9uOaePiudo6yn/Y3n5B6FakJ/bXqa49tIzp/FAqha6F6fPRvgsAADCS5yoatf90u+7YsnTEjHDD2mKV5Kbpx88eG/Vezl5fQE/ur9PbLigaaHGNp8iyo1MtsbfwHmvoVG1br64qG3khrCSluJ3aWJKjj1+xRHd/6CK9+KWr9epXrtW8OanDXrskL0P+oKXqCZ7ph9uOKmhZ+vy1Kyb8a5iIpA6lkZnSiaT5ZOdxOfT+DQtkjOK+kjlUKQ2F0ucrGvWvTx3Ru9cV6/bLS+L6OcmqvChL/qCl4w3Ts1ks4kBtm+rae/XhSxePeh3NgrlpunDBHD3x5hnVtw+/ozTC63JQKQUAABjF3c8eU/GcFL13/YIRn3c6jD6zZZn2n24ftYV2+5EGdfT5p6R1Vxp0LUxT7KE0cvYrV4weSkcy2v+LxrKB92Rzl363u1q3blqkheGgPVWSOu35wpuzZlKlVJI+d+0K3f+JS5WTHr8rYaTQBt6Wrj6dbO7S3z7wusoKM/V/3rcmofd2TqeV88IbeOunt4U30o79luVj3+96/Zp52lfTpldPnpU0cqXU6yaUAgAAjOTlymbtrjqrT165RB7X6DHmPevnq3hOin6w7ai6+4dvqX14b63yMjzaPEWdhItyI5XS2Aslz1U0anlBhuZnD696xmJJ3sRD6b89XSGX00xoi26skjqURiqlM2XRUUSqx6lLp+BfgtwMj4KWdPsvdisYtPR//8eGmPvY7agkN10elyPmIe5Y7TjaqJXzssadEb4+3MJ7/yvVMmbk9u3QTCntuwAAAOf7rxdOKDfdo1svXjTm6zwuhz537Qq9fqpVV/7Ls/r5CyfUGx6P6uj1advhBt2wtliuKerGnJPq1tw0t6pi3MDb3e/Xy5Ut2jJG6+5EZad5NDfNrcooQ+nhunY9vK9Wf31ZqQri3N05kqROLJGZ0qn6DTPTRELRieYu/fyvNmlxuHVgtnA5HVpekKHD07iBt7vfr1dPntVHLy8d97WLc9O1ujhLB2rblZ/pHbEt3etycE8pAADAeWpbe7TtUL0+ddXSqOZAb9m0UEsL0vW9pyp016MHdc/zlbrz6mVyOoz6/UG9a4padyNC18LEVindVdms/kBQV62I7xKmJfkZUd9V+r2nKpThdenTVy2J6xlGk9RpL3Lx7UyrlE6V4nB5/3PXrNDW8vhvErODsqLp3cD7cmWLfAFr3NbdiEi1dKTWXYmZUgAAgJE8sLtalqQPjVMlHWzD4hz99hOX6refuEQL5qbqKw/t1/9+8E0tzEnV+oXZU3dYSSW5aTHfVfrckUalup3aWBL9PZ/RKM2L7q7S106d1dMH6/WpK5coOy2+44ajSepQGpiB23en0kWLsvXo37xFf3vN1Pd9J6uVRVmqb+/T2a7+8V8cBzuONsnjcmhTSU5Ur79+TWg1+WhLrrwup/p8hFIAAIAIXyCoB145pS0r8mNauHPZ0jz94dObde/tm7R5Sa7uuGrZlO9cWZSbrtrWnpg64J6raNTmpblx3wxcmpeu+vY+dfUNn7Md7F+fPKK8DI9uj6ITMF6SOpT6BrbvEkqjYYzRBfPnzJrFRiMpKwotO5quFt4XjjXqktKcqP+jsTQ/Q+9YU6SrVoxcWfW4HMyUAgAADPKXg/Vq6OjTbZcsjvk9jDHaUlag+z95qT50SfTV1liV5KYpaEk1ZydWLa1q6lJVc3dc50kjoll29MLRJr10vFmf3bpM6d7pm/RM6lAaCEa27yb1MZFEyiMbeKehhbeurVcV9Z16y7LoWncjfnLbBv2PzSUjPkf7LgAAwFC/fvmk5men2mo8LbLbZaItvJGrYK6a4FUw0ViSnyFJYy47+uEzRzU/O3VagvtgSZ32/IGZuX0XUyc/w6ucdM+0VEpfOBa6CuaK5fH7j4bX7SSUAgAAhFU2durFY8364MULbTXStzh8LUzVBJcdPVfRqJLctClZWLo4N03GSCcaRz5TU2efdle16JaNC+V1xbd1eDzJHUoHtu/a5zcgEssYo/KizOkJpUcblZfhUXm4ZTgevC6H+ny07wIAAEjSb14+JZfD6JZNCxN9lAnJTfcow+uaUKW01xfQzuPN2lI2NRXhFLdTxXNSdaJp5A282480yrKka1ZOf0XaHqHURn8qgsQrK8pURX2HguHfP1MhGLT0wrEmXb4sT444/v70uhzqD1ApnW1+9sIJ/X5PdaKPAQBAUun1BfT/Xq3R2y4oUkHm1N+VGU/GGC3OTZvQtTB7qs6qxxeYktbdiCX56aO27247VK/CLK9WF2dN2eePJqlDKTOliMXKoix19wdUPcHB8ok4XNehps7+uLbuSmzfna1+/sIJ/cf244k+BgAASeXRN86orcenD09iwVEiLZ7gtTDbjzTI43LokiXR3eoQiyV56TrR2CXLGlq86fcH9XxFo64uL0zI0tSkTns+ZkoRg8gG3kNnpq6Fd8fR0BD6RJccjcfrZtHRbNPvD+pMW48qm7rU2NGX6OMAAJA0fr3rpJbmp+vSKQxpU2lxbrqqz3YPXHM5nucqQrc6pHmmbuttaV66Ovr8auocen3iyyea1dUf0LUJaN2VkjyUBpgpRQxWFGbKGOnIFM6VvnCsScsLMlQ0J76tJJH23alsPUZyqW3tUeQf96snWxJ7GAAAksT+023aW92q2y5ZbNvrDkty0+QLWKpt7Rn3tWfaenS0oXNKW3clqTS8gff8a2G2HWqQ1+XQZUvjW3CJVlKH0shMqZ02bSHxUj1OleSm6/AUXQvT6wvolRMtcW/dlUL3lEpirnQWOdVyrq3nlRNnE3gSAACSx29ePqkUt0M3bViQ6KNEp7dd6mkd8tCinNAG3Wg28L50rFmSdHmcu/DOF7mrtLLx3LIjy7K07XC9Ll+Wp1TP9G7djUjuUBr+H3M3M6WYoFXFWXqjpm1Yv3w87K5qUZ8/qCuWx/8/GpH128yVzh6RUFqal649VEoBAFB7r08PvV6rd11YrDmp7kQfJzoPfEh64LYhD5UXhbr3XjvZOso3nfPS8WblpHtUVhi/Wx1GUpydKo/LMaRSeqyhU9UtPQnZuhuR1Gkv0r7rpH0XE3RJaY5Ot/ao5uz47RIT9cLRJrmdZkqG0L3hSmmfn2thZovqlm55XA69Y02RDtS2q6vPn+gjAQCQUA+9flo9voA+fKlNFhw1HJaqdki1r0uDCiJz0z1aO3+Ong/vIhmNZVnaebxJm5fkxvVWh5E4HUYluWlDNvBuO9wgSbq6nFA6Iq6EQaw2L8mVJO083hz3995xtEkbFs+dkiH0c6GUSulscaqlWwvnpuri0lwFgpZeO0ULLwBg9rIsS7/edVJrF8zR2gXZiT5OdF69N/Sjr0tqqxny1JUr8rW3ulVtPb5Rv/1US7dq23p16dLcKTzkOaV56UMqpdsO1Wt1cZbmzUmdls8fSXKH0nD7rov2XUzQsoIM5WV4tKsyvqG0saNPB8+0T8k8qSR53eH2XULprHGqpVuLctJ00aJsOYy0u4pQCgCYvXZXnVVFfaduu2RRoo8SHV+PtO9+KTtc1W08MuTpK1fkKxC09NKxplHf4qVwEeWyaQqlS/IzdLK5S/5AUGe7+vXqybO6JoFVUinZQymVUsTIGKNLluRqV2VzXOdKXzoe+g/KVMyTSrTvzjaWZelUcyiUZqa4tao4S7tPMFcKAJi9fr3rpDJTXLrxwuJEHyU6B/8k9bZKb70r9PPGQ0OeXrcwW5le15gtvC8db1ZhlndgCdFUK81Lly9g6XRrj7ZXNChoSVevLJyWzx5NUofSQNCSMZry3mrMTJcuyVVtW++Q7aaT9erJs8rwurS6eE7c3nMw2ndnl7Yenzr6/FqYkyZJ2rg4R69Xn1U///wBALNQU2efnth/RjddtGBK7+qMq1d/7xhD4wAAIABJREFUIeUslVa9W0rPlxoPD3na7XTosmW5er6iacRCSWSe9LKledN29c3ABt6mLm071KC8DK/Wzp+a/7eNVlKHUl/AYvMuYhaZK41nC++Rug6VFWVO2TVFkSth2L47O5xsDv2ByaJwKL24NEe9vqAO1LYl8lgAACTE7/dUyxew9OFLbdK623BYOrVT2vDXkjFSfvmw9l0p1MJ7urVHxxuHXw1ztKFTTZ392jxNrbtSqFIqSUfrO/RcRaOuLs9PeBEwqRNfIBjkjlLEbGl+uvIzvXFbdmRZlirqO7RiCld1D1wJQ/vurBCp4i/KDVdKS+ZKCl07BADAbBIMWvrty6d06ZIcLSuY2mtRotbdIvV1jv78a7+UHG5p3YdCP88vC4XS8yqiV4Z3kewYoYU3Mms6XfOkkpST7tGcVLd+v6dGHb1+XV2e2NZdKclDqT9oycV1MIiRMUaXLsnVrsqWuMyVNnb26Wy3T2WFGXE43cho351dBkJpuFJakJmikty0GbvsyLIsHaht0zcfPahN//wX/a8/7Ev0kQAASeK5o42qOduTPNfAvPEH6QcXSvdcJXU2DH/e1yvt/a208kYpPbxrJL9c6muXOs4MeenCnDSV5qXr+YoRQunxZi3KSdOCuWlT8asYkTFGpXnpOtbQKY/TMWW7UoY4e3LMp5M7lAYslhxhUi5dkqO69l5VNU9+rrSiLvQnZSuKpu5P71LchNLZpLqlW3kZ3iFzM5tKcrSnqkXBYPwWdCVafXuv7nn+uK7/wQ6984cv6L6dVcrwuvTH10+rrq030ccDACSB3+w6qbwMr65bVZTYg/S2Sf/9CenBj0u5y6T2WulX75V6zvsD44MPhxYcbfjrc4/ll4V+PG+uVJKuXJ6nXZUtQ7rhAkFLuyqbp7VKGhGZK710aa7SvdMwv/vYF8Z8OrlDadCSk5lSTMKlcZwrPVzXLkkqm4b2XRbdzA6h62CG3gm2qSRHZ7t9Ot44RruQjbxc2azLv/2MvvX4YaV6nPrmey7QK1++Vr+8/WIFLUv3v3Iq0UcEACTY6dYePXO4QR/YtGBgv0ZCnNwp/fQt0v7/lrb+g/Sxp6VbfyM1VUi/fr/U13Huta/eK+UskUqvPPdYfnnox1HmSnt8Ae0Z1A11sLZd7b3+aZ0njYjMlU7LVTAVT0rHnh7zJUmd+PyBoNy072ISluSlq+D/Z+++w6Oq0geOf++U9EZ6SCGkEAKE3qsUARvYBdR17Y21u7u/tayu7uraFpW1u66ughVUVEAFVHoLJYEkECCkF9J7MjP398fJAIH0TEtyPs/DM2Fy594TDcm897zFQnWlRwoq8fdwws/D2QIra5kcCdO3mGeUnm3cQF+g98wr/WRnJp4uOjY+PIPV90zhxokD6OfuRISfG9NjA/h0dyaNRnkTRpIkqS9buTMTFVg83k4NjkxG2Ph3+O/FoNHALethxh9Bq4PoWXD1B5C7D1YuFnNJi9Igc9uZBkdm7gHg2q/FndKJUX7otUqzFF7zqEFzc06rKUyF6uZzUscP9MXHTc/coVauJzU0wLr/E7vObXDooNRoUmWjI6lbztSVdn9eaVpBlVWbHMFZjY5k991er9FoIres9rygNNLPDX8P517R7KimwcBPhwu4KCGEqIDza7FvnDiAgop6fj5cYIfVSZIkSY6gwWDi091ZzIoL7HpdZXd7h+z5D/z2Agy/Du7cDOHjmn8+/lK4/E3I2AKf3wS73hUNjkYsaX5cGx143Z11jB3gy6/NgtJiYgI9CPRy6d7621JwWNTF/vxUs6cnRPmx/8m5hHi7tvw6S9n5FpQcg/nPt3mYQw8AMphkTanUfZOi/fj2QC7HT1UT3cIb444wmVSOFlRy7dhwC6+uOWdZU9pn5JbVYlI5PaPUTFEUxkX26xVB6YaUQmobjVw2vOUB6DMHBxLq48rHO09yUUKIjVcnSZIk2VpqfgUlVQ3UG000GEzUG0yk5FVwqqq+6w2Oasvg/bkw5iaYdG/nX6+qsPs96D8Krnir9eNGXAcNVfD9Q+LvQ68Aj4DzjwuIg0Nfi/OeM3d0+qAA/rkulcKKOnzcnNidUcLVY8I6v+aOaqyFr24FQx3k2aG5YGUB/PoCxM6D2AvbPNTBg1ITOq1Db+ZKPcDZdaVdDUpzymqpaTASZ8UmRwBOWpm+21ec23n3bOMifVmbnE9eea3172Ba0ZoDuQR5OTO+KSX5XFqNwpIJEby4Po1jRVVd/vcpSZIkOb6dx4u57p0dLX4uyt+d6YNaCPA64rcX4VQabHgGhlwO3qGde/3JrSLdduG/2z923K0iMN3wNxh/R8vHBAwWDZCqi8Cjeb3m9EH+/HMd/Hb0FJF+btQ0GK3b5OjHJ6DwMISOgfxkMBpESrKtbPybCIjnP9fuoQ4d8cnuu5IlRPq5Eezlwo7jXd95SssXhe3WTt/VaBT0WkXulPYB584oPdu4SBHE7TrRc3dLy2sb+SWtiEsS+rdZhnHt2HD0WoVPdsiGR5IkSb3Zsp+PEuDpzMrbJ7L6nsl8f99Ufn5oOr89OpPv75vWtZK94mOw822InQuqSQSLnbX7PXDxgaFXduz4KffDn07CgMktf76NDrzxwV74ezjz25Eith0rRlFgwsB2gtKqQkj9QQSUnZG2Fna/C5OWwrjbwVgPJcc7d47uyEmEfZ/AxLvBL7rdwx06KJU1pZIliLpSX7Yf63pdaVqBOSi1/k6Os04ra0r7gMySGpy0GoI8z68jiQ/xxN1J26xDX0/z46F8GowmFoxsOXXXLMDTmfnDQvhybxa1DTJDQJIkqTfadaKE7ceLuXN6FJOi/RgV0Y+h/b2JCfQkws8NVydt107805OgdYIFr8Oke+Dgp5Czt+OvryyAlDUw8npw6kQ9q3Mb7wfb6MCr0ShMj/VnS/optqSfYkiIF/3cndq+1o9PwKeLYflY2PcxGBvbX19FHnx9DwQPh9lPQtAQ8XzhofZfawmqCmv/JBo/TX+0Qy9x6KC00aTK9F3JIiZG+XGqqp5jRdVdev2RgkpCfVzxdNFbeGXnc9ZpZPpuH5BZXEOYryuaFm686bQaRg/o2XWl3x7IJcLXjRFh3u0ee8OECCrqDKw5kGuDlUmSJEm29tqGo/h7OHP9hC7WjbbkxGZI/Q6mPQiewTD1IREErftLxxsfJX4EJgOMvcVy6/IMAWevFndKQdSVllQ3sOtESfupu411kPYDDJgCLt7wzb0iOE38X+vBqckEq+8UabNX/wd0zuAfB4oWCmwUlB78HLJ3wZy/gotXh17i0DWlRpNJpu9KFmGe/7T9uOhy1llp+ZU22SUFEZTKOaW9X0vjYM42LtKXf/18hLKaBnzc2r6L+kNSHn//PoU3bxjN8DAfSy+1005V1bPtWDF3zYhCUdr/GT5+oC+Dgjz4eOdJrh1n3WZikiRJkm3tPVnClvRTPHZxfNd3RM9lMsL6v4B3uEhPBRH8zHoc1twPh78WjYjaYjTA3g8gaib4tz2upFPa6MALMDXW//THk6P9WzzmtGMbob5CBNwxs8W8z1+eg2+XilraIQsgKAGCh4H/INDqYdtrcOJXuOw18I8V59G7iBTagsNd+5pqy2DLv6C2FFBFqrRK08fqWc81fXxsE/QffX534jY4dFBqMMr0XckyInzdCPF2YcfxYm7sZHe3RqOJ40XVzIjrYgF+JznrtbKmtJdTVZXM4hrGDOjX6jHTBwXwyk9H+N1/dvHaolFENg25Pvc8yzem8/JPRwDYfPSUQwSla5PyMJpULhvRduqumaIo3DBxAE9+c4gDWWWMCLf/1yBJkiSd0Wg08eBn+3HVa3nm8mG46DseXC77+Sh+7k5cP9GCM0gPrIT8g3DV+6A/qyHgqBvFuJafnoRBF4lgrDVH10NFDlz0guXWZRYQB0fWtfgpfw9nhoV6kZJXeXo2easOfy3qXaNmiGA3bj4MmgdHf4Qty2DnO6JWFEQac0AcFKZA/AIY/bvm5wocAnn7u/b1/Pg47P8E3AObOgor4lHRNH1M06NGPO/VHy5bJma+dpBjB6UmFRe9TN+Vuk9RFCZF+fHb0SJUVe3Q7o3ZyeJqGowm4qzc5MhMpu/2fuW1jVTWG9rcKR0Z7sNbN4zmT18lcclrm3l64TCuGh16+nu3rtHIH788yLcHcrliVCjbjp0ivbDKVl9Cm9YcyGNQkAeDgzuWsgNwxahQnl+bysc7TsqgVJIkyYGoqspfvz3EdwfzADhZXMM7vxvTbhYPQGJmKZuPnuLPFw3GzclCYUd9pWhoFDYOhl3V/HMaLcz7B3y0AHa8AdMeav08u98Dr1AYNN8y6zpbwGDY9z+oLgb381N0754RQ1p+BR7Obfw3MdSLZkXxC8QOqJmiiMB00DyRwnvqKBQkQ36SeAz3hgWvnTeOhqChIsitr2q7JvZcJ7eJr2XK/XBhFxpJdZBDR3wGk4q2ExG2JLVF1JU2dPqNe1q+ON7anXfNnHQauVPay7U1DuZs84eFsPb+aQwL9eaRLw5w36f7Ka9tpLCyjkXv7ODbA7k8Oi+OV64dwaAgT4cISnPLatmVUdLqbNLWeLrouXxUKN8eyKWspsFKq5MkSZI66/0tJ1ixM5O7L4jmtcWj2J9VxlVvbiOr6XdZW17bcJR+bvpOZ6m1acsyqCqAec+dH3iB2FWMuxg2vyI617ak+JhIjR3ze+uMSDnd7KjlutJLhofw0Ny4ts9hTt0dennrx2j1oonR8Gth7jNw42q4+XtwbSETK2hom2tqkaEBvnsQvCNgxp86/roucOiIT9aUSpZkTpFIzOxcR9O0gko0Cl2qRe0KZ52mT3ff3ZdZyoaUAhqNvfe/QVvjYM7V38eVFbdP5JG5g/ghKY+LX93M5cu3kpZfyVs3jObemTEoikJMoAfphVWYTF3rMG0p3zfdSe9o6u7Zrh4TRr3BxNb0YksvS5IkSeqCnw8X8PcfUpg/NJhH58axYER/Prp1PEWV9Vz55jaSc8pbfe3+rDJ+SSvitmlRuLe1I9gZZVmwfTkMuxrCx7V+3NxnRaOfDX9ruenRnv+ARnd+iqultDEWpsMONaXuDpxhmTUFNnXg7Uyzo+3Lxddw8YvgdH4ZkSU5dFAq55RKlhTh64azTtPp3aQj+ZVE+rl3qn6iO5x12j6bvmsyqdz18V5u/XAPk57bwLPfHT49I7YnKayo48o3tvLGL+ktft4clIb361j7ea1GYemsWL64axIaDZhU+OKuScwfFnL6mNhAT2objeSU1Xb/C2hDvcHIvzelc+P7O1mblHfemKVvD+QyPMy7xRrY9gzt74Veq3Awu8xSy5UkSZK66HBuBfd9uo9h/b3513UjT3eLnxjlx1d3T8ZJq+Hat7ezKa3l3cjXNxzFx03PTZMjLbeoX54Xj3Oeavs4v2iYcKdIO31nBuxfKdJhARprxWiV+MtE115r8A4DJ49Wmx21y1Avuu4OvhR07adJd4jPANC7dzwoLc2AX18Q/53irJDifA6HrynVaWVQKlmGVqMQHeDB0c4GpQWVNkvdBbFTWlrTe3cJ27Ivq4yCinpunhJJblkt/92WwXtbTjA8zJtrx4azaFy4w4+JyiuvZcm7OzlxqppjRdXcMmXgeTc0skpq8Pdw6vSd49ER/djw0AWYVPW8c8Y2dYdOL6oivJ204K76Ja2Qp749REZxDf4eztz9SSLDQr14eG4cFwwKIKO4hqScch6/JL5L53fWaYkP8eJgdut33iVJkiTrK6yo47YPd+Ploue9m8ae1zU3NsiTVfdM5uYPdnPzB7uJ9HMjPsSLwcFexId4otMqbEgt5OELB7VdN9kZpSfFHNJxt4FPBzq1z3lKBKc73oKv7xLNj8bdCno3qCuDsbdaZl0tURTRDberO6UdSd3tLI0GAuOhsAMdeFUVvn9E1OjO/6fl1tAGhw5KjbKmVLKw2CAP9mR0PH23rtFIRnE1l3YhFbGrnPV9dyTM+kP56LUKD8wZhLernuKqer7en8sXe7J4/OtkEjNLeenqES3O9nQE2aU1LHl3JyXVDTw6L44X16fxQ1IeV44Oa3ZcZklNlwNHJ13LPxNjApqC0oIqZsYFduncrckqqeGZ7w7z4+ECovzd+eiW8UyO9uPr/bks+/kIN3+wm3GR/ejv44qiiFqZrhoe5s03+3IxmVSH/f8sSZLUm9U1Grn9oz2U1jTyxV2TCPJquYNtkJcLn981iQ+3ZZCcU05KXgXrDuWfzpb1ctFx05RIyy1s66uAApPv69jxWr2YPzrmZhHk7XxLjFMBMbczcqrl1taSgMHiul1x6Gsxl9RSqbtmQUMg5TsRdLbV9PPw15D+k6jb9Q617Bpa4dBBaaPRhF6+KZEsKDbQg2/251Jdb+jQLlV6YRUmFZt13gVz+m7fC0pVVWVtch5TYvzxdhVd5vw8nLl16kBumRLJ6xvTeeWnI7g5aXlm4bBOdVC2hcziGha/u4OKukY+vm0CI8K8+XJvNit3ZbYYlI6OaH0cTFf0c3fC38OJo4WWTXf+YOsJnl+bikZR+OP8OG6dOhBnnbhjfvWYMBaM6M9nuzN5fWM6uzNKGT/QlxBv13bO2rrhoT58vCOTE8XVRAfYpo5bkiRJOuOf61I5kF3OOzeOYViod5vHejjruHfmmRmf1fUGjhRUkppfSZS/O14u+jZe3QkVeSLlduSSzgdJiiJmfMbMFp1qEz8SH1v7fURAHBxYIWZ7ttR4qDXm1N34yyyXumsWOFR8/VUFracu11XA2j9DcAKMv8Oy12+DQwelYqfUsd54Sj2buVnRsaKqDs1zNNczxgXbNn23L9aUHs6rIKuklnsvOH+AtaIo/GFWDNUNBt7+9TjuTjr+fNFghwlMT5yqZsm7O6htNLLy9omnf4kvHh/OP35IbZYC3mg0kVtWx+UjLZ9ia252ZClV9Qb+9t1hJkf78cLVIwj1OT/YdNJpuHFSJFePCWf1vhxGdnOcy/Bw8d/uYHaZDEolSZJsbE9GCf/dlsHvJg1g7tDO11u6O+sYFdGPURa+8cr25WAywNQHu3ce/1jRpdYWTnfgPQIREzr+utOpu1dYfk3mDrwFh1oPSjf9QwSti1ZYpzNxKxw6N1bWlEqWFhMoAoOOvnE/UlCJk1ZDZAe6pFpKXx0Jsz45H40CFw4JavHziqLw5/mDuXHiAN7+7Tivb2y5iZCtZZXUcN3b26k3mJoFpABXjQ7DSath5a7M08/lldVhNKlWqfuMCRQ10+c2H+qqtPxKVBVunjywxYD0bK5OWpZMiGBI/47PJm1JTIAHrnotB7JkXakkSZIt1TUa+eNXB+nv7cof5w+293LOqC4W3XITrgHfgfZeTcd1tQOvtVJ34UxQ2lpdaUM1JH4IIxZD2BjLX78Njh2UGk3oZE2pZEED/NzQa5UONztKK6gkOtDDps11+upImLXJ+Ywf6Iufh3OrxyiKwtMLhnLV6DBe+ekI720+bsMVtmzlrkxKqhv49I6JxIc0D8j8PJyZNyyYr/ZmU9codr9PllQD7c8o7YrYQE8q6wwUVtZb5HwpeRUADA6xXaaATqthaH8vktoYMyBJkiRZ3msbjnK8qJrnrkywXHMiS9jxhuiYO+0he6+kc3wiQOfauQ681ui6ezY3X/AIbr0Db9paaKyBUddb/trtcOiIzyDTdyUL02s1DPR352hBB3dK8yuJC7JtCqF5JIyldrt6gvTCKo4WVjG/A6lCGo3CP69K4OKEYJ79PoXPdme2+xprSswsJT7Eq9UOzYvHh1NRZ+CHJDG/8/SMUqsEpU3NjiyUwpuSV4GXi67dXVJLGx7mw6Hccgy9eFatJEmSI0nOKeft345zzZgwpg8KsPdyzqgtg13vwJAFZ3YeewqNVqQLd2an9Ngmkbo7xIJdd88VNKT1oDTpC/DsDxGTrXf9Vjh2UGpU0cv0XcnCYgM9Se9AM5iKukZyy+sYZMN6UhA7pSZV3JTpK9YfygfocP2KTqth2XWjmBrjz9NrDnOqyjI7g51lMJo4kFXO6IjW6ygnRfkx0N+dFTtF8JxZUoOTVtNqN8PuMNdMHy2wTLOj1PxKBod42bx2d0S4N3WNJo508OaRJEmS1HWNRhOPfnkQX3cnHr9kiL2X09yud0WQNu0Re6+kawIGd26n9HBT6m7UBdZaEQQOEWsyGpo/X1MC6T9DwlVifIyNOXRQKkfCSNYQHehBZknN6XTK1pjf2Nuy8y6IkTBAn6orXZecz4hwH/p3YkfOSafh6YVDqWs08samY1ZcXetS8yupbTQyekDrDR0URWHx+HD2nCzlSEElWSU1hPVztUoWSICnM14uuk7P4m2JyaSSmldBvI1vygAkNNXlJuWU2fzakiRJfc1bvxwjJa+CZy8fhrebhbrlWkJ9lUjdjZ0HIcPtvZquCYiDimzR0bY9hgZItWLqrlnQMDDWQ8k5JVCHvxbNpBKusd612+DQEZ/BZEIn03clC4sN9MCkio6pbUnLF2/sW0vLtBbzuI2+Mqs0u7SGpJxyLhrW+S5/0QEeXD0mjI93nCSnrNYKq2tbYqaYedveeJerx4TjpNWwYmcmmSU1RFipcZaiKMQGeVokfTertIbqBuN5dbK2EOnnjqeLjgPZsq5UkiTJEjakFDDzpV+47cPdvPJjGuuS88gsriEtv5LXN6ZzyfAQ5nWh265V7f0Aaktgeg/dJQUIjBePp462f2zGZqgvF6NgrCmoaTe88JwU3qQvxfzWYPvcAHCgKubmTCYVk4rsvitZXGxTjejRwqo233AfKajE3Ulr83o6Z515p7RvjIVZf6gAoMu/DO+bHcvX+3J5fcNRnr/Ktj9IE0+WEuDpTFi/tr9HfN2dmDcsmFWJ2ZjU9oPY7ogJ8ODnlIJun8fc5MgeQalGo5AQ6k2SDEolSZK6TVVVXvnpCJV1jZwsrmFjaiHmCiFFAR9XPU8vGGrfRZ6rsQ62vS460IaPt/dqui5omHjM3N5+N9u0taIxUtQF1l2TfxwoWlFXah47U54NJ7fCzMetP7+1FQ4blJrr6eROqWRpA/3d0SiQ3k7dXVp+JbFBnmhs/D14On23j3TgXZ+cz+BgTwb6u3fp9WH93FgyIYL/7TjJHdOjiLLhbMvEzDJGR/h0qOZyyfgI1hzIBazT5MgsNsiDz/ZkUVLdgK9719N/UvIq0Si2zxQwGx7mw/tbjlNvMJ7OHpAkSZI6b8fxEg7lVvDclQksHh9BXaORtPxKDudVkJpXwYVDgvFvo/O9Xez7n5iVedX79l5J9/QbAEEJkPItTF7a+nGqCkfWQfQs0Ft5M0TvAn7RUHDWWJikL8VjwlXWvXYbOpS+qyjKE4qipCuKsltRlMiznr9ZUZT9Z/2pVRQlVFGU2xVFyTrr+U6/ozA2BaWyplSyNGedlgF+7u3W3R0pqLR5PSmAk1b8c+kLNaWFlXXsPlnC/C6k7p7t3pkxOGk1/OvnDqTHWMipqnoyS2o6vOs5McqXqKbA2xozSs1iLNSBNyWvgkh/d1yd7BMQjgjzptGokpJnmaZNkiRJfdX7W47j6+7EFaNCAXDRaxkR7sPi8RE8vXAYU2P97bzCcxgaYOurED4BIqfaezXdN2QBZO2EitzWjylIhvIsiJtvmzUFDmmevpv0JYSOBd8o21y/Be1GfIqiDACuAOKAvwH/NH9OVdUPVFUdqarqSGAWcFhV1RzAD3jM/DlVVTudh9hoEm/IZfddyRpiAj3aDEqzS2sorm6w6XxGs76UvvvT4QJUlW4HpQGeztwyNZI1B3I5nNuBZgIWkHiyqZ60jSZHZ1MUhSUTIgBOB6fWcLoDbwc6TLclJb/CLqm7ZglhTc2OsmWzI0mSpK46XlTFhtRCbpg4ABd9D8k6OfiZCNCmP2q3VFKLGrJQPKZ81/oxaWsBBQbZKCgNGgqlGaKZVGEKFCTZrcGRWUe2IWcCa5sCy7XApFaOuwH4tOljX+BUdxZmNJp3SnvBN6PkcGIDPcg4VU1jK3MQN6UVATDNDncP+1L33XXJ+UT6uVlkR/qOadF4ueh4+cdOtF7vhsTMMnRNtY8d9fvJkay4fQKxVtyB7+/tipuTtls7pZV1jWSV1DLEjkFpqI8rfu5OstmRJElSN3ywNQO9RsONEwfYeykdYzLCllcgZATEzLH3aiwjIE6Mhjn8TevHpK2FsLHgEWibNQU11RAXpYpdUkUDw660zbVb0ZGgNJCmAFNVVQOgbSUd93fAx00f64EXFEU5pCjKi0oLBVeKotyhKMoeRVH2FBUVnXcyWVMqWVNskAcGk8rJ4pY78G5KLSTC141oG9Ynmpnr53p7TWl5TSPbjxUzf1iIReZgervpuXNGNBtSC9l7ssQCK2xbYmYpQ/t7derOs06rYXK0dW90aDQKMYEe3QpK0/LFLutgO4yDMVMUheFh3hyUO6WSJEldUlbTwBd7s7h8VH8CPB2sZrQ1h1aLUSXTHukdu6RmQxZC5jaoKjz/cxV5kJsIcRfZbj2BTR14C5Ih6QvRXMlWAXErOhKUquf8vaUAczRQpKpqXtNTD6mqOgwYCwwFztsPVlX1HVVVx6qqOjYgIOC8ixqa0nd1WllTKllebKB4s3204Pw37rUNRramn2LW4ECLBEudZU7fbTD27vTddYfyMJjUbqfunu3mKZH4ezjxwro0VPXcH12W02g0cTC7jFFW7KLbHTEBHi1+b3eUPTvvni0hzIf0wiqq6w3tHyxJkiQ188nOTOoaTdw61X51gp1iMsFvL4ldxcGX2ns1lhW/AFQTpLaQwntknXgcZMOg1GcA6N0h8SMoO2n31F3oWFCaB/gDKIqiA9QWakRvAT40/0VtejeoqmotsAYY0tmFGWT6rmRFUQGipq+lutLtx09RbzAxa7B97hj1le67K3ZlERPowYiwjqe/tsfNScfSmTHv0bJxAAAgAElEQVTsPFHC1vRii533XKl5ldQ1mjpcT2prMUEe5FfUUVnX2KXXp+RX4u2qJ8TbxcIr65wRYd6YVDhkozphSZKk3qLBYOLDbRlMi/Unzo5ZL52S9gMUpYhd0t7W6DRoKPhGt5zCe2SdCBLNM01tQaMR18vZC1pnh7gJ0JH/4z8D85tSdi8GNjel5M4FUBTFuen5r80vUBQlpOlRg2iAtL+zCzPK9F3JitycdIT1c20xKN2YWoibk5YJUb52WNlZ6bu9uKb0UG45B7LKWDI+wuK70YsnRBDo6cwbv6Rb9LxnS8wUTY7GOGhQas4E6GoKb0peBYODPe2SKXA2c7MjmcIrSZLUOd8dzKWwsp5bpw6091I6RlXhtxeh38AzszN7E0URKbwnNkPNWSVGDdVw/BeIu9j26cpBTXuGcfPBxb6ZUdCBoFRV1XzgAyANeAb4IzAA0cwI4HJgg6qqdWe97DFFUZKBfUAm0EZlb8tk+q5kbbEt1N2pqsrGlEKmxvjbbTaiUx/ovrtyVyZOOg1Xjg61+LmddVpunTqQbceKrRbMJGaWEuTlTH877yS25kwH3s4HpSaTSlp+pd1TdwECPV0I8XbhoGx2JEmS1GGqqvL+lhPEBnowY9D5JXIOKX0D5O2HaQ+BVmfv1VjHkIWgGiH1+zPPHf8FDHW2rSc1C2xqdpRwre2v3YIORXyqqi5XVTVGVdURqqpmqqp6raqqnzZ97jNVVW8/5/ilqqoOazr+YbULxV2y0ZFkbbFBnhwrqjq9Kw+QVlBJbnmd3VJ34eyRML1zp7SmwcDX+3K5NCEEHzcnq1xjyYQIPF10vPXrMaucPzGzlNER/ey+k9ia8H6uOOk0HOtCUJpZUkNNg9GunXfPJpsdSZIkdc6O4yUcyq3g1qkDHfb3VDPmXVKvMBi+yN6rsZ6QEeAT0TyFN20tOHvDgMm2X0/CNTDrCRg0z/bXboHDbkPKmlLJ2mICPGgwmMgqqTn93IYU0RVtpiMEpb20pnTNgVyq6g0sbprZaQ2eLnpunDiAtcn5HC/qesOflhRV1pNVUstoB21yBCLDJMrfvUs7peYmR/aY0duS4WE+ZBTXUF7TtfpYSZKkvkRVVd7dfBw/dycuH2X5bKRuq8iDvAOQvQdObhM7hXveh6wdMOV+0FnnZrVDMKfwHv8FastEY6cj6yB2Dmj1tl+Pux9Mf8Q+126B4walTbtXeq0MSiXriAk6P8VxU2ohw0K9CPKyX1rmmZrS3pm+u2JXFrGBHoy1cj3mzVMGotdqeHfzcYue11xPOnqAj0XPa2kxgR4cLazs9OtS8irQKDDIirNUO2N4U11pUo5M4ZUkqW9qMJj481cH+f5gXrvH/d+qJDamFnLzlMhOjSyziYytsGwYvD0d3psNH1wEHy2E7x8Gj2AYfaO9V2h9Qy4HU6MIRnP2QnWRqCeVcNikbWNTTam2t3XfkhzGmbq7Si4cEkRpdQOJmaUsnRlj13XptQqK0jvTd80Njp68dIjVU4oCPJ25ZkwYX+zJ5sE5gwi00I2GxMxS9FqFof0t1zXYGmIDPfk+KY/aBiOuTh1/Y3I4r5KoAA+HeTMzPFQE/weyy5gaa90Zr5IkSY5oY2ohn+7O4tPdWWxMDePphUPxcG7+Fr60uoG7Pt7LzhMlLJ0Zwz0X2Pe9zHmMBlj7R/AMgfnPg85Z7NBpncQfnwjQu9p7ldbXfzR4hYoU3oDBoGghZra9V+UQHDbiM6fvyppSyVq8XPQEe7mQ3jTP8dcjRZhUmBUfZNd1KYqCs05DQy8MSlfuysTZSg2OWnLH9CgMJhPvbz1hsXPuO1nG0P7eDhO0tSYm0ANVhWOdTF9OzReddx2Ft5ueAX5ubD5aRG1D78wekCRJasuqxGz8PZy5b1YMq/dlc8lrm9nXlLUDkF5YyeVvbGVfVhnLrhvJI/Pi0Dja++fE/0JBMsx9BuIvhdgLIeoCUUsZNhY87Fc2ZVMajZhZmr4BDn8tvn5Xxy0HsiXHDUployPJBmKDPEhvetO+MbUQfw8nhofafwfMWaftdTul1fWiwdElVmxwdK4Bfu5cnBDCJzsyKa/tfk1ig8HEgewyh64nNYttSk/vzFiYirpGsktrHaLz7tmuGBXKjuMlTH9xEx9tz+iVN2wkSZJaUlLdwKa0Qi4f2Z+H5sbx2Z2TMBhVrn5rO8s3HmVTaiFX/Hsb1fUGVt4+0THrSGtKYOOzEDlNpK/2dUMWgrEeSo7L1N2zOH5QKmtKJSuKDhBjYRqNJn5JK+SCuECHuLvopNP0uprS7w6KBkdLrNjgqCV3zYimqt7AJztPdvtcKXkV1BtMDl9PChDp545Wo3QqKE3NEzWojtJ51+yBOYP44q5JDPRz58lvDjHr5V/4cm92s87ZkiRJvdGaA7k0GlWuGhMGwLhIX364fxqXJITw0o9HuPm/uwnzdeObpVMddnY2vzwHdeUibbcndAO2tvAJooYWxIxQCXDkoNTYNKdU1pRKVhQb5EFNg5HvDuZSUWew6yiYsznrNL2u++6KnZnEBnrY/JfmsFBvpsX6858tGdQ1di/QP93kqAfslDrpNAzwc+tUs6PUfNF519F2SkG8Efvszol8eMt4fNz0PPLFAeYt+40DWXJcjCRJvdeqxGziQ7ya/Vz2dtXz6qKRLLtuJL+fHMmXd00i1MdB6zELDsHu92DsLRA8zN6rcQwaDYy7FaJngW+UvVfjMBw24jPvlMqRMJI1xQaK2rm3fz2OTqMwzUEaqTjrNL0qfTc5p5wD2eUsmRBhl5lpd18Qzamqer5KzO7WeRIzywj2cqG/o/7yP0dsoAdHCjq+U5qSV4GPm54gL2crrqrrFEVhxqAA1iydypvXj6a2wci1b2/n2wO59l6aJEk20mg0ce+KRPb3gRtS6YWVHMgu56oW+jAoisLlo0J5asFQ3J0dtG+pqsLaP4GLN8x8zN6rcSwz/gg3rrb3KhyKwwalRpm+K9lAbFMH3tT8SsYP9MXTxTFmNYma0t6TvvvJzpOiwdGoMLtcf1KUHyPCvFm+MZ3CyrpOv95kUtmWfortx4odNz2qBeMifTlxqprs0pr2D0Z03o0P9nL4YeuKonBRQgjfLp3CiDAf7lu5j5d/TMMk03klqddLyavg+4N5LN941N5LsbqvEnPQahQWjOxv76V0Tcq3kLFZBKRuvvZejeTgHDYobZTpu5IN9HN3ws9dNN1xlNRdAGd979gpTcmr4Mb3d7JyVxZXjArF280+Qb+iKDx7eQJlNY3c/MFuquoNHXpddmkNy34+wvQXN7HkvZ3UG4wsHm/bmtjuMH9Pb0wtbPdYo0nlSH6lQ6butsbPw5mPb5vAdWPDeX1jOvd8kkhNQ8f+30qS1DMdzBYzizemFpJf3vmbjD2F0aTy9b4cpsf6E+hpv9npXdZYC+sfh6BhMOZme69G6gEcNuIzyu67ko2Y55U6VFDaw9N388vrePSLA1z82mYOZpfz+CXxPL1wqF3XlBDmzRs3jCY1v5K7P97bZgfXnceLueG9nUx7YROvbjhKpJ87ry4aye7H5vSoWZlRAR5E+buzIaX9oPRkcTW1jUYGhzjOOJiOcNJpeP6qBB6/JJ4fD+dz9ZvbySmrtfeyJEmykuScclz1WkwqfLk3y97LsZrtx4rJK6873eCox9n6GpRnwkX/BK2DphdLDsVhv0tkTalkK9MHBaAi3sA7CmedljILjDCxtXqDkdc3pPPeluOYTHDrlIEsnRVjsxEw7ZkZF8hzVybwxy8P8uevDvLytSOaparWNhj557pU/rstgxBvFx6YPYirxoQS1s/NugtLWQN6N6sM0J41OJCPtp+kut7QZt1RioN23u0IRVG4bVoU0YEe3LdiH9e8uY3v7puGr7tjfN9JkmQ5B7PLGRvZD4NR5bM9WdxzQYxDdM23tFWJ2Xi66Jhj59npXVJ8DDa/DEOvgMip9l6N1EM47E6pwSiCUr3WYZco9RL3zozh8zsn2XsZzYjuuz2vpvTz3Vks35TOnPggNjw8g8cvHeIwAanZtWPDefjCQazal8OL69NOP7/3ZAkXv7aZ/27L4KZJA9jw8AzunxNr/YAUYN1f4PuHRVMIC5sVH0iD0cSW9FNtHrcvsxSdRjmdOdATzYwL5JPbJ3CqqoGHPt8va0wlqZepazRypKCS4WHeLBofTlZJLduOFdt7WRZXVW9gbXI+lw4PwUWvtfdyOkdV4YdHQOsE856z92qkHsRhd0qNJpFaJ3dKpb7ISadpM73UUR0trMLTRcfri0c5dLOcpbNiyKuo441fjuHr7kRRZT3vbD5OqI8rK26fwORoG6bo1pWLFCcQrfMt3DJ/XKQvni46NqYUMm9ocIvHNBhMfL0/h1mDA3veG6BzDA/z4cnLhvD418m8+esx7p0ZY+8lSZJkISl5FRhMKgmhPlwQF4C3q55Pd2f2qLKKjliXnE9to5GrRvfA1N1Dq+HYRrjoBfAKsfdqpB7EYYNSg6wplfow0X235wWlmSU1RPi6OXRACiLd85mFwyiqrOfZ71MAWDw+gscuicfD1q31Cw6d+TjlW4sHpXqthhmDAtiYVojJpLaY5vbj4XxOVTWwZELPaeLUlusnRLDrRAkv/5jG6Ih+TIr2s/eSJEmygKQc0eRoeJg3LnotV4wKZcXOTEqqG3pVuv6qxGwG+Ln1qG7vgLjJuu7/IGQkjLvN3quRehiHzY01p+/KkTBSXyS67/a89F1zUNoTaDUKry0axW1TB/LhLeN57soE2wekcCYo9YuFw99a5RKz4wMpqqw//YbuXCt2ZhLq48q02ACrXN/WFEXhH1cmEOnvzh9W7uvSGCBJkhxPUnY5fu5OhHiLbrSLxofTYDSxel+OnVdmOTlltWw/XsyVo8Ic7wbvwS/g8Detf37j36GqAC79F2h6dtaNZHuOG5Se3il12CVKktWImtKetVNqMqlkl9b2mKAUwNVJy+OXDmHGIDsGY/lJ4NpP3FUuSoFTlp+9N2NQIBoFNrQwGubEqWq2HStm8fjwXlUu4eGs483rx1BV38h9K/ed7uguSVLPlZRTTkKY9+lgbXCwFyPDffh0VyaqFWry7WHV3mxUFa4YFWrvpZyhqvDL87DqNvj8d/DlrVBb1vyYnETY/S6Mvx1CR9tnnVKP5rARn6wplfqynpi+W1hZT4PBRHgPCkodQsEhMcct/jLx9xTL75b6ujsxOqIfG1MLzvvcyl2Z6DQK144Nt/h17S0u2JNnL09gx/ESlv18xN7LkSSpG2obmpochXo3e37RuHCOFlaRmFnWyit7jqp6Ax9sy2BarD8Rfg7yu1RV4cfH4ZfnYOT1MPNxOPw1vDkFTmwWx5iM8N2D4B4Asx6373qlHsthg9JGo6wplfouZ52GBqOpR935zSypAehRO6V2ZzJC4WERlHqHQuhYK6bwBpGcU9Fs2Hy9wciXe7OZEx9EoFcPHM7eAVePCeO6seG8vjGdX9Lan9cqSZJjOpxXjkmFhDCfZs9fNqI/7k5aPtudaaeVWc4HW05QUt3Aw3Pj7L0UwWSENffB9uUw4S5YsBxmPAq3/gh6F/jwMvjxCdjxBuTth3n/ABfv9s8rSS1w2KDUaFLRKPTK2VOS1B5nvfin2ZN2S2VQ2gWlGdBYc6a50ZAF4hd76UmLX2p2fCAAG89K4V2XnE9Jde9pcNSapxcOZXCwJw9+tp/cslp7L0eSpC5Iyj7T5Ohs7s46LhvRnzUH8qisaz7fW1VVskpqekQ3+7KaBt757ThzhwQxMtyn/RdYm7ERvroNEj+C6Y/C/OfBXFIXOgbu/A3G3gzbXhM7qVEzYdhV9l2z1KM5bFBqMKmynlTqs5y0PTMo1SjQ38fV3kvpOfKTxGPQUPFoTuFN/c7il4oN9CCsn2uzFN4VOzOJ8HVjaoyNxymoKhSmQHXbs1MtxUWv5Y3rR9NoVFm6IpFGY8/5dyVJknAwp5wAT2eCWsjqWDQ+gtpGI2sO5FFR18i65Dz+b9VBpv5zE9Ne2MSfvzrYoWsUVtZR02Cw9NI75K1fj1PVYHCMXdLGOvjsBji0Ci78m0jJPbfpkpO7aGi0+FOIngWXvnL+MZLUCQ4b9RmMJtl5V+qznJtmRfakDrxZJTWEeLvipHPYHyuOp+AQKBoIiBd/942CoASrpPAqisLswYFsST9FXaOR9MIqdp4oYdH4cNtkpJTnwL5PxJ33l2LhjYnw0UKRHmYDUQEePH9VAomZZfxzbapNrilJkuUkZZefV09qNiLMm8HBnvzjhxRG/e0n7vo4kTUH8hgW6sWlw0NYtS+Hnw6fX1N/tpPF1cx++VcuenUzRwoqu7zOrgS1hRV1/HfbCRaO6E9csGeXr20RqgrfLoUj60XQOeX+to+PuwhuXC1+f0lSNzj0nFLZ5Ejqq5ybArue1IG3J42DcRgFyWIUjP6sO//xl4mGEpX54Bls0cvNjg/iw+0n2XbsFFvTi9FpFK4eY+Xh7EVH4IubRO0siEYYUReAeyDs+Dfs/wRG/866a2hy6fD+7D5RwntbTjA20pf5wyz731eSJOuorjeQXlTFJcNDWvy8oijcPzuW97ecYEKULzMGBTIqwge9VkODwcSxomr+sjqJcZH98HE7f55pXaORez5JRKMoVNcbueLfW3l10SjmDAnq1DrXJedz74pEPr9zUqdmjC7flI7BqPLAnEGdup5VbF0GSV/A7Cdh7C32Xo3UhzjslobRpMomR1KfdToo7WHpu+G+MnW3UwqSz9STmg1ZAKhWSeGdEOWLm5OWH5Ly+Soxm7lDgwj0tHKDo41/E7ukc5+Fu7bAw0fgqvdg3t8hbDxsfBbqu74r0Vl/uSSeEWHePPrlATKLa2x2XUmSuu5QbgWqen496dkuSgjhy7sn8+i8wYwf6Iu+qQzGSafhpWuGU1rdwNNrDrf42me/P8yh3ApevmYEa/4whagAD27/3x7+vSm9ww0HS6obeGx1EkaTyue7szr8tWWV1LByVybXjgsn0t+9w6+ziiPr4eenRW3o1Ifsuxapz3HYoNRgMqHTOuzyJMmqnHU9K323tsFIUWW93CntjLpyKMs8U09qFjBY7J5aIYXXWadlWqw/XyVmU1bTyJLxAyx+jWaKjkDKdzDhDpj8BwhOONMoQ1FEp8aqAtj6qnXXcRZnnZblS0ajAPes2EtdY8/4NyZJfVlSjmhyNKyV9N32DO3vzb0zY1jdQhrvN/tz+HhHJndOj2LOkCBCvF354q5JXDa8Py+uT+O+T/dT29D+z4mnvj1ERV0jYwf044ekvA7/bHl1w1EUReG+WbFd+tospihNlFeEDBdddmV9qGRjDhv1GYxyp1Tqu8zdd3tCx0CArFKx4yRnlHZCQdMd+6CE5s8rikjhzdgCNSUWv+zswUGoKgzwc2NytJ/Fz9/M1ldB5yJGCbQkfBwMuxq2LYfybOuu5ezL+rrxyrUjSc6p4JnvWt45kSTJcSRllxHs5dKtzI57Z8YQH+LFX1YnUVbTAMCxoir+siqJMQP68ci8Mw2GXPRaXl00kj/Oj+O7g7lc8/Y2CivqWjs16w/l8+2BXJbOjOWBOYOorDewIaX9EVTphZWsSszmpkkDCPa241iu2lJYuQh0zrBoBTjJ3+WS7TluUCprSqU+rKel75rTIOVOaScUJIvHc3dKQaTwqkZI/d7il505OBAXvYbfTYq0boOj8mw4+KmoF3Vvo7vvnL+CaoINz1hvLS1ddkgQd86I4pOdmXyzP8em15YkqXMO5pST0Ebqbkecncb71LeHqG0wcu8niTjpNCxfMup0uq+Zoijcc0EM7/1uLMeLqrnijW2kF1add96ymgYeW53MkBAv7pkZzaRoP4K8nFm9r/0bba/8dARXvZa7L4jp1tfWIbVlkPQlZO1qfsPTaIAvboayLLjuY/C2cp8BSWqFQwel5/6AkKS+4kz6bg8JSuWM0s7LTwLXfuDV//zPhYwE7whIWWPxywZ4OrPtz7O5ZUqkxc/dzLbl4nHy0raP84mASfeIADYn0bprOscjc+MYF9mP/1uV1OKbTUmS7K+yrpHjRdWtdt7tjKH9vVk6K4av9+ey+N0dpOZX8q/rRhLi3Xo/hNnxQXx2xyTqDUaufmsbe082z2B5es1hymoaePGa4ei1GrQahYUjQ/klrYiS6oZWz5uUXc4PSfncOi0KX/fzmy9Z3Lo/w1e3wvsXwgsD4Z8D4b0L4aMFcHwTXPIyREy0/jokqRUOG/UZTSa5Uyr1WWe67/aMerfMkhrcnbS2+cXaWxQcgqBhLdftmFN4j2+CyrbHGGAywb6P4c2pZ+aetsPX3QnFmvVC1cWQ+CEkXCOCzvZMfQjc/GH9Y2IcgY3otRpeXzwaV72Wez7Za7f5hJIktS45pwKg2zulZvfOjGFIiBf7s8pYOjOGC+IC231NQpg3q+6ego+rniXv7uTHQ/kA/Hy4gNX7crhnZgxD+59Z3xWjQjGYVL47mNvi+VRV5bm1Kfi6O3H7tIEW+bralLsfDqyEcbfDks9h7t9hyEKRrluWCdMfhTE3WX8dktQGhw1KG2VNqdSH9bT03aySGsJ93awb6PQmJqMYkRI0rPVjhl8rjls+Fn59Eepb2MkrOAz/vRi+uRcKkqzSHKlLdr0NjTUw5YGOHe/iBbMeg8xtVtkdbkuwtwvLFo3kaGEVj3+d3OFOm5Ik2UZyU5OjBAvslIK4GfXmDaP5v4sG88CcjjcXivBz46u7JzM4xIu7Pt7LO78d4y+rkxgc7MnSmc3Tb+NDvBgc7MmqxJZLAzYfPcW2Y8UsnRmDp4u+W19Pu1QVfnwc3Pxg9hMwaJ7IYLlsGfz+O3gwGWY9bt01SFIHOGxQajSp6LTyDa7UN/XE9F2ZutsJpRkiaGupntSs/0i4azNEToVNz8JrI2HHW2CoFwHqj0/AW1NFx8QFy0WAm73LZl9Cq+orYefbMPhSCBzc8deN+h0ExMNPT4qv0YamxQZw36xYViXm8MUe2zVckiSpfQdzygn1ccXPw9li5xzg586dM6I7PeXBz8OZlbdP4IK4QP7xQyrF1Q28ePUInHTnn+eKUaHszyrjeFHzG4omk8rza1MJ6+fK9RM7kEnSXUfWQcZmuOD/wMUygb0kWYPDBqWi0ZHDLk+SrMrcfbcnjIRRVZWsUhmUnqeyABpamYNpTrM9d0bpuYKGwuKVcOtPYlTMuj/B62Ph3xNg22swcgn8YS+MvhHCx0P2XrG7am0mkxhp05K9H0JdGUx9sHPn1OpgzlNQekLMyrOx+2bHMjXGnye+SeZwboXNry9JUsuSsssstktqCW5OOt65cQz3zozmmYXDWk0rXjgyFEWBr/c3T+FdczCXw3kVPDI37vQNaKsxNoobmH6xMOb31r2WJHWTw0Z9BqMJvUzflfqoMzWljr9TWlRVT12jiQg/GZQCIlVqx1uwLAG+vLnlYwoOgaIRgWZHhI+Hm9bADavAM1j8uWU9LFwObr7imLDx0FAJRamW+Tpa0lANu9+HNybA8xHw4WVw8PMzwbehHrYvh8hpEDa28+ePmQ1OnpD+s2XX3QFajcKyRSPxdtVz74pEKusabb4GSZKaK69tJKO4xmL1pJai02p4dN5glkxofacz2NuFKdH+fL0v53RZQL3ByIvr04gP8WLBiBaa3Fna3v9C8VG48G+gtXKasCR1k+MGpXIkjNSHme+eNhgdPyjNKpEzSk+rKoRPrhY7ml4hIm3q+K/nH1eQLO5c61vv+HgeRRFB220/we0bzu+SGD5ePGbv7vr6W1OeAz/9FV4ZAt8/BE7uMOV+KD0Jq26Hl+NgzQPwy3NQmQfTHuradbR6iJoBxzbatOGRmb+HM8uXjCajuJoPt2XY/PqSJDV3qKmedLiDBaUddfmoUDJLath7shSAFTszyS6t5c8XDbbuSC4Q2Sy/PCduEsZdZN1rSZIFOGxQKmtKpb7MqQftlMpxME2OrIc3JkHGFrj4JbhnB3iHixpJ0zn/HwuS264n7QrfKHD1hSwLB6Vr/yR2fbe9JgLGW9bD7ZvEnff79sNN38HgS+DAp7DlXxAyAqJmdv16MXOgPAtOHbHc19AJ4wf64uvmRG55nV2uL0nSGQebgtJh/XtmUDp/WDAueg2r9+VQWdfI6xvTmRztx/TYNmY3W8qWZVBTDHOfabnLuyQ5GJ29F9Aag9GEztlhlydJVqXVKOg0So+oKc0srkVRINSnE7t+vYmhXnQ23PWOaDZ01XsQGC8+N+txWH0nHFoFCVeL5+rKRQt+S9f3KAqEjbNss6OyLNj5Fgy9AuY8Df0GNP+8RgMDp4k/F70AaT9A/9HdewMUM1s8pv8MAXFdP0839HN3orSN+YKSJFlfdb2Bz3dnER3gTr8eOm7Mw1nHvKHBfHcwD08XPSXVDfxp/mDrd6ovy4Idb8Dw66D/KOteS5IsxCF2SltK0jKY5EgYqW9z1ml6RPfdzJIagr1ccNFbuWGDo9rzHxGQTrwHbttwJiAFSLgWghJgw9NnOsoWHBaPbY2D6arwcWKHsaak/WM74uRW8Tjt4fMD0nO5eMGIRRAwqHvX9IkA/zi71JWa+bo5tTn0XpIk63vq20OcKK7m2csT7L2Ubrl8VCjltY289esxLhkewohwH+teUFXh57+Kj2c9Yd1rSZIFOURQerSgki1HTzV7zihrSqU+zlmv7RE7peYZpX3WyW3gMwDmPwd6l+af02hg7t/Ezuju98RzBcni0RpBaVhTXWnOXsucL2MzuPhAoIVTjdsTMxsytrbevdjKfNz0lNXIRkeSZC/f7M/hi73ZLJ0Zw6RoP3svp1umxfjj7+GMTqPwyNwuZn/UVcAHF4tykLo2uoOXnoSPFkDyV6Lu3ye8a9eTJDtwiKBUVeGG93dy1//2nm6a0mg0oe/k/ChJ6k2cdZoeU1Papw3BA7gAACAASURBVOtJc/a23Wk2epaosfztRagtE0Gpiw94WaHzYugY0dU3y0IpvBlbxJxUW4/nipkNxvozO7U25uvuREmN3CmVJHvILK7h8dXJjI7w4f7ZsfZeTrfptBr+etkQ/rZwGAP93bt2ksQPxc/Dra/C66NFV92zx3+pquiM/uZkyNkHl70q5pJKUg/iEFHfoCBPHp0Xx69Hipjzyq+88mMadY0muVMq9Wk9IX23rtFIfkUd4f36aFBakQsVOaKWsy0XPi0C0i3/gvxkCE6wTuMJZw+xq2mJutLybCjNEEGprQ2YAjoXSN9g+2tzpqZUtUMHYEnqyxqNJu77dB8o8OqiUeh6yebEZSP6tzk+pk2GBtj+BgycLprM+UbDmvvh7elw4rczu6PfPyRukN6zXfQskM2NpB7GIToJKQrcOzOGK0eH8twPqby2MR2ACQN97bwySbIfZ52WBgcPSrNLawGI8LNgk6ONfxdzPAdOF38C4x33l2v2HvEY2s5MzpARouHEjjfF12LNIebh4+DgF+IuuqYbdb4ZTbuUA6ZYZl2doXcVwbCd6kp93ZwwmFSq6g14usjZfpJkK//66Qj7s8r495LRfbss5GzJX0JlLix8HUJHwy3r4NBqMabrw8tA6yT+XLpMBqNSj+YQQalZiLcrry0exQ0TB/DKT2lMiJJBqdR3Oes1Dl9TmmXpcTDFx0Saq7MXpH0vnnMPFMFp9EzROEjnQF0Yc/aARi92Ptsz6zHxRsJQZ516UrOwcaL5UlEaBA3p+nnM9aTWXGtbYubAuj+L3dp+kTa9tI+bCERLqxtlUCpJNrI1/RRv/nqMxePDuWR4SNsH11eCk0fvD8BMJpGyGzQMops6kysKDLtSzB7d/m8oPAxznhJN4iSpB3PIvIjxA3359I5JXDdO/gOT+i4nreOn75pnlFrsjva218Qd3z/sgfsPwoLlYjbmid/gm3vhP3NF4OoosveKgPTcBkct8YmACXeKjzsSxHaVudlRd1N4M7aIXVJb15OaxcwRj3ZI4fVtGj8h60olyTYKK+t48LP9RAd48OSl7TRWqymBl+Jg59u2WZw9Hf0RilJF06JzA3C9K0x/BK7+jwxIpV7BIYNSSZLMO6WOH5S66DUEeDh3/2SV+bB/BYy6HjwCxQiS0TeKuZ+PHIFrP4KSE6KO5sCn3b9edxkNkJvYfj3p2WY+BotWinRea/GLBldfyNrd9XOU50DpCYi0Q+qumV8MeEfYJSg1z0SUs0olyfpqGgzc9uEequoNvL54FK5O7ZQdZGyGxmrY/JLdOnTbzNZXwTtczIqWpF5OBqWS5KCcdY4/Esbcedcig8B3vAkmA0z+w/mfUxQYshDu3grBw2H1nbDqjrZb41tbUQo01rTdefdcehcYfLF1U84URQTK3dkpNXe9tUeTIzNFEV14T/wqGn3YUD+3pqBU7pRKklUZTSr3f7qf5JxyXl88ivgQr/ZfdGIzKFqoLhJdaXurrF2QuQ0mLQWtLCOQej8ZlEqSg+oJI2GyLDUOpq5c1EEOWQi+Ua0f5x0Gv/8OLvgLJH0Bb0+DnMTuX78rTjc5GmOf67clfBycOiLS3LoiYzO4eNuvntQsZg40VFmmm3An+DYFpSVyp1SSrOofP6Tw0+ECnrx0CLPjgzr2oozNEHUBRE6DLcugsc6aS7Sfra+Caz+RMSRJfYAMSiXJQTn6SBhVVckqqbFMPeme/0B9BUx5oP1jNVq44E/w+x9Eh9n/XSFa4tta9h6RJttWEG0v5rrSnL1de33G1qZ60m5077WEgdNBo7N5F15PFx1ajSJ3SiXJij7ansH7W07w+8mR/H7KwI69qKpQ1FgOnAYz/ghV+bDvf1Zdp12cOgqp38O428Gpi7NNJamHkUGpJDkoR0/fLaluoLrB2P2d0sY6kbobNRP6j+z46wZMgpvWiKHhX/weDPXdW0dn5ewRqbuO2P0xdDQoGsjuQl1pRS6UHLPPKJhzuXhB+ESbB6UajUI/Nz2lNY02va4k9RWbUgt56ttDzIkP5IlLO9ElPGOzeIycLnZKwyeK+c+2/vlvbdteB50zjL/D3iuRJJuRQakkOShnvcah55RmWmoczMFPoaoApnZgl/RcvgPh8n+LhkM/PtG9dXRGXYUYudLefFJ7cfaEwCGiJqmzMhygnvRsMbMgP0k0wjpbYx3k7gejdQJHHzcn2ehIkqzgUG45S1ckEh/ixauLRqHVdOLG3onN4OQpmsUpitgtrcgRTfJ6i8p8OLASRl4PHgH2Xo0k2YwMSiXJQTl6+q5FglKTUdTNhIyEgTO6do74y2DivbDrbTEH1BZyEwEVwhywntQsbJxI3zV18nvo5BZw9rbu2JrOMI+GObZR7OLu/S+sXAIvDIR3ZsDbM7rXabgVvm5OsqZUkiys3mDkDyv24emi5z+/H4e7s65zJ8jYIrJktE2vi54lbg5uecVqN6hsbtvrTU3/ltp7JZJkUzIolSQH5WTFoHRr+ine/rV78z6zmoLSsH7dCEpT1kDJcZj6YPfSYOc8JYKwb/4Ap9K7fp6OMqfFOmKTI7Pw8aJOtyi1c6/L2AIDJtu/ntQsKAHcA+GHR+GVeFhzv9g5HXk9XPwS1JXB+xfCdw+JhlkW0s9dT5lM35Uki3rn1+McP1XN81clEOTVgfnOZ6vIg+KjIm3XzLxbWpYJBz+z7GLtIXefKGcZeb1j9iuQJCuSQakkOShnnRajScVgtHxguuznIzy3NpWv9mZ3+RyZJTUEejq3P1OuNaoKW5eBb7TY7ewOnRNc/YG4e/7FTdBY273ztSd7L/jFis6Ijsrc7KgznWsr86E43b7zSc+l0cCke6H/KHHz4Z4d8MBBuOQlGH873LsTJtwFez+A5ePEbrmqdvuy/dycKJGNjiTJYjKLa1i+KZ2LE4K5IC6w8yfI2CIeB05r/nzsXJHO+9tLYn70uRrrLPIzweoMDfDNUnAPgLnP2ns1kmRzMiiVJAflrBP/PC29W1pe08jek6U4aTU88U0yx4uqunSezO6Ogzm2QdwVnvwHy+zK+YTDle9CQbLYVbMWVT3T5MiR+UWL7sCdSW01v+lzlHpSs6kPiFFAUx+EwPjmu+rOnnDR83DbBvAMFk2vPr+x22Mi+rmLmlK1J7yZlfqkeoOR3LJaDudWdKgp3qHccv5vVRJ7T3ZxVFQ3qKrKX79NRqdRePLSoV07ScZvYlRV8PDmzysKTP8jlJ6A5C9Ftsz+FbDmAXhjMvw9GL5/qGOB6amjUH2qa+vrri3/Er+/Lv0XuPrYZw2SZEcdSuZXFOUJ4CagFLhGVdWMsz53sul5gHdVVf23oii+wCogHFivquo9Fl21JPUBZwel7s6WO+9vR4swqbBs0UgeW53E0hX7WH3vZJx1nQsMs0pqmTDQt2uLMBpg/ePgMwBGLO7aOVoSeyFMexg2vyzm2CVcbblzm5WdFEPbHT0oVRSR0py5TbwZ60h6dMYWcPY6/01fTxA6Gm7bCNuXw89/hU+XwKIVoO9kimATXzcnDCaVynoDXi5ycL1kHU+vOcRXe7PxctXj46bH21X88XLRY1JVDEaVBqOJRqMJg1F8P56qqudUZT0VdWd2Bfu56blydBiLx4cTE+jZ7BqJmaUs35jOxtRCAHLKavnolvE2/TrXHypgU1oRj18ST7B31/5NcmJz66Oq4i4Wc5VX33nmOWev/2fvvsOjKtMGDv/OTHovJISEAIEAgdCLdAQEpEhTighYsIuuda276q5+a9u1N+yKFBEQAQsqIL2XIL1DSEhI73XmfH+8CQTSJskkmUme+7q4BmbOnHlHcDLPeZ+iPqf9wtTYsSbtoO/95Z//5DpYMBUcXeH6V6DbLVUvKynMh9MbVK2roQr7PvEHYcMb0GkyRIyp2msK0UBUGpRqmtYSmAS0B8YArwHTShySqev61XMcHgRW6Lr+pqZpSzVNG63r+i/WWrQQjYGzo/rBa+2xMOuOXsTHzZHrI4NwdjBw59e7eOXnI7w43vKr1ycTMolJzaFdkGflB5dl1xeQcBimza920FCuIc+qLwU/Pa6+wHg1s+75z+9St7baebekiLGw8m+qC2+LPpUff2YTtOhnO/WkVWV0ULuqbv6w4iFYNL0oMHWt8ql83Z0ASM0qkKBU1IrMvEIW7jhH+6aetA7wIC2ngNTsfOLScsnILcRo0HAwajgaDTgZDTgaDbg6GekQ5EWTcCeaeDjj7+GMm5OR3w7F8fUWNfezV0tfpvUOpZm3Kx+tP8HmE0n4ujny+Ih2XMzIY8GOcyRl5uHvYcWrnRXIyivk3ysPEhHkye39W1XvJGnn1U5oeSNSDAa1w7hvgRot1vwaCIhQ95vNKnti9bPgH64uXl7t3HZ1Ics/XO3G/vgAHFgK494GnxaWrVHXYdUjsG8+jHsHet5u2fNMhfDjHPW6o1+37DlCNECW7JQOBX7Rdd2kadovwAfFD2ia5gZklfGcYcDdRb9fDgwBJCgVogqKd0qtORbGbNbZcCyBwW0DMBo0ruvQlNkDwvhi82kGhDdhRMemFp3ni02ncXIwMLln86ovIjsZ1v2f6rYbMbbqz6+M0QEmfgQfD1RNcW75zrqzRGN2g4MLNK1mClpd6nQT/PYPdRGgsqA0I141Eelxa92srTb1mKX+zn98EBZOh+kLqxyY+rqpQDQ5O58W/jUceyREGX47GEdugZl/3tCRXq2qmXVSZGL3EBIz81i6+zzf7Yzm70v2A9DEw5nnxnTglj4tcHd24FBsOvO2neWXA3HM7NvSGm+jUu+uPU5sWi7vTu+Og7GaVWPl1ZOWFHqN+nU1gwFu/AS+uB6+vwPu+l2VARS7sB/mTwHPZjBruarp3PU5/P4CfNhP1bL3urPync8t76mA1NENtn4A3W+1bLd06/uqlGXyl+DuX/nxQjRQlnw6BAKJALquFwJGTdOKL6O7Aa00TduradpGTdN6Xv0c4CIQdPVJNU27R9O0XZqm7UpISKjRmxCiISpOp7VmTemB2DQSM/MZGnF59tlTo9sTGezF35dEcSGt8gZBKVn5LN1znkndQmhSnSvta1+GvAwY/Zp1g8WSmrSF616A46vVlwRrOr9TjbAx2sHumbMHdJmmmv9kV1JHdtZG60mrq/tMmPABnPoTFt4M+dlVenrxTqnMKhW1Zfm+WEJ8XOnRwjoN05p4OHPvtW1Y8/i1fHdPX96e1o1NTw3l7sGtL41e6dDMk/BAD1ZGxVrlNStzNC6DzzeeZlqv0JoF3qc3qsZygdW8GOjkDtMXgZMbLJh2uW404RjMmwQuXnDrj+DZVAWS19wNc7apIPfnJ+CrsZBypvzzH1sNvz8PHSeqXdLEY3D8t8rXlXgc1v0HIm6AyEnVe29CNBCWBKVXV4Zf+hap63oi0FTX9e7A88DXlT2nxHM/0XW9l67rvQICZDiwEFdzKq4pLbBeULruSILqCdH28v9zzg5G3r+lBwWFZh5euK/Sbr8Ldpwjt8DM7IFhVV9A3F+qS2rvu668Ul0b+twHLQfCr89AarR1zlmYr66q23o9aUm97gBTXuXB+d5vVWMke6wnLU/3GTDxQzi1HhZOq1Jg6udWFJRKB15RCxIy8th8IpEJ3YIxGKx7cU7TNPq09mdi9xBcHI2lHhvXJZgdZ5KJS6tZM7DK6LrOP5cfwNPFgadHR9TsZGc2FNWT1qA/p3dzuHkhZMbDdzNVQPjNBNAMKiD1Cb3yeJ8WMHOZurgVfwA+GghRZYyduXgYltwJzbqoLJ3ISeAdClverXg9ZrPK5nB0hbH/q72LtELYCUv+774ANAHQNM0B0HVdv1Tkphe1JtR1fR3gX7SLeuk5qF3Ti9ZctBCNweVGR9arKV139CJdm/uUqiUKa+LOSxM7seNMMh/9Wf780vxCM19vOcOgtk1oX9V6Ul1XAaKLDwx5ujrLrxqDASa8D2YTrHjQOiMB4v9SAZ49BaVNIyG0L+z6Un0JKsuJNXByLQz+++Wh9A1Ft1tg0sdqp2XBVMgvq+KkNN+ioDRZdkpFLfj5rwuYzDoTuoXU+Wvf0LWZKn/cX7u7pSuiYtlxJpmnR0dcyjyolpSzag5p2OCaL6p5TxVkntsKH/WHwhy4dbnqVl4WTVNZF/dtUp+lP9wDS++CnFT1eFaSysRwclMBr5ObyqLpez+c3azGh5Vn+8cQvQ1GvaI6hwvRyFkSlP4BjCoKNscAGzVNe0PTtJGapvlqmuYCoGlaV+BCUcC6Ghhf9PxJRecQQlRBeSNhMvMK+d9vR8nMK2MeWwWSMvOIOp/KkPZlZybc2KM547oG886a4xyISSvzmFX7Y7mYkcddg6ox1PvQj3BmIwx7DtxqVj9lMb8wGPmSSuHc9XnNz1f8BcMemhyV1Gs2JJ9Uuw1XM5tU7ZRPS+h9Z92vrS50vRkmzVVfEhdMsygw9XRxwGjQZKdU1Irl+2KICPKs+sU9K2gT4EFksBcr91+otdfILTDx+q9H6RTixZSeoZU/oSJnNqrbVhXUk1ZF58kw9B+qO+/MpZb1B/BtCbf/BEOfgwPL4ONBKgNj8a2QfkE1VPMucYGhx63g7A1b3yv7fInHYc2/oN0o63agF8KOVRqU6roeB3wJHAVeAp4EWgJ+QAiwU9O0vcCHwF1FT3sfGK5p2kngoq7rv9bC2oVo0Mrrvrtg+1neW3uCFfuqdpV74/FEdB2GVjC0/KUJkfi5O/H44qhSr6vrOp9tPE3bQA8Gt21SzhnKUZADv/1TtezveUfVnltTvWZD66Hw2/OQfLpm5zq/EzyCVBqYPek4QdVj7fqi9GP7F6sd4OueB4e66cZZL7pOuxyYzq98x9Rg0PB1cyQlu6COFigai3NJ2ew9l1ovu6TFxnUNJio6lXNJVau1ttQXm08Tk5rDc2M61jw9+fRGcGti3ZKPa/8OTxyHkJ6VH1vM6ADXPgmzV6tMnG/Gq1r8Ce+Xzp5x9oRet6uLsVfXoppNsPx+1TBv3DuStitEEYuS83Vdf1/X9XBd17vqun5O1/Wpuq4v0nX9gK7rnXVd767r+gBd1/cUHZ+j6/poXdfb6Lp+X+2+BSEaJucyakp1XWfhDlUf+fuhuCqdb93Ri/i7O9E5xLvcY3zcnHjtpi4cjc/gzd+PXfHYtlPJHLqQzp0Dw9Cq8kPUbII/X4W0czDq1bofN6Jp6kuDwQhLZkNmNaoJ8jLh+O+qA2TzXvb3JcLRBbrNgCM/QUaJfzcFOarxVHAPiLyx/tZXV7pMhUmfqNmt86eov9cK+Lo5SaMjG6PrOnvPpfDKL4d56/djFnUnT8spYMfpZDJybeMCw4/7YgAY3y243tZwQxc1KmtlLaTwJmbm8eG6kwzv0JR+bWrYTVbX1eduq4HW/9ytbn1qaG+VznvNvXD9f9TnSln63KfqVbd9dOX9W95TFzjH/FfSdoUooYEVDwnRcJSVvrv1VBKnE7MI9XNl88kksvIKL3VVrIjJrLP+WALDIgIrvWo9NCKQ6deE8smGU4zo0PRSx8TPN53C392Jid0tvLqfdFI1z4laBBmxajxJRe38a5N3c1VXuGQ2zB0MU76CFn3LP95sUl8aTq1Xqb/nd4K5AIxOqruiPep5hxo9sHeeqh0FVdOUfl79t6lJAxF70mWK+nK77G4VmM74XnUpLoOvm5PUlNoAk1ln99kUfjlwgdUH4ohNy8XBoFFo1tlxOpmPZ/XE27XsbtgHYtK4d95uYlJzMGgQEeRFr1a+9GrlR6+WvgT7VH2GbU3ous7yfTFc08qPkDp+7ZKa+7rRs6UvK6NimTM03KrnfvuPY+QWmHhmTA2bG4GaTZp+HsIerfm5rMnZE8ZUMlPUKxg6T4E98+Dap1TZysUjaiRah3EqjVgIcUkj+RYihP0pTt8tuROwcEc03q6O/HtCJ/ILzWw4Ztk4pX3RqaRmF1SYulvSc2M7EuLjyuPfR5GdX8iphEz+OHyRGX1blurmeAWzSQWiX4yC93rA5rchqBNM/UalTtaniLFw1x8qZeqrsbD1w9LNjwpyYOdnau1fXA9/vgIF2dDvAZj1Azx1VgU19qhJuJoNu/tr9feUlQQb31Q1TfV1saC+dJ4MN30G0dth/mQ1oqgMvu6OUlNaz47GZTDotbVMnbuV+dvP0THYm/9N6cruf4zgf1O6sutsMpM/2kJ0cuk01GV7znPTR1vQdZ23p3XjoWFt8XN3Ysnu8/xt4V76v7qWeVvP1On7ORibzsmErHrdJS02rkszjsRlcCy+7H//1XHiYgYLd0Qzo08L2gSUfbGnSk4X15NaoclRfej/EBRkqdIJUyEsv08FtGPfsr+MGyFqmeyUCmGjru6+m5SZx+oDcczo24JB4U3wcXPk98PxjO7crNJzrT96EYMGgyysBfVwduC/U7oy/dNtvPLzEQCcjAZmVTZsffeX8NPj4B+uBo53uRm8Kl9fnQnqDPf8CcsfgNXPqKCkuEPvzs/UzmFWgqozGvZPaDOs7poy1YXed6rGHCf+gJPrID8Thv+rvldVPzrdpFLrltwJ306GmUvUl8US/Nyd2HMutZ4WKOLTc7njyx0UmnXend6dYRGBeJTIDLmpZ3Oa+bhw77zdTPpwC1/c3osuzX0oMJn5z8+H+XLzGfqE+fHBjB5XzFQuNJk5EpfBA/P38OfRBGb1a1Vn72lFVCwOBo2xFnxu17YxXZrx71WHWBUVy2Mj21vlnP/5+QhuTkYeHt6u5ifLSVWfyZ7Bava0PWoaCW2ug+1zoTAPYvfClK/BQ0YhCnE1CUqFsFFOV6XvLt1znnyTmenXtMDBaGBY+0DWHrlIocmMg7HipId1RxPo0cIXHzfL2/L3be3P7AFhfL7pNE5GAxO7BxPgWUkjnHPb1ReIB3fZ7lVgVx+4eT5sfkd1P4zdA9nJKkALHw4DHqmd+iVb0H4MeDRVO8BxB6D7LAi0QoqdvYqcBGgqrfvbm2DGEnDxuvSwT1FNqa7rVaujFjWWmVfI7K92kppTwOJ7+9GpnFr4/m2a8MMD/bn9y51Mm7uNlyZ24vtd0Ww/nczsAWE8MyYCx6s+Hx2MBjqFeNMnzI+1Ry7W2d+v2ayzYl8s17YLqNmIFCsJ9HShb2t/Vu6/wKMj2tX4v8HmE4msPXKRZ0ZH4FfT91eQAwunqy61Mxbb9+dx/4dg3kTY8Lq6GBZppyUgQtQySd8VwkaVrCktbnDUq6Uv7Zqq3ZzhHZuSml3ArrMpFZ7nYkYuf8WkMTTCstTdkv5+fXvaBLiTbzIze2BY5U+4EAXNutr+FwhNg4GPwK0rwOAI7UerxhUzl6pUVltff3UZHdWogti96vdDn63vFdW/yIkw5UuI2a0C09z0Sw/5uTlRaNbJqOL4JVGx/EIzy/acJykzr8zHC01m5szfw5G4DD6Y0aPcgLRYeKAnPzwwgHZNPXji+yj2Rafy9rRuPD+uY6mAtKRuLXxIysrnfEpOjd6PpbafTiYuPZcJltbl14HxXYM5nZjFwdj0yg+ugMms8/JPh2nu68pt/VvVbFGmQpXBcG4r3DhXZazYs9ZD1M9F90DV3EgIUSYJSoWwUU7G4u67JradSuZ0Yha39Glx6fHB7QJwMhr4/VB8hedZf1TVnZY3n7QiLo5GvrrjGj6a0YOIIK+KD87PhqTj0KxLlV+n3oQNgr/tUfWFQZ3rezV1o8dtYHSGAQ9L58diHSfA5C/Vrvm3N0KumtNbvJuVmmUbXVtt2bmkbN787Sg5+aZKj/1i82keWxzF4NfX8ebvx0gv0RVX13X++eMB1h9L4OWJnSyugw/wdGbRPf14dHg7lj3Q36KGbN1CfQDYG103KdoromJwczIyvEPVLxDWllGdgnAwaKyMqlkX3qV7znP4QjpPjYqouO9AZXQdfnoUjv4Eo4t2Fu2dpsGs5XD/5oZVDiKElUlQKoSN0jQNZwcDeYVmFuw4h5eLA2NK1CF5ODvQP9yf3w/Fo1/dsKeEP48lEOjpTMdmlQSV5Qj1c7OobpX4g6CbIciOgtLGyCcUHtkPg5+s75XYlo7jVVfm2L0wbxLkpuHrpjq6Jkuzowql5RRw+1c7eHftCT7ZcKrCY9NzC/joz5NcE+bHkPaBvLvmOINfX8fc9SfJyTfx4Z8nWbgjmjlD2zD9mhYVnutqrk5GHh7elsjgindWi7Vv6omLo4F9FtQNf7X5NJ9tPFXtsTJ5hSZ+2n+B6yODcHOyncopHzcnBrcL4Ie9Mfy0/0K5u9cVOZuUxX9+Pkz3Fj6XRs1U29qXYc83qkN4n3tqdi5b4uYHHrZzMUIIW2Q7n4xCiFKcHQxcSMtl9YE4bunTotQV6BEdm/LcDwc4Fp9J+yDPUs8vNKkOvaM7BdV+zVRclLq1p53Sxkp2SMvWYZzqFL1oBmz7CN/W9wLIrNIKFJrMPLhgD9HJ2XRt7s3H608yrXcoQd4uZR7/2YZTpOUU8PwNHekU4s39MWn897ejvPLLET7ZcIqkrHwmdAvmCSs13qmIg9FA5xBv9kVXXAKRkVvASz8dxmTWeeeP48zo25I7BrSiqVfZ7/FqZrPOS6sOkZ5baPlIrTp096DW3PPNLuYs2ANARJAn/ds0oX8bfwa2bVLhzmdGbgF3fr0LgHemda/Zz5ntc2Hjf6Hn7TD0ueqfRwhhl2SnVAgb5uxo5NeDceSbzFek7hYb3qEpAH8cLjuF96stZ8jILWRkxzoIQi7sBxcf8A6t/dcSorZEjAWvEEg5i19RYzAZC1O+l386zMbjibw8sRPv39IDk1nn9dVHyjw2MTOPzzadZmyXZpfqRDuFePPVHdew+N5+tGvqyfAOgbw+uUudNZbqFurDgdj0K0ZvXW3bqWRMZp3nb+jI4PYBfLLhJANfW8uTS6I4cbHicSpms0pH/nbbOe69tjWDLeyAXpf6tfFn7/MjWPZAf/5+fXv87+lWmAAAIABJREFUPZyYv/0sd32ziwnvby5z3A6oOtKHF+3jTGIWH87oQQt/t+ovYsen8MtTEHEDjH2z4db1CyHKJUGpEDbM2cFAfqH5igZHJTX1cqFrqA+/lVFXejA2jdd/PcrIjk25ri5qmOylyZEQlfEMgowLl2pKk2WntEzzt5/lqy1nuHNgGNN6tyDUz43ZA8NYtieG/edLp8R+uO4kuQUmHhtRelzINWF+LLynL5/d1htnhxrUJFZRt1Bf8gvNHIkrv9HPpuMJuDoamdG3BR/c0oN1Twzh5t4tWBEVy4i3NvCP5X+Rll06rdds1nlu+QHmbz/H/UPa8PSoCJvt4uxgNNCjhS9zhoYz/66+RL0wko9m9OBCWg4TPtjMzjPJpZ7z+uojrD1ykRfHR9K/TTWDbV2H9W/Az0+ohnM3fQ6Guvv7F0LYDglKhbBhxWNhytolLTayY1OiolOJT8+9dF9OvomHF+3Dx82RV2+qg10HUwFcPCSpu6Jh8AyCjDi8XBwwGjTZKS3DlpOJvPDjQYa0D+DZMR0u3T9naBuaeDjx75WHrqh1j0nN4dttZ5ncszltAjzqY8ll6tZCNTvaV0Gzo43HE+nT2u9SsNzS352XJnZi81PDuK1fKxZsP8ew//3Jkt3nL71ns1nn2R/+YuGOc8wZ2oYnr29vswFpWVwcjYzu3Izlcwbg4+rILZ9uY/Gu6EuPL919nrnrTzGrb0tmVja/ujxmM6x+Fta9DF2nw9R54GhZSrQQouGRoFQIG+bsYCzV4OhqIzqWTuF95ZfDnLiYyX+ndK35vDhLJBwFUz4Eda391xKitnk2g4wLaJqGr5sjydJ99wqnE7O4/9s9hDVx593p3TEaLgdbni6OPD6yPbvOpvDzX3GX7n9vzXEAHh5eepe0PgV7uxDg6Vxus6PzKdmcSsxiUNvS3cv9PZx5cXwkKx8aSEt/N574Poqpc7dyKDadZ5b9xaKd0Tw0LJwnRtpXQFpS6wAPfnhgAH3C/HlyyX5eXnWIXWeSeWbZX/Rr7c/z4zpW78SmQvhxDmz7EPrcBxM+BKO0ORGiMZOgVAgbNv2aUP4xtmOFjSbaBnrQws/t0miYtUfi+WbrWe4cGMbgdlUfA1MtcfvVreyUiobAMwhyU6EgB183J1Jlp/QKDy/ai0GDz2/rjZeLY6nHp/YKJSLIk1d+OUxugYlTCZl8v/s8M/q2IMTHtR5WXD5N0+gW6lPuTumm44kADKqgFjQy2Jsl9/Xn9Zu6cOJiJmPe3ch3u6L523VteWxEO7sNSIt5uzny1R29ua1fSz7bdJqpc7fSzMeFD2f0qHAObLkKcmHxrRC1AIY8C6NeBYN8HRWisZPLUkLYsFv7tar0GE3TGNGxKfO2nuVsUhZPLtlPRJAnf7++gu6V2cmQeRECI6yz0AtR4OgG/uHWOZ8Q9cmzKDMhIw5fNyepKS0hISOP/efTeGpURLmNbYwGjX/e0JEZn23ni82nORSbjrODgQeG2ObnQ7dQH34/FE9adgHeblcG2RtPJNLUy5m2gRWnHBsMGlN7hzIysinvrjlBsI8Ldw1qXZvLrlMORgP/mtCJtk09+XbbWd6b3v1SzXWVmM3w/e1w7Bc1h7TPvVZfqxDCPsmlKSEagBEdm5JvMjNt7jYycgt5d3r3igeY/zgHPh4IJ9ZYZwEX9kPTTtKgQjQMXsVB6QV83R2lprSE3WfV+JRrwnwrPG5AeBOGd2jKe2tOsGr/BWYPCCPA07kullhl3UOL6kqvas5kMutsPpHIwPAAi3c7fdyceH5cxwYVkJY0s29Lfn1kMG3LaLxnkU1vSkAqhCiTBKVCNAC9Wvri4+ZIXHouz4yOKLNT7yVZSXD8N0CH72bB+d01e3GzGeL+ktRd0XB4Xg5K/dydSCmjs2pjtftsMk4OhksjXSry7JgICkxmvFwcuHuw7QZpnZt7o2mUqis9GJtGanZBham7dicvQ31m14dT62Hd/0GnyXDNPfWzBiGEzZL0XSEaAAejgdkDwjiblM1t/VtVfPDBZWAuhJnL4KfHYP5kmL0aAqrZgCTlNORnQJAEpaKB8Cya65sRh69bJClZ+ei6bve1gdaw62wKXUK8LRrb0jrAg3end8fLxRFv19K1p7bC08WRtoEe7ItOueL+jUX1pAPCazEoPbMJTq4DZ88rfzm5q3Ep5kL1y1SgbgtyICcFcpKLblMgN12N4+pwAzTrVnosl6kAjv0Ke+bBid9h0BMw7Lnae09lSb8AS+8E/7Yw7h0ZHSaEKEWCUiEaiL9d19ayA/cvhsBICL8OZv0An4+Eb29Ugal3SNVfWJociYbGxQccXFT6rpsThWadjLzCMpv6NCa5BSYOxKQxe2CYxc+pqHO4LSmuKy158WHT8UQ6NPOqvbRjUwEsvRsyYqv+XM2g/p26+qp6/pNrYON/was5RIxVvzwCYd98iFoEWQngEaSCwh2fwMBHwansmmCrMxXAkjsgPxtu/wmcbWckkBDCdkhQKkRjknwKzu+A4S+qP/u1hplL4cux8O1NcMfP4OZXtXNeiAKDAwRWczSAELZG0y7NKvX1V81cUrMKGn1Quv98GgUmnV4tq/gZYU2p0XByLZxaBw6uMOLf4FFJl/H4Q3B2MwREQFBncPUpdUi3UF8W7zrPueRsWvq7k51fyK6zydwxwPIAvMoOLlcB6S2LodVAlVqblwl56ZCfqQJPg6P6fDU6qFsHF/UZ7ex9ZcfarCS1G3pkFez5GnbMVfcbHKDdKOg+C8KHQ/R2+GoM/LUYet5ee++tpDX/gnNb4cbPIKCCBnxCiEZNglIhGpO/lqjbzlMu39esK0xfoILSBdPg1h+rdgX9wn4I6AAOttnERIhq8WwGGXH4uatANDk7v9xus43FzjPJAPRsWXGTI6vSdRWEHlutbpPUvFM8glTq6ok/4Ma50GZY6eeaCmDTW7D+dTCXqAv2DVOfe826QveZ4BFIt+JmR9GptPR3Z/vpZApMeu3Vk+o6bPtA7VyGj1ABppM7VLN/EO7+0H2G+pWfpZrYZcZDxwlqx7RYy/4qMN/2MfS4rfbTaA+vhC3vQe+7oMuUyo8XQjRa0uhICHtkqkbjFV2H/d9Bq0Hg3fzKx8IGw02fQ8wuWHa35Y0wdF2l70rqrmhoPIMg4wI+bmqnNEXGwrD7bAqtA9zxq84okOrQdfj9n6q8YM834NsSrv8PPLANHj8Cd69V6avzJsHvz1/5uRh/CD67TjXW6TgeHtytskKue159Xl3Yp3bw1r8OQLumHrg6Gtlb1Oxo47FEnBwM9G5VS7vC57ZC7F7oe7/1Z3Q6uav3fM3dVwakoILQPvdBwmE4vcG6r3u1lDOw/AEI7q7+3oQQogISlAphb85ugf+EwPldVXte7B5IOgFdppb9eMfx6ovDkVWw5kXLzpkRp2qVpMmRaGiKd0qLgtLGPqvUbNbZfTaF3nWVums2w89PXN5le+qMCir7zYHADiq4CuoE9/yp0lA3vwNfXA+JJ2DDf2HuYEiLganfwOQvoEm4Sl8d9Li67+Eo9eeiwMzBaKBzc2/2RaugdNOJBK5p5VfxaK2a2PqBCqi7Tq+d81ek02Rw84ftH9fea5hNsKxo5MuUryWTRghRKQlKhbA3a18GUx7s/Lxqz9u/GIxO0GF8+cf0uQ963am+4O3+uvJzSpMj0VB5BkF+Jr4OeQCNflbpyYRM0nIK6NnKCqm7pgI4tAKyk8t+3GyClQ/Bzs+g/0Mw5r/g6FL2sU5uqpvrlK/VRbf3e8Lal1Qn2jnbVfpqecIGQ+JRdXENNa/0UGw60cnZHIvPrL3U3eTTcOQn6DW77poNleToAj3vgKO/qLXUhk1vQfQ29Xfn27J2XkMI0aDYV1B6fhe80ValhAjRGJ3eqBp2uAfCoeVqFIAlTIVwYKlqeFFGk49LNE0NNW8zTI2LObW+4vNeiFK3QZ0tW4cQ9sIzGACvggSMBq3RB6W7zqpxKb3Kqye9EAUrH4Gc1LIfL2nTW7B4FrzZUT0n4djlx0wFsOwe2PstXPsUjHjJsrrHyIlw32a18zjlK/XLvZKgstUgdXtmE6A68OabzHy68RQAA2srKN0+VzUg6n137ZzfEr3vBINRBf7WFrsX/nwFIieVn5kjhBBXsa+g9EIUZF1Us7aEaIzWv6YafEz+Agqy4eAPlj3v1J8qzbbLtMqPNTqoL3T+4eqLY+Lx8o+9EAV+bdRcPSEakqJZpVpGHL5ujiRnVaOOuwHZdSYFf3cnwpq4l37wQhR8PR52fwm/PlPxiZJOqvTatiOh82TYtwA+6A3fTlZNi76/HQ4sgetegKHPVq0Rj08oTPpYBUOWaNZVdbE9rS6+dWuhLtgt2hmNv7sTHYK8Lh/7w/1q3TWVmwZ750GnG8GrHsfleAWrXeQ981THX2vJz1YXFdwDYeybMo9UCGEx+wpKs5PUbdRCld4jRGNyZhOc2QgDH1HjAwIi1G6CJfZ/p2batR1h2fEu3nDLd2ocwfwpatxAWaTJkWioPIsChow4fN2cSG3kO6W7zybTo6XvpRmel1zYD99MUBemet4OUQvgyM9ln0TXYdWjqr5w/Hsw4X149CAMeVYFtt/epGraR70Ggx6r9feEwQitBqgMFKCZtytNvZzJLzQzsG0TDIai95p0Ur2vtS+phks1secbNe6l7wM1XLwV9LkP8tLUdypr+eMFSDwGEz+s+ngxIUSjZl9BaVaCuk2PUTs/QjQmf74KHk3VFz9NU6MMzu+AhKMVPy8vU33Ri5xYtWYTvq1g+kJIj4VF00sHpjkpkHpOmhyJhsmzqbrNuICvu1OjbnSUkJHHmaTs0qm7cX/BN+PB0R1uWwmj31Cp/CsfLrtedP9itSt53fOXdqLxCIAhT8GjB2DSXJj2LfS9r/bfVLFWgyDltJp/CpdGwwwML5G6e3iFum3eG1Y9dindt8pMhSp1t+VACO5Wk1VbR/PeENxDrcnSjusVOfEH7PgE+twPbYbW/HxCiEbFzoLSRPAOVTs+++bX92qEqDtnNqtd0gGPgKOruq/LNFWXVNlu6dGfVaqvJam7Vwu9Bm76FGL3wSfXQsyey4/F/aVuZadUNETOnuDkWbRT6tioa0p3n1UBZq+STY7iDqiUXUc3uH0l+IWBgxNM/EhdsPrp8StPkp0Mq5+FkF6qwc/VHJyh683QYVwtvpMyhA1Wt2fUbmnvVn44GDQGtQ24fMyhFWqsyYwl6n1+NxOST1X9tQ6vgLRo6GcDu6RweTxM0nE4tbZm58pOhuVzVAbP8Bessz4hRKNiZ0FpAniFqML5w6vUDz4hGoP1r6oanV53XL7PI1A1LopaVPHc0v3fgXcLCO1bvdfuOAFm/6p+/8X1l7vyXmpy1LV65xXC1hXNKvVzdyIlu/HWlO46k4KTg4FOId7qjvhDaofUwUXtkPq1vnxwUGfVoOjgsitr3v94Qf3MHve2Spu1FYEdwdXvUgrvrH4t+fWRQQR5F3X7TY1W47Q6jFdN4qYvUvcvmKbqQ6ti24fgG6Y+t21F5CSVgbPlPcsb512tMB9+uE+VWN346eULp0IIUQX2FZRmJ6luet1mqJEYB5bW94qEqH1nt6hZegMeLv3DvvtM1fzr+O9lPzfhKJxcB12m1GxAe0gPuGe9qmVd+Tf4cQ5E71AdSj0CKn++EPbIM+hSTWlKVj66rtf3iurFrrMpdG3ujbODUdWFfjdDjZe6fRX4tyn9hIGPqp3FVY9B5kX1GbbnGzVj1NY6dRsMEDZIfcbqOs4ORsIDSzRuO7JK3RaP0vJvA1PnqZ3S7+9QKbmV0XXY9hGc3wl977etoNzBCa65R5VEvdoC3u+tGhVt+1h9xld0wROgIBcW3wrHV8PoVyVzRghRbfYVlGYlqKC0WVdo2snyJi9C2LM/XwX3gLJT3sJHqKvcZf2/kBGnOlq6+avZozXl7q/S1wb/Xb3e4RXyBUQ0bJ7NLu2UFpp1MvIsCEAamNwCEwdj0+jZsqhpTeIxFZBd+1TZASmoDt4TP4b8LFVfuupRla0x5Om6W3hVtBoE6edVbenVDq9Uu6lNwi/fFzYIbngLTq6BX5+uuB6zIAeW36+Oazcaetxm/fXX1MDHYOYy1e3YP1yNAvv1Kfh8BHw8CGJ2l/28/GzVb+DYLzD2f9D7rrpdtxCiQbGfoNRsVjulbk1UHUS3GWoWVvyh+l6ZELXn3DbVGGTAw2UPWTc6qDqsY79CRvzl+/MyVNfc7CSYsRi8Q6yzHoMRhv1DpbC5+kH4cOucVwhbVLRT6uPqCEBKI2x2FBWdSoFJv9zk6MQadRt+XcVPDIyAYc+pmvaEIzD2v+BUxjgZWxB2rbo9veHK+4t3eYt3SUvqcSv0exB2fgof9YP935feNU09p0oeohapDsM3LwBHl9p5DzVhMKi/z2ufVM3tnjgKjx1Wqbi5afDZcPjjRbUrWiwvExZMVZk449+XgFQIUWP2E5TmpIBuVjtGoOpKDQ7S8Eg0bOtfL3+XtFi3maCbVO0oqHSrxbdB/EGY+rVKo7O29qPhyVPyRUQ0bJ7NwJRHoGMWQKOsK911VvVu6FkclJ5cC/5twadF5U/u96DaHex5B7S7vhZXWUNN2qqMk6K60kuO/ATo5TdfGvES3PQ5aAZYdpeat7rnG1VjeXIdzL0Wks+o8VpDnqpZCUVd8wpW37PmbFObAJvegrmD4fwuFah+e5MK2G/8FHrMqu/VCiEaAPv5hMxOVLfuTS7fth+tvohXVvMghD2KP6TSw/rcV/EOQ0A7CO2jUmp1XaXLnVwD496xfC5pdWiaDEYXDZuXmlUaoKvArDHulO46k0ybAHd83Z3UTtmZTdBmmGVPNhjhlkWquZEt0zSVwltUV3rJ4RWqiVPTyLKfZzBA58lw32aYNh+cvWDFQ/BOF/j2RhXo3rPOtgPyyrh4q3myM5eq+aqXUnp3weQvVL8CIYSwAvsJSrOuCkpB7RBlJcDx3+pnTULUpm0fgoNrxbukxbrPhMSjsHiWyh4Y8oxcvRaipjxVUOprUiNRGtusUrNZZ/fZFHoV15NGb4PCHMuDUnsSNlg1jUs8pv6ck6KC1A7jK7/4ZjBAhxvgnj9hxlJVl9l5Ctz1R/l1t/YmfDg8sBW6z1JlIdO+VbOvhRDCSuwoKE1Qt24lgtLw4UVNXiSFVzQwWYlq0HzXm8HNr/LjIyepeYGHV6oA9dqnan+NQjR0nkEAeBWqi6KNbVbpiYRM0nMLL88nPbkWDI6qC3dDEzZI3RbXlR79FcyFZdeTlkfToO1w1ZX4xk/A2cP666xPLt4w/l14OlplqgkhhBXZT1B6dfouqCYvXaapJi+ZF+tnXULUhl1fqLFHfe+37HhnTxj8hMoeuOFtSasVwho8VFDqmnsRo0FrNEFpXqGJlVGxPLPsLwB6tSq6MHZiLbTo2/CCLVDzQ71DLwelh1eAV3M1DktcyZ5qY4UQdsOhvhdgseL0XTf/K+/vPhO2vAt758Ggx+t+XUJYW2Ee7PhUZQIEtLf8efLvXwjrcnQBV1+0zDh83bqRnNWw+xcciUvnu53R/LA3htTsAkJ8XHlhXEfCmrir7t7xf8F1L9T3MmtHcV3psV8gN111Ge41Wy7wCSFEHbGvoNTFB4yOV94f0F79IFnzb0g6CcP/BR4B9bNGIazhwDJV29T3gfpeiRDCsxlkxOHr5kRqA90pTcsp4L55u9l6Kgkno4ERkU25uXcoA9o0wWAoCspOrVO3DbGetFjYYIhaAJvfVpkq5XXdFUIIYXV2FJQmXB4Hc7Xpi2Djf2HL+3B4lZqj2Gu2Su8Vwp7oOmz7AAIiGvaXPyHshWcQZFzA193JLhodmcw6O04nsyIqhi0nk5jaK5QHhrRBK2fHLzu/kNlf7WT/+VSeHRPB5J6h+Lk7lT7w5FrV0yGoSy2/g3pUXFe65X31faNF3/pdjxBCNCL2E7VlJ11ZT1qSswcMfxG63gK//F392vsNjHpVzVIryIXCEr+8QqrXEa8wH46sUs1kwgZBj9tUy3tRMV1X88wuHoJed0o9SkXObIK4v9Q4F0kbE6L+eTaDhKP4BThxKjHz0t26rrPtVDJfbj7N2C7NmNAtpN6WqOs6f8WksWJfLCv3xxKfnoebk5HwQA/eWH2U+PRcXhgXidFw5WdKXqGJe+ftZu+5FD64pQejOzcr+wXMZhWUthnasD+/vZur2tKU0xAxVn6+CyFEHbKfoDQrQQ24rkhAO5i1HA4th9XPwVdjyz+21SDodQdEjAOHMq4Kl5R0EvZ8rbr8ZieqWWQHl8GOz2DUK9D62qq/n7Js+0gFbh5NwT1QpSF7NFUd73LTIDtZBec5RbdtrlNfEmyVqQAOLoet78OFfZfvv+bu+luTrdv2kaqb7jKtvlcihICindI4/Foa2JVVgK7rbDieyPtrj7PzjJpfmpVfWK9B6Vt/HOfdNcdxNGpc2y6Qf4wN5roOgbg4GHnt1yPM3XCKxMw83prWDWcHFWgVmsz8beFeNh5P5L9TupYfkALEH1A/gxtD9kbYYBWUVqXrrhBCiBqzo6A0EVr0q/w4TVPjMcJHqO55uhkcXEr8coLzO2H3V7BktkrR6T5Tzd5ycIHMePXDNzNedfQ9vQFOrwfNqFqg97xDBYKHV8Lv/4RvxkPEDTDyJTVku7oy4mH1s2qsR0G2WndlDq+Cv+21vR21nBTY/TVsnwsZseDfVnWEPbwSfn8ewq+r2X+rhirpJBz9WXXRdXSt79UIIUDtlOomQhyzScnOZ+KHW4iKTqWZtwv/Gh9J1PlU1hy+iK7r5abI1rb951NpE+DOsvsH4O12Zd+FZ8Z0oImHM//382FSsnbyya09cXdy4Mkl+1l9MJ4Xx3Vkcs/mFb/AybXqtjEEpT1vUxlVYYPreyVCCNGo2EdQajYVpe9WoYGRswd0u6Xsx8IGw4BH1A/aXV/C5ndg01tlH+vTAob+QwWuXiWuJEdOhHaj1C7gxjfhgz6q++mQpy1fY0kHlqhA9O51KrU4O7koQL6odkldvNUOmqufuj2wFFY8CLF7batl/dktsGiG2s0Nu1aloYYPVylfbUfCh/1g+Ry4/aeGnQZWHdvngsEBet9V3ysRQhTzVJ/7LZ3TMZl1krPyeOXGztzYIwRnByPzt59l2Z4YzqfkEOrnVi9LjEnJITzQo1RAWuzuwa1p4unE37/fz7S52+gc4s2yvTE8PqIdtw8Iq/wFTq6BwMhLc1sbtJCeasaoEEKIOmUfQWlOCqCXX1NaHQYjtB2hfqXFqF08B2fwCCxKnw1Qv69ox8rRpWg25Az49Wn48xX1A63tiKqvJ2oRBHdXKchQlLpbQRDe4QZY9agKTm0lKI36TgXKPi1h1g8Q3O3Kx71DVLrzjw/Ajrnlz+DMTlYXDCInNZ6anpxU2PstdLqpcXzxE8JeFAWlw5ub+fbOPvRp7Yej8fIFtU7B3gAcjE2rl6BU13ViU3MY2Lbin4+TujfH182JB+bv4dCFdO4Z3JoHh4VX/gL5WXBuG/S510orFkIIIUqzj6C0eEapNYPSkrxDoO991X++VzO48VPVoGb1s9B6SOnRNRW5eBji9sOo1yx/jquv2oE8+AOMeKl+dx11Hf58Fda/qmp1p81T6ytLt1vg0I/wx79UinWTq74Undum0qrTYyDxGAx9tvbXbwv2LYCCrPIDdSFE/Si6SOSSc5GBkaV/BrUP8sRo0DgQk86oThXUZdaS9JxCsvJNhPhUnvI/pH0gi+/tR9T5VG65poVl6cZnNoMpv3Gk7gohhKg39pE/mZWgbt1qKSi1BgcnuP7/VCC187OqPTdqkapZ7XRT1Z7X6UYVvJ3fUbXnWVNhHvxwrwpIu82AmcvKD0hB1b+Oe0f99/rxAZWaDaq74+Z34MsxKqBvPwbWvw4n19XN+6hPZrP6NxPap/TushCifnkEAhpkxJX5sIujkbaBHhyITavbdRWJSc0BINiCoBSgU4g3M/q0tLz+9eRa1W/Bkp4OQgghRDXZR1CaXbxTWoWa0vrQbpS6mvznK5CVZNlzzGb463u161lRum5Z2o9WXxYOLK36Wq0hOxm+mQj7v4Nh/4QJH1TeyRjUzvLoNyB6O2z7UJ1n0XTVBCliLNy7AW76XM3qXHZ3uV8GG4zTf0LySaklFcIWGR3Vz56MC+UeEhnszYGYNHRdr8OFKbFFQaklO6XVcnINtBwgzdeEEELUKvsISms7fddaNA2ufwXyMmHd/1n2nDMb1W5n12qMAHH2hHbXqxReU2HVn18ThXmwYBrE7FYB5OAnqtYFuMtUaD8W1rwEHw+CE2tUoDr1G9XUyckNpnyl6pmW3lX3768u7fxcNa/qOKG+VyKEKEvRWJjydArxIjEzn4sZeXW4KCU2rWo7pVWSGq2yfyR1VwghRC2zr6DU1a9+12GJwAi147X7S4g/WPnx+79Tc0/bj6ne63W6SaU3n91UvedXh67DyodV2vCNc6Hz5KqfQ9Ng3NuqS7LBCHf+Bn3uuTKwDYyAsW+qwH19Fept7UnaeTUGpsetqtGWEML2eDarcKe0U8jlZkd1LSY1BycHA/7uFmSpWCr9Amz/BL6/Tf1ZglIhhBC1zE6C0gQVkBrtoy8TQ55Wu32/Pq0CuPLkZ6umPx3HVz81qu1IcPKAA8uq9/zq2PIeRC2EIc+oDrnV5REIc3bCA9vK7yDcbTp0mwkb3rg8K68h2fWl+jfSa3Z9r0QIUR7PoAqD0g7NvNA0OBCTXoeLUmJTcwn2dsFgqOGM1LQY2PoBfD4S3oyAX/6ufkaNeg0CO1hnsUIIIUQ57CMozU60/dTdktz8YMizcHoDHPmp/OOO/gz5mdDl5uq/lqOr2mU9vAIK86t/HksdW61qPztOgMFP1vx87v4qVbciY95Q9aVL71ZfnBqKwnzY87WqRfZpUd/U4EGRAAAcL0lEQVSrEUKUxytYXRw1FZT5sIezA2FN3DkQU/c7pbGpOWWn7qaeg7+WQEZ85Sc58jO811N1jy/IVrO55+yAOdtUZ/qqlGYIIYQQ1WAfW49Zibbf5OhqvWbDri/gt+fU3NKyUjOjFoF3qGoiUROdboK/FsOpP6HdyJqdqyIXD8OSOyGoM0z8qO7G0BTXl346FN7qCM7e6iKFR6C69W0Fg54AV5+6WY+1HF6hvuhKgyMhbFvx7ODMePBuXuYhnYK92X02pQ4XpcSm5jAgvImqu4/eDsdXw7HfIOGwOsA9ECZ/DmGDyz7Bri/hp8egWVe48bPSY7qEEEKIOmBRVKFp2j81TTuhadpOTdNalbjfW9O0hZqmHSl6LLLo/rs1TYvWNG1f0S9jjVaZlagawdgTowOM+g+knIHl96sOsyVlXlTpqJ2n1Dy4azNMpQtb2oU3Pxu2fggfDVTNija+CWe3QEFO+c/JSoKFN6ud2ekLwcm9ZmuuqsAIuP0nGPYP6Hqz+gJlcIDE4yrl7KfH6nY9lsjPVoF8eXZ+Dr5hUq8lhK3zLJo/Wkmzo5jUHJKz6iBjpUiByUx8ei7X5a+DN1rDV2PU56FHAIz8v6IRXT7wzQRY/4bq9l5M12Hdf2DVI9DmOrhtlQSkQggh6k2lO6WaprUEJgHtgTHAa0Bxq9h8YJ6u69M1TRsHvAhMAfyB53Rd/8Yqq8xKgFYDrXKqOtVmmErjXf+aSuUd/bqqwdQ0lValm1SAVVMOTtBhPBxcDgW54OhS9nF5GSoQ2vq++m8a0hOSTsKxX9XjBkcV7AW0L52uFRulml/c/lO5OwW1LqRH2bWn69+AdS9Du9HQZUrdr+tquWmw49OicTdJ0O9BGP6iGi1RLP4gnNsCI1+uux1nIUT1FO+UVtTsKPhys6NBbesmsyc+PRezDl3T1oCjO4x7F9oMVRcpi929TjWmW/cyRG+DSZ+ox1c9AnvnQfeZcMPbV34+CSGEEHXMkvTdocAvuq6bNE37Bfig+AFd13OAn4v+eAbwLfq9H7DfKis0myAnxb5qSksa8hREjIEVD8GSO2D/Yhj7P9i/CJp1UwGgNXS6UX3BOPE7dBh35WM5KbDjM9j2gfp9m2GqHrRl0TD0rCTVSTd6O5zbrtKAr2ZwgEkfQWhv66zXmgY+qt73T49Di77gE1o/68hKUoHojk8hLw3CR6iZrFvfV6NzJn+p/gyw8zM1Y7bbjPpZqxDCchbslEYWBaUHYtLrLCiNTc0FwCf3PDTvBZETSx/k7AE3fQYt+6vme3MHgX84nF6vfg4MfVZqRoUQQtQ7S4LSQCARQNf1Qk3TjJqmGXVdN1113BRgddHvHYHXNU17AxW0PqlfNVVc07R7gHsAWrSooMlLdjKg219NaUlBneHOP2D7x7D2ZXi/NxRkwahXrfcarQaDWxOVwtthHGQmwJFVcHil+vJhLlQNdQY/Cc17Xvlcd39oP1r9skdGB5g0Fz4eCD/cB7etUGNm6oqpUM2l3f6xSoHuMA4GPQ7B3dTjYdeqixJzB8PkL9RudNR3qhbYzQ7GHAnR2Lk1Ac1Y4U6pt5sjzX1d63QsTGxqDgbMuGZGg9/48g/UNOh9p8o0WXybGrN1w1vS9VsIIYTNsCQovXqmSalLqpqmdQcmAn2K7npM13Vd0zRXYCkqYF18xUl1/RPgE4BevXqVPzclK0Hd2ltN6dWMDtD/QYgYC6sehZg9Kiix5vkjJ8Le+fDlGDi3FXSzqlnsN0fVrgZ1tt7r2Rq/MBj9Gvw4R+1MDni4bl63MA+WzFYXADpPhcFPlN797jwZmkbCd7Pgm/Gq4UhBljQ4EsJeGAxFY2HK3ykFlcJ7MLbuxsLEpObQjCQ0cwH4ta78CcHd4f7NqhQjoF3tL1AIIYSwkCVB6QWgA4CmaQ6AXnKXVNO0JsBXwPSidF6Kd0V1Xc/RNG0l0LHaK8xOVLf2vFNakl8YzPoBTPlld+Stia63qI6/OalqR7TDOBUMNZbUrG4zVH3smpdUinJtB+F5mfDdDJXuPOo1NTqhPIEd4J518OODcGg5BJdTHyuEsE2VzCoF1ezo14NxZOQW4OlS+zWaMak5dHZLAjPqZ4slnD0hwLNW1yWEEEJUlSUdVv4ARhV10B0DbNQ07Q1N00YWBamLgGd0XT9Y/ARN05oV3RqAYcC+aq+weKfUXmtKy6Jp1g9IQaXlPhMDD2yBoc9AUKfGE5CCeq83vKNSYpfeXXE34ZrKToZ5E1UDq4kfVRyQFnP2VKNtJn8JEz+svbUJIazPs5naYSyWFgOb3lJp+ds+BiAyRNWVHqqj3dLY1Bw6uSapP1iyUyqEEELYqEqDUl3X44AvgaPAS8CTQEtUM6MpQD/gPyXGv/gAz2madgDYC5wDfqz2CrOKfuA2lJ3S2ubkVt8rqF/u/irgSzisBsHr5WeGV1tGPHx1A1yIgqnfQLdbLH+upqmmVIEdrL8uIUTt8WwG6bGqHvybCfBWJPzxIiSdgi3vgtl8qQPvgToMSts5JIDRGTyD6+Q1hRBCiNpgSfouuq6/D7xf4q6pJX6/sIynPFiTRV0hKwHQwNW30kOFACB8OPR/CLa8B85eahyLtXaMU8/B1+PVnNlbFqvxC0KIhs8zSHXV/uEe8GkB1z4JXabBhX2qrvzsZgLCBtHUy5mDMbXf7EjXdWJScgj1i1OpuzJaSgghhB2zKCitV9mJKh2zLrupCvs3/N+QnwWb3wbNANc9X/PA1FQI39+uUndv/dE2x+MIIWpH5ylq1nO76yG07+Ug0LMZOHnC/u8gbBCdgr05UAcdeNNzC8nKN9G08AI0bVvrryeEEELUJtu/tJqVIKm7ouoMBhjzP+h5O2x6E9a+VPNU3s1vq3mj496SgFSIxsa3JYz4l5r3WXJX0slNNZU79CMU5BIZ4s2Ji5nk5F89Nc26YlNzAB3vnGjVZV0IIYSwY3YQlCapGXFCVJXBAGPfgh63wcb/qVmi1Q1M4w7An69Cx4nWHeUjhLB/XaZCXjoc+5XIYC/MOhyJq9260tjUHAJJxWjKtbzzrhBCCGGj7CAoTWhYnXdF3TIY4Ia3ofss2PAG/PlK1c9RmA8/3AeuPjD2TeuvUQhh38IGg0cQ7F9Mp5C6aXYUm5pDK61obqp03hVCCGHnbD8ozU6UoFTUjMEA496F7jNh/WuwZ17Vnr/hdYj/S53D3b921iiEsF8GI3SeDMd/I9gxG183x1pvdhSTmksb40X1BwlKhRBC2DnbDkpNhZCTIjWlouYMBhj3HrQcCKufg4w4y54Xsxs2vgldb4GIMbW7RiGE/eoyDcwFaIeW0ymk9psdxabm0NE1CQwO4B1aq68lhBBC1DbbDkqzi2aUusnulLACgwHGvQOFufDzE5UfX5ADP9yvRkGMqkbarxCi8QjqDAEdYP9iIoO9ORqXQX6hudZeLjY1h3DjRTWexmj7jfSFEEKIith4UJqobmWnVFhLk3AY8jQcXgmHVlR87NqXIfEojH9P1ZMKIUR5NE01PIreRh+fdApMOgdrcbc0NjWH5sRJ6q4QQogGwbaD0qwEdSs1pcKa+j+kdjV+fkKlh19N11VTpK3vQ6/ZEH5d3a9RCGF/Ok8BoE/mGgC2nEyqlZcpNJmJS88hsCBGglIhhBANgo0HpbJTKmqB0RHGv6/+ff32zysfK8yHHx9Uu6Sdp8KoV+tnjUII++MTCi0H4HZkKRFNPdhyMrFWXiYuPRcfPQNnU5YEpUIIIRoE+whKZU6psLbgbtD/Qdg7D06tV/flpML8m2Dft3Dt03DjJ+DgXL/rFELYly5TIek4k4MT2XUmhdwCk9VfIjY1l5ZavPqDBKVCCCEaANsOSrMTQTOAq299r0Q0REOeAd8wWPk3uHgYPh8JZ7fCxI9h6DOqRkwIIaqi4wQwOnG9aT15hWb2nC2jRKCGYlNzLgelvmFWP78QQghR12w7KM1KUJ13Dba9TGGnHF1h/LuQcgY+GgCZ8XDrcug2vb5XJoSwV66+0O56Qs7/jLPBzOZaSOGNSc2hlSEOHQ18W1r9/EIIIURds+1oLytRUndF7QobDH3nQJO2cNcf0Gpgfa9ICGHvOk/BkJ3AtKYxbD5h/WZHsak5tHNMQPMOlRIDIYQQDYJtDzfLSpTOu6L2jfqP6rgr6bpCCGtoPRQMjoxzPcC3p0NJzy3Ay8XRaqePTc2htfEi+EnqrhBCiIbBtndKsyUoFXVEAlIhhLW4eEGLvkRmb8esw/ZTyVY9fWxqLiHmOAlKhRBCNBi2HZRmJco4GCGEEPan7UjcUo/RyjGZzSesW1eakZqIpzlNOu8KIYRoMGw3KDUVQG6q1JQKIYSwP21HAHBrk+NWnVeanluAX36M+oMEpUIIIRoI2w1Ks4uaQ0j6rhBCCHsTEAHeoQw17udYfCYXM3KtctrY1BxayYxSIYQQDYztBqVZCepWglIhhBD2RtMgfDgt0nbgSCFbT1qnC++VM0pbWeWcQgghRH2z4aC0KN1JakqFEELYo7YjMRZkMcTlhNXqSmNSc2mpxWNybwpO7lY5pxBCCFHfbD8olZpSIYQQ9ihsMBidmOp9mM0nktB1vcanjE3NIcwQj8FfUneFEEI0HLYblGYX75RKUCqEEMIOOXtAy/70LtxNTGoO55Kza3zK2NQcwozxaH5trLBAIYQQwjbYblCalQCaEVx86nslQgghRPWEj8An6xQhJLD5RM3rShOTU2iip8iMUiGEEA2KDQelieDmDwbbXaIQQghRobYjARjvfojNVhgNY0g5rX4jnXeFEEI0ILYR8ZkKSt+XlShNjoQQQti3Jm3BpwVjXf9i68kkzOYr60pzC0zsi061qN600GTGPTta/UF2SoUQQjQgDvW9AADiD8CbkRDaG0L7QOg1kBkP7v71vTIhhBCi+jQN2o4kYs98MrOyOBKXQYdmnhyISef73dH8uC+WtJwCpvZqzis3dsFo0Mo91f6YNFoQp/7gK0GpEEKIhsM2glLv5tCiD0TvgIM/XL4/8sb6W5MQQghhDW1H4rDzM64xHOGN1cFcSMvlSFwGTg4Gro8Mws/Nka+3niUjt5C3b+6Gs4Ox1CnWHb3IQwv28pJzIiZnP4yu0m9BCCFEw2EbQal7AEz+Qv0+LQbO74CY3RAxrn7XJYQQQtRUq0FgdGaSxyEeP9qZrqE+vDyxE+O6BuPt6ghAC393Xlp1iMyvdzF3Vk/cnC7/eP56yxn+tfIgHZp5McY9B6MunXeFEEI0LLYRlJbkHQLekyByUn2vRAghhKg5JzdoNZDxyQfoeteHhAd6lDrkzoFheLo48PTS/cz6fAdf3N4bD2cHXlp1iK+2nGF4h6a8c3M3nD88Cy3718ObEEIIIWqP7QWlQgghREPTdgSOvz5NuEMCUDooBZjaKxQvFwceWriXmz/ZRpCXM+uOJnDXwDCeGdMBozkf0s5LPakQQogGxza67wohhBANWdFoGE78UeFhozo14/PbenMmMYsNxxP5v0md+McNHVVA+vsLgA4B7Wp/vUKI/2/v3oPtKs86jn9/nJByFQkJlwolQyA11GIL4Sa13KwFKrQ6Mi0D2AKdXhSd6dTOtNILIjq1F2e8QG0somOVjkNrgQoiVCnUC5AUUkJoBEZsh4sJtZTbcGny+Mdax3OyPUlOcvbeK9nn+5nZk33e9a61n5V5Zu317Pdda0kaIkdKJUkatH0WwT6HwT9+FP7zG80lKotPg7m7/7+ub1y8gOsuPoEXXl7PEQf+OKxbA1++CJ64D45+Nyw5q4MdkCRpcCxKJUkahnO+BHctg9VfhQdugDm7wuI3w2veBgcdBz92wP91XbzfnlAFd18FN1/SXJd6zpfg1ad3uAOSJA1GpvPA7kFbunRpLV++vOswJEkavA3r4bv/Bqu+Aquvg+efbNr32A8O+Onmtf8RsPIaWHMjLDoF3vY52HP/buOWJGkGkqyoqqVTLXOkVJKkYdppDBa+oXmd/qnmEWiP3QOPr4TH722uO60NMDYX3vx7cOz7YSdvASFJGl0WpZIkdWVsDrzq2OY17qXn4b/vhz0WwN4LOwtNkqRhsSiVJGl7Mnc3OOjorqOQJGlonA8kSZIkSeqMRakkSZIkqTMWpZIkSZKkzliUSpIkSZI6Y1EqSZIkSeqMRakkSZIkqTMWpZIkSZKkzliUSpIkSZI6Y1EqSZIkSeqMRakkSZIkqTMWpZIkSZKkzliUSpIkSZI6Y1EqSZIkSeqMRakkSZIkqTMWpZIkSZKkzkyrKE3ysSQPJbk7ycKeZRcleTDJqiRHtW3zktyW5OEkV/Y/bEmSJEnSKNhiUZrkYOAXgVcDlwG/P2nZrsCHgdcD5wBXtIsuBq6vqkXAfklO73PckiRJkqQRMJ2R0pOBm6pqPXATcPykZccCd1XVs1V1HzA/yW7AKcANbZ+vAif1L2RJkiRJ0qiYTlG6L/AkQFX9CBhLMta7rLUO2K+nfS2wf+9Gk7wnyfIky9etW7eN4UuSJEmSdmTTKUqr5+9sZtn48s2t06xYtayqllbV0gULFkwjDEmSJEnSqJkzjT6PA0sAkswBqp3KO75s/qS+C2hGS8fbf0Azarp2cx+wYsWKJ5P819aFPiPz2XiEdzbbC/hh10FsB8yJCeZEw5yYYE40zIkJ5kTDnJhgTjTMiQnmxATzonHYphZMpyi9FfhAko8DZwB3JPk0cAtwG7AsyR7AImBdVT2T5GbgLOCzNDdJWra5D6iqoQ6VJlleVUuH+ZnbqyTLquo9XcfRNXNigjnRMCcmmBMNc2KCOdEwJyaYEw1zYoI5McG8aCTZZE24xaK0qp5IcjWwBngOOBP4DDCvql5K8lHgHmAD8I52tT8BvpLkV4FbquofZrgPGpwbttxFs4w5oV7mhHqZE+plTqiXOaFem8yJVE11Weho89cK9TIn1MucUC9zQr3MCfUyJzQV82LLpnOjo1G02enEmpXMCfUyJ9TLnFAvc0K9zAlNxbzYglk5UipJkiRJ2j7M1pFSSZIkSdJ2YKSK0iQHJrkxyeoktyd5ZZJDkqxI8lCSSyb1/VjbdneShW3bsUnuTbIyycVd7Yf6ZytzYl6SO5Jc2rON85K8OPTgNRAzyYkkOyW5sl13dZLLOtsR9c1MjxNJfjfJw+33x82d7IT6aobHiX3aXBh/PTq5v3ZMfThOeI45YqabE1P1m7QNzzFbIzV9N8kCYElV3Z7k14FXAfsAXwOuA/4VuIDmLsJ/BxxN85ib86rq7UnuBt4N3E/zKJyLqurh4e+J+mUrcuJB4JvAKuB7VXVpu/75NI83Oq6qDhr+Hqjf+pATr62q+5LsDDwAnFhVjw5/T9QvfciJPwX+pqpu7yB8DcBMc6JnW9cCn6yq5UMKXwPQh+OE55gjZityYl1vv6r6kOeYGxupkdKqWjfppOARYG/gRODGqlpPkyQnAicDN7VtNwHHt+scXFUrq+pHNLcsPm2Y8av/ppsTVfUyzeOO7ujZxI00jzpaP5yINWgzzYmquq99+0rgReD7w4hbg9OH48Q8fCj6SOlDTgCQZD6wyIJ0x9eHnPAcc8RsRU5M1Q88x9zISBWlPc4GbgZ2r6oX2ra1wP7AvrQnEO3BYSzJGLA2yVFJ5gKnMpE0Gg2bywmqam3vClX1/fbAotG01TmRZG6SVcC3gI9MWk+jYatzAtgZ+GKSVUk+OJwwNUTbkhPjzgOuGWx46sC25ITnmKNtszkxRT/PMXuMZFGa5AyaIfRrgd75ydlEG8B7gc8B/wz8D800X42AaeSEZpltzYmqeqmqfgp4DXB5kkWDi1LDNIPjxC9V1ZHAG4BzkxwzoBA1ZH347vgV4Iv9jkvdmUFOeI45oqabEz391GPkitIkhwKfBM6p5oLZZ5Ls0i7el+ZXi8eB+W3/OUBV1fqq+peqOqaqTgDGaK4H0A5umjmhWaQfOVFVT9BMzzpyYIFqaGaSE21/quop4Bbg8AGHqyGY6XEiyVJgbVU9NthINSwzPE54jjmCppsTU/RTj5EqSpPsSTNN5p1V9XjbfAvwlnZ67pk0F5ffCpzWtp1Bz7z/JMcCrwduG1LoGpCtyAnNEjPJiSS7Jzlw/D1wAs1NK7QDm+lxIskB7b9zgZ8FVg42Yg1an747LgT+cnBRapj6dT7hOebomG5ObKKfeszpOoA+uxg4FLg6CcDzwFtpLij/NPDnVfUdgCRXA2topk+c2badC/wm8DTwjvZide3Ypp0TmjVmkhO7ANe2BekG4PNVtXrwIWvAZnqcuLKdxl3AX1XVPQOOV4M3o5xoR0rOALzGeHTMNCc8xxw908qJJB/p7VdVP9NNyNuvkXokjCRJkiRpxzJS03clSZIkSTsWi1JJkiRJUmcsSiVJkiRJnbEolSRJkiR1xqJUkiRJktQZi1JJkiRJUmcsSiVJGqAkb0pyfp+3+QtJ/mILfc7s52dKkjQoc7oOQJKkUVZVtwz7M5MsAd5J8xB3SZK2a46USpJmpSQ7J7k+yeokleSxJCuSfCLJt5Ps0o5IfivJyiTva9c7Kcm1k7bz5KT2Lyf5epLvJLmwbX9Xks+0729Lcnm7/buTLGjbL23jeCHJU0lOnSLehUn+PckK4O2T2n85yV1JHkjy8bb5KuDnktybZCzJWe2+rUzylgH9l0qStE0sSiVJs9XPA89U1eHANcD5wDzge1V1BM135B8ApwJHAxclOWQL2zwOOAs4Brgkye5T9Hm63f43gbOT7Am8C3gt8BvAH1fV16dY79J22VHA85Pav1ZVxwBHAO9NshPwW8CtVfU6YGfgw8Dx7b780Rb2QZKkobIolSTNVhuAXdv3uwCvAAJc37YtBh6qqh9U1UvAbTRF5+asqKrnquppYA1w2BR9xgvOh4EF7WeGpngcj2MqRwLjU4EnTwk+KcldwApgPrBbz3o/CRwO3AXcCuyyiWJZkqROWJRKkmarm4E9kzwA/LD9G+CF9t+iKRZ7Vc/fu056P/l7dap1AV6c3KctYK8C/gM4E/jDLYe+UQzLgAvb0dcnN9H/zqp6Xfv6iap6bhqfIUnSUFiUSpJmqzFgp6paUlUXVNX6nuVrgIVJ9k6yM3AicCdN4XdIGsex8cjk0Un2SrIXzQjlg9OM5XhgSVW9qaoe3USfe4BT2vdvnNT+CuCh9uZGB7RtzwF7TNqPQ5PsB5Bkn2nGJEnSUHj3XUnSbBWa4vJR4BngU5MXVtULST4A/FPb9wtV9TBAkjXAAzRTZu+ftNoamum/+wO/U1XPJZsaMN3IU8AjSZ4F/r6qLp6iz6XA3yb5BLAcGC+ir2g/907gG23bt4F57bTek4H3AzcmGQNWAedNJyhJkoYhVb2zkCRJGn1J/ozmJkHXJZkH3FNVB89geycBv1ZVZ2/leufSXC/6IZofi28CPlhV925rLJIk7UicvitJmq0WA4+07+cBz/Zhm9MaFp0iju9W1QaaonQ+8Nb2cS7jr8v6EJskSdslp+9Kkmary4G/TvIy8BLwvo7i+ALNtNwLaH4svqKqPg/8dkfxSJI0VE7flSRJkiR1xum7kiRJkqTOWJRKkiRJkjpjUSpJkiRJ6oxFqSRJkiSpMxalkiRJkqTOWJRKkiRJkjrzv3M8qUctz7LWAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sample_id = np.random.choice(ret_df['secID'].unique(),1)\n", "display(ret_df.loc[ret_df['secID'].isin(sample_id),['grouping_date','bm','bm_uqer']])\n", "ret_df.loc[ret_df['secID'].isin(sample_id),['grouping_date','bm','bm_uqer']].set_index('grouping_date').plot()" ] }, { "cell_type": "code", "execution_count": 162, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([,\n", " ],\n", " dtype=object)" ] }, "execution_count": 162, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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p1p2p3p4p5p6p7p8p9p10p10-p1
mean0.0026060.0084760.0094600.0100430.0125050.0120730.0123160.0123820.012270.0110690.008462
t-value0.3587231.1277171.2160451.3553131.6101751.6194781.6196581.6509431.638341.4946792.125298
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" ], "text/plain": [ " p1 p2 p3 p4 p5 p6 p7 \\\n", "mean 0.002606 0.008476 0.009460 0.010043 0.012505 0.012073 0.012316 \n", "t-value 0.358723 1.127717 1.216045 1.355313 1.610175 1.619478 1.619658 \n", "\n", " p8 p9 p10 p10-p1 \n", "mean 0.012382 0.01227 0.011069 0.008462 \n", "t-value 1.650943 1.63834 1.494679 2.125298 " ] }, "execution_count": 163, "metadata": {}, "output_type": "execute_result" } ], "source": [ "q = dict()\n", "keys = ['q'+str(i) for i in range(1, 10)]\n", "values = np.arange(0.1, 1.0, 0.1)\n", "q.update(zip(keys,values))\n", "\n", "quantile_df = pd.DataFrame()\n", "for key, value in q.items():\n", " quantile_df[key] = ret_df.groupby(['grouping_date'])['bm_uqer'].quantile(value)\n", "\n", "ret_df_q = pd.merge(ret_df, quantile_df, on='grouping_date')\n", "\n", "portfolios = dict()\n", "drop_cols = [col for col in ret_df_q.columns if col[0]=='q']\n", "\n", "portfolios['p1'] = ret_df_q.loc[ret_df_q['bm_uqer'] <= ret_df_q['q1']].copy().drop(drop_cols, axis=1)\n", "for i in range(2,10):\n", " idx = (ret_df_q[f'q{i-1}'] <= ret_df_q['bm_uqer']) & (ret_df_q['bm_uqer'] <= ret_df_q[f'q{i}'])\n", " portfolios[f'p{i}'] = ret_df_q.loc[idx].copy().drop(drop_cols, axis=1)\n", "portfolios['p10'] = ret_df_q.loc[ret_df_q['bm_uqer'] >= ret_df_q['q9']].copy().drop(drop_cols, axis=1)\n", "\n", "portfolios_crs_mean = dict()\n", "for k in portfolios.keys():\n", " portfolios_crs_mean[k] = portfolios[k].groupby(['ret_date'])['exret'].mean()\n", "\n", "mean_values = {}\n", "t_values = {}\n", "for k in portfolios_crs_mean.keys():\n", " y = portfolios_crs_mean[k]\n", " const = np.full(shape=len(y),fill_value=1)\n", " reg = sm.OLS(y, const).fit().get_robustcov_results(cov_type='HAC', maxlags=6)\n", " mean_values[k] = reg.params[0]\n", " t_values[k] = reg.tvalues[0]\n", "# Portfolio 10-1\n", "y = portfolios_crs_mean['p10'] - portfolios_crs_mean['p1']\n", "const = np.full(shape=len(y), fill_value=1)\n", "reg = sm.OLS(y, const).fit().get_robustcov_results(cov_type='HAC', maxlags=6)\n", "mean_values['p10-p1'] = reg.params[0]\n", "t_values['p10-p1'] = reg.tvalues[0]\n", "\n", "pd.DataFrame([mean_values.values(),t_values.values()],index=['mean','t-value'],\n", " columns=mean_values.keys())" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "# Double Sorting on Size and BM" ] }, { "cell_type": "code", "execution_count": 164, "metadata": { "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(178,)\n", "(178,)\n", "(178,)\n", "(178,)\n", "(178,)\n", "(178,)\n" ] }, { "data": { "text/html": [ "
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bm1_size1bm1_size2bm2_size1bm2_size2bm3_size1bm3_size2
ret_mean0.0095980.0034810.0158410.0052170.0207670.004902
t_values1.2321530.4939112.0286350.7207032.4415800.685649
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" ], "text/plain": [ " bm1_size1 bm1_size2 bm2_size1 bm2_size2 bm3_size1 bm3_size2\n", "ret_mean 0.009598 0.003481 0.015841 0.005217 0.020767 0.004902\n", "t_values 1.232153 0.493911 2.028635 0.720703 2.441580 0.685649" ] }, "execution_count": 164, "metadata": {}, "output_type": "execute_result" } ], "source": [ "q_size = dict()\n", "keys = ['q_size_1']\n", "values = [0.5]\n", "q_size.update(zip(keys,values))\n", "\n", "q_bm = dict()\n", "keys = ['q_bm_1','q_bm_2']\n", "values = [0.3, 0.7]\n", "q_bm.update(zip(keys,values))\n", "\n", "q_size_df = pd.DataFrame()\n", "for key, value in q_size.items():\n", " q_size_df[key] = ret_df.groupby(['grouping_date'])['size'].quantile(value)\n", "\n", "q_bm_df = pd.DataFrame()\n", "for key, value in q_bm.items():\n", " q_bm_df[key] = ret_df.groupby(['grouping_date'])['bm'].quantile(value)\n", "\n", "ret_df_q = pd.merge(ret_df, q_size_df, on='grouping_date')\n", "ret_df_q = pd.merge(ret_df_q, q_bm_df, on='grouping_date')\n", "\n", "portfolios_size = dict()\n", "portfolios_size['size1'] = ret_df_q.loc[ret_df_q['size'] <= ret_df_q['q_size_1'],\n", " ['secID','grouping_date','ret_date','exret','size','mktcap']]\n", "portfolios_size['size2'] = ret_df_q.loc[ret_df_q['size'] >= ret_df_q['q_size_1'],\n", " ['secID','grouping_date','ret_date','exret','size','mktcap']]\n", "\n", "portfolios_bm = dict()\n", "portfolios_bm['bm1'] = ret_df_q.loc[ret_df_q['bm'] <= ret_df_q['q_bm_1'],\n", " ['secID','grouping_date','ret_date','exret','bm']]\n", "portfolios_bm['bm2'] = ret_df_q.loc[(ret_df_q['bm'] >= ret_df_q['q_bm_1']) & \\\n", " (ret_df_q['bm'] <= ret_df_q['q_bm_2']),\n", " ['secID','grouping_date','ret_date','exret','bm']]\n", "portfolios_bm['bm3'] = ret_df_q.loc[ret_df_q['bm'] >= ret_df_q['q_bm_2'],\n", " ['secID','grouping_date','ret_date','exret','bm']]\n", "\n", "portfolios = dict()\n", "for bm_group in portfolios_bm.keys():\n", " for size_group in portfolios_size.keys():\n", " portfolios[f'{bm_group}_{size_group}'] = pd.merge(portfolios_size[size_group],\n", " portfolios_bm[bm_group][['secID','ret_date','bm']],\n", " on=['secID','ret_date'])\n", "\n", "mean_portfolios_ret = dict()\n", "for pf in portfolios.keys():\n", " mean_portfolios_ret[pf] = portfolios[pf].groupby('ret_date')['exret'].mean()\n", " print(mean_portfolios_ret[pf].shape) # print 看一下会不会存在某个月份上没有bm和size分组没有任何交叉\n", "\n", "# Fast merge by stacking\n", "mean_portfolios_ret_df = pd.DataFrame(np.vstack([pf for pf in mean_portfolios_ret.values()])).T\n", "mean_portfolios_ret_df.columns = mean_portfolios_ret.keys()\n", "mean_portfolios_ret_df.index = mean_portfolios_ret['bm1_size1'].index\n", "\n", "# Newey-West adjustment\n", "mean_values = {}\n", "t_values = {}\n", "for k in mean_portfolios_ret.keys():\n", " y = mean_portfolios_ret[k]\n", " const = np.full(shape=len(y),fill_value=1)\n", " reg = sm.OLS(y, const).fit().get_robustcov_results(cov_type='HAC', maxlags=4)\n", " mean_values[k] = reg.params[0]\n", " t_values[k] = reg.tvalues[0]\n", "\n", "pd.DataFrame([mean_values.values(),t_values.values()],index=['ret_mean','t_values'],columns=mean_values.keys())" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "# Fama MacBeth regression" ] }, { "cell_type": "code", "execution_count": 165, "metadata": { "editable": true }, "outputs": [], "source": [ "# ret_df[(ret_df['ret_date'] >= '2008-02') & (ret_df['secID'] == '000001.XSHE')]" ] }, { "cell_type": "code", "execution_count": 166, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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interceptbeta_coefsize_coefbm_coef
ret_mean9.3816650.364258-0.4036360.035598
t_values2.4606390.962976-2.4942570.607668
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" ], "text/plain": [ " intercept beta_coef size_coef bm_coef\n", "ret_mean 9.381665 0.364258 -0.403636 0.035598\n", "t_values 2.460639 0.962976 -2.494257 0.607668" ] }, "execution_count": 166, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ret_df['exret100'] = ret_df['exret'] * 100\n", "\n", "def fm_reg(df):\n", " df_ = df.dropna()\n", " if df_.shape[0] < 15:\n", " return None\n", " reg = LinearRegression().fit(y=df_.loc[:,'exret100'], X=df_.loc[:,['beta','size','bm']])\n", " return np.insert(reg.coef_, 0, reg.intercept_)\n", "\n", "temp = ret_df.groupby('ret_date').apply(fm_reg)\n", "reg_result_df = pd.DataFrame(temp.values.tolist())\n", "reg_result_df.index=temp.index\n", "reg_result_df.columns = ['intercept', 'beta_coef','size_coef', 'bm_coef']\n", "# Mean of coefs with NW adjustment\n", "mean_values = {}\n", "t_values = {}\n", "for k in reg_result_df.columns:\n", " y = reg_result_df[k]\n", " const = np.full(shape=len(y),fill_value=1)\n", " reg = sm.OLS(y, const).fit().get_robustcov_results(cov_type='HAC', maxlags=6)\n", " mean_values[k] = reg.params[0]\n", " t_values[k] = reg.tvalues[0]\n", "pd.DataFrame([mean_values.values(),t_values.values()],index=['ret_mean','t_values'],columns=mean_values.keys())" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "# Fama French 3 factors" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "Fama-French 3 factors 的构建：\n", "\n", "- Market return 就是 CAPM 里面的市场收益率\n", "- 另外还有 SMB，HML，也即 Small-Minus-Big, High-Minus-Low\n", "\n", "构建方法：\n", "\n", "- mktcap1 也叫做 Small, mktcap2 Big. bm1 Low, bm2 Medium, bm3 High. \n", "- 因此对应的，我们的\n", " - bm1_mktcap1: SL\n", " - bm2_mktcap1: SM\n", " - bm3_mktcap1: SH\n", " - bm1_mktcap2: BL\n", " - bm2_mktcap2: BM\n", " - bm3_mktcap2: BH\n", "- 在 Fama French (1993) 的构建里，mktcap 是在t年6月形成并保持到t+1年5月不变。bm和我们这里的构建一样，t年6月按照t-1年的BM ratio构建，保持到t+1年5月不变。\n", "- Fama French 计算了这6组资产组合每一年从7月到下一年6月(资产形成期的第二个月的收益率)的 value-weighted excess return。weight 是t年6月的mktcap占所在portfolio 总的 mktcap 的比重。\n", "- SMB: (SL+SM+SH)/3 - (BL+BM+BH)/3。这样构建的意思是把BM的影响平均掉。\n", "- HML: (SH+BH)/2 - (SL+BL)/2\n", "\n", "这里我们还是按照mktcap组合的构建日期，不改成和 Fama-French (1993) 原文一样的日期(t年6月)" ] }, { "cell_type": "code", "execution_count": 167, "metadata": { "editable": true }, "outputs": [], "source": [ "portfolios_vwret = {}\n", "for pf in portfolios.keys():\n", " temp = portfolios[pf].groupby('ret_date')['mktcap'].agg({'mktcap_sum':np.sum})\n", " portfolios[pf] = pd.merge(portfolios[pf], temp, on='ret_date')\n", " portfolios[pf]['weight'] = portfolios[pf]['mktcap'] / portfolios[pf]['mktcap_sum']\n", " portfolios[pf]['weighted_exret'] = portfolios[pf]['exret'] * portfolios[pf]['weight']\n", " portfolios_vwret[pf] = portfolios[pf].groupby('ret_date')['weighted_exret'].sum()\n", "\n", "portfolios_vwret_df = pd.DataFrame(np.vstack([pf for pf in portfolios_vwret.values()])).T\n", "portfolios_vwret_df.index = portfolios_vwret['bm1_size1'].index\n", "portfolios_vwret_df.columns = portfolios_vwret.keys()\n", "portfolios_vwret_df.rename(columns={\"bm1_size1\": \"SL\",\n", " \"bm2_size1\": \"SM\",\n", " \"bm3_size1\": \"SH\",\n", " \"bm1_size2\": \"BL\",\n", " \"bm2_size2\": \"BM\",\n", " \"bm3_size2\": \"BH\"},\n", " inplace=True)" ] }, { "cell_type": "code", "execution_count": 168, "metadata": { "editable": true }, "outputs": [], "source": [ "SMB = (portfolios_vwret_df['SL'] + portfolios_vwret_df['SM'] + portfolios_vwret_df['SH']) / 3 - \\\n", " (portfolios_vwret_df['BL'] + portfolios_vwret_df['BM'] + portfolios_vwret_df['BH']) / 3 \n", "\n", "HML = (portfolios_vwret_df['SH'] + portfolios_vwret_df['BH']) / 2 - \\\n", " (portfolios_vwret_df['SL'] + portfolios_vwret_df['BL']) / 2 \n", "\n", "factors_df = pd.DataFrame(np.vstack([SMB,HML])).T\n", "factors_df.columns = ['SMB','HML']\n", "factors_df.index = SMB.index" ] }, { "cell_type": "code", "execution_count": 169, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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SMBHML
ret_date
2007-05-0.031591-0.021360
2007-06-0.117856-0.000611
2007-070.0713170.021021
2007-08-0.0654460.030286
2007-09-0.0209480.048817
2007-10-0.1105600.005402
2007-110.1139380.017011
2007-120.065398-0.022715
2008-010.064591-0.012904
2008-020.0849020.007286
.........
2021-050.027934-0.018596
2021-060.028139-0.032799
2021-070.043955-0.032335
2021-080.0109600.052207
2021-09-0.0299210.046108
2021-10-0.014233-0.053258
2021-110.123285-0.031466
2021-120.0155070.051330
2022-01-0.0037980.069385
2022-020.023980-0.009554
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178 rows × 2 columns

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" ], "text/plain": [ " SMB HML\n", "ret_date \n", "2007-05 -0.031591 -0.021360\n", "2007-06 -0.117856 -0.000611\n", "2007-07 0.071317 0.021021\n", "2007-08 -0.065446 0.030286\n", "2007-09 -0.020948 0.048817\n", "2007-10 -0.110560 0.005402\n", "2007-11 0.113938 0.017011\n", "2007-12 0.065398 -0.022715\n", "2008-01 0.064591 -0.012904\n", "2008-02 0.084902 0.007286\n", "... ... ...\n", "2021-05 0.027934 -0.018596\n", "2021-06 0.028139 -0.032799\n", "2021-07 0.043955 -0.032335\n", "2021-08 0.010960 0.052207\n", "2021-09 -0.029921 0.046108\n", "2021-10 -0.014233 -0.053258\n", "2021-11 0.123285 -0.031466\n", "2021-12 0.015507 0.051330\n", "2022-01 -0.003798 0.069385\n", "2022-02 0.023980 -0.009554\n", "\n", "[178 rows x 2 columns]" ] }, "execution_count": 169, "metadata": {}, "output_type": "execute_result" } ], "source": [ "factors_df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "百度百科：中证800指数是由中证指数有限公司编制，其成份股是由中证500和沪深300成份股一起构成，中证800指数综合反映沪深证券市场内大中小市值公司的整体状况。" ] }, { "cell_type": "code", "execution_count": 170, "metadata": { "editable": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 170, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# index_info = DataAPI.SecIDGet(assetClass=\"IDX\",pandas=\"1\")\n", "\n", "# 用中证800作为market return\n", "sec_id = ['000906.ZICN']\n", "index_df = DataAPI.MktIdxdGet(indexID=sec_id,beginDate=START,endDate=END,field=['indexID','secShortName','tradeDate','closeIndex','CHGPct'],pandas=\"1\")\n", "index_df['tradeDate'] = pd.to_datetime(index_df['tradeDate'])\n", "index_df['ret_date'] = index_df['tradeDate'].dt.to_period('M')\n", "\n", "index_df.sort_values('tradeDate',inplace=True)\n", "index_df = index_df.groupby('ret_date',as_index=False).last()\n", "index_df['mktret'] = index_df['closeIndex'] / index_df['closeIndex'].shift() - 1\n", "\n", "index_df = pd.merge(index_df,rf,left_on=['ret_date'],right_on=['ym'])\n", "index_df['exmktret'] = index_df['mktret'] - index_df['rf']\n", "\n", "index_df.drop(['ym','mktret','rf','indexID','secShortName','tradeDate',\n", " 'closeIndex','CHGPct'],axis=1,inplace=True)\n", "\n", "index_df.dropna(inplace=True)\n", "\n", "factors_df = pd.merge(index_df, factors_df, on='ret_date')\n", "\n", "factors_df['ret_date'] = factors_df['ret_date'].dt.to_timestamp(how='end').dt.normalize()\n", "\n", "factors_df.set_index('ret_date',inplace=True)\n", "\n", "((1 + factors_df).cumprod()*100).plot()" ] }, { "cell_type": "code", "execution_count": 175, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 175, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ret_date
2007-05-310.098693-0.031591-0.021360
2007-06-30-0.074622-0.117856-0.000611
2007-07-310.1922400.0713170.021021
2007-08-310.167193-0.0654460.030286
2007-09-300.047263-0.0209480.048817
2007-10-31-0.010382-0.1105600.005402
2007-11-30-0.1573890.1139380.017011
2007-12-310.1373660.065398-0.022715
2008-01-31-0.1232540.064591-0.012904
2008-02-290.0240100.0849020.007286
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2021-05-310.0377990.027934-0.018596
2021-06-30-0.0150340.028139-0.032799
2021-07-31-0.0647760.043955-0.032335
2021-08-310.0141990.0109600.052207
2021-09-300.002272-0.0299210.046108
2021-10-310.001777-0.014233-0.053258
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2021-12-310.0181850.0155070.051330
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178 rows × 3 columns

\n", "
" ], "text/plain": [ " exmktret SMB HML\n", "ret_date \n", "2007-05-31 0.098693 -0.031591 -0.021360\n", "2007-06-30 -0.074622 -0.117856 -0.000611\n", "2007-07-31 0.192240 0.071317 0.021021\n", "2007-08-31 0.167193 -0.065446 0.030286\n", "2007-09-30 0.047263 -0.020948 0.048817\n", "2007-10-31 -0.010382 -0.110560 0.005402\n", "2007-11-30 -0.157389 0.113938 0.017011\n", "2007-12-31 0.137366 0.065398 -0.022715\n", "2008-01-31 -0.123254 0.064591 -0.012904\n", "2008-02-29 0.024010 0.084902 0.007286\n", "... ... ... ...\n", "2021-05-31 0.037799 0.027934 -0.018596\n", "2021-06-30 -0.015034 0.028139 -0.032799\n", "2021-07-31 -0.064776 0.043955 -0.032335\n", "2021-08-31 0.014199 0.010960 0.052207\n", "2021-09-30 0.002272 -0.029921 0.046108\n", "2021-10-31 0.001777 -0.014233 -0.053258\n", "2021-11-30 -0.006047 0.123285 -0.031466\n", "2021-12-31 0.018185 0.015507 0.051330\n", "2022-01-31 -0.085436 -0.003798 0.069385\n", "2022-02-28 0.010633 0.023980 -0.009554\n", "\n", "[178 rows x 3 columns]" ] }, "execution_count": 171, "metadata": {}, "output_type": "execute_result" } ], "source": [ "factors_df" ] }, { "cell_type": "code", "execution_count": 172, "metadata": {}, "outputs": [], "source": [ "factors_df.to_csv('./data/factors/ff3.csv')" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "## Long-only factors" ] }, { "cell_type": "code", "execution_count": 173, "metadata": { "editable": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 173, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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0lDVr1gT82tWrV7N06VI2b94cwq79UyAqIiKSrvqX5gI01cQnED3ZOyOagmdEj2yHV38JTpcCURHpY8qUKWzbtq37e/frm2++mf379/Poo49y4sQJcnJy+PrXv87DDz/M5z//+e7rhw0bxsGDBwd877y8PNauXXvG470D0muuuab7DOiGDRu6H581axbjx49n/vz5YfzqfFNproiISDrq9ECXp1ezohK7xqthUd1+O88UUjMjune9XQ+9HN99iEhSOXToECNHjiQvLw9jDJWVlZSUlAx47eOPP86CBQu6/7nlllvCuve6det4//vfH9Z7DEYZURERkXTU7u1Qm90/IxqvQPQAlM6Bir+lZkb07aftWrfPBvvuz1tEZBDLli3jwQcfZO7cuRhjWLZsGR/84AcHvHbVqlWsWrUqIve94YYbqKys5E9/+lNE3m8gCkRFRETSkTsiZUiv8S0AjQOPAIiqlnpoqYPS2TYQTbWM6MlDtiPxnPfB7j9Cxcv2axERP7Kysnj00Udjft/f/va3Ub+HSnNFRETSkafZrm5GdGghmIz4ZETd86Gj59g11TKibjZ0+Z2QlQcV0Wn8ISKSTBSIioiIpKP+gWhGJgwtis8ZUXd0S6k3EE21jOje9VA03WZ8Jyy2GVERSRi+5mxKcIL9OSoQFRERSUft3kDULc0FW57bVBP7vbijW0pm2TWVMqKtp+DgSzBzpf1+0vlwbCe0NcZ3XyICQG5uLrW1tQpGw+Q4DrW1teTm5gb8Gp0RFRERSUeefs2KAIaVxK80d9ho79gYAx1tsd9DtOz7i+1OPOMq+/2k88HphMqtUH5pfPcmIkyYMIHKykqqq+NwPj7F5ObmMmHChICvVyAqIiKSjtxmRdn9MqJ1W2K/l7oDUFgGxkB2Xs/eUsHbT9uxNBPPs99PWGLP4lZsViAqkgCys7OZOnVqvLeRllSaKyIiko66S3Pzex4bVgpNccgK1B2AUd6/CGblQkeKnBHt6oR3/gzTr4BM72f/uQUweq7tDiwiksYUiIqIiKSj7tLcvJ7H8ktsE6NYnl/0tMDpo1A4tWc/qdKsqHIrNNf2nA91TVoKldug0xOffYmIJAAFoiIiIulowNLcErvG8pzoyYN2LSyza1Zu6jQr2vsUZGTBtMv7Pj55qQ34j+2Mz75ERBKAAlEREZF01O7NiPYvzQVojGF5rju6pXdpbqpkRPc+DZMvhNwRfR+ftNSuGuMiImlMgaiIiEg68jTbpjmZQ3oe686IxjIQ9Y5u6S7NTZGMaN1+qNkLM68687nhY2zgrUBURNJYQIGoMabEGFNjjFlujCkzxmw3xrxrjPm3Xtd8zfvYVmPMlGhtWERERCLA0wLZ+bZTrcvNiMa0NPeAzRgOLbTfZ6XIGdG9T9t1xsqBn5+01HbO1exCEUlTgWZEvwO86f36q8C3gJnAdcaYOcaYycAq72P3AvdFeqMiIiISQe1NfRsVQU9GNKaluft7ynLBmxFNgUD07fVQMqsn09vfpPOhuQZq343tvkREEoTfQNQYsxI4DngPcXAJ8JTjOJ3AE97vLwXWex9bDyyNznZFREQkIjzNMGRo38cysyFvVGwzou4MUVcqjG9pPQWH/uY7Gwow+QK7qjxXRNLUoIGoMSYf+DI2y+nKdxzH/T9EFTAGKAVqABzH6QAyjTGZPt7zVmPMNmPMturqOMwqExERkZ7S3P7yS6AxRoFopwfqK/pmDbPzejr6JquKzdDVAeWX+b6maBoMLYJDAQaij90Mmx+MyPZERBKBv4zoN4FvO47T3Oux/ocZjI/HBuQ4zk8cx1nsOM7ikpKSwHcqIiIikTNQaS5AfmnsmhU1HAanM/UyogdftE2gJpzr+xpjvOdEAwhEm2rhzcfhxe9q9qiIpAx/gej7gQeMMQeBG4BfA6eNMbne50uxWdFjQDGAMSYLcLxluiIiIpKIBirNBRhWErtAtP/oFkiNjOjBl2D84oF/vr1NWmqbNZ0+Pvh1h7fYtakK3n4mMnsUEYmzQQNRx3GmOo4zxXGcKcBvgQ8CzwLv9ZbeXgs85/1npfexq4EXo7prERERCY+n2UdpbmnsmhV1j25JoYxo6yk4tgOmXOT/2kDniR7eDBnZMGw0vPZw+HsUEUkAocwRvQf4EvAO8CfHcd5yHOc48BCwF/gGcEfEdigiIiKR1948cGnusBJoa4jNCJWTB+24luFjeh5zA9FkHWtSsRmcLpi6zP+1Y+fbDwMOvuTnPbfAuAWw8CPwzp/h1NHI7FVEJI4CDkQdx7nZcZwNjuNUO45zvuM4ZY7jfLPX8/c7jjPNcZyzHcepiM52RUREJCJ8lebmu7NEY5AVrdsPo6b0nWWa7T39k6xZ0UDOh7oys23mdN9ffF/jaYWjr9pxLws/YoPcHWsjt18RkTgJJSMqIiIiyc5naa63kWAsRrj0H90CNkMKyR2ITjh34GzzQKatsAG5e162v2M7oLMdJp5vf1ZTL4ZXH4aursjtWUQkDhSIioiIpCOfpbnejGi0z4l2ddlGPb1Ht0BPRjQWpcGR1toAx14P7Hyoq3yFXfc9P/DzFZvtOvE8u57zcag/BAdfCH2fIiIJQIGoiIhIuun0QJcHhsQxI9p43GY9+wei3RnRJOyc654PDSYQLSqHEZPgXR/luYe3QGG5PbsLMOsayB0Jr/4y/P2KiMSRAlEREZF04/GOB88eaHxLjM6IDjS6BZI7IxrM+VCXMTDtMjjwwpkzQh3HBrdud12wP5/5fwd71kFzXWT2LSISBwpERURE0k27G4gOUJqbnQdDhke/NLd7dEsKZUQPvhTc+VBX+QpoPw2VW/s+XvMOtNTBpPP6Pn7Ox+y50Z2PhrdfEZE4UiAqIiKSbtyM6ECluWDLQKNdmnvyAJhMGDGx7+PJmhEN5Xyoq+wS+7N4t9850cPu+dDz+z4+Zi6MO8eW5ybrmBsRSXsKREVERNLNYKW5AEOLoakmuns4fdzOD83M7vt4lju+JckyoqGcD3XljrCZ1P5jXCq2QF4hFE8/8zXnfAyqdsOR7aHtV0QkzhSIioiIpJt2P4Fo3kib4YumpmrILz7z8awkzYiGcj60t/LL4Ohr0FTb81jFy7Zbbu85q665q+2/v9d/Fdr9RETiTIGoiIhIuukuzfURiOYUQNup6O6hqbqnQ29v2Uk6RzTU86GuaSsAB/b/1X7fWA11+2DS+QNfn1sA4xbCiTdDu5+ISJwpEBUREUk3nkGaFYEtFY16RrRm4EC0uzQ3iQLRcM6HusYthLxRsM8biB7eYldfgSjYRk+1+0K/p4hIHCkQFRERSTfdpbk+mhW5gWi0GuE4DjRWDZ4R9STRGdFDL4d+PtSVkQlly2Hf8/bnc3izLfUdu8D3awrLbVOp1ihnr0VEokCBqIiISLrxV5qbWwBdHdELBttOQ2db6mREwz0f6iq/DE4fg6o9tvnRuIU9XYQHUlRu15MHwruviEgcKBAVERFJN/665uaOsGu0ynObvDNKB82IJlMgGub5UFf5CrvufRKO7hi8LBegsMyuKs8VkSSUFe8NiIiISIy1N9nVVyCaU2DX1gYoGBv5+7uB6LABAtHMbDtTMxHHtzTVwE+W21LYjEzIyLL/nD4Kl3w5/PcfMR5KZsHmB6DLc+b80P7cQLROgaiIJB8FoiIiIunG0wImA7JyBn4+d6Rdo9U5d7CMKNjMYiJmRA9tgobDMP+DkDPMli93dQAGFn4kMvcoXwGbf2i/nnje4NcOyYfhY6FOpbkiknwUiIqIiKQbT7PNhg40nxLiW5oLNkBOxIxo5TZ7FvS67/sO4sNVfpkNRIumQ36R/+sLy1SaKyJJSWdERURE0k17k++yXLDNiiCKgWiNXYcWD/x8Vh50tEXn3uE48iqMmR+9IBRgyoX2383kCwK7vrBMpbkikpSUERUREUk3nhbfHXMh+hnRxipb/ps1ZODns3MTb3xLVyccfS1yJbi+ZOfBJ9bDiImBXV9UbjPMrad6PkAQEUkCyoiKiIikG7c015dYlOb6KssFb0Y0wc6IVr8FniYYvyj69xq3ILCyXOjVsGh/9PYjIhIFCkRFRETSjb/S3KxcyMiOYrOimsED0UTMiB7ZbtcJi+O7j/4KvbNEVZ4rIklGgaiIiEi68Veaa4zNikYtI1o18OgWV1Zu4mVEK7fZcmI3A5koCqfaVRlREUkyCkRFRETSjcdPRhTsecN4leZm5yVgRvRVW5brq9NwvLgjXGoViIpIclEgKiIikm7a/ZwRBW9GNAqluZ0eaDnp54xogmVE25ug6s3YnA8NRWG5SnNFJOkoEBUREUk3/kpzIXqluc21ds33MboFEi8jeux1cLoSNxAtKlNprogkHQWiIiIi6SaQ0tycKJXmNlbZNb/U9zVZOYmVEa3cZtdEDUQLy3pGuIiIJAkFoiIiIunG0xJYaW40uuY2Vds1mca3HNkOIycN3mApntQ5V0SSkAJRERGRdNLZAZ3ttsnNYKJVmttUY1e/41sSLBAdn2BjW3orcgNRleeKSPJQICoiIpJOPE12zc4b/LrcEeBpts2FIqnJW5o76PiWPOhsg66uyN47FKdPQMPhxC3LBRjlHeGizrkikkQUiIqIiKQTtwlQIKW5EPlzh03VkDnEnkH1JTvXrolQnnv0VbsmciA6ZCgMH6fSXBFJKgpERURE0km7mxENNBCtj+z9m2psWe5g8zizvNnaWAWiXZ2w+4/Q0X7mc5XbwGTC2LNjs5dQFZWrNFdEkooCURERkXTiabarv/EtbsYy0g2LmqoHH90CPRnRWI1w2fcX+M3H4C/fOPO5I9th9Bz/P694K5wKtcqIikjyUCAqIiKSTrpLcwNoVgSRb1jUWDX46BaIfUb02Ot2/dsP4OBLPY93dcGRVxO7UZGrsByaa6LTYEpEJAoUiIqIiKST9kCbFXkzopEObNzS3MFk5dg1VhnRE7ugYLydx/n4P0KLtxy5bh+0NST2+VCXOueKSJJRICoiIpJOAi3NjUazIscJsDTXzYi2Re7egzm+C8YthOt/CqeOwlNfso9XbrNrMgSihWV2VXmuiCQJBaIiIiLpJJ6luW2n7FiWYf5Kc92uuTHIiLY328zn6LkwYRFc8mV44zew63f2fOiQYVAyM/r7CJc7wkUZURFJElnx3oCIiIjEUKCluUOGAyaygWhTjV39lea6e/PE4Ixo1R5wumDMXPv9stvg3WfhiS/A0CKbKc3IjP4+wjVkqC0vViAqIklCGVEREZF00l2a6ycjmpFhO+dGsmtuU7Vd/ZXmxjIjeuINu472BqKZWbDqx9DZYYO6ZCjLdRWWqTRXRJKGAlEREZF04gai/uaIgi3PjWhG1A1EEygjenyXzf6OnNzzWFE5rPwP+/Wk86O/h0gpLLNlxiIiSUCluSIiIumkvRkwPZ1pB5NbENlmRY1VdvU7viWWGdFdMPosmwHu7ZyPwYTFUDon+nuIlKJyaK61XX/zRsZ7NyIig1JGVEREJJ14mm1ZrjH+r414RtR7RnRo0eDXxSoj6jhw4s2e86G9GWMD1EB+TonC7Zyrc6IikgQUiIqIiKQTT3NgZbkQndLc3JGQNWTw62KVEa0/ZM/Ajh4gEE1GhZolKiLJQ4GoiIhIOmlv9t8x15VTAG0RDkT9jW6BXoFolOeIHt9l15QJRDXCRUSShwJRERGRdOKW5gYiGhlRf42KwHauzcjqmXkaLSd2AQZGJ9E50MFk59kRLuqcKyJJQIGoiIhIOgmqNNfbrKirKzL3bqr2P7rFlZUHHVE+I3r8DXuuMtDAPBkUlUPtu/HehYiIXwpERURE0kkwpbm5IwAH2hsjc+9AM6IA2bmxyYgO1KgomRVNg9p3bCMmEZEEpkBUREQknQRbmguRKc/t9EDLSf+jW1zRzoi2noKTB2H0vOjdIx6Kptt/X8218d6JiMigFIiKiIikE0+QzYogMoGoO7ol0NLcaGdEq3bbNRUzoqDyXBFJeH4DUWPMRcaYLcaYncaYl40xs4wx3zLG7DPG7DDGPNPr2q8ZY941xmw1xkyJ5sZFREQkBO3NkB1kRrTtVPj3baq2a6CluVm5kcmItvrY+/E37JoqHXNdxd5AtKayXMkAACAASURBVOad+O5DRFKX48Cpo2G/TSAZ0X3Aex3HmQ/8BLgNKAI+4TjOAsdxrgQwxkwGVgEzgXuB+8LenYiIiESWpxmGBDFHFCKUEfUGooGMbwGbtQ0nI9rRBn/4NHx7KhzeeubzJ3bZX9+ICaHfIxGNmAQZ2cqIikj07Pod/M9Z9nhDGPwGoo7jHHMcp8YYY4DZwJtAIVDT79JLgfWO43QC64GlYe1MREREIi+Y0tzuQDReGdEQ54g2VsMvroMdayEzB5792pnNe47vsudDjQntHokqM8t2AlYgKiLR8trD4HT1zGIOUUBnRI0x/wIcBeYDDwDZwCPGmF3GmNu8l5XiDU4dx+kAMo0xmQO8163GmG3GmG3V1dVhbV5ERESC0NkBne3Bl+ZGMiMa8PiWXOgIISN64k346WVwbAfc8BBc+S2oeBn2PtVzTVenPSOaaudDXUXTFIiKSHScPg4HXrBfh/nnTECBqOM43wfGAc8APwKudxznHOAi4MPGmCVA/z7hA37E6DjOTxzHWew4zuKSkgA/FRUREZHweZrtGmhpbkSbFVVD5pCe9/QnOxc8QZ4R3bsefn6FDbY/8RTMvR4WfhSKZ8Bz99hAHGw5mac59c6HuoqnQd1+G3BHw8lDcOhv0XlvEUlsu35ns6FZubEJRAEcx3GAR4DzvV/jOE498CwwBzgGFAMYY7K8L4nSn4AiIiISNDcQDbQ0N2uIHaPSFoFAtLHajm4JtBQ2Ky+4jGjFZvjVTTYbeOtfYfwi+3hmFlx+D9S8bcvJoKdRUSpnRDvbob4i8u9duw9+djn84lr7tYikl52/gbFnw7hzwv4zIJCuuTOMMe51VwK7jDFjvc8NAZYBrwPPASu95bhXAy+GtTMRERGJrPYmuwZamgu2PDdSGdFAy3Ih+Izo0dcABz78GBSM6/vczKth0lLY8B/2Z3BiF5hMKJkd+Psnk6Lpdo10ee6po/DL90NXh81u//XfI/v+IpLYat6xxx7mfQCKyqE2vO7cgWREVwK7jTGvAR8Gbgd+ZIzZCWwFfu84zmuO4xwHHgL2At8A7ghrZyIiIhJZbhfaQEtzAXILIhiIBnEkJysvuPEtzXWAgaFFZz5nDLznXmg8AS//0DbYKJ5ug91UFI1Zok21NghtOQkf/T2c/0+w67dw7PXI3UNEEtsbjwEG5q62f840VUNLfchvl+XvAu/50O/3e3iVj2vvB+4PeTciIiISPd2lucEEoiMi1DW3BkafFfj12bnBjW9pqYO8kZBxRp9Ea+ISmH0dbPqePdtUtjzw9042+cX231ukZom2nYa1N9iztR/9PYxbaDvzbvs/eO7r9jERSW2OY8typy6DgrH2wzyAun09RyGCFPAZUREREUly3aW5wQaiYWZEHQeaqoIrzc3Kgy5P4A13musgr3Dwa1bcbbOszTWpez4UbAY4Up1zPa327O2x1+EDv4ApF9nHc0fAsttg3/M9HTRFJHUd2Q4nD9iyXOhVeRH6OVEFoiIiIukilNLcnAiU5radss1zginNdctmA82KttTBUD+BaPE0WHSz/Xr0vMD3koyKpkcmEH3+63DwRVj1IMy8qu9z534KCsbbjsT957SKSGrZ+Rs7l3nOdfb7UVPAZIT154wCURERkXQRamluW5iluU01dg3qjKg3EO1oC+z6QDKiAJd91f4z9eLA95KMiqbBqSM9WfBQdHXZUQ1z3gfzP3Dm89m5sPxOmynZsy70+4hIYuvsgDd/DzOu7JkvnZUDIyeFdQRAgaiIiEi6iFdpbmOVXUMKRAPNiJ70nxEFyBsFF3/JjqZJZcXhl81x9FXb4GnWNb6vOfsmKJ4Jz9/bM6dVRFLLgQ22MVH/D6TCrLxQICoiIpIuuktzgxnfUmDLaoMZpdJfU7VdgyrN9c46DfS+gWZE00X3+a0wGhbtfcqOuZl2ue9rMrNgxV32PjvWhn4vEUlcOx+zH0pOv6Lv40XT7IddIZbmKxAVERFJFx43I5oX+GvcMqxwsqKhBKLBZEQ72uyvbeio4PeWqgrL7RpORnTveph8gf9M86z3woRz4YX/Cv1eIpKY2pvhrSdsiX5WTt/nisrtn72nj4f01gpERURE0kV7M2B6grxA5I60a1iBqHtGNIiuucFkRJvr7JqnQLTbkKFQMCH081t1B6BqN8y82v+1xsBZq6Chws4bFZHU8e5z0N4I824887kwZxYrEBUREUkXnhZ7PtSYwF+TU2DXcBoWNVXbgDYzO/DXBJMRbTlpV5Xm9lUcxgiXvevt2r9Tri9h/oVURBJUzdt2Hb/4zOf8HQHoaIdjO32+tQJRERGRdOFpCm50C/Qqza0P/b6BjFbpL5iMaIs3IxrsPVKdO0s0lPNbe5+C0jlQODXwe0F4Z1JFJPE0VMLQooH/31Ew3s589nUEoOJl+PEyn2+tQFRERCRduBnRYOR6M6LhlOa2NvSU+AYqmIxod2muAtE+iqbbTLZ7Rre39V+B390ycJDaXAeH/hZ4NhRg5GTIyFZGVCTVNFTCiAkDP5eRYc+J+vrv/t3n7J8LPigQFRERSRftTSEEom5GNIzS3Jb6nvcJlJsRDWSOqDKiA3OzlP3Pida8A1sehDces7MB+3vnWXA6YeZ7A79XZpbNnoYxU1BEEtCpI/a8uS/+AtHJS32+VIGoiIhIuvA0h1GaG2ZGNC/YjKi3O6NHGdGQFfs4t7npe/bnWzoHnvk3aDvd9/m9T8Gw0TBuYXD3C3OmoIgkoMEyomA/8Dp5EDo9Z76uajdMe4/PlyoQFRERSRehlOZmD7WzJMMKREPIiGa5GdEAz4hm5QYfZKe6ERMhM6fvuc1TR+H1X8PCj8K134fTx2Djt3ue72izWYwZK23ZXTCKyqFuP3R1Rmb/IhJfrQ22vH/QQHQ6dHXAyUN9H3/3ObtOVyAqIiIioZTmGmODyFC75jpOaGdEs71nRAPKiJ5UNnQgGZlQWNa3kcjLPwSnCy74LEw81wakm38EVW/Z5w++aEc1zAqiLNdVPB0626G+IjL7F5H4ajhi1xHjfV/jq2P2O8/akt6SWT5fqkBUREQkXYRSmgs2EA01I+ppscFJ0KW5QWZEdT50YEXlPec2m+tg+xqYuxpGTbGPXX4PDBkG679kPzTYu95+WDH14hDu5f6F1EcHTRFJLg2Vdh0x0fc1ReV27R2IdrTD/o0w/fJBx4UpEBUREUkXoTQNAts5N9RmRe7Yl2Dvm5EBmUMCPyOaNyr4vaWD4ulw8gB0dsDWn9ts50Wf73k+vxhWfA0OvAC7fmcD0fLLeppFBaNoul01wkUkNTQctmvBIBnRoYW2IqV3IFr5CrSfhmmXD/r2CkRFRETSQWcHNNfaJjTBCicj6r4u2NJcsFlRZUTDUzTNnt+q3gNbHoDpV8Los/pes+gTMPZsWPd52yFz5tWh3Su/GHJGqGGRSKo4dcT2CBg+ZvDrivs1KnvnWcjIgqmXDPoyBaIiIiLpoLkGcCC/JPjXhhOItoSYEQV7TjTgjKgC0QG5Wcpn77YfRFz0hTOvyciEq79rMxgmA2ZcGdq9jLGdejXCRSQ1NFTabGhG5uDXFU3rG4i++xxMWtozh9oHBaIiIiLpoPGEXUPJiOaE0azIDWCDPSMKthOuvzmijgMtJ1Wa64t7bnPf8/Yvhr5m+k08Fy78HMz/oM1shnM/nREVSQ0NlYM3KnIVldsO3G2NtjP3iV1+y3IBsiKwRREREUl0jdV2HVYa/GvDKs11M6IhBKLZedDhJyPadgqcTpXm+pJfZIP0lpMDZ0N7e8+94d+vaDrsfNR2aB6SH/77iUj8NFTCxCX+r3M/8KrbB8det18PMrbFpYyoiIhIOujOiIYSiBbYJjedHcG/Nqwzojng8XNGtLnOrirN9W30XBg9D6ZfEf17uR006/ZH/14iEj1dnTa7OdgMUVfvES7vPAvDx0HpHL8vU0ZUREQkHTRV2TU/xIwo2OxjsJnH7jOig58VGlBWABnRFm8gqoyobzeuAcygYxQipth7JrXmHRgzL/r3E5HoaKyCLs/gHXNdhWWAsfOI92+As94f0J83yoiKiIikg8YqyM6HnGHBv9YNREMpz21tsHMqM7ODf212bgAZ0ZN2VUbUt/xiW6IbC4VldtU5UZHkduqIXQebIerKzrPX7fy1/cAygPOhoEBUREQkPTRWhVaWC5DjzWaGFIiGOLsUlBFNRkPyoWCCZomKJDt3hmggpblgy/LrK+zYlrLlAb1EgaiIiEg6aDwReiDauzQ3WC31oZ0PhQAzojojmnCKyjXCRSTZNVTaNZCuudBzTnTieQF/+KhAVEREJB00VYcfiIZamhvK6BbwZkT9BKItdYAJ/R4SecXTbWmu48R7JyISqoYj9lhFoB8kuufDAyzLBQWiIiIi6aHxRGiNiqCn0VBrCBnRcEpzs3P9B6LNdfb9/Q1cl9gpmg5tDfbDDxFJTg2HbVluoE3OJi6B7KEw+9qAb6GuuSIiIqmuo93OkRw2OrTXh5sRDbU0NyvPf2luS53Ohyaa3qMcQs3Ci0h8NVQG1jHXNW4h/OvRoLpzKyMqIiKS6tzM1LCS0F4fTrOilnCaFeX4b1bUXKfzoYmm2BuI6pyoSPI6dSTwRkWuIEdEKRAVERFJdY0n7BpqRjQjE4YMD75ZUWcHtJ8O/fxmdh50ddj38UUZ0cQzYiJk5tiMqEgi2vMENNXGexeJy9NiP8AMZHRLGBSIioiIpDo3IxrqGVGwWc1gM6Ju4BpyRjTXroNlRVtOQt6o0N5foiMj084TVSAqiejYTnj0w7DlwXjvJHGdOmrXQDvmhkiBqIiISKrrzoiGE4gWBB+IttZ7XxtGRhQGPyfafFKluYmoqFyBqCSmV39p16OvxXcfiax7dEuQpblBUiAqIiKS6hqr7BpWIBpCRrTFDUSjlBHtaLelvyrNTTzF06HuwOBl1SKx1t4MO39jvz62I31GDB16GdZ/Bbq6ArveDUSDaVYUAgWiIiIiqa6xyjYccjOMoRha1BPQBsoNXMM5Iwq+M6ItJ73vr9LchFM0Dbo8UH8o3jsR6bHnT3a00Oxr7ZGF08fjvaPY2Pwj2PIA7Fgb2PUKREVERCQimqogP8SOua6CccH/pS3c0tzujKivQLTOrsqIJp4i73B7ledKInn1l/b88tLP2u+P7YjvfmKhqxMOvGC/fv7rgVW2nKq0PQWyc6O6NQWiIiIiqa6xKvSOua7hY20mob0p8NeEW5qb7ScQbfYGojojmnh6zxIVSQQ178ChTXDOx2DMPDAZcOz1eO8q+o69bj8UvOCfoakGNn7b/2saKqPeqAgUiIqIiKS+xqrwzoeCzYgCnDoW+GvCLc3NcktzfZwRVUY0ceUX2ZJpzRKVRPHqLyEjC87+EAzJh+IZcDQNMqL7N9j1gn+xQfiWB6F67+CvaaiMeqMiUCAqIiKS+iIRiA4fa9fTRwN/TWu9/Ytf9tDQ7umvNFcZ0cRWNE0ZUUkMHe3w+q9gxkoY7q0OGXt2emRE92+A0XPt/wNW3AXZ+fD0V3w3anIcaDgS9RmioEBUREQktXlabUltvDKiuSPBmNDu6ZbmKiOanIqmKxCVxPD2etuc6JyP9zw29mz7wVqwTdiSiacFKjZD2XL7fX4xXHon7PsL7F0/8GtaToKnKeqNikCBqIiISGpr8v4lKz/cjOgYuwaTEW2pD/18KASWEc3MCT3jKtE19mw4fQwOvxLvnUi6e/WXNrCatqLnsbEL7JrKWdGKzdDZ1hOIApx7C5TMgmfuHLgj+akjdlVproiIiISlsdqu4TYryhkOQ4YH1zm3tSH086HQa3zLIBnRoYWhZ1wluhZ+xHZrfu7r6TOvURJP/WF493n7+zEjs+fxMfPsmsrnRPdvgIxsmLS057HMbFj5H3DyIGz+4ZmvcUe3qDRXREREwtJ4wq7DwhzfAlAwFk4FeUY0qhnRkzofmshyhsHFd8Chl2Df8/HejaSr1x6x64IP9308t8CeY07lES77N8DEJfa/xd7KL4NZ18AL3z3zz/TuQFSluSIiIhIOtzQ33Iwo2IZFp0M4IxoqvxnRk7YzqySuRTfDyEnw/L3Q1RXv3Ui66eq0gWj5pTBq8pnPp3LDouY6+2srWz7w81d+C7o64Nm7+z7eUGmzqOEe5wiAAlEREZFU5jbiyI9ERnRccM2KWurDK83tzoi2+Xj/OhiqQDShZQ2BS//N/oV49x/ivRtJNycPwqlKmPP+gZ8fuwAaDkNTbUy3FRMHXgAc34HoqClw4b/AG7+xZ0ld7gzRjOiHiQpERUREUlnjCZuVzMoJ/72Gj4XG44Flthwn/NJcY2ww2uEjI9pcp9LcZDDvRiiZDX/5JnR64r0bSScNh+1aWDbw82PPtuvxFMyK7v+rPdc/7hzf11z0BdvEaf0dNnsMNhAtiH6jIlAgKiIiktoiMUPUVTDOlnI1Vfu/1tNsrw2nNBdsIDpQZ0fH6WlWJIktI9POL6zbBzvWxns3kk7qK+w6ctLAz7uBaKANixzHNt9697nw9xZt+zfA1GWQmeX7miH58J57bcXCaw/bx04diUnHXAggEDXGXGSM2WKM2WmMedkYM8sYU2aM2W6MedcY82+9rv2a97Gtxpgp0dy4iIiIBKCxKjLnQyG4ES4t9XYNJyMKvjOibadtoKuMaHKYeRVMWAIb7vN95lck0uorwGT6nomZN9KWqAZ6TnTHWnjpv2HzAxHbYlTUHbBlyWXL/V87dzVMusCe426qtc2LYtCoCALLiO4D3us4znzgJ8BtwFeBbwEzgeuMMXOMMZOBVd7H7gXui86WRUREJGBNVZE5HwowfJxdAxnh0tpg13DOiAJk+8iIttTZVRnR5GAMXH63/RDjlZ/GezeSLuoP20qOwbKCY88OrHPuqaPw9L/aryu3JnbzrQMb7Vq23P+1xsBV99nmb098DpzOxMmIOo5zzHGcGmOMAWYDbwKXAE85jtMJPOH9/lJgvfex9cBSX+8pIiIiMRLJjGjBWLsGMsKlNVIZ0byBM6LN3kBUGdHkMeUiKF9hM0oDfbggEmn1Fb7Lcl1jF9jsYctJ39c4Dqz7PHS2w8Vfsh+01bwd0a1G1P4N9oPD4hmBXT92vu1wvWed/T4GM0QhwDOixph/AY4C84EHgHzHcdw/QaqAMUApUAPgOE4HkGmMyRzg7URERCQW2pugvTFyZ0TzS8FkBDbCxc2IhntGVBnR1HL+p+1f+A+8EO+dSDqor/AfVLnnRI/t9H3N67+Gd56xZ53Pvsk+dnhLZPYYaV1dsH+jzYYaE/jrLv1qzweHvkqZIyygQNRxnO8D44BngB8BTr9LjI/HzmCMudUYs80Ys626OoBmByIiIhIad3RLpALRzCybXQ1khEvEzojmQccAgWizN3uhjGhymboMhgyDt9fHeyeS6jo9thQ8kIwo+D4nevo4PP1lmHg+nPcPtgPv0CI4/Epk9xuKplrY9xc4sdv+mes4cOIN+0Fd2fLg3iu/CK74lu2OPmpKFDZ7pkEKpvtyHMcxxjwCbABOG2NyvVnRUmxWtB5buosxJsv7ks4B3ucn2LOmLF68uH/wKiIiIpHidreNVGku2L+kBNKsqPuMaJhzPrNzbWOi/pQRTU5ZOVB+Kex9Gt7738FlbESCceoIOF3+A9H8Ips1HeicqFuS29EG7/uh7QANtvFWZZwDUceB33wUDm3qeSx7qP0HoOyS4N/znI/Cwo/E7L9Lv4GoMWYG8K7jOF3AlcAubAnue40xfwCuBT6GDUS/YIy5C7gaeDFquxYRERH/Gk/YNVLNisAGonX7/V/nnhHNKQjvfll50DhABZV7RjTc0l+JvZlX27Nox16HcQvivRtJVd2jWwI47zj27IEzom88ZrP3V3wTiqf1PD5xiX28OY4jpN78vQ1CL/kKlMywlSqnjvZkgd0u58GK4YdDgWREVwKfNsa0AMeBW4FWYB3wHeD/HMd5C8AY8xCwF2jCBqgiIiISL92luRHMiBaMhUMv+b+upd4OUx+sW2Ugsn2Mb2k5act+w31/ib3pVwAG9q5XICrRU3/Yrv4yomDLc996AlpPQW4BVO2Bl38IOx+FCefas829TTzPrpVbYcaVkd13INqb4c93wZj5cMkdPZnaJOP3T2/v+dDvD/DU+QNcez9wfwT2JSIiIuFyA9H84si95/Cxtuy2vRmGDPV9XWtD+KNbwM4R9dWsKNyyX4mP/GL7F/m318Old8Z7N5Kq6isAAwUBjCJxGxa98mM49Dd77jIrDxZ8GJZ/5cxAb9xCyMiyDYviEYhu+l84VQmrf5q0QSgEcUZUREREkkxTlW2qkZkdufcscGeJHoOict/XtdaH36gIbCDqa3yLGhUlr5kr4bl7oOEIjIhNh05JM/UV9oOzrCH+r3Uz83/5pq0gueyrsOiT9vzoQIYMhTHz4tOwqL4CNn0P5q6GyRfE/v4RFFDXXBEREUlCjVV25EokDffOEvU3wqW1ITLnN7PzfGdE1agoec24yq5vPx3ffUjqajgcWFku2M7iV/47vP9B+PwbdlaoryDUNfE8OLIdOjvC32sw/vw1wMB77o3tfaNAgaiIiEiqaqyK3OgWl5sR9TfCpSXCGVGnX6N9ZUSTW8lMGDXVnhMViYb6Q4EHogBLPwMLbrKdnQMxcQl4muHErtD2F4oDL8LuP8CyL8KIAEqOE5wCURERkVTVeCLygWh3RtTPCJdInRHNzrMjGE4f7/t4y0llRJOZMTDzKjjwArQ1xns3kmo6O2zZdyAdc0PlNiyKVXluZwc8/RUYMQku+OfY3DPKFIiKiIikIsexc0Qj2TEXIGc4ZOf7z4hG6ozo7GvtXLzHPg4d7faxTg+0nVJGNNnNvAo622D/X+O9E0k1p4+B0xlcRjRYIyZAwXjbsCgWXvulzb5e8Q37AV0KUCAqIiKSitobbdlYJGeIgs1kFYwdPCPa2WHvH4kzoiUz7SD5w1tg/R32sZaTdlVGNLlNWgo5I2CvzolKhHXPEI1iIAp2tEtljDKiO39jGyTNeV9s7hcDCkRFRERSUTRmiLqGjz2zVLa31ga7RiIjCjD3erjw87D9Idj2kD0fChrfkuwys2H6e2zDoq7OeO9GUokbiI6IciA68Tx7L38VIuHqaIMjr8LUS+yHgSlCgaiIiEgq6g5EI3xGFGzDosH+4tVab9dInBF1rbgLylfAU1/q6bSqjGjym3kVNNfY7qP+NByBx/8J2k5Hf1+S3LoD0Sg39HHPiUY7K3p0hy1jn3R+dO8TYwpERUREUlHjCbtGIxAdPtaewerqGvh5NxCNRGmuKyMTbvi5/Yvlc/fYx3RGNPlNWwEmM7Duubt+C6//P3jz8ejvS5JbQwUMGwPZudG9z5h5trN3tBsWHd5sVzfwTREKREVERFJRU7Vdo1GaWzAOujzQXDvw8y1uIBqh0lxX3ij44P+zzYtAGdFUkDcKJl8QWCBa4W0Ks+v30d2TJL/6iuh2zHVlDYFx50S/YVHFZigsj84Hi3GkQFRERCQVNZ4AkwFD/QxlD8XwMXb11bDIPSMaydJc1+g5NjM67fKeUTKS3GZeBdV7esopB+I4NitkMu3Il6aa2O1Pkk99RfQbFbkmngvHXgdPa3Te33FsIJpiZbmgQFRERCQ1NVbB0GJb0hppw8fZ1dc50dYoZURdM6+Cj/zONruR5Fd2qV33b/R9Te27NgN/7i12LMfuP8Zmb5J8ujq9M0RjFYieB53tNhiNhpp3oKVOgaiIiIgkicaq6JVxFXgzkad9BaJu19woZEQl9ZTOhvxSODBIIFrxsl3PvQWKpumcqPh2+rg9OjAiBqW5ABOW2DVa5bnd50MViIqIiEgyaIpiIDpsNGB8B6It9ZCRnTJD1yXKjIGpF9uSW8cZ+JqKLbY5VfF0OOt6OLQJTp+I7T4lOXTPEJ0cm/sNK4FRUwLr/ByKis09v/dTjAJRERGRVNRYZbNM0ZCZbYPcU4OcEc0bmVLz7iTKyi6x55qr3xr4+YqXbWmiMXaurNOl8lwZWMNhu8aqNBdg9Fyo2hOd93bPh6bgn6cKREVERFKN49iuufnF0buHO8JlIK310TsfKqmpbLldBzon2lgNdft6zsiVzoaS2fCmuufKAOoP2TXaM0R7K5llf492tEX2fRur+v7eTzEKREVERFKNpxk6WqMfiPpqVtRSr/OhEpyRk2DUVNi/4cznBjojN/d6myVtOBKT7UkSqa+A/BIYMjR29yydDV0dtqlWJLnnTlPwfCgoEBUREUk97miLoVEMRAvGDj6+JRqjWyS1lV1iz352dvR9vGIzZObAuAU9j511vV13/yF2+5PkUH84tmW5AKVz7Brp8tyBfu+nEAWiIiIiqabZG4hGNSM6DlpOgqflzOdUmiuhmHoJtJ2Co6/1fbxiM4w/B7Jyeh4rngZj5sEuledKP/UVseuY6yqaBhlZULU7su870O/9FKJAVEREJNU01dp1aFH07tE9wuX4mc+1Nqg0V4I39WK7HtjQ81h7s53POPG8M68/63o4sg1OHorJ9iQJdHXZZkWxzohmDbHBaJWPZluhcH/vp+j5UFAgKiIiknrcjGg0A9HhPmaJOo73jKgyohKk/GIYPa9vw6Kjr9qZkJOWnnn9WavsqvJccTVVQWd77ANRsOdEI5kRdX/vp+j5UFAgKiIiknqavRnRaJbmFoyza/8RLu1N4HTqjKiEpuwSOPxKT8l3hduoaMmZ1xZOhXHnqDxXenTPEI1DIFoyG04etH8GRsJgv/dThAJRERGRVNNUAxnZkFMQvXv4yoi21ttVGVEJxdRLoLOt5y/hFZvtaIyhhQNff9YqOLYD3lRWVIhvIFo6G3Cgem9k3s/f7/0UoEBUREQk1TTX2LLcaA5Azx0BWXlnjnBpbfA+r4yohGDyBbbpy4GN9rzfwEvyoQAAIABJREFU4VcGPh/qWvgRGLsAHvs4/Omfoa0xdnuVxOMGorFuVgQ9nXOrI3BO1P29n8LnQ0GBqIiISOppqo1uWS7YIHegES4tyohKGHKGwfjF9pxo9R5oaxj8L+NDC+Hvn4WLvgCvPgw/vhiObI/dfiWx1FdAXqH9fRRrhVPtqJVInBN1f++n8PlQUCAqIiKSeppro9uoyDV8HNS8C12dPY+5pbk6IyqhKltuy23ffsZ+7y8rlDUELr8HPr4OOlrh51fAC/9lG2dJeqmviE9ZLkBGJpTMCH6W6NHX4JWfwp51ULndnrs/9Df7XIpnRLPivQERERGJsOYaGLcw+veZfQ08/RX4/a2w6kHIzFZproSv7BLY+J/w8g8hvxRGTQ3sdVOXwT9tgnWfg798w5b0Tl0W3b1KYmk4bM9VxkvpHDj4UuDXH3oZHn6//QClv2GjYdSUiG0tESkQFRERSTVNMcqInv9Ptrvp81+3nSJvXKPSXAnf+MWQPdR+oDL7uuDOOueNgqu+A7v/aM/qKRBNH45jM6LTr4jfHkpnw85HvbOU/fwZeOJN+NXfwYgJcNOv7Z+hp4/ZjOjpY7YjdDTP+ScABaIiIiKppKPdni0aGuUzoq5lX4Sc4fDU7fD/boQx8+3jCkQlVFlDbNOid58LrTRxWClk50Pd/sjvTRJXY5XNLMarNBfsCBeAqrdg0iBNtuor4JHV9gOXjz7ea88Lor7FRKIzoiIiIqmkpc6u+THIiLqWfApW/RgObrLllDkF9ryUSKjKlts1lEDUGCgsUyCabiq32nX03PjtodQNRAdpWNRUCw9fD55m+Mjv4hs4x5kyoiIiIqmkqcauscqIus7+IAzJh99+Uo2KJHyLPgHDxtjyxFAUTo3MGA1JHgc22gzjhHPjt4cRE2HIMN8Ni9oabeVIw2H46B9g9Fmx3V+CUSAqIiKSSpq9gWi0x7cMZPa18PEnerKyIqHKGQbzbwz99YVl8PbTtqOzsvPpYf8GmLTUlnbHS0YGlMy041cG8qfP2i65f7cWJi+N7d4SkEpzRUREUkl3RjSGpbm9TToPZl4Vn3uLuArLoLMdGirjvZPwPHkbPHt3vHeR+E4dhZq3e0q646l09sAZ0aq34M3HYdntMOvq2O8rASkQFRERSSXNtXaNdWmuSCIpLLNrMp8TdRzY9TvY8mBPN2oZ2IEX7Fp2SXz3AXaES1M1NFb3ffzlH0BWHpz3j/HZVwJSICoiIpJKmmsBA0ML470TkfgpKrdrMgeip49Dy0nbCfaNx+K9m8S2fyPkFcLoefHeSU/Dot7luaePw87fwMIPx7aRXIJTICoiIpJKmmrsLEWdi5N0NmyMzT4lcyDqdl7NHgqvPRLfvSQyx7GNiqYus2c04633CBfXlh9DVwcs/Ux89pSgEuDfloiIiERMc038zoeKJIqMDNs5t+5AvHcSOjcQveCf4dgOOP5GfPeTqGr3wakjMDUBynIBho+B3JE9//7aTsO2n9tmbm7JuAAKREVERFJLU218OuaKJJpknyVatQeGjbZnCjNz4NWH472jxHRgg13LlsdxE70YY8+Jug2LXnsEWhvggn+J774SkAJRERGRVNJcq4yoCNiM6MkD0NUV752E5sSb9rzh0EKYfQ3sfBQ8rfHeVeLZv8HO70ykbGPpLBuIdnrg5R/BpAtgwuJ47yrhKBAVERFJJc01yoiKgA1MOlrh9LF47yR4XZ1QvRdKz7LfL/wotNbD3ifju69E09UJB160ZbnGxHs3PUrnQFsDbH4AGirgQmVDB6JAVEREJFV0dUFznTKiItBrhMu++O4jFCcPQkdLTwfWqZfAiEkqz+3v+E4boCfC2Jbe3H9vG/4DimfA9Cvju58EpUBUREQkVbTWg9OpGaIikNyzRN1GN6Pn2DUjw47+2L8B6ivitq2Es3+jXadeHN999Od2zvU0w9LPJkY33wSkn4qIiEiqaK61q0pzRaBggm3yk4yB6IndgIGSWT2PLfiQXV9bG5ctJaQDG+3PaPiYeO+kr/wiyC+1/8z/u3jvJmEpEBUREUkVTTV2VWmuiM1CjZqSnIFo1W679yH5PY+NnGQ7w+5Ya89GpruONjj0cuJ0y+3vPffCdT+A7Nx47yRhKRAVERFJFc3eQFQZURGrsCw5Z4lW7bYNb/o756PQcNhmAtPd4VfsOdpEmR/a34KbYObKeO8ioSkQFRERSRXKiIr05c4SdZx47yRwnlao3ddzPrS3WddA3ijYvibm20o4BzaCyYApF8Z7JxIiBaIiIiKpws2IqlmRiFU41TaMaTwR750EruZt23TM7bzaW1YOnPNx2P0nqH479ntLJPs3wrhzIHdEvHciIfIbiBpjJhhjnjLG7DbGvGCMGWeM+ZYxZp8xZocx5ple137NGPOuMWarMWZKNDcuIiIi/TTXwZBhOpMk4nI759Ym0QgXt2OuO0O0v6Wfhew8ePG7sdtTomk9BUe2J97YFglKIBnRNuA/HceZAzwGfAEoAj7hOM4Cx3GuBDDGTAZWATOBe4H7orNlERERGVBTjcpyRXpLxhEuVbshIxuKygd+flgJLP4kvPGb5AqwI2nzj2zWeNZ7470TCYPfQNRxnGrHcV7wfnsQGAUUAjX9Lr0UWO84TiewHlgawX2KiIiIP80KREX6GDERMrKSKxA9sRtKZkJmtu9rLvwcZA5J/KxoezMc3wW7/whbf2bLaZtqw3vPhiOw6Xsw5/0wflFk9ilxkRXk9TcC64APAY8YY4YADzmO812gFG9w6jhOhzEm0xiT6Q1MuxljbgVuBZg0aVK4+xcRERFXU03izdMTiafMrOQb4VK1Byb7yecMK7VZ0S0/hou/ZM/CJorqvbD+Dqh5B04dGfia4WNh9FkwYQlc9Hl79jVQz99rx9e85+uR2a/ETcCBqDHmauD/s3ff4VGV2QPHv3fSezKBECAhISSEXqQKKKAgoIIUNSou9u4quNa1/+yKvaGsFSxItYEISAdBeg8JCamkkN4zk7m/Py4TStpMMpNJ4Hyex2dkbjvjAjsn533P6QQsAhapqqoqiuIP/KUoykbg3HZkSm33UVX1c+BzgIEDB7aiFmZCCCFEC1eaC+16WXeJoRSdosPdWfaVivOUuXNua1CWD4WptY9uOdewh+CfL2DTO9q8ypZi7w9wfBP0vg70XSAwQnv1aqMlp5kHIPOgVild96q2xHb0fy27d+pO2PcjjHhE+wGDaNUsSkQVRYkEXgfGqerp/teqquYrirIK6AGcALqfOt9ZO6zKtF0hhBCiOaiqtjTXy7qlufetvg8fVx8+uvwjOwUmhIPpIyBpq/ZnRKm1TtJyZB/RXtvV0ajoTL7tYcCtsOMLuORRCAiza2gWS9oKHfrDlDk1j/mFQJfRp3+9+C7Y+A70mqYtR66PqsIfT4JXEFzyiG1jFg5hSddcH+AH4BZVVU+ceq/9qVdX4BJgL7AaGK8oihNwJbDRXkELIYQQ4hyVJWAst2qPqKqqHMk9wvrU9cTmxtoxOCEcSB8BlUWn5+y2ZJkHtdfaRrfUZvjD2izNTe/aLyZrGMohfRd0srBVzLhXwdULfp0JJlP95x5YDKnb4fLnwM2n6bEKh7Oka+6DQCTw1alxLVuATxRF2Qf8AyxRVXW3qqoZwFdALPAS8Li9ghZCCCHEORoxQzS/Ip9SYykAXx38yh5RCeF4ralzbtZhcPXRmixZwq8j9P8X7J4PBan2jc0S6bugqhLChll2vndbuOIlSN4Ce+bXfZ6hDFY9D8G9od9NtolVOJwlXXNfU1U14NSoln6qqg5TVXWKqqp9VFXtq6rq7DPO/UhV1chT7yfbN3QhhBBCVDN3ovSyPBFNK9YaiYT7hvNH4h+kF6fbIzIhHKs6EW0Fo06yDmnVUGuWEI+Ypb0uvgs2zIa9P2p7NHMTocponzjrkrRFew0dYvk1/f8FYcPhz2ehOLv2c7Z8pO2dHfca6JyaHqdoESypiAohhBCipSs9lYhaURFNLdYqKI8NegwFhXmH5tkjMiEcy78TKE4tvyKqqloi2s6CRkVn8g+Fy56Gk7Hw10uw9B74+ir4oB98PhLKC+wTb22St0Lb7uCpt/waRYGr39W2F6w8p2mRsRL2/KAtPe4+ETpfYtt4hUNJIiqEEEKcD8xLc61oVpRWpFVEB7QbwPjO41kct5iCimb80ipEc3By0ZLRlp6IFmVAWZ5lHXPPNWIWPJ4A/z0BD+6Efy2D8a9rzY+W3N3w/ktbMFVBynboNNT6a9tGaw2I9v8Ex/7SOoBvfBve6w3L7tUaMY171fYxC4eSRFQIIYQ4H5gbsVjRrCitOA1/N3+8XLy4teetlBnLWHh0oZ0CFMKBWsMIl6xD2mtjElEzV09oE6l1ph16n5aMHv0D1r5imxjrk3kQKgot3x96rhGPQGCktsT43Z7avNCg7nDzYrhvi/bDBHFekURUCCGEOB+UngSdC7j5WnxJWnEaHb07AhCtj2Z4h+HMPzSfiqoKe0UphGPoIyAnQVv+2lLZIhE916A7tT2YG2fDwaW2u29tkrdqr5Z2zD2XiztM/ECbK9pjMty7GWYsg8gxLX/sjmgUSUSFEEKI80FpjtaoyIovbGcmogC39rqVnPIcfjv2mz0iFMJx9BFQUaAtfTWZIPso7PsJ1r6mLQNtCbIOg3c7q2cB10tR4Kq3IWQwLLsfMvbb7t7nSt4KviHantXGCh8OTxyHKZ9CcC+bhSZaJklEhRBCiPNBSY5VjYpMqon04nQ6+pxORIcED6G7vjtfH/wak9oMe8qEaC7mzrnfToLXO8HHg2DJXbD+ddj7g2NjMzN3zLU1ZzeImQfufvDDTac7bNuSqkLSVghrZDVUXJAkERVCCCHOB6UnrepUmVWahcFkIMQ7pPo9RVG4rddtHC88zrqUdXYIUggH6XiRtsfQyQ363gDXfKztO/QLhdQdjo5OU5AK/mH2ubdPMMR8B8WZ8EMMFKTVfW5pLvw4Hd7oDCuegBwLxt7kJUJxRuOX5YoLkrOjAxBCCCGEDZSc1L5sW8g8Q/TMRBRgbNhY2nu1Z3HcYi7rdJlNQxTCYbyDYGYty1JDBraMRLTKqP0Z9gm23zNCBsC0ubD0Xvh0GEx8H3pOPvuclH9g0W1aB9/Iy+GfL2DbZxB1BQy5B7pcVvvy/+S/tVdJRIUVpCIqhBBCnA9Kc61ammtORM9cmgvgrHOmT9s+JBYk2jQ8IVqkkEFQkKwlXo5UkgWo2h5Re+pxDdy7SVuqvPAWbd9oeaG2tHbLh/DVeFB0cMdKuGkBzDoAI5+A9F0wfyp8PgoqimreN2kLuPtD2272jV+cVyQRFUIIIVo7Y6XWiMXL8kQ0tSgVBYX2Xu1rHAv1CeVE8QmMJqMtoxSi5QkZpL06uipqToTtWRE1C+wCd/wJlz6u7Y+dMwK+uxb+fAaiJ8A9G6DjgNPxjH4KZh3UKqgn9sKfz9a8Z/JWrRqqk9RCWE5+twghhBCtXemp5iNW7BFNK04jyDMIVyfXGsdCvEMwqkYyShxcJRLC3oL7aGOPUv9xbBzFmdqrdzMkogBOLnDZ03DbCkCFhPUw4U24fh54+Nc839kNBtwKwx6EnV9B/JrTx4qzISceOg1tntjFeUMSUSGEEKK1Kz2pvVqxNDe1KPWs0S1nCvXRxi+kFqc2OTQhWjQXd2jfB9J2OjaO6oqonZfmnqvTULj/b3h4j7YHtKHxT6OfgTbR8Mu/oSxfe888PzRsmH1jFecdSUSFEEKI1q7kVCJqxdLctOI0QnxCaj1mTkRTilKaHJoQLV7IIEjbpTUMchRzRdQrqPmf7eoFfrX/XVCDi7s247MoA1b+V3sveSs4u0P7fvaLUZyXJBEVQgghWrvqpbmWJaKVVZVklWbVWREN8gzCWecsiai4MIQMAkMJZB92XAxFGeAZCM41l8q3OB0HwIhZsOc7iF2hJaIdB7aO2EWLIomoEEII0dqZE1ELK6InSk6gotaZiDrpnAjxDiG1SJbmigtAyEDt1ZH7RIszm29/qC2MfALa9YJfHoIT+yBMxrYI60kiKoQQQrR2JScBBTwCLDo9rejU6JY6ElHQxrpIIiouCP5h4NXWsZ1zizKaf39oUzi7wpQ5UJYHapU0KrrApBSl8MymZygxlDTpPpKICiGEEK1d6UktCdU5WXS6uQlRXXtEAUK9Q0kpSkFVVZuEKESLpSja8lypiFonuDdc9ow2PzR0iKOjEc3oywNf8vOxn1kat7RJ95FEVAghhGjtSnOsblTkrHOmrUfbOs8J9Qml2FBMQUWBLSK84JUaShm/eDyLji5q1PUbUjewP3u/jaNq2JHcI3x94GvWJK3haN5RyoxlzR5DswgZCCePahW+5mYyaYloa6qImo2YCY8dAzcfR0cimklxZTG/J/wOwPdHvsekmhp9L2dbBSWEEEIIBynJsWp0S1pxGh28OuBUTwXVXC1NKUrB372WuYLCKssTl5NWnMYnez5hYpeJuDm5WXxtYkEiD/31ECoqt/e6nfv73o+Lk4sdo9VUVFUwc+1M0orTzno/yDOILn5d6B/Un35B/ejbti+eLp52j8euQgZpr2k7IXJM8z67LBdMxtZXETVzknTiQvJrwq+UGcu4teetfH3wazalbeLSkEtrPddgMrAwdmGd95LfOUIIIURrV3oSAiMtPj2tKK3e/aFw9izR3m17Nyk8AYuOLsLX1ZfssmyWxS0jpluMxdd+uPtDXJ1cGRs2lv/t/x+b0jbx6ohXiQqIsmPEMO/QPNKK0/hg9AcEeQWRUphCUmESyUXJxObG8uneT1FRcVKc6BrQlasiruKWnrfYNSa76dAfULR9os2diDpqhqgQVlJVlZ9if6JnYE8euughlicuZ/6h+XUmot8f/p7ZO2bXeT9ZmiuEEEK0diUnrV6a29Gn/kTUnKjKCJemO5RziIM5B7m/3/30bduXLw58gcFksOjaAycPsCppFbf0vIVXRrzCB6M/IKs0i5jfYvjm4DdNWhZXn6zSLD7f9zmXhV7G6E6j6RnYk/Gdx3NP33t4ZcQrLJq0iM03bmbOmDnc2ftOdIqO2TtmcyT3iF3isTs3Hwjq4Zh9osWnEtHWWhEVF4xdWbuIz48nJjoGF50LN0TfwNYTWzmWf6zGuZklmXyy5xMu6XhJnfeTRFQIIYRozYoytYqoPsKi00sNpeRV5BHiXf8Ae08XT9p4tGnWzrklhhIO5zhwlqOdLDq6CDcnNyZ2mcg9fe7hRMkJfjv2W4PXqarKezvfQ++u55YeWqVxdKfRLJm0hBEdRzB7x2xe+vslu8T8/q73MZqMPDrw0TrP8XH1YXjH4TzY/0E+v+JzPJw9mHdonl3iaRYhA7WKaHM36CrK1F6lIipauAWxC/Bx9WF85/EATOs6DVedKz8c+aHGubN3zMZoMvLU4KfqvJ8kokIIIURrdnyj9ho+wqLTzR1zG6qIgrY8tzkrorN3zGbGihlUmaqa7Zn2Vmoo5feE3xkXPg5fV19GdBxBd313/rf/fw1+zq3pW9mWsY27+9yNt6t39fuBHoG8P/p9bu5+M4uOLuLgyYM2jXl/9n5+OfYLM3rMINQ31KJrfF19mRw5meWJy8kuzbZpPM0mZBCU50NOzeqOXUlFVLQCOWU5rEpaxTVdrsHD2QMAvbueKyOu5Jdjv1BYWVh97t8n/uaP439wZ+876/07RBJRIYQQojU7vhHcfCG4r0Wnm2eINlQRNZ/TXIloqaGUFYkrKK8qJ78iv1me2RyWJy6n1FjKdV2vA0BRFO7pcw/JRcmsPL6yzutMqon3dr1HR++O1deeSVEUHuj3AHp3PW/884bNxuyYVBOvb3+dNh5tuKvPXVZde3P3m6kyVfFj7I82iaXZmRsWNffy3KJM7c+waytv+CTOa0vjl2I0Gbku+uy/j27qdhNlxrLqUS6GKgOv/P0KId4h3N779nrvKYmoEEII0ZolboSwYRZ3rqyuiDbQrAi0imhWaRYVVRVNCtESq5NXVw9Hzyt3wAgNO1l0dBGR/pH0bXv6BwWjO40m0j+Sufvn1rnHc+XxlRzOPcwD/R7A1cm11nO8Xb15qP9D7M7azcqkupNaa/ye8Dv7Tu7j4YsexsvFy6prO/l2YmToSBbGLqTcWG6TeJpVm65aQtjciWhxBnjLslzRclWZqlh0dBGDgwcT4Xf2NpDugd25KOgifjjyA1WmKr459A3HC4/z1JCnGuwOLomoEEII0VoVpkPuMQivuxnEudKK0/B09sTfreGRLCE+IaioNcZ32MOy+GXoFO1rSW55rt2f1xzMTYqu7XotiqJUv69TdNzV+y7i8+NZm7y2xnWGKgMf7v6wuhNtfSZHTiY6IJp3d7zb5OSv1FDKezvfo2dgTyZ1mdSoe8zoMYO8ijx+S2h4D2yLo9NBxwGOqYj6yLJc0XJtTt9MWnEa10dfX+vx6d2nk1acxoLYBdVNzurqpHsmSUSFEEKI1irRuv2hcGp0i0/HsxKjulSPcLFzw6KUwhT+yfiHceHjAMitOD8S0TObFJ1rXPg4wnzD+GzfZ2ctq1VVlYVHF5JSlMLDFz1cnZzXxUnnxOODHie9JL3JjYI+3vMxWWVZPDn4yQafW5eB7QbSTd+N+YfmW7VcuMXsCw4ZBJkHobKk+Z4pFVHRwv0U+xNtPNpwWafLaj1+WafLCPYK5rXtr6GqKk8MfsKi+0oiKoQQQrRWxzeCux8EWz7nM7U41aJluaBVRMH+I1yWHdOqobf2vBWA3LLWn4ie26ToXE46J+7odQeHcw9zz6p7uHn5zYxfPJ7B3w3mte2vMaDdgHrHHpxpcPvBXN7pcubun0tWaVaj4v3xyI98e+hbru96Pf2C+jXqHqDtXf1Xj39xrOAYW9O3WnTNgiMLuOTHS1rGkuyQQaBWwfHNzfM8VZWKqHAIo8lo0Z+59OJ0NqRuYGrUVFx0LrWe46xz5oboGwC4u8/ddPDuYFEMkogKIYQQrdXxjRA2AnROFp2uqtoyW0saFQEEugfi4exh14polamKn+N/ZliHYXQN6IqCQl5FC0hImujcJkW1ubrL1QwOHkx2WTbuzu70C+pHTHQM/xnwH2aPnG1R1drsPwP+g9Fk5INdH1gd6x/H/+DVba8yKnQUTw2pe9SCpcaHj6eNRxu+Pfxtg+dmlGTwzs53KDIUsSF1Q5Of3WShg8FDDz/cAL/8G/Lt3KyrohCMZVIRFc3qcM5hbvz9RsYtHkdGSUa95847NA+douPaqGvrPe+m7jfxwsUvVP9A0RKWdTYQQgghRMuSnwJ5x2HIvRZfkleRR5mxzOKKqKIohPiE2DUR3XZiG5mlmTw+6HGcdc74ufmdFxXR2poUnctF58IX476wyfNCfUO5ucfNfHXgK27sfiM9A3tadN3W9K08tfEp+gf1561L38JZ1/Svhq5OrsREx/Dxno9JyE8gwr/uGbevbXsNk2oiwC2A9anruSbymiY/v0k8/OH+v2HTu7DjC9j7Iwy4FS75j32qltUzRKUiKuyvoqqCz/Z+xpcHviTAPQCDycDcfXN59uJnaz3/ZNlJFh5dyNURV9Peu3299/Zw9mBa12lWxSMVUSGEEKI1qp4fakWjolOjWyxNRMH+I1yWxi/Fz82PUaGjAG0uXWuviG5J38LBnINcH329VVXNprq7993o3fW8vu31OrvxnungyYPMXDuTzn6d+fDyD3F3drdZLNdHX4+rzpV5h+vet7omeQ1/pfzFvX3vZUzYGDanbaayqtJmMTSaTzuY8Do8tBv6TYcdX8L7feH4Jts/q3qGqFREhX3tydrDdb9ex9z9c7k64mqWXbOMaVHTWBK/hBPFJ2q95tuD32IwGawe5WQpSUSFEEKI1ihxo7aEMKiHxZeYu9929LE8EQ31CSW1ONVmcyrPVFBRwJrkNVwdcXX1iJIA9wByynJs/qzmUmWq4q1/3iLEO4RpUdZVB5rK29WbWQNmsSd7D78e+7Xec48XHOe+1fcR4B7AnDFzat3H2hR6dz0Tu0xkWfyyWmMpMZTw2rbXiPSPZEbPGYwKHUWpsZQdGTtsGkeT+IXAxPfgwR3g5Ab7F9r+GVIRFc1gReIKZqyYQbmxnDlj5vDyiJfxc/Pjzt53oqAwd//cGtfklefxY+yPTOg8gTDfMLvEJYmoEEII0dqoqlYRDR+hjZywkHmGqKV7REFLRCuqKsguy7Y6zIb8nvA7BpOBKZFTqt9r7RXRpfFLic+PZ9aAWXXO/7SnSV0m0bdtX97Z+Q6FlYW1nlNUWcQDax4AYM6YOQR5BtklllkDZjEgaAD/3fRf3t357lmdcT/a/RGZpZk8f/HzuOhcGBw8GHcnd9alrrNLLE2i7wwhAyDVDkmyVERFM/jhyA909uvM0muWMrzj8Or3g72CmRo1laXxS0kvTj/rmnmH5lFuLOfu3nfbLS5JRIUQQojWJu84FKRYtSwXtIpogFsAni6eFl9jz865y+KX0V3fnWh9dPV7end9q50jWmIo4cPdH9I/qD9jw8Y6JAadouPpIU+TX5HPR7s/qnHcpJp4etPTpBen897o9wj3C7dbLH5ufnw69lNiomP48sCXzFw7kxJDCYdyDvH9ke/P6tDr7uzO0A5DWZ+y3i7V9yYLGQxZh6CiyLb3LcoAZ3et+7UQdpBVmsXurN1M6DwBLxevGsdrq4oWVBTw/ZHvuSL8inr3eDeVJKJCCCFEa2Peq9bZykS0KM2q/aFgv1miR3KPcDj3MJMjJ5/1vt5dT0FFAUaT0abPaw5f7P+C3PJcHhv4WLPuDT1X98DuxETHsCB2AYdzDp917MsDX7I2ZS2PDnqUi9pdZPdYXHQuPDP0GZ4e8jQb0zZy8/KbeW7zcwS4BfDwgIfPOndUyCjSS9KJy4+ze1xWCxkEqgnSdtr2vsWZWjXUgb+5lsYIAAAgAElEQVRfROtwsuwkOzN3YqgyWHXdmuQ1AFwRdkWtx4O9gpkWNY1lccuqt298d/g7Sgwl3N3HftVQkERUCCGEaH2ObwSvttC2m1WXpRWnWbU/FKCDVwd0is7mFdFfjv2Ci86FqyKuOuv9APcAAPIr8m36PFuoMlXxU+xPpBTW/G+RXpzONwe/4aqIq+jd1vK5rvbyYP8H8Xfz55Vtr1Q3LtqSvoUPd3/IhM4TuKnbTc0azw3dbuDTMZ+SWZpJbF4sTw5+ssa+1EtDLgVgfcr6Zo3NIiEDtNfUf2x736IM2R/ayny0+6Nm/z2qqiqz1s7i1j9uZfiPw3lwzYN8d/g7jhccb3AFwaqkVXTx61JvZfOO3negKApz982lqLKI+Yfmc3mny+ka0NXWH+UskogKIYQQjlZlgJVPw8n4hs9VVa1RUfgIq6ooBpOB9OJ0Ovl0sio0FycXgj2DbZqIqqrK6qTVDO8wHD+3s5ck6t31AC1yee6a5DW89PdLTPllCnP2zjmrw+v7u95HURQe7v9wPXdoPr6uvjwy4BH2Zu/l5/ifSS9O54kNTxDhF8ELF7/gkIrtxR0uZsFVC3h1xKuMCx9X43hbz7b0CuzVMveJegRAm2hIsXEiaq6IilYhpSiFz/Z9xmvbX2vWVRv/ZPzDnuw93BB9A5O6TCKhIIHXt7/OxGUTuWvVXXV2yTZXUceG179VINgrmGu7XsvP8T/z9o63KTIU2b0aCpKICiGEEI4Xuxy2fgR75jd8bm4CFKVbvT80ozgDo2qkk691iSic7pxrK4dyDnGi5ASXh11e41hLTkR/iv2J9l7tGRkyko/3fMy0X6bx94m/2Ze9j+WJy5nRY0aDs/aa08QuE+kf1J93d77LzLUzMZqMvDvqXav2CNtaqG8oE7tMrDMRHhk6kv3Z+1tm5+SQQVpFtL4KlKrWf/xcRZlSEW1mpYZS7lt9H1vTt1p97R+JfwDa6pLVyattHVqdPt//OW082vDooEd5ZugzLJ+6nOVTlnN7r9vZdmIba1PW1nrdX8l/YVJNjOk0psFn3NHrDnSKjsVxixkZMpIegZZ3ZG8sSUSFEEIIR/vnC+3Vkq6ciRu0186XWvWIpKIkAKsroqA1LLLlHtFVSatwUpwYHTq6xrHqRLSsZSWiiQWJbMvYxnVdr+PtUW/z6ZhPqVKruOvPu7h/zf0EugdyR+87HB3mWcyNiwoqCzice5hXRrxi1+ZEtjAqdBQqKhtSNzg6lJpCB0FZrvbDoNoYK+G9PrBhtmX3M5RBRYFURJvZnL1z2JS2iS/2f2H1tcsTl9OnTR/CfMP45sA3zdJYa1/2Prad2MYtPW7Bzcmt+v1Q31D+3f/fhHiHMHff3FpjWZ20mjDfMIuW2Lbzase1Xa8F4J4+99juA9RDElEhhBDCkU7GQ+J6cPWBtF1Q1cByr+MbtS+ugZFWPSa5MBmgURXREJ8QcstzKTGUWH3tuVRVZXXyagYFD6qxLBdOJ6ItbYTLoqOLcFacmRKljZoZ0XEESyYt4d6+91JhrOA/A/9Ta0dKR4vWR/PM0Gd47uLnuKzTZY4Op0HRAdEEewWzLmWdo0OpKWSQ9pqyvfbjieuhIBk2vwelFvwgpejU6BapiDab+Lx45h2aR4BbANsytlm15SAuL474/HiujLiSGT1mcCDnADsy7T/3du6+ufi5+XF99PU1jjnrnLmz950czDnIlvQtZx3LL89ne8Z2xoaNtXgp/swBM/lm/DfNts9dElEhhBDCkXZ8CTpnGPUkGEog+3Dd56qq1jE3/BKru2ymFKXg6exJoHug1SGaR7jYoioanx9PUmFSneNN/Nz80Cm6FrU0s9xYzs/HfubysMtp49Gm+n13Z3ce6PcA26ZvY2KXiQ6MsH7Xdb2O67pe5+gwLKIoCiNDRrL1xFYqqiocHc7Z2nbTfmBUV8Oig0vBxRMqS2Drxw3frzhTe/WWRLQ5qKrKy9texsvVi8+v+BwFhZ/jf7b4+hWJK9ApOsaFj2NSl0kEuAXwzcFv7BgxxObGsi51HdO7T69zSf2kLpNo59mOz/d9ftb7a1PWUqVWMSas4WW5Zh7OHs3STdtMElEhhBDCUQxlsOc76D4Rup3qHltXtQUgO1b78mrlslyApMIkOvl2alSTGvMIF1s0LFqdvBoFpc7qnE7R4e/m36IqoquSVlFQUcD1XWtWJECLWdjOyJCRlBnL2H6inj8LjqBz0rrnptYSl7ESDv8GPSZDj2tg22cNV0WrK6KyNLc5/JbwGzszdzLzopl003djWMdhLItfRpWpqsFrVVVlReIKBgcPpo1HG9yd3bmx242sT11PQn4dS7UttCdrD5vTNtd67H/7/4eXi1e9Xa5dnFy4rddt7MraxY6M0xXaP5P+pKN3R3ro7b/Xs7Hkb04hhBDCUQ4uhfJ8GHg7BIRrI1nq2yeasE57jRhl9aNSilKqE0pr2XKW6Oqk1fQP6n9WZfFcend9i9oj+lPsT4T7hjMoeJCjQ7kgDG4/GA9nD9anaiMyVFUlNjeWrw98zf9t/T+KKoscF1zIIMg8qFU9z5SwVtvv2XMKjHwcKovg70/rv5dURJtNYWUhs3fMpk+bPkyNmgrA1MipZJZmsvVEw02LDpw8QGpxKld2vrL6vZhuMbg5ufHtoW8bHVdSYRL3rLqHe1ffy4tbX6TMWFZ97HjBcVYeX0lMdEyt2xjONDVqKnp3PXP3zwW0z/v3ib+tWpbrCJKICiGEEI7yzxfQpuvppbbmrpx1SVyvJawBYVY9xmgyklqcSpivddeZ+br64uvq2+SKaHJhMkfzjnJ5p5rdcs+kd9e3mIpobG4se7L3cF3X61r0F7rziZuTGxe3v5jVSat5cuOTjP5pNNf+ei1v73ybhUcX8tY/bzkuuJDBoJq0/dxnOrgU3P20HxK16wndJ8G2OVBWz+/jogxtWb6n9cvlhXU+3PUh+RX5PDP0meoVDKNCR+Hv5s/SuKUNXr88cTkuOpezOn3r3fVMjpzML8d+4WTZSatjMlQZeHzD47g4uXBTt5tYdHQRN/x2A7G5sQB8eeBLXJ1c+VePfzV4Lw9nD2b0mMGW9C0cOHmA9SnrMZqMdW6BaCkkERVCCCEc4cReSNuhVUPNCU7IQMiJq31JX5VR2x8aMcr6R5WcwGgyNqpjrlmoT2iTE1HzuIOG9iwFuAe0mPEtC48uxM3JjWsir3F0KBeUK8KvIKc8h63pWxnSfggvDX+JVdeu4o5ed7A0fimb0jY5JrCQgdrrmT8wMlbAkd+h20RwdtXeG/kEVBTC33PqvldxJngFgU6+jtvTwZyDLIhdwI3dbqR7YPfq912dXLk64mr+SvmLvPK6f2BQZapi5fGVjOg4Al9X37OO/avHvzCajHx/+Hur4/pg9wccyjnEi8Ne5KkhT/HZ2M8oqCjgpt9v4tO9n/LrsV+ZFjWt3tUjZ4qJjsHH1Ye5++byZ9KftPNsR682vayOqznJ73whhBDCEXZ8Cc4e0PeG0++Zu3KeW20BSN+tfbGNGGX1o1IKtQSysUtzAcL9wkksTGz09aAty+0R2IMO3h3qPU/vrm8RiWiJoYRfj/3KuPBxDS6NE7Z1ZecrWTltJWuvX8sbl77B5MjJBHsFc3+/++ni14UXtrzgmCW6nnqtY/WZieixv7Q/mz2nnH4vuBd0u1pbnluWX/u9ijJkf6idmVQTL299mUCPQB7o90CN41OjpmI0Gfk94fc677EzcyfZZdlnLcs1C/MN47JOl7EgdgGlhlKL49qctpmvD35NTHRM9QqRYR2GsXjSYga3H8wnez4BBW7rdZvF9/R29ebm7jfzV8pfbErbxNiwsS1+/3rLjk4IIYQ4H5UXwr6F0GsaeAScfr9Df1B0tS/PNe8PDbe+UVFykTa6pbFLcwEi/SPJKMlo9Jf/jJIM9p/cb9FSsQD3AIoqizBUGRr1LFtZnricUmNprWMThH0pikIH7w41vki7Orny0vCXyC7LZvYOC+d12lrIIK2pmHlu48Gl4O4PESPPPm/kE9q+0W2f1X6f4kzZH2pncXlxHMg5wH1978PH1afG8aiAKHoF9mJJ/JI6Z4IuT1yOh7MHI0NH1nr81p63UlhZyNL4hpf4ApwsO8l/N/2XSP9IHh346FnHAj0C+fjyj3nu4ud4buhzBHtZ9/tjevfpeDp7topluWBBIqooSoiiKMsVRTmkKMoGRVE6KIoSoSjKTkVR4hVFefqMc5899d4/iqKE2zNwIYQQotXat0Ab1TLo9rPfd/OBoB51J6LBfcDL+v1kSYVJeDh7WLzEqzaR/trc0mP5xxp1/ZrkNQAN7g8FqkfMOHKfqKqqLIxdSHRANH3a9HFYHKKm3m17c1vP21gSt8QxS3RDBkHpScg7DoZyOLJc63zt5HL2ee37QPRV8PfHUF5Q8z4WVETrSo6EZfZm7wXg4vYX13nOlKgpxOXFcTDnYI1jhioDq5JWMTp0NB7OHrVe3y+oH33a9OGn2J8a/N/LpJp4ZtMzlBhKeOvSt3B3dq9xjk7RcV3X66pnFlvDz82P23vdTqR/JP2C+ll9fXOzpCJaAbyuqmoPYCEwC3gGeAWIBiYpitJDUZQwYMqp9/4PeMM+IQshhBCtmLFCW5bbvi90qGVeW8hAbe+oyXT6vcoSbWTEuRUXC5k75jal2Y45EY3Pj2/U9auTVhPpH0lnv84NnhvgrlWJHbU816Sa+HTvpxzOPcz10ddLk6IW6L5+9xHhF+GYJbqhg7XX1H/g2BqtQ27POpKGkY9rSei5HXSrDFoy20BF9P4193PXn3dZtexTnLY3ey96d331LOTaTOg8AXcn91qbFm1J30JhZWGty3LPdG3Xa0koSKhOfOsy79A8Nqdv5vFBjxMZEGnZh7DSPX3vYek1S1v8slywIBFVVTVbVdUNp355HAgARgLLVVWtAn479evRwIpT760A6v7RgxBCCHGhKc6GdW/Au70g6xAMvf90k6IzhQzSvrjmnJHwJW+FqspG7Q8FbWluU5blAnTw7oCHs0ejKqI5ZTnsytplUTUUtD2i4JhEtMRQwqy1s/h076dM6jKJKZHWVyWE/bk5ufHy8Jcds0S3bXdw8dIS0YNLwUNf92zfDv20uaKb34eCM8YfFWdpr/VURPdl72NT2ib+PvE3D/31EOXGcht+iAvD3uy99G3bt94fJvm4+jA2bCzLE5dXj08xmAxklWaxLH4Zvq6+DOswrN7njAsfh6ezJ4vjFtd5TlFlEXP2zuHSkEu5rut1jftA5xlrU+XrgJWAl6qq5j8NWUAwEAScBFBV1Qg4KYridO4NFEW5W1GUHYqi7MjOzm585EIIIURrkHkIfn4A3u0J617Vvpj+ayn0ian9fHPDojOX5yasBydX6GT9z3irTFWkFqU2qVERaMvFuvh1IS4/zupr16asxaSaLN6z5KiKaHJhMtN/n8761PU8MegJXh7+Mi7nLrcULUbvtr25teetLIlbwtRfpvLh7g85mHPQ/stZnZyh40WQuBFiV9S+LPdMY1/SRr6seu70e8UZ2qt33Yno/MPz8Xbx5pkhz7A9Yzuz1s2isqrSRh/i/JdbnktSYZJFS1SnRE2h2FDMlJ+ncOmPl3LRvIu4fOHlrE5ezbjwcQ3+PeDp4smEzhNYeXwlxZXFtZ7zU+xPFBuKeaDfA7LK4hRnS09UFOVKoBOwCPjg3MPAuX/qa/0vrKrq58DnAAMHDpSF70IIIc5feUnw+UhtVmD/m2HofdAmqv5rAqPAzU9LRPtP195LWKfNL3T1sjqEjNIMDCZDk0a3mHXx79KoPXnbTmyjnWc7ugZ0tej86j2i9YxUsLXNaZt5bMNj6BQdc8bOYWj7oc32bNF4D/Z7kCDPIFYnreZ/+//H5/s+p51nO64Iv4KZF83E1cnVPg8OGQSb3tH+va5luWYBYTB8Jqx/HQbdCWHDoChTO1bH0tzMkkxWHV/Fjd1vJKZbDE46J17c+iKPb3ict0a+hYtOfkDSkH3Z+wDo27Zvg+cObDeQaVHTyK/Ip41HGwLdAwn00P6x9O+CaVHTWBy3mBXHV9SoeFZUVTD/8Hwubn8xPQJ7WP9hzlMWJaKKokQCrwPjVFVVFUUpUhTF/VRVNAitKpoPdD91vjOgnlqmK4QQQlyY0nZqS2pv/wM6DrDsGp0OQgZA6g7t1yU5kLEfRj9d/3V1SC7UOuZ28m16IhoVEMXPx34mrzyvumppifj8eLrpu1lcBfBx9cFJcWq2iuiG1A38+69/E+kfyfuj3693P5loWVycXJjefTrTu08nrzyPDakbWJ20mnmH5tFN341JXSbZ58HmfaKegRB+ScPnD38Yds+H5Y/DPetPV0TrWJq7IHYBVWoVN3a7EdD2IFZUVfD69td5euPTvHbJazjpaiw8FGfYk7UHZ8WZnoE9GzxXURReGPZCk57Xq00vogKiWHJ0SY1E9Ndjv3Ky7CSvXfJak55xvrGka64P8ANwi6qqJ069vQq46tTS24nA6lP/jD/13pXARvuELIQQQrQS2Ue0cSxBDX8ROkvIIMg6CBXFcHwDoDa6UVF1ImqjiihY17DIUGXgeMHx6mZHltApOgLcA5qlInqi+AT/3fRfovyjmDdhniShrViAewDXRF7D+5e9j7+bP9tPbLffw8xL6LtP1JbqNsTVE8a9DJn7Ydc3pyuiXkE1Ti03lrPw6EJGh44+a0n99O7TmXnRTFYcX+G40TWtyN7svXTTd6u1M609KIrCtKhpHMg5QGxubPX7VaYqvjrwFT0DezIkeEizxNJaWLJH9EEgEvhKUZQ9iqJsAV4AHgPigF9UVT2iqmoG8BUQC7wEPG6fkIUQQohWIuswBHQGFyu/CIUM0vaUpe/W9oe6+tTeYdcCyUXJuDu509azbaOuP1NjRrgcLzyOUTVa3SFS764npzzHqmusZTAZeGzDYxhNRt4e9TaeLp52fZ5oHjpFx6DgQWzL2Ga//aJebeCmhXDZs5Zf02MyhI2ANS9pP6TyDATnmkuHlycuJ78in5t73Fzj2B2972Ba1DR+jP2RzJLMpnyC85rBZODAyQPNPsLk6oircdW5siRuSfV7a5LXkFyUzO29bpe9oeewpGvua6qqBqiq2u/UP8NOddIdqqpqhKqqL59x7keqqkaqqtpXVdVk+4YuhBBCtHDZRyCou/XXmZfxpm7X9oeGj7Cs6lKL5KJkQn1DbdLKv51nO7xdvK2qiJrPjfJvYG/sOaytiKqqavUYjw92fcDe7L28cPELTe4qLFqWIcFDyCjJIKUoxX4P6XqFlpBaSlFgwhtQng+HltW6P1RVVeYdmkfXgK4MbDew1tvc2ftOTKqJ745819jIz3tH845SXlVu0f5QW/Jz8+PysMv5NeFXyo3lqKrKlwe+JMw3zOKu4ReSlj9gRgghhGiNjBWQc6xxiainHgIj4cBSyEts9NgW0Jbm2mJZLmhLzyL9I61KROPy4nBSnCyaH3omvbveqj2if6X8xbAfhnHFoit4auNTLDq6iMSCxDorYutS1vH1wa+JiY5hfOfxVsUmWr7B7bU9nNsytjk4knME94KBt2v/Xsv+0O0Z24nPj+fm7jfXWT0L8QlhbNhYFsYurLND64VuT9YegGaviILWtKiosog1yWvYnrGdgzkHubXnrbKntxaSiAohhBD2cDIO1Cpo261x14cM1vaTQaMT0SpTFSlFKTZLREHbJxqfH2/xksf4/HjCfMOs7l6qd9dbVRHdlbkLV50rvdv0Zmv6Vl7c+iKTlk1i9E+jeX7L86xLWVc9hzG9OJ2nNz1Nd313Hhv0mFVxidYh3DecII8g++4TbazRT2vLcvURNQ7NPzQfvbueKyOurPcWt/W8jWJDcb1zKy9ke7P3EuQZRLBX7V2J7WlQ8CBCvENYEreEL/Z/QRuPNkzsMrHZ42gNGrfORwghhBD1yz6ivTamIgoQMhD2fq8t32sb3ahbZJVmaaNbbNAx1ywqIIrFcYvJKc+hjUfDyxLj8+Pprrf+v4HeXU+xoZiKqgrcnNwaPD8uL47IgEjeHvU2qqqSVJjEzsydbDuxjT+P/8mSuCV4OHswvMNw0orTMKkm3h75tkX3Fq2PoigMaT+EzembUVW1Ze3N89TDfVtrjGNKKUxhfep67upzV4O/L3u26cnAdgOZf3g+N3W/Sca5nGNv1l76tW3+aihoe5SndZ3G+7veB2DWgFny90wdpCIqhBBC2EPWYVCctCW2jWHuyhkxUttb1ghJRUmAbTrmmpkbFsXlxTV4bqmhlNSiVKsbFQHV42EsrYoezTtaPadUURTC/cKZ1nUab458kw0xG/hszGdM6jKJfSf3cTj3MC8Me4FQ39AG7ipas8HtB5NbnktcfsO/V5udTztw8z7rre+PfI+TzomY6BiLbnFbr9vIKMlg5fGV9oiw1coqzSK9JL3Z94eeaVKXSTgpTni7eNcY5SJOk0RUCCGEsIesw1oS6tzIn4QH9YDuk+CiWxodgi1niJqZR7hY0jk3sSARFdXqRkWgVUQBi/aJ5pTlkFOeU+dzXJxcGNZxGM8MfYZV165i7fVrGRc+zuqYROsyOFjbJ9oil+fWYk3yGkaFjCLIs+ZIl9qM6DiCCL8Ivj7wda1L5atMVVRUVdg6zAaVG8tZn7Lefh2LG7A3ey/gmP2hZkGeQdzb914eG/QYPq4+DoujpZNEVAghhLCH7MMQ1Mj9oaB1yY2ZB+HDG32LlKIU3JzcLP5ia4lA90AC3AIsalhkrkRZM0PUzJpE1PycrvquDZ6rU3QWLSkWrV8H7w6E+oS2vIZFtSg1lHKi5ATdAy1fxq5TdNzS8xZi82L5+8TfZx3bn72fqb9MZczCMSyNW4pJNdk65Dp9vu9zHvzrQd7a8ZZDktG9WXtx1bk2akuALd3b916mRk11aAwtnSSiQgghhK0ZyiA3Edo69otQUmESoT62Gd1ipihKdcOihsTnxeOqcyXUx/olsOZE1JKluUdzjwJUL80VwmxI+yHsyNiB0WR0dCj1SixMBLC6u/TVEVcT6B7INwe/AaCyqpL3dr7HzStuptRYSphvGM9teY5bVtxCbG6szeM+l9FkZFn8MrxcvJh3aB5z9s2x+zPPtSd7Dz3b9MTFSfbNtnSSiAohhBC2dvIooDatImoDKUUpjUoCGxLpH8mx/GMNVjvi8uPo4t+lUWMLzHtELamIHs07SqB7YHXyKoTZkOAhFBuKOZJ7xNGh1CshPwGACL+anXTr4+rkyvTu09mcvpmf438m5rcYvjjwBZMjJ7Nk0hK+nfAtLw1/iaTCJGJ+i+GN7W/YdeTL5rTNZJdl8/Lwl5nUZRKf7PmE+Yfm2+1556qsquRQziGH7g8VlpNEVAghhLC1rMPaa1APh4VgUk2kFKUQ5htm83tH+kdSbCgmszSz3vPi8+IbtSwXwMfFB2eds8VLc6UaKmozMHggQI2lqy1NYkEiTopToxqLXR99PR7OHjyz+RkKKwv55PJPeHHYi/i4+qBTdEyOnMyvU35latRUvjv8HTctv4kyY5kdPgUsiVuC3l3PyNCRvDjsRS7vdDlv/PMGy+KX2eV55zqUcwiDyeCwjrnCOpKICiGEELaWdRh0LrXOCWy2EEqzqKiqsE9FNKDhzrkFFQVklWU1qmMuaEuA9W76BhNRo8nIsfxjkoiKWrXxaEOkf2SLb1iUWJBIqE9oo5aT+rn58ejAR7mp200svWYpl4RcUus5z138HJ+M+YTEgkQ+2PWBLcI+y8myk2xI3cCkLpNw0bngrHPmzUvf5OL2F/P8ludZnbTa5s88l7lRUd8gqYi2BpKICiGEELaWfQTaRIED9yjZo2OumbnKWd8+UfOxxlZEAfQe+gb3iCYXJVNRVUFUgPWdecWFYWj7oezO2k1lVeVZ7xuqDPyW8BulhlIHRXZaQkGC1ftDz3R99PU8NeQpfF196z1vRMcR3BB9A98d/o5dmbsa/bza/HrsV4yqkSlRU6rfc3Vy5b3R79G7TW8e2/AYu7N22/SZ59qbvZeO3h2lIVkrIYmoEEIIYWtZh6GtY/eHJhdpiWiYj+2X5vq5+dHWo239iWiedqwxo1vMAtwCGqyImquyUhEVdRkcPJjyqnL2Ze+rfq+osoj71tzHUxuf4ssDXzowOjCYDCQXJlu9P7SxZg2YRQfvDjy7+VmbLdFVVZUlcUvo17Zfjc/h6eLJx5d/TAevDsxaO4uMkgybPLO2GPZm7ZX9oa2IJKJCCCGELVUUQ36SQ/eHglYRddW50s6rnV3u31Dn3Lj8OLxcvAj2Cm70M/QeDS/NPZp3FCfFiQh/xy2DFi3bgOAB6BRd9RiXjJIMbvnjFnZm7CTMN4zFcYsxVBkcFl9qUSpG1dikiqg1PF08+b9h/0dyUTIf7v6wxnGjycj/9v+Pm5ffzN1/3s0j6x7h+S3PM/uf2SyNW1prk7I92Xs4Xni8znElfm5+fHDZB5QZy5i5diblxnKbf67dWbvJKsty6PxQYR1JRIUQQghbOnlqRIKDO+YmFyUT4hNi09EtZ4r0jyQhP6HO+YTx+VqjIkVRGv0MvbtliWiYbxhuTm6Nfo44v/m6+tJD34PtJ7ZzNO8o05dPJ704nY/HfMzjgx7nZNlJ1qSscVh8CQWN65jbFIPbDyYmOob5h+aftVzW/N/n/V3vY1JNlBhLiM+PZ2PqRhbELuC5Lc/x5j9v1khGl8QtwdPZk3Hh4+p8Zhf/Lrx+yesczDnIi1tftOmM0YKKAp7c+CQh3iFMjJhos/sK+3J2dABCCCHEeSXr1JgIB88QTS5Ktsv+ULNI/0jKq8pJK0oj1PfshkiqqhKfH8/YsLFNeobeXU+ZsYwyYxkezh61nhOXF0fvNr2b9Bxx/hvcfjDfHvyWW1bcgqezJ9+M/4ZofTRVpio6endkwRm1cNgAACAASURBVJEFjA8f75DYEgsaN0O0qR4Z8Aib0jbx7OZn+fGqH5l/eD6f7fsMX1df3h75NleEX3HW+aqq8uY/bzL/8Hx0io5HBz6KoiiUGEpYeXwlEzpPwNPFs95nju40mgf6PcDHez6mm74bt/S8pcmfQ1VVXtz6Itml2Xw74Vu8Xb2bfE/RPKQiKoQQQthS9mFwcgN9836pPFOVqYrkwmS77A81M3fDrW157smykxRUFDSpURFQPRe0roZFJYYS0orTZH+oaNDQ9kMxqkaCvYL57qrviNZHA+CkcyImOoYdmTuq9zU3t8SCRII8gpo9gfJ08eTFYS+SVJjEuMXj+HjPx4ztNJZl1yyrkYSC1sn68UGPc1O3m/j20Le8u+tdVFXlj8Q/KDOWMSVySi1PqenuPnczptMY3tn5DlvStzT5cyyKW8SqpFU8dNFD9G4rP5RqTSQRFUIIIWwp6zC07Qo6J4eF0BydZLv4dQFqT0Tj8rUGQk1pVARasyKoOxE1NyqSjrmiIUPbD+WdUe/wzYRvauxbnhw5GVedKwtiFzgktoT8BDr7O+YHV0PaD2FGjxm4O7nz3qj3eHPkmwS4B9R5vqIoPDn4SWKiY/jqwFd8uPtDlsQvIcIvwuImQTpFxysjXiHCL4LH1j/WpB8AxOfF88b2NxjWYZhNqquieUkiKoQQQthS1hGHL8ttjgTN29Wb9l7ta01EzV8sGztD1EzvoVVEc8pzaj1+NO8oIB1zRcMURWFs2Nhax5sEuAcwvvN4fk34lRJDSbPGpaoqiYWJzbo/9FyPDnyU1det5vKwyy06X1EU/jvkv1zb9Vrm7p/Lvux9TI2aatV+cE8XTz647APcnNy448876p1JXJdyYzmPbXgMLxcvXhnxit32wwv7kf/FhBBCCFspL4TCVIc3KorPj0en6Oz+5TZaH83fJ/4msySzxvP17vrqpbWNpXerf2nu0byjeLtoCbEQTRETHUOJoYTfjv1W63FbNtY5U1ZpFiWGkmbfH3omRVGsbiqmU3Q8O/RZru16LX5uflwdcbXVzw31CeXLcV/irDhz5593WpWMlhnLeH3768Tnx/PaiNdkbmgrJYmoEEIIYSvZpzrmtoCKaCefTrg7u9v1Of/u/2/KjeX8+69/U2oorX4/Pj++ycty4XRFtK7OuXF5cUQFRDWpM68QAL3b9Ka7vjs/xv5YI+n8PeF3LllwCb8e+9Xmz00s1BoVObIi2lg6RcfzFz/P2uvWEugR2Kh7hPuF8+X4L3HWOXPHyjuqVzmcqbiymLXJa/l076c8su4RJi6dyNDvh7I4bjG39byNYR2HNfWjCAeRRFQIIYSwlaxD2muQYxNR8+gUe+sa0JW3Rr7FkdwjPL3paUyqCZNq0p7fxGW5AJ7OnrjqXGutiKqqSlxenCzLFTahKAo3dLuB+Px4dmXtArSq2/NbnufJjU9SWFHIvEPzbP7chPzmH91iay5OLk26Psw3jK/GfYWLkwt3rryT2NxYkguTmXdoHnf+eSeX/HgJD619iE/3fEpsbixd/Ltwd5+7eX/0+zx80cM2+hTCEWR8ixBCCGEr2UfAxRP87dettiHlxnKSi5KZ0HlCszzv0pBL+c/A/zB7x2w+2v0RU6OmUmYss0kirCgKeg99rXtEM0oyKDIU2aTyKgTAhM4TmL1jNguOLMDP1Y9H1z9KQkECd/W+Cz83P2bvmE1sbmx1x11bSChIwNvF+4JfWtrJtxNfjfuK21fezg2/3YBRNQLamKgZPWdwScdL6BHYo8HxMKJ1kURUCCGEsJWsw9CmK+gct+AooSABk2pqloqo2YweM0gsSGTu/rmkFqcC2Oz5AW4BtS7NNXfm7aqXiqiwDQ9nDyZHTuaHwz+wNmUtni6ezBk7h2EdhpFXnsd7u95jWfwynhj8hM2eebzgOBF+EbK8nNPJ6NcHvybCP4KRISMJ8QlxdFjCjmRprhBCCGErWYcdvizXESNNFEXh6SFPMyh4ECsSVwC2S0T1Hvpal+aa95I1Z8Itzn8x0TEoikLftn1ZNHERwzpo+w8D3AMYHTqa3xN+x1BlsNnzEgoSHNqoqKUJ9Q3l2YufZXr36ZKEXgAkERVCCCFsoSwPijOgrWM75sblxeGqcyXUJ7RZn+vi5MI7I9+hk08nQn1C8Xb1tsl99W76WiuiR3OP0sGrAz6uPjZ5jhCg7Vdcfd1qPr/ic9p6tj3r2OTIyeRV5LE+db1NnlVUWUR2WbYkouKCJUtzhRBCCFtI2qq9BvdyaBjx+fF08e+Cs675/y/e392feVfOo7iy2Gb31LtriWiVqQonnVP1+3H50qhI2EddY4eGdRhGW4+2LItfxpiwMU1+TmJB6+2YK4QtSEVUCCGEsIV/5oJPe+g80qFhmEeaOIreXU8n3042u19UQBQVVRVM+WUKyxOWU2WqorKqksSCRId+TnHhcdY5M6nLJDambSS7NLvJ90soONUx118SUXFhkkRUCCGEaKrso3DsLxh0BzRxlEFTFFQUkFWWdV7tm5zYZSJvj3wbJ8WJJzY+wZRfpvDFgS+oUqukIiqa3eTIyZhUE78m1D5TNLc8F6PJaNG9EgsScdG50NG7oy1DFKLVkERUCCGEaKrtn4OTK1x0q0PDcESjInvTKTquCL+CxZMWVyekn+z5BEASUdHswv3C6de2H8vil6Gq6lnH/kj8gzELx/DM5mcsuldCQQJhvmEOWUYvREsgiagQQgjRFOUFsOd76HUteLdt+Hw7is+PB87PTrLnJqT39b2PcL9wR4clLkBToqaQWJDI3uy9AKiqytcHvuaxDY/h4+rD7wm/sy5lXYP3SSxIlEZF4oImiagQQgjRFHu+B0MJDLnb0ZEQlxeHj6sP7TzbOToUuzEnpPf3ux+dIl9jRPMbFz4OD2cPlsUvo8pUxavbXuXtnW8zLnwcy6cuJyogipe2vkRhZWGd96isqiS1KFUSUXFBk7/BhRBCiMYymWDbZxA6BDr0d3Q0xOXHEeUfhaIojg5FiPOWl4sXY8PG8sfxP5i5diY/xv7IbT1v481L38TLxYuXhr3EyfKTvL3j7TrvkVyYTJVaJR1zxQVNElEhWrsqIxxZDpWl1l9bUQQfD4XFd0Jugu1jE+J8F78K8hJhyD2OjgRVVYnPiz+v9ocK0VJNjpxMiaGEDWkb+O+Q//LIwEeqK/Q92/Tk1p63siRuCVvTt9Z6fXXHXElExQVMElEhWjOTCX55EH68Eb65GoqtbCcfvxqyD8PBpfDRIPj9USjKtE+sQpyPtn2mjWzpPsnRkZBZmkmRoYgof0lEhbC3ge0Gcluv2/jwsg+5sduNNY7f1/c+wn3DeXHri5Qaav6g2DxDNMw3zO6xCtFSSSIqRGulqrD8Udj7A/S+HjIPwRdj4GSc5fc4shw89PDwPrhoBuz4Ej7oB2tegsoS+8UuxPngZBwcWwMDHTuyxczcMTcy4PxrVCRES6MoCo8MeIRLQy6t9bi7szv/N/z/SC9O5/1d79c4nlCQQAevDni6eNo7VCFaLOkXLURrpKqw6lnY8QUMnwljXoC0XfD99fDFWLjhBwi7uP57VBkgbiVEXwV+HeHqd+HiB+Gvl2HjbMhPhmlzm+PTCNE6mUe2DLjV0ZEA2v5QOD875grRGvUP6s+N3W7khyM/VC/bLa8qp9xYztb0rfRo08PBEQrhWFIRFaI1Wv8mbPkQBt2lJaGKAiED4M7V4BkI314DBxbXf4+kLdrYiW5Xnn4vsAtc9xWMfAL2/6Qt3RVC1FQ9smWaw0e2mMXnxRPkGYSfm5+jQxFCnPLwRQ/TTd+NH478wLL4ZaxLWceerD0EegRydcTVjg5PCIeSiqgQrc2Wj2Ddq9BvOkx4U0tCzfSd4Y5V8ONNsOh2cPODqDG13yd2OTi7Q5fLah675D/avtHfZsH9f4Orl30+ixCt1Z7vobK4RTQpMovLj5NGRUK0MJ4uniy4eoF0shaiFlIRFaI1SdkOfz4NPSbDxA9AV8sfYU89/GsZ+IXCpndrv4+qwpHfIWJU7UmmsxtMfF9bnrv2VVt+AiFaP5NJW5YbMrhFjGwBMJqMJOQnSKMiIVogSUKFqJ0kokK0Jru+BVdvmPwJONWzoMHFXavUJG2C9N01j2fsh4IUiL6y5jGzsGHa3re/P6n9HkJcqOJXa+OOWlA1NLkomUpTpVREhRBCtBqSiArRWlSWwsFlWjXUkqWyF83Qktatn9Q8FrscUCB6Qv33GPMieAXBLw9p80qFELBtDngHQ49rHB1JteqOudKoSAghRCshiagQrUXscqgsgr43WHa+u5+WjB5cAoXpZx878juEDgbvoPrv4eEPV74JGfu0yqgQFzrzyJZBLWNki1l8fjw6RUeEX4SjQxFCCCEsIomoEK3Fnu/BrxOEDbf8miH3gHpqP5tZfoqWWNa3LPdM3SdpI17Wvgq5idbFLMT5poWNbDGLy4ujk08n3J3dHR2KEEIIYRFJRIVoDQpPQMJa6BtTe4OiugSEQ7erYceXUFGsvRe7QnvtdpVl91AUuGo2/D979x1eRbU1cPg356T3Dim0hJBGL0F6L9KUoiAqehXFfq1cy7Wjn10RxIpcFVQQpEiRXqUmlBBIQgrpgfRGSDtnvj82CQTS20lgv8/jEzNnyh5JZNbstddSNLD3wzoNW5JuKoW54oVQwLSaswmakaqqRGRGyPWhkiRJUqsiA1FJag1OrxIzm91rmZZ7rQFPiZ6Hp34T30dsAkdvcKrDQ6uNG/S8B0L/hEsZdR+DJN0Mylu2PGrokVTw89mfScxPZIDbAEMPRZIkSZJqTQaiktTSqSqc/E20inCqRyGSdoHg3les8SzIhNgD4FvLtNxr9ZsLuiI48XPdj81JhMvZdT9OkloKvR6Ofgse/cC9j6FHU+5Q8iE+C/6MMR3GMMN7hqGHI0mSJEm1JgNRSWrpUk5BWljtixRdT1FgwJOi3cTmF0FfKtZ81pWLH3QcItJ89braHaOqEPwTLOoDXw+Ci2fqfl1Jagmid15p2fKYoUdSLik/ifn75uNp68mCQQtkr0JJkiSpValVIKooioOiKPsVRXnryvfvKYoSrSjKSUVRtl6z3+uKokQpinJMUZSOTTJiSbrVnPpdFEfpOq3+5/CbArbtIHQNWDqDR9/6naffw5AdD5Hba963+BKsfQz+ekbMIqk6WDoOonbW79qSZEhlLVv8phh6JAAUlhby3O7n0Ol1fDHiCyyMLQw9JEmSJEmqkxoDUUVRjIEtwLlrNjsC/1JVtaeqquOu7NcBmAr4AO8AsqqJdHO4lA5JwYa5tq4ETv8h+n2a29f/PFojUUEXoMt40Gjrdx7fSWDtCse+r36/tAj4fiSErIThr8Cc9TB3J9h3gBV3wfF6pPdKkqGknYOoHdD3ITAyMfRoUFWVtw+9TXhmOB8M/YAONh0MPSRJkiRJqrMaA1FVVUuAycD+azY7AOnX7ToC2KKqqg4RuMqqCdLNYc1c+HE85Kc2/7WjdkBBOvS4p+Hn6j0HvEaKh+n60hqLthVROyAjuvJ9Tq+G74aLAP7+tTD8ZRH42rrDv7aA53DY8DTsfEesu5Oklm7XO2Bs2bDfnUa0ImwFG2M28kTPJxjqMdTQw5EkSZKkeqlVaq6qqtc/gRsDyxVFCVUU5YUr21y4EpyqqloKaBVFuWHaRVGURxVFCVIUJSgtLa0BQ5ekZhC1Q7RN0RXDiV+a7jqqCnkXxQzotU7+ChZO0Hl0w69hZisCQ/feDTtPnwdBYyTWil7vxHJY8zC49oDHDoDXiOvGYAOzV4pz7P8Udr3bsLFIUlOL/QfC/oIhz4GVs0GHUlhayAdHP+DDYx8yot0IHu3esqr3SpIkSVJdGNXzuGmqqqqKotgBuxRF2Q+o1+1TadUEVVW/A74D6Nu37/XH1IuqqnyyLYJLRTom93ClVzt7NBpZtEFqIL0Otr0henFau0HQMhj0bP3TWqsTsQV+vwcULdi1AwdPsO8E5/6Gvg+LmciWwrot+E0WQeeI18Dkytq0Uyth/VNi1nXWb2BsVvnxWmOY9AXkJos1q6PfbL6xS1Jd6PWw9VWw8RBtkAzobMZZXtn/CjE5Mcz2nc1zfZ5Do8h6g5IkSVLrVa9AVFVV9crXbEVRtgP+QArgB6AoitGV3WpZWrNh1p5I4qvd0WgU+N/BWNxszZjY3ZVJ3d3o7mErKwlK9XPqd0g9AzOWieBz1RyI3CbWaza203+AhSP0+RdknRfVORODxUxp7zmNf72G6jcXzqwVgWTv+0V/0XWPQachMOvXqoPQMooCniPEf8/cZNGnVJJamtOrIOUkTPsejM0NMgSdXseyM8v46uRX2Jva8+3obxnoPtAgY5EkSZKkxlSvQFRRFFdVVVMURTEBhgC/IwLR5xRFeQOYQMU1pU3mQk4hb244Q98O9ix9sB+7wi+y8VQK/zsYy/f7z/PEcC/mj/dtjqFIN5PiAti1QPQLDJgqWp5Yu8KxHxo/EC0pFAFZ1+kw6vWr21VVXLclzYaW6TAInP1E0SJzO7GOtt1tcM/vtX9gb3+b+Bp/uGEVgSWpKRQXwI63wa03dDVMf06dXse8HfM4knKEsR3G8vptr2NnZmeQsUiSJElSY6tvXs8SRVFCgGPAn6qqnlBV9QKwDIgA3gXmN9IYq6SqKv9ZE0KpTuWTu3pga27M1F4eLH2wH0GvjSGwkwPbz15s6mFIN6PDSyAvGcYuELN31xbpyYxp3GvF7IHi/BvbQpRdtyVSFAicK3qcrpojAvZ7V4GJZe3P0ba7KAATf6jpxindmnSlos1QQxxaLP4fMO590BgmBfZ46nGOpBzh+T7P88mwT2QQKkmSJN1Uav23q6qq/1NV9a0r/z5VVdXuqqr2UFX1k2v2Wayqaucr2xv4FFCzlccS2HsujZdv96WjU8UHYFsLYwZ6ORKdlk9BcWlTD0W6meSnwYEvwGcidLgmBa73A2INZ9Cyxr1e+F9gagOdWln1y+4zwdwBXHvCfavB1Lpux2uNoF0/GYhKjW/D07Cwhyj2VR95F8T/A/ymQAfDFYDfGrsVcyNzZvrMlEtMJEmSpJtOq610kJhVwIJNYQzwdOT+2yrvodbVzRa9CmEpuc08OqlV2/cRlBTA6LcqbrdxBb9JonpuyeXGuZauFMI3Q5dxLaI/YZ2YWsOTR+GhraIib320HwAXz0BhTuOOTbp1hW+GU7+CpTOsexyOfFv3c+xaICplj3m78cdXS6X6UrbHbWeox1AsjC0MNg5JkiRJaiqtMhDV61Xmrw5BVVU+mtG9ygq5Xd3Fw3FokgxEpVpKjxJtSfo8AM5dbvy878NwOQvOrGuc68UfhMuZogpta2Tl3LAAuv1toOoh4VjjjUm6dRVkwsZnoU03eDoYfCfBlvmw92Ox5ro24g+LitD954nq1QYSfDGYzMJMxnUcZ7AxSJIkSVJTapWB6C+H4zgYncHrk/xp51D1m+I2NqY4WZkQmiRnW6Ra2vM+aE1h+CuVf95pKDh6i6JFjSHsLzAya5w+oa2RRz+R7izTc6XGsGU+FGTA1K/FjP1dP0H3WbB7AWx/veZgNP4ILJ8B9h1g6IvNM+YqlKXlDnYfbNBxSJIkSVJTaXWBqF6v8vmOcwzxdmJmv3bl20v0JajXPWQoikKAmy2hydXPiOYVlpBXWNIk45VakcwY0ZKk38Ng5VL5PooiWpckBUHyiYZdT6+HsI3gNapuRX5uJiaW4NpDzEJJUkOc3SDaIA37D7TtJrZpjeDOr8Xv7MFFYu1oUV7lx8cdguXTxCz/g5vB3L75xn6dUn0pO+J2MNxjOOZGhmkbI0mSJElNrdUFolFp+WQXlHBnT/fy4g0luhJmbpzJf//57w37d3W3IfJiHoUlVbc0fWLFceb9EtxkY5ZaiYOLQGMEA56sfr8es8DYAo4tbdj1kk+IqpytNS23sbQfIAL70iJDj0RqSXKSav+C4lI6bHxOvNQY/FzFzzQamPAJDHlBrO/+shcc/R5017x8jP0Hlk8H67YiCLV1b7z7qIdjF46RVZQl03IlSZKkm1qrC0SPx2UB0LvD1bfVG6I3EJkVyYboDexPrNi+tKubLaV6lYgLlb8Fzy8q5XDCGY4mn5GzoreyvItwYgX0nC0eRqtjbgfd7oLTq+Fydv2vGbZBBL5dbvGHzQ4DoLRQtIKRpDLrHoNlt0PE3zXvu/lFUfDqzm8qb3mkKDDqDXh4Bzh1Eft/FSgyIM7vgxUzwNYDHtwkipIZ2NbYrVgYWTDIfZChhyJJkiRJTab1BaLxWThYmtDRUawNLdGX8P3p7/F39KejTUfeP/I+haWF5fuXFyxKrnyd6NHzGRi7rsTUdTlHYjKa/gaklunwEtCXwMBnard/nweh9DKErq7f9VRVrA/tOAQsHOp3jptFu9vE17iDhh2H1HJknhcBosYYVj8EF05XvW/w/0RAOeIVaONf/Xnb9RPB5uxVYm32Hw/CT5PBrgM8uLHml1DNoERfws74nQxvNxwzIzNDD0eSJEmSmkwrDESz6dXOrjwt96/ov0jKT+LJnk/y+m2vk5ifyPenvy/f38PeHFtz4yor5+6KiENjmoLGNJ2/z1XzsCPdvApzRKVc/zvB0at2x7j1EuvQjv9cv2umhUNmtGgHc6uzcgbHznKdqHTVyV8BBf61WWQg/DoTclMq7qPXw8534K9/g+dwGPjv2p1bUUQWwmMH4I4loh/ugxurXhfezI6lHCO7KFum5UqSJEk3vVYViGYXFBOVml+elluiL+G7kO8IcAxgiPsQAl0Dmew5mR9DfyQmJwYQBYu6uttwpooZ0X0JR1AUUeToUMre5rkRqWU5thSKcmHws7U/RlGg9wMinTT5ZN2vGfYXoIj2EpJo45JwWAQX0q1NrxOBqNdI8OgLs1eKl0W/zYLiS2KfonxYdT/s/1T8Hs7+QxQmqguNFnrdC9O+A0unxr+PetoatxVLY0uZlitJkiTd9FpVIHoiQazH69XeDoCN0RtJyk/iiZ5PlM+QvtD3BcyNzFlweEF5Fd2ubraEp+RRoqv4kJuaW8jF4rNoFRMcjNuTxQnS82XBlJtS5A6xBlR/XdGqkssiLddrlCh0UhfdZoj0vvrMiob9Be0CW0QqYIvQfqDoz5oeceNnR74TAYd0a4jZA7mJ0Os+8X3bbjDjR7gQAn8+CtnxsGw8RGyGcf8Hkxc2rJdtC1KiL2FH3A5GtBuBqdbU0MORJEmSpCbVugLRuCw0CvTwsKNEX8K3Id+Wz4aWcTR35Nnez3LswjE2xmwEIMDdlmKdnsiL+RXO9090OlrLGHzsujLcfTQa8wS2hUc16z1JTUyvh93vw4rpsP4J+GEUJB2/+vnJFXApDYY8X/dzm9uD/x2iZURxQe2Py4oVD9VyNvSq9lfWiV7fTzT0T9jykkjBbKzerVLLdmK5+N3ynXh1W5dxIugM3wiL+kJmrFjnOeAJkZ1wkziScoTc4lyZlitJkiTdElpVIHo8PhvftjZYmhqVz4Y+3uPx8tnQMjO6zKC7c3c+CfqEnKIcurrZABCaVDE9d/e5eLSmKQxr15+7/cehKCqbonY22/1ITawwF36fDXs/hJ73wtTvIDcZvh8Jm16Egkz450vw6Acd6pkG1/sBkdZ7dn3tjzm3VXy99kH7VufgCZYuFdeJXgiF9U9Cu/7gPRa2/AdiDxhujFLTK8gUwWb3mWB03Yxg/3kw8GmxjnvudvAeY5gxNqGtsVuxMrZioNtAQw9FkiRJkppcqwlEdXqVkwnZ9OlgX7421N/Rn6EeQ2/YV6NoeOO2N8gpyuGbU9/Q0dESK1OjCpVzVVXln6SjoKj0c+2Hv5MfJjgSlnvohvNJrVB6lJj9jNwGt38Ed3wFPWbCU8cg8FEIWgqfd4XsONF3sL6zKh0GgoNX3dJzz+8Du/a1L4x0K1AU0cYl7srvX0GmeIlgZgt3/wLTfxDB6qo5kBVn2LFKTef0H6ArvpqWey1FgbEL4IlD4OLX/GNrYiU6US13ZPuRmGhvjlRjSZIkSapOqwlEI1PzyC8qpXcHOzZGbyQxP7HS2dAyPg4+jO80nvXR6ylRi/F3s6kwIxqdlk8+59AqxnR37o6iKPjbDqDYOJzI1MxKz3k+5zwnU+tRmEZqXjF7xaxnQQbMWS9mUsp+TsxsYcJH8MhuaBMAHoHQ5fb6X0tRoPcciD8Iaedq3l+vh7h/RNsWqaL2AyAnXgSaq/8FeSkwcwVYtxF/brN+A12pCFDLitZIrc+VtfuVOvGLWKvdtlvzjaeF2JWwi7ziPG7v1ID/H0mSJElSK9JqAtHjcVcKFbWz48fQH/Fz8GOYx7Bqj5niOYW84jz2Je6jq5stZ1Ny0enFQ9CByHS0FjH4O3QrLwoxxXsMiqaU30K333CuwtJCHt/xOC/te6mR70xqdFtfBUtHeHQPdKoi4HPrKdL7Ht4Gmgb+GvScDRojOFGLWdGLoaIojwxEb1S2TvT3e0XBmkmfg0efq587dYa7foTUs7Du8eoDGqllUlXxkuGbwZAaVvGzlFOiX2iv+w0zNgNbc24NrpauDHAdYOihSJIkSVKzaDWBaHBcFo6WJmCcTmxuLNO9p1c5G1qmv2t/nM2d2RC9ga7uNhSW6IlJEwWL9kQloDVLZpBHYPn+d/gOBr0Z+5NubOOyLHQZSflJXLh0gbzivMa9OanxXEoXwV7Pe0X6a00ao9CJlQv43A4nf4PS4ur3LVvjWFWAfCtr0w1MrODiaQicV3l6ZufRMOYdsSZXVtJtfU6ugDNrRer8dyNESnvZC4UTy0FrKqpR32IS8xI5lHKIqZ2notVoDT0cSZIkSWoWrSYQPRGfRa/29hxKEWvIalPMQavRMtFzIgcSD9DeWTzshCbnUKLTE5QSLNaHtulXvr+J1oQ2Rj25UHKC9oLRjwAAIABJREFUUl1p+faEvASWhi7F1US0jYnOjm7MW5Ma0/l94mun6mfLG13vB6AgHc5tqX6/2P1g3wlsPZpnXK2J1khUEvYaBePeq3q/AU+B9zg48o2cFW1NchLh71egw2B45oRoX7ThaVgzV7xAClkFfpNFxdxbzNqotSgoTPWeauihSJIkSVKzaRWBaNalYmLSL9G7gx2Hkg/hbuVOO5t2tTp2kuckStVSwvP2YWas4XRiLiGJ2ZSYRJWvD73WINdhoM1nc+SR8m0fHf0IjaLhgwyxxjQ6W7Z4abHO7wMTa3Dr1bzX9RoJNu4Q/FPV++h1EPuPnA2tzrRv4b41oDWueh9FERVTL6WJ4EZq+VQV1j8lfgfuWAw2rnD/Whj5XzjzJyzqDYXZlc+C3+RK9aWsi1zHIPdBtLWUfYUlSZKkW0erCERPJGQB0N3DmmMXjjHArfZraHwcfPCx92Hz+U34udoQmpzDgcgMtBYxdHXsipmRWYX97+k2FlXVsjZCtNjYm7CXPYl7eLzDRHqmx2Gu1xN18UTj3ZzUuM7vhY6DxOxac9JoxUN09C7Ijq98nwshUJQj14fWpDbp0m69xdfk49XvJ7UMQT9CzG4Y+w44dBLbNFoY+hI8uAmMLUX16ebOZGgBDiQdIPVyKjO8b72UZEmSJOnW1ioC0eNx2Wg1ClrzBPJL8uvcY22y12ROp5+mY9tLnE3OZU9kPFqzZPq79bthX982LhgXdyY0+xBFuiI+OPoBnWw7cV9mBhqgU0kJ0elnG+nOpEaVnQCZMYZ7mC2bzTmxovLPz+8XX2Ug2nBtu4LGGJJkINrkfpkK+z6u//GZ52Hb6+A5HPo+fOPnHQbC00Ewd0fDC4e1Qmsi1+Bo5sjQdje2IpMkSZKkm1mr+Fv/eHwW/q42nEg7ikbRENg2sOaDrjGh0wQ0ioZC06PkF5USkn4KFD392t4YiAL42txGIRd448AbJOYn8mrflyB0HbuV/ngWlxJ9KakxbktqbOXrQxvvgU5VVb48/iXzts9Dr+qr39muvUjRPbFcpCBeL3Y/OHYWaYlSwxiZimBUzog2rewEMct/dn39jtfrRUquRgtTFlc9221iCRYO9R9nK5VakMr+xP3c0fkOjDXVpKNLkiRJ0k2oxQeipTo9pxKy6d1erA8NcAzA1tS2TudwtnBmgOsAwvP3Anq0FjFoFSN6OPcQOyQFQ8LR8v0neI0GYHPsZkZ4jCF083GMi3NYUToC+2JzUnWXyS3ObaxblBrL+X1g4QQu/jd8pKoqusqCw2qoqspHxz7i+9PfczD5IIeTD9d8UO85kJsoHt6vpSuFuENyNrQxufWG5JMi2JGaRtnP8cUz9evdeux7iDsA494Hu9qt67+VrItah07VMd17uqGHIkmSJEnNrsUHohEX87hUrMPfw4TT6afrtD70WpO9JpNeeAFTqziMLc/T1akr5kbm4sMN/4YVM0TlRmCCnx+6Qjc0qin7jwygfeIGLhk7Mm36fahFzoCsnNviqKoIRDsNuSG9r7C0kEe3P8qcv+dQqi+t4gTXn07l/47+H8vDljPbdzb2pvasjlxd84E+E0QwfPy6okUpp6A4TxYqakzuvaEoFzLl72KTKQtEVX3d06CLC2DPB+A54pYsQlQTvarnz8g/CWwbSHubWrSakiRJkqSbTIsPRI/HZ4t/MYtGp+qqbvadkwiFVc9Sjmw/EgsjC5zcgtGYJxJYlpZblA+pZ6AwB3a9C4CjlSmuxQ+QH/cgAVaWjDE6hWXfe+jW3pHsQlFoIyr1VKPdo9QIMqIgL/mG9aGl+lJe2vsSh1MOE5IWwsqIlTWeSq/qee/Ie/wW/hsP+D/Ay4EvM9lrMrsTdpNxOaP6g41MoOc9ELEF8lOvbo+9kjYsZ0QbT1nBIrlOtGnodRCzR7xcAUg8Wu3uNzj1K1zOFAWJGqNf703mSMoRkvKTmOY9zdBDkSRJkiSDaPGB6Im4LJysTDmXG4S5kfnVdNoyqgrHfoCFPWHjs1Wex9zInDEdxpCrDQL09G3bV3yQclK87W/TVbTeSBEB5rsTxrLg9in81D8RRV8C3WfiYW9OGn6Y6/XEpAQ30R1L9XJ+r/h6zfpQvarnzYNvsidxD//t/18Gug1k8YnFpF9Or/I0elXPgsMLWBmxkn91/Rcv9H0BRVGY7j2dUn0pG6I31DyWXnNAXwqnfrtmfPvByQesXOp7h9L1nH1EtdUk+bvYJJJPipYqXaeDozckBtX+WL0ODi0RLws61K243K1iTeQabE1tGd1htKGHIkmSJEkG0eID0ePxWfRub8fhlMMEtg3E+Nr+gsUFsO5x2PQCGJtDxN9QUljluSZ7TQbASDGip3NPsbHs4erun8HCEbb8B1SVgV5O3BPYHu3pVWLNYdtuKIqC2qYrXiUlRGVFNtUtS/URsxds24GDJyBSaz8N+pQN0Rt4sueTzPSdycuBL1OoK+Tz4M8rPYWqqrx3+D3+OPcHc7vN5bnez6FcmcnxtPOkl0sv/oz8E1VVqx+LcxdoPwCO/yxelOhKIP6wTMttbBotuPZo3oJFlzJEL9hbQfROQBHVbj36iXX0Nf3sl4nYIlKmBz4tZ0MrcSDpADvjdzLZczKmWlNDD0eSJEmSDKJFB6Lp+UXEZhTg7VZMfF58xfWhmTGwdAyc+h2GvwozfoSSS1dnxirRr20/2lq2patTVyyMLcTGpCCw6wCOXjDqdYg/BKFrxGcZ0ZB4DLrPLH+Ycndvj1uxhujC1CquIjU7vV5UpO00tPzPaWnoUn4++zP3+t3LvO7zAOhk24k5/nPYEL2Bk6knK5xCVVU+C/6MVedW8VDXh3im1zPlQWiZ6d7Tic2NJehiLWaGej8g0oXjDkLyCfGz2UhpuakFqXUuvHTTcu8NF06LYL+2zm2FdU/WrchRYQ7sfh8W9oD/TYDI7XUfa2sTvUsE+pZO0K4fFKRD1vnaHXtwEdi2B78pTTvGVqZYV8yHRz/k8R2P09GmIw8EPGDoIUmSJEmSwbToQDQoNgsAjeU5gKvrQyO3w7fDxbrQe/+A4f8RawNNbSB8Y5Xn0ygaloxawoLBC65uTAwWb/sBet0PbbvD9jdEhciQlYAC3e8u393P1QazIlvS1BJyinIa83al+rp4Gi5nla8PXRe1joXHFzLRcyLz+82vEFDO6z6PNhZteO/IexWCuW9CvuF/Z/7HLJ9ZPNv72RuCUICxHcdibWzNmsg1NY/J/w7x83j856ttZRohED2ccphRf4xi6MqhPLv7WX4N+5Xo7OiaZ2mrkFOUw/x983lx74t8ffJrtsZuJSoripK6BHaG5NYLSgshtQ69fQ8ugpPL4dyWmvctyof9n8IX3WHvh9B5JDh4wZb5UFpU/3G3dIW5YgbUa6T43uNKy6zapOcmHIOEwzDgCdAaNd0YW5mY7Bhmb5pdXgDtt4m/0dayraGHJUmSJEkG06ID0eC4TEyMNCQUnqKNRRs62XaCgkxYNUf0bJy3F7zHiJ2NTKDzaJESVs1skbe9Nx1sOohvcpNFgRuPK+tFNVqY8DHkJsGBz8Vsq+cwsHErP97P1YbiItEHMjq9Dg+/UtO5pn9oqb6UL49/SW+X3rw76F00SsUfcQtjC17s9yLhmeH8ce4PAH4+8zNLTi5hitcUXun/SqVBKIh1xhM8J7A9dnvNLyFMLKDbXXB2HYRvApcAsHRs8K0uC12Gk7kTo9qPIiwjjP87+n/cuf5ORv0xioXHF3Lx0sVan6tIV8Qzu55hR9wOzmac5etTX/Pi3heZumEqgSsCeevgW+QV5zV4zE3KvY4FiwoyxSw1wL6Pq081jTsIX/aEne9A+9tg3j6Rwj/hY5GRcfDLho29ueRdhBV3w5Fva59aG7sfVN3VQNTFD0ysKrS5qtKhRWBmKyvlXmP1udXM3DiT1IJUFo9czCv9X8HMyMzQw5IkSZIkg2rRgWhQXBbdPaw5duEoA9wGiADhxC9QUgBTvwH7jhUP8J0Il9JEOm1tlL3dd+97dVv720QAsf9TyI6D7rMqHOLTxpqMIm8AohIP1vPOpEYVsxecuoCNKweTD5J2OY05/nOqbBA/rsM4+rftz5cnvuTH0B/5OOhjxnQYw9sD374hcL3ejC4zKNYXszGm6pn3cr3niNm65OPQcXB97qyCyKxIDiYfZLbvbN4Z9A5bZ2xly7QtvD3wbQKcAlh6einj14xn/t75nEqrvqqzXtXz6v5XOZ56nPcHv8/maZs5eu9R/pj8Bx8M+YBp3tNYG7WWO9fdye743Q0ee5Ox7wTm9rVfJxq1QwRYveeIlOnr+72WKS2C9U+CiSU8vANmrxRpqgCdR4HfZNj3KWTHN859NJXsBFh2O0RuE7O4656odh19uaidohBUu/7ie41WBP01Vc7NPA9hf0Hfh8DUuuHjvwkcu3CMtw+9TU+XnqyZsoZh7YbVfJAkSZIk3QJabCBaWKIjNCmHjm5Z5BbnirRcXSkc/V6kOLbteuNB3mNAYyxmoGojKUjs37Zbxe2j3wYjMzC2EA+c1zA30VJk1QsLvZ5o2cKlUTy580lWhK2o38G6EjFzdaVa7rqoddib2jPUY2iVhyiKwiv9X+FyyWU+D/6cQe6D+HDIhxhpak4j9HXwxd/RnzWRa2pOh3XreTV4aYRCRcvDlmOmNeOuLneVb/Ow9mCa9zQWjVzEpmmbuMfvHvYn7ee+zfdx76Z72ZOwp9Jxfhr0KdvitvFi3xcZ32k8AGZGZvg6+DLRcyKvD3idXyf8iq2ZLc/sfoaX9r5Uc+saQ1AUkZ6bdKJ2+4dvAqu2cPvHYOMuXjhV5uCXYtZz0udifeT1xv2f+Lr11fqNuzlkRMOP40V/5Ie2irX0p34Va1xzk6s/NnqX+Jk1Mrm6zSMQLoSKInFVOfw1KFoInNc493AT+PnMz9ib2rN41GKcLZwNPRxJkiRJajFabCB6KiGbEp2K1kJUp+3v2h8iNkNOAvR/rPKDzGxFQBK+sXYpaInBIgg1vi5FytYd7lwCt38IplY3HGbv1oWOxTqi8+LqelvSdS5eusi+xH3siKjFusvKJAWLQkCdhpFdmM2ehD1M9JxYsbpyJbzsvHi2z7OM7ziez4d/XuP+15ruPZ3IrEhOp5+ueefbnhA/lw2cEc24nMHG6I1M8ZqCnZldpfu0s27H/H7z2XHXDl4JfIXMwkye3vU09225j8Mph8sD0uVnl5cXcprjP6fKawY4BbBy4kqe7PkkO+J3cOf6Owm6UIcWHs3FvY9YI1pdgARiljNqJ/iMF7/zA5+BuH+upuqWyY4Xs51+U66mpl7Prh0MfUHM/kXtbJz7aEwXz4ggtLQQHtwI7fuLtfQzV0BaBHw3HOKPVH5sZowoSnT9vXv0E7PJyVUE/QWZImOl211g49qot9NaxeXGsTdxL3f73N2iq+PGZVwiLe8mXvMsSZIktUgtNhANihOFipKKTuHn4IejuaNY42TbHnxur/pA3wniQSotovoL6EpFOp9H38o/D5gq0vcq4edmh32xGVHF2bW5FakawVfSPsNyotCrdahiWub8PkCBjoPZdH4TJfoS7ux8Z60OfSDgAT4e9jHmRuZ1uuSEThMwNzKvXdGiHrNg/nmRPtoAq86tolhfzH3+Na+7szS2ZLbfbDZM3cCbA97k4qWLPLLtER7e9jA/nP6Bj459xKj2o3ip70tVroctY6w15rEej7F68mqsTax569BblOhbWCEjt94iQLpQw4uB2P1QnAc+E8T3veeApTPs+6TifltfFTOt496v/nwDnxHtgrbMh9Li+o+/sSUGw7IJoDGCf20B1+5XP/ObBHN3iGyP/02EkD9uPD76Siq216iK28uKulWVnhv0o1g2MfCpht/DTeLXsF/RarTM9Jlp6KFUKTGrgElfHuCh/x2rd9EzSZIkSaqPFhuIBsdl4eViypnMEDEbmhICcQcg8BGxXqkqZQ+Z1VTPBSAtTDw0uVcRiFbDz9UabZEjGYqe7MuZdT5euioocgMAl1CJz4qq+wmid4lZbQsH1ketx8/BDx8Hn0YeZUVWJlaM7zieLee3kFlYiz//6n5ea6FIV8Tv4b8zxH2IKNhVS8YaY2Z0mcGmaZt4OfBlYrJjWHh8Id2du/PBkA/Q1mFcXnZevBz4MnG5caw+t7o+t9F0ygoW1bRONGKLCMCupHFjYgEDnhT9MpOCxbaoHWKWc+iLYtazOkamcPtHok3P4a8adg+NJTsBfrlTzMI/tEX0tL2eix88ulush1/7KJy+7s8zepd44efoVXG7paOoGJxQyRr8whw4vEQEr20CGu9+WrHc4lzWRq1lQqcJLTYlV69XeemPEPKKSjmdlMPhGPn3mSRJktR8WmQgqterBMVm4u2RR6m+lO7O3eHot+Ihsvf91R9s4yZS9SI2V79fWaGiqmZEq+HnasOlIvGQGp1woM7HS1cFZ4XjWloKQFhUDX9m18uKE31f/acQkRlBWGZYrWdDG+rBgAcp0hXxfcj3TX6tzTGbySzM5H7/Gn72q2CqNeVev3vZPG0zHwz5gK9GfVWvip1D3IcQ2DaQr09+3bKq6Vq3BWu36ivnqqoIRL1GgvE1M+B9HwYzO9j/mUjd3TxfBFsDajmr5z0GfCbC3o8gP61h99FQqgqbXwJ9KTyw4cZibtcytxcFmNoPgD8fhTNrxXZdicgy8BpR3pO3Ao9+ohjc9TNnB76AggzRi1kCYG3kWi6XXuZev3sNPZQq/XQolkMxGbw9JQBHSxOWHogx9JAkSZKkW0iLDESj0vLJLSzFxu4CAAHmbiKFrMes2qU4+k4UMxzVFeRIChLncvCs8/ja2piRqxdv/aOTDtX5eEnIyIohhhKmW3fBRFU5W9cqxCGrxNfuM1kXtQ5jjTETPSc2/kAr4WnnyRSvKayMWElKfkqDz1eqLyUhL+GG1DhVVfkl7Be87b25zfW2Bl3DwtiCiZ4TsTW1rdfxiqLwQt8XyCrKYunppQ0aS6Nz7311VrMyKadEW6ayjIkyZjZizXn4Rvjr35AZDRM+ErOdtTXqDZFdEfJ7/cbeWML+Er1RR7xafRBaxsQSZq8SweXqh8XxScFQlCsqA1emXT+4lCoqipfJSRSzod3uFoWjJEr1pfwa9it92vTB39Hf0MOpVFRqPh9sCWekrwtzBnTgvts6sCMslei0fEMPTZIkSbpFtMhANChWrA8t1sbjYOaAa9hm0BXVvhKjz5VgpLpZ0cRgkZZbwxq5yiiKgrlLP6z0eqIywup8vCQcP/UTAP0DZtNFNeZsbh3exqsqnPoNOg6hxNqVjTEbGdFuRL2DrPp4oscTAHwT8k29z1GkK2JVxComr53MhD8ncN/m+9ibsLc8ID2ccpjIrEju97u/xvWczcHf0Z/JnpNZHra8UQLwRuPWSwSRl6tYtx2xGRQNdBl342f954kemad+E1WyO4+u27VdfEWbk+M/175PZ2MrzBFrVdt0g/6P1/44Uyu4b7XIIvnjQdi1QPx36lRF1WmPQPH12vTcXe+J+5azoeV2J+wm+VIy9/vVL4uhqZXo9Dy/6iQWJlo+mN4NRVG4f0AHTIw0LD1w3tDDkyRJkm4RBg1Eq3pmC4rNxNHShLj8cPwd/FCCfgTP4eKBrzacfUR6XVVtXApzIS28Xmm5Zbq4OeNarCHqUgt6GG9lghL2Yq6qBPhMxc+qHWHqZdSiWr6NTwoWgUf3mexJ3EN2UXazpeWWcbVyZaaPmI09n1O3h7f84nx+DP2R8WvG8+7hd7E3s+fpXk+TUZjBU7ue4u6Nd7M9bjs/n/0ZBzMHJnhOqPmkzeTpXk8DsOjEIgOP5Brl60SrqOgasVkEi5ZON35m4SBScU1tay5QVJVe90P6OUioohJtU9u1APIuwOSFoK25DVEFptYiGHXtIQo6ufepOvPExV/0Fy3r1XzhtAjg+88Du/YNu4ebyC9nf8Hdyp3h7YYbeiiVWrI7mpDEHN6b2g0Xa5Gm72RlyrRe7qwJTiTzUgsqviVJkiTdtAwaiGZcqrxcfFBcFr06WBCTE0OAagR5yXV7y68oIj33/H4xU3C95BOAWq9CRWX8XG2wKrYkWnep3ue4pRXlE1x4ge4mThgbmeDvGkieRkPiuVr2gD31m+j16n8H66LW4WLuwkC3gU075krM7TYXU60pi08srvUxZ9LPMHb1WD4P/hxvO2+Wjl3KigkreLT7o/w19S8WDFpAYWkhz+95ngNJB5jlO6tFtX5wtXLlPr/7+CvmL85mnK3wWVxuHF8e/5JPjn3CqohVHE45THJ+cv0qItdFWUpoZQWLshNEwFRdte3hL8PzZ+sfTAVMFbOqx3+p3/ENkRgk+isHPgoefep3DjNbuO9P8J1UdXssEEGue++rlXO3vQ7mdjDkhfpd9yYUmh7KidQT3Ot3b50KgjWXkMRsFu2K5M6ebkzoVrHNzsODO1FUqmf5YdmaTJIkSWp6Bg1EU/OKyLlcsRVEam4h8ZkFtGubhV7V0zXxNNh3Au+xdTu570TQl0Dk9hs/S7pSqKhsFqUe/F1t0Be2IVOrkJV+rt7nuVXlhG/gnLERfd1F8OjXWQQJZ85vq/ng0iIIXQO+k0jTF3Eg6QCTvSYb5KHP0dyROf5z2Ba37YagrCrLzixDo9Hw+8Tf+W7sdwS6Bpan3RprjLmj8x2su2MdHw39iCleU5jtO7spb6FeHu72MPam9nwa9Ckl+hJ2xO3gkW2PMGntJJaFLuP3iN959/C7PLLtEcatGUe/5f147cBrXCppohc3Zeu9KytYFLFFfPWpZv2wolTaM7jWTK2g63Q486fIuGguuhKxttW6LYz8b8POZW4Hs1ZAtxnV7+fRTwT2YRshZjcMnS+OlQBYHrYcS2NLpnaeauihVOq/60JxsjLl7Sldb/jMu401w32c+flQLIUluuYfnCRJknRLMWggqtOrLNldsWVHWf9QE8skAAKSQqDnbNDUcage/cDSBQ4uuvHBMDFIpO5aONR77J1drMgpFoWOouJ21fs8t6oTYatRFYU+Xe4AwNu5G0YqhKXV0AsSIHIbXM6CHvfwV8xf6FV9s6flXuuBgAewNbXly+Nf1rhvTlEOu+J3MbHTRAKcqm5zodVoub3T7bw3+L1mXfdaW9Ym1jze83GOXjjKqFWjeG7Pc8TmxvJUz6fYNmMbR+89yvYZ21k6dilvDniTOzvfycaYjczaOIvIrMimGVT7gSLo3PIfKLimDUXEZnD0BqfOTXPdMr3niKJFobXoL9tYDi+Bi6GijYyZTfNc06OfqMy7/glRFKnf3Oa5bivwT9I/bD2/lamdp2Jl0oAXG00kMauAkMQc5g7phK2FcaX7PDLEk/T8YtafTGrm0UmSJEm3GoMGog5mRSw7GEtiVkH5tqDYLEyNNGSWxuBiYoezTi/WLNWVRguTPhcPacunXw1GVVUEog1YHwpgZqwFS9HgPfpCDf0LpYpKCglKD8EYhe4uPQEw0ZrgbWLP2ZKsqgvOlDn1O1i1oaTjIFZFrKKXSy862nZs+nFXwdrEmrld5/JP8j8cu1BJj8VrbD6/mRJ9iUED58Yyo8sM+rftT4BTAItGLuLvaX8zr8c8nC2c0Sga2lq2JdA1kBldZvD6gNf5fsz35BXnMXvTbNZHrW/8AY19F/o8AEe/g0W9RbpqQSbEHgDfZlhj695HrKE80UzpuQWZsOcDUQnYb3LzXBNEIApi2cOoN8HIpPmu3UKlFqTy4t4XeWzHY3hYe/BgwIOGHlKl9kSIFkMjfF2q3GeglyN+rjb8sP/8DVW8JUmSJKkxGTQQ1WjyUIBPtkaUbwuOy6RHOzvCM88SYHTlDX99WwL4TYIZy8S6sbJgNCdBtB9owPrQMp3a+GKhV4nKjqp5Z+mqmD0Em2joZtOpwtpHf0d/zpoYo8b+U/WxBZlwbit0u4v15zeRlJ/E3G6Gn5GZ5TsLFwsXvjz+ZbUPb+ui1uHr4Iufo18zjq5pGGuM+WHcD3w9+muGtxteY2p0oGsgq6espptzN/77z3958+CbFJYWNt6ALBzEy6d5+6FtN9j8IizuK1L0r2/b0hQURcyKJgXDhdDK9ynIFP1v06Pg4hmxXj0xCFLDITcFigtqX3k3cruYgR3yYr2qf9eblTM4+YiANKBlpp82F51ex4qwFUxZN4Xd8bt5qudTrJmyhjaWbQw9tErtiUilvYMFnk6WVe6jKApzB3ciMjWfvecM3BtXkiRJuqkZNBDN0xfx0KCOrDuZTEhiNgXFpYQm59K9nQmxubF0LSkF2/YNSqHFf0rFYDRmj9he36Ie157azRbnImNCC1O5mJ8s3x7X0qWzawkzMaFv+xEVtvu3G0KuVkty9NaqDw5dA/oSirvN4NuQb+nu1J0h7kOaeMQ1MzMy4/Eej3My7SQ743dWuk9EZgRnM87eFLOh9eVk7sR3Y77jkW6P8Gfkn7x64NXGv0jbrjBnA8xcISrC2rW/OovX1LrPBK3JjbOiej3sfBc+8oSF3WFxH/h6IHw3HH4YBUv6w2e+8L4rLHCBz/whJaT6a53bIpYfGKJ355x1cO8fzRsAtzAZlzOYvXk2Hxz9gJ7OPVl3xzrm9ZiHibZlzhAXluj4JyqDET7ONbaCmtzDDRdrU34+JIsWSZIkSU2njnX+G1cJKiO6XWZVkAnvbw7jmVHe6PQqTo5pkAYBWRfArUfDL1QWjK7+lwhItaai314D+bnasPZ4B86YRzN6zTgczBzwsfehk603PR0HcHvnwQ0f+81GV8qJ2B3oHCzo4xpY4SN/F/FnfTbpEO5VHX/qd2jTjTW54Vy4dIG3B77dIvprAtzZ+U5WhK3gs+DPGOYxDGNtxTVY66LWYaQxYkKnltOKxRCMNEY80/sZAH44/QOxObGNn1qtKCIjost40YO4uQpZWTiIyrMhK2ElhZX9AAAgAElEQVT022BsBkV58Oc8iNgE3WdBpyEiWC37R6MV+xTmXP3nyDeiL+nETyq/jq4EonaK/7fVdf18Y7Bxa/5rtjCLTy7mXNY5Ph72MeM6jGsx/x+qypHzmVwu0TG8mrTcMiZGGiZ1d2P5kTgKikuxMDHoo4IkSZJ0kzLojKgCHDy/ln+P9uZwTCafbxfVZ3XGCQAEZMQ33tv+smAUxDkbYV2Tn6s1ERf/xWe5drySW8RAl9s4l36RX8NW8NKBJ9gWU02K6a0q7gDBmhKMFA09nXtW+Mjb3hsjFM4WXoT8SlLC0iMhKYjCbjP4PuR7erv0ZoDrgGYaeM2MNEa80PcFEvIS+C38twqflehK2BSziRHtRmBvVkWPxlvMbL/ZGGuM+eVsE66p1BqBSdVpiE2i9xxRTCt8I2Sehx/GwLm/RUGhqd9Ar/ug+90QcKdYu9plnKhU2+9hGPI8jHlbbDu7DnSllV8j7iAU5UKXalrSSE0mJjuGPyP/5O4udzO+4/gWH4QC7A5PxdRIwwBPx1rtP9LXheJSPf9EZTTxyCRJkqRbVa0CUUVRHBRF2a8oyltXvvdUFCVYUZQoRVFeu2a/169sO6YoSseazmupV9mRuJdZ/drh6WTJsdgsurSxIjo3DHczR+z0enDtWdNpas9/CszdCXd81Sinc7E2w8nKgmOWTzErI42ue+OIDXmUXupC1GInXj0wn9SC1Ea51k0j7C+CzC3wd/DDwtiiwkemWlO8rDwIMzGB2P03Hnvqd1A0rLQwIu1yGk/1eqrFPQAOdh/MILdBfBvyLTlFV3vY7kvcR1ZR1i2dlns9J3MnJnlNYn30erIKsww9nMbTaZhIB973CXw/EvJS4P4/of+82qeydp0Ol9Ig7kDln5/bKjI7PIc31qilOvji+BeYG5kzr8c8Qw+l1vZEpDLQy1EU2quFwE4OWJpo2RUu/w6TJEmSmkaNgaiiKMbAFuDaZpn/Bd4DfIApiqL4K4rSAZh6Zds7wIc1ndtGhbjiLOLzzjN/vC8AfTo4cDbjLF21DSxUVBW3no3axsHP1YbvzlnxW+kIZrGV7fe58PO/hjHS4SUKdZd5ZucLlOqrmNW41eSmcPnMWkJNTW5Iyy3j36Y3Z01NUcvW8pYJXQOHvqLAaxQ/Rv5Bf9f+9GvbTOv+6uiFvi+QX5LPN6e+Kd+2LmodzubODHQbaMCRtTxz/OdQpCtiZcRKQw+l8Wg00Ot+SAsDKxd4dHfdA0bvsWBiVXkrGFUV60M7DWlY71OpXo5fPM7uhN081PUhHMwaUL+gGcWk5RObUcDIWqTlljEx0jC0izO7wi/K+geSJElSk6gxEFVVtQSYDFw7RTUM2Kyqqg7YeOX7EcCWK9u2ADXmTFprzVBU2BG/g3EBbXhtgh8z+tmSlJ9EQHFxwwsVNYPZge2Z3MONHnM+Rmtmjffx90BVeX3sCPSpMziTeZIvT9TcX/KmV5gDK2YQotFRikrfNpVXLfZzCiBLq+FC3D6xQa+HXe/B6ofAtQe/+g0hszCTp3o+1YyDrxtve2+mdp7K7+G/E5cbR/rldPYn7Wey12SMNHKt1bW87LwY7D6Y38J/o0hXZOjhNJ7bHofxH8LD28HBs+7HG5uLSr9nN0BpccXP0iMhM0asf5WalaqqfBr8KS7mLtzvf7+hh1Nru6+0bRnuU/tAFESbl4u5RZxJzq15Z0mSJEmqo1ql5qqqen1ujqWqqmV9F1KBtoALkH5l/1JAqyhKtTlARiZW9CwqYmfsNhRF4ZGhnlzWxAPQNStFzF62cLd3c2XRPb3o2sULhr8CMbshYgsuNmY83Gs6xVn9WRa6jN3xuw09VMMpLYLf74W0cIJ7342CQi+Xyme6/R39ATh7+QKknYM/5sC+j6DnfeTP/pX/Rf7BYPfB9HRp2T8bT/V6ChOtCZ8Hf87G6I3oVJ1My63CAwEPkFmYyaaYTTd8pqoqP5z+gcUnFreuWRlTa7jtMTCzqf85uk6Dwuyrlb7LnPtbfJWBaLPbGb+TkLQQnuj5BOZG5oYeTq3tiUils4sV7Rwsat75GiOuBK67ZXquJEmS1ATqW6zo+idCpYptN1AU5VFFUYIURQnKuVzKqEsFhGdHkpAnChSFpov+e37pca0iEK2g31xw9oWtr0JpEY8O9cQ8dyrm+g689s9r5fd4S9Hr4M9HxZrPO78mqCQDXwdfrE2sK929i30XNCicNTGBH0ZD+CYY+x7csZhfIlaRU5TTomdDyziZO/Fwt4fZGb+TpaFL6eHcg062nQw9rBapf9v++Nj78POZnysEm6qq8uGxD1l4fCHfhnzLwuMLDThKA/AaCWa2N6bnnvsb2nQFu3aGGdctqkRfwhfHv8DL1os7Ot9h6OHU2qWiUo7EZDLCx7nOxzpbm9KjnR07ZSAqSZIkNYH6BqJ5iqKYXfl3F8SsaArgBKAoihGgXknTrUBV1e9UVe2rqmpfWydXRhUUALArfhcAZzLO0NHcBStVbdxCRc1Bawzj/w+yzsPBRVibGfPMSH/SYmZSqlP5z77/GHqEzUtV4e+XRfXPsQu44DWUk6knCWxb+fpQAHMjczztvDhrYQWoMHsVDHyKv2O38m3It4zpMIYAp4Dmu4cGuN//ftpYtCG7KFvOhlZDURQeCHiA6JxoDiSJ4jx6Vc+7h99lRdgK5vjP4a4ud7E0dCk/nfnJwKNtRkam4DdZvIwpuZKAUpAJ8YdvidlQVVUp0ZcYehjl/jz3J3G5cTzb59lWlWJ/MDqDYp2+fHazrkb5unAqMZv0/JsodV6SJElqEeobiG4HJl5JvZ0M7Ljyz/gr2yZQcU1pFVfX4mHXGT9M2RG3A4Az6Wfoqr0yW2aIRu0N5TUSfCbCrnfh5zu43ykCd0s3TPLHcjr9NGkFlbQluVkd+ByOfgcDnoKBT/NdyHeoqNzjd0+1h/k7+nPW2gH1sX/Aewx/Rf/Ff/b/hx7OPXh30LvNNPiGMzcy55X+r+Bj78P4jjd/4NAQ4zuOx8XchZ/O/oROr+PNg2/yx7k/mNttLi/2fZHX+r/GmA5j+CToEzZEbzD0cJtPwDQozoOo7eL7qJ2g6sDn5m7botPreG7Pc4xbPY6QtBBDD4ecohy+PvU1fdr0YZjHMEMPp052hadiZWpE3471q7cw0tcFVYU9EbfQ312SJElSs6hvIPoW8BIQCWxQVTVcVdULwDIgAngXmF+rM3n0Y1ReLifTTnIm4wxpl9MIKCoS7Q9aeKGiKk37Dka/BWkRGP8+k83GL+GXnghAWGaYQYfWbBKOwc53oOsMGPMuCXkJrI1cywzvGbhbuVfYNaeghKLSq5Pn/o7+ZBbnkGpiyrqodbx24DX6tunL16O/xtK4mXtCNtCo9qNYPWU1Viayuml1jLXG3Ot/L0dSjvD4jsdZF7WOJ3o8wTO9nkFRFLQaLR8M+YD+rv1545832JOwx9BDbh6dhoGF49X03HNbwNIZ3HobdlxNbOGJheyM30mxvpiHtj7ElvNbDDaWfYn7mL5hOtlF2bzQ54UW1zKqOqqqsicilcGdnTAxqt9f9wFuNrSxMWVX+MVGHp0kSZJ0q6v130yqqv5PVdW3rvx7mqqqt6mq6qmq6oJr9lmsqmpnVVV7qKoaX6sTe/RldI5omL3oxCIAumYmt7603GuZWsHg5+DfITD1O6wtLfhcJx4kwzJugUBUVwIbnwUbN5j8BWg0fHPqG7QaLY90f6TCrqU6Pbcv3MeHWyLKt5UVLPok6BPe+OcNbnO9jcWjFt/Qd1S6uczoMgMLIwsOpRzi373/zeM9H6/w0G+iNWHhiIX4Ovjy4t4XCb4YbMDRNhOtEfjfIfqGXs6GqB3gPU60iLlJbYjewLLQZdzd5W423LmBAMcA5u+bz5KTS5q1YFVOUQ6v7n+VJ3c+ibWJNb/c/gvdnLs12/UbQ8TFPFJyChnhW/f1oWUURWGkrwv7zqVTXKpvxNFJkiRJtzrDP820C8SzpJSOpo78k/QPWkWLT0YrLFRUGSMT6DETZd5+Ijs/SfuSEk4mBhl6VE3v8BK4GAoTPgZTa2KyY9gYs5FZPrNwsai4TulobCbJOYUcjsko3+Zj74OCwt+xfzPQfSCLRi2qd4XKUp2efefS+M/qEL7cGdmg25Kalo2JDe8Meof3B7/P3G5zK93H0tiSr0d/jaulKy/tfYnLpZfrfJ3k/GR2xu1s6HCbT9fpUFIAO94SbZB8mi/NW6fX8dDWhyr0xG1KJ1NP8tbBt+jXth8v938ZBzMHvh/7PVO8pvD1qa+Zv28+haWF1Z5Dp9exKWYTbx96m5/O/MTB5IOkX06v9RhUVWVH3A7uWHcHW85vYV73eayctLLVBaEAu8Pr17bleiN8XMgvKiUoNrMxhiVJkiRJABi+4oKzL4qJFaM1NvxABl4WbTBXz7fuGdHrKQpWvqPwPfwbJ7LCDT2appUVB7v/D3wnge9EAJacWoKp1pSHuj10w+5/h14AxJv7whIdZsZaLIwtGOYxDFMjU94f/D4mWpM6DUGnVzkWm8lfp5L5O/QCGZeK0WoUdHqVYV2c6dHOruH3KTWJcR3H1biPvZk9bw18iwf/fpDfwn/joa43/lxVRa/qeX7P85zJOMMPY3+gv2v/hgy3ebQfANauELwMtCbgOaLGQ/KL8wlJC+F46nFOpp4kIiuCad7TeLb3s3VKLV0fvZ5jF45xPuc8j3R7BK2m2o5cDZKSn8K/d/+btpZt+WzYZxhrjAExE75g0AI8bT1ZeHwhkVmRzOgyg/GdxuNk7lR+vKqq7IzfyVcnvyIqOwoLIwsKSgvKP3cwc8DXwZfAtoEMcBuAr4MvGuXqu9jIrEi2xm5la+xWYnNj8XPw45sx3+Dr4Ntk99zUdoen4u9qQxsbs5p3rsagK6m9O8NTGdjZqeYDJEmSJKkWDB+IarTg3pvR2Rn8YAoBmitr6VpjoaJqtPPti+++ErbpssktzsXGpAH9BVsqVYXNL4o/09s/BCAiM4KtsVt5pNsjOJhVXPOr16v8HXoBW3Njci6XcCY5lz4d7AFYNGpRPYegMu+XIHaEpWJurGWUnwuTe7jRr6MDYz/fy3ubw1j56G2tap2XdKM+bfow2H0wP4b+yF1d7qqyHdD1NsVs4kzGGcyNzFlweAFrpqyp84uOZqfRgv+dcORr6DhEpP5XIbMwk+f3PM+J1BPoVT0aRYOPvQ/+jv78GPojxbpi5vebX6uf/4KSAhafWIy1sTXpl9MJvhhMoGvVFa9r62zGWULTQ3E0c8TB3AEHMwcsjS15ZvczFOuKWTZuGXZmFV8WKYrCw90exsvOiyUnl/DhsQ/5OOhjBrgOYKLnRKxNrFlycglhmWF0tOnIx8M+ZmyHsWQXZROZFcm5rHNEZkVyOv00Xxz/gi+Of4GdqR39XfvjYeXBnoQ9ROdEo1E09GvTjwcDHmRK5ynlwXBrdCGnkKC4TJ4a6d3gc1maGjHA05Fd4am8Psm/EUYnSZL0/+ydd1hWZR/HP+d52HvvDSKggIC4996VOytN08zRzrJ6KzPb07LlTFPL0nLvvVFBEWQIInvv/fCs9w+S9yVBAVGG53NdXOVz7nOf+zDOuX/r+2v/VFQpqZQrMdbVRCK5931nSaWc/LIqnMz02s0+tuUNUQCHYHzOLGfa0JcYmRTetoWK6kFH3wgrefVmOTY/lmCb4BZe0X0gajvEHYQRn4CxAwArrlRvZGd0mnHb8LDkArJLZLw50ouP98VwNbWwxhBtKruuZnA4OpsXBnnw3AB39LT+9yv+0hBP/rM9ksPR2Qz1sb6n67QUMoWS7ZfTGOVri6FO290kNwfPBzzPlN1T2BC1gQVdFtx1fIWigm/CvqGTeSeeD3ie5w4/x5qINczrMu+O56nVarLKs0goTOBG0Q1UahWPez3+YA1Y34nVhqjXqHqHyJQyXjz6IlF5UczxnUOgdSD+lv7oa+qjVqv57OJnbIzeCNAgY3RD1AZyKnL4eejPvHTsJfYl7rsnQ1SlVrEmYg3fX/ke5e2dvZAIElYMWoGbiVu9cwxwHMAAxwE16f57b+7lrdNvAWBvYM+HfT5klOuomvYqZjpmdLftXivynVuRy/mM85xLP8f59PMcTDxIoHUgb3u9zRDnIbWirG2Zvy6nolLDhED7uw9uAIO8rHhv5zUSckpxsxTF10RERETqQq5UcSouh7/C0jgUlYVMoUIigKmeFqb6WpjpaTGisw0ze7s0ypg8E5/LS1uukFMiw8VcjyHe1gzxsaarsyka0pavtGwqrcYQFVQK3rQbDBe2ta+03P/DTNMVSCY6L7r9GaKVRbDvjeqfXbdnAYjIieB4ynEWdlmIsbbxbafsjchESyphWncn1py+ydXUontaQnGlnA92R+HnYMyLQzyR/sv7NCXYkbVnbvLJvmgGdrRs0B+uTKHkcFQ2FXIlE4Mc7ml9zcFvIcks2RXFX2FprJ/VDR3N+5cq2drxMfdhmPMwNlzbwONej98Wcf8366+tJ7s8m8/6fUaQdRAjXUayKmIVI11H4mLsctv44ynHWXl1JTcKb9RK8QQIyQjh64Ffoy3Vbs5bqh+HrvDMoXrVclVqFW+ffpsrOVf4sv+XDHMZVuu4IAg1xuevUb+iUqtY3G1xvS/B3Ipc1kWuY7DTYHrZ9WKA4wAOJx3mre5vNSlKmFuRy1un3uJcxjlGuozk+YDnKZWXkl+ZX/PVwbQDvex6NWg+NxM3Xgh8gecDnudy9mVyK3IZ6DSwQWuz0LVgjNsYxriNQa1WU6GoaHdCaGq1mq2hqQS7mOJs3jxK47cM0aMx2aIhKiIiIvIvojOK+f1CMruuZpBfVoWpniaTuzriaqFPQXkV+WVVFJRXkZRXztLdUeSXVfHqMM+7GqMKpYpvj8Tx3bF43Cz0mdvPjVNxuWw4l8Tq0zcx0dNkSrAji0d4tckoaeswRO27Vv837hAU3ITAp1p2PfcJI6suWFYmEJZ+hemdprf0cpqXI0uhLAembQGJFLVazbeXv8VU25QnfZ68bbharWZ/ZAb9PC0w1NHEz8GE8NTCe1rCVwevk1sqY82MrrcZoQCaUgmLR3jx7K+h/H4xhSd7ONc7V3RGMX9cSmH75TQKyuUA2Bjp0KdDy0VLVCo1G84lYW2kzYXEfOZvCuPnp4LQbMOesHtlQcACDicfZk3EGhYFL6p3XHZ5Nmsj1zLUeShB1kEAvN7tdU6nnWZZyDJWDV1V6wG+MWojn138DFdjVx71eBQ3YzfcTNxwN3HnaPJRlp5byvNHnmf5oOVNFtJqNI71RyO/DfuWA4kHeCXolduM0FsIgsCirosQENgQtQE1at7s9madL66fwn+iSlnFS4EvAdV9Xvfd3EdIRgh97Ps0atnnM86z+ORiSuWlLOm5hPEdxjfby1IQBAKtm97KRhCEdmeEAlxOKSQhp4y5/eqPLjcWRzM9PK0NOBiVxey+zTeviIiISFtGpVLz44kbfHXoOlKJwFAfax7rYk8/T8s622apVGre3h7BimPxyJUqFo+s34DMLKrkhd8vc+FmPhMCHVj6SCf0tTWY3deNUpmCk9dz2HEljZ9PJGBpoN0mn82tYwdrYAmmLhC2ofrf7TQiauQSgFeVnOhW0KC9Wbl+EC6uhm5za2p7111bx/mM88z1n1tn78/w1CLSiyoZ0dkWAH8HYxJyyiiulDdpCZFpRWw4l8hTPZzxc6hfjGiojzXdXMz45vB1SmWKWsfUajW7wtMZt+I0I5efYtP5ZHp5WLD26a64mOvxzo5IKuW3pxQ+KE7F55KQW8abI71Z9mhnjsZk8+of4ShVD66lRWvDzdiNsW5j+T3mdzLLMusdt+LyChQqBS8HvlzzmYWuBS8EvkBIRgh7bu4BqhVXP7nwCZ9e/JRBToP4fczvvNn9TaZ4TSHYJhgzHTMmek5kae+lnM84z8IjCymXl9d32QfC1utbWRO5hkmek3i609N3HCsIAq91fY0ZPjP4LeY3Xjr2Etnl2bXGJBQlsPX6ViZ6TqyJFPex74OhpmGj+3mujVzLswefxVjbmM2jNzPBc0Kb9Ni2NbaFpqKjKWGUr22zzjvWz44LN/NJyitr1nlFRERE2iLZJZVMX3uBzw/EMqKzDRfeGsz30wIZ4mNdb+9miUTgw0d9md7TmZ9PJrB0d9RtrclKKuX8cSmFUd+eIiK1iC8m+fPlZH/0tf8XPzTQ1mCUry0/PRnE8E7WfLIvpk0qm7cOQxTAoRuU/yOx386Eim5h5xWMV1UVmVXZd21B0GYoSIS/5oCNLwx5D6hOafwm9BtGuoxkmte0Ok/bF5mBhkRgqHd1rabfP0q2kU1Iz1Wq1Lz9dwRm+tq8OqzjHccKgsBbo73JLa1i5YkbNZ9fzyph2qoQnv/tMhVVSt4b60PIPw+UQV7WfPBoZ27mlvHj8Rt3mP3+8suZm1gYaDPK15Ynujvzxggvdoan8+6OyHvqr5hbKqOkiQ6A1sC8LvNQoeLnqz/XeTw6L5rt8dt5wvsJHI0cax2b5DkJXwtfPr/4OZllmbx8/GU2RW/iKZ+n+LL/l/VGOx/1eJSP+n7EpaxLzDs8jzJ5y2zMz6adZdn5ZfS2781b3d9qkJEnCAKvdn2VV4Je4XTaaR7Z/ghbYragUlf3iFweuhwdDR2e83+u5hwtqRaDnAZxNPkoVcqqBq1te/x2vg79mmEuw/ht9G94mno27SZFGkWlXMnO8HRGdm7+OvKJXR2QCPDnpdRmnVdERESkrXHyeg6jlp/iUlI+n4z3ZcXjAZjoNUw7QiIReH9cJ2b1dmXdmUTe2RFJmUzB7qvpzP31EkHLDvP61qtYG+mw6/k+dywNEwSBzyf542Cqy4LNYeSWyprrFh8IrcgQ/admsh0KFd1C28wJ1yoJKkFNfGF8Sy/n3pFXwJanADVM/hU0dYkriOONk2/gbe7N+73fr3NjrFar2ReRSS8PC4z1qjdKfvbVNaThTTBEN4ckEZ5axDtjvDHWvfvGq4ujCWP8bFl16ibx2aV8sDuKkctPEZVRzLJHO7P/pX7M7O2Kqf7/Hih9O1gyzt+OH4/f4EZOaaPXeK8k5pZx/HoOT3R3qvGyzRvgzrwB7mwKSebT/bFNMkYPR2XR/aMj+L9/kNHfnmLJzmvsjcggp6TtPMjsDeyZ5DmJ7XHbSS5OrnVMrVbzxaUvMNE2YY7fnNvOlUqkvNvzXQplhYzbPo4TqSd4s9ubvB78+l1blYxxG8On/T4lPCecZw89+8CdSwWVBbx64lU8TDz4sv+XNQI9DUEQBGZ2nsnfj/xNJ4tOLAtZxox9M9h2fRtHU44yq/MszHXNa50zwnUEpfJSTqedvuv8IRkhvH/2fbrbdufjvh+3y/TX1sqhqCxKKhVMCGz+mnZbY136eVqyNTT1vmRiqNVqNoUksXTX7RECERERkeaiSqHidFzubZlxDUGlUvP5gRimr72Amb4WOxf2YWo3p0Zn+wiCwDtjvJnb342N55PpsvQgCzdfJiy5kGndnNg2ryd7nu+Dh9Xda/KNdDT54YkgCsvlvPDb5fuaKZddUskvZ24y/oczdHp3P9EZxfc0X+uoEYVqMQ5ot2m5AAgC5hJ7oJjovGg6W3Ru6RXdG3tfg8yr8PgWMHMlvzKf548+j76mPt8O/LbeaFJURjHJ+eXMG+Be85mpvhZOZnpcbWSdaHZJJZ8diKW3hznj/O0afN7rw704cC2TYV+fQA1MDXZi0fCOmOnX7836zxhvjsVm8872SDbN7v5AUww3nEtCKgg80d2p1uevD+9IcYWcn07cYPvlNPp7WtLP05I+/2fk18eVlEIW/haGj60RA72suHgzn98vJvPL2USg2mCfEGjPWH+7Bnv5Wopn/Z7l77i/ee3Ea3ibeyMRJEgFKWXyMi5kXuDt7m/X2zLJy8yLpztV9yRdPnA5AxwHNPi6I1xGIBWkvHL8FX648gOvdH2lme7o7uy7uY9SeSkf9vmwzvT3huBk5MSqoavYlbCLzy9+zpJzS7DSs+Ipn9vr9LvbdsdE24T9ifsZ5DSo3jkTihJ4+fjLOBs589WAr9p0C5S2yLawVOyMdejpbn73wU1gSldH5m0K4+T1HAZ6WTXbvLmlMl7fepWjMdWp4rP6uOBgKjowREREmo/47BK2XEzhr7A08sqqCHAyYeMz3WulvN4JmULJa39eZVd4OlO6OrJkXCd0tZouGikIAotHeGGhr01Sfhmjfe3o5mpWp87J3fCxM2LZo51ZtPUqXx2KZdHw//XALiyv4uyNPGIyS6iUK6moUlIhr/6q/L//v9VuRg1YG+lgb6KLnYkOtsa6qIH9kRmcu5GHSg1eNoZoakh4Z3skf8zt2eT2NK3HELXuDMZO0GFoS6/kvmJm5ouB8hSX0iOY1HFSSy+n6YSuh8sbod8i6DgCuVLOK8dfIac8h19G/IK1fv3tUfZHZiIRYNi/Wqj4ORhzOblxhujHe2OQyVUsfaRzowxDJ3M9Xh7qyZn4XN4Y4XXHutJbWBnq8MYIL/6zPZLtV9J4LODBqOiWyRT8eSmF0X62WP2rMb0gCHzwSGcCnUw5GpPNvsgMtlxKQSJAoJMpzw/uQH9Py9vmTMor45lfLmJpqM3ap4OxNKxWf5UrVUSmFXH2Rh47r6Tzzo5rLN0dxWAva8YH2jPY27pJD8j7jYWuBYuCF7EmYg2n006jUqtQqpQo1Up62/VmoufEO57/UuBLzPOfh46Gzh3H1cVQ56FM6DCB9VHrGeYy7IE5mHbe2ImXmRcdze6cjn43BEFgnPs4+tr3ZVXEKvrY96nTiaQp0WSI8xD2JOyhQlFR55j8ynzmH56PpkST74d83z77JbdisoorOXk9h/kDPO7b3+lgb2vM9bXYcjGl2QzRYzHZLNoaTnGlguk9ndlwLonLyYWiISoiIjlNop0AACAASURBVNIs7AxP55czNwlLLkRDIjDE2xo/R2O+OBDLs79eYu3TwWhr3NmgLK6UM3dDKOcS8lg80ou5/dyaJSAhCAJzmklYblJXR0KTCvj+2A1M9bTIK6viTHwuEWlF3Eoy0dWUoqslRUdDgo6WtPrfmlIMtDWwMNBGV1OKGsgqquTCzXyyiitR/BNhdTHXY+FAD8b629HB2pA/Lqbw+rarbAtLZVJXx/oXdgdajyGqoQUvR7T0Ku47Bk4BeMUd4Vr2lZZeStNJvwx7F4H7IBjwJmq1mg9DPiQ0K5RP+n6Cr6XvHU/fG5FBd1dzzA1qt77wdzBh99UMcktlWBjcvS1GSEIef19OY+FAD9yb0E5g/gAP5g/waNQ507o5sS0slWW7oxnU0fquUcfm4K+wVEpkCmb0cqnzuEQiMCHIgQlBDiiUKsJTCzkRm8PO8HRmrL3AYwH2vDPGpybam1cqY8baCyjVan6Z2a3GCIVqZeEAJ1MCnEyZP8Cda+nF/BWWxo4raey/lskjXez4ZkqXVik4M7njZCZ3nNykcwVBaJIReotXu77KqbRTvHPmHbaM2XLfe4zeKLzBtbxrvB78erPNaapjetf5RrqMZOv1rZxMPclwl+G1jlUqKnnh6AvVrV+Gr8PeoHn6V4o0nL8vp6FSw/hm6h1aF1oaEsYH2rPuTGKDn9X1USZT8Nn+GNafS8LLxpBNs3vgZqnPH5dSCEsuYGwjslxE2jaVciXJ+eV4Whu29FJE2hkhCXm88Ntl3C31eXuUN48F2tc8t6wMdXjtz3Be+O0y308LrLetX1ZxJTPWXiA+u5SvJvsz/j6UPjQXS8Z1IiKtiGV7otGQCAQ6mfLSYE/6dDDHz8Gk0Z0WlCo1OSUyKuRKXMz1au3/JgY58PvFZD7ZF8MwH5sm7YlbT43oQ4JVhyC8qqpIr0xBqWo5BdYmI6+EP6aDgRWMXw0SKSuvrmRb3Dbm+M5htNvoO54el1XCjZwyRvna3HbMz6G6TrQh6bkKpYr3dl7D3kSXBQMbZ0zeC7fUzgor5HyyP+a+X0+tVvPL2UT8HIwJcLx71FZDKiHI2YxXhnVk/0v9eGGQB7uvpjP4y+P8fTmViiolszdcIqOokjUzut7RgBcEgc72xrw71ofzbw1m4UAPdlxJ59fzSc15i+0CQy1D3u3xLvGF8ayKWHXfr7fzxk6kgpSRriPv+7X+nyDrICx0Ldh/c3+tz2PzY1l4ZCFXc67ycd+P7+qMEml+1Go120JTCXI2ve99PqcEO6JQqfk7LK3R5xaWV7EtNJU5Gy4R+MEh1p9LYlZvV7Yv6E1HG0M0pRL87E0anR0j0naJSC1i7HenGfb1STacS2zp5Yi0M1Yci8fCQIs9L/RlTj+3Ws6ziUEOvDfWhwPXsnhjWwSqOmor47NLGP/DWVLyy1k3M7hVG6EAOppS1s/qxoZZ3bjy3jD+eK4nLw7pQJCzWZPa/UklAjbGOrha6N8WhJBIBD54tDMF5VV8frBpe+LWExF9SNCy7UTHKgVyFCQWJ+Ju4n73k1oTSaehMBmmbgZ9c/68/icrrqxgnPs4FgYsvOvpeyMyEQQY3ul2Q7SzvTESAcJTihjkVX9qL8Cv55OIySzhpycD7yk/vyn42Bkxq7cLq0/fZGqwI/4NMBCbyun4XG7klPHVZP9GRyF1NKW8Mqwjo/3seGPbVV7eEs6HBjHklcn48YlAgpwbLgqmKZXwylBPrqUX8cHuKHztjQlwMm3s7bRr+jv2Z4zbGFZfXc0QpyH3nDJbH0qVkt0Ju+lj3wcL3Qfb11YqkTLMeRjb4rZRWlVKdnk2P4T/wIHEAxhoGvBez/cY6ty+yytaK1dTi4jLLuXj8fffCeBhZUigkwm/X0xmdl/XBj2brqQU8sWBWM4n5KFQqbEx0mFqsCOPBNgT+K9nSYCzCWtP36RSrkRH88E+30WaTk6JjOT8sgZHXeRKFT8cu8F3R+MwN9Cil7s57+64hlQi8ET3+vt8i4g0lPCUQk7FVZdg1fcsmdnblZJKBV8duo6hjgbTezoTmlRAWHIhl5MLiM0qwVxfmy1ze9L5H2HN1o6FgTb96ijLuh90sjNmek8X1p9LZHJXxwaVuv0/oiH6oNHUxUZtDqiJzotue4Zo3CHQ0AH3QRxOOsyy88voY9+HJb2WIBHu/OJRq9Xsi8wgyMn0tlpHAH1tDTysDO4aEc0pkfHVwev07WBRp0H7IHhxiCfbr6Tz7s5r/D2vV5OLtO/G+rOJWBhoMdqv6f0AO9oYsm1eL349l8i3R+N5f1ynmv6tjUEiEfh6ShdGf3uaBZvC2P1C3zuKOz2MvBH8BmfTz/LOmXfYPHpzo5RsG0pIZgjZ5dnNmpbbGEa6jmRzzGbmHppLZF4k2lJt5vjOYUanGRhrt42XdHtka2gq2hqSe3pWNIYpwY68sS2CsOSCuzq15EoVL/1+mVKZkjn93BjeyQY/e+N6n5sBjqbIlQlcSy9qlMNMpGVIzC1j5akEtoamUqVQYaitQT9PSwZ5WTGgo+VtZTgA8dmlvPrHFcJTi3ikix1Lx3VGR0vCvI1hvP13JBoSgSnBTnVcTUSk4Xx/LB5jXU2e7HHn36XnB3lQVCFnzembNYKNhjoaBDiZMqKzDZO7OmJnUrcApwi8MsyT3VczeGd7JH/P792oPbFoiLYAlkbeaKqucSE9gjHuY1p6OY0j7iC49uNiXiRvnHyDzhad+bL/lw1SxjwVl0tMZgkfPVa/x97PwYRjMdmo1ep6veyf7IuhUqFkybhOLVaraKCtwZsjvXjlj3C2hqUyuYlF2v9GplByI7uMmMxiotKLORKTzcKBHnctor8bUonA071dmdHL5Z6+ZyZ6Wvz4ZCATfzzHS1uu8MvTwffNCG+LmOiY8Hb3t3n1xKv8cu0XJnSYQFppGmmlaaSXppNTkYOehh5GWkYYaxtjpGWEoZYhgiDU9PFUqVXoaOjgZ+FX589q141dGGoZNkrdtznxs/TDwcCB2IJYnvJ+ilm+szDTEY2FlmTP1Qy2hqYyvJMNRs3cO7Q+RvvZ8f6uKLZcTLmrsfjnpVQS88pZPb0rQ3zunO0CEOhc7VG/nFwoGqKtmMi0In48cYN9ERloSCRMCHKgt4c5Z+JzORKdzZ6IDAQB3Cz0b4uQ3swtQ1dLyvfTAms5T354IpBnfw1l8V8RSCWSO/ZPFBG5E7GZJRyMyuLFwR3u2lNZEAT+M9qbjjaGqFRqAp1N8bA0EPc3DcRIR5O3R3vx8pZwfr+YwuPdHMksriQqvXovG3WHFi+iIdoC6Dv445l2mauZ4bcdy6/M51ruNfo69G2Bld2FvBuQn0Bsl0m8cPQF7A3t+X7Q9w3qEahWq1l+JA5bYx0mBNUvpOHvYMzW0FTSCivqVEwMTcpnW1gqz/V3b5JAUXPyWIA9m0KS+Wx/DCM6N30DKFeq+P1iCpvOJxGfXVqjTqalIaGbi1m9IkVNoTkMdz8HE94b58Pbf0fy3dF4XhzSgVKZgrCkAi7czCc0qQBrI20e6WJPnw4WTapJaMsMcxnG0MShLA9bzvKw5bWO6WroUqmoRM3de3w96f0kb3R7o9ZnZfIyjiQfYYzbGLSlTReJuRckgoQNIzcgESS39RoVebBUypV8tDeaDeeSCHAy4a1R3g/s2gbaGozxs2X31QzeHdsJg3raH1TKlSw/cp1AJxMGezdMZdfKUAcHU13Ckguac8kiTaBUpuBQVCbhKUXklVWRXyYjr7SK3NIqcktlGGpr8Gw/d2b1dqnJdBrjZ4dareZaejFHorOJyvifYuctApxMeXlIh9uyo3Q0pax8Kohn1l9k0dZwNCQCjwaIwmcijef7Y/Hoa0mZ2dulQeMFQWi2oMLDyKNd7Pn9Qgof7I7i8wMxFJTLa465mNdvJ4iGaAtg7h6EV8IqdpffqBX5K5eXM/fgXGIKYtj+yPbWl7Ybd5ByQWBe+gH0NPX4ecjPmOg0LBf8THweoUkFfPBIpztG927lll9NLbrNEFWq1Ly74xq2xjo8P+jBCRTVhyAIvD+uE2NXnGb54TjeGePTqPNVKjV7IjL48mAsiXnldHE0YW5/N7xsjPC2NcTFXL9eBbeWZlo3J0ITC/jmyHUORWcSnVGCUqVGKhHwsTUiKraY7VfSMdXTZLSfLY92qa4De1i8i+/0eAcXIxfMdMywM7DD3sAeWwNbjLSMUKqUlMpLKZYVU1xVTIm8BAABoSa9fd/NfWyM3oirsWstJeBDSYeoUFQwzn1ci9zXLSz1HkztiUi1V19TKuBirl/r7ycpr4wFm8OITCvm2X5uLBre8YE7faYEO/HHpVT2XE2vN41yw7lEsoplLJ8a0ChHWICTKZcS85tppSKNQaZQ1iivH47OolKuQl9LiqWhNuYG2jiZ6RHgZIqHlQGTujrU6YS9JXbXlJo6HU0pq6cH8/S6C7y05QpbLqbwZA9nhvpYo6XROt+JIq2LxNwydl9NZ05ft1bfB729IAgCH4335d0dkTia6uFjZ4SPrRFetkYYaGsg1FNNJBqiLYCmnR/eVVVso5L0snTsDexRqVUsPrWY6wWxABy7sQf3oBdaeKX/Iu4gR6zdyJHls3rYamwNGlaLVB0NvY6NkQ6Tg+/sbfKyNURTKhCeWsgo39rzbw5J4lp6MSumBTS4+fD9prO9MVODnVh/NpGpwY50aKD0/Om4XD7dH0NEWhEdrQ1ZM6Mrg7ysWmVblLoQBIEPH/Mlu0RGlVLF/AHudHM1I9DJFH1tDaoUKk5cz2HHlTS2hqay8Xwy3V3N+OGJwDrrhdobpjqmvBBY99+vVCLFWNv4jvWUgVaBZJZl8lHIRzgYONDLvhdQnZbrZOiEv6X/fVm3SOsivbCC0d+eQqFSo6clxdu2+sVubaTNzycSkEiEBqe73g8CnUzwsDLg55MJDPWxua1mvLhSzg/Hb9DP05Iebo2Lngc6mbArPJ2MogpsjdtHbVZ8dgnv74piYpADj3RpXVG+UpmCU9dzOBydzaGoTIorFZjpazEpyJFHutg9cEeirpaUdTODWXcmkc0hySzYHIaFgTZTgh2YGuyEo5nYY1akfn48fgNNqYRn+rq29FIeKtwtDdg0u0ejzmkdu/mHDUMbXBTVL+yo3GjsDez5JuwbjqUcY3FBCTv1tDiReIDZrckQrSqDxNPs8fDDVlNKsE1wg089dyOPi4kFLL1LNBRAW6N6s3U1pajW5zvD01m6O4reHuaM9n0wYhwNZdHwjuyNyGDJrmtsfKb7XY3JraGpvPZnOPYmunw5yZ9HA+zvW/P5+4mulpSNs7vXeUxLQ8JQH2uG+lhTKlPwd1gqy/ZEM27FGVZOD6KTnShqcyekEimf9/+cp/Y9xasnXmXjqI3oauhyIfMCC7osaDMOC5F74+/LaShUat4Z40NKfjlR6cX8fTmNUpmCLo4mrJgWUGcJw4NCEASWjuvEzF8uMm3VeTbP6VHLGF19MoHCcjmvD2+8gvQtVe7LyYXY+rZ9Q3THlTTe/CuCCrmSszfy0NWUMqyFxPZukVMiY29EBoejswhJyKdKqcJYV5Mh3taM7WJHH4+WLa3Q09JgwUAPnuvvzsm4HDadT+bH4zf46UQCzw/yYOFAj1abNSTScqQXVvDX5VSmdXPCyrDp/cFFHgyiIdoSCAI2eh4I6kxC0iIolZewLnIdU6QWTCvLpVgi8GNpMnkVea2nBuvmSfLUCs5X5TLD8+m7KuTeQq1W883hOKyNtBuce+/nYMyOy+moVOpqj/+pBJbtiaabqxk/PBHU6jbhZvpavDrMk3d3XGN/ZCYj72AoV8qVfHEglgAnE36b0+OhaE1goK3BUz1d8Hc0Ye6voUz88RxfTPJ/YOqebRV9TX2+H/Q9j+95nAVHFjDQcSAAY93HtvDKRB4EarWabWGpBLuY8kyf/3n1VSo1WSWVWBnqtAoHVi8PC9bMCOaZ9dXG6KbZ3TE30Ca3VMbq0zcZ7WvbpPRMH1sjtDQkhCUV3JYd05aolCv5YHcUm0KSCXYx5dMJfrzyRzgLf7vM+pnd6One8He8QqlCEIRm+bkfj83mpS1XKCyX42apz9O9XRjsZUWQs2mrM+6kEoGBHa0Y2NGKtMIKPt8fwzeH4zh5PYdvpgTgdIf6M5GHj5UnE1Cr4dn+ray8TaROWtfT5iFC384PF7mcw8n7WXp+KT1NvHgjPgyhz8sMMOqAGjiZerKll/k/4g5xwMgUJSpGu41u8GnnEvK4kJjPvP7uDTa6/BxMKJEpSMgt5aO90SzbE83IzjZsmNUNY90HowjZWKZ1c8LLxpBle6IplSnqHbfhXCKZxZUsvkNPq/aKn4MJOxb2xsfOiAWbw/jyYGydzaNF/oetgS3fDfqO3IpcNkZvpKt1V+wNWldKn8j9ITy1iIScMib8q3m6RCJga6zbKozQW/TpYMHap4NJzCtj2qoQcktlfH8sHplCxSvDPJs0p5aGBF97Yy6n3LmdV0uQUyJr0LMrOa+ciT+dZVNIMnP7u7F5Tg/cLA1Y93QwzmZ6zNlwiYjUojvOUaVQcSQ6i5e3XKHL0kN4vL2XoA8OMezrEzy+8jzP/3aZFUfjCE0qQKFU3XVNSpWaLw/GMvOXi9gY6bDvxb4cfXUAb43yprubeaszQv+NvYku30wNYPnULsRllzJy+Un+vJSC+t9qSCIPJQk5pfx2IZnxgfbYi+1W2gRiRLSFMHENxDvzb/ZWpeNq5MoX6WloGjtBr+fxUimxTtjAiaRDPNbhsZZeKqjVEHeIveaWdDCxx9O04RuL5YfjsDLUZmq3hvcD8/9HsOjZDaEk5JYxvacz743t1Ko2Xv9GQyrhw8c6M+mncyzbHcUnE/xuG1NUIef7Yzfo72lJ90bWS7UXrAx12DynO+9uv8Z3R+PJK6u6YzsfEfC19OWjPh+x6OQiJnlOaunliDwgtv3TF3RUG8kc6O1hwdoZwcxaf5EpP58jJb+CiYEO96RuHuhkwvpzSVQpVK1CpCYlv5yP90WzNyITexNdJgTaMzHIsVZErkqh4uT1HHZdTefgtSw0pQKrpndl6P/V8Zrqa/HrM92Z+NNZZqy7wB9ze+JhVf19UqvVZJfIiEgt4mBUJgeuZVFUIcdYV5PRvrZYG+uQVyojt7RavfZqaiG7wtOB6xhoa9Dd1YxeHhYEOZviaW2Antb/tnk5JTJe/P0yZ2/kMaWrI+8/0qnNOkQf6WJPkLMpr/wRzqKtV9kfmUkHa0NUajVKVfWXgbYGc/q5tVoHtkjzUlQuZ/b6S+hra/DikKY5wEQePKIh2kJo2PrSp7yCk3qWfGTUB6PwD9nk9AFrvw2hY6UeA4wq2JlxHplS1mJtGmrIiSWlLI1wM3tedB3V4NPOJ+QRcjOf98b6NOpl52FlgJ6WlITcMhYN78j8Ae6tLh23LoKczXiuvzs/HL/BYG/rWhsPgFUnEyiqkLOoCfVS7QltDSmfTPBFT1vKujPVIk+31JJF6maYyzB62vXEUKthYlgibRuZQsmuq+kMe4B9QZuDXh7VkdFZv1wE4IUhHe5pvkAnU1aduklURjFdHBv2jCiplN+1Z2BjKZMp+OF4PKtO3UQqCMzp60psVinfHYvn26PxdHc1Y4yfLdfSi9kXmUlRhRwTPU0eDbBj/gCPOoV1bIx1+PWZ7kz66SzT14Qwxt+O6Izqnnt5ZVVAdVnDMB9rxvrb0dvDol5jPL+sivMJeZyJz+XsjTyOxGQDIAjgbKZHRxtDPKwM+PNSKkUVcj6b6Ncu2lQ4mOrx25werDyZwIqjcZyKy0UiAakgIJEIlMoUxGeX8uOTgW1iD9GeSC0oZ8eVdDpYGRDsYoap/v1VrpUrVczfHEpKQTmb5/QQo6FtCNEQbSksOzKqTEZhkR12ics5r/bmk2RPOtlpcyTHiVWacrYYybmQcaHle4rGHWSfvj4AoxpoiMqVKr46eB1LQ20eb0Q0FKrrQZaMq+5L19Zqg14a4snx2BwWb7tKF8d+WBpWOxGySypZc/omY/3tmlQv1d4QBIGXh3qy80o6y/ZEs+XZHuJG4S6IRujDw7GYbArL5UwIbHtp2L3cLdj6XC8Ky+X3vBm8JVgUllTQIEN0c0gyb/0dQQ83M6YEOzKys+09RfxyS2Ucjc7mi4OxZJfIeCzAntdHdKxR8U0vrOCvsFT+DE3lnR3X0NeqFiAa629LHw/Lu0ZxXS30WT+rG0+uDuGXs4l0tDZkiLc1PnZGeNsa4edg3KD1m+lrMcrXtuZ9mV5YQURaETEZJcRmFROTUcLBqCxczauv521r1OTvSWtDKhGYN8CdeQNurwf8+cQNPt4Xw5aLKY3KympNKJQqPtwbTURqEWb6WpgbaGNhoIWZvhZ9O1jWRNJbE+mFFUz5+TxphRU1n3WwMiDY1YyuzqZ42xrhbmnQrFkOS3dFcSY+j88n+hHsYtZs84rcf0RDtKXQ0EZu1oEn8o8BApYTvuJK555IBBj85QlMq5zQU5dxPOV4ixui6rgD7DExI9CqC3YGdncdn1cqY8HmMC4k5vPxeN8mbQTaqrdWS0PCN1O7MOa707z511VWTe+KIAh8fzQeuVLFq0PFdJFbGOlo8vJQT/6zPZID17IY0bllFSRFRFoL28LSsDTUpo+HRUsvpUk0l7PNxlgHO2OdBtWJFlfK+eJgLB5WBmQUVfLylnDe3XGNR7vY80gXO0z1tdCSStCQCmhKJUgFgUqFkvIqJRVV1f8tqpATlV5MRFoRkWlFZBZXAuDvaMJPTwUR+I9hfAs7E10WDurAgoEexGWX4miqh65W4953neyMCXlrCIJAsynU2pnoYmeiy/D/U+WtlCvRkkoeml7OAHP6unEqLpf3d0XR1cWsVRptd0KuVPHCb5fZF5lJoJMJSXnlhCUXkl8mQ6UGQx0Nti/ofU/p781NTomMJ1eHUFwhZ9u8nqjUcOFmPhcT89l1JZ3NIckAaEgEXC306WhjiL+DCdN7Od+1q0J9bDiXyK/nk5jbz41JbXTv+DAjGqItiI6DP+THQNDTuPv1qvn80QB7jh/zpGf5GY6nHOM/Pf7TctGiymJiMy6RYGfFOw0QKYpKL2bOhkvklMr4eoo/jwU43PWc9oantSFvjPDig91RbLmYQi93CzZfSGZKsCMuFvotvbxWxdRgR9afTeTjfdEM8rJqFXVgIiItSV6pjGMx2czq49rqhWMeBAFOpoQlFdx13A/HbpBfVsWGWd3wsTXi/M08/riYwh+XUvj1fFKDrycI1ZHK7m5m+Nob4+dgQlfnO/fQFAQBzwb2kK6LB/Hca6u1oPeCRCLw1WR/Riw/xYu/X+av+b2abOw8aKoUKhZuDuNgVBb/Ge3N7L5uNcdUKjUJuWVM+fkcc9Zf4u/5vTHWa/kU/oKyKp5aE0JGUSW/PtONIOfqyOStCKVSpSY+u5TYrBJiM4uJzSwlPLWQ3VczuJJayLdTA+rVAqmUK9l5JR1tTQn2/zharAy1OZeQx/u7ohjsZcXrI7we2L2KNB+iIdqSuA+CxDMw6D+1Pn60iz1vHfFmUvkhjlTkEJ0fjY+5T8usMeE4e/R00BAkDHMedsehe65m8Nqf4RjravLn3J74N7Cmpz0ys5cLR6KzWLo7iiBnU6QSgRcG31u9VHtEQyrh7dHePL3uIhvOJdZ62YqIPIzsCk9HoVIzvg2m5d4PApxM2BORQXZxJVZGdfcETC0oZ+2Zm4wPsK+JxvZyt6CXuwXvl8s5l5CHTKGkSqFCoVIjV6pQKNXoaErR07r1pYGBjgYeVgYYaItbo/aClZEOn03wY/aGS3xxIJa3R7fQXqoRyBRK5m8M40hMNu+P68SMXi61jkskAh5WBvz0VBDTVp1n4W9hrHs6uE7HVXphBdfSi9HVlKKrJUFHU4quphRTPa1G1W3KlSqS8sq4nlVKcYUcPwcTOtoY1hiOJZVyZqy7QEJuGeueDqZrHemxUolARxtDOtoYgv//sutWnUzgw73RmOlpsfSRTrcFXgrLq5i9/hKX/uWQkkoEJAJ4WBqw/PH6jViR1o34tG1J/KeC35RqF+z/4WSuh8qhGz1zqxCA4ynHW8wQVV0/wF5DA3rb9cZEp27DUqFU8c3hOFYciyfQqTqF6WFvIiyRCHwxyZ/h35zkVFwuz/V3x7qeTdTDzoCOVvTztOTbI3FMCHSo9XLMK5Xxw/EbaGlIWDSs40OVVibycLItLI1OdkZ42bSfOr57oaZONLmw3vT9Lw7EIgCv1SEEZ6ynKab9P+QM8bFmek9nVp26Sd8OlvTztGzpJdVLpVzJ3F9DOXE9hw8f68wT3Z3rHRvsYsaHj/ry+rarLNsTzZJxnWrNs/JkQk0bpbow09fC3VIfd0sD3C0NsDDUokympEymoEymoFSmJLukkrisUhJyS5Era7fIMdTWIMDZlGBnU07G5RCVXsxPTwbRu5ElBXP6uZFbJuPnEwmYG2jx0v8p3qYWlDNj7QVS8itYPrULneyMSCusJL2wgvTCCkplCmb3dROdR20Y8SfX0tSTcjsy0J20vc74KtUcTznO/C7zH/DCALWa0KQjZJto8pr72DqHJOaW8fIfV7icXMiUro4sfbRTm0l9ud/YmejyxSR/1py+yTyxsfIdeXuUNyOXn2T5kTiWjOtEpVzJujOJ/HAsntIqBWp1de3JpxP8RK+nSLvlelYJEWlFvDOm9UdtHhSd7Y3Qkkq4nFJQp0EZnlLI9ivpLBjojp2olClSD2+N8uZ8Qh6v/BHOlGAH9LQ0aqLhxrpaDOho2SrSl1/47TIn43L4bIIfk4PvXu84OdiR61klrD59E09rQ6Z1d+JoTBbv74oiKa+c0X62zOrtglJVbZxWyJVUypXklMi4kVPGjZxSDkdn8fvFlFrzCgLoa2lgqq9JBytDBnhZ0tHaEE9rQ/S1E0ujqQAAIABJREFUNbicXMClpAJCEwv48tB1JAIsnxrAkH91C2goi0d4kV9axTeH4zDX1+Kpni5cSy9i5rqLVMqVbHimGz3+aXvnYSUK97UnREO0lTLG15Y/9ngzsOgUy6VyMssysdF/wF7dc9+zRyJDV6LHAMcBtQ6p1Wp+u5DCB7uj0JQKfPt4AOP87y5k9LAxvJNNLcEIkbrpaGPI1G5ObDyfhL2JLr+cTSStsIIh3tYsHunF7qvpfHM4DpVKzeeT/EVjVKRdsi0sFalE4JEu4rP0FtoaUnzsjLicdLtgkVqt5sO90Zjra/Gc6OwTuQM6mlK+fTyA2esv8dOJBJSq2tG9bi5mrJretUVrLUOT8jkYlcWi4R0bZITe4s1R3sRll/Lujkj2RKRzJj4Pd0t9Ns3u3uDoZGF5FfllVRhoa6CvrYGupvSOGUiuFvqMD6zWACkql1OpUN5T1pcgCHw83peCcjnv7rxGelElv55LwlBHg63zet1TDbZI60Y0RFsppvpaVNr1ZGz+PpabGXEi5QRTvKY8uAXE7qP80DsccnFmsPMwdDX+52nOLqlk8bYIjsZk08fDgs8n+dXI2YuINJWXh1S3c/lwbzSd7Y34YpI/Pd2rPaAvDfFEQyLwxcHrKFRqvprsLwq5iLQrqhQqtl9OY4CnJRYGLdw7upUR6GTKxvNJfLo/hgmBDjXqp4eisrhwM58PHu3c7L1DRdofXjZGnH5jEGq1miqlinKZknK5knM38njrrwgm/nSW9bO6tVhk/buj8ZjpazGzt0ujzpNKBL6bFsD4H85yObmQN0d6MbO3a6NEsEz0tDDRa1qvT2M9TYy5978/DamEFdMCmL7mAj8ev4GXjSHrZgaL+8t2jmiItmK8ug3Daefb2An6HE89/uAM0cxIFNtm85qjK6WCkileU2sOFZZXMfrb0xRXyFky1ofpPV3Euj2RZsHSUJufnwqioLyKUZ1tb/u9WjioA1KJhE/3x6BUqflmapcmtTsITykkNKmAp3uJv7sirYcP90SRVSzjs4kuLb2UVsecfq4k5pWx8mQCPx6/QRdHEyYGObD29E3cLfWZ2ojokYiIIAhoa0jR1pBiCkwMcsDORIe5G0IZ/8NZfpkVfFuNtkyhJDKtiE52Devt2liuphZyPDaHRcM7oqfV+K25kY4m2xf0RqFUNdmgbA3oaEpZ/XRXtoWmMj7QAWNd0cHU3hEN0VZMfz83Yne60rVUyb6MEFJLUnEwvM/tUEqyUP82lWXmJpySynm3x7t0sepSc3hTSDI5JTL+nt+rRkRCRKS5uFsa0bwB7mhKBZbtiUYQ4LvHAxrV2igsuYCnVodQVqUktaCCd8Z413v+hZv5rD19k6IKOSUyOaWVCkplCgy0NfjhiSB87EQxGZHmYceVNNafS2JOX1f6t2IhlZbC1liXtU8Hk11SyY7L6WwNTeU/2yMBWD29a7P13xR5eOnlbsEfz/VkxtoLTPrpHCuf6oqzuR7HY3M4FpvNmfhcyquUjPa1ZcW0xr13GsKKo/EY6WgwvWf94kR3o70I9hjpaDKzt2tLL0PkAdE+fmvbKTqaUvIsujIjfyfHTTyYdWAWa4atwdHoPnl/5RXw+zRWSsvZpqPPHN85TPKcVHP4loBMf09L0QgVaTFm93WjSqnis/2xdHU25ekGvrAi04qYsfYCFobajHUzZ+2Zm5joadbZVmdXeDqv/hGOsZ4mrub6WBnq4GahgaGOBkdjsnlyTQi/zelRLUPfSlGr1ShUapQqNSq1ukledpH7T2xmCYu3RdDNxUzsg3cXrAx1mNPPjdl9XbmWXszN3DIGe1u19LJE2gnetkb8Nb8XT6+7yBOrz3OrjNTeRJfxgfZIBIEN55Loft6M6T1dmu260RnFHIzK4sXBHcQUc5GHDnFn0sqx8h2M5/E/WGj6FD8UbWLmgZmsGb4GZ6Ome83qRK2GHQvZURTNCktzxrmP4/mA52sN2XEljdxSGXP7ib0eRVqWef3dCU0s4KO9MQQ5m+HrYHzH8TGZxTy5JgQjHU02z+mBrZEOcqWarw5dx0hHo5Yxu/pUAsv2RNPNxYyV04NuS3NKzC1jyspzTFt1nt+f7UGHViSicD4hj1f/CCejqIJ/aXEwKciBj8b7Njp6pFKpEQSaPQIgUt17b97GUAx0NFgxLUCM7DUQQRDobG9c0zNURKS5cDDVY+tzPfnuaDxWhtoM9LKig5UBgiCgUqlJLajgg91RdHE0wc+heXqlrzgWj4G2BrPEKKDIQ4j41mvleAYPQ4WAdmw0a4avQa6SM2v/LG4W3WzeC0Xv5Gz8bpZYWtDDtgdLei6ptfFUqdSsPJlAJzujGgEZEZGWQhCq+7RaGGixYHMYxZXyesfGZ5fy5OoQtDUkbJ7THXsTXSQSgU8n+DLUx5olu6L4KywVlUrNst1RLNsTzcjONmx4pludtTYuFvr8NqcHEonA46tCiM8uvZ+32iDUajXrzybyxOoQtDUlLBjowUtDOvDaME/eGOHFkz2c+DM0lWc3XKK8StHgeZPzyhnwxXGW7o66j6t/OFGr1Sz68ypJ+eWseDwAK7HPsIhIq8BET4t3xvgwt787ntaGNXshiUTgy0n+WBpoM39TGEXl9b93Gkp8dil7IzKY3tO5RRV7RURaCtEQbeVI9M3I1XPHKv8SBQXmrBm2BoVawawDs0goTGiei6iUpB5fxis2VriZduDrAV+jKa39QDwWm82NnDKe7ecmRkZEWgWm+lp8Ny2AtMIK3vwrArVafduY2MwSnlh9HoBNs3vgbK5fc0xDKuG7xwPo5W7Ooq1XeWJ1CKtP32RGT2dWTAu8oyCFm6UBv83pAaiZtuo8N3PLmv3+GkqlXMnrW6/y3s5rDOxoyfYFvXl1WEdeGuLJwkEdmDfAnWWP+vLRY76cuJ7DtFUhFJRV3XXelPxyHl91nuT8cjadTya7uPIB3M3Dw6pTCey/lsniEV50dxOdeyIibQFTfS1WPBFIZlElr20Nr/O90xh+OBaPjoaUZ/qI0VCRhxPREG0DGHTsT5AkjjlrT3Mzw5B1w9cBMPPATIpkRfd+gchtfEE+KqkWKwatwEDL4LYhP59MwN5El1G+tvd+PRGRZiLI2YzXhnVkz9UMNoUk13x+ObmA534NZcTyk1QpVGyc3b2m5cP/o6MpZeX0rnS2M+JcQh6LR3qxZFynBvUp9bAyYPOcHihUah5feZ7YzJJmvbeGkFlUyZSV5/kzNJUXBndg5VNdMaqnxmhadyd+fDKIqIxiJv50lrTCinrnTSusYNrq85RUyvl+WiBylYr15xLvz008BMgUSsKSC1h9KoEFm8Lo+fERPtobw8jONszuK25ARUTaEoFOprw5yptDUVmsOV07O02hVJFTIkP179qIOkjKK2NHeDpPdHfCXGzZJPKQItyrN+de6Nq1q/rSpUstdv02Q+Jp+GU0J7X6MbP0OT6b4I+rQzYz9s/goz4fMdZ9bNPnVso5/1NX5hioeKHL88zxf/a2IVdSCnn0+zP8Z7Q3s/uK9aEirQuVSs3MXy5yLiGP98b6sPNKOiE38zHS0eCpns483csVS8M7v+TLZAoS88roZNf4mrOYzGKmr7lARZWSH58Mok+HhjUQv1cKyqoY/s1JymQKvpzchRGdbRp0XkhCHrM3XEJfS4P3H+lEvw6W6Gr9L/pbbdyeI7+sik2zu+PnYMK8jaGcvZHH2cWD0G8nyowPir/CUnl3xzVKZdUp0Q6mugQ6mdLVxZRJQY61vvciIiJtA7VazXMbQzkSnU3fDhbklMrIKpaRWypDrYYujiZ8PaULrhb6dZ6vUKp49c9w9kVmcvr1gWJqvki7RxCEULVa3fW2z0VDtI1w6is48j5/GT7BKzmjeXtURzZmPEMP2x582u/TJk+rCP2FSWEfU2low/ZJB9GW3r5hX7ApjJNxOZx7c3C7kQcXaV/klcoY9e0psopl2BjpMLuvK1O7OT2w39f0wgpm/XKR+OxSPnrMl8kPoK/hx3ujWXkqgR0LejdaNCMms5hZ6y6SXlSJtoaEvh0sGOJtja+DMQs3XyanRMavz3SrUccOSy5g/A9nWTLWp8EqxfVRJlNQpVBhql9/r7symYKd4emUVymZ1s2pTRprlXIlS3dHsTkkmW6uZszq7UKgk6m44RQRaScUVchZuDmMvNIqrI20sTbSwcpIB20NCT+fuIFcqeadMT483s2xVknT6bhcPtgdRWxWCc/1d2fxSFEtW6T9IxqibR21GnYuhMsbWWf9Ju8n+eIfsJd89VWOTz6OVNKEjZpCxuaVQXysL/DNgG8Y7Dz4tiHVYiXHeLaf+LAUad1czyohNrOE4Z1s0NJ48FUHJZVyFmy+zMnrOcwf4M5rwzoiaUCKb1PILq6k3+fHGNXZlq+mdLn7CXVQpVBxMTGfQ1FZHIrKqknV1deSsuGZbgQ5m9UaP+HHs2SXVHL8tYENSl3+N8WVctaevsmaUzcprVIQ7GLGyM42jOhsg62xLlDdxmBTSBLbL6fXRBDtjHV4c5Q3Y/xs20x9ekp+OfM3hRGRVsRz/d15bZgnGqIirojIQ0NGUQWv/RnOmfg8hnhb8fF4P8pkCpbtieZwdBZOZnq8Ncqb4Z2s28xzTUTkXhAN0faAogo2jkedEsJPTl/xZVoyuva/s2nUJvws/Ro9XeHZbxkd8xPeZt6semRrnQ/DJTuvsSkkidNvDMJa9OSLiNwRuVLFuzuu8duFZMb42fLhY74Y6za/EuJ7OyLZGJLM0Vf71xJgaipqtZrYrBKOx+bQw82cLo63R1j3R2by3MZQfngisFG14qUyBevPJrLyZAJFFXKGd7Kmo40RByIzic2qrqsNcDJBIgiEJhWgpSFhjK8tT/RwQqFUs2RXFNEZxXRzNWPJ2E742Bnd8/02N2q1GplCRUWVkouJ+SzaehWVWs1Xk7sw1Me6pZcnIiLSAqhUan45m8gn+2PQ05JSJlOgJZWwYJAHs3q73lEQT0SkvSEaou2FigJYPRRVeR6DSl4jr8PPzPN/jvld5jdunqpylq3tylYdKVsf+QsP0w63DTkVl8OzG0IZ7WfLF5P8m+kGRETaN2q1mp9PJvDp/hjM9LR4bXhHJnd1rDOKmJhbxqm4HFws9PF3NKlXaOj/SS0oZ+AXx5kY5MDH4xvvgGoqSpWawV8ex1hPi+3ze93Vi69QqthwLonvjsZRUC5nsJcVLw/1rNX78UZOKfsjM9kXmUGVQsXkro5MCHSolbarVKn5/WIyXxyIpahCzpRgJ14c3AEb45Z1jJ24nsO7OyLJL62iXK5E+X/iJD62Rvz4ZGCzOAlERETaNtezSvjP9khczPV4bVhHMT1f5KFENETbE3k3YPUQ0qt0GWfniqeNMZtHb27UFLFH32Ny8jamOgzizSHf1jpWKlPw0d5oNock426pzy8zu+FoptecdyAi0u6JTCti6a4oLiTm42NrxHtjfejuZk6ZTMGeiAy2XkrlQmJ+zXhBgA5WBv8I2Zgx1t8WbY3bPeaLt13lr7A0ji8agJ2J7oO8JX49n8Q72yP587meBLuY1TvuWnoRi7dFEJFWRB8PC14b3rHOKGtjKCqX8/Xh62wKSUIQBJ7o7sS8Ae5YGT74Td3uq+m8vOUKrhb69PGwRE9Liq6WFF1NKSZ6mozytRWjHSIiIiIiIv8gGqLtjesHYPNkxhgOI9niv+3deZBV1Z3A8e8PaHZEkGaMYRUVjRpUMKKIuM2EqJiYSdQMSmJMNIupcrLoJJnUOGbRlDPJJCYatWLG0qgz6qilMYs7GFKJO6JgFDWQhggoNFsDTfdv/ugntsjS633N4/uponicd+7r361f3cf59Tnn3hd55IxHGNx72wPD5jasXsLnbj2Bl3tWce+ZMxnY6+0ZitkLlnPR7XOoWVnHZyfvzZf/fj8HVFIbZSb3zlnCZffNY3Hteg4fNYjnF69i3cYGRg/px8fGD+NDB+1Jzco6nvrLSp5auIKnF65g1fpNjB85iJ+dNf4dd/x9dflaTvzBo5w9cSSXnHpg4edTt7GBoy5/kAmjBnPdjHf9f8L6+gb+64GXuG7WKwzqW8W/TTuww/d2LnpzHVc+9BJ3PFVDVfdgxpGj+PSk0Qwd0KvT9uQ2d/MfF/LNu55jwshB/PxTh7doFluSpF2ZhWilaWxg0xX7cV39CK4avpzLJl/GKXufstWumcmrq15lds1sfr/wIZ742+OsD/jW/p/i9CO+AjQto/vOr+bx37NfY/SQfvzHx9//rpuVSGqbuo0NXDNzAXc+XcPE0Xvw8QnDGD9y0FYLtMbG5FfPLeFrtz/LHv16cd2MCZv3RV5469P89vnXefSiY8syEwjwg9+9yJUPv8z9/zyFQX2rWLp6A8tWb2DxyjqufnQBf3ljHadPGMY3TjqA3ftu+8647fXa8rX8+MGXuOuZGhoTugUM7FPF7n17MrBPFXvt3pvJ+1Zz3NihHbaM9+pHFvD938znuLHVXDV9/E55N19JkorWoYVoRFQBK4GXSk3/DjwL3AYMBH6Rmd/d0edYiLZP3nMha5+8hcmj9uaDY6Zw+eTL39Xn1dpXueDBC1i4eiEAoxrgqHV1HHPkVzjqsM9tHghfdt88rpn5Cp86ahQXT93fAZZUZnNravnMDU9QW1e/+Xl0U380k/PLfAfrZas3MOn7D7FxU+O73hu1R1++d9rBHLVPMc9SBXh56RoeeXEptXX1rFxXz4p1G6mtq2fB0jUsrl0PNO3ZPH7/oRw5Zg+G9O/FwD5V7NanB32qum93tra+oZHaunpW1dVz6+OLuHbmK5w6bi/+8/RxVHkXXEmSWqSjC9E9gZsz8/hmbdcD9wJ3A7OBczLzhe19joVoOy14CG48jeOHHE39kFU8esajdIu3B0eZyfn3n8/c5XO5cOx0Js26iveuXw3T74Dhh2/u95u5S/jcTU9x9sSRfPsjB5XjTCRtxdJV6/nsjU/y7KKVjBjclxVrNzLr4uM6daaxJe55djHz/7aK6v69GLpbb6oH9KK6fy+GDerTZR5Tkpn8+fU1PDR/KQ/PX8qTC1e844ZCAFXdg/69etC9WxARdAvoFkFjJqvXb2LdxoZ39D9r4gguPfWgQpYAS5JUKbZViLb1ae+DgeVbtE0BvpCZDRFxb+nf2y1E1U6jJlPfc3eOWbuWOwas5Pnlz3Nw9cGb335k0SP8YckfuHj/szn9oR82PYv0k/fCe96+0+Yry9bw1dvmMG747vzrKQeU4ywkbcPQ3XrzP+dN5F/umMNdzyzmwhP3LXsRCjBt3F5MG7dXucPYrohg7J4DGLvnAD5/7Bhq19XzXE0tK+s2sqpuE7V19dTW1bNmQz2N2fT1mJk0ZhIEA3r3KM2cVjGwTxV7DuzNEaMH+8w/SZI6SFsL0Z7A5IiYAywGvgD0y8z1pfeXAlsdpUTEecB5ACNGjGjjjxcA3avodsDJfHrOXdzBEB6reWxzIbqxYSNXPHEFY/oP54yHfwI9+8OMu2HI249pWbdxE5+/6SmqugdXTT9sq3folFRevau688MzDuHsI0cxbtjAHR+grRrYt4qj9y1uybAkSdq+Nq2hysxngL0y8/3ALcBPgC3X+G7118aZeW1mTsjMCdXV1W358Wqm+4EfZkSuZbcNg5lVM2tz+40v3Mii1Yu4qL43VQ2b4Jz73lGEZibfvHMuf166mh+deSjvLfgxEJJaLiIYP3JQl1n2KkmS1F5tHtXk25tL/xd4H7A6It66NeFQmmZF1dn2Ppb6Hv0Zt6aBucvnsmL9CpatW8a1c67l2OrDOGr+gzDx8zBo1DsOu+mPC7nz6Rq+fOJ+HLOfvxCQJEmSVJw2Lc2NiGrgzcxsAE4EngFqgJMj4i5gGjCjw6LUtvXoReO+U/mnBfczi4HMXjyb2YtnU99Yz0Vv1tLQayBffHUSf71yFnUbG1hf30hdfQMr1m3kuLHVfPG4fcp9BpIkSZJ2MW3dIzoO+FFEbABWAJ8FVgP3AFcA12fm/I4JUTvS6/0f4ch5t1PVOJQbnr+BeW/O49wRH2L4o9fwk27T+dOSBg4dPoDePbvTu0d3+vTsxuB+vTj36NHe/VGSJElS4dpUiGbmA8CBW3lrYvvCUZuMOYHGbr3Zd01PXug2j+o+1cx4+WneYHd+mVO59fyJ7Pd3A8odpSRJkiQB7dgjqi6kZ1/Wjz6R0+peB+C8oScwuOZPXMNHue4zUyxCJUmSJHUpFqIVov8hH+X0dcuZsuYfOXTmHdRkNVNnXMxB7/VxD5IkSZK6FgvRChH7/QON0ZMvvX4bYxsXUDfpaxy2957lDkuSJEmS3sVCtFL0GsC64VMY2+2vrN1tb/Y54dxyRyRJkiRJW2UhWkF2m3A6AP2mXgLd23pDZEmSJEnqXFYrleSgj8HgMTBsfLkjkSRJkqRtcka0knTrZhEqSZIkqcuzEJUkSZIkFcpCVJIkSZJUKAtRSZIkSVKhLEQlSZIkSYWyEJUkSZIkFcpCVJIkSZJUKAtRSZIkSVKhLEQlSZIkSYWyEJUkSZIkFcpCVJIkSZJUKAtRSZIkSVKhLEQlSZIkSYWyEJUkSZIkFcpCVJIkSZJUKAtRSZIkSVKhLEQlSZIkSYWyEJUkSZIkFcpCVJIkSZJUKAtRSZIkSVKhLEQlSZIkSYWKzCzfD49YBvylbAHs2ECgttxBFGwIsLzcQRTIHFc+c1z5zHHlM8eVzxxXPnNc+baV45GZWb1lY1kL0a4uIq7NzPPKHUeRIuKJzJxQ7jiKYo4rnzmufOa48pnjymeOK585rnytzbFLc7fvnnIHoE5njiufOa585rjymePKZ44rnzmufK3KsYXodmSmF0yFM8eVzxxXPnNc+cxx5TPHlc8cV77W5thCVFu6ttwBqNOZ48pnjiufOa585rjymePKZ463wz2ikiRJkqRCOSMqSZIkSSqUheguICKGRcR9EfFCRMyMiL0iYu+IeDIiXo6Ibzbr+61S2+MRMarUdkREPBMRz0bEBeU6D21bK3M8OCJmRcQlW3zGWRGxofDg1SLtyXFEdIuIq0rHvhARl5btRLRN7b2OI+K7EbGg9H3927KchLarndfxHqXcvvWnpnl/dQ0dcB075uriWprjrfVr9hmOuXBp7i4hIqqBAzJzZkR8CRgB7AHcC9wNzAbOAdYCdwKHAycBZ2XmGRHxOPAZ4HngAeDczFxQ/JloW1qR45eAx4C5wKLMvKR0/NnAqcDEzBxe/BloRzogxwdn5nMRUQXMA6ZkZk3xZ6Jt6YAc/wy4OTNnliF8tUB7c7zFZ90OXJ6ZTxQUvlqgA65jx1xdXCtyvGzLfpn5Ncdcb3NGdBeQmcuaDUxeAwYBU4D7MrOBpgtnCnAc8OtS26+BI0vHjMzMZzNzE023ZZ5aZPzasZbmODPrgWnArC0+4j7gTKChmIjVWu3NcWY+V3q5F7ABeKOIuNVyHXAdD2bXenD6TqcDcgxARAwBxliEdj0dkGPHXF1cK3K8tX7gmGszC9Fdz8eB3wL9MnN9qW0psCcwlNIgpvQF2D0iugNLI2J8RPQETuDtC0ld0/ZyTGYu3fKAzHyj9OWpnUOrcxwRPSNiLvAU8PVmx6lranWOgSrgpoiYGxFfKSZMtUNbcvyWs4BbOjc8dYC25Ngx185luzneSj/HXM1YiO5CIuIkmpYP3A5suSY7ttEGcD5wNfAw8CZNS3jVBbUgx9rJtTXHmbkxMw8CDgS+ExFjOi9KtUc7ruOPZuZhwNHA9Ij4QCeFqHbqgO/qGcBNHR2XOk47cuyYayfR0hxv0U/NWIjuIiJiH+By4BPZtDF4dUT0Lr09lKbf3iwBhpT69wAyMxsy8/eZ+YHMnAR0p2k/g7qYFuZYO7GOyHFm/o2mpWCHdVqgarP25LjUn8xcCdwPvK+Tw1UbtPc6jogJwNLMXNy5kaqt2nkdO+baCbQ0x1vpp2YsRHcBETGApiU8n8zMJaXm+4GTS0tvp9G0If4BYGqp7SS22LcQEUcAhwKPFBS6WqgVOdZOqj05joh+ETHsrdfAJJpuhKEupL3XcUS8p/R3T2Ay8GznRqzW6qDv6k8DN3RelGqPjvr/2DFX19XSHG+jn5rpUe4AVIgLgH2AX0QEwDrgwzRtgr8CuD4z5wNExC+AF2laCjKt1DYd+CqwCjiztMFeXUuLc6ydVnty3Bu4vVSENgLXZOYLnR+yWqm91/FVpSXXCdyYmU93crxqvXbluDTjchLgHuCuq705dszV9bUoxxHx9S37ZeZR5Qm5a/LxLZIkSZKkQrk0V5IkSZJUKAtRSZIkSVKhLEQlSZIkSYWyEJUkSZIkFcpCVJIkSZJUKAtRSZIkSVKhLEQlSZIkSYWyEJUkqYNExBERMbQF/fpHxGs76DOtwwKTJKmLsRCVJKnjfBnYYSG6IxHRA/h2+8ORJKlr6lHuACRJ2hlExCjgx8AmYAPwZ+AUYD0wAxgHnASMi4grM/OnWxzfC7gJ2B+YCWSpfSxwPdAPWAScBlwKHBQRzwDnA28CPwd2B27PzEs78VQlSep0zohKktRyHwS+AfwAGJWZ44HLgUsy8/+AJ4GPbVmElnwCWJmZBwOPAVFqfxk4NjMPARqBIzLzG6W+h2TmH0s/7xzgEOCUiNin805RkqTOZyEqSVLLvZSZ84FJwAmlGcvv0bLluIcB95de39+sfTRwb0Q8BxwNDNrKsUcCdwBPAdWlYyRJ2mm5NFeSpJZb3+z1TzPzsjZ+TjZ7fQlwd2ZeFRG/3Eb/htKMqSRJFcEZUUmSWu/3wMmlmwoREXuU2tcC/bdxzNPA8aXXxzRrHwA8HxF9gInN2hsjonvp9RMR8aHSz9otIqo64BwkSSobC1FJklopMx8H7gaejIg5wPTSWzcDt0TEl7Zy2M3A0IiYB5wB1JbarwZ+CfytnwxIAAAAf0lEQVQOeLBZ/9uA5yJiEnAB8NXSUuAHgV4dfEqSJBUqMnPHvSRJkiRJ6iDuEZUkqYNFxBHANc2aFmfmSeWKR5KkrsYZUUmSJElSodwjKkmSJEkqlIWoJEmSJKlQFqKSJEmSpEJZiEqSJEmSCmUhKkmSJEkqlIWoJEmSJKlQ/w9r9Xv2wZCi7gAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "small_only = (portfolios_vwret_df['SL'] + portfolios_vwret_df['SM'] + portfolios_vwret_df['SH']) / 3 \n", "\n", "high_only = (portfolios_vwret_df['SH'] + portfolios_vwret_df['BH']) / 2 \n", "\n", "factors_long_df = pd.DataFrame(np.vstack([small_only,high_only])).T\n", "factors_long_df.columns = ['small_only','high_only']\n", "factors_long_df.index = small_only.index\n", "\n", "factors_long_df = pd.merge(index_df, factors_long_df, on='ret_date')\n", "\n", "factors_long_df['ret_date'] = factors_long_df['ret_date'].dt.to_timestamp(freq='day',how='end').dt.normalize()\n", "\n", "factors_long_df.set_index('ret_date',inplace=True)\n", "\n", "((1 + factors_long_df).cumprod()*100).plot()" ] }, { "cell_type": "code", "execution_count": 174, "metadata": { "editable": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 174, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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s+539eykiIiJyPoZh8PLiLF5ftodr+nZjQPcAtuWWsS2vlENlNQCYTRDf1c+WnHYPYGBUIH26+eHmcvFs/vvX93v4+6JM5j04gjA/D+6fuZH0w+X8Zmw8v7oyDrPZ5OgQpTmsVnh7JFjq4aG1bU9EAf59OXgGwi++bPtYrWAymTYahpF65vWL+6OjVti/fz+BgYF4eXlhGAZ5eXnEx8c3eu/8+fN5/vnnT/47NTWVd955p9VzL1iwgAkTJrT6eREREZHOxGo1eH7BTt7/cT+3pnbnrzf1x+WUhKrwWM3JpHRrXhmLdh1mblouAO6uZvpG+J9cOR0QFUjPUJ8LMiErq67n3yv2MjohjJSYIADmPTiCP8zfzsuLs9h+sIyXbxmIn2cbzhpKx9j1ORTugknv2icJBdv23O2f2JJcs/N8OKMV0RZqaGjgjjvuYOfOnZhMJi677DJef/11XFxc2nXeyZMnk5eXx5dffklYWFiznnH276WIiIhIUxosVp76dBufbT7ItMt68IfrEjGZzp1EGoZB7pFqtuaVsjW3lG15ZWw/WEZ1vQUAPw9X+h9PSn8+PIaIQK+O+FLa3cvfZfLasj0s/PWl9I0IOHndMAxmrMnhxYXpxIR485+7UokL83VgpHJOVgu8ORxMJnhwDZjtlF9sng1fPAQPr4cufewzZgtoRdROXF1dmTt3bofP++mnn3b4nCIiIiKOUFNv4ZE5m1m8q4Anx8Xz8JVx501CwdadIDrEm+gQb24YGAGAxWqwp7CCrbmlbM2zJafvrMxmW14pH04b1t5fSrsrqajl3VX7+Fn/8NOSULB9P+4e2YOEbv786sNNTPjXal6+ZSDj+jpX9VQ5bud8KM6Em2fYLwkFWwsXsJ0TdUAi2hTnWZsVERERkYteRW0D98zYwOJdBfx5fF9+Nbp3s5LQpriYTfTp5sctg7vzl4n9WfDIpfx6TG/W7C1hf0nnryz79oq9VNdb+M1VvZu8Z3ivEBY8cik9u/jwy5kbefm7TKxWx+2KlEZYGmD5/4OwvpA43r5jh/QGD384mHb+ezuQUyaijtwufKHQ91BEREQ6m9KqOu58Zx3r9h3h5VsG8vPhse0yz82pUZhNMHdDbruM31EKymv44Mf9TEiOJC7M75z3RgR68fH9w7k5JYrXlu3h3vc3UFZd30GRynnt+BRK9sCo39n/HKfZDBHJTtfCxekSUU9PT0pKSpRItYFhGJSUlODp6enoUERERESapbC8hlv/vZZdh8p5645B3DQoqt3mCg/w4so+YXyyMY96i7Xd5mlvbyzbg8Vq8NiYxgtnnsnTzYX/nTyAFyb0Y+XuYsa/sYqsgmPtHKWcl6UBVrwE3fpDwvXtM0dUKhTshPrq9hm/FZzujGhUVBR5eXkUFRU5OpROzdPTk6io9vsBLiIiImIvuUequOOddRRX1DL97sGMjAtt9zlvHdydpRmFLMso5OpOeGYy90gVH204wK2DuxMd4t3s50wmE3cNiyGhmx8PzbadG/3HzQO5rn94O0Yr57RtLhzJhilz2q+qbWQKWBsgfytEO8fZaKdLRN3c3OjRo4ejwxARERGRDrC74Bh3vruOmnors+8bSnJ0UIfMOzohjDA/D+ZuyO2UieirS3djNpl4ZHTTZ0PPZXBsMF89cikPztrIQ7M38eCoXjw5rs9p7XGkg6x+BcKToM+17TdH5PGCRQc3Ok0i6nRbc0VERETk4rAtr5Rb/v0jVgM+vn94hyWhAK4uZm5OjWJ5ZiH5Zc6zXbE59hZV8NmmPO4aFkO3gNYfxerq78mcXw7j9qHRvLV8L1Onr6e0qs6Okcp51ZRDcRZccqOtbUt78esKAd1tlXOdhBJREREREelwP+4t4fb/rsPX05VPHxhOn27nLrbTHm5NjbYlwRvyOnzutvjn4iw83Vx4YFSvNo/l4erCXyf252839Wdd9hFueGMVuw6V2yFKaZaiTNtrl8T2nytykFMVLFIiKiIiIiIdaml6Ab+Yvp7wAE8+uX8EMSE+DokjOsSbkXEhfJyWi6WTtDPZdaicr7blc8/IHoT6etht3ClDopl7/zDqGwxuems1X2w5aLex5RyKMmyvHdHfMzIVSvdDZXH7z9UMSkRFREREpMN8seUg98/cSEI3P+beP7xNW0vtYcrgaA6WVrNqj3P8cn4+Ly/OxN/TlWmX97T72MnRQSx45FIGRAby6Edb+HyzktF2V5QBrp4QFNv+c0Wm2F47antufQ2s/2+TbysRFREREZEOMXPtfh6bu4WUmCBm3zeUYB93R4fEuL5dCfJ246P1BxwdynltOnCUJemF3H9FLwK83Nplji5+HsyeNpSeoT7M29S5tix3SkUZENobzC7tP1dEEphcOm577q4v4Osnm3xbiaiIiIiItLs3l+/h6c93MLpPGO/fMwQ/z/ZJpFrKw9WFSYOiWLyrgKJjtY4O55xe/i6LEB93po6Ibdd53FzMjL2kK+uyj1BR29Cuc130ijKhS0LHzOXuA2GXwMEOWhFNew+Cmz7HrERURERERNqNYRj87ZsM/vfbTMYnRfD2XSl4unXA6k8LTBnSnQarwWdOvAL4494SVu0p5sFRvfDxaP8OjKMTwqizWFm1u3NsWe6Uao9BWW7HJaLwU8Eio53PRBfshNy1kHpPk7coERURERGRdvNxWi5vr9jLncOi+ectSbi5ON+vn3FhfqTGBDF3Qy5Ge/+C3gqGYfCP7zLp5u/JncNiOmTOlJgg/DxdWZZR0CHzXZSKsmyvHZmIRqVCTRmU7G3fedKmg4sHJN3e5C3O95NARERERC4INfUW/rl4N8nRgbwwvh9mczv2SWyjKUOiyS6uZN2+I44O5SzLM4vYuP8oj4yJ67DVZDcXM1fEd2FZRhHWTlJRuNM5UTE3rANat5wQmWp7bc/tubUVsPUj6DsRvIObvE2JqIiIiIi0i1lr93O4vIb/uboPJpPzJqEAP+sfjp+nq9MVLTqxGhod7M0tqd07dO4xiWEUV9Sy/WBZh8570ShKt60aBnbMKjdgaxPj7tu+BYt2fAp1x865LReUiIqIiIhIO6iobeDN5Xu5NC6UEb1CHR3OeXm5uzAhKZKvdxymrKre0eGc9O2Ow+w8VM6jY3p3+LbmK+LDMJtgWUZhh8570SjKtFXMdWn/M78nmV0gIrn9WrgYBmx4F8L6Qvch5w6lfSIQERERkYvZe6v2caSyjiev7uPoUJrt1sHdqWuwMn+zcxQtslgN/m9xFnFhvkxIjuzw+YN93EmODlIi2l6KMjr2fOgJkYPg8HZoaIcq0Yc2weFtkHo3nGcXhBJREREREbGro5V1/PeHbK7u25Wk7oGODqfZ+kUG0D8ygI+cpGjRF1sOsqewgsevisfFQedrRyeEsf1gGQXlNQ6Z/4JVWwGlBxyUiKaCtd6WjNpb2nvg5gMDbj3vrUpERURERMSu3v5hLxV1DTwxrvOshp4wZUh3Mg4fY2ueY89F1lusvLJkN30j/LmmbzeHxTEmMQyA77Uqal/FxyvmhjkgEY06XrDI3ttzq0th+zzoPxk8/c97uxJREREREbGbgvIa3l+Tw8SkSOK7+jk6nBa7cWAEXm4uDi9a9ElaHgeOVPHkuD4OrTbcp6sfkYFeLFUial8nKuY6YkXUPwL8wu1fsGjbXGiotm3LbQYloiIiIiJiN28s20ODxeCxsfGODqVV/DzduH5AOF9uPURFbYNDYqipt/D6st0Mig5kVJ8uDonhBJPJxOiEMFbvKaam3uLQWC4oRRng4g5BPRwzf2SKfVu4GIZtW27EIFsxpGZQIioiIiIidnGgpIo56w8wZUh3okO8HR1Oq00ZEk1VnYUFWw85ZP7Z6w6QX1bDk07S9mZ0QhhVdRan7LHaaRVlQkgHV8w9VWQKHMmGKjv9Nz3woy25Pk/LllMpERURERERu3hlaRYuZhOPjO7t6FDaZFB0IPFdffloQ26Hz11Z28Cb3+9hZFyI07S9Gd4rBE83M8vSCxwdyoWjMN3W09NRTpwTPbjJPuOlvQceAdDvpmY/0qxE1GQyBZtMppUmk+m5U67daTKZas+472mTybTHZDJtMJlMsc2OQkREREQ6tayCY8zffJCpI2Lp6u/p6HDaxGQyMWVwNFtzS0nPL+/QuWesyaGkss6pCj15urlwaVwoSzMKnaKacKdXV+m4irknRCQDJvtsz60shl1fwMAp4O7T7MfOm4iaTCY34Bsg65RrdwHjgcJTrsUAE4E+wJ+Bl5odhYiIiIh0ai9/l4WPuysPXNHL0aHYxcTkSNxdzB1atKisup5/r9jLmIQwBkUHddi8zTE6oSt5R6vZU1jh6FA6v+IswHBMxdwTPPxsibA9ChZtmQ2WumYXKTrhvImoYRj1wA3AylMufw1MAU49sXwl8I1hGBZsievwFkUiIiIiIp3S1txSvt15mGmX9STIx93R4dhFkI871/TrxvzNBzusSM87K7Mpr2ng8XHOV+hpdIKtjYuq59pBUabt1ZErogBRKbYWLm1Z5bZaIW06xIyEsMQWPdqsrbmGYRSe8e+S4wnnqcKA4uPvNwAuJpPJ5cyxTCbTL00mU5rJZEorKipqUbAiIiIi4nz+8V0mwT7u3HuZgyqAtpMpQ7pTXtPANzvy232ukopa3lu1j58NCKdvREC7z9dS3QI8uSTcn2XpSkTbrCgDzG4Q3NOxcUSmQvUROLqv9WPsW257vgVFik6wZ7GiM1PpRkt8GYbxH8MwUg3DSO3SxbHlqEVERESkbX7cW8LK3cU8NKoXvh4OqgDaTob3DCE2xJs569u/aNHbK/ZSXW/hN07c9mZMYhhp+49QWlXn6FA6t8IMCIkDFzfHxhGZYnttS8GitPfAOwQSb2jxo/ZMRPOBUACTyeQKGI2smoqIiIjIBcIwDP6+KINu/p7cOSzG0eHYnclk4tbB0azfd4S9Re13NrKgvIYPftzPTYOiiAvzbbd52mp0QhhWA1ZkaVdjmxRlOLZi7glhl4Crl217bmuU50PG15B8J7h6tPhxeyaiS4Brjm/HvY7Tz5SKiIiIyAVmWUYhmw6U8usxvfF0O+tE1gVhUkokrmYTH7dTK5eC8hqmTt+AYcCjY5y77c3AqEBCfNxZpnOirVdfDUdzWnyesl24uEJEUusLFm2eCYYFUqa26nG7JaKGYRwGpgOZwAvAU/YaW0RERESci9Vq8PdFmcSEeHNzapSjw2k3YX6ejEkM49ONedQ1WO069p7CY9z05hoOlFTyzi9S6R7sbdfx7c1sNjGqTxjLM4tosNj3e3HROFEx1xlWRMG2PTd/KzS0cLu1pQE2zoBeo1t91rXZiahhGDMMw3jujGuxZ/z7DcMw4gzDGGgYRsfVuhYRERGRDvXV9nwyDh/j8avicXOx5yY75zNlSDQllXUsSS+w25gbco4w6a0fqbNYmXv/cC6P7xy1U8YkhlFWXc+mA6WODqVzOlkx1wlWRAGiUsFSCwU7WvbcnsVQfrBVRYpOuLB/aoiIiIiI3dVbrLz8XSYJ3fy4YUCEo8Npd5f37kJEgCdz7NRT9Jvt+dzxzjpCfN357MER9It0viq5TbmsdyiuZhNLM+yXlF9UCtPB7Or4irknnCxY1MLtuWnvgV84xF/T6qmViIqIiIhIi8zbmEdOSRVPjOuD2dxoo4QLiovZxM2p3Vm1p5jcI1VtGmv66n089OEm+kcGMO+BEU6/HfdMfp5uDO0ZrDYurVWUCcG9wNVJ+u0GdAefsJYlokdzYPdiGPTzNlX+VSIqIiIiIs1WU2/h1aW7SY4OZGximKPD6TC3DO4OwCdprStaZLUa/L+v03l+wS6uSuzK7PuGEuTjJMlIC41O6Mruwoo2J+UXpaIMCEtwdBQ/MZlsq6ItSUQ3vm97btDP2zS1ElERERERabbZ6w6QX1bD/1zdB5Ppwl8NPSEy0Isr4rvwcVpeiwv11DZYeGzuFv79QzZ3DYvhrTtTOnWV4dEJtg8gVD23hepr4Og+6OJEiShAVIqtiFJ1M879NtTZquXGXwMBbStSpkRURERERJqloraBN7/fw6VxoYzoFerocDrclMHRHC6vaVEfzfKaeqa+t4Evtx7it9ck8OfxfRnTTBAAACAASURBVHHp5NuZe4T60DPUh6VKRFumZDcYVuepmHtCZKrt9dCm89+b8RVUFrWpSNEJSkRFREREpFmmr9pHSWUdT17tZL9Id5AxiWGE+nrwUTN7ih4uq+GWt39kQ84R/nnrQB4c1euCWUUenRDG2r0lVNY2ODqUzqMww/bqLBVzT4hItr02Z3tu2nsQGG1r29JGSkRFRERE5LxKq+r4zw/ZjLukK0ndAx0djkO4uZiZnBLFsoxCCstrznlvVsExJr65mryj1Uy/ezATky+sXqujE8Oos1hZtafY0aF0HkUZYHKBkF6OjuR0XoEQGg9550lEi7IgZyWk3A3mtm8tVyIqIiIiIuf19opsKuoaeGLcxbkaesKUwd2xWA0+2ZjX5D1rs0uY/NYaGqwGc+8fxmW9O0eP0JYYHBuMn4cr32t7bvMVZdiSUFePdhm+uqGayvrK1j0cmQIH08Awmr5n4wwwu0Hyna2b4wxKREVERETknArLa5ixZh8TkiLp083P0eE4VGyoD8N7hjB3Qy5W69m/tH+17RA/f3c9Xfw8mP/QCPpGdJ4eoS3h5mLm8vguLMsobPT7II0oymjX86FPrniSu765C6vRsmJagC0RrSyCsia2nddXw5bZkHgD+NqnWrYSURERERE5pze+30ODxeCxsb0dHYpTmDKkOweOVPFjdslp199dtY9H5mxmQFQA8x4cQVRQ5+oR2lKjE8IoPFbLzkPljg7F+TXUwpHsdquYm1OWww95P7D76G7WHFrT8gGijhcsyktr/P2dn0NNqV2KFJ2gRFREREREmpR7pIo56w9w6+DuxIT4ODocp3B1324EersxZ/0BwNYj9MWvdvHCV7u4+pJuzLpvKIHenbNHaEuM6tMFkwmWZhQ4OhTnV3yiYm77JKIfZ32Mi8kVX9dAZqXPavkAYX3BxaPpgkVp70FIb4i9tG2BnkKJqIiIiIg06Z9LsjCbTDwyWquhJ3i6uTAxOZLvdhZwuKyGX3+0mXdW7eMXw2P41x2DOnWP0JYI8fUguXug+ok2R9GJirn2T0SrG6r5OOMzasr6UlMynNUHV5Ndlt2yQVzdIXxg44no4e2Qt962GmrHqs9KREVERESkUVkFx5i/+SC/GBFLtwBPR4fjVG4bEk2dxcp1r63kq235/O7aBJ67sfP3CG2p0QlhbMsro/DYuasIX/SKMsFkhlD7fqBjGAaPLniPWmslvrWXUVqQgpvZnQ/TP2z5YFGpcGgLWOpPv542HVw9YeAU+wR9nBJREREREWnUy99l4ePuygNXOFm7CScQ39WPlJggjtXU8+qUJB644sLpEdoSoxO6ArA8o8jBkTi5onQI7mnXirn1FitPfrKVVYVf4mfuzvQpt2JYfOkXcAVf7v2Sstqylg0YmQIN1VCY/tO12mOwbS70vQm8g+0WOygRFREREZFGbM0t5dudh7nvsh4E+1z45x1b4807BvHNo5czPinS0aE4TGK4H+EBnjonej5FmXbdlltV18C0D9KYv+tHXDwP8evBP6dvRAB+nq741Y6muqGaz3Z/1rJBI1NsrwdPKVi0/VOoq7BrkaITlIiKiIiIyFneXL6HQG837r20h6NDcVpd/T2JC/N1dBgOZTKZGJ0QxsrdxdQ2WBwdjnNqqIOSvXZLREsqarntv+v4IauIYUmZ+Lj5cEOvGzCbTaTEBJGV68fgboP5MONDGqwNzR84KBa8Q346J2oYtiJFXfv/VFXXjpSIioiIiMhpco9UsXhXAbcPicbP083R4YiTG5MYRlWdhfX7jjg6FOdUsgcMi10S0dwjVUx++0cy8sv5vym9yTy2kht63oCPm62i9eDYYHYXVjCx5xQOVx5m2YFlzR/cZLKtiuYdT0QPboLD2yD1brsWKTpBiaiIiIhIK3274zDD/99Stue18CyWk5u5dj8mk4k7h8U4OhTpBIb3DMXD1czSdFXPbdTJirl92jTMjoNlTHxzDUcq6/hw2lCOuqyizlrHrX1uPXlPakwQAJ71/Yj0jWx5K5fIVFu8NeW21VB3XxhwS5vibooSUREREZFWWLTzML/6cBP5ZTXMXrff0eHYTVVdAx+tP8A1fbsREejl6HCkE/Byd2FkXChLMwowDMPR4Tifoow2V8xdtbuYKf9Zi4ermXkPDiepewAfZ35MatdU4oLiTt43sHsgbi4mNh0o547EO9hcuJmdxTubP1FkCmBA9nLYMQ/63wwefq2O+1yUiIqIiIi00Hc7D/Pw7E30iwzg6r5dWbg9n5r6C+N83PzNBymvaeDukbGODkU6kdEJYeQeqWZvUYWjQ3E+RRm285durftg54stB7l7xnqigryY9+AI4sL8WH1oNQcrDnJrwq2n3evp5kK/yADSco4wIW4C3q7eLVsVjRxke138tK2CbhuKFBmGcc4kWImoiIiISAss2VXAwx9uom9kAB/cO4Q7h8VwrKaB7zM6/7ZEwzCYsTqHfpH+pBzf4ifSHKMTwgC0PbcxRZnQJbFVj76zMptHP9rCoOgg5t4//GQ/37mZcwn1CmVM9JiznhkcG8zWvDLczd5MiJvAtznfUlTVzPY63sG2NjNHcyBqMIQPaFXcAEsPLGXKwqZ7jyoRFREREWmmpekFPDh7I5eE+/PBPUPw93RjRK9Qwvw8mL/5oKPDa7M1e0vYXVjB1BE9LsqemNJ6EYFeJIb7s+wC+EDmBMMwOFZTT05xJWk5R/h2x2Fmrd3PK0uyePrzHfx6zmbeWZnNvuLKpgdpqLMVK2rh+VCr1eAvC3fx4sJ0ruvfjffvGUKAl61wWN6xPFbmrWRy/GTczGcXE0uJCaKuwcqOg2Xcnng7FquFuZlzmz955PEKuW1YDa2qr+Jv6/9GfFB8k/e4tnp0ERERkYvIsowCHpy1icRwfz64d+jJXwpdzCbGJ0UwY00ORyvrCOrEPTenr95HiI871w8Id3Qo0gmNTujC2yuyKauqJ8DbOastW60GR6vqKK6oo7ii9vgf299Ljv+95JRrtQ3WRscJ9HbD282FL7ce4sWF6fTs4sPYxK6MSQgjJSYIV5fj631HssHa0KKKuXUNVv7n0618seUQPx8ew7M39MXF/NMHQx9nfYzZZGZS70mNPn+iYNGGnKM8ENOLy6Mu55OsT5g2YBoeLh7nD+CSG6E4C/pObHbMZ3pr61sUVBXwjyv+wWc03s9UiaiIiIjIeXyfUcgDMzfRp5sfM+/5KQk9YUJyJP9duY+F2/M7baXZ/SWVLM0o5JEr4/B0c3F0ONIJjU7oyr++38uK3UXcODCiw+c3DIOjVfUcKq3mcFkN+WXVHCqrIb/U9nr4+J86y9nJpavZRIivO6G+HoT4etArzJdQXw9Cfd0J8fEg1M+DEB93uvh5EOTtjrurLdHMPVLF0vQClmYUMn31Pv7zQzYBXm6M6tOFMYldGWPdgQ9AWPMS0YraBh6YuZFVe4r5n6v78NCoXqftTqi11DJ/93yu7H4l3Xy6NTpGiK8HPUN9SMs5Alf04s5L7mTad9P4Zt83TIibcP4gEm+w/Wml3Ud3M3PXTG7qfRNJYUlN3qdEVEREROQclmcWcv/MjcR382XWvUMbXem5JNyf+K6+fL75YKdNRD/4cT8uJhN3dNL4xfGSugcS7OPOsvSCdklEy6rryS+rJr+0hkOnvNqSTlviWVN/epLp5mKiq78nEQFeJHUPJLy/J+H+noT6eZxMNEN9PQjwcmvVdvTuwd5MHdmDqSN7cKymnlW7i1mSXsj3mYV8seUQv3H9mkdcTbyf6coot0p6hPo0OVbhsRrumbGB9Pxj/H3yAG5O7X7WPd/lfEdpbSlTEpo+ewmQGhvE4l0FWK0GQ7sNJS4wjlm7ZjG+1/h23XZvGAYvrn0RX3dfHhv02DnvVSIqIiIi0oQVWUX8cuZGendtOgkFMJlMTEiO5H+/zeRASRXRId4dHGnbVNY28PGGXK7rH05Xf09HhyOdlIvZxKg+XViWUYjFapy2nbQtCspr+N28bXyfeXrBHRezia5+HnQL8OSSCH/GJoYRHuBFRKAn3QK8iAjwJNTXA7Od4jgfP083ru0fzrX9w7FYDbbkHsX7i/c4XNqN57/N4flvc5rcwruvuJKfv7eO4mN1vPOLVK7sE9boHB9lfESsfyxDug05ZyypscF8nJZHdnEFcWF+3Jl4J8/9+BxpBWkM7jbY7l/7CV/s/YJNhZt4fsTzBHmeu+CZElERERGRRvyQVcS0D9KI6+LL7PuGEuh97rOf45NsiejnWw7y6zGt7xfoCJ9tyuNYbQNT1bJF2mhMQlc+23SQzQeOkhob3ObxFm7L54+fb6em3sKvR8cR383vZLLZxdfjp7OYTsbFbCIlJhhcDkJcEiuvufKsLbyB3m6Miu9CSkwQ/1yyG4A5vxxGUvfARsfcWbKTbcXb+N2Q3513VfPUc6JxYX78rOfPeGXTK8zaNavdEtGy2jJeTnuZgV0GNmsLsBJRERERkTOs3G1LQns1MwkFiAz0YljPYD7ffJBHRsd1mqqzVqvB9DU5DIwKILmJX4BFmuuy+FBczSaWZhS2KREtq67n2S928PmWQwyMCuDlW5Po1cXXjpF2AEu9rWJu/NVnbeFdubuYJekFLM8s4vMth4gK8uKDe4bQ8xxf48eZH+Pl6sUNvc5/frNHqA8hPu6k5RzltiHReLp6cnP8zbyz/R1yj+XS3e/sbb9t9cqmVyivK+fpYU9jNp3/AwLn/AhBRERExEFW7S7mvvfT6BHqw+z7hraoCu7E5EiyiyvZllfWjhHa18o9xWQXVXL3SLVskbbz93RjcGwwy9rQT3T1nmKueeUHFmzL57Gxvfn0wRGdLwmF4xVz6yHs9B6ifp5uXNc/nJdvSWLDH8fy1SOX8s2jl50zCS2rLePr7K+5rsd1+Lv7n3dqk8lEamwQafuPnLx2a59bcTG5MCdjTuu/piZsLdrKvKx53J54O32Cm9eqRomoiIiIyHGr9xRz7/sb6BHqw4fThhHcwlYs1/QLx93V3Kl6is5YvY8ufh5c118tW8Q+xiSGkVlwjNwjVS16rqbewvMLdnLHO+vwcnfhswdH8NjYeNycdPvteRVl2F7P0UPUxWyiX2QAfp7nbnfz5d4vqbHUnLdI0alSY4LZX1JF4bEaALr6dOWq2KuYv3s+lfXn6H3aQg3WBl5c+yJdvLrwcNLDzX6uk/5XFREREbGvNackobPvG9riJBQgwMuNsYlhLNh6iPpGWkQ4m33FlXyfWcQdQ6NPtqMQaavRCbZCO99nNn9VdHteGde/vorpq3OYOiKWhY9cxsDOvlW8KBMwQWjzVgibYjWszM2cS1KXJBKCm9+PNDXWdk50Y87Rk9fuTLyTivoKPt/zeZtiOtXczLlkHMngqSFP4ePWdFXgM+knjoiIiFz0ftxbwj3vbyAm2JaEhvg2o+k7QH01/PgvqPtpdWFichQllXWs2l3cTtHaz/trcnBzMXH70GhHhyIXkJ5dfIkN8WZZxvkT0QaLldeW7mbim6upqGlg5r1DeO7Gvni5XwC9bAvTITAa3NtWRXtt/lr2l+/n1oRbW/Rc34gAPFzNbDglER3QZQADugzgw/QPsRpt/7CssKqQ1ze/zsiIkYyLGdeiZ5WIioiIyEVtbXYJ98zYQHSwN7OntSAJBVj9Giz6A2z/5OSlK+K7EOjt5vTbc4/V1PNJWi7XD4ggzE8tW8S+Rid0Zc3eEqrqGpq8J7uogslv/8jLi7O4rn84ix67nMt6d+nAKNtZUSZ0af4KZlPmZswlyCOoxYmeu6uZpO6BbDzlnCjYVkUPHDvAyryVbY7tHxv+Qb2lnj8M/UOLz5grERUREZGL1rrsEu6evoGoIC8+nDaM0JYkoeX5sPoV29/3LD152d3VzPUDwvlu12Eqapv+JdzRPt2YR2WdhakjYh0dilyAxiSGUddgZfWekrPeMwyDmWv387PXVrGvuJLXb0vmtduSm+zT2ylZGqBkN4S1LRE9XHmY5XnLuan3Tbi7tPy4wODYYHYcKj/tA4GxMWMJ8w5jVvqsNsW25tAavsn5hvv630e0f8t3VSgRFRERkYvS+n1HuHvGBiJbk4QCfP8iWBug55WQvdzWquG4icmR1NRbWbTjsH2DthOr1eD9NTkMig7s/OfwxCkNjg3G18OVZRkFp10vKK9h6vQNPP35DlJjg1j02OXcMDDCQVG2o6P7wFLX5hXRjzM/xjAMbulzS6ueT4kNwmI12HKg9OQ1N7MbtyXcxtr8tew+urtV49ZZ6vjrur8S7RfNPf3vadUYSkRFRETkorPrUDlTp68nPMCTD6cNpYtfC5PQ/G2weTYM+SWk3gO15ZC34eTbg6KDiA72dtrtuSuyisgpqWLqyB6ODkUuUO6uZi6PD2VZRiGGYQCwcFs+V7/yA+v2lfDC+L58cM8QugVcoNvCm1Ex93zqLfXM2z2PK6KuIMK3dcn6oOggTCZI23/0tOuTe0/Gw8WD2emzWzXuezveY3/5fv449I94uLTw5+dxSkRFRETkovPF1oPUW6zMmTas5ecjDQO++yN4BcHl/wM9rwCTC+xZcvIWk8nEhORIVu8tpqC8xs7Rt917q/fR1d+Da/t1c3QocgG7sk8YBeW1/JhdwmMfbebhDzcRE+zNwl9fxl3DYy/svrWFxxPRNlTMXXJgCUdqjrS4SNGpArzc6NPVjw05p58TDfQM5Pqe1/NV9lccrTnaxNONyy3P5b/b/su4mHGMiBzR6tiUiIqIiMhFJz3/GL3D/Ajzb8VqTNYi2PcDjPodeAWCZwB0H3paIgowISkCw4AvtxyyU9T2safwGCt3F3Pn0JjO259ROoVRfcIwmeCud9ezYFs+j43tzacPjqBXF19Hh9b+ijIgIBo8Wv+1fpTxEVG+UYyIaH2yB7Y2LpsPlGKxGqddvzPxTmottczbPa/ZYxmGwV/X/xVXsytPDX6qTXHpp4+IiIhcdHYdKicx3L/lD1rqYfHTEBJn25J7QtwYyN8KFT+1q+jZxZeB3QOdbnvu+2v24+5qVssWaXdd/Dy4rHcXYkO8+ezBETw2Nr71H34c2gLr/m3bkdAZFGW2qVBR1tEsNhVu4tY+t2I2tS1lGxwbTEVtAxmHy0+7HhcUx7DwYczJmEO9tb6Jp0+39MBSVh1cxcNJD9PVp2ub4lIiKiIiIheVomO1FFfUcklEKxLRjTOgOAuuegFcTqnwGTfW9rp32Wm335Qcya78cjIPH2t9wHZUVl3PvE153DgwomVtakRaafrUwSx5/Iq2F8X6+n/gm6d+qlTtzKwW28+JNpwPnZsxFw8XDybETWhzOCkxQQCk5Zy9BfeuS+6isKqQJfuXnPXemarqq/jb+r8RHxTP7Ym3tzkuJaIiIiJyUUnPt60KJIb7tezBmjJY/v8g9jLoc+3p73UbAD5dztqee/2AcFzMJqdZFf0kLZcqtWyRDuRiNrX9LGhRFuStB99usOR5yFhon+Day9EcsNS2umJuRV0FC7IXcE3sNQR6tr2qdWSgF+EBnmcVLAK4NPJSYvxjmtXK5a2tb1FQVcDTw57G1eza5riUiIqIiMhFZdfxRPSSlm7NXfl/UHUExr0IZ/5ibTZDrzG2fqJWy8nLIb4eXBHfhS+2HMRqdeyWQovV4P0fcxgcG0S/yACHxiLSIltm2wqC3bsIIpJh3jQ4vN3RUTWtMN322iWxVY8vyF5AdUM1UxKm2CUck8lEamwwG/YdOVnB+ASzycztCbezrWgb24q2NTlG1tEsZu6ayaTek0gKS7JLXEpERURE5KKSnl9ORIAngd4taA5/NAfWvgUDb4OIJn4JixsL1Ucgf8tplyckR5JfVsO6fUcaf66DLMsoJPdINVNHqGWLdCKWBtg6B3qPg6BYmPKhrUDYnNtOO5PtVE62bolv8aOGYTA3Yy59Q/rSL7Sf3UJKjQnicHkNB0urz3pvfNx4fN18m1wVtRpWXlz7In7ufjw26DG7xaREVERERC4qrSpUtOR524rMmKebvqfXlYDJtip6iqsSu+Lr4cr8zXktD9aOZqzZR3iAJ1f3bVuBEZEOtXcpVBRA8h22f/uHw20fQmUxzL0TGmodG19jijIhoDt4tHD7P5BWkMbesr3c2qf1LVsakxprOye6sZHtuT5uPtzU+yYW5yymoLLgrPe/2PMFmws383jK43bZKnyCElERERG5aNTUW8gurmxZoaLc9bDzMxj5a/A/R1N5n1DbtsEzzol6ubtwTb9ufLP9MDX1liYebl9ZBcdYvaeEu4bH4KqWLdKZbJ4F3qHQ++qfrkUkw8S3IHcdLHjU+SrpFqW3ulDRRxkf4e/uz7U9rj3/zS2Q0M0fXw/Xs/qJnnBbwm1YsTI3c+5p10trSnl548skdUlifNx4u8bk0J9EDQ4+KyEiIiIXl90FFVisRvNXRA0DFv3BViRlxK/Pf3/vqyBvA1SfvuowMTmSY7UNLE13zFbC6atz8HA1M2WwWrZIJ1JZApnfwIBbwfWMrfR9J8KoP9i27a5+1THxNcZqgeLdrSpUVFhVyLIDy5gYNxFP11b0OD4HF7OJ5OjARivnAkT5RXFl9yv5JOsTahpqTl5/dfOrHKs7xp+G/anNbWTO5NBE9EhlnSOnFxERkYvMrvwygOYnojvn2xLL0X9qXmP6uLFgWGHv96ddHtYzhK7+Hg6pnltaVcf8zXlMSIok2KcF52JFHG37x2Ct/2lb7pmueAr63gRLnoOMrzs0tCYdzYGGmlYlovN2z6PBaOCWPrfYPy5s/UQzC45RVt14z9A7Eu+gtLaUhdm2qsRbi7byadan3JF4B32CW9+KpikOTUSPVtadVblJREREpL2k5x/D292FmGDv899cXwNLnoWu/SCpmT3zIgaBZ+BZ50RdzCYmJEWyPLOwwz+In7shl5p6K1NHxnbovCJtYhi2bbkRydC1b+P3mEww4U1bAbF598HhHR0bY2OKMm2vLUxE6631fJr5KSMjRhLt3z47F1JjgjAM2HSg8VXR1K6pJAQnMCt9Fg3WBl5c+yJh3mE8lPRQu8Tj0ES0zmJlQxPLwyIiIiL2tiu/nIRufpjNzehruP7fUHrA1q7F7NK8CVxcbUWL9iw569zahORIGqwGC7cdakXkrdNgsfLBj/sZ1jO45QWaRBwpfysU7ICkJlZDT3DzgilzwNMf5kyBiqKOia8prayYuzx3OYXVhXYvUnSqpOhAXMwmNjaRf5lMJu5IvIM9pXt46oenyDiSwW8H/xYfN592icehiajZXMcnabmODEFEREQuEoZhkJ5f3rxCRZXF8MM/bAVSel3ZsonixkLFYSjYedrlxHB/Err5dej23CXphRwsVcsW6YS2zAYXD+g/+fz3+ofb2ro4QyXdogzwj7S1mGmBuRlzCfcJ5/Koy9spMPB2d6VfhH+TBYsAru1xLcGewSzev5iRESO5KuaqdovHoYmoh0ctC7fnU1nb4MgwRERE5CKQd7SaYzUNzVsZXP43qKuEcS+0fKJeY2yvZ1TPBduq6KYDpewvqWz5uK0wffU+IgO9GJsY1iHzidhFfQ1s+xgSrwevoOY9EznItk03dy0seMxxlXSLMlpcMTe7NJt1h9dxS59bcGnu7otWSokJZktuKXUN1kbf93Dx4I7EO/By9eIPQ/+AydSM3SOt5NBE1EIVVXUNfLPjsCPDEBERkYvArvxyoBmFioqyIO09SL27dS0Y/MNt50obSUTHJ0VgMtEhq6K7DpWzbt8Rfq6WLdLZZH4NNaXn35Z7pn43wajfw9YPYc1r7RPbuVittp8fXRJb9NjHWR/janZlYtzEdgrsJ4Njg6htsLLzUFmT99zb714WT17cbmdVT3Bs+xajnqiwUj7dqO25IiIi0r7S88sxmSCh23mazC9+Gtx9bL/QtlbcGDiwFmqPnXY5PMCL4T1D+HzzwXYv2Pj+mhy83FzUskU6ny2zbdtbe45q+bNX/NbW2mXxs41W0i2vK2fVwVXt8/+/0v3QUN2iD7DKasv4fM/njIsZR4hXiP1jOkNKrG2Fuak2LgAuZhcCPFq2tbg1HJqImjAR3+MAa7OPcKCkypGhiIiIyAUuPb+cHiE+eLu7Nn1T9nLI+hYuewJ8Qls/WdxYW9uJfSvPemtCciQ5JVVsyS1t/fjncaSyjs+3HGTioEgCvN3abR4Ruys7CHuX2SpVt2abqskE449X0v1s2mlntZfsX8L4z8fz4JIHWXZgmR2DPq4VFXNn7ppJZX0l9/a/1/7xNCLMz5OYEG/S9jd9TrSjODQR9XL1osy8FZMJPt2U58hQRERE5AK3K7+cxHMVKrJaYNGfICAahj7Qtsm6DwN330a3517brxsermY+b8ftuXPWH6C2wcrUEbHtNodIu9g6x9aLt7ktkxrj7m0rXuTuCx9Oobg4k8eXP85vlv+GUK9QonyjeHPrm1iNxs9JtlpRuu21mSuiZbVlzE6fzdjoscQHtazKblukxgSTlnPU4W00HZqI+rn7kVW6i6FxrszbmIfVqp6iIiIiYn/lNfXkHqnmknOdD906Bwq2w1XPgZtn2yZ0dYceV8CexWcVTfHzdOOqS7qyYFs+9RY7/yIM1FuszFq7n5FxIcR3Pc82ZBFnYhi2bbkxIyG4Z9vG8o/AmDKb+ZRz41c3syJ3BY8OepQPf/YhDyc/TNbRLJbsP/uDojYpygS/cPAKbNbts9NnU1FfwQMD2/jBVwulxgZRUlnHvuKOKZrWFIcnogBxsQc4WFrN2uwSR4YjIiIiF6iMfNtZzcTwJhKzukpY+gJEDYa+N9ln0rgxtj6kJXvPemticiRHKuv4Icv+PQ+/21lAflmNWrZI53NgLRzJhuQ72zxU7rFcfrnr3zwT7E/v2mo+9e7Pff3uxc3sxrWx19IjoAdvbnkTi9Vih8CPK8po9rbc8rpygnLtFAAAIABJREFUZu2axejuo+kT3IqiaG0w+MQ50f1NnxPtCI5t3+LiQZRvFCXWzfh5uvLpRm3PFREREftLP14x95LwJgpwrH7N1vtz3F9sZ8zsIe5EG5fFZ711eXwXgrzd7Fo91zAMvt2Rzwtf7aJ7sBejE9SyRTqZLbNs22kvGd/qISxWCx/s/IBJX05ie/F2nh72NNN7302P7fNhzeuArRjPQwMfYm/ZXr7b/519YrdabSuizUxEZ6fP5lj9sQ5fDQXo1cWXIG830s7RT7QjOLyW96juo1h/eB3XDgjm6x35HKupd3RIIiIicoFJzy8nyNuNrv4eZ79ZfsjW6qHvRIgear9Jg2IhpHej50TdXMzcMDCCxbsK7PK7T8bhcu54Zx0PzNpEgJcbb9w2CBdz+/X/E7G72grYMR/6TrBVrW6FrKNZ3PXNXfw97e8M6TaEz8d/zi19bsE86ndwyQRY/AxkfgPAuNhxxAXG2W9VtCwX6quadT70WN0xZu6ayajuo0gMaVmrF3swmUykxASds3JuR3CKRLTOWkfvmHxq6q18vT3f0SGJiIjIBWZXfjmXRPg33px92YtgbYCxz9l/4rixkLMK6qvPemticiS1DVa+bUM/9dKqOp75YgfXvbqSXfnlvDC+Lwt/fSkDuzfvjJrI/2fvvsOjqrbH/7/PpPfee0gIIRBCb0ISCAhIL2IDUYq9//Rar42rXvXq93o/KiKCgggKQgANEARC7xJKEiAJJKR30stkZn5/DEFCCikzmQnu1/P4DM45Z+8VUZw1e+219UbiFpBXQv957X60TlHHl/FfMnfbXDLLM/l49Mf8b8z/cLVwVd8gk8H0r8GtH/y6CPISkEkyngx7krSyNGKuND3mpd0KLqhfnW+fWP6U9BPldbpZDW0wyNeey4WVFFXU6iwGnSeiA1wGYGVkxdXqE/RwsmDDSVGeKwiCIAiC5tQrlFzMLSfYtZlGRTlnIP4ndZdcO1/NTx4QBfU1kH6oyaUwL1t8Hcw7VJ5br1Cy5kgaEZ/G8ePRdB4a5sPelyKYN9wXQwOdf7wThPaLXwsOAeDVvqqE+Px45mybw7Izy5jgN4Et07cw0W9i0y+djM3h/nXq0t9190FFAWO9xxJkF8SyM8uoV9Z3Lv6GRNSx9e63FXUVrE5cTbhnOCEOIZ2bsxP0YZ+ozv+kMpIZcZfnXezP2s+sge6cTC/ReQcnQRAEQRDuHGlFldTWKwm+tWOuSgU73wAzO/W5odrgOxIMTSFld5NLkiQxvb8HRy4XkVPadMW0JUdSi5j8v4O8tSWBXq5W/P7sKN6b1gc7C2NNRi4IXacoVf1lTdgDbd6jXSWv4qPjHzF/+3yq6qv4auxXfDjqQ+xM7Vp+yNod7v8JKvLh54eQKeQ8GfYkV8uv8tvl3zr3MxRcBEsXMLdv9bb1F9dTVlfGE/2e6Nx8ndTHwwZjQ5lO94nqPBEFiPSKpLimmF4+Jcgk2HgqQ9chCYIgCIJwh0jIvt6o6NYzRC/tgLQDEPFam49baDcjM/VRFM3sEwWYHuaBSgVb47NvO1RmSRVPrf2T+789SnlNPV89OIB1i4c1TbAFobuJ/wkkGfS7v023H8o6xIwtM/gp6Sfu63Uf0dOiGeU5qm1zeQxUl+lmHIVtzxHpGUFvh94sO7MMubIT+7Xzk27bqKhKXsUPCT8wymMUIY66Ww0FMDE0oJ+nzd97RRRgpMdIDCVDzhQfJrynE7+eykIhzhQVBEEQBEEDknLKMTKQ6OFk+debCjnEvqVuJjToEe0GEBAFhZegJL3JJV9HC/p727Zanltdp+DzXZcY+5997L6QxwtRPdn9UjiT+ro1v+dVELoTpUJ9hm+PseoVy1Zcq7nGGwff4PE/HsfE0IQfJv7A60Nfx8Konc2N+sxUfwF1Zh3S4S94Kuwpsiqy2JqytWM/g0rVpo656y6s41rtNZ2vhjYY6GPP+axSauQaPMKmHfQiEbU2tmag60DiMuKYPdCL3LIaDqUU6josQRAEQRDuAIk5ZQQ4W2FseNPHnnMboSgZxr8PBkbaDSAgSv2a2rQ8F2Bmfw8u5JbfOGKmgUql4rez2Yz9Txz/3Z3MuN4u7H4pgueiAjE1MtBuzILQVS7vhbIs6P9gq7f9mfcn07ZMI+ZyDEtCl7Bhygb6O/fv+Lzh/4A+s+CPdxhVVkqoYyjfnP0GuaIDq6KlGepGS84tJ6INq6EjPUbS16lvx+PWoMG+dsgVKs5kXNPJ/HqRiIK6PPdy6WWCvGqwMTNigzhTVBAEQRAEDUjKKaP3reWrmcfBxAZ6TtB+AI6BYOvd7D5RgHtC3TGUSUTftCqamF3GfcuP8vRPp7E1N+aXx4bzfw8MwMPWTPvxCkJXOr1WvU87aFKrt30Z/yVGMiPWT17PM/2fwcSgmaOY2kOSYNqX4N4fafNjPOU9kZzKHDYlb2r/WAUX1a+trIj+fPFnSmpL9GY1FGCgj24bFulNIhruGQ7A4ewDTAtzZ2dCLqXV4kxRQRAEQRA6rqC8loLyWoLdrBpfyEsEl5A2N0bpFElSr4pe3gf1dU0u21sYExHkxJb4bAoranlj8zkm/+8AyfkVfDCjL9ueuYshfq03QBGEbqmqGC78Dn3vBcOWE8ui6iJO5p1kesB0guxvf05nmxmZqTvpmtowfNeH9HcIYfm55dQq2nmkSUPH3BYS0Sp5Fd8nfM8I9xH0c+rXyaA1x9bcmEBnS07oqGGR3iSinlaeBNoFEpcZx5yBXtTVK9l25vYb9wVBEARBEFrSUO7aqFGRSqVuLOLSu+sCCYiCunL1Smwzpvf3ILeshrv+vYf1JzJ4eIQve1+K4IGh3hjIxD5Q4Q51/ldQ1N62LHf31d0oVUrG+YzTfAxWrvDAeqTqYp7KzSK/Kp+Nlza2b4z8C2Dh3GLH3A2XNlBcU6xXq6ENBvnacyq9BKUO+vPoTSIKEOEZwZ95f+LlqCLIxYqNojxXEARBEIROuJGI3lyaW5oJtaXg3IWJqN9okBlC8q5mL0cFu+Blb8ZgX3t2PDeKt6eEYGOu5b2rgqBrp38El77g1voqYWx6LL7WvvS0a/2Mzg5z6wczlzMk8yyDZFasOLeCmvqatj9fcAGcml+pra6vZuX5lQxzG0aYc5iGAtacwb52lNfUcym/vMvn1qtENNIrEoVKwcHsg8wZ5El8xjVSdPAPRRAEQRCEO0NiThluNqbYmt90xmZegvrVpQuPTzCxAu/hLe4TNTUyYP/LkaxZOJRAF6tm7xGEO0rueciJh/4PtXpbcU0xJ3JPMM5nnHa7RAdPQRr7T57MSqGwupBfLv7StucaOuY6Bzd7ecNF/V0NBRjko17FPZHW9ftE9SoRDXEMwdHMkbiMOKaFeWAgk0TTIkEQBEEQOqzZRkX51xPRFj44ak3AWMg7B2U5zV4WR7EIfyvxa0FmBH3ntHrbnqt7UKqUjPcdr/2Y7nqRwT1nMLS6hu/iv6JKXnX7Z8qy1GX3zayI1tTXsCphFUNdhzLAZYAWAu48L3sznK1MOKWDfaJ6lYjKJBnhnuEczDqIrbmMyCBnNv2ZRb1CqevQBEEQBEHoZmrkClILKgm+NRHNSwQbbzC16dqAbhzjsqdr5+2ohGjY/b6uoxDuRPV1cPZnCJoIFg6t3hqbFou3lTdBdhpsUtQSSYKpX/CUkQfF9ZX8fPyz2z/TSqOijZc2UlhdyOP9HtdwoJojSRKDfO3Eiiioy3Mr5ZWcyDvBnEGeFJTXsj+5QNdhCYIgCILQzSTnVaBQqho3KgLIT+zaRkUNXPqApQuk/NH1c7dXwmbY+Agc+BSKUnUdjXCnSd4JVUXQf16rt5XUlHA89zjjfcd3XcWAoQn9525gZJ2KlZfWU1mU0vr9+Q2JaOMKi1pFLSvPr2Sw62AGuQ7SUrCaMcjHnqxr1eSUVnfpvHqXiA51G4qpgSlxGXFEBjljb2EsmhYJgiAIgtBuDY2KGq2I1tdB4aWubVTUoOEYl9Q9oFR0/fxtdSkWfl0Mrn3Vf5+0VbfxCHee0z+CpSv0GNPqbXuu7kGhUjDepwvKcm9m6cSTo5ZyTSaxbvMDUNdKiW7BBTB3bLKyu/HSRgqqC/R2b+jNBvuq94me7OJVUb1LRE0NTRnuPpy4jDiMDCSmh3nwR2I+JZVNz90SBEEQBEFoSWJOGebGBvjYm//1ZuElUNZ3baOimwWMhZprkPWnbua/nbSD8Ms89Yrxw9vAvT8kbdN1VMKdpDxP3T26331gYNjqrbvSd+Fp6Ukv++bP59Sm0F7TGW3bi1WyCio2LwZlC1sFCy40KcutVdSy8txKBroMZLDr4C6ItnOC3awwNzbgZBfvE9W7RBQgwiuCnMocLpVcYvZAT+oUSraKM0UFQRAEQWiHxJwyerlaIbv5HM78RPWrrhJR/0iQZPpZnpt5Cn6aC3a+8NBm9R7a4KmQdUp95I0gaMLZ9aBS3LZbbmltKcdyjnVtWe4tnrzrHcoMDPgxZz/Efdj0hhsdcxsnopuSN5Ffnd8tVkMBDA1k9Pe25WS6Hq6ISpJkL0nSAUmS3rn+9/6SJJ2SJClFkqQ3brrvrevvnZAkybejQY32HI2ExN6MvfR2tybE3ZoNpzI6OpwgCIIgCH8zKpWKpJyyZhoVJag7dToE6CYwc3vwGKR/iWheAvw4EywcYV70X2WGwVPVr2JVVNAElUpdlus1FBwDW711z9U91Kvqu6ZbbgtCHEKI9IpktZ0DZQc/gbMbGt9QngO1ZY1WROsUdXx37jsGOA9giOuQLo644wb52JOUU0Z5jbzL5rxtIipJkhGwHbh009tvAv8CgoCpkiT1liTJB5hx/b33gH93NChHM0f6OvUlLiMOgNkDPTmfVXZjr4cgCIIgCEJrMkuqKa+pb9qoKC9BfcyCgZFuAgP1PtGsU1DV9cclNKsoFVZPByNzmL8FrN3+uuYYoN5PKxLRbuFK6RWmbJ7C83uf55eLv5BRrmcLOZkn1eXxYQ/e9tbY9Fg8LD3oba+D/dw3eTLsScpRsMY7BLY8pf4ZGuQnqV9vSkQ3J28mryqPx/s93q2OZBrka4dSBaevXuuyOW+biKpUKjkwBThw09vhQIxKpVIAv13/+0hg+/X3tgPDOxNYpFckCUUJ5FXmMS3MAyMDSTQtEgRBEAShTZptVATXO+bqqCy3QUAUoNKPY1yuZcDqaepSyflb1GW5twqeCumHoSK/y8MT2mfZmWXkVuaSWJTI+0ffZ9KmSUzaNImlR5ey++puyuvKdRtg/I/qLzxCZrR6W2ltKUdzjjLeR3dluQ162fdinM841hjVU2rjCuvuV/93A+qyXLiRiNYp6lhxfgVhTmEMcxumo4g7pr+3HTKJLi3PbVNprkqluvVPHguVSlVz/df5gCvgDBRev78eMJAkyeDWsSRJWiJJ0klJkk4WFLR8LEuEZwQA+zL3YW9hzNheLkSfzkIuzhQVBEEQBOE2knLKkSTo5Wr115vVJerD53XRMfdm7mFgZq/78tzyPHUSWlMG8zaDU89Gl+My4lh6dCmqXpMBFVz4XTdxCm2SXpbOjrQd3B98Pztn7WTb9G28NuQ1etj0YFvqNp7f+zyj1o9iXsw8vo7/mvj8eOqV9V0XYF0VnPsVek8DU+tWb92bsZd6pW7Lcm/2RL8nqKqv5ocB06G+Rp2M1laoGxWZ2atL2oHolGhyK3N5ot8TOk+g28vSxJBgN2uNNixSKlXsvdDyF1itt6pqmeqWv5daeK/pgyrVcmA5wKBBg2595oYetj3wtPQkLiOOe4PuZc4gT3Yk5LL3Qj7jQ1w7GLYgCIIgCH8HiTml+DlYYG5800edhjI6Xa+IygzUx1ak7FZ34pTpoHdkVTGsmaHe4zYvGtz6Nbll2ZllJBQlEOU9lmH2/upjXAY90vWxCm2y4twKjGRGzO89H0mS8LXxxdfGlweCH0CukHOm4AyHsw9zJPsIX5/5mq/OfIWVkRVD3YYy3H04I9xH4Gnlqb0Ak7ZBXXnbynLTYnG3cCfEQcf/rV4XaBfI3b5382P6DuZN+x92Gx6BTUugMh+cg0GSkCvkrDi3glCnUIa7d6owVGcG+9rzy8kM5AolRgYd/3NJoVTx+7kcvtyTwsW8llfhOzpDuSRJptd/7Yx6VTQHcASQJMkQUF0v0+0QSZKI8IrgWM4xquRVhPd0wtHSRJTnCoIgCIJwW0k55c03KgLdr4iCujy3Mh/yznX93LXlsHY2FCXDfT+B99Amt6SVppFQpP7ntSrhewieAlf2q1eVBb2TVZHFb6m/MbvnbBzNHJtcNzIwYpDrIJ4d8CzrJq9j/9z9fBL+CeN8x3G+6DzvH32fiZsmcs+me7RXxhv/o7r022dkq7eV1ZVxJOcI43zG6dWq4hP9nqBWUcuq6ssw4SO4+DtknlDvOQe2pG4hpzKnW66GNhjka0dVnaLDfXnqFUp+PZXJuM/38ey60yhUKv57X1iL93c0Ed0F3HO99HYK8Mf1vyZcf28SjfeUdkikVyR1yjqO5BzB0EDGzAEe7LmQT2FFbWeHFgRBEAThDlVeI+dqcVXzjYpMbcHaXTeB3azHGPVrV5fnyqvVZYXZ8TDnB+gR2ext269sR0Li/l73czj7MBc9+6vPX724o2vjFdpk5bmVSJLEgpAFbbrf1tSWCb4TeHfEu8TOimXr9K28OuRV/Gz82Jq6lef3Ps/o9aP5/bKGyrFL0tRfZIQ9eNsKgLiMOL0qy23gb+vPJL9JrL+wnsK+s2DQo+oLTsHIlerV0L6OfRnp3nqirc8G+dgDcCKtfV841dUrWX/8KmP+s4+XNpzB2EDGVw8OIPb50UwL82jxuY4mou8ALwPJwFaVSnVBpVLlAquAi8D7wCsdHPuG/i79sTK2atQ9t16pIvp0VmeHFgRBEAThDnUhV72SE+xm1fhCQ6MifVitsHIB11B1eW5Xqa+DX+ZD2kGY8Q30mtTsbSqVipgrMQx2HcxTYU9hbmjOqoJjYO2hLs8V9EpeZR6bUzYzPWA6rhbt374mSRJ+Nn48GPwg/zf2/zh03yFW3r2SQLtAPj/1OXWKus4HGb8OkKDf/be9NTYtFjcLN/o69u38vBr2eL/HqVPUsSphFUz8GCZ9Cn1nsy11G1kVWd2uU+6tXG1M8bQz41R62/aJ1tYrWHM0nchP43h10zlszIxYPm8gMc+OYlJft8ZnODejzYmoSqX6XqVSvXP91wUqlWqYSqXyV6lUS2+65/9UKlWASqXqp1KprrZ17JYYyYwY5TGK/Zn7USgV9HSxop+nDRtPZaJStbi9VBAEQRCEv7FmO+aqVJCXqB9luQ0CoiDjGNSUan8upQI2LYbkWJj8OYTOafHWxOJE0srSmOQ3CRsTG2b3nM2OtB1k94xSJ861FdqPV2iz7xO+R6lSsrDvQo2MZ2RgxGDXwTwX9jR5VXlsTe3klw9KJcT/BP4RYOvV6q3ldeUczj6sd2W5DXysfZjsP5mfL/5MQe01GLIYuakVy88uJ8QhhFEeo3QdYqcN9rXnRFpJq7lWdZ2ClQevMPrjvbwVfR4XaxNWPTKYrU+PZHyI620T0AY62B3fPpFekRTXFHOuUL2HYvYgLy7klpOQLc4UFQRBEAShqcTsMuzMjXC1Nv3rzWtX1Y1SXDSfiHb4y/GAKHW565X9mg3oVkolbH0WEqNh/L9u23Ao5nIMhjJDonyiAJjXex4SEmtMJVDUqpNZQS8UVRex8dJGJvtPxsOy5RLIditMYcTPi+lTW8uKA28j/yIMvh0La+fApsdgx2uw7xM4sQLOb4LLcZBzFkqz1OXfN0vbD6VXof9Dt502LiMOuVLOOJ9xmvtZNOyxfo9Rr6znu/PfAfBb6m9kVWR1672hNxvoY0dBeS1Xi6uaXKusreebfamM+ngP7/2WiK+DBWsXDeXXJ0YQGeTc7p+/o11zu8xIj5EYSobszdhLmHMYU0Pdef+3RDaeyqSPh42uwxMEQRAEQc8k5ZQR7Gbd+ENRfqL61aWPRueKz4/nsV2P8c24bwhzbrkpR7O8hoCJtXqfaPAUjcZ1g0oFO19TN4oJfxVGPN3q7Qqlgh1XdjDKYxQ2JurPWa4Wrkz0m8ivV//gcUsnbJK2Qp+Z2olXaJfViaupU9axqO8izQ1aXQLr5iIp5TzmMZFnCvcT4+TNNLkMKvIg/wJUF0NdKyvjhmZgbq8+2qTmGpjYQK97bjt1bHosLuYuhDqFau7n0TAvKy+mB0xnw8UNzOs9j2/PfUtvh96M9hyt69A0YrCvep/oybQSfBwsACirkbP6cBrfHbxCSZWcuwIceWZMAEP9HTo1l94nolbGVgxyHURcRhwvDHwBG3Mjxvd2ITo+i9cm9cLEsMlRpYIgCIIg/E3VK5RcyC3noWE+jS/c6JgbrLG55Eo57x55l6r6KtZdWNf+RNTACPzD1eWuKpV29q7u/RccWwbDnoKIV297+6m8U+RX5/Oy/8uN3l/QZwHbLm/jZ+8QllyKBXkNGJm2MIrQFa7VXGP9hfXc7Xs3vja+mhlUIYcNC6AkHR7eRrj3MIK2zWGFopbJc3/FQHbT5+76WnXSWlWsTkyrim76dfFf14zNYfhTYGTW6tQVdRUczjrMvUH3IpP0u2hzcehitqRu4bFdj5FRnsEXkV/cEauhAIHOllibGnIyvZioYBdWHrrCqkNXKKupJzLIiWfGBjLA204jc+l9IgoQ4RXBR8c/Ir0sHR9rH2YP9OS3sznsScpnYl83XYcnCIIgCIKeSCuqpLZeSe9bj27JTwRbbzCxav7BDvgx8UdSrqXQy74Xf6T/QWlt6Y1VxDYLiFKfr1h46cYxEBpz8P/B/k9gwHy4+19tSnRjrsRgbmhOuGd4o/d72vVkpMdI1uaf4eH6SkxS97TY7EjoGmsvrKWqvoolfZdobtAdr6nLbKd9CT7DkYAloUt4ad9L7EzbyST/m37PDU3AylX9lwbsy9xHnbJO77rlNsfD0oOZATP55dIvBNsHE+EVoeuQNEYmkxjoY0fMuVy2ncmhorae8b1deGZMIH09NVuNqt9fN1zX8Jvb0D13VKATLtYmbBBnigqCIAiCcJPEnIaOuc0c3eIcorF5siuy+frM10R4RbB05FLqlHX8dvm39g/UY6z6VdPHuJxYAX+8DX1mweT/16YktE5RR2x6LGO9x2Jm2HT16tGQRymWV7DV1lGdPAs6U15XztrEtUR5RxFgF6CZQU+sgBPfwvCnG+3njPKJoodND7499y1KlVIzczUjNi0WZ3Nn+jn109ocmrQ4dDHeVt48P+D5O2Y1tEF4TyfKauSEBzmx/blRLJ8/SONJKHSTRNTD0oOedj1vJKIGMomZAzzZd6mA/LIa3QYnCIIgCILeSMwuw8hAIsDZ8q8362uhMFl9dIuGfHT8IwBeG/IaQfZBhDiEsCl5U/sbF9l6gVMvSN6lmcAq8tUrob+/BD0nqo9pkbVtG9PBrIOU15U3XvW6yWDXwYQ4hPCDvT2KizHqMk5BJ9ZfWE+5vJzFoYs1M+DlOIh5BQLvhnHvNbokk2QsDl1MyrUU9lzdo5n5blEpr+Rg1kHG+YzT+7LcBq4Wrvw+83dGeIzQdSgaN2+4L6ffGseXDwxo+qWeBnWP32nUq6Kn809zreYaAHMGeqJQqtgszhQVBEEQBOG6pJwyApytMDa86SNO4SVQKTTWMXfP1T3szdjL4/0ex93SHYCZgTO5VHKJxKLE9g8YEAXph6CusmMBFabAof/Cd+Ph057qldAeY2DO9+p9qG0UcyUGe1N7hroNbfa6JEks6LOAdGUNe2W12u/2KzSrSl7F6sTVjPYcTW8HDfw7XZQKvzwMjj1h1opmv7iY4DsBH2sflp9drpUjFPdn7leX5frof1nu34GBTMLW3Fjr83SfRNQzAoVKwYGsAwD4O1ky0MeODeJMUUEQBEEQrlN3zL1lH2je9eRQA6W5VfIqPjr+EQG2AczrPe/G+5P8JmFmaMavyb+2f9CAsaCog7RDbbtfqYTMk/DHO/B/Q+D/BsKuf6qPzYh4DR4/CA9talczoUp5JXEZcYz3GY+RrOXkNco7Ck9LD1bZ2qJK7OT5kkKHbLi0gWu111gSqoG9odXX4Ke5IMng/nVg2vzql4HMgEV9F5FUnMT+TM1/ARGbFouTmVP7G34J3Vq3SURDHENwNHO8UZ4LMHugJyn5FZzJ7IKDoAVBEARB0GuFFbXkl9c2bVSUdx4MjMGhR6fnWHZ2GTmVObw17K1GCZulsSXjfMYRcyWGKnnT8/da5T1CfdxFa/tE62vV5bvbnoPPgmHFWDj0hbpRzMSP4flz8PgBiPgHuPZtdwfePVf3UKuobbEst4GhzJCHQxZw1sSIPy9vB6WiXfMInVNTX8Oq86sY5jas83spFfWw8REoSYO5P4K9X6u33+N/Dx6WHnxz9huNLgJVyas4kHWAKJ+oblOWK2hGt/ndlkkywj3DOZR9iDpFHQD3hLphaiTjm32pKJViVVQQBEEQ/s6ScsoAmu+Y6xTUrjLV5iSXJLMmYQ0zAmYwwGVAk+uzAmdRKa8kNj22fQMbmYLfqKaJaHUJnP1FXTb5sT+snQ3nNoL3UJj5LbySCg9vhaGPqTsCd8LvV37H3cK9TcnNtIBp2Bmas8pECRnHOjWv0D6bkjdRVFOkmdXQ2DcgdQ9M/gx8R972diOZEY/2eZRzhec4kn2k8/Nftz9zP7WKWlGW+zfUbRJRgEivSCrllZzMPQmAtakRT0cGsP18Lu/9lihKdAVBEAThbywxW52INu2Ym9jpslylSsnSo0uxNLbkhYEvNHtPf+f++Fr7sil5U/snCIiC4lRIOwjHlsPqafBJAGxaDOmHoe9seGADvJwK966G0HvBTDNn+RXXFHM0+ygT/Sa2aUXKzNCM+3vdzz5zM1LPrtVvaihqAAAgAElEQVRIDMLtyRVyVp5fyQDnAQx2Hdy5wU6uun6+7JPq433aaHrAdFzMXfjm7Dedm/8msemxOJg60N+5v8bGFLqHbpWIDnUbiqmBKXsz9t5476nIAB4d6cf3h9P4fNclHUYnCIIgCIIuJeWU4WZjip3FTU02qoqhPLvTjYq2pGzhz/w/eXHgi9iZNp8ASpLErMBZnM4/zeXSy+2bICBK/fr9PbD9ZSjLVh+jsfAPeOkiTPkv9Bzfrn2fbRWbFotCpbhtWe7N7gt5GFMkvs/eB2IhoEtsSd1CXlUej4U+1rmBruyHmP9P/e/cuPfb9aixgTGP9HmEP/P/5ETuic7FwfWy3Ex1Wa5BG7s7C3eObpWImhqaMtx9OHGZcTdWPyVJ4q3Jwdw7yJMv9qSwfH+qjqMUBEEQBEEXknLKm66G5ne+UVFJTQn/OfUfBjgPYFrAtFbvndJjCoaSIZuTN7dvEoceEPkGRL0LT5+Ep0/AuHfBazDItPtxLeZKDAG2AfS069nmZ+xM7Zju0J/fTCDv8m4tRicA1CvrWXFuBX0d+zLcfXjHByq+DL/MB/seMHslGBi2e4hZgbNwMHXQyKrogawD1ChquNv37k6PJXQ/3SoRBXV5bm5lLhdLLt54T5IkPpwZyj193fgg5gI/HbuqwwgFQRAEQehqNXIFKQUVzTQqup6IduIM0c9PfU5lXSVvDnvztqWrDmYORHhFsDV1K/L2nrMZ/grc9Tw4BnY41vbKqsjidP5p7vG/p93Pzh/yMkpgbfzXmg9MaCTmSgxZFVksCV2C1M5GVDfUlMJP96l//cB6MLXp0DCmhqYsCFnAsZxjxOfHdyyW63al78Le1J4Bzk33XAt3vm6XiI7yHIWE1Kh7LqjPu/l8bhiRQU68EX2OLfHifFFBEARB+LtIya9AoVQ1syKaoN5LaeXaoXH/zPuTzSmbmRcyj0C7tiWIMwNnUlxTTFxmXIfm7Erbr2wH1OdEtpeXcx/GS1ZsKL9EeW2ZpkMTrlMoFXx79luC7III9wzv2CBKBWxcqN6HfO8asPfvVEz3Bt2LrYltp1ZFq+ur2Z+5nyhvUZb7d9XtElFHM0dCnUKbJKIAxoYyvn5oIEN87XnxlzPEJuR2fYCCIAiCIHS5hkZFvd1baFTUgVUkuVLO+0ffx83CjcdDH2/zcyPcR+Bi7tKxpkVdLOZKDGFOYXhaeXbo+QX+U6mQYOPprzQcmdBgV/ou0srSOrcaGvsWpOyCSZ+qOzR3krmROfN7z+dg1kESihI6NMbBrINU11cz3ld0y/276naJKECEVwQJRQnkVeY1uWZqZMB3CwbTx8OGp386zcHkQh1EKAiCIAhCV0rMKcPc2AAfe/O/3lQq1XtEO9ioaE3iGlKupfDakNcwNzK//QPXGcgMmB4wnUNZh8it1N8vxS+VXCK5JLldTYpuFRL2KEOra/gxZVP7S5HvAGcLzrIrfZfWTm5QqpR8c/Yb/G38ifKJ6tggf66Go1/C0Mdh0CMai+3+XvdjZWzF8jPLO/R8bFos9qb2DHQZqLGYhO6lWyaikV6RAOzL3NfsdUsTQ354ZDB+jhYsXn2SU+nFXRmeIAiCIAhdLCmnjCBXK2Sym1aMSq9CXUWH9odmV2Sz7MwyIr0iifSObPfzMwJnALA5pZ1Ni7rQ9ivbMZAMOnd+o5ULjxh7kq+o5vcrv2suuG4guyKbx/94nBfjXuSlfS9RWluq8Tn2Zuwl5VoKi0MXt+lonSbSDsFvL0KPMTD+XxqNzdLYkoeCH2JPxh4uFl+8/QM3qamvYV/mPsZ4j8FQ1v6GScKdoVsmov42/nhZeTVbntvA1tyYNYuG4GJtwoJVJ0jI1vwfDoIgCIIg6J5KpSIxp6zlRkUd6Jj74fEPAXhtyGsdisnD0oNhbsOITo5GqVJ2aAxtUqlUxFyOYZj7MBzMHDo11ojgOfSsreP7M8v18mfVBrlSziv7X0GpUrKo7yL2Xt3L7G2zb5x1rwkqlYrlZ5fjZeXVoT28lKTBzw+BnS/MXtWhDrm382Dwg5gbmvPtuW/b9dyhrEPqstzOfAkidHvdMhGVJIkIrwiO5RyjSl7V4n3OVqb8uGgoViaGzP/uOCn5FU3uUalUpF5LZW3SWqJTorUZtiAIgiAIWpB1rZrymvrmGxUBOPdq13h7ru4hLiOOJ/o9gZulW4fjmtlzJtmV2RzNOdrhMbTlTMEZsiuzucev/d1ybyUFT2FBaRmpFRkczDqogej039fxX3Om4AxvD3+b5wY8x5pJazCWGfPozkf53+n/IVd2vkz5YNZBEosSWdx3cftXDWvK1B1yVUp44Gcws+10PM2xMbHh/l73E5sWy+VrbT87d2f6TuxM7BjsOlgrcQndQ7dMREFdnlunrONI9pFW7/O0M+fHRUORJHhoxTEyiqsoqCpgW+o23jj4BlEbopi+ZTofHf+Itw69ddvxBEEQBEHQL0k55UBzjYoSwNYHTKzaPFaVvIoPj39IgG0AD/V+qFNxjfEag62JrV42Lfr98u+YGJgwxntM5wez9WKCTRCuKhkrz6/s/Hh67kj2EVacW8HMwJlM9JsIQB/HPmyYsoHpAdNZfnY5C7YvIKM8o8NzqFQqvjn7DW4Wbkz2n9y+h5UK+HURFF6Ce1erz6jVovkh8zE1NGXFuRVtur9WUcu+DFGWK3TjRDTMOQxrY2v2Zuy97b2utjJemKqg0vJXJm+ezpgNY3j94Ovsz9xPf5f+vD38bbZM34KvtS//PPxPKuqarpwKgiAIgqCfErPLkCTo5XpLwpmXCC592jXWsjPLyK3M5Z/D/4mRzKhTcRkbGDPZfzK7r+6mpKakU2NpklwpJzY9lgivCCyMLDQyplHwVOYVF3Eq7xRnC85qZEx9VFhdyOsHX8fPxo9/DP5Ho2vmRua8N/I9Pgn/hCulV5izbQ7bUrd1aJ4TuSc4U3CGhX0WYmTQzn8P/3gbknfCpI/Bv4PHvbSDvak9c3rOIeZKDBllt0++D2Udoqq+SpTlCt03ETWSGTHKcxT7M/ejUCoaXatX1hOfH8+yM8t4ePvDjFw3ko9Ov4yR3TEUdZZYVk7j27E/sm/uPj4N/5TZPWfjb+PP0ruWkl+VzycnP9HRTyUIgiAIQnsl5ZTh62CBufFNqyv1tVCU0q6OuZdKLrE6cTUzA2fS37m/RmKbGTiTemV9hxMSbTiWc4zimmIm+XW8W24Tvacxq7wCK5kJ3yd8r7lx9YhSpeTNg29SXlfOJ+GftNhJeYLvBDZO3UiQXRCvH3ydf+z/B+V15e2a65uz3+Bk5sT0wOntCzLjOBz+HwxepP6riywIWYCBZMCK87dfFY1Nj8XGxIbBbqIs9++u2yaioD7GpaS2hDMFZ7hSeoWfkn7i2T3PMmr9KOZtn8dX8V9Ro6hhfsh8lo9bzuH7D7F83HKKskfyr83lVNY2TmD7OfVjQcgCNiVvYn/mfh39VIIgCIIgtEezjYoKLoJKAc5tS0SVKiVLjy7FytiKFwa8oLHYAu0CCXUKZVPyJq0d8dFeMZdjsDK24i6PuzQ3qEMPLJx6c1+9MX+k/0F6WbrmxtYTPyT8wKHsQ7wy+BV62vVs9V53S3dW3r2Sp8OeZmfaTuZsm0N8fnyb5jmdf5rjucd5pM8jmBiYtC/IcxvA0BSi3m3fc53kZO7EzMCZbE3ZSnZFdov31SpqicuIY6z32E5XHAjdX7dOREe6j8RQMmTJriVMjZ7Kh8c/5FLJJSb4TeDT8E/ZN3cfP0/+mRcGvsBw9+GYGpoyIsCRrx4YQFJOGQu/P0l1XeNk9KmwpwiwDeDdw+9qpQ23IAiCIAiaU14j52pxFcFut5blXm9U1MajW6JTojmdf5oXB76IralmG7vMDJhJamkqZwt1X7JaXV/N7qu7Ge8zHmMDY80OHjyFBzIvYCgz5IeEHzQ7to6dLTjLF39+wTifcczpOadNzxjIDHis32N8P+F7ABbsWMCyM8uaVPLd6puz32Bvas/snrPbF6RSCYlbIXAcmFi271kNWNh3IUi0uk/4SPYRKuWVoixXALp5ImplbMWjfR9ltOdo3hr2FjEzY9gxawdvD3+bu33vxs7Urtnnonq78NncME6kF/P4j6eoq/+r1bixgTFL71pKUU0R/z7+7676UQRBEARB6ICLuS00KspPAAMTsL99o5aSmhI+O/UZA5wHMC1gmsZjnOA3ATNDM71oWrQvcx9V9VWaLctt0HsqjgoFU22C2JKyhcLqQs3PoQNldWW8sv8VnM2deWfEO0iSdPuHbhLmHMaGKRuY4DeBL+O/5NGdj7a4aphQmMChrEPM7z0fM0Oz9gWacRQqcqF3O8t5NcTVwpVpPaaxKXkT+VX5zd4TmxaLtbE1Q9yGdHF0gj7q1okowDP9n+GziM+4N+hevKy82vzc1H7ufDijL/suFfDc+tPUK/5KRkMcQljUdxHbLm9jz9U92ghbEARBEAQNSMwpA2h6dEteIjgFtensxM9OfUZlXSVvDXsLmaT5j0YWRhZM9JvI9ivbqZRXanz89oi5HIOzmTMDXQZqfnDn3mDvz8MlpciVctZdWKf5ObqYSqXi3cPvkluZy8fhH2NtbH37h5phZWzFR6M+4oO7PuBiyUVmb53Njis7mtz3zdlvsDa2Zm7Q3PZPkhCtLsvt2YEzRzVkYd+FKFVKVp1f1eRanaKOvRl7GeM9RpTlCsAdkIh2xn1DvHnznmC2n8/l1U3nUCr/2rvxWOhjBNkF8d6R97hWc02HUQqCIAiC0JKknDJszY1wtTZtfCE/sU1luafyThGdEs38kPkE2AVoKUp106Lq+upmk4+uUlpbyoGsA0zwm4CBzEDzE0gSBE/FL/0Yke4jWX9hfavnvXcHG5M3EpseyzP9n6GfU79OjzelxxQ2TNmAn40fL+9/mTcPvnnjy4mLxRfZm7GXh4IfwtK4naW1SiUkbYWAKJ2U5TbwsvLiHv972HhpI0XVRY2uHc05SoW8QpTlCjf8rRNRgEWj/Hk+KpCNpzL5V0zSjfeNDIz4113/orSulA+OfaDDCAVBEARBaElitrpRUaNyyapiKM+5baMiuULO0qNLcbdw57HQx7QaZ6hjKAG2AWxK0V157h/pf1CvrGeSvxbKchv0ngrKeh4x96OsrkwvypE7KrkkmX8f/zfD3YbzSJ9HNDaul5UX30/8niWhS9iaupV7t93L+cLzfHvuWyyMLHgg+IH2D5pxTP3vfMgMjcXZUYv7LqZWUcsPiY33Ce9M24mVsRXD3IbpKDJB3/ztE1GA58YG8nHQBbyP/pOzGX+d8xVkH8TjoY+zPW07sWmxOoxQEARBEIRbKZQqLuaVN1OW27ZGRasTV5NyLYXXh77e4lEcmiJJEjMCZnC24CzJJclanaslMVdi8LX2pbd924+0aTf3AWDtSdjV0wxwHsDqxNXIlXLtzacl1fXVvLzvZSyNLPlg1AcaL9k2khnxTP9nWHn3SuqUdcyLmUdsWiz397ofGxOb9g+YGK3eE93zbo3G2RG+Nr5M8J3A+gvrb1QVyhVy9mbsJdIrsv3nogp3LJGIAhIwq2IdDxvuInrLhkbt1R/t+yi9HXqz9OjSJiUGgiAIgiDozpXCSmrkyqaJaH6i+rWVRDS7IptlZ5YxxmsM4V7hWozyL1N6TMFQZqiTVcK8yjxO5J5gkt+kdjfbaRdJguApkLKbBUFzyanM6ZZf5v/7+L9JLU3lg1Ef4GjmqLV5BrkOYuOUjYz1GYuDmQPzes9r/yBKJSRuud4t1+r293eBxaGLqa6vZk3SGgCO5ByhvK6cu311nygL+kMkogD5SRgUqb+djMz7gX2XCm5cMpIZ8a+R/6JCXsH7R9/XmzPABEEQBOHvrqFRUZMzRPMSwMweLF1afHZzymbqlHW8OuRVbYbYiJ2pHWO9x7Lt8jbqFHVdNi/AjrQdqFAx0W+i9icLngKKWsIrK/Gz8WPV+VXd6vPTjis7+DX5Vxb2WcgI9xFan8/GxIZPwz9l95zd2Jvat3+AzOPqslwddcttTqBdIFHeUfyU9BNldWXsSt+FlZEoyxUaE4koqMsZkFAMfYpRBueJ3haN4qbGRQF2ATwV9hS7r+4m5kqM7uIUBEEQBOGGpJwyjAwkApxvac6Sl6BeDW1l5S8+P54guyDcLN20HGVjMwNnUlpbyp6Mru3KH3MlhhCHEHxtfLU/mfcwsHBCduF3Hgl5hIslFzmSc0T782pARnkG7x55l1CnUJ7q/1SXzt3h8t+E62W5QbrrltucJaFLqJBXsCZxDXuu7iHCK0LzZ9cK3ZpIREH9H7DPSAzGvEadkQ2TS39i8+msRrcsCFlAqFMoHxz7gIKqghYGEgRBEAShqyRmlxHgbIWx4U0fZ5RKyE9qtSxXoVRwtuCsRrqgttcwt2G4W7iz6VLXledeKb1CYlGids4ObY7MAHpNhkux3OM1Biczp2aP89A3coWcf+z/BxISH4/+uHscMdJQlhsQpTdluQ2CHYIJ9wzn27PfUlZXxnhf0S1XaEwkovlJUHgRQqaDiRWGI58myuA023Zsp0auuHGbgcyApSOXUquo5d0j73arEhNBEARBuBMl5ZQR7HbLh+9r6SCvbLVjbvK1ZKrqqwhzDtNyhE3JJBnTA6dzJOcImeWZXTLn9ivbkZCY4NeFK2bBU0BeiXHaIR4MfpCjOUe5WHyx6+bvgP+d/h/nCs/xzoh38LD00HU4bZN5Asqz1Z9j9dCS0CUoVAosjCy6pMxZ6F50m4jW6fZQZ0C9Gor63CsA2dAl1BtZcm/NL3x/OK3RrX42fjzb/1n2Ze5ja+rWro9VEARBEAQACitqyS+vbX5/KLS6IhqfHw+gk0QUYEbADCQkolOitT6XSqUi5koMQ1yH4GzurPX5bvAbDaY2kLSVWYGzMJIZ8Wvyr103fzsdzDrIqoRV3Nvz3u61cnejW65+leU2CHUKZVqPaTzQ6wFRlis0odtEtEoPutAmbgGfEWB1vaGBmS2Gwx5nosEJtu+No6SycTOBh3o/xADnAfz7+L/JrczVQcCCIAiCICS11KiooWOuU68Wn40viMfJzAl3C3dthdcqVwtXRnqMJDolGoVScfsHOiGxKJH0snTtnh3aHAMjCJoEF2OwNTQnyieK3y7/RnV9ddfG0QYFVQW8cfANAmwDeHnwy7oOp+1ulOWOBVPr29+vI0vvWsqzA57VdRiCHtJtIlp9DWordDd/wUUoSGraZWzYk2BoyiOKX/lyb0qjSzJJxtKRS6lX1fP24bdFia4gCIIg6EBDItrsGaJ2fmBi2cxTavH58YQ5h2n3GJPbmBk4k7yqPA5nH9bqPL9f+R0jmRFjvcdqdZ5mBU+FmlJIO8DswNmU15WzK31X18fRCoVSwWsHXqNKXsWn4Z9iamiq65DaLusklGXpVbdcQWgP3SaiKoX6mxxdaSjL7T218fsWDsgGL2SqwRHijhwjo7iq0WUvay9eGPgCh7MP63WZiSAIgiDcqZJyynGzMcXO4pZyv/zEVstyC6oKyKrIIsxJN2W5DSI8I7A3tdfqmaIKpYIdV3YwymMUNiY2WpunRT3GgJEFJG1lsOtgvK28+fWSfn1uWnl+Jcdyj/Ha0NfoYdtD1+G0T0I0GBjrXbdcQWgr3SaihiZwZp3u5k+MBu/hYOXa9NqIZ5AMjHjMYAv/iW26uX5u0FyGuA7hkxOfkFWR1fR5QRCatffqXtLL0nUdhiAI3VxidlnT1VB5NRSltNqoKL5At/tDGxgZGDG1x1TiMuIorC7Uyhwn805SUF3Q9WW5DYxMoed4uPA7kkrJrJ6z+DP/Ty5fu6ybeG5xOv80X8Z/yUTficwImKHrcNqnoSy3x1j1XlxB6IZ0m4ia20PaAShJ6/q5Cy6pvzVtqcuYlSvSgPnMNNjP8fiznM8qbXRZJsl4b+R7ALx96G2UKqW2IxaEbu9y6WWe3fss82LmkVKScvsHBEEQmlEjV5BaUNG0Y27BRVApwaWVRDQ/HmOZMcH2wVqO8vZmBM6gXlXPttRtWhk/5koM5obmhHuGa2X8NgmeCpUFcPUoU3tMxVAy1ItqstLaUv6x/x+4Wbjx1vC3dFqm3SFZp6AsU2+75QpCW+g2ETWzByQ4s77r505s3C23WSOfQybJeNb0dz7afqHJZQ9LD14e/DLHco/x88WftRerINwh1iauxVhmjIHMgEWxi7hSekXXIQmC0A2l5FdQr1TR2+2WlaCGRkUufVp8Nr4gnj6OfTAy0P0Zkf42/vR37s+m5E0a7zlRp6hjV9ouonyidLvvMXCcuqtr0lYczRyJ9I5ka+pW6hR1t39Wi9478h4FVQV8Ev4JVsb6df5mmyQ2lOVO1HUkgtBhuk1EDYzV7b3jf1KXGHSlhGjwHgbWbi3fY+uFFHY/s2V7uZiSzP5LBU1umRU4i5HuI/n81OdklGVoMWBB6N6u1Vxja+pW7vG/h+/Gf4cKFYt2LuJq2VVdhyYIQjeTeKNR0S0JRF4CGJqCvX+zz9UqakksSqSfcz9th9hmMwNnklaWxun80xod90DWAcrl5Uzy01FZbgMTK3VX16RtoFIxK3AW12qvsfvqbp2FlFCUQGx6LEv6LaGPY8tfWugtlep6We4YUZYrdGu6TUQBwh5UHz59Vbtd4xopTIb8hLZ1GbvrBQxU9bxkuZMPt19AqWz8jaUkSbwz4h0MJUPePPSmKNEVhBZsTN5IjaKGh3o/hL+tP9+O/5Y6ZR0LYxeKfdaCILRLUk4Z5sYG+DhYNL6QlwBOQSAzaPa5xKJE6pX19Hfq3wVRts14n/FYGFlovFw15nIM9qb2DHUbqtFxOyR4irq7a+5ZhrsPx93CXadNi1aeW4mlkSUPBT+ksxg6JesUlGaIbrlCt6f7RDR4MhhbqVdFu0rC9QOkb+2W2xx7f6S+c5it2kVeTibR8U0/MLtauPLKkFf4M/9Pfkz8UcPBCkL3J1fKWZe0jmFuw+hp1xOAnnY9WT5uOZXyShbuXCjO5RUEoc0Ss8sIcrXCQHbLvr78RHBuuWNuw6qjPq2ImhuZM8lvErFpsZTXlWtkzIq6CvZl7uNu37sxlBlqZMxO8Rutfk07hEySMTNwJsdyj+mkkiytNI1d6bu4r9d93bMkFyBhM8iMRFmu0O3pPhE1tlBvtE6I7rozRROjwWsYWLfxIOu7XsRAUcOrdnv4T+wlauRND5+e1mMa4Z7hfHH6C7HvTRBuEZsWS351PvN6z2v0frBDMMvHLae0tpSFOxeSX5WvowgFQeguVCoVSTnNdMytLIKKvFaPbonPj8fH2gd7U3stR9k+MwNnUqOoYfuV7RoZb0/GHmoVtbovy21g4wm2PpB+CIDpAdORSTKdNC1albAKYwNjHgx+sMvn1giVChK3qstyzWx1HY0gdIruE1FQl+fKK9X7B7StMAXyzrevy5hzL6TeU5lZH0P5tQJWH0lrcoskSbw9/G1MDEx47cBrnM4/jULZNGEVhL8blUrFmsQ1+Fr7cpfHXU2u93Hsw9dRX1NYXcii2EVaO8ZAEIQ7Q9a1aspq6ul9ayKan6B+baFjrkql4kzBGfo56c9qaIMQhxB62vW87Zmi9cp6CqoKuFB8gUNZh9iaupWV51fyyYlPePXAqyyOXczMrTNZenQpHpYe+vWz+t4F6YdBqcTFwoXRHqOJTolGrpR3WQh5lXlsTd3K9IDpOJo5dtm8GpX1J5ReFd1yhTuCHtRroG4aZOcH8Wsh7H7tzpW4Wf3aWrfc5ox+GcPELbztcoh399hy7yAvbM0bH6LtZO7EOyPe4ZX9rzB/+3zsTe0J9wwnwiuC4e7DMTM009APIQjdR3xBPAlFCbw59E1kUvPffYU5h/Hl2C95cveTLI5dzMq7V2JnatfFkQqC0B0k5ajLV5usiOZdT0RbKM3NKM+guKZY5+eHNkeSJGYGzuSj4x/xQ8IPKFQKiqqLKKoporC6kKLqIopriimpKUFF0+66ZoZm2Jva42DmgKelJ2FOYYzzGadfR5L4jFB/ziu8CM7BzO45m7jMOPZl7CPKJ6pLQliduBqVSsWCkAVdMp9WJDaU5erJarcgdIJ+JKKSpF4V3bsUStLBzkd7cyVsAa+hYOPRvudc+0LPiUxL38LbtaP5Ki6V1yc1PYNsnM849s3dx8HMg8RlxLErfRebUzZjYmDCcLfhRHpHMtpzdPf9Jk4Q2mlN4hqsja2Z0mNKq/cNch3EF2O+4OndT7Nk1xJWjF+BjYnoBigIQmNJOWVIEvRybaZjrrkDWDo3+1x8QTyAXjUqutlk/8n898//8unJTwF1culg6oCDmQPeVt70d+6Pg5kDjqaOOJip33cwdcDRzBFzI3MdR98GPiPVr2kHwTmYkR4jcTZ3ZmPyxi5JRK/VXGPDpQ1M8JuAp5Wn1ufTCpVK/Tm2R6QoyxXuCPqRiAL0m6tORM+sh4h/aGeOolTIOwd3f9ix50f/fxiuGMuHXid56bAF84f74GnX9A9/a2NrJvlPYpL/JOQKOSfzThKXEcfejL3EZcYhIRHqFEqEVwSRXpH42/jr17eWgqAhmeWZ7L66mwUhC9r0QWmY2zD+G/lfntnzDI/teoxvx3/bfZtJCIKgFaevluDnYIGFyS0fYfIT1ftDW/j/6en801gZWeFv2/zRLrpmY2LDtunbqFPU4WDm0D2Sy/aw8wUrd3V57pDFGMoMmREwg+Vnl5NdkY27ZRv7dnTQugvrqK6vZmGfhVqdR6uyr5flRryq60gEQSP0Y48ogK339TNF12rvTNGE62W5vad17HnPQeAfyaSKDZhSy2exl277iJGBEcPdh/Pa0NfYOWsnG6ds5ImwJ5Ar5fz3z/8yfct0Jm+ezCcnPuFk7knqlfUdi00Q9NBPF35Choz7e7W95H6kx0g+j/iciyUXefyPx+rPwYMAACAASURBVKmUV2oxQkEQupOK2noOpRQR2euWVU+lEvKTWu2YG58fT6hzaItbBPSBi4ULXtZed14SCuovCHxHqhNRlbq8eGbgTAA2p2zW6tRV8irWXlhLhGcEgXaBWp1Lqxq65fYSZbnCnUG//jS+caboEe2MnxgNnkPaX5Z7s9EvY1BVyH96xLM5PouE7NI2PypJEkH2QTzR7wl+nvwzu2bv4s2hb+Jl7cW6C+t4ZOcjRP4SyRsH32BX+i6q5FUdj1MQdKyiroJNyZsY5zsOVwvXdj0b7hXOp6M/JaEwgSf/eFL8tyAIAgB7L+RTp1Ayoc8tf6aUXAF5VYuNisrqyki9lkqYk/7tD/1b8RkBFblQfBkAd0t3RriPYFPyJq1+Ef9r8q/q7ux9u/FqaENZrn8EmIkeCsKdQb8S0eApYGwJZ7RwpmhRKuSe63yXMd+R4D2CsUXrcTCBj7Zf6PBQrhauzO01l2VRy9g/dz//Cf8Pd3ncRVxGHC/GvUjELxEkFiV2Ll5B0JHolGgq5ZXMC553+5ubMdZnLB+N+oj4gnie3fMsNfU1Go5QEITuZsf5XBwtTRjgfcsH8fzr/69sYUX0XME5VKj0slHR34rP9c7p149xAZjdczb5VfkcyjrUwkOdI1fI+SHhBwa6DOzev//ZoluucOfRr0T05jNF6zRcjpcYrX7taFnuzcJfRlaRw+e9EjmQXMiB5IJOD2lpbMl43/F8OOpD9s3dx3fjv0OulGvsTDFB6EoKpYIfk34kzCmMvk59OzzOBL8JLB25lOO5x3lu73PUKmo1GKUgCN1JjVzB3ov5jA9xwUB2yz7QvERAAudezT4bXxCPTJIR6hiq/UCFljkGgoUTpP2VdIZ7heNg6sDG5I1amfK3y7+RV5XHor6LtDJ+l0mIBpmh6JYr3FH0KxEFdXluXYXmzxRNiAbPwepDlTvLPxI8BnJXzmq8bYz4aPsFlMqm7dQ7ylBmyBC3IfRz6sexnGMaG1cQukpcZhxZFVnM692x1dCbTekxhXdHvMvh7MO8GPcickXXnTknCIL+OJBcSFWdgom3luWC+gxRez/1F9rNOJ1/miC7oDtz72V3Iknq8tz0wzfeMpIZMS1gGgcyD5Bfla/R6RRKBSvPr6SXfS9Guo/U6NhdSqVSL6j4R4C5va6jEQSN0b9E1Hu4urNa/FrNjVl8GXLPQm8NlTNIEox+Gan0Kp/1TiYhu4ytZ7I1M/ZNhroN5ULxBa7VXNP42IKgTWsS1+Bu4c4Y7zEaGW9G4AzeGvYW+zP38/L+l7v0AHRBEPTDjvO5WJsaMszfoenFvARwbn5/aL2ynnMF5+jn1E/LEQpt4jNSXWJ67eqNt2YFzkKhUhCdEq3RqfZk7CGtLI2FfRZ279MJsk+r/3lp6nOsIOgJ/UtEJQn6PQBX9jf6Q6pTEjRYltug5wRw6cvAq6vo42rBJzsvUluv0Nz4qI+yUKHieO5xjY4rCNqUWJTIqbxTPBD8AIYyzZ0QdW/Qvbw65FV2X93N6wdeFx2mBeFvRK5Q8kdSHlG9XTAyuOWji7xa/YWzS/P7Q1OupVBVX9W99wfeSXxGqF9vWhX1tvZmqOtQNiVvQqnSzMkJKpWK7859h7eVN+N8xmlkTJ1JvF6W2+seXUciCBqlf4kowP/P3n2HR1VmDxz/3pnMpPceSIWENEhCJ0CUjigggiCi66JrF1bX1f25rrpiV9aua1nFLhY6UqULEmoCSQiEFNI76W3a748LkZCeTCrv53l4ojN37j0hCZlz3/OeE3ab/DF2tXHOl7AeBowEO0/jnA8urYo+jlSUxBshaWSVVPP17xeMd34g1CkUCxMLUZ4r9ClfJ3yNhYlFfVt+Y1oStITHRzzOtrRtPHPwGXR64978EQShd4pOKaa0WsPMkCbKcgsSwaBvNhGNyY8BEIlob+ESAma2kPZbg4fnB8wnqyKLw9mHjXKZwzmHiS+KZ2noUpQKpVHO2SMMBnlBxfc6UZYr9Du9MxG19wafiRDzXf2sqQ4rToWc2K7pMhY0B5wCCEr6mKjBDry3+zylVcYrGVQpVIxwHUF0rkhEhb4hvyqfbWnbmOc/D2u1dZdc48+hf2ZZxDI2p2xm5bGVXXINQRB6l61xOViolUQFODd+Mq/ljrkxBTG4mLvgYenRhREKbaZQgFfDfaIAU7ymYGdqZ7SmRZ+d/gxnc2fmDJpjlPP1mJwYebSh6JYr9EO9MxEFuWnRxVRI7+SdscvdcoO64B8ihRImPg75CbwYnElZjYYP95036iXGuI/hQtkFcitzjXpeQegKqxNXo9PrWBK4pEuvc9+w+5jvP5/ViavFz4Yg9HM6vYHt8XlMGuKCmaqJla28eDAxl5sVNSEmP4Ywl7C+vUewv/EZD8XJUP7Hv99qpZrZg2azJ30PhdWFnTr96YLTROdG86fgP6FWqjsbbc+KXw+SEgJv6ulIBMHoem8iGjxHnina2aZF8evBY7i8ytoVQheAvQ9ecR8wL8yDVQflMl1jGes+FpBLTAShN6vR1vDTuZ+Y5DkJTxsjlsE3495h92LAwDcJ33T5tQRB6Dkn0y9SWFHLjKa65YLcMdd5iHxz+OqnqvLJqsgi3FmU5fYq9ftEG84OXeC/AK1By8bkjZ06/Wdxn2GttubWIbd26jw9rr5brijLFfon43USMTa1pdwdLH493PBasy3ZW1ScKpc0TFth/PguU5rAhL/BpuU8NTqbzacVvLo1kfsm+lGr1VGn1VNb/0dHrVZf/1hdo8euOF6jR5LgqVlDsDe1JzonmpsHi7IMoffanLKZktoS7gi+o1uuN8BqANN9pvNz0s/cF3YfNmqbbrmuIAjda1tcLmqlgklDmijLBbk01396k0/FFsQCYn9or+MWJi82pB2E0Pn1D/vZ+THcZThrk9ayNGRph1axU0pS2JW+i/uG3YelqgPvHXuTnFi4mCa/zxSEfqj3JqIA4Ysh5hs4sxnCFrX/9Qkb5I/G7JbblLDFsO81nI+/y58j3+KTA6lsasc4F5VSwtREidpEgemlP2oTBefyKgh2t2G0+2iic6IxGAyitEjolQwGeWUyyCGIka4ju+26S0OWsjV1Kz+d/Yl7ht7TbdcVBKF7GAwGtsXnMsHfCWszVeMDKgqgMh9cmx7dEpMfg1qhJsghqIsjFdpFaQKeYxrtEwW5adHTvz3NsbxjjHIb1e5Tfx73OWZKM5YEde0WkW6RIMpyhf6tdyeiXpFg5y2X53YoEV0PHhHyXNKuZKKG8Y/C1id4/LpChnuPQKmQ6hNKOblsmGiamigxVSlQKxUoFE0nl7Pf+439SQUsnDyG7WnbSS1Nxc/Or2s/F0HogEPZh0guTeblCS93682SIMcgxrmP45sz33Bn8J19fy+QIAgNxGeXkXmxmuWT/Zs+ID9e/tjMDNGYghhCnUJRKZtIYoWe5TMedq2AyiKw/GM27DTvabwa/So/n/u53YloTkUOv6T8wqLARTiY9fFS1vpuuVEN/n4EoT/pvXtEQe6sFn55pmhG+157MU0eANxdw3+H3wmWLpgeXMnMUDemBbsSFeDMWD9HIrzsCfawYbCLFZ4OFrjYmGFrocJMpWw2CQWICnDiRHoJIQ4jALFPVOi9vk74GidzJ2b6zOz2ay8NXUphdSGbUzZ3+7UFQeha2+JyUSokpga7Nn3A5Y65TYxuqdHWkFCUIMpyeyvv8fLH9IarouYm5tw06CZ+vfArJTUl7TrllwlfAnBX8F1GCbFH5Z6Sm3aKbrlCP9a7E1G4NFPUAKfaOVP0cllud/0Aq8whchmk7oOMI0Y55UR/Z3R6AxdyzPGw9BDzRIVeKbkkmYPZB7ltyG09suow1n0sgQ6BrIpbZbRB6IIg9A7b4nMZ4+uAg2Uz1Q758WDpDFYujZ5KKEpAq9eKRkW9lcdwMDGT94leZb7/fOr0dWxK2dTm0xXXFLPm3Bpm+c3C3crdmJH2jPpuubN7OhJB6DK9PxG19+nYTNH49eAe3vVluVcaeTeYO8D+Nzo//xQY7mWPpVrJgfOFjHEfw9G8o+j0OiMEKgjG882ZbzBVmrJwyMIeub4kSSwNWUpaWRr7Mvb1SAyCIBjf+fxyzudXMLO5brkgr4i2UJYLEOYS1hXhCZ1looaBoxp1zgUY4jCEoU5DWXNuDYY2vp/67sx31OpquSe0H/QLuNwt13eiKMsV+rXen4iCXJ5bnAIZbVwRLEmH7BPdX85gagXjHoakHbDqBkjZ26mEVG2iYNwgJ/afK2C022jK68o5U3zGePEKQiddrLnIpuRN3OR3E/Zm9j0Wx3Sf6XhYerAqflWPxSAIgnFtj88DYHpwM4moXgf5Z5osywW5UZG3jXff3yvYn/lMgNzTUN24BHe+/3ySS5PrOx+3pFJTyXeJ3zHZa3L/6KWRe1p+3xsyr6cjEYQu1TcS0aA5oLJs+0zR+m65PVBXP/5RuOENuHgBvpoLn8+E5N0dTkivC3Ai82I1HqbDALFPVOhdfj73M7W6Wu4I6p6RLc0xUZjwp5A/cTL/JCfzT/ZoLIIgGMfWuBwivOxwszVr+oCLaaCtbnJF1GAwEFsQS5izWA3t1bwjAUOTCw03+N6AhYkFP5/7udXT/HT2J8rryvvHaihc0S1XlOUK/VvfSERNreQRLHHroK6q9ePj14N7GDj4dn1sV1OawJj7YPlJmLVSXp39eh58Nh3O72p3QhoVIM9NO52uZ7DdYLFPVOg1NDoN3yd+T6RHJIPtB/d0OMwbPA9bU1tWxYlVUUHo6zKKq4jLKmNmSEtluZc65jaxIppenk5xTTERLhFdFKFgFANHgUIFab81espCZcEsv1lsT9tOeV15s6eo09XxVcJXjHEbw1DnoV0Zbfeo75YrynKF/q9vJKIgl+fWlUNiK50xSzIg61jPrIZeSWUGo++Fv8bAjf+Bsiz45hb4bBok/drmhNTb0RIvBwv2nytgjPsYTuafpFZX28XBC0LrtqVto6C6gDuD7+zpUAD5TcviwMXszdhLSmlKT4cj9BEXK+uo04omV73N9vhcgJb3h+YnABI4BzZ6KiZf3h8qGhX1cipzGDCiyXmiAAv8F1Cjq+GXlF+aPcXG5I0UVBf0n1nSeXFQnNzz72MFoRv0nUTUezzYeclNi1rS3d1yW2NiCqP+Iq+Q3vgmlOfCt/Phf1MhaWebEtKoACd+TylihMtoanW1xOa3vl9CELqSwWDg64Sv8bP1Y7zH+J4Op97iwMWolWq+iv+qp0MR+oDyGg3XvbGHmW/v51ByYU+HI1xhe3wuQe42eDtaNn9QXhw4+IHaotFTMQUxWKus+8d+wf7OZzzkxEBtRaOngh2DCXQIZE1S002LdHodq+JWEeIYwlj3sd0Rbde73C03SJTlCv1f30lEFQoIu11uAFSa2fxxCevBbZj8y6k3MTGFUffAshNw09tQkQ/fLoBPJ8O57S0mpFH+zlTV6VDUDkIhKcQ+UaHHncg/wZniMywJWoIkNT8Lt7s5mDlw8+Cb5TvkVQU9HY7Qy22Ny6WsRktFrZbbP43mbz/EUFghKk56Wn55DccuXGy5LBfkjrmuzXTMzY9hmMswFFLfeZtzzfKOBL0WMhuPvpMkiQX+C0gsTiShKKHR8zvTd5Jens49Q+/pVb+LOuxyt1yfCWDp1NPRCEKX61v/Ql+eKRrbzEzR0kzIPNp7VkObYqKGkUth2XGY/S5UFcJ3C+HTSXB2W5MJ6bhBjpgoJI6lVhPqGEp0rtgnKvSsrxO+xtbUltmDet8d2z8F/wmdQce3Z9rY3Ey4Zq09kYmvkyX7n5zEI5MGs+lUNlP+s4/VR9LR6zs/gkvomJ0JeRgMrZTl1lXJXUVdQxs9VVZXRnJJsijL7Ss8x8grgM2U587ym4W5iTk/JzVsWmQwGPjs9Gf42PgwxWtKd0Ta9fLioeh8734fKwhG1LcSUQdf8J7Q/EzRnuyW214mahhxl7xCOuc9qCqG7xfBJ9dD4pYGn5+1mYrh3vb1+0TjC+OpqGtcwiII3SGjPIPd6btZGLAQcxPzng6nES8bL6Z6TeXHsz9Sqans6XCEXirzYhWHU4qZFzEAM5WSv88Ywta/TmSImzX/t/Y0Cz/+nXN5zTdIEbrOtrhcfJ0sCXC1av6ggkTA0GTH3FMFpzBgEI2K+gpTa7nBZFrjeaIA1mprpntPZ0vKFqo0fzSsPJh9kMTiRO4Ovbv/rHwnrAdJIbrlCteMDv3kSrJPJEmKkyRppyRJHpIk+UmSdFySpPOSJD1t7EDrhS+WN3FnNC7hIH49uA0Fx0FddnmjU6pg+J/kFdK5H0BNCaxeDB9HQeIfm/Oj/J2Izy5jiO1wdAYdx/KO9WDQwrXsuzPfoZSULBqyqKdDadbS0KWUa8rb1PZfuDZtiMkGYF7EgPrHBrtY88N9Y3l9wTCSCyqY9c4BXtuWSHWdrqfCvOaUVNXxe3IRM0LcWi61bKFjbkx+DApJwVCnftBB9VrhM15uNKmpafLpBQELqNJWsTV1a/1jn53+DFcLV27yu6m7omza6Z9h06Pw6/Nw6D04+a28oJB+GArOQWUh6LStn+dyt1yfCWDl3PVxC0IvYNLB180CnAwGQ6gkSTOAlwE98BKwATgkSdI6g8HQuKC/s4LnwpYn5JmiXmP+eLw0U95fMPkZo1+yWyhVEHEHDFsEp36E/W/A6tthyRrwn0pUgDMrd5yjtMQDU6Up0TnRXO95fU9HLVxjKuoqWHd+HTN8Z+Bq6drT4TQr1CmUUW6j+Drha24PvB2VUtXTIQm9iMFgYM2JTEb7OuDp0LDRjSRJLBzpydQgV17ecob/7k1m86lsVswNZdIQlx6K+Nqx60w+Wr2h5bJckDvmmpiDvU+jp2IKYhhiPwQLVeMmRkIv5T1eTuKyjsmJ2FXCnMMYZDuINUlrmB8wn5j8GI7lHePJUU/23L/vmmrY8nc4+Q2Y2oKmUt7r2hwzWzC3B3MHsHC44uOlxwx6KEqCsQ923+cgCD2so4loCHAQwGAwbJck6b+AAXjIYDDoJEnaDFwHGD8RNbWWk9H4dXDDa3Lrb4CEjZcim2f0S3YrpQoilkDofHhlIKQdAP+phHrY4mCp5vD5MsJdwkXDIqFHrE1aS6WmslMjW2o0OkwUEibKri2lWhqylId2PcTWtK3MGTSnS68l9C2xmaWkFFRyf1TzTe0cLNWsvDWMBSMG8vS60yxddZQbh7rz7OxgXG3MujHaa8u2+Fzcbc0IG2jb8oF58eASBAplg4e1ei2nC06Ln/m+xmssIMn7RJtIRCVJYkHAAl47+hpni8/y2enPsDW1Zb7//O6PFeT9yT/+CXJPQ9QTcP1TckltbTlUF8vbraqLoeqi/LH64hWPFUNVERQmQXUJ1Jb+cV6lqeiWK1xTOpqIpgB3SJL0FjAB8ASKDAbD5ZqKfMDDCPE1Lfx2iP1eLl0dukB+LGE9uPaxstyWqMzkkqPskwAoFBITBjuxP6mQ+2eP4d2YdymsLsTJXHRVE7qHTq/ju8TvGO4ynBDHxuVwbVGj0RH1+h7Ka7QMHWhLhJcdEZ72DPeyw8XIb+4nDJjAYLvBrIpbxWy/2f2jo6JgFGtPZGJqouCGoe6tHjvWz5Etf53IJ/tSeG/PefafK+DvM4Zwx1hvlArxPWVMlbVa9p8rYPFor9Z/XvMTIGBGo4eTLiZRpa0i3EU0KupTzO3lxlNpv8F1TzZ5yOxBs3nr+FusPLaSwzmHeSjsoZ5Z9U78BdY9CJIEt//Y8PvQzEb+08RKfbN0GjkhrS4GEzOwEpUXwrWjo0sS64AcIBaYCZQir4heqcnfIpIk3SdJ0jFJko4VFHRwvIL3BLD1kstzAUqzICMaQuZ27Hy9lUe4PFvrUuOiqABnCitqcVHLXQKP5DSxT1YQusiejD1kVWR1ajV0a1wO+eW1TAt2pU6r5/PfUnngm+OMfnkX41/dzcPfnuB/B1I4fqGYGk3n9uVJksTdoXdzvuQ8B7IOdOpcQv9Rp9WzMTab6SFu2Ji1raTP1ETJsin+7Hg0inAvO57bGM8tHx4kLqu09RcLbbb3bAG1Wn3rZbkV+VBZAC5N7A8tiAEQjYr6Iu9Iuf+Htq7Jp21NbZnqPZXDOYcxNzHn9qDbuzc+nRZ2Pidvm3Lwhfv3N3kzpN2UKnlPqPMQsPfu/PkEoQ/p0IqowWDQAQ8CSJI0BLkM11mSJLNLq6IuyKuiTb32E+ATgJEjR3asP75CITct2ve6nISeuVSWG9zHy3Kv5hEBx7+Ai6ng4MdEf3n1MzPXEWuVNdG50czym9WzMQrXjK8TvmaA1QAmeU7q8Dm+P5KBj6MF79wWjiRJ1Gh0JOSUcTK9hJPpFzmZXsIvp3MAUCklgt1tiPCyJ9zTjggvO7wcLNq1sjnTdybvnnyXL+K/IGpgVIfjFvqPPWfzKanScMvwAa0ffBUfJ0u+uns0G2OzeWFzAnPe/40/R/ryt+kBWJl2tMBIuGxbfC6OlmpG+Ti0fGB9o6LGHXNj8mNwMXfB3bL11W6hl/EZD0c+lm/Ae45u8pAFAQvYkrqFWwNuxda0lfJtYyrPgzX3yNulRiyFma/KlWuCIHRKp35zSpKkBFYAXwHDgBslSVoPzAb+1PnwWhB2G+x7DU6thnM75JIOp8Fdeslu53Hpjm72SXDww9XGjEA3a35LKmbk4JFE54h5okL3SC1N5UT+CZ4Y+QTKq/ZktVVKQQVHUot5cuaQ+mTSTKVkuJc9w73sAV9AHmYfk17CyQw5Of3xWAZfHEoD5H17EZeS0nBPe0b62GOmaj4elULFHUF3sPLYSk4XnGaos+iiea1beyITJytTJg7u2LYGSZKYGz6A6wNceH17IqsOpbI1LofnZocwI8RVlIB3UI1Gx+4zecwO82i95Dn/UvuJJmaIxhbEEuYSJr4OfZFXpPzxwsFmE9GRriN56/q3GOcxrvviunAIfloKNaVw80fyQoggCEbR4W4hkiT9BhwHziOvcP4beAJIAjYaDIZEYwTYLAc/+R+tI/+DjMN9Y3ZoezkHgVIN2TH1D0UFOHPsQjHDXUaRVZFFRnlGDwYoXCvOFp8FYIz7mFaObN4PRzNQKiQWjBjY4nEu1mZMD3HjHzMDWX3fOE49N50tyyfy0rxQJge6cKG4ipU7znHHZ9E8/O2JVq+7IGAB1iprVsWv6nDsQv9wsbKO3Yn53Bzu0elmWbYWKl6aN5Q1D0Zia67igW+Oc+dnRziQVIChqTnXQosOJRdSWadrvSwXIOcUWLqAZcObCflV+WRVZBHuLPaH9klWzuA0pNl5oiDfCJrqPRVLlWXXx2MwyJ18v7gJ1JZw7y6RhAqCkXV4RdRgMFzd1qwAGNu5cNop/HbY+Ij83yH9MBE1Uct3fC81LAKI8nfmk/0pqOuGABCdE42ntWdPRShcI1JKU5CQ8Lbp2P6VOq2en49nMiXQBRfr9pUzmSgVBHvYEOxhw5Ix8vVLqzWs3H6Wb6IvUFBei7O1abOvt1RZsihwEZ+d/oz0snS8bLw69DkIfd/mU9lodAZuGd7yzZD2GO5lz6ZlE/jyUBof7Uvhzs+OEOhmzX1Rftw0zAO1Sdd2h+4vtsXlYm1qQuSgVlaqNdVwdgsE3tjoqZh8+aataFTUh3lHynM59bpGHZG7VU0pbHgYzmySu9jO/UAevyIIglH17d+QITeDykJuWODk39PRdA2PcMiJBb0e4FIpooKzGRY4mzuL8lyhW6SUpjDAagBmJh3bE7PrTB5FlXUsHm2cJNDWXMXi0V4YDPDrmbxWj18StAQThQlfxn9plOsLfdOaE1kEulkT7GFT/9iRnCPkVbb+PdQSlVLBXyb6cfD/JvH6gmHo9Ab+9mMsE1/fzUf7kimt1nQ29H5Nq9OzMyGPKUEurSfuib9AbRmEL2n0VExBDKZKU4IcgrooUqHL+UyAunLIPdVzMeTGwSfXQ+IWmP4iLPxaJKGC0EX6diJqag3zPoJZb/R0JF3HI0L+pXsxFZD31I3xdeRAUiGj3UdzJPcIeoO+h4MU+ruU0hQG2XV8NNL3RzNwtzUjKsDZaDEFuVvj6WDOtrjcVo91MndizqA5bEjeQFF1kdFiuJJGryG2IFb8PPZSyQUVxGSUMP+K1dCsiiz+suMv3Ln1TnIqcjp9DVMTJQtHerLjsSi+WDqKwS5WvLo1kchXdrFiUwIZxVWdvkZ/dCS1mItVmraV5cZ8C3Ze4D2+0VOx+bGEOIagUratG7LQC3lf3id6qGeuH/M9/G8q1FXBnzdD5DJ5TIsgCF2ibyeiAMFz5U5r/dWVDYsuiQpwJqWwEn/rCIprikm6mNRDwQnXAp1ex4XSC/jZ+nXo9ZkXqziQVMCtIz2NOndRkiRmhrhxKLmQsprWV5zuCrmLOl0d3yd+b7QYLjtbfJYlvyzhji13cOeWO0koSjD6NYTOWXciC4UEc8P/GHG9LmkdAOV15dy7814KqwuNci1Jkrh+iAvf/mUsm5dNYFqwK1/9nsb1K/fyyHcnOJVZYpTr9Bfb4nMxUylav1FVmgXJeyDsdrl7/hVqtDUkFCeIsty+zsYD7H1b3CfaJTQ1sOmvsP4BGDhSHs1yOSkWBKHL9P1EtL9zDgSlaYNE9LoAeQ9NXYW8QiXKc4WulFWRRZ2+Dl9b3w69/sdjmQAsHGm8fXmXzQhxQ6MzsCexyWlRDfja+jLJcxLfJ35PlcY4K1MavYb/xv6X2365jbyqPB4Ie4DMikxu23wbLx5+kdJaMWeyN9DrDaw7mcUEf2dcbOTycp1ex7rz64gcEMl/p/6X/Kp87tt5n9G/ZqEDbHn7tgj2PzmJeyb4su9sAXPeP8iij3/n14Q89Ppru7GRXm9ge3wu1wU4Y6FupW3FqdWAQe6af5WEogS0eq1oVNQfeI+H9EP1W5K63MU0LzJjwQAAIABJREFU+Hy6PC5v/KNw53qwdu2eawvCNU4kor2dUgVuQxt0zh3kbIWHrRmxqRJe1l5E54pEVOg6KaUpAPjZtX9FVKc38NOxDCb6OzPQ3sLYoTHcyx5na1O2x7denguwNHQpZXVlrDu/rtPXTixO5PZfbufDmA+Z7j2dDXM38HD4w2yat4nbg27np3M/MXvdbNYmrRXluj0sOrWYrJJq5l8xO/Rg9kHyq/KZ7z+fcJdw3pn0DmmlaTyw8wEqNZVGj8HDzpx/zgri0FOT+deNQWQUV/GXr44x9a19fH8knRqNzujX7AtiMkvIK6vlhtBW5n4aDBDzHXhPAIfGN8VO5ss3a8NcwroiTKE7+YyH6otQcKbrr3VuO3x8HRSnwW3fw7TnQSlmAgtCdxGJaF/gEdGgYZEkSUQFOHMwuZBRbqM5lnsMjV40wxC6RnJJMkCHSnP3nysgp7SGxaO6prOzQiExLdiVPYkFbXojH+4SznCX4XwV/xVavbZD19ToNHwY8yGLNy+moKqAtye9zWtRr2FnZgeAjdqG/xv9f/x404/42Prw3KHnRLluD1t7IhNLtZLpwX/sQVybtBYHMweuH3g9AOM8xvGf6/7DmeIzPLLrEaq11V0Si7WZir9M9GPfk5N457ZwLNRKnlp7mvGv7uadX5Morqzrkuv2VtvjclEpJSYFurR8YOZRKDovd8tvQkxBDN423jiYOXRBlEK36q59ovvfgO8Wgp0n3L8XAmd17fUEQWhEJKJ9gUe43EWuOLn+oYn+zpTXaHFVhVKlrSK+ML4HAxT6s5TSFJzNnbFWW7f7td8fScfRUs2UoK4rc5oZ4ka1RseBpLbt71saupTsymx2pO1o97XOFJ3htl9u47+x/2Wm70w23LyBKV5Tmjx2iMMQvpj5BS+Of1GU6/ag6jodW07nMGuoO+ZqeRxEYXUh+zL2MWfQnAaNbSZ5TeLlCS9zPO84f9v7NzS6rrvBp1IqmBs+gE2PTOD7e8cS5mnHW7+eI/LVXfxnx9kuu25vYjAY2BqXS+QgJ2zNW2kwFPMtqCzlvhBNnCc2P1aU5fYXdt5gMxDSfuu6ayTvgd0vQugCuGenPJteEIRuJxLRvqCJhkUTBjuhkOBikTwO43DO4Z6ITLgGpJamdmg1NL+8hl2J+SwYMbDhSIa6SqPu/Rnr54i1mUmbuucCRA2MwtfWl1XxqzAY2rY/r05Xx3sn32PxL4u5WHOR9ya/xysTX8HWtOWW/gpJwdzBc9k0bxOLAxfXl+uuS1onynW7yY6EXCrrdA1mh25M3ojWoGWe/7xGx8/ym8Vz457jt6zf+MeBf3R45bytJEli3CBHPv/zKH79WxQT/Z15b/d5UgoquvS6vcGZnHLSi6ta75arqYa4tXISamrV6On08nQu1l4UjYr6C0mSV0UvHJJLso2ttgI2LQfHwTD3fVCZG/8agiC0iUhE+wKnIWBi3iARtbVQEeZpx5HkOgIdAkXDIqFLGAwGUkpTOtSo6Ofjmej0BhZeWZar08K7w2HnM0aLUW2iYGqQK7sS89DoWk/uFJKCpSFLSSxO5Pec31s9Pr4wnkWbF/HJqU+40e9G1s1dx/We17crRhu1DU+NeYofbvoBbxtvnj30LH/a+ifOFHXDHqhr3NoTWQywM2eMr1yyaTAYWJu0luEuw5u9wTI/YD5PjnqSnRd28tyh57rtpsFgF2temBuKJMHG2OxuuWZP2hafiyTBtOBWKibObL40O7SZstx8uYeCWBHtR3zGQ2U+FCW3fmx77X4BStJhjkhCBaGniUS0L1CaNGpYBBDl78ypzBLCnUYSWxDbZXuahGtXflU+lZrKdjcqMhgM/HA0g9G+DgxyvmIFIzcWKnIh+iMoPG+0OGeEuFJSpeFIanGbjr/R70aczZ1ZFbeq2WPqdHW8e+JdlmxZQlltGR9M+YCXJrzU6ipoSwIdAvnyhi95cfyLZJRncNsvt/HS4ZdEuW4XyS+r4UBSAfMiBqC4NDroeN5xLpRdYH7A/BZfe2fwnTwc/jAbkzfycvTLbV497yw3WzPG+jqyMSa7267ZU7bH5TLKxwEnK9OWD2xhdijIjYqsVdYdaqgm9FKXv9YXjFyem34Yoj+GUfeC9zjjnlsQhHYTiWhfUd+w6I+GLFEBzugNYKYNRKPXcDLvZAsnEIT2u9wxd5DtoHa97veUIi4UVbF49FVNii7v+VGqYde/jRChLCrAGTOVos3dc9VKNXcE38HhnMNNNhGKK4xj0eZFfHr6U2YPms26m9cRNTDKKLFeWa67aMgifjz3I3PWz2H9+fWiXNfINsRkozfAvCu65a5NWouVyopp3tNaff39w+5nachSfjj7A2+feLvbEsO54R6kFFYSl1XWLdfrCSkFFZzNK+eG1spySzMhZW+Ts0Mviy2IZZjLMBSSeEvTbzgOBksX4zYs0tTAxmVgOxCmPme88wqC0GHiX+2+wiMcNJVQmFT/UNhAW6zNTMjKdcNEMuFwrtgnKhhXR0e3/HA0Axszk8YjGdIOglMATHgMzmyCC62XxraFhdqEKH9ntsfntnku460Bt2KpsuSL+C/qH6vV1fL28bflVdC6Mj6c8iEvjH8BG7WNUeK8ko3ahn+O+Sc/3PQDntaePHPwGe7aeheJxYlGv9a1as2JTMI97epX5cvqythxYQc3+t2IuUnrJXmSJPHYiMdYNGQRn8d9zqenP+3qkAG4IdQdlVJiQ0xWt1yvJ2y7dNNoRkgriWhs87NDQf6ani85T4RzhJEjFHrU5X2iaQeNt090/+tQeA5mvwOm7W++JwiC8YlEtK+43LAo54/yXBOlggmDnTiUVMFQ56Fin6hgdKmlqVirrXE0c2zza0qq6tgal8u8iAGYqZR/PKHTQvrv4DMBxj0MVm7yXlEjvcmYGepGXlktsZklbTreWm3NrQG3siNtB5nlmZwqOMXCTQv5LO4z5g2ex/q565k4cKJRYmtJoEMgX93wFS+Mf4H08nQWbV7Ey9EvU6Wp6vJr92cJ2WUk5pY3mB26JWULtbpabvG/pc3nkSSJf475J7P9ZvPeyff4JuGbrgi3AVsLFdcPcWHTqWx0bbyx0tdsj8slbKAtHnYt3BBoZXYowKmCUwCiUVF/5DMByjLl/ZydlRMLv70N4UtgcNOdzgVB6H4iEe0rnAJAZdGgYRHIJYm5ZTX4W0dwpuiM2GsmGFVKaQp+tn5IktTm16w7mUWdVs+iUV4Nn8g9JTcc8R4PakuY/C95NmDCeqPEOiXQFROFVL/S0hZLgpYgSRLLdi/jzq13UqWt4qOpH/HvyH93aFxNRykkBTcPvpmNN29kYcBCVieu5sXDL3bb9fujtScyUSklbhrmAcj7ltckrSHIIYhgx+B2nUshKVgxfgVTvaby2tHXWJe0ritCbmBuuAd5ZbVEpxR1+bW6W1ZJNbGZpcxorSw344g8tqyZJkUgNypSSAqGOg01cpRCj6ufJ3qwc+fRaWDDw2DpBDNe6nxcgiAYjUhE+wqFEtyGNW5YFOAMgL5qMAYMHM092hPRCf1Ucklyu0a3GAwGVh/JIGygLcEeV5WzXn4z4TNB/hh+O7iEwK/Pg7au07HaWqgYN8iR7XG5bd7L52bpxmy/2ZwvOc8t/rewbs46xg9ouiFKd7A1teXpsU9zf9j9bErZxO703T0WS1+m1elZH5PN5EAX7C3VACQUJ5BYnNiu1dArmShMeC3qNcYPGM9zh55jW+o2Y4bcyJRAVyzVSjbE9L/uuTsu3Sya2VpZbguzQ+sPyY9hiP0QLFQWxgxR6A2cg8DcvvOJ6KF3Ifc0zFopn08QhF5DJKJ9iUeEvKqk+2Ou3QA7cwY5W5KU6Yi5ibmYJyoYTWltKcU1xe1KRE9mlHA2r7zxaijIjYocB4P1pTefCiVMWwEXU+HYZ0aJeUaIG2lFVZzLa/sMxn+O+Sfr567nuXHPYaVuPKOwJ9w39D4CHQJZ8fsKSmraVmos/OHA+UIKK2obzA5de24tZkozZvnN6vB51Uo1b13/FsNdh/PUgafYm7HXCNE2zVytZEaIG1vicqjV6lp/QRcydpOmbXG5BLha4efcws9bXRXEr2t2diiAVq/lVOEpwpzDjBqf0EsoFOB1aZ9oRxWcg72vyd9HwXOMF5sgCEYhEtG+xCMcNFXyZvsrTPR35mhKKeHOwzmSe6SHghP6m440KvrhSAYWaiVzwj0aPqHXyd0PL6+GXjZ4CvhdD/teg+rOJ1zTg12RJNrcPRfAzMSMQXbt6wrc1VRKFS+Of5HSulJejn65p8Ppc9aeyMLOQsWkIS4AVGmq2JK6hek+0zvdeMrcxJz3J79PoEMgj+99vEtv/s2NGEB5jZa9Zwu67BqtySurIeKFncx8ez//2XGWmIySNjcEa0phRS1H04qZeXUjs6sl/tLi7FCApItJVGurxf7Q/sw7Ur5ZWdaBygC9DjY+AmoLeTVUEIReRySifUkTDYsArgtwplarx00dSmppKnmVeT0QnNDfpJTIiaivbdNNQq5WUatl06lsZg/zwMrUpOGTuaflN5U+VzX/kSSY9oKchB74T6djdrExY7iXPdvi2p6I9lZDHIbwwLAH2Jq2lR1pO3o6nD6jrEbDjvhcZg/zQG0i/4rbeWEnFZqKDpflXs1KbcV/p/4XLxsvlu9eTkx+TOsv6oDxgxxxtFSzsQfLc785fIHSag3WZiZ8sOc8N39wkDGv7OKptafYdSaPGk37Vmt3JuShN7SxLLeF2aEAMQXy33uEi+iY22/5XJ4n2oExLkf/BxnRMPNVsHIxblyCIBiFSET7EsfBoLZq1LBojJ8DaqWCqlI5YRCrooIxpJSmYKo0xcPSo/WDgU2x2VTV6Vh09exQ+GN+aFNvKt2HQdhieci4EbojzghxJSGnjIzivt919u6hdxPsGMyLh1+kqLr/Na3pCltP51Cr1XPLVbNDfWx8GO4y3GjXsTOz49Ppn+Ji4cJDvz7EmaIzRjv3ZSZKBTcNc+fXM3mU12iMfv7W1Gh0fBedzpRAF356IJLj/5rGmwvDGO3jwMaYbO758hjhK3Zw71fH+PFoBgXlta2ec1tcLl4OFgS5t9AMrA2zQ0HeH+pi7oK7ZSurq0Lf5ToU1Nbt3yd68YLcf2DwVBi2qGtiEwSh00Qi2pfUNyxqmIhaqE0Y5WvPqRRL7EztxD5RwShSSlPwtfVFqVC2fjCw+kg6Q1ytifC0a/zkhYPgMAhsmnnDOPlf8urorhc6EbHs8lzC9pTn9lYqhYqXxr9EhaaCl6JfMvpevf5ozYks/JwsCb/0fZhSmsKJ/BPc4n9Lu7o/t4WTuROfTvsUS7Ul9++8v76KwJjmhA+gVqtne3z3V7psis2mqLKOpePlm5z2lmpuGT6QD5YM58Sz0/jq7tEsHOlJfFYpT645xeiXf2Xehwf5YM95zuWVN/p+La3WcCi5kJmhbi1/LVqZHXpZTH4MYS5hRv+6Cr2I0gS8xrZvn6jBAJuWy79Tbnpb/igIQq8kEtG+xiNCLnO8omERyPtEz+VVMtRxBNE50eINq9BpqaWpbS7LTcguIzazlEWjPBu/KdTr5ETUp4VutLYDYOxDcPrHRjda2svb0ZJAN+t+kYgCDLYfzMPhD7Pzwk62pXVtp9a+LqO4iiOpxdwyfED99+G6pHWYSCbMHjS7S67pbuXO/6b/D4Wk4IFfH6C4ptio5x/uZcdAe3M2xGQZ9bytMRgMrDqYRoCrFZGDGs8RNjVREhXgzIq5oRz8v8n8snwCj04JQKsz8Mb2s0x/az/XvbGXFZsSOJRciEanZ09iPhqdof5mUTMXbnV2KEBeZR7ZldmEO4v9of2edyQUnoWKNu6VPvmNvKI+7Xmwa6JCRxCEXkMkon2NRwRoa6AgscHDUf7yGBdrQyB5VXlcKLtg9EtrdBqqtdVGP6/Q+1Rrq8muyG5zx9wfjqajNlE0KIeslxcHNaWN94debcKjYOEIO56R34x2wsxQN45duNimUsG+4K6QuxjmNIyXol+isLqwp8PptdadlJO1myPk70ONTsPG5I1c73k9TuZOXXZdbxtvPpjyAcU1xfxt79/Q6IxXRitJEnPDPTh4vrBbv5+PpBaTkFPGnyN9W11xlCSJEA9b/jrVn03LJnD4qSm8NC+UQc6WfBN9gds/jWbECzt5bVsirjamTVdNXNaG2aEAsQWxAKJR0bXgcpO79DbsEy3Lge1Py9tARtzdtXEJgtBpIhHtazwu/dK9atUoyN0aZ2tTCgrksRnROdFGvWxKSQqz18/m3h33itXWa0BaaRoGDG1KRGs0OtadzGJmiBt2FuomTnappKqFpiMAmNnCdf8HaQfg3PYORP2HGSFuGAxyY5T+wERhwgsTXqBaU82K31eIn8EmGAwG1p7IZKyfAwPt5ZmSezP3UlxTbLQmRS0JcQphReQKjucd5+UjLxv1azQ3fAB6A/xyqvuaFn1xKA07CxXzIpq4udQKN1szlozxZtXS0Zx8Zhof3TGC6SFuaPUGbhvlhULRQmLbhtmhIDcqMlWaEuQQ1O74hD7GPRxMzFtvWGQwwJa/g64W5rzX4v5iQRB6B/FT2tc4DJI37l/VOVeSJCb6O3E8WYmbhRvRucZLRI/mHuWOrXeQV5lHbEFs/Z1oof9KLk0GaFMiujUuh7IaLbc11aQI5EZF9r5y+W1rRi6Vv8d3Ptuo/Lw9At2s8Xa0YFs/Kc8F+WuxfPhy9mTsYXPK5p4Op9c5kV5CWlFVg9mha86twdXClUiPyG6JYZbfLO4JvYefz/3MD2d/MNp5A1ytCXK3YUNs9ySimRer2B6fy22jvDBXt22PeHMsTU2YGerGylvDOPr0VB6bFtD8wW2YHXpZbH4sIY4hqJSqTsUn9AEmavAc3fo+0fh1kLgZJv0THHvXSC5BEJomEtG+RqEA97Am99FdF+BMaZUWf5sIjuQeQW/Qd/pym5I3cd/O+3A2d+bH2T9iqbJk9dnVnT6v0LullKSglJR423i3euz3RzLwcbRgnF/jfWTo9Zf2h05o/FxTlCp5X0/hWTj5dTuj/oMkScwIcePQ+UJKq7u/22hXuSPoDiJcInjlyCtiTNNV1p7IxEyl4IZQef9hdkU2h7IPMc9/XpsbbhnDsohlXDfwOl498ipHcozXwXxuuAcn00tIL+r6btBf/34BSZK4c1zrP/9G1YbZoQA12hoSihNEWe61xHu8vM2j+mLTz1cWwZYn5O1LYx/u3tgEQegwkYj2RR7hkBsHV+1DGj9Y3gNlUhtAaW0picWJTb26TQwGAx/Hfsw/f/snw12G8/Wsr/G392fuoLlsT9su9qn1c6mlqXhae7a62pBSUMGR1GIWNtWkCCA/HmpK2p6IAgTeBJ5jYc/LUFvRzsj/MONSKeCexPwOn6O3USqUvDD+BTQ6Dc///nyfKtE1GAxEpxTx5aE0o48iqdXq2Hwqhxkhblibyd+z68+vB2De4HlGvVZrlAolr058FW8bbx7f9zgZ5RlGOe/sMHmM0sbYrm1aVFWn5fsj6cwIcWWAnXmXXquRNswOBYgviker14pGRdcSn/GAAdKbmQqw/Sm5F8HcD+ROu4Ig9AkiEe2LPCLkPRD5DefWOVmZEjrAhvRs+Q1LR/eJavQanjv0HO/HvM9sv9l8NPUjbNQ2ACwKXIRWr2Vt0trOfQ5Cr3Z5dEtrfjiagVIhsWDEwKYPaGl+aHMkCaa/CJX5cOjdtr/uKhGedrhYm7ItrnvKc7U6PXvO5nMsrZiM4ipqNLouuY63jTePjniUA1kH6pOt3qxWq2PN8Uxueu83Fn1ymOc2xjP5P/vYEJNltER6T2I+pdWa+rJcnV7HuvPriPSIxMOqbXNwjclKbcV7k99Db9CzfPdyKjWVnT7nADtzRvs4sD4mu0tvQKw7mUVZjbZ+ZEu3aePsUJDHtgCEuYR1Q2BCrzBgBCjVTc8TPbcDTv0AEx8H15Duj00QhA4Tt436Io8I+WP2SXAf1uCpKH9nPt5fTvAoX6JzolkaurRdpy6vK+fxvY/ze87vPBD2AA+FPdRgpcvP1o+x7mP58eyP3B16NyYK8S3U32j0GtLL0pnsNbnF4+q0etacyGRKoAsu1mZNH5T2G9j7tL+FvucoCL4ZDr0HI5Y2P3+0BQqFxPQQV9Ycz6K6TtfpvW6teX/Ped7+NanBY7bmKlysTXG1McPF2hRnG1Ncrc1wsfnjMRdrs3bHtjhwMb9e+JXXj77OOI9xuFm2MA7DCKo0VWRWZGKtssZKbYWlyhKF1HKyUFRRy7fR6Xx9+AIF5bUMdrHi5XlD8Xe14oXNCfx1dQyrj2SwYm4I/q7WnYpvzYksXKxNGX9pzMjvOb+TW5nLEyOf6NR5O8PLxouV163kwV8f5KkDT/H2pLdb/TtrzZxwD/61Po6EnDJCPGyNFOkfDAYDXxxMI8TDhpHe9kY/f4vaODu0SlPF+vPrGWQ7CAczh+6JTeh5KnMYMLLxPtGaMtj8KDgHyYmoIAh9isgi+iJ7XzC1vdSw6K4GT0UFOPPh3mQGmA3jRP4ONDpNm5s55Fbm8tCuh0gtSWVF5Arm+Tdd0nZb4G08uudR9mbsZar31M5+NkIvk1GegdagbbVR0a4zeRRW1LF4tFfTB1zeHzrkxo4FMvU5ec/Y3pflDogdMDPEnW8Op7M/qaDl2YWdlJRXzgd7znNDqBuLRnmSX15LflkN+eW15F36GJ1aSX55DRpd49UsazOT+sTU1cYMVxszpgS5MNLbvsmSZ4WkYMX4FczfOJ9nDz7Lx9M+bnXERkfty9jHisMryK/6o8RZQsJSZYmV2gorlRXWamus1dZYqazQak05n6vlXI4WrcaUQA9nlgb7MtbHGmt1LS4Wtqx7aDyrj6bz+raz3PDOAe6Z4MvyKf5Ymrb/V1JxZR17EvO5e4IvJko50VubtBZ7U3smeU4y2t9DR4zzGMcTo57g1SOv8kHMByyLWNap880a6s6/N8azMSa7SxLRg+eLSMqvYOWtYV32/dSkNs4OBXg5+mUulF3g0+mfdlNwQq/hHQm/vQW15WB66ebVzmehPAcWfi03NRIEoU8RiWhfpFCAR9MNi4Z72WOpVlJX7ke1tprYglhGuo1s9ZSJxYk8/OvDVGmr+HDqh4zzGNfssdcNvA43SzdWJ64WiWg/lFqSCrTeMXf10Qzcbc2ICnBu+oCCM3JjifbsD72Sgx+MvheiP4KxD4FL+8c0jPFzwNZcxfb43C5LRPV6A/9YcworUxNevDkURyvTFo8tqdaQX15DXtkfyWp+2aX/L6/hSGox+eU1fLQvmQBXK5aM8Wbe8AHYmDW8oeRp7cnjIx7nxegX+TnpZ24NuNWon1dJTQmvHn2VX1J+YbDdYB4d/igavYbyunIqNBVU1FVQVldGRV0F5XXlpBTnkF9ZQo2uCklZg4mTDhMgDfhvovwHwFRpypvXv8mSMVHMDHHj9W1n+Xh/ChtisnnmpmBmDXVrVxK0KTYbrd5QP8O2qLqIPel7WBK0pFd0VL098HbOXTzHJ6c+wd/en5k+Mzt8LgdLNVEBzmyMzeYfMwNbHoPSAasOpuJkpWZ2WPsrEDrl8uzQVla0NiVvYkPyBu4fdj9j3Md0U3BCr+EzHg6slL9fBk+B1ANwfBWMewQGjujp6ARB6ACRiPZV7uHyG3RtXYO7gGoTBeMGOXLmghaFq4Lo3OhWE9EDmQf4+76/Y2Nqw5c3fEmAfQvt9ZFnGi4asoh3TrxDckkyg+xEm/T+JKU0BaDFPaKZF6vYn1TAssn+KJt7M3x5f6hPO/aHXi3qCTj5rXzXe8lP7X65SqlgSpALvybkodHpUSmNvy3+m+gLnEgv4a1FYS0moSCXCztYqnGwVBPYQl5cVadlc2wO30Zf4LmN8by6NZE5YR4sGevFsIF29cfdOuRWdqbvZOXRlUR6RDLAqv0zH5uyI20HL0W/RFltGQ+GPci9Q+9tMqmrrNWy5kQmq06kkVpYiZuNGfdEenPbSE/MzfRykqopp6Kuov6/Pzv9GY/ueZR3Jr3DxIETeW3BMBaO8uSZ9XE8/N0JJvo78e85IQxybnl8x2VrT2QS7G5DoJu8j31T8ia0Bm23zA5tC0mSeHrM06SUpPDMb8/gbe1NkGPHZ1/ODfdgd2I+R9OKGdNUp+oOSiusZPfZfJZN9sfUpPu6DANtmh2aVprGC4dfYLjLcB4Ie6AbgxN6jYGjQVLKlTZe42DjMvmG5aSnezoyQRA6SDQr6qs8IkBXB/kJjZ6KCnAmq0hikE1gqw2Lfjr3E8t2L8PbxptvZ33bahJ62S3+t6BSqFidKEa59DfJpcm4WbphobJo9pgfj2UCsHBkM02KANIOyB0w7Zop3W0LCweIehySdsiNTDpgRogbZTVaolOKOx5HM7JLqnltayJRAc7cHG6cJBDAQm3CwlGebHhkApsemcDNER5sjM1mzvsHmf3eb/xwNJ2qOq1cohu5AkmSePbgs50e2VRYXchjex7j8X2Py1UPN63mofCHGiWhWSXVvLLlDONe2cWzG+KxMTPhndvCOfCPSTx0/WAcrEwxNzHH2cIZP1s/hjkPI3JAJDN8ZvDp9E/lFdY9j/JblnyzYoS3PZuWTeD5OSHEZJQw8+39vL4tkaq6lmfJns8vJzaztH411GAwsCZpDREuEfjZtT4Dt7uolWremvQWtqa2LN+zvFNdx6cFu2KuUhp9puiXv6dhopC4Y0wnfl47og2zQ2t1tTyx/wlMlaa8FvWa6E1wrTK1kt/7XDgEe16Ci6kw+11QN/+7ShCE3k0kon2Vx6W29U2U50b5y6WS9opgThecpkrTeO6c3qDnreNvseL3FUR6RPLFzC9wsXBp8+UdzByY4TODjckbqajr+IgNofdJKUlpsSxXpzfw07EMJvo7M9C+mTdofiOzAAAgAElEQVQAer38ZsFnYucDGn0/2HrBjmfk87ZTlL8z5iol2+JzOh/LFQwGA/9aH4feAC/dHNple+qGDrTllVuGEf30FF6YG0KdVs8/1pxmzEu7eG5DHOUV1jwx8gmO5B7hh7M/dOgaBoOBjckbmbt+Lvsz9/Po8Ef5dta3DHEY0uC4E+kXefi7E0S9vodPD6Qwwd+JNQ+OY/3D45kbPqBNK862prZ8Ov1TBtkN4q+7/1qfjCoVEndF+rD78euZHebBh3uTmfbmfrbH5zbbJXbtiSwUktzEB+Bk/knSytJ6zWrolZzMnXh38ruU1JTw2J7HqNPVdeg8FmoTpoe4suV0DnXazs+KBiiv0fDTsUxuHOqOi00zjce6Shtmh648upLE4kReHP9ilzfmEno570jIPAqHP5Qb2fka4XeMIAg9RiSifZW9L5hdbljUkI+TJV4OFpRd9EZr0HIs71iD52t1tfxj/z/4PO5zFgYs5N3J77a4+tWcxYGLqdJWsSllU4c/DaF30Rv0pJWltZiI7j9XQE5pDYtHtdAJtyARqoraN7alOSozmPIM5J6C0z+2++XmaiXXBTizIz4Pvd54Yy82ncphd2I+f58xBE+Hrr8jb2Om4s5xPmx7dCJrHhzHtGBXvj+awYy39/P9bjcGW43gzWNvklHWvrmVl5uUPf3b0/jZ+vHznJ+5Z+g9VNYaOJ1Zyi+ncvhw73lu/uAgt3x4iP3nCrhngi/7n5zEh0tGMMLbod1J+NXJ6MGsPzphOlub8ubCcH68fxxWpibc//Vxln5xlAtFDUeg6PUG1p3MIirAub5r85qkNViprJjuPb1d8XSXYMdgXhj/AjEFMbwU/VKHx7DMDfegpErD/nMFRonr5+OZVNT2wMgWaHV26K8XfmX12dXcGXwn13le183BCb2OzwTQa8HaHaat6OloBEHoJJGI9lWSJJeoNLEiChAV4ERCiiNqhbpBeW5JTQn37biPbWnbeGzEY/xr7L86XOY01GkowY7BrE5c3aVz7YTuk1uZS7W2usX9oauPpuNoqWZKkGvzJ7o8662jjYquFroA3MNg1wugqW73y2eGupFfXsvJjBKjhHOxso7nN8YT5mnHnyN9jHLOtpIkiRHeDry5KJzop6bw9Kwgiio0xJycTo0G7tr8OMkF5a2eR6PV8cnJb5m9bi7R2UcZYbUU6+K/svyrLMKe30HY8zuY/f5vPPzdCV7fdpayag3Pzwnh96em8M9ZQc2vhrfR5WTUz86P5buXN0hGAUb7OrB5+QT+dWMQx9IuMu2t/by581z9fNbDKUXklNbUzw4tqytjR9oOZvnO6tCNte4y03cm9w69l7VJa/ku8bsOnWOivzP2FiqjlOfq9Qa+PJTGcC87wjztWn+BMbUyOzSrIotnDz1LiGMIjw1/rHtjE3on70jwHAs3fwhmNj0djSAInSQ2WvRl7uHw+wegrQWThk1SJvo7883hdIKsQ+oT0YyyDB7c9SA5FTm8cd0bnereCPIb4sWBi3nm4DMczT3KaPfRnTqf0PMuNypqbkU0v7yGXWfyuWeCL2qTFu5jpR0AW0+w9zZOYAoFTH8RvpwtN+ma0L43pZMCXTBRSOyIz2WEEeYjvvjLGUqrNXw7f2jzzZq6gb2lmnuj/Lhngi+/pxTx5u/5nNN8xszPX2aM480sGeOFp4MF6cVVXCiqIr24ioziKlJL07lo/i1Ky2S0lYOoybmFg3onPO0r8HSwINzTDi8HCzwdLC59NMfazPgdaG1Nbfl02qfcu/Nelu9eznuT3yNyQGT98yqlgr9M9GN2mAcvbznDu7uSWHcyk3/PDmHL6VysTU2YHizfENmaspUaXQ23BPS+styrPRLxCOdLzvPG0Tfws/VrsUt5U1RKBbOGurPmRCaVtdoOjb25bO+5fNKKqvjb9CGtH2xsLcwO1eg1PLn/SfQGPW9EvdErOiALvYCpNdyzvaejEATBSEQi2pd5RIBeA3nxMGB4g6ciBzliopAw0w4htnI1ezP28uzBZzFg4H8z/keES4RRQpjpM5OVx1byfeL3IhHtB1JK5ES0uU7Ia45nodUbWNhSWa7BIA8d959m3OB8oyBgJhx4EyLuBEunNr/U1lxF5GAntsXn8n83BHZqP+f+cwWsOZHJssmD6zu19jSFQmL8YCciB/2V+3ac5ai0g/N5YTz4bcOmOPaWJti6RlPhtAEzScl0t+XM8ZuHt5MlbjZmPZJU25nZ1Sejy3Yva5SMArjamPHObREsGuXJsxviuefLY0gSLBzhiZlK7vC6JmkNgQ6BBDsEd/vn0F4KScErE1/hji138Pd9f+f7G7/Hy6Z9TYLmhg/g2+h0dibkcXNExxtlrTqYhpuNGTeEdvPeS4NBLsttZnbo+yff51TBKd6IegNPmxb+vREEQRD6LFGa25d5XEommyjPtTZTMdzLntw8+Rf4st3LsFJb8c2sb4yWhAKYmZhxy+Bb2JOxh9zKXKOdV+gZKaUp2JvaY2/WeNXQYDDww9F0Rvs6tDxao+AsVBUaZ3/o1aY+D3UVsO/1dr90RogrF4qqOJvXetlqc6rqtPxz3Wn8nC15eNLgDp+nq0iSxEsTn8dSZcagkM18sXQEH90xgi3LJ7Lpb/6EjPiaItOfGD9gDFvmb+TV6fcSOdiJAXbmPbqyezkZ9bX1Zfme5RzKPtTkcZGDnNiyfCJP3RCIp70Fd46TV9wTihI4U3yGW/xv6bKmUcZmqbLk3UnvIkkSy3Yva3fTt5He9gywM2dDTFaHY0jKK+dAUiF3jvPuktFGLcqIhuKUJpsUHcw6yOdxnzPffz4zfTtXuSMIgiD0XiIR7cvsvMDcvsV9oucz7HAydyHMOYxvZn2Dt42RSiWvsHDIwv9n777jqq73B46/vsBh7w0yBQQ1t2nuPXPbsGxYtm5l3dvuduvXbdy6dbPbvLdhNtUWZubWTHNU5kgFUfEAooAgWzac7++Pr3BV4LDOAHw/Hw8eBw/f8aYEz/t83p/3G4Nq4KujLW8kI9qXlMKURveH7tbnkppbyg2DmlidSP1ZezTV/tAL+cdB/1vg9yWQe6JFp07oEYCiwPrDrX/DZPHGY5zKL+PlOb3rVuLaG39nf54c/CQHc/4gtWo943v4siv3KxZsuIHUolT+MfwfvDPunXbXfdTT0ZMPJn5AhHsED/zYeDJqb2fD3aOi2P7YGK7o4gFA/PF4HGwdmBo51ZIht1moeyivjXqNtKI0nvj5CWoMNc0+18ZGYXqfYLYfP0vuuYpW3f/jXanY29kwz1iFg7k0Mjs0pzSHv+74K9Ge0Tw+6HHLxyWEEMJiJBHtyGobFjXQORe0eaJgyz1d/8unUz7F29HbLGGEuIUwMmQk3x7/ttUjCYT1qarKicITjc5f/HJPOu6Odky5Isj4hdJ2gnsIeEWYPkiA0X8FWwfY/GyLTvN3c2RAmBcbEs606rZ/pBfw0c4U5g8OY1CkeX6WTOXqyKsZGzqWt/a/xQ1rbuCNfW8wKnQU3838julR09vtqqGXoxcfTPyAcPdwHvjxAXZn7G7ynLLqMtbq1zIhfAIeDh4WiNK0BgcN5vFBj7Pt1DbePvB2i86d2TeYGoPK2kMtH01UWFpF/L7TzOobjI+rQ9MnmFJlKRyuPzu0xlDDkz8/SWlVKf8a9S+c7JwsG5cQQgiLkkS0owvqC9lHoKq83pd6Bnvg5azj1xMl2Cjm/V99Q9wN5JXnsSltk1nvI8wnrzyPworCBhsVFZZWse5wFrP7dTG+EqiqkLoDIoZpb5SYg1sADHsQjnwPJ39t+vgLTL4ikCOZRZzMrT9b15iqGgOPf3sQfzdHHp8S16JzrUFRFJ4e8jQuOheyS7NZPHoxi0cvxtep+ftqrcXL0YsPJ35IuHs4i35c1GQyujltM8VVxe1ydmhzzYudx9yYuXx46EPW6tc2+7y4QDe6Bbiy6kDLu+eu2HOSsqoa64xsSfoBKovrleV+eOhDfs36lScHP9noPnUhhBCdhySiHV1wP22m1pmEel+ytVEYHuPH9uNnTTo/sSFDgocQ5hbG8qTlZr2PMB9jHXP3ncynstrAlF5NrIaePQYlOeYpy73Q0PvBNRDWPQo1Vc0+bVJPrRx1Q0LLynPf364nKauY52ddgbsZuseag6+TL/Ez41kzew0Twk3cOMrMapPRMPcwFv24iF8yf2n02G+Pf0u4ezgDAwZaMELTUhSFpwY/RX///jyz6xkSztb/fd7YeTP7duH3tHzS85r/5kp1jYFPd6dxVVdvugdZoeFWA7ND957Zy7t/vMuUyCnMjp5t+ZiEEEJYnCSiHV1dw6J9DX55ZIwvZ89VcCSryKxh2Cg2XB97PX/k/MGR3CNmvZcwj5TCFKDhRDQxU/v70yO4iRetqTu0R3M0KrqQvQtMfRUy/4CfXm72aaHezvQIcmd9CxLREznneGPLca7uFcSEHkZmp7ZDvk6+uNobaSzVjnk5erFk4hLC3MO4f8v9DSajqYWp7D2zt0M1KWqMzlbH4tGL8Xb0ZtGPi0gtTG3WeTP6BAOw+mDzV0U3HznD6YIyFgy1wmpoQTrot100OzS/PJ/Htj9GiGsIz1z1TIf/fymEEKJ5JBHt6DxCwNkHMoztE4W/fHmAv3x5gFc3JPHFr2lsPZrNsTPFFJc3fzWpKTOjZ+Jk58SKoytMdk1hOfpCPU52Tg02sUnMKCLM27np1cDUHeAWDN4N7zM1qR4zoN9NsGMxpDW9l7DW5CsC2Xcyn+yi+uXslzIYVJ6MP4SjnQ3/N6P9jwXpbC5aGd2yqG4mcq345HjsFDtmRM2wUoSm5ePkwzvj3qFGreH2DbfXVSkYE+rtTP8wT75vQXnuRztTCfFyss4bKwcvnh2qqipP73ya/PJ8Xh31aod940QIIUTLSSLa0TXRsCjA3ZEHxkbj4aTjt5Q8/rtNz1MrD3Pb0j1MfH07vZ7dSK9nNzD539u5/eM9/O27Q7yzNZnv9p/mt5Q8TuWXUlVjaFYoHg4eTI2cyhr9GgorCk35XQoL0BfoifSIbHA1IjGziB5NlfCpqtaoKGK4+faHXmryy1qJ38q7oLx5f+cm9QxEVWFjYtNNi5bvOclvKXn87eoe+Ls5tjVa0Qrejt58OPFDQtxCuH/L/XXJaJWhilXJqxgVOqpD7H1trhivGJZMXKIlo+tv50RB092hZ/XrQlJWMUnNqHxJyCjkt5Q8bh0SYfmRPaoKB5ZdNDv0s8TP2HZqGw8PfJgePvJmjxBCXE7srB2AMIHgfvDzYq0Tob1zvS8/NDG27vPqGgM55yrIKCjjdEE5GQVl5z+0z/edzKeg9OJVUhtF6zga7OnIvEFhXDew8Vb/N8TdwLfHv+W75O+4teetpvsehdnpC/UMChxU7/lzFdWk5pYwu18X4xfITYZzZ7RGRZbi4AZzPoCPJsO6x2H2f5s8pVuAK5G+LmxIyOKmqxofZ5RVWM7La5MYGuXDtQNDTBm1aCFvR2+WTFrCwg0LuX/L/bw97m2KK4vJK8/r0E2KGhPtFc3SSUtZuHEht2+4nQ8nfkiMV0yjx0/tFcTfVyfy/YEM4iYbf8Po452pOOlsjf4eN5va2aEjHgEg4WwCr+97nTGhY7gxrv48USGEEJ2bJKKdQVBfUGvgzGEIrZ9IXMjO1oYgDyeCPJwY0Mhr8JKKajIL/5ec1iat+9PzeWbVYSZ0D8DLxb7Bc2O9Y+nv358VSSu4ucfNZu/WK0yjpKqEM6VnGhzdcjSrCFWl6RXRuvmhI8wQoRGhg2Dko7DtZYiZCFcYT0wURWFizwCW/JxCYWkVHs71y41VVeXpVYeprDHw0pxesmetHahdGb1j4x3cv+V+QtxC8Hf2Z1iwBd/4sKCunl35aNJH3LHhDhZuWMgHEz8g1ju2wWN9XR0YHu3LqgMZPDIxFptGVjpzz1Ww6o8MrhsY0uDfe7O7YHZocWUxj2x7BF8nX54f9rz8jAkhxGVIsoTOoK5hUcPluS3l4mBHtL8bI7v5MW9QGA9NjOW16/rw7vz+lFcZWLEn3ej58+LmcercKXac3mGSeIT51TYqivSo37wkMaO5jYp2ap1sLbE/9FIjH4EuA+GHP0Ph6SYPn9wzkGqDyo9HGy7PXX84i02JZ3hoQjfCfVxMHa1oJR8nn7oy3eSCZGZHz8bWxsg4oQ4u0iOSpZOXYm9rz8KNC402gpvZN5jT56taGrP8t5NUVhus06To5C9wYDlcMQfV3oXndj9HZkkmr4x8pUPOfxVCCNF2koh2Bu7B4OIHGfvNepu4QHeGdPXhs92pVBvZNzo+bDy+Tr6sSJKmRR2FsdEtiZlFeDrrCPIwskeybn6oBfeHXshWB3Peh5pq+O4eMBjf19wnxJMAdwfWH67fPbewtIpnvk+gZ7A7C4db4QW7MKo2Gb2j1x3c1P0ma4djdmHuYSydvBRnO2cWblzY6GiXiT0DcbCzaXSmaFWNgc9+SWNkNz+i/S3cEKjwFHx5M3iGwsTn+fb4t6xPXc99fe+jn38/y8YihBCi3ZBEtDNoomGRKS0YFkFGYTmbjDR60dnquKbbNew4vYP0IuOrp6J9OFFwAjsbO0Ld6u8bS8zQGhUZLZ3L08O5LPPPDzXGJwqmvAwp2+GXd4weamOjMKlnINuO5VBWWXPR1/6x9gh5JZX8c25v7GzlV2R75OPkw4P9H8TT0dPaoVhEqFsoSycvxd3enTs33smhnEP1jnF1sGN8jwDWHMpssMHc2kOZnCmq4LahERaI+AJVZbBivvY4bznHy8/y8m8vc1XQVSzstdCysQghhGhX5FVWZxHcD3KSoLLErLcZ3z2AEC8nlu5KNXrctd2uxVax5cujX5o1HmEa+kI94W7h2NlcvG28usZAUlZxC/aHWjERBeh3M8RNgy3PQVb9F+sXmtQzkPIqA9uO5dQ9tyv5LF/+ns4dIyK5oouUC4r2o4trF5ZOWoqHgwd3bbqLA9n133ic2SeYvJJKdiSfrfe1j3el0tXXhVHnR3pZhKrC94u0eb9zP6DMO5xHtz2Ki86Fl0a8JD0EhBDiMif/CnQWQX1BNTT54rutbG0Ubh0SwW8peSRkND4uw9/Zn7FhY4lPjqesusxs8VQbqs127ctJSmFKg42KUnNLqKg2NGN/6A5wDQCfaDNF2EyKAtPfBCcv+PZOqGp8VuigSG88nXVsSNDKc8uranhy5SHCfZz5y/hulopYiGYLcg1i6eSleDt6c/emu9l3Zt9FXx8d64+Hk67eTNED6QXsP1nArUMjGm1kZBa73qT48DfsG3oHX6pFPPjjg+gL9bw04qVONXJHCCFE60jX3M7iwoZFYVeZ9VbXDQxl8aZjfLwzlVev7dPocfPi5rExbSPrUtaZfMRCRU0Fi39fTPzxeD6b+hlx3nEmvf7lpLKmkvTidCZFTKr3tYTmNCpSVa1RUfgw6+wPvZSLD8x6Fz6fC5uf1cp1G6CztWFcXACbErOorDbw783HScstZdmdg3HUdd4GOKKVKs5BcRYUZzb+aGOrzbX1DD//GAZeEdqji59Jfj4CXQJZOnkpCzcs5J7N9/DOuHe4MvBKAOztbJjaK5BVBzIorazG2V77J37pzhTcHOyYO8B8Y4iqaqrQF+o5XnCc4/nHOX5qN8fPHiIrIhQyN0DmBlx1rvxlwF8YGjzUbHEIIYToOCQR7Szcg7SOpWZuWATg4axjTv8ufL33FE9MicPH1aHB4wYGDCTaM5rlScuZHT3bZO35UwpTeGz7YyTlJWGr2PL10a95esjTJrn25SitKA2Dami0UZG9rQ1Rfkaam+TpoTjD+mW5F4oeD4Puhl//AzETIHpcg4dNviKQb/edYsmOFD74Wc/1A0MZGiUrNZeV6krt729xFhRlNJJkZkFlcf1zdc7gFqR9dOkPhmooOKmVopbmXnysndP5xDS84WTVyavZiaq/s7822mXjHdy7+V7eHvc2g4MGAzCjTxeW/5bO5iPZzOgTzJmictYczOSWIRG4OrT9n3yDaiDjXIaWbBYcJzk/meMFx0ktTKVa1SpU7BQ7Iisr6K84EdPnTrr59iTGM4ZAl0AZ0yKEEKKOJKKdSXBfiySiAAuGRvDFrydZsSed+8Y0XI6pKAo3xN3A8788zx85f9DXv2+b7/v9ie954ZcXcLB14K2xb7ExdSNrU9byyJWP4GTn1ObrX45qO+ZGeUbV+1piRhHdAl3RGWvak7ZTe7T0/NCmTPg7pGyD7+6FP+3SVkovMSLGF2d7W/65PglfVwf+OrW7FQIVVlNdAW/2g6JLRv7YOoBboJZgBvTU3tio/fOFjw5ujSePFcVQkA4FaVpymp/2v8/Tf4XyS7Y22Lv9LzENHwJDHzCamPo5+7Fk0hLu3Hgn9225jzfHvsnQ4KEMivQm0N2R7w+cZkafYL74JY0aVWVBK5sUpRelszNjJ0l5SXWJZ2l1ad3Xu7h2IcYzhjGhY4jxiiHGOZDwr+5AV1YOd63Xvh8hhBCiAZKIdibB/eDYBq2EzMG87fljAtwYEePLZ7vTuGtk10YTlWldp/H63tdZnrS8TYloSVUJL/zyAj/of2BAwABeHvEygS6BuOhcWK1fzea0zUyPmt7q61/O9IV6FBQi3CMuel5VVRIzihjX3d/4BVJ3gIs/+MaYL8jW0DnBnA/gg7Hww4Nw3Wf1Xtg76mwZHevH2kNZPDezJx7OOisFK6zixFYtCR35mLaloTbBbMHqZKMc3CCgh/bRkLICLSmt+zifpObp4dg6KMuH8c8avYWvk29dMrpoyyLeGPsGw7sMZ3qfIJbuTOVMUTlf/HqScXEBhPk4NyvsKkMV+8/sZ9upbWw/tZ3UolQAPB08ifGKYWb0TC3h9Iwh2jMaV/sL/q0x1MDyeZCfCrd8L0moEEIIoyQR7UyC+gIqZB2EcPPvwVkwNIKFn/zO+sNZTO8T3OAxzjpnZkbP5MujX/Jo2aOtalCRkJvAY9se49S5U9zb917u6nVX3RD7gQEDCXULZWXySklEWymlIIVg12Ac7S6eE5pTXEFuSaXxjrl180Pbyf7QSwX1hnFPw6Zn4MAX0K/+3Mm/jO/GkChfplwRaIUAhVUlxIOjJ4x8FOzsLXtvJ0/tI6j3xc+rKvzwF9jxurbd4qp7jF7G29GbJROXcOemO3ngxwf495h/M7NvHz74OYVFy/eTW1LJbcMijF4jtyyXHad3sO3UNnZn7OZc1Tl0NjoGBgxkXtw8RnQZQahbaNNltT8+D8c3wtWLtd8JQgghhBGSiHYmwedXHDMOWCQRHRPrT7iPMx/vSm00EQW4PvZ6vjjyBd8e+5a7+9zd7OurqsrnRz5n8d7F+Dj6sGTiEgYGDrzoGEVRmBU9i7f2v0V6UTqh7vXnYArj9IX6BveHJmTWNioyMsYkP1VbUQpvxy86hyyC45tg3ePaz4X3xd9rTIAbMQFuVgpOWE1VOSSthZ6zLJ+EGqMocPVrUJID658AV3+4wnizN09HTz6c+CF3bbqLB7c+yOJRi+nq58JvKXnEBrgxNOrisnRVVTmSd4Ttp7az/dR2Dp89jIqKn5MfkyImMSJkBEOChuCsa94qKgCHvtGS5wG3wZUyH1QIIUTTZHxLZ+IWCG7BFtsnanN+lMvetHwOnipo9LhIj0iGBA3hq2NfNXvcSn55Pot+XMQre15heJfhfDP9m3pJaK0ZUTOwUWxYmbyyVd/H5azGUKONbmmoUdH5jrlxQUaStNQd2mN72x96IRsbmP1fUGwh/i6okZE/AkjerDUg6jnb2pHUZ2MLcz+E0MGw8m5I+bnJUzwcPPhg4gd09+7OQ9seok9sOgALhkWgKAqlVaVsObmFZ3c9y/ivx3P9D9fz7oF3Abi37718Oe1Ltly7hWeHPsu4sHEtS0Iz9sOq+yBsKEx5pVXfshBCiMuPJKKdjQUbFgFcMzAEF3tbPt6ZavS4eXHzyC7NZmv61iavuSdrD9d8fw27MnbxxKAneHPMm3g6ejZ6fKBLIEODh7LqxCpqDDUt/RYuaxnnMqg0VDY4QzQxs4gwb2fcHY3sm0zdAc6+4BdrxihNwCMEpi2GU3vg59esHY1oDxLiwdkHIkdZO5KG6ZzghuXgFQkrboSsw02e4m7vznsT3qOHTw+25r3GlCFpVDhv4+5NdzN8xXD+vPXPbEjdQB//Prww7AW2XreVZVcv454+99DDp0frOtqey4YV87XfA9d92r5Wl4UQQrRrkoh2NsH9IDcZyosscjt3Rx3XDAhh9cEMsovLGz1uVMgoglyCWJG0otFjqg3VvL3/bRZuWIizzpkvpn7B/O7zm/XiaE7MHLJLs9mVsatV38flqrZjbkMrokcyipreH5q2s/3uD71Ur2ug9/Ww7Z+Qvsfa0QhrqiyFo+uh+wywbcc7VJy94aZvwd4VvrhGa2bUBDd7N94b/x69/Xqzo+A/vLb3FTLOZXBj3I0smbiE7fO2s3j0YmZGz8THqX4n6RaproQvb4bSPLhhGbj6te16QgghLiuSiHY2wf2oa1hkIbcOjaCqRmX5r+mNHmNrY8t1sdfxW9ZvnCg4Ue/rWSVZLNywkPcOvsf0qOl8Oe1Luvs0f5TG6JDReDl4SXluC9UmopEekRc9X1JRTUpuCT2CjSSiBWlQmN6+y3IvNfVVcA+G+Du17tLi8nR8I1SVtM+y3Et5hmrJaGUpfD5XS/qa4Grvyn/H/5fXR7/O2tlrWT17NY9c+QiDggahszFRZ2hVhbWPQPovMOsdCOpjmusKIYS4bEgi2tkE1TYsslx5blc/V0bH+vH5r2lUVhsaPW5OzBx0NjqWJy2/6PkfT/7I3O/nkpSXxD+G/4MXh7/Ysv1JgM5Wx7SoaWxN30peedMv1IRGX6jHx9EHD4eLGxIlZRWjqtDd2Ipo6vn5oe25UdGlHD1g9ntakwgb5yUAACAASURBVKX1T1g7GmEtCfHayKGI4daOpHkCemgrjvlpsOx6LSltgrPOmfHh483XwG3Ph7DvExj+EFwx1zz3EEII0alJItrZuPqBe4jWOdeCFgyNIKe4grWHMhs9xtvRmymRU1h9YjXnKs9RUVPBP379Bw9ufZAurl34avpXbRrBMjt6NtWGatbo17T6GpcbfaGeKM+oes8n1nXMNZaI7tD22PnFmSs884gYBsP/Avs/gyOrrR2NsLSKc3BsI/SYqTUF6igihsPcD7R9zt8utG7TrZSftTdyYibB2L9ZLw4hhBAdmiSinZGFGxYBjIzxo6uvC0t3pRo9bl7sPEqrS/nPH/9h/pr5LE9azk3db+LzqZ8T7h7ephhivGLo5duL+OPxqKrapmtdDlRVJaUgpV5ZLmgdcz2cdAR7ODZw5nmpO7TVUJsO+Gtk9JNaKeH3D0BxlrWjEZZ0bD1Ul3WMstxL9ZipdaU9uhbWPKSVx1pafhp8fas2BmnuBx0rmRdCCNGudMBXkKJJwX0h7wSUF1rsljY2CguGRfBHegH7T+Y3elwvv1709OnJp4mfcqb0DG+PfZvHBz2Ova1pOi3Oip5FckEyCbkJJrleZ3a27CzFVcUNj27J1BoVNdooKj8NCk92nNLGS9nZw5wPoaoMvrsXDI2XlItOJmEluAVB2BBrR9I6g+/SymH3faI13rKkyhKtg29NNcxbrpW6CyGEEK0kiWhnFNxPe8z8w6K3ndM/BDcHO5Y2Mcrl4YEPMyNqBt9M/4ZRoaYdnTAlcgqOto7EH4836XU7oxOFWtOoS0e3VNcYSMosMl6Wm3Z+f2hHTUQB/LrBpBfgxBZtpEueXus2LavpnVd5ERzfBD1mdcyV/FrjnoE+N8JPL8HvSy1zT1WF7/4E2Ylw7UfgG22Z+wohhOi02nHfetFqQecT0Yz9EDnSYrd1dbDj2oGhfLo7laeu7k6Ae8NlnVcGXsmVgVeaJQY3ezcmhE9gXco6Hr3yUZzsnMxyn85AX9Dw6JbU3BIqqg3GR7ek7gQnL/BrfmfjdmngQm2/4NYXtA8AW3tt76uzL7icf3T2AZdLHp19tc+dvKQ8saM4ug5qKjpmWe6FFAVmvAklOVqJrmsAxE017z23/wsSV8GE5yF6vHnvJYQQ4rIgiWhn5OIDHmEWb1gEcOvQcJbuSuGLX9J4aGKsxe8PMDtmNqv1q9mctrlNzY86O32hHledK35OF8/+S8hoTqOinzvu/tALKQpc/xnot2kv6ktzofQslJx/LM3VypBLc6Gisdm8ipaMuviCZxhMeklbbRXtT0K81swtxDxvhFmUrQ6u/Rg+mQ7f3Aa3fA9hg81zr6Q12hs1va+HoYvMcw8hhBCXHUlEOysrNCwCCPdxYVycP1/8epL7xkbjYGf5laKBAQMJdQtlZfJKSUSNSClMoatH13r7QBMzi7C3tSHKz7XhEwvStRmiV91rgSgtwM4Buk1s+rjqCm2GY+lZKDmfpJbmnv/8/HNpO2HJeJi3rGOXLXdGZQWQvAUG393x30Cp5eAK87+GJRNg2XWwcCP4megNwLJ8rVogabX2GNwPpr+hvXkjhBBCmIAkop1VcF848r32YsLJy6K3XjA0ks1HfmX1H5lcMyDEovcGUBSFWdGzeGv/W6QXpZtvjl4Hpy/UMyy4/gzQxIwiYgJcsbdr5MV63f7QDjQ/1BTsHMA9SPtoTH4qfHEdfDoLZr4Dfa63WHiiCUlrwFAFPedYOxLTcvGFm+JhyUT4fK6WjLoHt+5ahae1jrxHVms/54ZqrbFTv5tg1OOgk60OQgghTKeTvC0s6rFSwyKAYdE+RPu7snRnitXGqMyImoGNYsPK5JVWuX97V1RZxNmys/VmiKqqSmJGURP7Q38GR0/w72nmKDsgrwhYuAHCroKVd8FPL0vzo/YiIR48w6FLf2tHYnrekdrKaFk+fH6NtvrbHKoK2Una/s/3R8PrPWDtI1CcqZXg3vEj/CURpi0GtwCzfgtCCCEuP5KIdlZBfbVHK5TnKorCgqERJGQUsTet8VEu5hToEsjQ4KGsOrGKGkONVWJozxprVJRTXEFuSWUT+0N3do79oebi5KWtUNV2Nf3uT1Bdae2oLm+leaD/SWtS1FlLS4P7avudzx6FFfOhqrzh4wwGSP8NNj0Dbw2AdwfDj8+DYgvj/g/u2wP374Hxz0LIAPk5F0IIYTbyL0xn5eytvftvhYZFAHP6d8Hd0Y6lu1LNep+SiurGY4iZQ3ZpNrsydpk1ho4opTAFqJ+IJmaeb1TU2Ipo4SnIT5H9j02xs4dZ78KYp+CP5fD5HG21SljHkdVamWlH75bblKixMOs/kLZDW5GvfROuugKOb4bVf4bFcdqe0t3vgFc4XP0aPHQE7twCIx6SRltCCCEsRvaIdmbB/ayyIgrgbG/HvEFhLNmRQkZBGcGept1bVFZZwzOrDhO//zSr7x/e4Are6JDReDl4sTJ5JSNCRpj0/h2dvlCPvY09wa4X7yWrTUS7N7YimtoJ5odaiqLAqMe0N4S+v1/bwzf/a618V1hWQjx4d4WgPtaOxPx6XwfFWbDpaYi/Uyu/Pb4JKotB5wIxEyBumvbo5GntaIUQQlzGZEW0Mwvuq3U3Lc2zyu1vviocVVX5/Jc0k143Ofscs97ZyTf7TmFQVdYnZDV4nM5Wx7SoaWxN30peuXX+G7RXJwpOEOERge0l8y8TM4oI9XbC3VHX8IlpO8DRAwJkf2iz9bkebl4J57Lhg3Fw6ndrR3R5KTkLKdu1JkWdtSz3UkMXwVX3weFvte/9itlw41fwmB6u+wR6XytJqBBCCKtrdSKqKMqbiqIcVBTld0VRBiqK0lVRlL2KoiQrivKUKYMUrVTXsMg65bmh3s6M7x7A8t9OUl5lmn2a3+0/zYy3d3D2XAWf3DaIAWFebE3KbvT42dGzqTZUs0a/xiT37yz0hfp6ZbmgrYgab1S04/z+UMuP5enQIobDHZu1cRsfXw2Jq6wd0eUjcRWohs5flnshRYFJL2r7PR85BjPegm6TQOdo7ciEEEKIOq1KRBVFGQT0VFW1N/Aw8BzwN+BFIBaYoShKD5NFKVqntgzNSuW5ALcNiyS/tIrvD2S06TrlVTU8GX+IP395gCuCPVjzwAhGdvNjTJw/h04Xkl3ccGOOGK8Yevn2Iv54vNU6+LY35dXlZJzLqJeIllZWk3K2hB5BHg2fWJQBeXotERUt5xsDd2yBwF7w1a2w6y3pqGsJCSvBt9vlt4qvKNp+T3nTSAghRDvV2hXRc4CLoigK4AHkA6OAtaqq1gA/nP+zsCYnL/CKtGoielVXb+IC3fioDaNcUs6WMPvdXSz/7SR/Gh3FsjsHE+ihvbM/OtYPgG1Hcxo9f1b0LJILkknITWjV/Tub1KJUVFQiPSMvej4pqxhVpfGOubI/tO1cfOHW1dBjBmz8G6x5GGoab7gl2qj4jLaKfzmV5QohhBAdRKsSUVVVE4FNwC/AX4FHABdVVWuXpbKBQJNEKNomuB9ktHGWaEUxZB6EhO8g+0iLTq0d5ZKUVcyvKS3fp/nDwQymv7WDzMIyli64kscnx2Fn+7+/tj2C3Alwd2Dr0cbLc6dETsHR1pH44/Etvn9n1NjolsSM8x1zG01EfwYHD21FT7Sezgmu+RiGPQi/L4Hl87SfMWF6iasA9fIqyxVCCCE6iFZ1zVUUxReYArwOLAQmApcudzX49rOiKHcBdwGEhYW15vaiJYL7ah0jS3LBxafx48oKtLLLPD3kpVzwuR5KLkjyvKNg0d4WrS7M7NuFl9cn8fHOVK7qaiSGC1RU1/DimiN8ujuN/mGevHVjf7o00HlXURTGxPqz5mAmVTUGdLb131txs3djQvgE1qWs49ErH8XJzrQdfDsafaEeG8WGCPeIi55PzCzCw0lHsEcj+8jSdkL4ECn1MwUbG5jwnNZBd80j8NEUmP8VuAc3eapogYR48O8B/nHWjkQIIYQQl2jt+JYbgR9UVf1CUZTvgSNAsaIojudXRf3RVkXrUVX1feB9gIEDB8oGKXOra1i0H4L7X5xgXvhRmnvxeW7B2riDbpO0R++ukHUIfv4X5Bxt0Qs7J3tbbhgUxnvbTnAqv5QQL2ejx5/MLeW+Zfs4dLqQO0dE8tjkuAYTzFpj4vxZsSed31PzGRLVcKI7O2Y2q/Wr2Zy2melR05sde2ekL9QT6haKva39Rc8nZmiNipSG3mQozoLcZBiwwDJBXi4G3g4eYfD1rVpH3flfyYqzqRRlwMndMOZv1o5ECCGEEA1obSJaCtQ2I/IBytBKda9WFOU7YDpwS9vDE21W27Bo2TwwVF3wBQU8QrQEs/uM/yWb3l21VRr7BpLF0EFaIpr0Q4tXGG66Kpz3t+v5bHcaT07t3uhx6w9n8eg3f6AA7988gIk9m67wHhbti85W4aej2Y0mogMDBhLqFsrK5JWXfSKaUphCpMfF+0NrDCpJWUXMHxze8EnZidpjUF8zR3cZihkPt6+HL66DjybDtR9rMx5F2yR8pz1KWa4QQgjRLrU2Ef0cmKwoyh9oJbn3AgeA1cCrwEeqqiaZJkTRJo4eMPZv2orWhcmmZ3jLW/m7B0OXAZC0BkY+0qJTu3g6MamnNsrlwfExONtf/FevstrAy+uS+GhnCn1CPHj7xv6EehtfOa3l6mDHoEhvth7NbjTJVRSF2dGzeXP/m6QXpRPqHtqi+Jtr68mtBLoE0t2n8WTbmqoN1aQWpTIyZORFz6ecLaG8ytD46Ja8FO3RJ8rMEV6mAnvBnVtg2XWw7HqY+ipcudDaUWkMBjiyCvJTIXo8BFzRMRr/JMRr/119o60diRBCCCEa0NpmReWqql6jqmofVVX7qqq6SVXVHFVVr1JVtauqqi+YOlDRBiMfhatfgyH3QewU8Itt/Ty5uKshY59W9tZCtw2LpKi8mu/2X3zuqfxSrntvNx/tTGHB0Ai+umdIs5PQWmNi/Tl25hyn8ksbPWZG1AxsFBtWJq9scexNMagGFu9dzANbH+BPm/9EQXmBye9hCunF6VQbqus3KsrUGhV1bzQR1YOdE7hKDzKzcQ+G29ZB9DhY85CWkJ7eZ714VBWOrof3RsDXC2Dzs/Df4fDvXlq33+TNUF1hvfiMKTgJp/Zo3XKFEEII0S61dnyLuFzFTdMej65t8akDw73oGezOx7v+N8ply5EzXP3mDk5kn+Pd+f15dkZPHOxa3gxnTJw/AFuNjHEJcAlgWPAwVp1YRY2hpsX3aEx5dTmPbnuUpYeXMiliEoUVhfxzzz9Ndn1T0hc23jFXZ6sQ7e/a8Il5KeAdqTXZEebj4AbzlsPYp+HkL/DBGPjiWjj1u2XjSPkZlkyE5ddDVSnMXQIPJcGMtyCwNxxYBp/PhVe6wpc3wf4voOSsZWM0RspyhRBCiHZPXlWKlvHtBj7RWnluC9WOcjl25hzbj5/lpbVHWPjJ73TxdGL1ouFM7RXU6rC6+roQ7uPM1qTGx7iA1rQouzSbXRm7Wn2vC+WV53HHxjvYlLaJRwY+wqsjX+WO3nfwg/4Hfkr/yST3MKWUQq3E9tI9oomZRcT4u2Fv18ivhPwUbSatMD9bO630/c+HYNwzWhL64Tj4bA6c/NW89z69Fz6dBZ9Mg6LTMP1NuO836HUNuAdB/1vghmXwmB5u/Bp6Xwen9sKqe+HVaPhwAvz8mjbmqZVzg00iIV5r1OYtf2eFEEKI9koSUdEyiqKV56Zs10a+tND0PsH4uNhz5ye/8952PfMHhxF/71AifF3aGJY2xmXXibOUVzW+2jk6ZDReDl4mKc9NLUzlprU3kZSXxGujX+PWnreiKAp39bqLGK8Yntv9HIUVhW2+jynpC/T4O/vjan/xymdiRlHj80MNhv+tiArLcXSHEQ/Dnw/C+L9D5gH4aCJ8MgPSTPNGSp3sJFgxHz4YC1kHYdI/YNE+GHAr2OrqH69zgm4TYdrr8FAi3LUNRj8BNZWw5Tl49yp4ow+sexxObIXqStPGa0xeCmTsl7JcIYQQop2TRFS0XNw0MFRre8RayFFny8IRkdjb2fDGvL68OLsXjjrTzKUcHetHeZWB3frcRo/R2eqYFjWNrelbySvPa/W99p7Zy03rbqKkqoQlk5YwIfx/XU51tjpeGPYCeeV5vLrn1Vbfwxz0hfp6ZbnZxeWcPVfReKOic1lQXaY1uRKW5+AGw/+srZBOfEHrYLx0Cnw8TSuhbYv8VFh5j5Y4pmyHMU/Bg39o+8mbu49cUbR5xaOfgLu3wUNHYNq/wb877P0YPpsFr0bBV7fCHyugtPU/d82ScP5Npp6zzHsfIYQQQrSJJKKi5boMBBd/bYxLK/xpVBT7np7AzL5dTBrWVV19cNTZ8FNT5bnRs6k2VLNG3/LyYoC1+rXcufFOvBy8+Hzq5/Tx61PvmB4+Pbj9ittZdWIVP59qY7JgIqqqklKYQpTnxZ1vEzO0RkWNrojmaftKZUXUyuxdYOgiePAgTHoJzh7TSmiXTgX9Ty0rhS3O0hoOvTVQS9yGPaAloKMe0xLftnAPhoG3wY1fwmMp2p7XnrO0mZ4r74bXr4DUHW27hzEJ8RByJXiGme8eQgghhGgzSURFy9nYQNxUOL6pVV0zFUVpfC9iGzjqbBkW5cvWozl1zZAaEuMVQy/fXsQfjzd63KVUVeWDgx/w+M+P09uvN59P/ZxQt8bHwNzT5x6iPKJ4dvezFFcWt+h7MYczpWcorS5tRcfc86NbZEW0fbB3hiH3aonjlFe0Nwo+nanNIE3eYjwhLc2DTc/AG3211cr+t8ADB2DCc+DsbZ5Y46ZqTY4eSoI7ftTmFy+7HtJ/M/39ziZD1iEpyxVCCCE6AElERevETYPKc1o5XzsyJs6fk3mlnMgpMXrcrOhZJBckk5Cb0KzrVhmqeHb3s7y5/02mRk7l/Qnv4+HgYfQce1t7Xhj+AmfLzvLa7681+3swF32BtrJZr1FRRhEhXk54ODWwFxC0RMdGB+4h5g5RtITOCQbfrSWSU/8Fhenw+Rz4cLz2JtGFCWlFMWx7Vdu3ufNN6DET7v8dpi3WmhBZgo0NhAyAW78HV3+t627GftPeo7Yst8dM015XCCGEECYniahonciRYO/a6vJcc6kd4/LTUePluVMip+Bo60j88fgmr1lcWcx9m+8j/ng8d/W+i5dHvIy9rX2z4rnC9woW9FzAt8e/ZddpEzeYaaFGR7dkFjW+PxS0RNQrXOvmKtofnSMMuhMe2K81Dzp3Br64Rhv9krQGdr+rrYBufUH7ub13N8x5z3ql1m6BcOtqcPKEz2ZD1mHTXTthJYQNAQ/Tlv0LIYQQwvQkERWtY+cA0eMhaa3WVbWd6OLpRGyAGz82sU/Uzd6NCeETWJeyjrLqskaPyzyXyS3rbmFP1h6eG/oci/otQlGUFsV0b997ifSI5Nndz3Ku8lyLzjWlE4Un8HDwwNvxfyWYpZXVpJwtaXx/KMjolo7CzgEG3q51u53xllaGu+JG2PAkBPbSymLnfaE1EbI2jxAtGdU5a2XFOUfbfs2co5CdIGW5QgghRAchiahovbhpUJINp3+3diQXGR3nx57UPIrLq4weNztmNueqzrE5reHuv4m5icxfO5+skizeHf8us2NmtyoeB1sHnh/2PGdKz7B47+JWXcMU9AVax9wLE+mkrGJUlcZXRFX1/OgW2R/aYdjZa3s/F+2Faz+BBWvhlu+0stj2xCsCbvkebGy1kTS5J9p2vcPxgAI9ZpgiOiGEEEKYmSSiovViJoCNXfsrz431p6pGZWdy42NcAAYGDCTULbTBmaLb0rexYP0CbG1s+XTKpwwJHtKmmPr49eHm7jfz9bGv+TXz1zZdq7VSClPqleUeyWyiY25pLlQUSSLaEdnqtG61EcOsHUnjfKPhllVgqNKS0fy01l1HVbWy3IjhWumvEEIIIdo9SURF6zl5QsQIbR9aOzIg3As3Rzu2NlGeqygKs6NnsydrD+lF6XXPr0hawQNbHyDCPYJlU5cR4xVjkrju73c/4e7h/N+u/6O0qtQk12yu/PJ88ivyG2xU5O5oRxdPp4ZPlNEtwtz8u8PN30FlMXwyHQpPt/wa2Ylw9ij0bF3VghBCCCEsTxJR0TZxV0NuMuQcs3YkdXS2NoyM8WPr0ewmx7PMiJqBjWLDyuSVGFQDr+55lRd/fZERXUbw8eSP8XP2M1lcjnaOPDf0OTLOZfD63tdNdt3mMNqoKNi98X2vMrpFWEJQb7h5pbav9dMZUHymZecfjgfFBrpLWa4QQgjRUUgiKtomdqr22N7Kc+P8yS6uICGjyOhxAS4BDAsexqoTq3j4p4f5NPFTboi7gTfGvIGzztnkcfUP6M/87vNZcXQFe7L2mPz6jalNRKM8o+qeqzGoJGUW0yPIyBiaPL32At8zzNwhistdlwFw0zdQlKk1MCoxXlpfp7YsN3IkuJrujSMhhBBCmJckoqJtPLpAcP92V547qpv2grSpMS6gNS3KLs1my8ktPHblYzw56ElsbWzNFtuifosIdQu1aImuvkCPk50TgS7/2z+XmltCWVWN8Y65eXqtw6mdgwWiFJe9sKvgxhVap+bPZkJZftPnZB2EvBPSLVcIIYToYCQRFW0Xd7XWObco09qR1PFzc6B3iAdbj+Y0eezokNHMiZnDv8f8m5t73Nzi8Swt5axz5u9D/056cTpv7X/LrPeqlVKYQoR7BDbK/37kE8+vFhudISqjW4SlRY7UxszkHIXP50K58aoGDsdrTdO6T7dMfEIIIYQwCUlERdvFTdMej661bhyXGBPrz/6T+eSXVBo9Tmer4+9D/87YsLEWigyuDLySebHz+OLIF+w7s8/s99MX6unqWX9/qM5WIdrftfET8/SyP1RYXvR4bfRM5h/wxbVQ0cj83dqy3K6jwdm74WOEEEII0S5JIirazi8WvKPaXXnumDh/DCpsP970qqg1/GXAXwh2DeaZXc9QVl1mtvuUVpWSWZJZv1FRRhEx/m7Y2zXya6CsQBvfIomosIa4qTB3CZz6DZbPg6oGfkYy9kFBmpTlCiGEEB2QJKKi7RRFK89N2Q7lhdaOpk7vLh74uNjzYxNjXKyltkQ3rSiNd/a/Y7b7pBRqnW8b65jbqPzajrlSmiuspOcsmP0epO6AL2+C6oqLv344Hmx0WtIqhBBCiA5FElFhGnHTtKH0xzdZO5I6NjYKo2L92HYshxqD8TEu1jI4aDDXdbuOz458xoHsA2a5R0OjW7KLy8kprjC+P1RGt4j2oPd1MONNSN4MX98GNVXa86oKCd9B9Dhw8rJujEIIIYRoMUlEhWmEDAQX//ZXnhvrT0FpFQfSC6wdSqMeGvgQAc4BPLPrGSpqKpo+oYX0hXrsFDtC3UPrnjuSWQzQdMdcAK8Ik8ckRIv0vwWm/guOroFv74Caaji1B4pOSVmuEEII0UFJIipMw8YWYidrK6KXls9Z0cgYP2xtFLa20/JcABedC88OfZaUwhTePfCuya+vL9AT6h6KzkZX91xtx9zugU2siLoFgb2LyWMSosUG3QkTX4TE72DVfXDoG7B1gNgp1o5MCCGEEK0giagwnbhpUFkMKT9bO5I6Hs46BoR5sbUZ80StaWjwUObGzOXjhI85lHPIpNfWF+qJ8oi66LnEzCK6eDrh4axr5Cy0FVEZ3SLak6H3w9i/wcEV8Nv7EDMBHI28mSKEEEKIdksSUWE6kaNA5wJJP1g7kouMifMnIaOIM0Xl1g7FqIcHPoyfkx9P73yayhrjI2eaq6qmivTidCI9Lk4oEzMKjZflgtasSPaHivZm5KPaB6q2f1QIIYQQHZIkosJ0dI4QMx6OrgODwdrR1BkT5wfAT+18VdTN3o3/G/J/nCg8wX//+K9Jrnmy+CQ1as1FM0RLK6vRny0x3qiosgSKM6VjrmifxjwFD+yHHjOtHYkQQgghWsnO2gGITiZuGiSu0ub7hQy0djQAxAa4EezhyNakHK6/Msza4Rg1ImQEM6Nm8tHhjxgXPo5oz2jKq8spqy6jvLqc8pryxv98/vML/3z63Gng4o65R7OKUdUmGhXlp2qPkoiK9khRZLVeCCGE6OAkERWmFTMBbOy08tx2kogqisLoOH++P5BBZbUBe7v2XQjw6JWPsjtjN/N+mNeq8x1sHXC0c8TR1hEnOyeGBA0h2jO67uuJmVqjIhndIoQQQgghrEUSUWFaTl4QMVwb4zL+WWtHU2dMrD/Lfj3J76l5DI32tXY4Rnk4ePDfCf9lY9pGHGwdcLJzwtHWUUsu7RxxsnXCwc6h7vPa52uPsVGMJ9qJGUW4OdoR4uXU+EF1o1tkRVQIIYQQQpieJKLC9OKmwdpHIOcY+HWzdjQADIv2wd7Whh+Tstt9IgoQ4xVDjFeMWa6dmFlEjyB3FEVp/KA8PTj7gJOnWWIQQgghhBCXt/Zdoyg6ptq5fkfXWDeOCzjb2zG4q3e7H+NibjUGlaTM4qY75sroFiGEEEIIYUaSiArT8wiB4H5aeW47MjbOnxM5JZzMLbV2KFaTmltCWVWN8f2hIKNbhBBCCCGEWUkiKswj7mo4tQeKs6wdSZ0xsf4Al/WqaGLG+UZFxlZEqyug8JQkokIIIYQQwmwkERXmETdNezy61rpxXCDC14VIX5fLOhE9klmEzlYhxt+t8YMKToJqkERUCCGEEEKYjSSiwjz84rQ9hu2sPHdMrD+7T+RSVllj7VCsIjGziGh/N+MjbOpGt8geUSGEEEIIYR6SiArzUBStPFe/DcqLrB1NnTFxflRUG9itP2vtUKwiMaOo6f2htaNbZEVUCCGEEEKYiSSiwnzipoGhCpI3WzuSOoMivXG2t+XHpMuvPDenuILs4ormdcx1cNfGtwghhBBCCGEGkogK8wkdBM6+7ao818HOlmHRvmxNfTA1BgAAHLRJREFUykFVVWuHY1FHMs83KmrOiqhXhLaqLYQQQgghhBlIIirMx8ZWmyl6fCNUV1o7mjpjYv05XVBGcvY5a4diUYnNTURldIsQQgghhDAzSUSFecVNg4oiSP3Z2pHUGRPnB3DZlecmZhTRxdMJD2dd4wfVVEN+miSiQgghhBDCrCQRFebVdRToXNpVeW6QhxNxgW6X3RiXxMyipveHFp3S9vVKIiqEEEIIIcxIElFhXjoniB6nzRM1GKwdTZ2xcf78nppPUXmVtUOxiLLKGvQ555qxP1RGtwghhBBCCPOTRFSYX9w0KM6EjP3WjqTOmDh/qg0qO45fHmNcjp4pxqDSvI65ICuiQgghhBDCrCQRFebXbSIotpD0g7UjqdMv1BMPJx1bL5N9ookZLeiYa+cEroEWiEoIIYQQQlyuJBEV5ufkBRHD29U+UTtbG0Z282Pr0RwMhs4/xiUxsxA3RztCvJyMH5iXoo1usZFfDUIIIYQQwnzk1aawjLhpcPYonD1u7UjqjIn14+y5ChLOrxZ2ZokZRXQPckdpajaojG4RQgghhBAWIImosIy4qdpjO1oVHdXND0Xp/GNcagwqSVnFTZflGgzaiqg0KhJCCCGEEGYmiaiwDI8QCOrTrhJRH1cH+oR4mmyMi8GgciSziILSSpNcz1TScksoraxpulHRuSyoLpMVUSGEEEIIYXZ21g5AXEbipsHWf0DxGXALsHY0gDbG5fXNx8g9V4GPq0OLzlVVlZN5pexIPsuu5Fx2nThLfmkVA8K9+OaeIU2XwVpIYmZzGxXJ6BYhhBBCCGEZkogKy4m7Gra+CMfWwYAF1o4GgDGx/izedIxtx3KY0z+kyeNziivYdUJLPHckn+V0QRkAge6OjI0LwNnels9+SWP94Sym9Aoyd/jNkphRhJ2NQkyAq/EDZXSLEEIIIYSwEElEheX499A6siataTeJaM9gd3xdHdh6tOFE9FxFNb+l5LIzOZedyWdJyioGwM3RjqFRPtw9qivDon3p6uuCoihU1xj4RZ/LP9cnMa57APZ21q9+T8wsItrfFQc7W+MH5unBRgfuTSfkQgghhBBCtIUkosJyFEUrz/3tfagoBgc3a0eEjY3CmFg/NiRkUV1jQAUOpBew4/hZdp04y/6TBVQbVOztbLgywotHJ8UyPNqXK7p4YGtTv/TWztaGv07tzm0f72HZr2ksGGb9MtfEjCKGx/g2fWCeHjzDwFZ+LQghhBBCCPOSV5zCsuKuht1vQ/Jm6Dnb2tEAMCbOn6/3nmLe+7+QmFlEaWUNigK9u3hw58iuDI/2ZUC4F466JlYUzxsd68fQKB/e2HKcOQNCcHfUmfk7aFxOcQXZxRVN7w8FGd0ihBBCCCEsRhJRYVmhg8HZRyvPbSeJ6IgYX3xdHcgrrWRu/xCGRfsypKsPHs6tSyAVReGvU7sz7a0d/OenEzw+Oc7EETffkdpGRU11zFVVrVlR6FUWiEoIIYQQQlzuJBEVlmVjC7FT4OBXYGsPva+DiBHa81bi5qhjz1PjTNrl9oouHszu14WPdqRw81XhBHs6mezazaWqKp/sSsVRZ0PPYA/jB5fmQkWRrIgKIYQQQgiLsH4nFXH5GfuMloAeWQ2fzoTXr4CNT8OZBKuFZI5RKw9P7IYK/GvjUZNfuzm+3XeaLUnZPDopDg+nJlZ36zrmWn9PqxBCCCGE6PwkERWW5xYAM9+BR47BNUshqA/88i78Zyj8ZxjsfAOKMqwdZZuFeDlz27AIVu4/TUJGoUXvnVVYzt9XJ3BlhBe3DY1o+oS6GaKyIiqEEEIIIcxPElFhPTonuGIO3LgCHj4KU/8Fdo6w6RlY3AM+mQEHlmkddjuoe0dH4+mk46W1SaiqapF7qqrKk/EHqaox8Oo1fbBpoLtvPXl6UGy0rrlCCCGEEEKYmSSion1w8YVBd8KdW2DRPhj1GBSkwXd/gldj4JuFcGwj1FRZO9IW8XDSsWhsDDuSz7LtWI5F7vnN3lNsPZrDY5PiiPB1ad5JeXptfqidg3mDE0IIIYQQAklERXvkEwVj/goPHICFm6DvjXBiCyy7Fl6Lg3WPw+m9WqfXDuCmq8IJ93HmpbVJ1BjMG3NWYTnP/ZDIoAhvFjSnJLdWforsDxVCCCGEEBYjiahovxQFQgfBtMXw8DGYtwwihsHvS+GDsfD2ldp+UoPB2pEaZW9nw2OT4jh6pphv9qab7T6qqvJE/EGqa1ReuaZ380pya+XpZX+oEEIIIYSwGElERcdgZw9xV8N1n2pNjqa/qc0j3fQMHF1r7eiaNLVXIP3CPHlt4zFKK6vNco+v957ip6M5PD45tvkluQBlBdr4FklEhRBCCCGEhUgiKjoeJ08YcCss+AEcPSHpB2tH1CRFUXhqaneyiyv48OcUk18/s7CM51cnMjjSm1uGRLTs5PzajrlSmiuEEEIIISxDElHRcdnqoNtkOLquQzQxGhjhzaSeAby37QQ5xRUmu66qqjz+7SGqDWrzu+ReSEa3CCGEEEIIC5NEVHRs3adBeQGk7bR2JM3y+OQ4KqoN/HvzMZNd86vf09l+LIcnpsQR5uPc8gvk6bVHrwiTxSSEEEIIIYQxkoiKji1qHNg5wZH2X54L0NXPlRsHh7FiTzrJ2efafL2MgjJe+OEIV3X15uarwlt3kbwUcA0E+xbsKxVCCCGEEKINJBEVHZu9M0SNhaQ17b57bq0Hx8XgpLPl5XVJbbqO1iX3EDWqyitzW1GSWys/RcpyhRBCCCGERUkiKjq+7tOgOAMy9ls7kmbxcXXgT6Oj2HzkDL/qc1t9nS/3aCW5T7a2JLeWjG4RQgghhBAWJomo6Pi6TQbFtkN0z611+7BIAt0d+cfaIxgMaovPP11QxgtrjjCkqw/zB7eyJBegsgSKM6VjrhBCCCGEsChJREXH5+wNEcM6VCLqZG/LI5Ni+eNUIT8cymzRuaqq8sS3BzGoKq9c07v1JbkA+anaoySiQgghhBDCgiQRFZ1D3HQ4ewxyTNeN1txm9+tC9yB3XlmfREV1TbPPW7EnnZ+Pn+XJqd0J9W5DSS7I6BYhhBBCCGEVkoiKziHuau0xabV142gBWxuFv06N41R+GZ/tTmvWOafyS3lxzRGGRvkwf1BY24OoG90iK6JCCCGEEMJyJBEVnYNHFwju32HGuNQaEePHyG5+vPVjMoWlVUaP1UpyD6GqKv+c28aS3Fp5enDyBifPtl9LCCGEEEKIZpJEVHQe3adBxj4oPG3tSFrkySlxFJVX8fbW40aPW/bbSXYkm6gkt5aMbhFCCCGEEFYgiajoPOKmaY9Ja6wbRwt1D3Lnmv4hfLIrjfS80gaPSc8r5R9rjjA82pf5g01QkltLRrcIIYQQQggrkERUdB5+seAT06G659Z6eGIsNjbw6oaj9b6mqipPxB9EURRentsLRTFBSS5AdQUUnpJEVAghhBBCWJwkoqJz6T4NUndAaZ61I2mRQA9H7hjele//yODgqYKLvvbFryfZmZzLX6d2J8TLRCW5AAUnQTXI6BYhhBBCCGFxrUpEFUV5SlGUAxd8lCuK0lVRlL2KoiQrivKUqQMVolnipoNaA8c2WDuSFrt7VFd8XOx5cc0RVFUFtJLcl9YeYUSMLzcMCjXtDWV0ixBCCCGEsJJWJaKqqr6oqmpfVVX7AvOBTcDfgBeBWGCGoig9TBemEM0U3A/cgjtkea6bo44/j4/h15Q8Nh/JxmBQefzb2pLc3qYrya1VO7pFElEhhBBCCGFhpijNXQh8CowC1qqqWgP8cP7PQliWjY02UzR5C1Q23PinPZs3KIyuvi68vO4In+5OZdeJXJ66ujtdPJ1Mf7M8Pdi7gbOP6a8thBBCCCGEEW1KRBVF0QFXA98DLqqqlp//UjYQ2Mg5dymK8ruiKL/n5OS05fZCNKz7NKgugxNbLH/vXW9Dwsr/b+/eo+yq6gOOf/fM5EUIJIFECJKEV5igJbwk0SSoxILABEWsBBE0lUq1olUBq3RpSqXFgkBFsYJAWzVYeXRRMLwENAmIvAwPyQuG8AquBCYvQxKTzO4f5yKTYZKZuffcc+6c+/38c++cu/c+v73u2mvmN3ufvcuu3q+xga8e18yzK9cz69anmXrA7sx4V8pLct/Q1po8H5r2TKskSZLUjUpnRKcD98YYNwGx02dd/nUbY7wqxnhEjPGIESNGVHh7qQtjJsPAobAw4+W5KxbCXefDzZ+BV54ou5ljDnobR+4znCEDmqqzJPcNniEqSZKknFSaiP418F+l9+tCCANL70eSzIpK2WvsB+M+CEvugK2bs7vvvEuh32AYNAxuOhM2byirmRAC133qXdz5paOqsyQXYOsWWPW8iagkSZJyUXYiGkLYE9gnxvhg6dLdwAkhhEaSmdJfphCfVJ7xLbBxNTx/fzb3a2uFp26EI2bCh38Ary6Gu79RdnODBzQxqlpJKMDal6B9s0e3SJIkKReVzIieAczu8PMs4FxgKfB/McZFFbQtVWa/adA0KLvlufMvh4Z+8J6zYf9pMOlz8NBVsOSubO7fWx7dIkmSpByVnYjGGL8dY7yww88rY4yTYoz7xhi/lU54Upn675QkhIt+Ae3t1b3X2uWwYDYc+gkYUtqja9o3YeQ74JbPwR9rcFMuj26RJElSjtI4vkWqTc0tsG45LP9dde/zwBUQ22HyF9+81m8gnPwj2LgWbvk7iJ338spZWys0DYSdu9zcWpIkSaoqE1EV17hjITTColurd4/1r8Ij18HBH4NhY7b97G0HwV9eAEvvhEeuqV4M5Wh7Dobtk5y7KkmSJGXMv0JVXDsNh7FTqvuc6INXwpaNMOXLXX8+8azkedU7z4eVi6sXR295dIskSZJyZCKqYmtugdeWwsol6be9YTU8dDUcdCKMGNd1mRDgw1dC/8Fw06dhy6b04+it9vZkRtQdcyVJkpQTE1EVW/MJyWs1luc+fDVsWgtTv7LjckP2gBO/B394Eu6tgX28/vgH2LLBRFSSJEm5MRFVse26F4w6LP3luX9aD7+5Eg44Bvac0H355uPh8JnJxkatv043lt7y6BZJkiTlzERUxTe+BZY/BmteTq/NR/8TNrTB1HN6XufYC2G3/eB//xZeb0svlt7y6BZJkiTlzERUxdc8PXld9It02tuyKZnZHDsVRk/seb3+g5MjXdavgNv+Pr8jXdpaoaEJdnl7PveXJElS3TMRVfGNGAe7j0vvOdEFs2HdK90/G9qVUYfC+8+Hp29J2slDWysMHQONTfncX5IkSXXPRFT1obkFlt1f+ZLYrVvg/suT5073fV95bUz+IoyZAref9+Yy2Sx5dIskSZJyZiKq+tDcAnErLLmzsnaeuglWLYOjzkmOZilHQyOc9B8QGuHmzyTJbVZiLB3dYiIqSZKk/JiIqj6MOhSGjIJFFeye294O8y+FkQfBuOMqi2fo3jD9MnjpYZh7cWVt9cbrryVHznh0iyRJknJkIqr60NCQnCn6zD3wp9fLa2PRbbByUfJsaEMKQ+edJ8PBM2Duv8ELv628vZ7w6BZJkiTVABNR1Y/xLbBlAzx7T+/rxgjzLkkSuHeclF5Mx18Mu+4NN/8NbFybXrvb49EtkiRJqgEmoqofYybDwKGwsIzluc/cA688DlO+lDzjmZaBu8BHroI1LyabF1VbWysQYOjo6t9LkiRJ2g4TUdWPxn5w4HGw5HbYurl3deddkpy7efCM9OMaPQmOOhcevz7ZDKma2lqTGdimAdW9jyRJkrQDJqKqL80tsHENLJvf8zrPPwAv/AYmfwGa+lcnrqPOg72OgNu+BGteqs49oHR0ixsVSZIkKV8moqov+x0NTYNg0S96XmfuJTB4BBx2RvXiamyCk6+G9q1w81nJazW0tfp8qCRJknJnIqr60n8n2H9akoi2t3df/uXHks2NJn0O+g2qbmzD94Xjvg3Pz4cHvpt++xvXJMe3OCMqSZKknJmIqv40t8C65bD8d92XnfcdGLgrvOvM6scFcMhpcNCH4N4LexZfb3h0iyRJkmqEiajqz7hjITTColt3XG7FwuTs0CPPSna3zUII0HJ5shT4xk/DhlXpte3RLZIkSaoRJqKqPzsNh7FTuj/GZd6l0G8wTPpsNnG9Yafh8NFrYfULcMOner/D7/a8kYgOG5tOe5IkSVKZTERVn8ZPh9eWwsrFXX/e1gpP3QhHzEwSw6yNeTdM/3do/RXc8bV02mx7DnbeA/oPTqc9SZIkqUwmoqpPzSckrwu3szx3/uXQ0A/ec3Z2MXV26GnJ/R++Gh66uvL2Vj3nslxJkiTVBBNR1addRsFeh3d9jMva5bBgNhz6CRiyR/axdfSBf4JxH4TbvwrP3ldZWx7dIkmSpBphIqr61XwCLH8M1ry87fUHroDYDpO/mE9cHTU0wsk/ghEHwg2fhFeXltfOn9bDuldg+NhUw5MkSZLKYSKq+tU8PXntOCu6/lV45Do4+GMwbEw+cXU2YAic+rNkqfDsU+D1tt63sWpZ8uqMqCRJkmqAiajq14hxsPu4bY9xefBK2LIRpnw5v7i6MmwMnPITWPNieTvpeoaoJEmSaoiJqOpbcwssuz+ZZdywOtkU6KATkyS11ryxk+5zv4bbz4MYe173z0e37FOd2CRJkqReMBFVfRvfAnErLLkj2Z1201qY+pW8o9q+Qz6ePLv6yLW920m3rRUGDYdBQ6sXmyRJktRDTXkHIOVq1GGwy17wxM/hD0/AAcfAnhPyjmrHpn0z2bTojn+A3faD/ad1X8ejWyRJklRDnBFVfQsh2T239T54/TWYek7eEXWvoRE+chWMaIYbZsLKJd3X8egWSZIk1RATUam5JXkdOxVGT8w3lp4aMAQ+/jNo6g/Xd7OT7pZNsOYlGO7zoZIkSaoNJqLSmMlw+Kfg2AvzjqR3ho6GU36aJJk/P2P7O+mufiE5F9UZUUmSJNUIE1GpsSnZjbbWnw3tyuiJcOIVsGwezDm36510PbpFkiRJNcbNiqS+bsIMWLkI5l8GI8fDxLO2/dyjWyRJklRjTESlIjj6G2/upDt8PzjgA29+1tYK/YfA4N3zi0+SJEnqwKW5UhE0NMBJP4SR74AbZ8LKxW9+tuq5ZKOiEPKLT5IkSerARFQqigE7w6nXQ9MAmN1hJ12PbpEkSVKNMRGVimTo3jBjNqx9Gf7ndNi8AVY979EtkiRJqikmolLR7H0knPg9eH5+cqxL+2ZnRCVJklRT3KxIKqIJp8Cri2Hed5KfTUQlSZJUQ5wRlYrq/f8IzS1AgN0OyDsaSZIk6c+cEZW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exmktretsmall_onlyhigh_only
ret_date
2007-05-310.0986930.0664890.067012
2007-06-30-0.074622-0.193444-0.132361
2007-07-310.1922400.2627290.238425
2007-08-310.1671930.0966610.145653
2007-09-300.0472630.0296470.069091
2007-10-31-0.010382-0.118364-0.052297
2007-11-30-0.157389-0.041802-0.093127
2007-12-310.1373660.2028110.151152
2008-01-31-0.123254-0.062278-0.105938
2008-02-290.0240100.1063500.060789
............
2021-05-310.0377990.0717720.046185
2021-06-30-0.0150340.026750-0.005860
2021-07-31-0.064776-0.001619-0.044277
2021-08-310.0141990.0570720.068252
2021-09-300.002272-0.0246470.013658
2021-10-310.001777-0.025143-0.041059
2021-11-30-0.0060470.1303580.043855
2021-12-310.0181850.0376710.050223
2022-01-31-0.085436-0.086267-0.045570
2022-02-280.0106330.0495480.034011
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178 rows × 3 columns

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