{ "cells": [ { "cell_type": "code", "execution_count": 2, "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": 3, "metadata": { "editable": true }, "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": 4, "metadata": { "editable": true }, "outputs": [], "source": [ "START = '2007-01-01'\n", "END = '2023-03-31'" ] }, { "cell_type": "code", "execution_count": 5, "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'].unique()" ] }, { "cell_type": "code", "execution_count": 6, "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.XSHE000004ST国华STGHXSHEEL1991-01-14CNYCNE0000000Y25.0NaN
4000005.XSHE000005ST星源STXYXSHEEL1990-12-10CNYCNE0000001L76.0NaN
5000006.XSHE000006深振业ASZYAXSHEEL1992-04-27CNYCNE0000001647.0NaN
6000007.XSHE000007全新好QXHXSHEEL1992-04-13CNYCNE0000000P08.0NaN
7000008.XSHE000008神州高铁SZGTXSHEEL1992-05-07CNYCNE0000001C69.0NaN
.......................................
25333900950.XSHG900950新城B股XCBGXSHGEDE1997-10-16USDCNE000000TH11429.02015-11-23
25334900951.XSHG900951退市大化TSDHXSHGEDE1997-10-21USDCNE000000TJ71430.02020-08-27
25335900952.XSHG900952锦港B股JGBGXSHGEL1998-05-19USDCNE000000W88763.0NaN
25336900953.XSHG900953凯马BKMBXSHGEL1998-06-24USDCNE000000WP81431.0NaN
25337900955.XSHG900955退市海BTSHBXSHGEDE1999-01-18USDCNE000000YC21063.02022-07-13
25338900956.XSHG900956东贝B股DBBGXSHGEDE1999-07-15USDCNE000000ZS51432.02020-11-23
25339900957.XSHG900957凌云B股LYBGXSHGEL2000-07-28USDCNE0000013W91433.0NaN
29888DY600018.XSHGDY600018上港集箱SGJXXSHGEDE2000-07-19CNYNaN618.02006-10-20
\n", "

5239 rows × 12 columns

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" ], "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 ST国华 STGH 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 全新好 QXH XSHE E \n", "7 000008.XSHE 000008 神州高铁 SZGT XSHE E \n", "... ... ... ... ... ... ... \n", "25333 900950.XSHG 900950 新城B股 XCBG XSHG E \n", "25334 900951.XSHG 900951 退市大化 TSDH XSHG E \n", "25335 900952.XSHG 900952 锦港B股 JGBG XSHG E \n", "25336 900953.XSHG 900953 凯马B KMB XSHG E \n", "25337 900955.XSHG 900955 退市海B TSHB XSHG E \n", "25338 900956.XSHG 900956 东贝B股 DBBG XSHG E \n", "25339 900957.XSHG 900957 凌云B股 LYBG XSHG E \n", "29888 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", "25333 DE 1997-10-16 USD CNE000000TH1 1429.0 2015-11-23 \n", "25334 DE 1997-10-21 USD CNE000000TJ7 1430.0 2020-08-27 \n", "25335 L 1998-05-19 USD CNE000000W88 763.0 NaN \n", "25336 L 1998-06-24 USD CNE000000WP8 1431.0 NaN \n", "25337 DE 1999-01-18 USD CNE000000YC2 1063.0 2022-07-13 \n", "25338 DE 1999-07-15 USD CNE000000ZS5 1432.0 2020-11-23 \n", "25339 L 2000-07-28 USD CNE0000013W9 1433.0 NaN \n", "29888 DE 2000-07-19 CNY NaN 618.0 2006-10-20 \n", "\n", "[5239 rows x 12 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_info" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5239" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(stk_id)" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "## ST" ] }, { "cell_type": "code", "execution_count": 8, "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": 9, "metadata": { "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 552823 entries, 0 to 552822\n", "Data columns (total 3 columns):\n", "secID 552823 non-null object\n", "tradeDate 552823 non-null object\n", "STflg 552823 non-null object\n", "dtypes: object(3)\n", "memory usage: 12.7+ MB\n" ] } ], "source": [ "st_df.info()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "editable": true }, "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
............
552815900955.XSHG2022-06-06*ST
552816900955.XSHG2022-06-07*ST
552817900955.XSHG2022-06-08*ST
552818900955.XSHG2022-06-09*ST
552819900955.XSHG2022-06-10*ST
552820900955.XSHG2022-06-13*ST
552821900955.XSHG2022-06-14*ST
552822900955.XSHG2022-06-15*ST
\n", "

552823 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", "552815 900955.XSHG 2022-06-06 *ST\n", "552816 900955.XSHG 2022-06-07 *ST\n", "552817 900955.XSHG 2022-06-08 *ST\n", "552818 900955.XSHG 2022-06-09 *ST\n", "552819 900955.XSHG 2022-06-10 *ST\n", "552820 900955.XSHG 2022-06-13 *ST\n", "552821 900955.XSHG 2022-06-14 *ST\n", "552822 900955.XSHG 2022-06-15 *ST\n", "\n", "[552823 rows x 3 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "st_df" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "editable": true }, "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": { "editable": true }, "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": { "editable": true }, "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": 12, "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": 13, "metadata": { "editable": true }, "outputs": [], "source": [ "fundmen_df = pd.read_pickle('./data/fundmen_df.pkl')" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDpublishDateendDateendDateRepactPubtimefiscalPeriodTShEquityTEquityAttrPminorityInt
0000001.XSHE2022-10-252021-12-312022-09-302022-10-24 20:52:23123.954480e+113.954480e+11NaN
1000001.XSHE2022-08-182021-12-312022-06-302022-08-17 18:30:42123.954480e+113.954480e+11NaN
2000001.XSHE2022-04-272021-12-312022-03-312022-04-26 17:45:15123.954480e+113.954480e+11NaN
3000001.XSHE2022-03-102021-12-312021-12-312022-03-09 17:58:07123.954480e+113.954480e+11NaN
4000001.XSHE2022-03-102020-12-312021-12-312022-03-09 17:58:07123.641310e+113.641310e+11NaN
5000001.XSHE2021-10-212020-12-312021-09-302021-10-20 17:39:15123.641310e+113.641310e+11NaN
6000001.XSHE2021-08-202020-12-312021-06-302021-08-19 17:20:35123.641310e+113.641310e+11NaN
7000001.XSHE2021-04-212020-12-312021-03-312021-04-20 17:54:36123.641310e+113.641310e+11NaN
..............................
275342900957.XSHG2009-08-012008-12-312009-06-302009-07-31 18:00:00124.902596e+084.369354e+0853324231.94
275343900957.XSHG2009-04-182008-12-312009-03-312009-04-17 18:00:00124.902596e+084.369354e+0853324231.94
275344900957.XSHG2009-03-262008-12-312008-12-312009-03-25 18:00:00124.902596e+084.369354e+0853324231.94
275345900957.XSHG2009-03-262007-12-312008-12-312009-03-25 18:00:00124.363166e+083.769447e+0859371874.07
275346900957.XSHG2008-10-242007-12-312008-09-302008-10-23 18:00:00124.363166e+083.769447e+0859371874.07
275347900957.XSHG2008-08-252007-12-312008-06-302008-08-24 18:00:00124.363166e+083.769447e+0859371874.07
275348900957.XSHG2008-04-242007-12-312008-03-312008-04-23 18:00:00124.363166e+083.769447e+0859371874.07
275349900957.XSHG2008-04-082007-12-312007-12-312008-04-07 18:00:00124.363166e+083.769447e+0859371874.07
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275350 rows × 9 columns

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secIDpublishDateendDateendDateRepactPubtimefiscalPeriodTShEquityTEquityAttrPminorityInt
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" ], "text/plain": [ " secID publishDate endDate endDateRep actPubtime \\\n", "147653 300720.XSHE 2020-04-24 2018-12-31 2019-12-31 2020-04-23 21:04:35 \n", "147654 300720.XSHE 2019-10-30 2018-12-31 2019-09-30 2019-10-29 19:22:34 \n", "147655 300720.XSHE 2019-08-28 2018-12-31 2019-06-30 2019-08-27 19:42:06 \n", "147656 300720.XSHE 2019-04-26 2018-12-31 2019-03-31 2019-04-25 23:27:06 \n", "147657 300720.XSHE 2019-04-26 2018-12-31 2018-12-31 2019-04-25 23:27:06 \n", "\n", " fiscalPeriod TShEquity TEquityAttrP minorityInt \n", "147653 12 4.555515e+08 4.555515e+08 NaN \n", "147654 12 4.555515e+08 4.555515e+08 NaN \n", "147655 12 4.555515e+08 4.555515e+08 NaN \n", "147656 12 4.555515e+08 4.555515e+08 NaN \n", "147657 12 4.555515e+08 4.555515e+08 NaN " ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fundmen_df[(fundmen_df['secID'] == '300720.XSHE') & (fundmen_df['endDate']=='2018-12-31')]" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "- `publishDate`: 实际公告日期\n", "- `endDate`：数值所在日期\n", "- `endDateRep`：数值所在报表日期。03-31是一季报，06-30是半年报，09-30是三季报，12-31是年报。后面的报表可能会对初始值做修改。\n", "\n", "比如，300720.XSHE在2020-04-24公布了数据截止至2019-12-31的报告，里面包含了数据截止至2018-12-31的报表数据。\n", "\n", "300720.XSHE在2019-08-28公布了数据截止至2019-06-30的报告，里面包含了数据截止至2018-12-31的报表数据。\n", "\n", "在t年6月分组时，应当取最新更新过的t-1年12月31日的Book数值。" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "editable": true }, "outputs": [], "source": [ "fundmen_df.drop(['actPubtime','fiscalPeriod'],axis=1, inplace=True)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "editable": true }, "outputs": [], "source": [ "fundmen_df[['publishDate','endDate']] = fundmen_df[['publishDate','endDate']].apply(pd.to_datetime)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "editable": true }, "outputs": [], "source": [ "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" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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1000001.XSHE2022-08-182021-12-312022-06-303.954480e+113.954480e+11NaN820222021
2000001.XSHE2022-04-272021-12-312022-03-313.954480e+113.954480e+11NaN420222021
3000001.XSHE2022-03-102021-12-312021-12-313.954480e+113.954480e+11NaN320222021
4000001.XSHE2022-03-102020-12-312021-12-313.641310e+113.641310e+11NaN320222020
5000001.XSHE2021-10-212020-12-312021-09-303.641310e+113.641310e+11NaN1020212020
6000001.XSHE2021-08-202020-12-312021-06-303.641310e+113.641310e+11NaN820212020
7000001.XSHE2021-04-212020-12-312021-03-313.641310e+113.641310e+11NaN420212020
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secIDpublishDateendDateendDateRepTShEquityTEquityAttrPminorityIntpub_monthpub_yeardata_year
0000001.XSHE2022-10-252021-12-312022-09-303.954480e+113.954480e+11NaN1020222021
1000001.XSHE2022-08-182021-12-312022-06-303.954480e+113.954480e+11NaN820222021
2000001.XSHE2022-04-272021-12-312022-03-313.954480e+113.954480e+11NaN420222021
3000001.XSHE2022-03-102021-12-312021-12-313.954480e+113.954480e+11NaN320222021
4000001.XSHE2022-03-102020-12-312021-12-313.641310e+113.641310e+11NaN320222020
5000001.XSHE2021-10-212020-12-312021-09-303.641310e+113.641310e+11NaN1020212020
6000001.XSHE2021-08-202020-12-312021-06-303.641310e+113.641310e+11NaN820212020
7000001.XSHE2021-04-212020-12-312021-03-313.641310e+113.641310e+11NaN420212020
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275350 rows × 10 columns

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" ], "text/plain": [ " secID publishDate endDate endDateRep TShEquity \\\n", "0 000001.XSHE 2022-10-25 2021-12-31 2022-09-30 3.954480e+11 \n", "1 000001.XSHE 2022-08-18 2021-12-31 2022-06-30 3.954480e+11 \n", "2 000001.XSHE 2022-04-27 2021-12-31 2022-03-31 3.954480e+11 \n", "3 000001.XSHE 2022-03-10 2021-12-31 2021-12-31 3.954480e+11 \n", "4 000001.XSHE 2022-03-10 2020-12-31 2021-12-31 3.641310e+11 \n", "5 000001.XSHE 2021-10-21 2020-12-31 2021-09-30 3.641310e+11 \n", "6 000001.XSHE 2021-08-20 2020-12-31 2021-06-30 3.641310e+11 \n", "7 000001.XSHE 2021-04-21 2020-12-31 2021-03-31 3.641310e+11 \n", "... ... ... ... ... ... \n", "275342 900957.XSHG 2009-08-01 2008-12-31 2009-06-30 4.902596e+08 \n", "275343 900957.XSHG 2009-04-18 2008-12-31 2009-03-31 4.902596e+08 \n", "275344 900957.XSHG 2009-03-26 2008-12-31 2008-12-31 4.902596e+08 \n", "275345 900957.XSHG 2009-03-26 2007-12-31 2008-12-31 4.363166e+08 \n", "275346 900957.XSHG 2008-10-24 2007-12-31 2008-09-30 4.363166e+08 \n", "275347 900957.XSHG 2008-08-25 2007-12-31 2008-06-30 4.363166e+08 \n", "275348 900957.XSHG 2008-04-24 2007-12-31 2008-03-31 4.363166e+08 \n", "275349 900957.XSHG 2008-04-08 2007-12-31 2007-12-31 4.363166e+08 \n", "\n", " TEquityAttrP minorityInt pub_month pub_year data_year \n", "0 3.954480e+11 NaN 10 2022 2021 \n", "1 3.954480e+11 NaN 8 2022 2021 \n", "2 3.954480e+11 NaN 4 2022 2021 \n", "3 3.954480e+11 NaN 3 2022 2021 \n", "4 3.641310e+11 NaN 3 2022 2020 \n", "5 3.641310e+11 NaN 10 2021 2020 \n", "6 3.641310e+11 NaN 8 2021 2020 \n", "7 3.641310e+11 NaN 4 2021 2020 \n", "... ... ... ... ... ... \n", "275342 4.369354e+08 53324231.94 8 2009 2008 \n", "275343 4.369354e+08 53324231.94 4 2009 2008 \n", "275344 4.369354e+08 53324231.94 3 2009 2008 \n", "275345 3.769447e+08 59371874.07 3 2009 2007 \n", "275346 3.769447e+08 59371874.07 10 2008 2007 \n", "275347 3.769447e+08 59371874.07 8 2008 2007 \n", "275348 3.769447e+08 59371874.07 4 2008 2007 \n", "275349 3.769447e+08 59371874.07 4 2008 2007 \n", "\n", "[275350 rows x 10 columns]" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fundmen_df" ] }, { "cell_type": "code", "execution_count": 27, "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": 28, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDpublishDateendDateendDateRepTShEquityTEquityAttrPminorityIntpub_monthpub_yeardata_year
147651300720.XSHE2020-04-242019-12-312019-12-314.783596e+084.783596e+08NaN420202019
147652300720.XSHE2020-04-242019-12-312020-03-314.783596e+084.783596e+08NaN420202019
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" ], "text/plain": [ " secID publishDate endDate endDateRep TShEquity \\\n", "147651 300720.XSHE 2020-04-24 2019-12-31 2019-12-31 4.783596e+08 \n", "147652 300720.XSHE 2020-04-24 2019-12-31 2020-03-31 4.783596e+08 \n", "\n", " TEquityAttrP minorityInt pub_month pub_year data_year \n", "147651 4.783596e+08 NaN 4 2020 2019 \n", "147652 4.783596e+08 NaN 4 2020 2019 " ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fundmen_df[(fundmen_df['secID']=='300720.XSHE') & (fundmen_df['endDate']=='2019-12-31')]" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "editable": true }, "outputs": [], "source": [ "fundmen_df = fundmen_df.groupby(['secID','endDate'],as_index=False).first()" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "editable": true }, "outputs": [], "source": [ "fundmen_df['bm_date'] = fundmen_df['endDate'].dt.to_period('M')" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDendDatepublishDateendDateRepTShEquityTEquityAttrPminorityIntpub_monthpub_yeardata_yearbm_date
0000001.XSHE2007-12-312008-03-202007-12-311.300606e+101.300606e+10NaN3200820072007-12
1000001.XSHE2008-12-312009-03-202008-12-311.640079e+101.640079e+10NaN3200920082008-12
2000001.XSHE2009-12-312010-03-122009-12-312.046961e+102.046961e+10NaN3201020092009-12
3000001.XSHE2010-12-312011-02-252010-12-313.351288e+103.351288e+10NaN2201120102010-12
4000001.XSHE2011-12-312012-03-092011-12-317.538058e+107.331084e+102.069747e+093201220112011-12
5000001.XSHE2012-12-312013-03-082012-12-318.479878e+108.479878e+10NaN3201320122012-12
6000001.XSHE2013-12-312014-03-072013-12-311.120810e+111.120810e+11NaN3201420132013-12
7000001.XSHE2014-12-312015-03-132014-12-311.309490e+111.309490e+11NaN3201520142014-12
....................................
47857900957.XSHG2014-12-312015-04-102014-12-314.072107e+083.940309e+081.317981e+074201520142014-12
47858900957.XSHG2015-12-312016-03-302015-12-314.106786e+083.973929e+081.328570e+073201620152015-12
47859900957.XSHG2016-12-312017-03-252016-12-313.938268e+083.930721e+087.546643e+053201720162016-12
47860900957.XSHG2017-12-312018-04-102017-12-314.238426e+084.231040e+087.386715e+054201820172017-12
47861900957.XSHG2018-12-312019-03-302018-12-314.515278e+084.508051e+087.226781e+053201920182018-12
47862900957.XSHG2019-12-312020-04-252019-12-314.768689e+084.761021e+087.667705e+054202020192019-12
47863900957.XSHG2020-12-312021-04-092020-12-314.987276e+084.979110e+088.165551e+054202120202020-12
47864900957.XSHG2021-12-312022-04-202021-12-315.263733e+085.255741e+087.991940e+054202220212021-12
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47865 rows × 11 columns

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" ], "text/plain": [ " secID endDate publishDate endDateRep TShEquity \\\n", "0 000001.XSHE 2007-12-31 2008-03-20 2007-12-31 1.300606e+10 \n", "1 000001.XSHE 2008-12-31 2009-03-20 2008-12-31 1.640079e+10 \n", "2 000001.XSHE 2009-12-31 2010-03-12 2009-12-31 2.046961e+10 \n", "3 000001.XSHE 2010-12-31 2011-02-25 2010-12-31 3.351288e+10 \n", "4 000001.XSHE 2011-12-31 2012-03-09 2011-12-31 7.538058e+10 \n", "5 000001.XSHE 2012-12-31 2013-03-08 2012-12-31 8.479878e+10 \n", "6 000001.XSHE 2013-12-31 2014-03-07 2013-12-31 1.120810e+11 \n", "7 000001.XSHE 2014-12-31 2015-03-13 2014-12-31 1.309490e+11 \n", "... ... ... ... ... ... \n", "47857 900957.XSHG 2014-12-31 2015-04-10 2014-12-31 4.072107e+08 \n", "47858 900957.XSHG 2015-12-31 2016-03-30 2015-12-31 4.106786e+08 \n", "47859 900957.XSHG 2016-12-31 2017-03-25 2016-12-31 3.938268e+08 \n", "47860 900957.XSHG 2017-12-31 2018-04-10 2017-12-31 4.238426e+08 \n", "47861 900957.XSHG 2018-12-31 2019-03-30 2018-12-31 4.515278e+08 \n", "47862 900957.XSHG 2019-12-31 2020-04-25 2019-12-31 4.768689e+08 \n", "47863 900957.XSHG 2020-12-31 2021-04-09 2020-12-31 4.987276e+08 \n", "47864 900957.XSHG 2021-12-31 2022-04-20 2021-12-31 5.263733e+08 \n", "\n", " TEquityAttrP minorityInt pub_month pub_year data_year bm_date \n", "0 1.300606e+10 NaN 3 2008 2007 2007-12 \n", "1 1.640079e+10 NaN 3 2009 2008 2008-12 \n", "2 2.046961e+10 NaN 3 2010 2009 2009-12 \n", "3 3.351288e+10 NaN 2 2011 2010 2010-12 \n", "4 7.331084e+10 2.069747e+09 3 2012 2011 2011-12 \n", "5 8.479878e+10 NaN 3 2013 2012 2012-12 \n", "6 1.120810e+11 NaN 3 2014 2013 2013-12 \n", "7 1.309490e+11 NaN 3 2015 2014 2014-12 \n", "... ... ... ... ... ... ... \n", "47857 3.940309e+08 1.317981e+07 4 2015 2014 2014-12 \n", "47858 3.973929e+08 1.328570e+07 3 2016 2015 2015-12 \n", "47859 3.930721e+08 7.546643e+05 3 2017 2016 2016-12 \n", "47860 4.231040e+08 7.386715e+05 4 2018 2017 2017-12 \n", "47861 4.508051e+08 7.226781e+05 3 2019 2018 2018-12 \n", "47862 4.761021e+08 7.667705e+05 4 2020 2019 2019-12 \n", "47863 4.979110e+08 8.165551e+05 4 2021 2020 2020-12 \n", "47864 5.255741e+08 7.991940e+05 4 2022 2021 2021-12 \n", "\n", "[47865 rows x 11 columns]" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fundmen_df" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "editable": true }, "outputs": [], "source": [ "fundmen_df.fillna(0,inplace=True)" ] }, { "cell_type": "code", "execution_count": 33, "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": 34, "metadata": { "editable": true }, "outputs": [], "source": [ "fundmen_df = fundmen_df[fundmen_df['book'] > 0]" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDendDatepublishDateendDateRepTShEquityTEquityAttrPminorityIntpub_monthpub_yeardata_yearbm_datebook
0000001.XSHE2007-12-312008-03-202007-12-311.300606e+101.300606e+100.000000e+003200820072007-121.300606e+10
1000001.XSHE2008-12-312009-03-202008-12-311.640079e+101.640079e+100.000000e+003200920082008-121.640079e+10
2000001.XSHE2009-12-312010-03-122009-12-312.046961e+102.046961e+100.000000e+003201020092009-122.046961e+10
3000001.XSHE2010-12-312011-02-252010-12-313.351288e+103.351288e+100.000000e+002201120102010-123.351288e+10
4000001.XSHE2011-12-312012-03-092011-12-317.538058e+107.331084e+102.069747e+093201220112011-127.331084e+10
5000001.XSHE2012-12-312013-03-082012-12-318.479878e+108.479878e+100.000000e+003201320122012-128.479878e+10
6000001.XSHE2013-12-312014-03-072013-12-311.120810e+111.120810e+110.000000e+003201420132013-121.120810e+11
7000001.XSHE2014-12-312015-03-132014-12-311.309490e+111.309490e+110.000000e+003201520142014-121.309490e+11
.......................................
47857900957.XSHG2014-12-312015-04-102014-12-314.072107e+083.940309e+081.317981e+074201520142014-123.940309e+08
47858900957.XSHG2015-12-312016-03-302015-12-314.106786e+083.973929e+081.328570e+073201620152015-123.973929e+08
47859900957.XSHG2016-12-312017-03-252016-12-313.938268e+083.930721e+087.546643e+053201720162016-123.930721e+08
47860900957.XSHG2017-12-312018-04-102017-12-314.238426e+084.231040e+087.386715e+054201820172017-124.231040e+08
47861900957.XSHG2018-12-312019-03-302018-12-314.515278e+084.508051e+087.226781e+053201920182018-124.508051e+08
47862900957.XSHG2019-12-312020-04-252019-12-314.768689e+084.761021e+087.667705e+054202020192019-124.761021e+08
47863900957.XSHG2020-12-312021-04-092020-12-314.987276e+084.979110e+088.165551e+054202120202020-124.979110e+08
47864900957.XSHG2021-12-312022-04-202021-12-315.263733e+085.255741e+087.991940e+054202220212021-125.255741e+08
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46883 rows × 12 columns

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" ], "text/plain": [ " secID endDate publishDate endDateRep TShEquity \\\n", "0 000001.XSHE 2007-12-31 2008-03-20 2007-12-31 1.300606e+10 \n", "1 000001.XSHE 2008-12-31 2009-03-20 2008-12-31 1.640079e+10 \n", "2 000001.XSHE 2009-12-31 2010-03-12 2009-12-31 2.046961e+10 \n", "3 000001.XSHE 2010-12-31 2011-02-25 2010-12-31 3.351288e+10 \n", "4 000001.XSHE 2011-12-31 2012-03-09 2011-12-31 7.538058e+10 \n", "5 000001.XSHE 2012-12-31 2013-03-08 2012-12-31 8.479878e+10 \n", "6 000001.XSHE 2013-12-31 2014-03-07 2013-12-31 1.120810e+11 \n", "7 000001.XSHE 2014-12-31 2015-03-13 2014-12-31 1.309490e+11 \n", "... ... ... ... ... ... \n", "47857 900957.XSHG 2014-12-31 2015-04-10 2014-12-31 4.072107e+08 \n", "47858 900957.XSHG 2015-12-31 2016-03-30 2015-12-31 4.106786e+08 \n", "47859 900957.XSHG 2016-12-31 2017-03-25 2016-12-31 3.938268e+08 \n", "47860 900957.XSHG 2017-12-31 2018-04-10 2017-12-31 4.238426e+08 \n", "47861 900957.XSHG 2018-12-31 2019-03-30 2018-12-31 4.515278e+08 \n", "47862 900957.XSHG 2019-12-31 2020-04-25 2019-12-31 4.768689e+08 \n", "47863 900957.XSHG 2020-12-31 2021-04-09 2020-12-31 4.987276e+08 \n", "47864 900957.XSHG 2021-12-31 2022-04-20 2021-12-31 5.263733e+08 \n", "\n", " TEquityAttrP minorityInt pub_month pub_year data_year bm_date \\\n", "0 1.300606e+10 0.000000e+00 3 2008 2007 2007-12 \n", "1 1.640079e+10 0.000000e+00 3 2009 2008 2008-12 \n", "2 2.046961e+10 0.000000e+00 3 2010 2009 2009-12 \n", "3 3.351288e+10 0.000000e+00 2 2011 2010 2010-12 \n", "4 7.331084e+10 2.069747e+09 3 2012 2011 2011-12 \n", "5 8.479878e+10 0.000000e+00 3 2013 2012 2012-12 \n", "6 1.120810e+11 0.000000e+00 3 2014 2013 2013-12 \n", "7 1.309490e+11 0.000000e+00 3 2015 2014 2014-12 \n", "... ... ... ... ... ... ... \n", "47857 3.940309e+08 1.317981e+07 4 2015 2014 2014-12 \n", "47858 3.973929e+08 1.328570e+07 3 2016 2015 2015-12 \n", "47859 3.930721e+08 7.546643e+05 3 2017 2016 2016-12 \n", "47860 4.231040e+08 7.386715e+05 4 2018 2017 2017-12 \n", "47861 4.508051e+08 7.226781e+05 3 2019 2018 2018-12 \n", "47862 4.761021e+08 7.667705e+05 4 2020 2019 2019-12 \n", "47863 4.979110e+08 8.165551e+05 4 2021 2020 2020-12 \n", "47864 5.255741e+08 7.991940e+05 4 2022 2021 2021-12 \n", "\n", " book \n", "0 1.300606e+10 \n", "1 1.640079e+10 \n", "2 2.046961e+10 \n", "3 3.351288e+10 \n", "4 7.331084e+10 \n", "5 8.479878e+10 \n", "6 1.120810e+11 \n", "7 1.309490e+11 \n", "... ... \n", "47857 3.940309e+08 \n", "47858 3.973929e+08 \n", "47859 3.930721e+08 \n", "47860 4.231040e+08 \n", "47861 4.508051e+08 \n", "47862 4.761021e+08 \n", "47863 4.979110e+08 \n", "47864 5.255741e+08 \n", "\n", "[46883 rows x 12 columns]" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fundmen_df" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "editable": true }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.allclose(fundmen_df['book'],fundmen_df['TEquityAttrP'])" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDendDatepublishDateendDateRepTShEquityTEquityAttrPminorityIntpub_monthpub_yeardata_yearbm_datebook
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" ], "text/plain": [ "Empty DataFrame\n", "Columns: [secID, endDate, publishDate, endDateRep, TShEquity, TEquityAttrP, minorityInt, pub_month, pub_year, data_year, bm_date, book]\n", "Index: []" ] }, "execution_count": 37, "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": 38, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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日期_Date年份()_Year月份_Month月无风险收益率_MonRFRetUnnamed: 4
01989-02-01198920.006300NaN
11989-03-01198930.006300NaN
21989-04-01198940.006300NaN
31989-05-01198950.006300NaN
41989-06-01198960.006300NaN
51989-07-01198970.006300NaN
61989-08-01198980.006300NaN
71989-09-01198990.006300NaN
..................
4012022-07-01202270.001620NaN
4022022-08-01202280.001366NaN
4032022-09-01202290.001342NaN
4042022-10-012022100.001413NaN
4052022-11-012022110.001676NaN
4062022-12-012022120.001931NaN
4072023-01-01202310.002013NaN
4082023-02-01202320.002013NaN
\n", "

409 rows × 5 columns

\n", "
" ], "text/plain": [ " 日期_Date 年份()_Year 月份_Month 月无风险收益率_MonRFRet Unnamed: 4\n", "0 1989-02-01 1989 2 0.006300 NaN\n", "1 1989-03-01 1989 3 0.006300 NaN\n", "2 1989-04-01 1989 4 0.006300 NaN\n", "3 1989-05-01 1989 5 0.006300 NaN\n", "4 1989-06-01 1989 6 0.006300 NaN\n", "5 1989-07-01 1989 7 0.006300 NaN\n", "6 1989-08-01 1989 8 0.006300 NaN\n", "7 1989-09-01 1989 9 0.006300 NaN\n", ".. ... ... ... ... ...\n", "401 2022-07-01 2022 7 0.001620 NaN\n", "402 2022-08-01 2022 8 0.001366 NaN\n", "403 2022-09-01 2022 9 0.001342 NaN\n", "404 2022-10-01 2022 10 0.001413 NaN\n", "405 2022-11-01 2022 11 0.001676 NaN\n", "406 2022-12-01 2022 12 0.001931 NaN\n", "407 2023-01-01 2023 1 0.002013 NaN\n", "408 2023-02-01 2023 2 0.002013 NaN\n", "\n", "[409 rows x 5 columns]" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.read_csv(\"./data/rf-monthly.csv\")" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "editable": true }, "outputs": [], "source": [ "rf = pd.read_csv(\"./data/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": 40, "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
.........
4012022-070.001620
4022022-080.001366
4032022-090.001342
4042022-100.001413
4052022-110.001676
4062022-120.001931
4072023-010.002013
4082023-020.002013
\n", "

409 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", "401 2022-07 0.001620\n", "402 2022-08 0.001366\n", "403 2022-09 0.001342\n", "404 2022-10 0.001413\n", "405 2022-11 0.001676\n", "406 2022-12 0.001931\n", "407 2023-01 0.002013\n", "408 2023-02 0.002013\n", "\n", "[409 rows x 2 columns]" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rf" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "## Beta" ] }, { "cell_type": "code", "execution_count": 41, "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": 42, "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": 43, "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
............
554352689009.XSHG2022-070.7987
554353689009.XSHG2022-080.8589
554354689009.XSHG2022-090.9106
554355689009.XSHG2022-100.7083
554356689009.XSHG2022-110.7363
554357689009.XSHG2022-120.6919
554358689009.XSHG2023-010.7379
554359689009.XSHG2023-020.7453
\n", "

554360 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", "554352 689009.XSHG 2022-07 0.7987\n", "554353 689009.XSHG 2022-08 0.8589\n", "554354 689009.XSHG 2022-09 0.9106\n", "554355 689009.XSHG 2022-10 0.7083\n", "554356 689009.XSHG 2022-11 0.7363\n", "554357 689009.XSHG 2022-12 0.6919\n", "554358 689009.XSHG 2023-01 0.7379\n", "554359 689009.XSHG 2023-02 0.7453\n", "\n", "[554360 rows x 3 columns]" ] }, "execution_count": 43, "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": 44, "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": 45, "metadata": { "editable": true }, "outputs": [], "source": [ "stk_df = pd.read_pickle('./data/stk_df.pkl')\n", "stk_df['closePrice'] = pd.to_numeric(stk_df['closePrice'])\n", "stk_df['negMarketValue'] = pd.to_numeric(stk_df['negMarketValue'])\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": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Int64Index: 11600428 entries, 0 to 11600427\n", "Data columns (total 5 columns):\n", "secID object\n", "tradeDate datetime64[ns]\n", "closePrice float64\n", "negMarketValue float64\n", "ym period[M]\n", "dtypes: datetime64[ns](1), float64(2), object(1), period[M](1)\n", "memory usage: 531.0+ MB\n" ] } ], "source": [ "stk_df.info()" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "### Exclude ST" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "editable": true }, "outputs": [ { "data": { "text/plain": [ "(11600428, 5)" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_df.dropna().shape" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "editable": true }, "outputs": [ { "data": { "text/plain": [ "(11600428, 5)" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_df.shape" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "editable": true }, "outputs": [], "source": [ "stk_df = pd.merge(stk_df, st_df, on=['secID','tradeDate'],how='left')" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "editable": true }, "outputs": [], "source": [ "stk_df = stk_df[stk_df['STflg'].isna()].copy()" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "editable": true }, "outputs": [], "source": [ "stk_df.drop('STflg',axis=1,inplace=True)" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "editable": true }, "outputs": [ { "data": { "text/plain": [ "(11048078, 5)" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_df.shape" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "### Monthly trading df" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "editable": true }, "outputs": [], "source": [ "stk_df_m = stk_df.groupby(['secID','ym'],as_index=False).tail(1)" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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secIDtradeDateclosePricenegMarketValueym
116000001.XSHE2007-06-29870.8704.266117e+102007-06
138000001.XSHE2007-07-311146.4985.616330e+102007-07
161000001.XSHE2007-08-311202.5105.890714e+102007-08
181000001.XSHE2007-09-281265.1676.197651e+102007-09
199000001.XSHE2007-10-311520.5427.448652e+102007-10
221000001.XSHE2007-11-301141.7515.593078e+102007-11
241000001.XSHE2007-12-281221.4976.574629e+102007-12
263000001.XSHE2008-01-311053.7785.850212e+102008-01
..................
11600307900957.XSHG2022-08-310.6441.175760e+082022-08
11600328900957.XSHG2022-09-300.5921.080080e+082022-09
11600344900957.XSHG2022-10-310.6031.100320e+082022-10
11600366900957.XSHG2022-11-300.6171.126080e+082022-11
11600388900957.XSHG2022-12-300.5681.037760e+082022-12
11600404900957.XSHG2023-01-310.5901.076400e+082023-01
11600424900957.XSHG2023-02-280.5781.054320e+082023-02
11600427900957.XSHG2023-03-030.5681.037760e+082023-03
\n", "

552758 rows × 5 columns

\n", "
" ], "text/plain": [ " secID tradeDate closePrice negMarketValue ym\n", "116 000001.XSHE 2007-06-29 870.870 4.266117e+10 2007-06\n", "138 000001.XSHE 2007-07-31 1146.498 5.616330e+10 2007-07\n", "161 000001.XSHE 2007-08-31 1202.510 5.890714e+10 2007-08\n", "181 000001.XSHE 2007-09-28 1265.167 6.197651e+10 2007-09\n", "199 000001.XSHE 2007-10-31 1520.542 7.448652e+10 2007-10\n", "221 000001.XSHE 2007-11-30 1141.751 5.593078e+10 2007-11\n", "241 000001.XSHE 2007-12-28 1221.497 6.574629e+10 2007-12\n", "263 000001.XSHE 2008-01-31 1053.778 5.850212e+10 2008-01\n", "... ... ... ... ... ...\n", "11600307 900957.XSHG 2022-08-31 0.644 1.175760e+08 2022-08\n", "11600328 900957.XSHG 2022-09-30 0.592 1.080080e+08 2022-09\n", "11600344 900957.XSHG 2022-10-31 0.603 1.100320e+08 2022-10\n", "11600366 900957.XSHG 2022-11-30 0.617 1.126080e+08 2022-11\n", "11600388 900957.XSHG 2022-12-30 0.568 1.037760e+08 2022-12\n", "11600404 900957.XSHG 2023-01-31 0.590 1.076400e+08 2023-01\n", "11600424 900957.XSHG 2023-02-28 0.578 1.054320e+08 2023-02\n", "11600427 900957.XSHG 2023-03-03 0.568 1.037760e+08 2023-03\n", "\n", "[552758 rows x 5 columns]" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_df_m" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "#### Fill na months" ] }, { "cell_type": "code", "execution_count": 55, "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": 56, "metadata": { "editable": true }, "outputs": [], "source": [ "full_dates = np.sort(stk_df['ym'].unique())" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 34.5 s, sys: 96 ms, total: 34.6 s\n", "Wall time: 34.6 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": 58, "metadata": { "editable": true }, "outputs": [], "source": [ "stk_df_m.reset_index(drop=True, inplace=True)" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDymtradeDateclosePricenegMarketValue
0000001.XSHE2007-062007-06-29870.8704.266117e+10
1000001.XSHE2007-072007-07-311146.4985.616330e+10
2000001.XSHE2007-082007-08-311202.5105.890714e+10
3000001.XSHE2007-092007-09-281265.1676.197651e+10
4000001.XSHE2007-102007-10-311520.5427.448652e+10
5000001.XSHE2007-112007-11-301141.7515.593078e+10
6000001.XSHE2007-122007-12-281221.4976.574629e+10
7000001.XSHE2008-012008-01-311053.7785.850212e+10
..................
567892900957.XSHG2022-082022-08-310.6441.175760e+08
567893900957.XSHG2022-092022-09-300.5921.080080e+08
567894900957.XSHG2022-102022-10-310.6031.100320e+08
567895900957.XSHG2022-112022-11-300.6171.126080e+08
567896900957.XSHG2022-122022-12-300.5681.037760e+08
567897900957.XSHG2023-012023-01-310.5901.076400e+08
567898900957.XSHG2023-022023-02-280.5781.054320e+08
567899900957.XSHG2023-032023-03-030.5681.037760e+08
\n", "

567900 rows × 5 columns

\n", "
" ], "text/plain": [ " secID ym tradeDate closePrice negMarketValue\n", "0 000001.XSHE 2007-06 2007-06-29 870.870 4.266117e+10\n", "1 000001.XSHE 2007-07 2007-07-31 1146.498 5.616330e+10\n", "2 000001.XSHE 2007-08 2007-08-31 1202.510 5.890714e+10\n", "3 000001.XSHE 2007-09 2007-09-28 1265.167 6.197651e+10\n", "4 000001.XSHE 2007-10 2007-10-31 1520.542 7.448652e+10\n", "5 000001.XSHE 2007-11 2007-11-30 1141.751 5.593078e+10\n", "6 000001.XSHE 2007-12 2007-12-28 1221.497 6.574629e+10\n", "7 000001.XSHE 2008-01 2008-01-31 1053.778 5.850212e+10\n", "... ... ... ... ... ...\n", "567892 900957.XSHG 2022-08 2022-08-31 0.644 1.175760e+08\n", "567893 900957.XSHG 2022-09 2022-09-30 0.592 1.080080e+08\n", "567894 900957.XSHG 2022-10 2022-10-31 0.603 1.100320e+08\n", "567895 900957.XSHG 2022-11 2022-11-30 0.617 1.126080e+08\n", "567896 900957.XSHG 2022-12 2022-12-30 0.568 1.037760e+08\n", "567897 900957.XSHG 2023-01 2023-01-31 0.590 1.076400e+08\n", "567898 900957.XSHG 2023-02 2023-02-28 0.578 1.054320e+08\n", "567899 900957.XSHG 2023-03 2023-03-03 0.568 1.037760e+08\n", "\n", "[567900 rows x 5 columns]" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_df_m" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 567900 entries, 0 to 567899\n", "Data columns (total 5 columns):\n", "secID 567900 non-null object\n", "ym 567900 non-null period[M]\n", "tradeDate 552758 non-null datetime64[ns]\n", "closePrice 552758 non-null float64\n", "negMarketValue 552758 non-null float64\n", "dtypes: datetime64[ns](1), float64(2), object(1), period[M](1)\n", "memory usage: 21.7+ MB\n" ] } ], "source": [ "stk_df_m.info()" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "editable": true }, "outputs": [], "source": [ "stk_df_m.drop('tradeDate',axis=1,inplace=True)" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDymretmkt_capmkt_cap_date
0000001.XSHE2007-06NaNNaNNaT
1000001.XSHE2007-070.3164974.266117e+102007-06
2000001.XSHE2007-080.0488555.616330e+102007-07
3000001.XSHE2007-090.0521055.890714e+102007-08
4000001.XSHE2007-100.2018516.197651e+102007-09
5000001.XSHE2007-11-0.2491167.448652e+102007-10
6000001.XSHE2007-120.0698455.593078e+102007-11
7000001.XSHE2008-01-0.1373066.574629e+102007-12
..................
567892900957.XSHG2022-080.0933791.074560e+082022-07
567893900957.XSHG2022-09-0.0807451.175760e+082022-08
567894900957.XSHG2022-100.0185811.080080e+082022-09
567895900957.XSHG2022-110.0232171.100320e+082022-10
567896900957.XSHG2022-12-0.0794171.126080e+082022-11
567897900957.XSHG2023-010.0387321.037760e+082022-12
567898900957.XSHG2023-02-0.0203391.076400e+082023-01
567899900957.XSHG2023-03-0.0173011.054320e+082023-02
\n", "

567900 rows × 5 columns

\n", "
" ], "text/plain": [ " secID ym ret mkt_cap mkt_cap_date\n", "0 000001.XSHE 2007-06 NaN NaN NaT\n", "1 000001.XSHE 2007-07 0.316497 4.266117e+10 2007-06\n", "2 000001.XSHE 2007-08 0.048855 5.616330e+10 2007-07\n", "3 000001.XSHE 2007-09 0.052105 5.890714e+10 2007-08\n", "4 000001.XSHE 2007-10 0.201851 6.197651e+10 2007-09\n", "5 000001.XSHE 2007-11 -0.249116 7.448652e+10 2007-10\n", "6 000001.XSHE 2007-12 0.069845 5.593078e+10 2007-11\n", "7 000001.XSHE 2008-01 -0.137306 6.574629e+10 2007-12\n", "... ... ... ... ... ...\n", "567892 900957.XSHG 2022-08 0.093379 1.074560e+08 2022-07\n", "567893 900957.XSHG 2022-09 -0.080745 1.175760e+08 2022-08\n", "567894 900957.XSHG 2022-10 0.018581 1.080080e+08 2022-09\n", "567895 900957.XSHG 2022-11 0.023217 1.100320e+08 2022-10\n", "567896 900957.XSHG 2022-12 -0.079417 1.126080e+08 2022-11\n", "567897 900957.XSHG 2023-01 0.038732 1.037760e+08 2022-12\n", "567898 900957.XSHG 2023-02 -0.020339 1.076400e+08 2023-01\n", "567899 900957.XSHG 2023-03 -0.017301 1.054320e+08 2023-02\n", "\n", "[567900 rows x 5 columns]" ] }, "execution_count": 62, "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": 63, "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", " \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", " \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", " \n", " \n", " \n", " \n", "
secIDymretmkt_capmkt_cap_date
1037000007.XSHE2021-03-0.0468011.254329e+092021-02
1038000007.XSHE2021-040.0180851.195629e+092021-03
1039000007.XSHE2021-05NaN1.217255e+092021-04
1040000007.XSHE2021-06NaNNaN2021-05
1041000007.XSHE2021-07NaNNaN2021-06
1042000007.XSHE2021-08NaNNaN2021-07
1043000007.XSHE2021-09NaNNaN2021-08
1044000007.XSHE2021-10NaNNaN2021-09
..................
1047000007.XSHE2022-01NaNNaN2021-12
1048000007.XSHE2022-02NaNNaN2022-01
1049000007.XSHE2022-03NaNNaN2022-02
1050000007.XSHE2022-04NaNNaN2022-03
1051000007.XSHE2022-05NaNNaN2022-04
1052000007.XSHE2022-06NaNNaN2022-05
1053000007.XSHE2022-07NaNNaN2022-06
1054000007.XSHE2022-080.0909022.276947e+092022-07
\n", "

18 rows × 5 columns

\n", "
" ], "text/plain": [ " secID ym ret mkt_cap mkt_cap_date\n", "1037 000007.XSHE 2021-03 -0.046801 1.254329e+09 2021-02\n", "1038 000007.XSHE 2021-04 0.018085 1.195629e+09 2021-03\n", "1039 000007.XSHE 2021-05 NaN 1.217255e+09 2021-04\n", "1040 000007.XSHE 2021-06 NaN NaN 2021-05\n", "1041 000007.XSHE 2021-07 NaN NaN 2021-06\n", "1042 000007.XSHE 2021-08 NaN NaN 2021-07\n", "1043 000007.XSHE 2021-09 NaN NaN 2021-08\n", "1044 000007.XSHE 2021-10 NaN NaN 2021-09\n", "... ... ... ... ... ...\n", "1047 000007.XSHE 2022-01 NaN NaN 2021-12\n", "1048 000007.XSHE 2022-02 NaN NaN 2022-01\n", "1049 000007.XSHE 2022-03 NaN NaN 2022-02\n", "1050 000007.XSHE 2022-04 NaN NaN 2022-03\n", "1051 000007.XSHE 2022-05 NaN NaN 2022-04\n", "1052 000007.XSHE 2022-06 NaN NaN 2022-05\n", "1053 000007.XSHE 2022-07 NaN NaN 2022-06\n", "1054 000007.XSHE 2022-08 0.090902 2.276947e+09 2022-07\n", "\n", "[18 rows x 5 columns]" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_df_m[(stk_df_m['secID']=='000007.XSHE') & (stk_df_m['ym']>='2021-03') & (stk_df_m['ym']<='2022-08')]" ] }, { "cell_type": "code", "execution_count": 64, "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", " \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", " \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", " \n", " \n", " \n", " \n", "
secIDymretmkt_capmkt_cap_date
0000001.XSHE2007-06NaNNaNNaT
190000002.XSHE2007-01NaNNaNNaT
385000004.XSHE2011-06NaNNaNNaT
517000005.XSHE2008-06NaNNaNNaT
672000006.XSHE2007-01NaNNaNNaT
867000007.XSHE2007-01NaNNaNNaT
871000007.XSHE2007-05NaN726805286.62007-04
872000007.XSHE2007-06NaNNaN2007-05
..................
567531900955.XSHG2022-01NaNNaN2021-12
567532900955.XSHG2022-02NaNNaN2022-01
567533900955.XSHG2022-03NaNNaN2022-02
567534900955.XSHG2022-04NaNNaN2022-03
567535900955.XSHG2022-05NaNNaN2022-04
567536900955.XSHG2022-06NaNNaN2022-05
567538900956.XSHG2007-01NaNNaNNaT
567705900957.XSHG2007-01NaNNaNNaT
\n", "

20973 rows × 5 columns

\n", "
" ], "text/plain": [ " secID ym ret mkt_cap mkt_cap_date\n", "0 000001.XSHE 2007-06 NaN NaN NaT\n", "190 000002.XSHE 2007-01 NaN NaN NaT\n", "385 000004.XSHE 2011-06 NaN NaN NaT\n", "517 000005.XSHE 2008-06 NaN NaN NaT\n", "672 000006.XSHE 2007-01 NaN NaN NaT\n", "867 000007.XSHE 2007-01 NaN NaN NaT\n", "871 000007.XSHE 2007-05 NaN 726805286.6 2007-04\n", "872 000007.XSHE 2007-06 NaN NaN 2007-05\n", "... ... ... ... ... ...\n", "567531 900955.XSHG 2022-01 NaN NaN 2021-12\n", "567532 900955.XSHG 2022-02 NaN NaN 2022-01\n", "567533 900955.XSHG 2022-03 NaN NaN 2022-02\n", "567534 900955.XSHG 2022-04 NaN NaN 2022-03\n", "567535 900955.XSHG 2022-05 NaN NaN 2022-04\n", "567536 900955.XSHG 2022-06 NaN NaN 2022-05\n", "567538 900956.XSHG 2007-01 NaN NaN NaT\n", "567705 900957.XSHG 2007-01 NaN NaN NaT\n", "\n", "[20973 rows x 5 columns]" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_df_m[stk_df_m['ret'].isna()]" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDymretmkt_capmkt_cap_date
0000001.XSHE2007-06NaNNaNNaT
190000002.XSHE2007-01NaNNaNNaT
385000004.XSHE2011-06NaNNaNNaT
517000005.XSHE2008-06NaNNaNNaT
672000006.XSHE2007-01NaNNaNNaT
867000007.XSHE2007-01NaNNaNNaT
872000007.XSHE2007-06NaNNaN2007-05
873000007.XSHE2007-07NaNNaN2007-06
..................
567531900955.XSHG2022-01NaNNaN2021-12
567532900955.XSHG2022-02NaNNaN2022-01
567533900955.XSHG2022-03NaNNaN2022-02
567534900955.XSHG2022-04NaNNaN2022-03
567535900955.XSHG2022-05NaNNaN2022-04
567536900955.XSHG2022-06NaNNaN2022-05
567538900956.XSHG2007-01NaNNaNNaT
567705900957.XSHG2007-01NaNNaNNaT
\n", "

20313 rows × 5 columns

\n", "
" ], "text/plain": [ " secID ym ret mkt_cap mkt_cap_date\n", "0 000001.XSHE 2007-06 NaN NaN NaT\n", "190 000002.XSHE 2007-01 NaN NaN NaT\n", "385 000004.XSHE 2011-06 NaN NaN NaT\n", "517 000005.XSHE 2008-06 NaN NaN NaT\n", "672 000006.XSHE 2007-01 NaN NaN NaT\n", "867 000007.XSHE 2007-01 NaN NaN NaT\n", "872 000007.XSHE 2007-06 NaN NaN 2007-05\n", "873 000007.XSHE 2007-07 NaN NaN 2007-06\n", "... ... ... ... ... ...\n", "567531 900955.XSHG 2022-01 NaN NaN 2021-12\n", "567532 900955.XSHG 2022-02 NaN NaN 2022-01\n", "567533 900955.XSHG 2022-03 NaN NaN 2022-02\n", "567534 900955.XSHG 2022-04 NaN NaN 2022-03\n", "567535 900955.XSHG 2022-05 NaN NaN 2022-04\n", "567536 900955.XSHG 2022-06 NaN NaN 2022-05\n", "567538 900956.XSHG 2007-01 NaN NaN NaT\n", "567705 900957.XSHG 2007-01 NaN NaN NaT\n", "\n", "[20313 rows x 5 columns]" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_df_m[stk_df_m['mkt_cap'].isna()]" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "editable": true }, "outputs": [], "source": [ "stk_df_m.dropna(inplace=True)" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDymretmkt_capmkt_cap_date
1000001.XSHE2007-070.3164974.266117e+102007-06
2000001.XSHE2007-080.0488555.616330e+102007-07
3000001.XSHE2007-090.0521055.890714e+102007-08
4000001.XSHE2007-100.2018516.197651e+102007-09
5000001.XSHE2007-11-0.2491167.448652e+102007-10
6000001.XSHE2007-120.0698455.593078e+102007-11
7000001.XSHE2008-01-0.1373066.574629e+102007-12
8000001.XSHE2008-02-0.0045045.850212e+102008-01
..................
567892900957.XSHG2022-080.0933791.074560e+082022-07
567893900957.XSHG2022-09-0.0807451.175760e+082022-08
567894900957.XSHG2022-100.0185811.080080e+082022-09
567895900957.XSHG2022-110.0232171.100320e+082022-10
567896900957.XSHG2022-12-0.0794171.126080e+082022-11
567897900957.XSHG2023-010.0387321.037760e+082022-12
567898900957.XSHG2023-02-0.0203391.076400e+082023-01
567899900957.XSHG2023-03-0.0173011.054320e+082023-02
\n", "

546927 rows × 5 columns

\n", "
" ], "text/plain": [ " secID ym ret mkt_cap mkt_cap_date\n", "1 000001.XSHE 2007-07 0.316497 4.266117e+10 2007-06\n", "2 000001.XSHE 2007-08 0.048855 5.616330e+10 2007-07\n", "3 000001.XSHE 2007-09 0.052105 5.890714e+10 2007-08\n", "4 000001.XSHE 2007-10 0.201851 6.197651e+10 2007-09\n", "5 000001.XSHE 2007-11 -0.249116 7.448652e+10 2007-10\n", "6 000001.XSHE 2007-12 0.069845 5.593078e+10 2007-11\n", "7 000001.XSHE 2008-01 -0.137306 6.574629e+10 2007-12\n", "8 000001.XSHE 2008-02 -0.004504 5.850212e+10 2008-01\n", "... ... ... ... ... ...\n", "567892 900957.XSHG 2022-08 0.093379 1.074560e+08 2022-07\n", "567893 900957.XSHG 2022-09 -0.080745 1.175760e+08 2022-08\n", "567894 900957.XSHG 2022-10 0.018581 1.080080e+08 2022-09\n", "567895 900957.XSHG 2022-11 0.023217 1.100320e+08 2022-10\n", "567896 900957.XSHG 2022-12 -0.079417 1.126080e+08 2022-11\n", "567897 900957.XSHG 2023-01 0.038732 1.037760e+08 2022-12\n", "567898 900957.XSHG 2023-02 -0.020339 1.076400e+08 2023-01\n", "567899 900957.XSHG 2023-03 -0.017301 1.054320e+08 2023-02\n", "\n", "[546927 rows x 5 columns]" ] }, "execution_count": 67, "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": 68, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDendDatepublishDateendDateRepTShEquityTEquityAttrPminorityIntpub_monthpub_yeardata_yearbm_datebook
0000001.XSHE2007-12-312008-03-202007-12-311.300606e+101.300606e+100.000000e+003200820072007-121.300606e+10
1000001.XSHE2008-12-312009-03-202008-12-311.640079e+101.640079e+100.000000e+003200920082008-121.640079e+10
2000001.XSHE2009-12-312010-03-122009-12-312.046961e+102.046961e+100.000000e+003201020092009-122.046961e+10
3000001.XSHE2010-12-312011-02-252010-12-313.351288e+103.351288e+100.000000e+002201120102010-123.351288e+10
4000001.XSHE2011-12-312012-03-092011-12-317.538058e+107.331084e+102.069747e+093201220112011-127.331084e+10
5000001.XSHE2012-12-312013-03-082012-12-318.479878e+108.479878e+100.000000e+003201320122012-128.479878e+10
6000001.XSHE2013-12-312014-03-072013-12-311.120810e+111.120810e+110.000000e+003201420132013-121.120810e+11
7000001.XSHE2014-12-312015-03-132014-12-311.309490e+111.309490e+110.000000e+003201520142014-121.309490e+11
.......................................
47857900957.XSHG2014-12-312015-04-102014-12-314.072107e+083.940309e+081.317981e+074201520142014-123.940309e+08
47858900957.XSHG2015-12-312016-03-302015-12-314.106786e+083.973929e+081.328570e+073201620152015-123.973929e+08
47859900957.XSHG2016-12-312017-03-252016-12-313.938268e+083.930721e+087.546643e+053201720162016-123.930721e+08
47860900957.XSHG2017-12-312018-04-102017-12-314.238426e+084.231040e+087.386715e+054201820172017-124.231040e+08
47861900957.XSHG2018-12-312019-03-302018-12-314.515278e+084.508051e+087.226781e+053201920182018-124.508051e+08
47862900957.XSHG2019-12-312020-04-252019-12-314.768689e+084.761021e+087.667705e+054202020192019-124.761021e+08
47863900957.XSHG2020-12-312021-04-092020-12-314.987276e+084.979110e+088.165551e+054202120202020-124.979110e+08
47864900957.XSHG2021-12-312022-04-202021-12-315.263733e+085.255741e+087.991940e+054202220212021-125.255741e+08
\n", "

46883 rows × 12 columns

\n", "
" ], "text/plain": [ " secID endDate publishDate endDateRep TShEquity \\\n", "0 000001.XSHE 2007-12-31 2008-03-20 2007-12-31 1.300606e+10 \n", "1 000001.XSHE 2008-12-31 2009-03-20 2008-12-31 1.640079e+10 \n", "2 000001.XSHE 2009-12-31 2010-03-12 2009-12-31 2.046961e+10 \n", "3 000001.XSHE 2010-12-31 2011-02-25 2010-12-31 3.351288e+10 \n", "4 000001.XSHE 2011-12-31 2012-03-09 2011-12-31 7.538058e+10 \n", "5 000001.XSHE 2012-12-31 2013-03-08 2012-12-31 8.479878e+10 \n", "6 000001.XSHE 2013-12-31 2014-03-07 2013-12-31 1.120810e+11 \n", "7 000001.XSHE 2014-12-31 2015-03-13 2014-12-31 1.309490e+11 \n", "... ... ... ... ... ... \n", "47857 900957.XSHG 2014-12-31 2015-04-10 2014-12-31 4.072107e+08 \n", "47858 900957.XSHG 2015-12-31 2016-03-30 2015-12-31 4.106786e+08 \n", "47859 900957.XSHG 2016-12-31 2017-03-25 2016-12-31 3.938268e+08 \n", "47860 900957.XSHG 2017-12-31 2018-04-10 2017-12-31 4.238426e+08 \n", "47861 900957.XSHG 2018-12-31 2019-03-30 2018-12-31 4.515278e+08 \n", "47862 900957.XSHG 2019-12-31 2020-04-25 2019-12-31 4.768689e+08 \n", "47863 900957.XSHG 2020-12-31 2021-04-09 2020-12-31 4.987276e+08 \n", "47864 900957.XSHG 2021-12-31 2022-04-20 2021-12-31 5.263733e+08 \n", "\n", " TEquityAttrP minorityInt pub_month pub_year data_year bm_date \\\n", "0 1.300606e+10 0.000000e+00 3 2008 2007 2007-12 \n", "1 1.640079e+10 0.000000e+00 3 2009 2008 2008-12 \n", "2 2.046961e+10 0.000000e+00 3 2010 2009 2009-12 \n", "3 3.351288e+10 0.000000e+00 2 2011 2010 2010-12 \n", "4 7.331084e+10 2.069747e+09 3 2012 2011 2011-12 \n", "5 8.479878e+10 0.000000e+00 3 2013 2012 2012-12 \n", "6 1.120810e+11 0.000000e+00 3 2014 2013 2013-12 \n", "7 1.309490e+11 0.000000e+00 3 2015 2014 2014-12 \n", "... ... ... ... ... ... ... \n", "47857 3.940309e+08 1.317981e+07 4 2015 2014 2014-12 \n", "47858 3.973929e+08 1.328570e+07 3 2016 2015 2015-12 \n", "47859 3.930721e+08 7.546643e+05 3 2017 2016 2016-12 \n", "47860 4.231040e+08 7.386715e+05 4 2018 2017 2017-12 \n", "47861 4.508051e+08 7.226781e+05 3 2019 2018 2018-12 \n", "47862 4.761021e+08 7.667705e+05 4 2020 2019 2019-12 \n", "47863 4.979110e+08 8.165551e+05 4 2021 2020 2020-12 \n", "47864 5.255741e+08 7.991940e+05 4 2022 2021 2021-12 \n", "\n", " book \n", "0 1.300606e+10 \n", "1 1.640079e+10 \n", "2 2.046961e+10 \n", "3 3.351288e+10 \n", "4 7.331084e+10 \n", "5 8.479878e+10 \n", "6 1.120810e+11 \n", "7 1.309490e+11 \n", "... ... \n", "47857 3.940309e+08 \n", "47858 3.973929e+08 \n", "47859 3.930721e+08 \n", "47860 4.231040e+08 \n", "47861 4.508051e+08 \n", "47862 4.761021e+08 \n", "47863 4.979110e+08 \n", "47864 5.255741e+08 \n", "\n", "[46883 rows x 12 columns]" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fundmen_df" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDymretmkt_capmkt_cap_date
1000001.XSHE2007-070.3164974.266117e+102007-06
2000001.XSHE2007-080.0488555.616330e+102007-07
3000001.XSHE2007-090.0521055.890714e+102007-08
4000001.XSHE2007-100.2018516.197651e+102007-09
5000001.XSHE2007-11-0.2491167.448652e+102007-10
6000001.XSHE2007-120.0698455.593078e+102007-11
7000001.XSHE2008-01-0.1373066.574629e+102007-12
8000001.XSHE2008-02-0.0045045.850212e+102008-01
..................
567892900957.XSHG2022-080.0933791.074560e+082022-07
567893900957.XSHG2022-09-0.0807451.175760e+082022-08
567894900957.XSHG2022-100.0185811.080080e+082022-09
567895900957.XSHG2022-110.0232171.100320e+082022-10
567896900957.XSHG2022-12-0.0794171.126080e+082022-11
567897900957.XSHG2023-010.0387321.037760e+082022-12
567898900957.XSHG2023-02-0.0203391.076400e+082023-01
567899900957.XSHG2023-03-0.0173011.054320e+082023-02
\n", "

546927 rows × 5 columns

\n", "
" ], "text/plain": [ " secID ym ret mkt_cap mkt_cap_date\n", "1 000001.XSHE 2007-07 0.316497 4.266117e+10 2007-06\n", "2 000001.XSHE 2007-08 0.048855 5.616330e+10 2007-07\n", "3 000001.XSHE 2007-09 0.052105 5.890714e+10 2007-08\n", "4 000001.XSHE 2007-10 0.201851 6.197651e+10 2007-09\n", "5 000001.XSHE 2007-11 -0.249116 7.448652e+10 2007-10\n", "6 000001.XSHE 2007-12 0.069845 5.593078e+10 2007-11\n", "7 000001.XSHE 2008-01 -0.137306 6.574629e+10 2007-12\n", "8 000001.XSHE 2008-02 -0.004504 5.850212e+10 2008-01\n", "... ... ... ... ... ...\n", "567892 900957.XSHG 2022-08 0.093379 1.074560e+08 2022-07\n", "567893 900957.XSHG 2022-09 -0.080745 1.175760e+08 2022-08\n", "567894 900957.XSHG 2022-10 0.018581 1.080080e+08 2022-09\n", "567895 900957.XSHG 2022-11 0.023217 1.100320e+08 2022-10\n", "567896 900957.XSHG 2022-12 -0.079417 1.126080e+08 2022-11\n", "567897 900957.XSHG 2023-01 0.038732 1.037760e+08 2022-12\n", "567898 900957.XSHG 2023-02 -0.020339 1.076400e+08 2023-01\n", "567899 900957.XSHG 2023-03 -0.017301 1.054320e+08 2023-02\n", "\n", "[546927 rows x 5 columns]" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_df_m" ] }, { "cell_type": "code", "execution_count": 70, "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": 71, "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
............
41551900957.XSHG2014-123.482069
41552900957.XSHG2015-121.465227
41553900957.XSHG2016-121.893849
41554900957.XSHG2017-122.373042
41555900957.XSHG2018-123.977318
41556900957.XSHG2019-124.653798
41557900957.XSHG2020-125.379798
41558900957.XSHG2021-124.526753
\n", "

41559 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", "41551 900957.XSHG 2014-12 3.482069\n", "41552 900957.XSHG 2015-12 1.465227\n", "41553 900957.XSHG 2016-12 1.893849\n", "41554 900957.XSHG 2017-12 2.373042\n", "41555 900957.XSHG 2018-12 3.977318\n", "41556 900957.XSHG 2019-12 4.653798\n", "41557 900957.XSHG 2020-12 5.379798\n", "41558 900957.XSHG 2021-12 4.526753\n", "\n", "[41559 rows x 3 columns]" ] }, "execution_count": 71, "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": 72, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDymretmkt_capmkt_cap_daterfexret
0000001.XSHE2007-070.3164974.266117e+102007-060.0026200.313877
1000001.XSHE2007-080.0488555.616330e+102007-070.0026820.046173
2000001.XSHE2007-090.0521055.890714e+102007-080.0029340.049171
3000001.XSHE2007-100.2018516.197651e+102007-090.0032500.198601
4000001.XSHE2007-11-0.2491167.448652e+102007-100.003545-0.252661
5000001.XSHE2007-120.0698455.593078e+102007-110.0036430.066202
6000001.XSHE2008-01-0.1373066.574629e+102007-120.003731-0.141037
7000001.XSHE2008-02-0.0045045.850212e+102008-010.003753-0.008257
........................
542043900957.XSHG2022-07-0.0232171.100320e+082022-060.001620-0.024837
542044900957.XSHG2022-080.0933791.074560e+082022-070.0013660.092013
542045900957.XSHG2022-09-0.0807451.175760e+082022-080.001342-0.082087
542046900957.XSHG2022-100.0185811.080080e+082022-090.0014130.017168
542047900957.XSHG2022-110.0232171.100320e+082022-100.0016760.021541
542048900957.XSHG2022-12-0.0794171.126080e+082022-110.001931-0.081348
542049900957.XSHG2023-010.0387321.037760e+082022-120.0020130.036719
542050900957.XSHG2023-02-0.0203391.076400e+082023-010.002013-0.022352
\n", "

542051 rows × 7 columns

\n", "
" ], "text/plain": [ " secID ym ret mkt_cap mkt_cap_date rf \\\n", "0 000001.XSHE 2007-07 0.316497 4.266117e+10 2007-06 0.002620 \n", "1 000001.XSHE 2007-08 0.048855 5.616330e+10 2007-07 0.002682 \n", "2 000001.XSHE 2007-09 0.052105 5.890714e+10 2007-08 0.002934 \n", "3 000001.XSHE 2007-10 0.201851 6.197651e+10 2007-09 0.003250 \n", "4 000001.XSHE 2007-11 -0.249116 7.448652e+10 2007-10 0.003545 \n", "5 000001.XSHE 2007-12 0.069845 5.593078e+10 2007-11 0.003643 \n", "6 000001.XSHE 2008-01 -0.137306 6.574629e+10 2007-12 0.003731 \n", "7 000001.XSHE 2008-02 -0.004504 5.850212e+10 2008-01 0.003753 \n", "... ... ... ... ... ... ... \n", "542043 900957.XSHG 2022-07 -0.023217 1.100320e+08 2022-06 0.001620 \n", "542044 900957.XSHG 2022-08 0.093379 1.074560e+08 2022-07 0.001366 \n", "542045 900957.XSHG 2022-09 -0.080745 1.175760e+08 2022-08 0.001342 \n", "542046 900957.XSHG 2022-10 0.018581 1.080080e+08 2022-09 0.001413 \n", "542047 900957.XSHG 2022-11 0.023217 1.100320e+08 2022-10 0.001676 \n", "542048 900957.XSHG 2022-12 -0.079417 1.126080e+08 2022-11 0.001931 \n", "542049 900957.XSHG 2023-01 0.038732 1.037760e+08 2022-12 0.002013 \n", "542050 900957.XSHG 2023-02 -0.020339 1.076400e+08 2023-01 0.002013 \n", "\n", " exret \n", "0 0.313877 \n", "1 0.046173 \n", "2 0.049171 \n", "3 0.198601 \n", "4 -0.252661 \n", "5 0.066202 \n", "6 -0.141037 \n", "7 -0.008257 \n", "... ... \n", "542043 -0.024837 \n", "542044 0.092013 \n", "542045 -0.082087 \n", "542046 0.017168 \n", "542047 0.021541 \n", "542048 -0.081348 \n", "542049 0.036719 \n", "542050 -0.022352 \n", "\n", "[542051 rows x 7 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": 73, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDym_xretmkt_capmkt_cap_daterfexretym_ybeta
0000001.XSHE2007-070.3164974.266117e+102007-060.0026200.3138772007-060.4614
1000001.XSHE2007-080.0488555.616330e+102007-070.0026820.0461732007-070.6423
2000001.XSHE2007-090.0521055.890714e+102007-080.0029340.0491712007-080.7722
3000001.XSHE2007-100.2018516.197651e+102007-090.0032500.1986012007-090.7596
4000001.XSHE2007-11-0.2491167.448652e+102007-100.003545-0.2526612007-100.7988
5000001.XSHE2007-120.0698455.593078e+102007-110.0036430.0662022007-110.9560
6000001.XSHE2008-01-0.1373066.574629e+102007-120.003731-0.1410372007-120.9468
7000001.XSHE2008-02-0.0045045.850212e+102008-010.003753-0.0082572008-010.9654
..............................
523663689009.XSHG2022-070.1150562.264534e+102022-060.0016200.1134362022-060.9071
523664689009.XSHG2022-08-0.1126562.525082e+102022-070.001366-0.1140222022-070.7987
523665689009.XSHG2022-09-0.1299112.240616e+102022-080.001342-0.1312532022-080.8589
523666689009.XSHG2022-10-0.1647091.949535e+102022-090.001413-0.1661222022-090.9106
523667689009.XSHG2022-110.0431251.637440e+102022-100.0016760.0414492022-100.7083
523668689009.XSHG2022-12-0.0865791.708055e+102022-110.001931-0.0885102022-110.7363
523669689009.XSHG2023-010.0885541.560173e+102022-120.0020130.0865412022-120.6919
523670689009.XSHG2023-02-0.0057251.698332e+102023-010.002013-0.0077382023-010.7379
\n", "

523671 rows × 9 columns

\n", "
" ], "text/plain": [ " secID ym_x ret mkt_cap mkt_cap_date rf \\\n", "0 000001.XSHE 2007-07 0.316497 4.266117e+10 2007-06 0.002620 \n", "1 000001.XSHE 2007-08 0.048855 5.616330e+10 2007-07 0.002682 \n", "2 000001.XSHE 2007-09 0.052105 5.890714e+10 2007-08 0.002934 \n", "3 000001.XSHE 2007-10 0.201851 6.197651e+10 2007-09 0.003250 \n", "4 000001.XSHE 2007-11 -0.249116 7.448652e+10 2007-10 0.003545 \n", "5 000001.XSHE 2007-12 0.069845 5.593078e+10 2007-11 0.003643 \n", "6 000001.XSHE 2008-01 -0.137306 6.574629e+10 2007-12 0.003731 \n", "7 000001.XSHE 2008-02 -0.004504 5.850212e+10 2008-01 0.003753 \n", "... ... ... ... ... ... ... \n", "523663 689009.XSHG 2022-07 0.115056 2.264534e+10 2022-06 0.001620 \n", "523664 689009.XSHG 2022-08 -0.112656 2.525082e+10 2022-07 0.001366 \n", "523665 689009.XSHG 2022-09 -0.129911 2.240616e+10 2022-08 0.001342 \n", "523666 689009.XSHG 2022-10 -0.164709 1.949535e+10 2022-09 0.001413 \n", "523667 689009.XSHG 2022-11 0.043125 1.637440e+10 2022-10 0.001676 \n", "523668 689009.XSHG 2022-12 -0.086579 1.708055e+10 2022-11 0.001931 \n", "523669 689009.XSHG 2023-01 0.088554 1.560173e+10 2022-12 0.002013 \n", "523670 689009.XSHG 2023-02 -0.005725 1.698332e+10 2023-01 0.002013 \n", "\n", " exret ym_y beta \n", "0 0.313877 2007-06 0.4614 \n", "1 0.046173 2007-07 0.6423 \n", "2 0.049171 2007-08 0.7722 \n", "3 0.198601 2007-09 0.7596 \n", "4 -0.252661 2007-10 0.7988 \n", "5 0.066202 2007-11 0.9560 \n", "6 -0.141037 2007-12 0.9468 \n", "7 -0.008257 2008-01 0.9654 \n", "... ... ... ... \n", "523663 0.113436 2022-06 0.9071 \n", "523664 -0.114022 2022-07 0.7987 \n", "523665 -0.131253 2022-08 0.8589 \n", "523666 -0.166122 2022-09 0.9106 \n", "523667 0.041449 2022-10 0.7083 \n", "523668 -0.088510 2022-11 0.7363 \n", "523669 0.086541 2022-12 0.6919 \n", "523670 -0.007738 2023-01 0.7379 \n", "\n", "[523671 rows x 9 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": 74, "metadata": { "editable": true }, "outputs": [], "source": [ "ret_df.drop(['ym_y'],axis=1,inplace=True)" ] }, { "cell_type": "code", "execution_count": 75, "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": 76, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDret_dateretmkt_capmktcap_beta_daterfexretbeta
0000001.XSHE2007-070.3164974.266117e+102007-060.0026200.3138770.4614
1000001.XSHE2007-080.0488555.616330e+102007-070.0026820.0461730.6423
2000001.XSHE2007-090.0521055.890714e+102007-080.0029340.0491710.7722
3000001.XSHE2007-100.2018516.197651e+102007-090.0032500.1986010.7596
4000001.XSHE2007-11-0.2491167.448652e+102007-100.003545-0.2526610.7988
5000001.XSHE2007-120.0698455.593078e+102007-110.0036430.0662020.9560
6000001.XSHE2008-01-0.1373066.574629e+102007-120.003731-0.1410370.9468
7000001.XSHE2008-02-0.0045045.850212e+102008-010.003753-0.0082570.9654
...........................
523663689009.XSHG2022-070.1150562.264534e+102022-060.0016200.1134360.9071
523664689009.XSHG2022-08-0.1126562.525082e+102022-070.001366-0.1140220.7987
523665689009.XSHG2022-09-0.1299112.240616e+102022-080.001342-0.1312530.8589
523666689009.XSHG2022-10-0.1647091.949535e+102022-090.001413-0.1661220.9106
523667689009.XSHG2022-110.0431251.637440e+102022-100.0016760.0414490.7083
523668689009.XSHG2022-12-0.0865791.708055e+102022-110.001931-0.0885100.7363
523669689009.XSHG2023-010.0885541.560173e+102022-120.0020130.0865410.6919
523670689009.XSHG2023-02-0.0057251.698332e+102023-010.002013-0.0077380.7379
\n", "

523671 rows × 8 columns

\n", "
" ], "text/plain": [ " secID ret_date ret mkt_cap mktcap_beta_date \\\n", "0 000001.XSHE 2007-07 0.316497 4.266117e+10 2007-06 \n", "1 000001.XSHE 2007-08 0.048855 5.616330e+10 2007-07 \n", "2 000001.XSHE 2007-09 0.052105 5.890714e+10 2007-08 \n", "3 000001.XSHE 2007-10 0.201851 6.197651e+10 2007-09 \n", "4 000001.XSHE 2007-11 -0.249116 7.448652e+10 2007-10 \n", "5 000001.XSHE 2007-12 0.069845 5.593078e+10 2007-11 \n", "6 000001.XSHE 2008-01 -0.137306 6.574629e+10 2007-12 \n", "7 000001.XSHE 2008-02 -0.004504 5.850212e+10 2008-01 \n", "... ... ... ... ... ... \n", "523663 689009.XSHG 2022-07 0.115056 2.264534e+10 2022-06 \n", "523664 689009.XSHG 2022-08 -0.112656 2.525082e+10 2022-07 \n", "523665 689009.XSHG 2022-09 -0.129911 2.240616e+10 2022-08 \n", "523666 689009.XSHG 2022-10 -0.164709 1.949535e+10 2022-09 \n", "523667 689009.XSHG 2022-11 0.043125 1.637440e+10 2022-10 \n", "523668 689009.XSHG 2022-12 -0.086579 1.708055e+10 2022-11 \n", "523669 689009.XSHG 2023-01 0.088554 1.560173e+10 2022-12 \n", "523670 689009.XSHG 2023-02 -0.005725 1.698332e+10 2023-01 \n", "\n", " rf exret beta \n", "0 0.002620 0.313877 0.4614 \n", "1 0.002682 0.046173 0.6423 \n", "2 0.002934 0.049171 0.7722 \n", "3 0.003250 0.198601 0.7596 \n", "4 0.003545 -0.252661 0.7988 \n", "5 0.003643 0.066202 0.9560 \n", "6 0.003731 -0.141037 0.9468 \n", "7 0.003753 -0.008257 0.9654 \n", "... ... ... ... \n", "523663 0.001620 0.113436 0.9071 \n", "523664 0.001366 -0.114022 0.7987 \n", "523665 0.001342 -0.131253 0.8589 \n", "523666 0.001413 -0.166122 0.9106 \n", "523667 0.001676 0.041449 0.7083 \n", "523668 0.001931 -0.088510 0.7363 \n", "523669 0.002013 0.086541 0.6919 \n", "523670 0.002013 -0.007738 0.7379 \n", "\n", "[523671 rows x 8 columns]" ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ret_df" ] }, { "cell_type": "code", "execution_count": 77, "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": 78, "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
...........................
523663689009.XSHG2022-070.1150560.0016200.1134362022-062.264534e+100.9071
523664689009.XSHG2022-08-0.1126560.001366-0.1140222022-072.525082e+100.7987
523665689009.XSHG2022-09-0.1299110.001342-0.1312532022-082.240616e+100.8589
523666689009.XSHG2022-10-0.1647090.001413-0.1661222022-091.949535e+100.9106
523667689009.XSHG2022-110.0431250.0016760.0414492022-101.637440e+100.7083
523668689009.XSHG2022-12-0.0865790.001931-0.0885102022-111.708055e+100.7363
523669689009.XSHG2023-010.0885540.0020130.0865412022-121.560173e+100.6919
523670689009.XSHG2023-02-0.0057250.002013-0.0077382023-011.698332e+100.7379
\n", "

523671 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", "523663 689009.XSHG 2022-07 0.115056 0.001620 0.113436 2022-06 \n", "523664 689009.XSHG 2022-08 -0.112656 0.001366 -0.114022 2022-07 \n", "523665 689009.XSHG 2022-09 -0.129911 0.001342 -0.131253 2022-08 \n", "523666 689009.XSHG 2022-10 -0.164709 0.001413 -0.166122 2022-09 \n", "523667 689009.XSHG 2022-11 0.043125 0.001676 0.041449 2022-10 \n", "523668 689009.XSHG 2022-12 -0.086579 0.001931 -0.088510 2022-11 \n", "523669 689009.XSHG 2023-01 0.088554 0.002013 0.086541 2022-12 \n", "523670 689009.XSHG 2023-02 -0.005725 0.002013 -0.007738 2023-01 \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", "523663 2.264534e+10 0.9071 \n", "523664 2.525082e+10 0.7987 \n", "523665 2.240616e+10 0.8589 \n", "523666 1.949535e+10 0.9106 \n", "523667 1.637440e+10 0.7083 \n", "523668 1.708055e+10 0.7363 \n", "523669 1.560173e+10 0.6919 \n", "523670 1.698332e+10 0.7379 \n", "\n", "[523671 rows x 8 columns]" ] }, "execution_count": 78, "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": 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" ] }, { "cell_type": "code", "execution_count": 80, "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
....................................
523663689009.XSHG2022-070.1150560.0016200.1134362022-062.264534e+100.9071202272021
523664689009.XSHG2022-08-0.1126560.001366-0.1140222022-072.525082e+100.7987202282021
523665689009.XSHG2022-09-0.1299110.001342-0.1312532022-082.240616e+100.8589202292021
523666689009.XSHG2022-10-0.1647090.001413-0.1661222022-091.949535e+100.91062022102021
523667689009.XSHG2022-110.0431250.0016760.0414492022-101.637440e+100.70832022112021
523668689009.XSHG2022-12-0.0865790.001931-0.0885102022-111.708055e+100.73632022122021
523669689009.XSHG2023-010.0885540.0020130.0865412022-121.560173e+100.6919202312021
523670689009.XSHG2023-02-0.0057250.002013-0.0077382023-011.698332e+100.7379202322021
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523671 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", "523663 689009.XSHG 2022-07 0.115056 0.001620 0.113436 2022-06 \n", "523664 689009.XSHG 2022-08 -0.112656 0.001366 -0.114022 2022-07 \n", "523665 689009.XSHG 2022-09 -0.129911 0.001342 -0.131253 2022-08 \n", "523666 689009.XSHG 2022-10 -0.164709 0.001413 -0.166122 2022-09 \n", "523667 689009.XSHG 2022-11 0.043125 0.001676 0.041449 2022-10 \n", "523668 689009.XSHG 2022-12 -0.086579 0.001931 -0.088510 2022-11 \n", "523669 689009.XSHG 2023-01 0.088554 0.002013 0.086541 2022-12 \n", "523670 689009.XSHG 2023-02 -0.005725 0.002013 -0.007738 2023-01 \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", "523663 2.264534e+10 0.9071 2022 7 2021 \n", "523664 2.525082e+10 0.7987 2022 8 2021 \n", "523665 2.240616e+10 0.8589 2022 9 2021 \n", "523666 1.949535e+10 0.9106 2022 10 2021 \n", "523667 1.637440e+10 0.7083 2022 11 2021 \n", "523668 1.708055e+10 0.7363 2022 12 2021 \n", "523669 1.560173e+10 0.6919 2023 1 2021 \n", "523670 1.698332e+10 0.7379 2023 2 2021 \n", "\n", "[523671 rows x 11 columns]" ] }, "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ret_df" ] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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secIDret_dateretrfexretmktcap_beta_datemkt_capbetayearmonthbm_date
261423300349.XSHE2013-01-0.0351970.003246-0.0384432012-127.245000e+080.6363201312011
261424300349.XSHE2013-020.0257510.0032400.0225112013-016.990000e+080.5292201322011
261425300349.XSHE2013-03-0.0736400.003236-0.0768762013-027.170000e+080.6351201332011
261426300349.XSHE2013-040.0255190.0032350.0222842013-036.642000e+080.7784201342011
261427300349.XSHE2013-050.3515750.0032350.3483402013-046.811500e+080.8078201352011
261428300349.XSHE2013-060.0002440.004241-0.0039972013-059.180000e+080.7089201362011
261429300349.XSHE2013-070.1725200.0039720.1685482013-069.182250e+080.5040201372012
261430300349.XSHE2013-080.0282020.0038800.0243222013-071.076625e+090.5452201382012
261431300349.XSHE2013-09-0.0873920.003884-0.0912762013-081.107000e+090.5464201392012
261432300349.XSHE2013-10-0.0022210.003897-0.0061182013-091.010250e+090.46692013102012
261433300349.XSHE2013-110.1667380.0039200.1628182013-101.008000e+090.65222013112012
261434300349.XSHE2013-12-0.0545340.004417-0.0589512013-111.176075e+090.64512013122012
\n", "
" ], "text/plain": [ " secID ret_date ret rf exret mktcap_beta_date \\\n", "261423 300349.XSHE 2013-01 -0.035197 0.003246 -0.038443 2012-12 \n", "261424 300349.XSHE 2013-02 0.025751 0.003240 0.022511 2013-01 \n", "261425 300349.XSHE 2013-03 -0.073640 0.003236 -0.076876 2013-02 \n", "261426 300349.XSHE 2013-04 0.025519 0.003235 0.022284 2013-03 \n", "261427 300349.XSHE 2013-05 0.351575 0.003235 0.348340 2013-04 \n", "261428 300349.XSHE 2013-06 0.000244 0.004241 -0.003997 2013-05 \n", "261429 300349.XSHE 2013-07 0.172520 0.003972 0.168548 2013-06 \n", "261430 300349.XSHE 2013-08 0.028202 0.003880 0.024322 2013-07 \n", "261431 300349.XSHE 2013-09 -0.087392 0.003884 -0.091276 2013-08 \n", "261432 300349.XSHE 2013-10 -0.002221 0.003897 -0.006118 2013-09 \n", "261433 300349.XSHE 2013-11 0.166738 0.003920 0.162818 2013-10 \n", "261434 300349.XSHE 2013-12 -0.054534 0.004417 -0.058951 2013-11 \n", "\n", " mkt_cap beta year month bm_date \n", "261423 7.245000e+08 0.6363 2013 1 2011 \n", "261424 6.990000e+08 0.5292 2013 2 2011 \n", "261425 7.170000e+08 0.6351 2013 3 2011 \n", "261426 6.642000e+08 0.7784 2013 4 2011 \n", "261427 6.811500e+08 0.8078 2013 5 2011 \n", "261428 9.180000e+08 0.7089 2013 6 2011 \n", "261429 9.182250e+08 0.5040 2013 7 2012 \n", "261430 1.076625e+09 0.5452 2013 8 2012 \n", "261431 1.107000e+09 0.5464 2013 9 2012 \n", "261432 1.010250e+09 0.4669 2013 10 2012 \n", "261433 1.008000e+09 0.6522 2013 11 2012 \n", "261434 1.176075e+09 0.6451 2013 12 2012 " ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ret_df.loc[(ret_df['secID']=='300349.XSHE')&(ret_df['ret_date']>='2013-01')&(ret_df['ret_date']<='2013-12')]" ] }, { "cell_type": "code", "execution_count": 82, "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", "523663 2021-12-31\n", "523664 2021-12-31\n", "523665 2021-12-31\n", "523666 2021-12-31\n", "523667 2021-12-31\n", "523668 2021-12-31\n", "523669 2021-12-31\n", "523670 2021-12-31\n", "Name: bm_date, Length: 523671, dtype: datetime64[ns]" ] }, "execution_count": 82, "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": 83, "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": 84, "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
..............................
523663689009.XSHG2022-070.1150560.0016200.1134362022-062.264534e+100.90712021-12
523664689009.XSHG2022-08-0.1126560.001366-0.1140222022-072.525082e+100.79872021-12
523665689009.XSHG2022-09-0.1299110.001342-0.1312532022-082.240616e+100.85892021-12
523666689009.XSHG2022-10-0.1647090.001413-0.1661222022-091.949535e+100.91062021-12
523667689009.XSHG2022-110.0431250.0016760.0414492022-101.637440e+100.70832021-12
523668689009.XSHG2022-12-0.0865790.001931-0.0885102022-111.708055e+100.73632021-12
523669689009.XSHG2023-010.0885540.0020130.0865412022-121.560173e+100.69192021-12
523670689009.XSHG2023-02-0.0057250.002013-0.0077382023-011.698332e+100.73792021-12
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523671 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", "523663 689009.XSHG 2022-07 0.115056 0.001620 0.113436 2022-06 \n", "523664 689009.XSHG 2022-08 -0.112656 0.001366 -0.114022 2022-07 \n", "523665 689009.XSHG 2022-09 -0.129911 0.001342 -0.131253 2022-08 \n", "523666 689009.XSHG 2022-10 -0.164709 0.001413 -0.166122 2022-09 \n", "523667 689009.XSHG 2022-11 0.043125 0.001676 0.041449 2022-10 \n", "523668 689009.XSHG 2022-12 -0.086579 0.001931 -0.088510 2022-11 \n", "523669 689009.XSHG 2023-01 0.088554 0.002013 0.086541 2022-12 \n", "523670 689009.XSHG 2023-02 -0.005725 0.002013 -0.007738 2023-01 \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", "523663 2.264534e+10 0.9071 2021-12 \n", "523664 2.525082e+10 0.7987 2021-12 \n", "523665 2.240616e+10 0.8589 2021-12 \n", "523666 1.949535e+10 0.9106 2021-12 \n", "523667 1.637440e+10 0.7083 2021-12 \n", "523668 1.708055e+10 0.7363 2021-12 \n", "523669 1.560173e+10 0.6919 2021-12 \n", "523670 1.698332e+10 0.7379 2021-12 \n", "\n", "[523671 rows x 9 columns]" ] }, "execution_count": 84, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ret_df" ] }, { "cell_type": "code", "execution_count": 85, "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
............
41551900957.XSHG2014-123.482069
41552900957.XSHG2015-121.465227
41553900957.XSHG2016-121.893849
41554900957.XSHG2017-122.373042
41555900957.XSHG2018-123.977318
41556900957.XSHG2019-124.653798
41557900957.XSHG2020-125.379798
41558900957.XSHG2021-124.526753
\n", "

41559 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", "41551 900957.XSHG 2014-12 3.482069\n", "41552 900957.XSHG 2015-12 1.465227\n", "41553 900957.XSHG 2016-12 1.893849\n", "41554 900957.XSHG 2017-12 2.373042\n", "41555 900957.XSHG 2018-12 3.977318\n", "41556 900957.XSHG 2019-12 4.653798\n", "41557 900957.XSHG 2020-12 5.379798\n", "41558 900957.XSHG 2021-12 4.526753\n", "\n", "[41559 rows x 3 columns]" ] }, "execution_count": 85, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bm_df" ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "editable": true }, "outputs": [], "source": [ "ret_df = pd.merge(ret_df,bm_df,on=['secID','bm_date'])" ] }, { "cell_type": "code", "execution_count": 87, "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
.................................
416073601999.XSHG2009-12-0.0250750.001516-0.0265912009-111.709400e+091.14282008-121.413367
416074601999.XSHG2010-010.1456600.0015530.1441072009-121.666480e+091.15622008-121.413367
416075601999.XSHG2010-020.1124050.0016040.1108012010-011.909200e+091.06572008-121.413367
416076601999.XSHG2010-030.0459810.0016190.0443622010-022.123800e+091.03072008-121.413367
416077601999.XSHG2010-04-0.1059540.001616-0.1075702010-032.221480e+090.98312008-121.413367
416078601999.XSHG2010-05-0.0924060.001646-0.0940522010-041.986160e+090.98182008-121.413367
416079601999.XSHG2010-06-0.1625270.002004-0.1645312010-051.802640e+090.88132008-121.413367
416080601999.XSHG2010-070.1619670.0021340.1598332010-061.509600e+090.92912009-120.880399
\n", "

33946 rows × 10 columns

\n", "
" ], "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", "416073 601999.XSHG 2009-12 -0.025075 0.001516 -0.026591 2009-11 \n", "416074 601999.XSHG 2010-01 0.145660 0.001553 0.144107 2009-12 \n", "416075 601999.XSHG 2010-02 0.112405 0.001604 0.110801 2010-01 \n", "416076 601999.XSHG 2010-03 0.045981 0.001619 0.044362 2010-02 \n", "416077 601999.XSHG 2010-04 -0.105954 0.001616 -0.107570 2010-03 \n", "416078 601999.XSHG 2010-05 -0.092406 0.001646 -0.094052 2010-04 \n", "416079 601999.XSHG 2010-06 -0.162527 0.002004 -0.164531 2010-05 \n", "416080 601999.XSHG 2010-07 0.161967 0.002134 0.159833 2010-06 \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", "416073 1.709400e+09 1.1428 2008-12 1.413367 \n", "416074 1.666480e+09 1.1562 2008-12 1.413367 \n", "416075 1.909200e+09 1.0657 2008-12 1.413367 \n", "416076 2.123800e+09 1.0307 2008-12 1.413367 \n", "416077 2.221480e+09 0.9831 2008-12 1.413367 \n", "416078 1.986160e+09 0.9818 2008-12 1.413367 \n", "416079 1.802640e+09 0.8813 2008-12 1.413367 \n", "416080 1.509600e+09 0.9291 2009-12 0.880399 \n", "\n", "[33946 rows x 10 columns]" ] }, "execution_count": 87, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ret_df[ret_df['ret_date']<='2010-07']" ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "editable": true }, "outputs": [ { "data": { "text/plain": [ "22" ] }, "execution_count": 88, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gc.collect()" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "# Sorting on BM" ] }, { "cell_type": "code", "execution_count": 89, "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": 90, "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": 91, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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q1q2q3q4q5q6q7q8q9
bm_date
2007-120.1649410.2157840.2692670.3194760.3775180.4429530.5418840.6387710.832079
2008-120.4077480.5289420.6425220.7585670.9041191.0722941.2710451.5536932.021077
2009-120.1553730.2089790.2539320.3077310.3630820.4141680.5182150.6876700.926290
2010-120.1456000.2094280.2652090.3233510.4082390.5173880.6795190.9179261.266695
2011-120.2482620.3477200.4462470.5512240.6845220.8455151.0937221.4314391.936640
2012-120.2672480.3684930.4675940.5704200.6939800.8607291.0735001.3838081.837972
2013-120.2099010.2991020.3915240.4690510.5603930.6633690.7956270.9896551.332001
2014-120.1764420.2457480.3076760.3694870.4277290.4978320.5894960.7142110.907696
2015-120.1147310.1639490.2039590.2498190.2959920.3528600.4145030.5168540.712337
2016-120.1648440.2279400.2859540.3410080.3953550.4639950.5436220.6627940.880151
2017-120.2194400.3034500.3869040.4609450.5380400.6308240.7434080.8763991.140541
2018-120.3172190.4380590.5444720.6454810.7619520.8678761.0282481.2240021.564038
2019-120.2348290.3387850.4306940.5230640.6160380.7271100.8750991.0743921.391731
2020-120.1892330.2848240.3690580.4562950.5549920.6635820.8121821.0110781.379245
2021-120.1679100.2505400.3282660.4025520.4964010.6051250.7492510.9734591.324875
\n", "
" ], "text/plain": [ " q1 q2 q3 q4 q5 q6 q7 \\\n", "bm_date \n", "2007-12 0.164941 0.215784 0.269267 0.319476 0.377518 0.442953 0.541884 \n", "2008-12 0.407748 0.528942 0.642522 0.758567 0.904119 1.072294 1.271045 \n", "2009-12 0.155373 0.208979 0.253932 0.307731 0.363082 0.414168 0.518215 \n", "2010-12 0.145600 0.209428 0.265209 0.323351 0.408239 0.517388 0.679519 \n", "2011-12 0.248262 0.347720 0.446247 0.551224 0.684522 0.845515 1.093722 \n", "2012-12 0.267248 0.368493 0.467594 0.570420 0.693980 0.860729 1.073500 \n", "2013-12 0.209901 0.299102 0.391524 0.469051 0.560393 0.663369 0.795627 \n", "2014-12 0.176442 0.245748 0.307676 0.369487 0.427729 0.497832 0.589496 \n", "2015-12 0.114731 0.163949 0.203959 0.249819 0.295992 0.352860 0.414503 \n", "2016-12 0.164844 0.227940 0.285954 0.341008 0.395355 0.463995 0.543622 \n", "2017-12 0.219440 0.303450 0.386904 0.460945 0.538040 0.630824 0.743408 \n", "2018-12 0.317219 0.438059 0.544472 0.645481 0.761952 0.867876 1.028248 \n", "2019-12 0.234829 0.338785 0.430694 0.523064 0.616038 0.727110 0.875099 \n", "2020-12 0.189233 0.284824 0.369058 0.456295 0.554992 0.663582 0.812182 \n", "2021-12 0.167910 0.250540 0.328266 0.402552 0.496401 0.605125 0.749251 \n", "\n", " q8 q9 \n", "bm_date \n", "2007-12 0.638771 0.832079 \n", "2008-12 1.553693 2.021077 \n", "2009-12 0.687670 0.926290 \n", "2010-12 0.917926 1.266695 \n", "2011-12 1.431439 1.936640 \n", "2012-12 1.383808 1.837972 \n", "2013-12 0.989655 1.332001 \n", "2014-12 0.714211 0.907696 \n", "2015-12 0.516854 0.712337 \n", "2016-12 0.662794 0.880151 \n", "2017-12 0.876399 1.140541 \n", "2018-12 1.224002 1.564038 \n", "2019-12 1.074392 1.391731 \n", "2020-12 1.011078 1.379245 \n", "2021-12 0.973459 1.324875 " ] }, "execution_count": 91, "metadata": {}, "output_type": "execute_result" } ], "source": [ "quantile_df" ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "editable": true }, "outputs": [], "source": [ "ret_df_q = pd.merge(ret_df, quantile_df, on='bm_date')" ] }, { "cell_type": "code", "execution_count": 93, "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+101.06722007-120.1978220.1649410.2157840.2692670.3194760.3775180.4429530.5418840.6387710.832079
1000001.XSHE2008-08-0.0288460.003604-0.0324502008-074.455369e+101.09662007-120.1978220.1649410.2157840.2692670.3194760.3775180.4429530.5418840.6387710.832079
2000001.XSHE2008-09-0.2579220.003591-0.2615132008-084.326849e+101.03862007-120.1978220.1649410.2157840.2692670.3194760.3775180.4429530.5418840.6387710.832079
3000001.XSHE2008-10-0.2719590.003522-0.2754812008-093.210865e+101.11842007-120.1978220.1649410.2157840.2692670.3194760.3775180.4429530.5418840.6387710.832079
4000001.XSHE2008-110.0740750.0030630.0710122008-102.330715e+101.19912007-120.1978220.1649410.2157840.2692670.3194760.3775180.4429530.5418840.6387710.832079
5000001.XSHE2008-120.0522790.0019080.0503712008-112.503361e+101.21922007-120.1978220.1649410.2157840.2692670.3194760.3775180.4429530.5418840.6387710.832079
6000001.XSHE2009-010.2304460.0012560.2291902008-122.634237e+101.22062007-120.1978220.1649410.2157840.2692670.3194760.3775180.4429530.5418840.6387710.832079
7000001.XSHE2009-020.1855670.0010880.1844792009-013.241281e+101.25142007-120.1978220.1649410.2157840.2692670.3194760.3775180.4429530.5418840.6387710.832079
............................................................
455411689009.XSHG2022-070.1150560.0016200.1134362022-062.264534e+100.90712021-120.1377160.1679100.2505400.3282660.4025520.4964010.6051250.7492510.9734591.324875
455412689009.XSHG2022-08-0.1126560.001366-0.1140222022-072.525082e+100.79872021-120.1377160.1679100.2505400.3282660.4025520.4964010.6051250.7492510.9734591.324875
455413689009.XSHG2022-09-0.1299110.001342-0.1312532022-082.240616e+100.85892021-120.1377160.1679100.2505400.3282660.4025520.4964010.6051250.7492510.9734591.324875
455414689009.XSHG2022-10-0.1647090.001413-0.1661222022-091.949535e+100.91062021-120.1377160.1679100.2505400.3282660.4025520.4964010.6051250.7492510.9734591.324875
455415689009.XSHG2022-110.0431250.0016760.0414492022-101.637440e+100.70832021-120.1377160.1679100.2505400.3282660.4025520.4964010.6051250.7492510.9734591.324875
455416689009.XSHG2022-12-0.0865790.001931-0.0885102022-111.708055e+100.73632021-120.1377160.1679100.2505400.3282660.4025520.4964010.6051250.7492510.9734591.324875
455417689009.XSHG2023-010.0885540.0020130.0865412022-121.560173e+100.69192021-120.1377160.1679100.2505400.3282660.4025520.4964010.6051250.7492510.9734591.324875
455418689009.XSHG2023-02-0.0057250.002013-0.0077382023-011.698332e+100.73792021-120.1377160.1679100.2505400.3282660.4025520.4964010.6051250.7492510.9734591.324875
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455419 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", "455411 689009.XSHG 2022-07 0.115056 0.001620 0.113436 2022-06 \n", "455412 689009.XSHG 2022-08 -0.112656 0.001366 -0.114022 2022-07 \n", "455413 689009.XSHG 2022-09 -0.129911 0.001342 -0.131253 2022-08 \n", "455414 689009.XSHG 2022-10 -0.164709 0.001413 -0.166122 2022-09 \n", "455415 689009.XSHG 2022-11 0.043125 0.001676 0.041449 2022-10 \n", "455416 689009.XSHG 2022-12 -0.086579 0.001931 -0.088510 2022-11 \n", "455417 689009.XSHG 2023-01 0.088554 0.002013 0.086541 2022-12 \n", "455418 689009.XSHG 2023-02 -0.005725 0.002013 -0.007738 2023-01 \n", "\n", " mkt_cap beta bm_date bm q1 q2 q3 \\\n", "0 4.140495e+10 1.0672 2007-12 0.197822 0.164941 0.215784 0.269267 \n", "1 4.455369e+10 1.0966 2007-12 0.197822 0.164941 0.215784 0.269267 \n", "2 4.326849e+10 1.0386 2007-12 0.197822 0.164941 0.215784 0.269267 \n", "3 3.210865e+10 1.1184 2007-12 0.197822 0.164941 0.215784 0.269267 \n", "4 2.330715e+10 1.1991 2007-12 0.197822 0.164941 0.215784 0.269267 \n", "5 2.503361e+10 1.2192 2007-12 0.197822 0.164941 0.215784 0.269267 \n", "6 2.634237e+10 1.2206 2007-12 0.197822 0.164941 0.215784 0.269267 \n", "7 3.241281e+10 1.2514 2007-12 0.197822 0.164941 0.215784 0.269267 \n", "... ... ... ... ... ... ... ... \n", "455411 2.264534e+10 0.9071 2021-12 0.137716 0.167910 0.250540 0.328266 \n", "455412 2.525082e+10 0.7987 2021-12 0.137716 0.167910 0.250540 0.328266 \n", "455413 2.240616e+10 0.8589 2021-12 0.137716 0.167910 0.250540 0.328266 \n", "455414 1.949535e+10 0.9106 2021-12 0.137716 0.167910 0.250540 0.328266 \n", "455415 1.637440e+10 0.7083 2021-12 0.137716 0.167910 0.250540 0.328266 \n", "455416 1.708055e+10 0.7363 2021-12 0.137716 0.167910 0.250540 0.328266 \n", "455417 1.560173e+10 0.6919 2021-12 0.137716 0.167910 0.250540 0.328266 \n", "455418 1.698332e+10 0.7379 2021-12 0.137716 0.167910 0.250540 0.328266 \n", "\n", " q4 q5 q6 q7 q8 q9 \n", "0 0.319476 0.377518 0.442953 0.541884 0.638771 0.832079 \n", "1 0.319476 0.377518 0.442953 0.541884 0.638771 0.832079 \n", "2 0.319476 0.377518 0.442953 0.541884 0.638771 0.832079 \n", "3 0.319476 0.377518 0.442953 0.541884 0.638771 0.832079 \n", "4 0.319476 0.377518 0.442953 0.541884 0.638771 0.832079 \n", "5 0.319476 0.377518 0.442953 0.541884 0.638771 0.832079 \n", "6 0.319476 0.377518 0.442953 0.541884 0.638771 0.832079 \n", "7 0.319476 0.377518 0.442953 0.541884 0.638771 0.832079 \n", "... ... ... ... ... ... ... \n", "455411 0.402552 0.496401 0.605125 0.749251 0.973459 1.324875 \n", "455412 0.402552 0.496401 0.605125 0.749251 0.973459 1.324875 \n", "455413 0.402552 0.496401 0.605125 0.749251 0.973459 1.324875 \n", "455414 0.402552 0.496401 0.605125 0.749251 0.973459 1.324875 \n", "455415 0.402552 0.496401 0.605125 0.749251 0.973459 1.324875 \n", "455416 0.402552 0.496401 0.605125 0.749251 0.973459 1.324875 \n", "455417 0.402552 0.496401 0.605125 0.749251 0.973459 1.324875 \n", "455418 0.402552 0.496401 0.605125 0.749251 0.973459 1.324875 \n", "\n", "[455419 rows x 19 columns]" ] }, "execution_count": 93, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ret_df_q" ] }, { "cell_type": "code", "execution_count": 94, "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": 95, "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
.................................
455307688777.XSHG2022-070.0972960.0016200.0956762022-061.979643e+100.99412021-120.219181
455308688777.XSHG2022-08-0.0027480.001366-0.0041142022-072.172275e+100.95042021-120.219181
455309688777.XSHG2022-09-0.0188370.001342-0.0201792022-082.159338e+100.84832021-120.219181
455310688777.XSHG2022-100.2400070.0014130.2385942022-092.500795e+100.79702021-120.219181
455311688777.XSHG2022-11-0.0613170.001676-0.0629932022-103.101025e+100.72262021-120.219181
455312688777.XSHG2022-12-0.0011030.001931-0.0030342022-112.984336e+100.36772021-120.219181
455313688777.XSHG2023-010.0133220.0020130.0113092022-122.981054e+100.35752021-120.219181
455314688777.XSHG2023-020.0647610.0020130.0627482023-013.020767e+100.29202021-120.219181
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45663 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", "455307 688777.XSHG 2022-07 0.097296 0.001620 0.095676 2022-06 \n", "455308 688777.XSHG 2022-08 -0.002748 0.001366 -0.004114 2022-07 \n", "455309 688777.XSHG 2022-09 -0.018837 0.001342 -0.020179 2022-08 \n", "455310 688777.XSHG 2022-10 0.240007 0.001413 0.238594 2022-09 \n", "455311 688777.XSHG 2022-11 -0.061317 0.001676 -0.062993 2022-10 \n", "455312 688777.XSHG 2022-12 -0.001103 0.001931 -0.003034 2022-11 \n", "455313 688777.XSHG 2023-01 0.013322 0.002013 0.011309 2022-12 \n", "455314 688777.XSHG 2023-02 0.064761 0.002013 0.062748 2023-01 \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", "455307 1.979643e+10 0.9941 2021-12 0.219181 \n", "455308 2.172275e+10 0.9504 2021-12 0.219181 \n", "455309 2.159338e+10 0.8483 2021-12 0.219181 \n", "455310 2.500795e+10 0.7970 2021-12 0.219181 \n", "455311 3.101025e+10 0.7226 2021-12 0.219181 \n", "455312 2.984336e+10 0.3677 2021-12 0.219181 \n", "455313 2.981054e+10 0.3575 2021-12 0.219181 \n", "455314 3.020767e+10 0.2920 2021-12 0.219181 \n", "\n", "[45663 rows x 10 columns]" ] }, "execution_count": 95, "metadata": {}, "output_type": "execute_result" } ], "source": [ "portfolios['p2']" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "## return by portfolios" ] }, { "cell_type": "code", "execution_count": 96, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Int64Index: 45663 entries, 0 to 455314\n", "Data columns (total 10 columns):\n", "secID 45663 non-null object\n", "ret_date 45663 non-null period[M]\n", "ret 45663 non-null float64\n", "rf 45663 non-null float64\n", "exret 45663 non-null float64\n", "mktcap_beta_date 45663 non-null period[M]\n", "mkt_cap 45663 non-null float64\n", "beta 44580 non-null float64\n", "bm_date 45663 non-null period[M]\n", "bm 45663 non-null float64\n", "dtypes: float64(6), object(1), period[M](3)\n", "memory usage: 3.8+ MB\n" ] } ], "source": [ "portfolios['p2'].info()" ] }, { "cell_type": "code", "execution_count": 97, "metadata": { "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.0072350610306788\n", "0.008844749962653749\n", "0.010848977354697274\n", "0.011225529519617812\n", "0.011914979445716047\n", "0.012396644493784302\n", "0.012503499785698357\n", "0.012803981513003439\n", "0.011792982655524654\n", "0.009439461559541758\n" ] } ], "source": [ "for k in portfolios.keys():\n", " print(portfolios[k].groupby(['ret_date'])['exret'].mean().mean())" ] }, { "cell_type": "code", "execution_count": 98, "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": 99, "metadata": { "editable": true }, "outputs": [ { "data": { "text/plain": [ "ret_date\n", "2008-07 0.066537\n", "2008-08 -0.257046\n", "2008-09 -0.075499\n", "2008-10 -0.280811\n", "2008-11 0.210002\n", "2008-12 0.066290\n", "2009-01 0.161217\n", "2009-02 0.085603\n", " ... \n", "2022-07 -0.005150\n", "2022-08 -0.045974\n", "2022-09 -0.085512\n", "2022-10 0.025376\n", "2022-11 0.055098\n", "2022-12 -0.030963\n", "2023-01 0.081680\n", "2023-02 -0.006816\n", "Freq: M, Name: exret, Length: 176, dtype: float64" ] }, "execution_count": 99, "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": 100, "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['p9'] - 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['p9-p1'] = reg.params[0]\n", "t_values['p9-p1'] = reg.tvalues[0]" ] }, { "cell_type": "code", "execution_count": 101, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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p1p2p3p4p5p6p7p8p9p10p9-p1
mean0.0072350.0088450.0108490.0112260.0119150.0123970.0125030.0128040.0117930.0094390.004558
t-value1.1031641.3687361.6836171.7448121.8613521.8433391.8836471.9686201.7968471.4998822.170096
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" ], "text/plain": [ " p1 p2 p3 p4 p5 p6 p7 \\\n", "mean 0.007235 0.008845 0.010849 0.011226 0.011915 0.012397 0.012503 \n", "t-value 1.103164 1.368736 1.683617 1.744812 1.861352 1.843339 1.883647 \n", "\n", " p8 p9 p10 p9-p1 \n", "mean 0.012804 0.011793 0.009439 0.004558 \n", "t-value 1.968620 1.796847 1.499882 2.170096 " ] }, "execution_count": 101, "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": 102, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDret_dateretrfexretmktcap_beta_datemkt_capbetabm_datebm
382000060.XSHE2008-07-0.0064250.003682-0.0101072008-069.792492e+091.41882007-120.161566
383000060.XSHE2008-08-0.2412010.003604-0.2448052008-079.729630e+091.40212007-120.161566
384000060.XSHE2008-09-0.0132500.003591-0.0168412008-087.382785e+091.39252007-120.161566
385000060.XSHE2008-10-0.3806330.003522-0.3841552008-097.285000e+091.31342007-120.161566
386000060.XSHE2008-110.1455180.0030630.1424552008-104.512090e+091.31972007-120.161566
387000060.XSHE2008-120.0540540.0019080.0521462008-115.511400e+091.27902007-120.161566
388000060.XSHE2009-010.3525550.0012560.3512992008-125.809067e+091.28432007-120.161566
389000060.XSHE2009-020.0540330.0010880.0529452009-017.857136e+091.35172007-120.161566
.................................
455411689009.XSHG2022-070.1150560.0016200.1134362022-062.264534e+100.90712021-120.137716
455412689009.XSHG2022-08-0.1126560.001366-0.1140222022-072.525082e+100.79872021-120.137716
455413689009.XSHG2022-09-0.1299110.001342-0.1312532022-082.240616e+100.85892021-120.137716
455414689009.XSHG2022-10-0.1647090.001413-0.1661222022-091.949535e+100.91062021-120.137716
455415689009.XSHG2022-110.0431250.0016760.0414492022-101.637440e+100.70832021-120.137716
455416689009.XSHG2022-12-0.0865790.001931-0.0885102022-111.708055e+100.73632021-120.137716
455417689009.XSHG2023-010.0885540.0020130.0865412022-121.560173e+100.69192021-120.137716
455418689009.XSHG2023-02-0.0057250.002013-0.0077382023-011.698332e+100.73792021-120.137716
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45634 rows × 10 columns

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" ], "text/plain": [ " secID ret_date ret rf exret mktcap_beta_date \\\n", "382 000060.XSHE 2008-07 -0.006425 0.003682 -0.010107 2008-06 \n", "383 000060.XSHE 2008-08 -0.241201 0.003604 -0.244805 2008-07 \n", "384 000060.XSHE 2008-09 -0.013250 0.003591 -0.016841 2008-08 \n", "385 000060.XSHE 2008-10 -0.380633 0.003522 -0.384155 2008-09 \n", "386 000060.XSHE 2008-11 0.145518 0.003063 0.142455 2008-10 \n", "387 000060.XSHE 2008-12 0.054054 0.001908 0.052146 2008-11 \n", "388 000060.XSHE 2009-01 0.352555 0.001256 0.351299 2008-12 \n", "389 000060.XSHE 2009-02 0.054033 0.001088 0.052945 2009-01 \n", "... ... ... ... ... ... ... \n", "455411 689009.XSHG 2022-07 0.115056 0.001620 0.113436 2022-06 \n", "455412 689009.XSHG 2022-08 -0.112656 0.001366 -0.114022 2022-07 \n", "455413 689009.XSHG 2022-09 -0.129911 0.001342 -0.131253 2022-08 \n", "455414 689009.XSHG 2022-10 -0.164709 0.001413 -0.166122 2022-09 \n", "455415 689009.XSHG 2022-11 0.043125 0.001676 0.041449 2022-10 \n", "455416 689009.XSHG 2022-12 -0.086579 0.001931 -0.088510 2022-11 \n", "455417 689009.XSHG 2023-01 0.088554 0.002013 0.086541 2022-12 \n", "455418 689009.XSHG 2023-02 -0.005725 0.002013 -0.007738 2023-01 \n", "\n", " mkt_cap beta bm_date bm \n", "382 9.792492e+09 1.4188 2007-12 0.161566 \n", "383 9.729630e+09 1.4021 2007-12 0.161566 \n", "384 7.382785e+09 1.3925 2007-12 0.161566 \n", "385 7.285000e+09 1.3134 2007-12 0.161566 \n", "386 4.512090e+09 1.3197 2007-12 0.161566 \n", "387 5.511400e+09 1.2790 2007-12 0.161566 \n", "388 5.809067e+09 1.2843 2007-12 0.161566 \n", "389 7.857136e+09 1.3517 2007-12 0.161566 \n", "... ... ... ... ... \n", "455411 2.264534e+10 0.9071 2021-12 0.137716 \n", "455412 2.525082e+10 0.7987 2021-12 0.137716 \n", "455413 2.240616e+10 0.8589 2021-12 0.137716 \n", "455414 1.949535e+10 0.9106 2021-12 0.137716 \n", "455415 1.637440e+10 0.7083 2021-12 0.137716 \n", "455416 1.708055e+10 0.7363 2021-12 0.137716 \n", "455417 1.560173e+10 0.6919 2021-12 0.137716 \n", "455418 1.698332e+10 0.7379 2021-12 0.137716 \n", "\n", "[45634 rows x 10 columns]" ] }, "execution_count": 102, "metadata": {}, "output_type": "execute_result" } ], "source": [ "portfolios['p1']" ] }, { "cell_type": "code", "execution_count": 103, "metadata": { "editable": true }, "outputs": [], "source": [ "portfolios[k]['1+ret'] = portfolios[k]['ret']+1\n", "portfolios[k]['1+rf'] = portfolios[k]['rf']+1" ] }, { "cell_type": "code", "execution_count": 104, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDret_dateretrfexretmktcap_beta_datemkt_capbetabm_datebm1+ret1+rf
60000016.XSHE2008-070.0773250.0036820.0736432008-062.249663e+091.02762007-121.4256491.0773251.003682
61000016.XSHE2008-08-0.1856350.003604-0.1892392008-072.423636e+091.00782007-121.4256490.8143651.003604
62000016.XSHE2008-09-0.0243260.003591-0.0279172008-081.973704e+091.08772007-121.4256490.9756741.003591
63000016.XSHE2008-10-0.1152670.003522-0.1187892008-091.925711e+091.07452007-121.4256490.8847331.003522
64000016.XSHE2008-110.1514190.0030630.1483562008-101.703744e+091.01762007-121.4256491.1514191.003063
65000016.XSHE2008-12-0.0367120.001908-0.0386202008-111.961706e+091.06402007-121.4256490.9632881.001908
66000016.XSHE2009-010.0857350.0012560.0844792008-121.889712e+091.05352007-121.4256491.0857351.001256
67000016.XSHE2009-020.2660600.0010880.2649722009-012.051688e+091.03062007-121.4256491.2660601.001088
.......................................
455395688819.XSHG2022-070.2105400.0016200.2089202022-064.443776e+091.01912021-122.6992311.2105401.001620
455396688819.XSHG2022-08-0.0690370.001366-0.0704032022-075.379307e+091.11292021-122.6992310.9309631.001366
455397688819.XSHG2022-09-0.1557700.001342-0.1571122022-085.069038e+090.97532021-122.6992310.8442301.001342
455398688819.XSHG2022-100.2468200.0014130.2454072022-094.279481e+091.11592021-122.6992311.2468201.001413
455399688819.XSHG2022-11-0.0702140.001676-0.0718902022-105.335693e+091.02722021-122.6992310.9297861.001676
455400688819.XSHG2022-12-0.0367040.001931-0.0386352022-114.961076e+090.65042021-122.6992310.9632961.001931
455401688819.XSHG2023-010.0743070.0020130.0722942022-124.778970e+090.58772021-122.6992311.0743071.002013
455402688819.XSHG2023-02-0.0587850.002013-0.0607982023-015.166623e+090.55942021-122.6992310.9412151.002013
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45646 rows × 12 columns

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" ], "text/plain": [ " secID ret_date ret rf exret mktcap_beta_date \\\n", "60 000016.XSHE 2008-07 0.077325 0.003682 0.073643 2008-06 \n", "61 000016.XSHE 2008-08 -0.185635 0.003604 -0.189239 2008-07 \n", "62 000016.XSHE 2008-09 -0.024326 0.003591 -0.027917 2008-08 \n", "63 000016.XSHE 2008-10 -0.115267 0.003522 -0.118789 2008-09 \n", "64 000016.XSHE 2008-11 0.151419 0.003063 0.148356 2008-10 \n", "65 000016.XSHE 2008-12 -0.036712 0.001908 -0.038620 2008-11 \n", "66 000016.XSHE 2009-01 0.085735 0.001256 0.084479 2008-12 \n", "67 000016.XSHE 2009-02 0.266060 0.001088 0.264972 2009-01 \n", "... ... ... ... ... ... ... \n", "455395 688819.XSHG 2022-07 0.210540 0.001620 0.208920 2022-06 \n", "455396 688819.XSHG 2022-08 -0.069037 0.001366 -0.070403 2022-07 \n", "455397 688819.XSHG 2022-09 -0.155770 0.001342 -0.157112 2022-08 \n", "455398 688819.XSHG 2022-10 0.246820 0.001413 0.245407 2022-09 \n", "455399 688819.XSHG 2022-11 -0.070214 0.001676 -0.071890 2022-10 \n", "455400 688819.XSHG 2022-12 -0.036704 0.001931 -0.038635 2022-11 \n", "455401 688819.XSHG 2023-01 0.074307 0.002013 0.072294 2022-12 \n", "455402 688819.XSHG 2023-02 -0.058785 0.002013 -0.060798 2023-01 \n", "\n", " mkt_cap beta bm_date bm 1+ret 1+rf \n", "60 2.249663e+09 1.0276 2007-12 1.425649 1.077325 1.003682 \n", "61 2.423636e+09 1.0078 2007-12 1.425649 0.814365 1.003604 \n", "62 1.973704e+09 1.0877 2007-12 1.425649 0.975674 1.003591 \n", "63 1.925711e+09 1.0745 2007-12 1.425649 0.884733 1.003522 \n", "64 1.703744e+09 1.0176 2007-12 1.425649 1.151419 1.003063 \n", "65 1.961706e+09 1.0640 2007-12 1.425649 0.963288 1.001908 \n", "66 1.889712e+09 1.0535 2007-12 1.425649 1.085735 1.001256 \n", "67 2.051688e+09 1.0306 2007-12 1.425649 1.266060 1.001088 \n", "... ... ... ... ... ... ... \n", "455395 4.443776e+09 1.0191 2021-12 2.699231 1.210540 1.001620 \n", "455396 5.379307e+09 1.1129 2021-12 2.699231 0.930963 1.001366 \n", "455397 5.069038e+09 0.9753 2021-12 2.699231 0.844230 1.001342 \n", "455398 4.279481e+09 1.1159 2021-12 2.699231 1.246820 1.001413 \n", "455399 5.335693e+09 1.0272 2021-12 2.699231 0.929786 1.001676 \n", "455400 4.961076e+09 0.6504 2021-12 2.699231 0.963296 1.001931 \n", "455401 4.778970e+09 0.5877 2021-12 2.699231 1.074307 1.002013 \n", "455402 5.166623e+09 0.5594 2021-12 2.699231 0.941215 1.002013 \n", "\n", "[45646 rows x 12 columns]" ] }, "execution_count": 104, "metadata": {}, "output_type": "execute_result" } ], "source": [ "portfolios[k]" ] }, { "cell_type": "code", "execution_count": 105, "metadata": { "editable": true }, "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
............
3957688722.XSHG2021-121.234767
3958688728.XSHG2021-120.838615
3959688737.XSHG2021-120.996613
3960688739.XSHG2021-120.668178
3961688767.XSHG2021-120.752507
3962688799.XSHG2021-121.125962
3963688819.XSHG2021-121.074326
3964688981.XSHG2020-120.730670
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3965 rows × 3 columns

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" ], "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", "3957 688722.XSHG 2021-12 1.234767\n", "3958 688728.XSHG 2021-12 0.838615\n", "3959 688737.XSHG 2021-12 0.996613\n", "3960 688739.XSHG 2021-12 0.668178\n", "3961 688767.XSHG 2021-12 0.752507\n", "3962 688799.XSHG 2021-12 1.125962\n", "3963 688819.XSHG 2021-12 1.074326\n", "3964 688981.XSHG 2020-12 0.730670\n", "\n", "[3965 rows x 3 columns]" ] }, "execution_count": 105, "metadata": {}, "output_type": "execute_result" } ], "source": [ "portfolios[k].groupby(['secID','bm_date'],as_index=False)['1+ret'].prod()" ] }, { "cell_type": "code", "execution_count": 106, "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": 107, "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.XSHE2014-120.9566221.030621-0.0433780.030621-0.073999
4000004.XSHE2015-120.6964451.036393-0.3035550.036393-0.339948
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
........................
4047688390.XSHG2021-121.2411431.0134520.2411430.0134520.227691
4048688516.XSHG2021-121.0640731.0134520.0640730.0134520.050621
4049688518.XSHG2021-121.0408751.0134520.0408750.0134520.027423
4050688536.XSHG2021-120.6761111.013452-0.3238890.013452-0.337341
4051688556.XSHG2021-120.9157411.013452-0.0842590.013452-0.097711
4052688595.XSHG2021-120.7314151.013452-0.2685850.013452-0.282037
4053688598.XSHG2021-120.7338281.013452-0.2661720.013452-0.279624
4054689009.XSHG2021-120.7415731.013452-0.2584270.013452-0.271879
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4055 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 1.030621 -0.043378 0.030621 -0.073999\n", "4 000004.XSHE 2015-12 0.696445 1.036393 -0.303555 0.036393 -0.339948\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", "4047 688390.XSHG 2021-12 1.241143 1.013452 0.241143 0.013452 0.227691\n", "4048 688516.XSHG 2021-12 1.064073 1.013452 0.064073 0.013452 0.050621\n", "4049 688518.XSHG 2021-12 1.040875 1.013452 0.040875 0.013452 0.027423\n", "4050 688536.XSHG 2021-12 0.676111 1.013452 -0.323889 0.013452 -0.337341\n", "4051 688556.XSHG 2021-12 0.915741 1.013452 -0.084259 0.013452 -0.097711\n", "4052 688595.XSHG 2021-12 0.731415 1.013452 -0.268585 0.013452 -0.282037\n", "4053 688598.XSHG 2021-12 0.733828 1.013452 -0.266172 0.013452 -0.279624\n", "4054 689009.XSHG 2021-12 0.741573 1.013452 -0.258427 0.013452 -0.271879\n", "\n", "[4055 rows x 7 columns]" ] }, "execution_count": 107, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pf_year_ret['p1']" ] }, { "cell_type": "code", "execution_count": 108, "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": 109, "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": 110, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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p1p2p3p4p5p6p7p8p9p10p10-p1
mean0.0688640.0879740.1181350.1263760.1354230.1517500.1488700.1585740.1431950.1174290.048565
t-value1.2847891.7334572.1830062.3079702.7538792.2734732.3947272.3397132.0984851.8068681.290878
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" ], "text/plain": [ " p1 p2 p3 p4 p5 p6 p7 \\\n", "mean 0.068864 0.087974 0.118135 0.126376 0.135423 0.151750 0.148870 \n", "t-value 1.284789 1.733457 2.183006 2.307970 2.753879 2.273473 2.394727 \n", "\n", " p8 p9 p10 p10-p1 \n", "mean 0.158574 0.143195 0.117429 0.048565 \n", "t-value 2.339713 2.098485 1.806868 1.290878 " ] }, "execution_count": 110, "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": 111, "metadata": { "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.14615646005784907\n", "0.2558344970352621\n", "0.33475874587831866\n", "0.41124253266416716\n", "0.4935211173021713\n", "0.5892265347229694\n", "0.7088981617503399\n", "0.8728986534940015\n", "1.1189011915748628\n", "2.143066708794292\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": 112, "metadata": { "editable": true }, "outputs": [ { "data": { "text/plain": [ "bm_date\n", "2007-12 134\n", "2008-12 143\n", "2009-12 153\n", "2010-12 189\n", "2011-12 222\n", "2012-12 237\n", "2013-12 244\n", "2014-12 261\n", "2015-12 288\n", "2016-12 303\n", "2017-12 339\n", "2018-12 346\n", "2019-12 364\n", "2020-12 394\n", "2021-12 438\n", "Freq: M, Name: secID, dtype: int64" ] }, "execution_count": 112, "metadata": {}, "output_type": "execute_result" } ], "source": [ "portfolios['p1'].groupby('bm_date')['secID'].nunique()" ] }, { "cell_type": "code", "execution_count": 113, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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p1p2p3p4p5p6p7p8p9p10
bm_date
2007-12134132133131132133132132131132
2008-12143142144142144141142142142141
2009-12153152151151151151150152149150
2010-12189186187186187185186185185185
2011-12222218216216215215216215215214
2012-12237240234235237236235234234235
2013-12244242242240241240241240239240
2014-12261254252252254251254253252251
2015-12288275273274273271274272274271
2016-12303294294292298294293294293292
2017-12339337339339338335335336335335
2018-12346344340340342343341341341342
2019-12364360360354355355354355353354
2020-12394389388388386389387387387386
2021-12438438437438437438438437438437
\n", "
" ], "text/plain": [ " p1 p2 p3 p4 p5 p6 p7 p8 p9 p10\n", "bm_date \n", "2007-12 134 132 133 131 132 133 132 132 131 132\n", "2008-12 143 142 144 142 144 141 142 142 142 141\n", "2009-12 153 152 151 151 151 151 150 152 149 150\n", "2010-12 189 186 187 186 187 185 186 185 185 185\n", "2011-12 222 218 216 216 215 215 216 215 215 214\n", "2012-12 237 240 234 235 237 236 235 234 234 235\n", "2013-12 244 242 242 240 241 240 241 240 239 240\n", "2014-12 261 254 252 252 254 251 254 253 252 251\n", "2015-12 288 275 273 274 273 271 274 272 274 271\n", "2016-12 303 294 294 292 298 294 293 294 293 292\n", "2017-12 339 337 339 339 338 335 335 336 335 335\n", "2018-12 346 344 340 340 342 343 341 341 341 342\n", "2019-12 364 360 360 354 355 355 354 355 353 354\n", "2020-12 394 389 388 388 386 389 387 387 387 386\n", "2021-12 438 438 437 438 437 438 438 437 438 437" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 113, "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": 114, "metadata": { "editable": true }, "outputs": [ { "data": { "text/plain": [ "bm_date\n", "2007-12 9.520442\n", "2008-12 24.801299\n", "2009-12 23.330185\n", "2010-12 7.387187\n", "2011-12 4.310136\n", "2012-12 6.852996\n", "2013-12 24.818758\n", "2014-12 30.266811\n", "2015-12 46.521432\n", "2016-12 49.633862\n", "2017-12 25.116625\n", "2018-12 23.554655\n", "2019-12 25.433189\n", "2020-12 27.416370\n", "2021-12 23.515428\n", "Freq: M, Name: mkt_cap, dtype: float64" ] }, "execution_count": 114, "metadata": {}, "output_type": "execute_result" } ], "source": [ "portfolios['p10'].groupby('bm_date')['mkt_cap'].mean()/1e9" ] }, { "cell_type": "code", "execution_count": 115, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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p1p2p3p4p5p6p7p8p9p10
bm_date
2007-125.715808e+094.795101e+092.882950e+093.434211e+093.840968e+093.304378e+092.571075e+092.325255e+091.692608e+099.520442e+09
2008-128.177124e+099.130319e+097.149702e+096.599234e+097.352643e+095.789497e+095.991050e+095.301314e+097.721629e+092.480130e+10
2009-121.067969e+108.379840e+097.375834e+098.714215e+099.299599e+091.347153e+101.388075e+101.177886e+104.367188e+092.333018e+10
2010-129.788895e+096.836670e+095.396383e+095.858096e+095.716337e+091.145075e+101.965841e+101.488090e+107.589464e+097.387187e+09
2011-128.593670e+095.343388e+095.859148e+098.330202e+091.316118e+108.513601e+091.432788e+107.445566e+093.744470e+094.310136e+09
2012-127.913350e+097.126788e+095.878258e+096.422015e+098.262964e+091.419814e+101.351916e+104.738759e+096.146973e+096.852996e+09
2013-121.039635e+109.947602e+098.807784e+098.933609e+099.016754e+097.248690e+091.388587e+101.694208e+101.861084e+102.481876e+10
2014-121.116183e+101.250609e+101.256581e+101.090602e+101.280498e+101.061681e+101.042628e+101.919891e+101.367151e+103.026681e+10
2015-129.341924e+099.016056e+091.004300e+109.985461e+099.288530e+091.068221e+101.002104e+101.043531e+101.542386e+104.652143e+10
2016-128.644937e+091.294780e+108.000102e+099.380542e+091.058725e+108.635930e+091.149724e+101.146498e+101.620886e+104.963386e+10
2017-121.783157e+109.153566e+097.178220e+099.078995e+098.969729e+098.541627e+098.122416e+091.129418e+101.205107e+102.511663e+10
2018-122.197334e+101.316444e+108.406058e+098.504631e+091.027894e+109.095954e+091.229830e+101.528553e+101.136744e+102.355466e+10
2019-124.097754e+101.884871e+101.227371e+108.953288e+091.343037e+101.333013e+101.564740e+101.098639e+101.182802e+102.543319e+10
2020-125.045492e+101.591886e+101.302870e+101.052350e+108.420125e+091.188646e+101.356596e+101.104882e+101.155407e+102.741637e+10
2021-123.762744e+101.515851e+101.245366e+101.022247e+109.673234e+098.130061e+098.767111e+091.278490e+101.199148e+102.351543e+10
\n", "
" ], "text/plain": [ " p1 p2 p3 p4 p5 \\\n", "bm_date \n", "2007-12 5.715808e+09 4.795101e+09 2.882950e+09 3.434211e+09 3.840968e+09 \n", "2008-12 8.177124e+09 9.130319e+09 7.149702e+09 6.599234e+09 7.352643e+09 \n", "2009-12 1.067969e+10 8.379840e+09 7.375834e+09 8.714215e+09 9.299599e+09 \n", "2010-12 9.788895e+09 6.836670e+09 5.396383e+09 5.858096e+09 5.716337e+09 \n", "2011-12 8.593670e+09 5.343388e+09 5.859148e+09 8.330202e+09 1.316118e+10 \n", "2012-12 7.913350e+09 7.126788e+09 5.878258e+09 6.422015e+09 8.262964e+09 \n", "2013-12 1.039635e+10 9.947602e+09 8.807784e+09 8.933609e+09 9.016754e+09 \n", "2014-12 1.116183e+10 1.250609e+10 1.256581e+10 1.090602e+10 1.280498e+10 \n", "2015-12 9.341924e+09 9.016056e+09 1.004300e+10 9.985461e+09 9.288530e+09 \n", "2016-12 8.644937e+09 1.294780e+10 8.000102e+09 9.380542e+09 1.058725e+10 \n", "2017-12 1.783157e+10 9.153566e+09 7.178220e+09 9.078995e+09 8.969729e+09 \n", "2018-12 2.197334e+10 1.316444e+10 8.406058e+09 8.504631e+09 1.027894e+10 \n", "2019-12 4.097754e+10 1.884871e+10 1.227371e+10 8.953288e+09 1.343037e+10 \n", "2020-12 5.045492e+10 1.591886e+10 1.302870e+10 1.052350e+10 8.420125e+09 \n", "2021-12 3.762744e+10 1.515851e+10 1.245366e+10 1.022247e+10 9.673234e+09 \n", "\n", " p6 p7 p8 p9 p10 \n", "bm_date \n", "2007-12 3.304378e+09 2.571075e+09 2.325255e+09 1.692608e+09 9.520442e+09 \n", "2008-12 5.789497e+09 5.991050e+09 5.301314e+09 7.721629e+09 2.480130e+10 \n", "2009-12 1.347153e+10 1.388075e+10 1.177886e+10 4.367188e+09 2.333018e+10 \n", "2010-12 1.145075e+10 1.965841e+10 1.488090e+10 7.589464e+09 7.387187e+09 \n", "2011-12 8.513601e+09 1.432788e+10 7.445566e+09 3.744470e+09 4.310136e+09 \n", "2012-12 1.419814e+10 1.351916e+10 4.738759e+09 6.146973e+09 6.852996e+09 \n", "2013-12 7.248690e+09 1.388587e+10 1.694208e+10 1.861084e+10 2.481876e+10 \n", "2014-12 1.061681e+10 1.042628e+10 1.919891e+10 1.367151e+10 3.026681e+10 \n", "2015-12 1.068221e+10 1.002104e+10 1.043531e+10 1.542386e+10 4.652143e+10 \n", "2016-12 8.635930e+09 1.149724e+10 1.146498e+10 1.620886e+10 4.963386e+10 \n", "2017-12 8.541627e+09 8.122416e+09 1.129418e+10 1.205107e+10 2.511663e+10 \n", "2018-12 9.095954e+09 1.229830e+10 1.528553e+10 1.136744e+10 2.355466e+10 \n", "2019-12 1.333013e+10 1.564740e+10 1.098639e+10 1.182802e+10 2.543319e+10 \n", "2020-12 1.188646e+10 1.356596e+10 1.104882e+10 1.155407e+10 2.741637e+10 \n", "2021-12 8.130061e+09 8.767111e+09 1.278490e+10 1.199148e+10 2.351543e+10 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 115, "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_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": 116, "metadata": { "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.7285225763373466\n", "1.0551583098923218\n", "0.8486621563464765\n", "0.8389765772572633\n", "0.934024016924777\n", "0.9659718376251994\n", "1.1611995866399725\n", "1.1060784470939953\n", "1.0264631375974875\n", "2.349862499307394\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": 117, "metadata": { "editable": true }, "outputs": [], "source": [ "del portfolios, portfolios_crs_mean" ] }, { "cell_type": "code", "execution_count": 118, "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": 119, "metadata": { "editable": true }, "outputs": [], "source": [ "fundmen_df = pd.read_pickle('./data/fundmen_df_pit.pkl')" ] }, { "cell_type": "code", "execution_count": 120, "metadata": { "editable": true }, "outputs": [], "source": [ "fundmen_df[['publishDate','endDate']] = fundmen_df[['publishDate','endDate']].apply(pd.to_datetime)" ] }, { "cell_type": "code", "execution_count": 121, "metadata": { "editable": true }, "outputs": [], "source": [ "fundmen_df.sort_values(['secID','publishDate','endDate'],inplace=True)" ] }, { "cell_type": "code", "execution_count": 122, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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secIDpublishDateendDateendDateRepactPubtimefiscalPeriodTShEquityTEquityAttrPminorityInt
121000001.XSHE2007-04-262007-03-312007-03-312007-04-25 18:00:0037.106094e+097.106094e+09NaN
120000001.XSHE2007-08-162007-06-302007-06-302007-08-15 18:00:0067.698478e+097.698478e+09NaN
119000001.XSHE2007-10-232007-09-302007-09-302007-10-22 18:00:0098.363553e+098.363553e+09NaN
118000001.XSHE2008-03-202007-12-312007-12-312008-03-19 18:00:00121.300606e+101.300606e+10NaN
117000001.XSHE2008-04-242007-12-312008-03-312008-04-23 18:00:00121.300606e+101.300606e+10NaN
113000001.XSHE2008-04-242008-03-312008-03-312008-04-23 18:00:0031.404138e+101.404138e+10NaN
116000001.XSHE2008-08-212007-12-312008-06-302008-08-20 18:00:00121.300606e+101.300606e+10NaN
112000001.XSHE2008-08-212008-06-302008-06-302008-08-20 18:00:0061.694330e+101.694330e+10NaN
..............................
433177900957.XSHG2022-04-202020-12-312021-12-312022-04-19 17:15:56124.987276e+084.979110e+08816555.06
433173900957.XSHG2022-04-202021-12-312021-12-312022-04-19 17:15:56125.263733e+085.255741e+08799194.04
433172900957.XSHG2022-04-302021-12-312022-03-312022-04-29 15:36:38125.263733e+085.255741e+08799194.04
433169900957.XSHG2022-04-302022-03-312022-03-312022-04-29 15:36:3835.341491e+085.333509e+08798170.28
433171900957.XSHG2022-08-162021-12-312022-06-302022-08-15 16:24:24125.263733e+085.255741e+08799194.04
433168900957.XSHG2022-08-162022-06-302022-06-302022-08-15 16:24:2465.483870e+085.476224e+08764620.52
433170900957.XSHG2022-10-282021-12-312022-09-302022-10-27 16:41:28125.263733e+085.255741e+08799194.04
433167900957.XSHG2022-10-282022-09-302022-09-302022-10-27 16:41:2895.566301e+085.558669e+08763140.90
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433293 rows × 9 columns

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" ], "text/plain": [ " secID publishDate endDate endDateRep actPubtime \\\n", "121 000001.XSHE 2007-04-26 2007-03-31 2007-03-31 2007-04-25 18:00:00 \n", "120 000001.XSHE 2007-08-16 2007-06-30 2007-06-30 2007-08-15 18:00:00 \n", "119 000001.XSHE 2007-10-23 2007-09-30 2007-09-30 2007-10-22 18:00:00 \n", "118 000001.XSHE 2008-03-20 2007-12-31 2007-12-31 2008-03-19 18:00:00 \n", "117 000001.XSHE 2008-04-24 2007-12-31 2008-03-31 2008-04-23 18:00:00 \n", "113 000001.XSHE 2008-04-24 2008-03-31 2008-03-31 2008-04-23 18:00:00 \n", "116 000001.XSHE 2008-08-21 2007-12-31 2008-06-30 2008-08-20 18:00:00 \n", "112 000001.XSHE 2008-08-21 2008-06-30 2008-06-30 2008-08-20 18:00:00 \n", "... ... ... ... ... ... \n", "433177 900957.XSHG 2022-04-20 2020-12-31 2021-12-31 2022-04-19 17:15:56 \n", "433173 900957.XSHG 2022-04-20 2021-12-31 2021-12-31 2022-04-19 17:15:56 \n", "433172 900957.XSHG 2022-04-30 2021-12-31 2022-03-31 2022-04-29 15:36:38 \n", "433169 900957.XSHG 2022-04-30 2022-03-31 2022-03-31 2022-04-29 15:36:38 \n", "433171 900957.XSHG 2022-08-16 2021-12-31 2022-06-30 2022-08-15 16:24:24 \n", "433168 900957.XSHG 2022-08-16 2022-06-30 2022-06-30 2022-08-15 16:24:24 \n", "433170 900957.XSHG 2022-10-28 2021-12-31 2022-09-30 2022-10-27 16:41:28 \n", "433167 900957.XSHG 2022-10-28 2022-09-30 2022-09-30 2022-10-27 16:41:28 \n", "\n", " fiscalPeriod TShEquity TEquityAttrP minorityInt \n", "121 3 7.106094e+09 7.106094e+09 NaN \n", "120 6 7.698478e+09 7.698478e+09 NaN \n", "119 9 8.363553e+09 8.363553e+09 NaN \n", "118 12 1.300606e+10 1.300606e+10 NaN \n", "117 12 1.300606e+10 1.300606e+10 NaN \n", "113 3 1.404138e+10 1.404138e+10 NaN \n", "116 12 1.300606e+10 1.300606e+10 NaN \n", "112 6 1.694330e+10 1.694330e+10 NaN \n", "... ... ... ... ... \n", "433177 12 4.987276e+08 4.979110e+08 816555.06 \n", "433173 12 5.263733e+08 5.255741e+08 799194.04 \n", "433172 12 5.263733e+08 5.255741e+08 799194.04 \n", "433169 3 5.341491e+08 5.333509e+08 798170.28 \n", "433171 12 5.263733e+08 5.255741e+08 799194.04 \n", "433168 6 5.483870e+08 5.476224e+08 764620.52 \n", "433170 12 5.263733e+08 5.255741e+08 799194.04 \n", "433167 9 5.566301e+08 5.558669e+08 763140.90 \n", "\n", "[433293 rows x 9 columns]" ] }, "execution_count": 122, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fundmen_df" ] }, { "cell_type": "code", "execution_count": 123, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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secIDpublishDateendDateendDateRepactPubtimefiscalPeriodTShEquityTEquityAttrPminorityInt
121000001.XSHE2007-04-262007-03-312007-03-312007-04-25 18:00:0037.106094e+097.106094e+09NaN
120000001.XSHE2007-08-162007-06-302007-06-302007-08-15 18:00:0067.698478e+097.698478e+09NaN
119000001.XSHE2007-10-232007-09-302007-09-302007-10-22 18:00:0098.363553e+098.363553e+09NaN
118000001.XSHE2008-03-202007-12-312007-12-312008-03-19 18:00:00121.300606e+101.300606e+10NaN
117000001.XSHE2008-04-242007-12-312008-03-312008-04-23 18:00:00121.300606e+101.300606e+10NaN
113000001.XSHE2008-04-242008-03-312008-03-312008-04-23 18:00:0031.404138e+101.404138e+10NaN
116000001.XSHE2008-08-212007-12-312008-06-302008-08-20 18:00:00121.300606e+101.300606e+10NaN
112000001.XSHE2008-08-212008-06-302008-06-302008-08-20 18:00:0061.694330e+101.694330e+10NaN
..............................
433177900957.XSHG2022-04-202020-12-312021-12-312022-04-19 17:15:56124.987276e+084.979110e+08816555.06
433173900957.XSHG2022-04-202021-12-312021-12-312022-04-19 17:15:56125.263733e+085.255741e+08799194.04
433172900957.XSHG2022-04-302021-12-312022-03-312022-04-29 15:36:38125.263733e+085.255741e+08799194.04
433169900957.XSHG2022-04-302022-03-312022-03-312022-04-29 15:36:3835.341491e+085.333509e+08798170.28
433171900957.XSHG2022-08-162021-12-312022-06-302022-08-15 16:24:24125.263733e+085.255741e+08799194.04
433168900957.XSHG2022-08-162022-06-302022-06-302022-08-15 16:24:2465.483870e+085.476224e+08764620.52
433170900957.XSHG2022-10-282021-12-312022-09-302022-10-27 16:41:28125.263733e+085.255741e+08799194.04
433167900957.XSHG2022-10-282022-09-302022-09-302022-10-27 16:41:2895.566301e+085.558669e+08763140.90
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433293 rows × 9 columns

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" ], "text/plain": [ " secID publishDate endDate endDateRep actPubtime \\\n", "121 000001.XSHE 2007-04-26 2007-03-31 2007-03-31 2007-04-25 18:00:00 \n", "120 000001.XSHE 2007-08-16 2007-06-30 2007-06-30 2007-08-15 18:00:00 \n", "119 000001.XSHE 2007-10-23 2007-09-30 2007-09-30 2007-10-22 18:00:00 \n", "118 000001.XSHE 2008-03-20 2007-12-31 2007-12-31 2008-03-19 18:00:00 \n", "117 000001.XSHE 2008-04-24 2007-12-31 2008-03-31 2008-04-23 18:00:00 \n", "113 000001.XSHE 2008-04-24 2008-03-31 2008-03-31 2008-04-23 18:00:00 \n", "116 000001.XSHE 2008-08-21 2007-12-31 2008-06-30 2008-08-20 18:00:00 \n", "112 000001.XSHE 2008-08-21 2008-06-30 2008-06-30 2008-08-20 18:00:00 \n", "... ... ... ... ... ... \n", "433177 900957.XSHG 2022-04-20 2020-12-31 2021-12-31 2022-04-19 17:15:56 \n", "433173 900957.XSHG 2022-04-20 2021-12-31 2021-12-31 2022-04-19 17:15:56 \n", "433172 900957.XSHG 2022-04-30 2021-12-31 2022-03-31 2022-04-29 15:36:38 \n", "433169 900957.XSHG 2022-04-30 2022-03-31 2022-03-31 2022-04-29 15:36:38 \n", "433171 900957.XSHG 2022-08-16 2021-12-31 2022-06-30 2022-08-15 16:24:24 \n", "433168 900957.XSHG 2022-08-16 2022-06-30 2022-06-30 2022-08-15 16:24:24 \n", "433170 900957.XSHG 2022-10-28 2021-12-31 2022-09-30 2022-10-27 16:41:28 \n", "433167 900957.XSHG 2022-10-28 2022-09-30 2022-09-30 2022-10-27 16:41:28 \n", "\n", " fiscalPeriod TShEquity TEquityAttrP minorityInt \n", "121 3 7.106094e+09 7.106094e+09 NaN \n", "120 6 7.698478e+09 7.698478e+09 NaN \n", "119 9 8.363553e+09 8.363553e+09 NaN \n", "118 12 1.300606e+10 1.300606e+10 NaN \n", "117 12 1.300606e+10 1.300606e+10 NaN \n", "113 3 1.404138e+10 1.404138e+10 NaN \n", "116 12 1.300606e+10 1.300606e+10 NaN \n", "112 6 1.694330e+10 1.694330e+10 NaN \n", "... ... ... ... ... \n", "433177 12 4.987276e+08 4.979110e+08 816555.06 \n", "433173 12 5.263733e+08 5.255741e+08 799194.04 \n", "433172 12 5.263733e+08 5.255741e+08 799194.04 \n", "433169 3 5.341491e+08 5.333509e+08 798170.28 \n", "433171 12 5.263733e+08 5.255741e+08 799194.04 \n", "433168 6 5.483870e+08 5.476224e+08 764620.52 \n", "433170 12 5.263733e+08 5.255741e+08 799194.04 \n", "433167 9 5.566301e+08 5.558669e+08 763140.90 \n", "\n", "[433293 rows x 9 columns]" ] }, "execution_count": 123, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fundmen_df.sort_values(['secID','publishDate'])" ] }, { "cell_type": "code", "execution_count": 124, "metadata": { "editable": true }, "outputs": [], "source": [ "fundmen_df = fundmen_df.groupby(['secID','publishDate'],as_index=False).last() #不涉及上上个报表的信息" ] }, { "cell_type": "code", "execution_count": 125, "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
..............................
195857900957.XSHG2021-04-092020-12-312020-12-312021-04-08 18:13:16124.987276e+084.979110e+08816555.06
195858900957.XSHG2021-04-272021-03-312021-03-312021-04-26 16:30:4535.070935e+085.062701e+08823373.23
195859900957.XSHG2021-08-122021-06-302021-06-302021-08-11 16:03:1065.136414e+085.128208e+08820511.29
195860900957.XSHG2021-10-292021-09-302021-09-302021-10-28 15:35:4295.197039e+085.188844e+08819528.99
195861900957.XSHG2022-04-202021-12-312021-12-312022-04-19 17:15:56125.263733e+085.255741e+08799194.04
195862900957.XSHG2022-04-302022-03-312022-03-312022-04-29 15:36:3835.341491e+085.333509e+08798170.28
195863900957.XSHG2022-08-162022-06-302022-06-302022-08-15 16:24:2465.483870e+085.476224e+08764620.52
195864900957.XSHG2022-10-282022-09-302022-09-302022-10-27 16:41:2895.566301e+085.558669e+08763140.90
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195865 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", "195857 900957.XSHG 2021-04-09 2020-12-31 2020-12-31 2021-04-08 18:13:16 \n", "195858 900957.XSHG 2021-04-27 2021-03-31 2021-03-31 2021-04-26 16:30:45 \n", "195859 900957.XSHG 2021-08-12 2021-06-30 2021-06-30 2021-08-11 16:03:10 \n", "195860 900957.XSHG 2021-10-29 2021-09-30 2021-09-30 2021-10-28 15:35:42 \n", "195861 900957.XSHG 2022-04-20 2021-12-31 2021-12-31 2022-04-19 17:15:56 \n", "195862 900957.XSHG 2022-04-30 2022-03-31 2022-03-31 2022-04-29 15:36:38 \n", "195863 900957.XSHG 2022-08-16 2022-06-30 2022-06-30 2022-08-15 16:24:24 \n", "195864 900957.XSHG 2022-10-28 2022-09-30 2022-09-30 2022-10-27 16:41:28 \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", "195857 12 4.987276e+08 4.979110e+08 816555.06 \n", "195858 3 5.070935e+08 5.062701e+08 823373.23 \n", "195859 6 5.136414e+08 5.128208e+08 820511.29 \n", "195860 9 5.197039e+08 5.188844e+08 819528.99 \n", "195861 12 5.263733e+08 5.255741e+08 799194.04 \n", "195862 3 5.341491e+08 5.333509e+08 798170.28 \n", "195863 6 5.483870e+08 5.476224e+08 764620.52 \n", "195864 9 5.566301e+08 5.558669e+08 763140.90 \n", "\n", "[195865 rows x 9 columns]" ] }, "execution_count": 125, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fundmen_df" ] }, { "cell_type": "code", "execution_count": 126, "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": 127, "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": 128, "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": 129, "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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195857900957.XSHG2021-04-092020-12-312020-12-312021-04-08 18:13:16124.987276e+084.979110e+08816555.064.979110e+08
195858900957.XSHG2021-04-272021-03-312021-03-312021-04-26 16:30:4535.070935e+085.062701e+08823373.235.062701e+08
195859900957.XSHG2021-08-122021-06-302021-06-302021-08-11 16:03:1065.136414e+085.128208e+08820511.295.128208e+08
195860900957.XSHG2021-10-292021-09-302021-09-302021-10-28 15:35:4295.197039e+085.188844e+08819528.995.188844e+08
195861900957.XSHG2022-04-202021-12-312021-12-312022-04-19 17:15:56125.263733e+085.255741e+08799194.045.255741e+08
195862900957.XSHG2022-04-302022-03-312022-03-312022-04-29 15:36:3835.341491e+085.333509e+08798170.285.333509e+08
195863900957.XSHG2022-08-162022-06-302022-06-302022-08-15 16:24:2465.483870e+085.476224e+08764620.525.476224e+08
195864900957.XSHG2022-10-282022-09-302022-09-302022-10-27 16:41:2895.566301e+085.558669e+08763140.905.558669e+08
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195800 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", "195857 900957.XSHG 2021-04-09 2020-12-31 2020-12-31 2021-04-08 18:13:16 \n", "195858 900957.XSHG 2021-04-27 2021-03-31 2021-03-31 2021-04-26 16:30:45 \n", "195859 900957.XSHG 2021-08-12 2021-06-30 2021-06-30 2021-08-11 16:03:10 \n", "195860 900957.XSHG 2021-10-29 2021-09-30 2021-09-30 2021-10-28 15:35:42 \n", "195861 900957.XSHG 2022-04-20 2021-12-31 2021-12-31 2022-04-19 17:15:56 \n", "195862 900957.XSHG 2022-04-30 2022-03-31 2022-03-31 2022-04-29 15:36:38 \n", "195863 900957.XSHG 2022-08-16 2022-06-30 2022-06-30 2022-08-15 16:24:24 \n", "195864 900957.XSHG 2022-10-28 2022-09-30 2022-09-30 2022-10-27 16:41:28 \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", "195857 12 4.987276e+08 4.979110e+08 816555.06 4.979110e+08 \n", "195858 3 5.070935e+08 5.062701e+08 823373.23 5.062701e+08 \n", "195859 6 5.136414e+08 5.128208e+08 820511.29 5.128208e+08 \n", "195860 9 5.197039e+08 5.188844e+08 819528.99 5.188844e+08 \n", "195861 12 5.263733e+08 5.255741e+08 799194.04 5.255741e+08 \n", "195862 3 5.341491e+08 5.333509e+08 798170.28 5.333509e+08 \n", "195863 6 5.483870e+08 5.476224e+08 764620.52 5.476224e+08 \n", "195864 9 5.566301e+08 5.558669e+08 763140.90 5.558669e+08 \n", "\n", "[195800 rows x 10 columns]" ] }, "execution_count": 129, "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": 130, "metadata": { "editable": true }, "outputs": [], "source": [ "# fundmen_df['publishDate+1'] = fundmen_df['publishDate'] + dt.timedelta(days=1)" ] }, { "cell_type": "code", "execution_count": 131, "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": 132, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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0000001.XSHE2007-06-20987.0074.835036e+102007-06NaTNaTNaN
1000001.XSHE2007-06-211085.7405.318694e+102007-06NaTNaTNaN
2000001.XSHE2007-06-221120.2335.487665e+102007-06NaTNaTNaN
3000001.XSHE2007-06-251113.9045.456661e+102007-06NaTNaTNaN
4000001.XSHE2007-06-261113.9045.456661e+102007-06NaTNaTNaN
5000001.XSHE2007-06-271019.6024.994705e+102007-06NaTNaTNaN
6000001.XSHE2007-06-28953.7804.672266e+102007-06NaTNaTNaN
7000001.XSHE2007-06-29870.8704.266117e+102007-06NaTNaTNaN
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11105027900957.XSHGNaTNaNNaNNaT2015-08-152015-06-303.952715e+08
11105028900957.XSHGNaTNaNNaNNaT2016-04-232016-03-314.005150e+08
11105029900957.XSHGNaTNaNNaNNaT2016-08-062016-06-303.906354e+08
11105030900957.XSHGNaTNaNNaNNaT2017-03-252016-12-313.930721e+08
11105031900957.XSHGNaTNaNNaNNaT2019-03-302018-12-314.508051e+08
11105032900957.XSHGNaTNaNNaNNaT2019-08-102019-06-304.618426e+08
11105033900957.XSHGNaTNaNNaNNaT2020-04-252019-12-314.761021e+08
11105034900957.XSHGNaTNaNNaNNaT2022-04-302022-03-315.333509e+08
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11105035 rows × 8 columns

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11105034900957.XSHG2022-04-30NaNNaNNaT2022-04-302022-03-315.333509e+08
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11105035 rows × 8 columns

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11105035 rows × 8 columns

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secIDtradeDateclosePricenegMarketValueympublishDateendDatebook
11048078000001.XSHE2007-04-26NaNNaNNaT2007-04-262007-03-317.106094e+09
0000001.XSHE2007-06-20987.0074.835036e+102007-06NaTNaTNaN
1000001.XSHE2007-06-211085.7405.318694e+102007-06NaTNaTNaN
2000001.XSHE2007-06-221120.2335.487665e+102007-06NaTNaTNaN
3000001.XSHE2007-06-251113.9045.456661e+102007-06NaTNaTNaN
4000001.XSHE2007-06-261113.9045.456661e+102007-06NaTNaTNaN
5000001.XSHE2007-06-271019.6024.994705e+102007-06NaTNaTNaN
6000001.XSHE2007-06-28953.7804.672266e+102007-06NaTNaTNaN
...........................
3813000001.XSHE2023-02-221834.1432.720658e+112023-02NaTNaTNaN
3814000001.XSHE2023-02-231838.0682.726479e+112023-02NaTNaTNaN
3815000001.XSHE2023-02-241813.2122.689609e+112023-02NaTNaTNaN
3816000001.XSHE2023-02-271790.9722.656619e+112023-02NaTNaTNaN
3817000001.XSHE2023-02-281802.7462.674084e+112023-02NaTNaTNaN
3818000001.XSHE2023-03-011853.7672.749766e+112023-03NaTNaTNaN
3819000001.XSHE2023-03-021862.9242.763350e+112023-03NaTNaTNaN
3820000001.XSHE2023-03-031869.4662.773053e+112023-03NaTNaTNaN
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3824 rows × 8 columns

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secIDtradeDateclosePricenegMarketValueympublishDateendDatebook
11048078000001.XSHE2007-04-26NaNNaNNaT2007-04-262007-03-317.106094e+09
41000001.XSHE2007-08-161147.1315.619431e+102007-082007-08-162007-06-307.698478e+09
84000001.XSHE2007-10-231300.6096.371272e+102007-102007-10-232007-09-308.363553e+09
184000001.XSHE2008-03-20917.7055.094780e+102008-032008-03-202007-12-311.300606e+10
208000001.XSHE2008-04-24869.9214.829500e+102008-042008-04-242008-03-311.404138e+10
290000001.XSHE2008-08-21639.5454.328991e+102008-082008-08-212008-06-301.694330e+10
330000001.XSHE2008-10-24380.6892.576831e+102008-102008-10-242008-09-301.837466e+10
428000001.XSHE2009-03-20632.5274.268801e+102009-032009-03-202008-12-311.640079e+10
...........................
3318000001.XSHE2021-02-022968.8484.517660e+112021-022021-02-022020-12-313.641310e+11
3368000001.XSHE2021-04-212934.4154.465264e+112021-042021-04-212021-03-313.726170e+11
3451000001.XSHE2021-08-202496.0663.768598e+112021-082021-08-202021-06-303.771930e+11
3488000001.XSHE2021-10-212570.6133.881151e+112021-102021-10-212021-09-303.888580e+11
3582000001.XSHE2022-03-101872.6922.827385e+112022-032022-03-102021-12-313.954480e+11
3614000001.XSHE2022-04-272011.5053.036964e+112022-042022-04-272022-03-314.061750e+11
3691000001.XSHE2022-08-181602.5862.377180e+112022-082022-08-182022-06-304.120980e+11
3733000001.XSHE2022-10-251393.2692.066691e+112022-102022-10-252022-09-304.253840e+11
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63 rows × 8 columns

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" ], "text/plain": [ " secID tradeDate closePrice negMarketValue ym \\\n", "11048078 000001.XSHE 2007-04-26 NaN NaN NaT \n", "41 000001.XSHE 2007-08-16 1147.131 5.619431e+10 2007-08 \n", "84 000001.XSHE 2007-10-23 1300.609 6.371272e+10 2007-10 \n", "184 000001.XSHE 2008-03-20 917.705 5.094780e+10 2008-03 \n", "208 000001.XSHE 2008-04-24 869.921 4.829500e+10 2008-04 \n", "290 000001.XSHE 2008-08-21 639.545 4.328991e+10 2008-08 \n", "330 000001.XSHE 2008-10-24 380.689 2.576831e+10 2008-10 \n", "428 000001.XSHE 2009-03-20 632.527 4.268801e+10 2009-03 \n", "... ... ... ... ... ... \n", "3318 000001.XSHE 2021-02-02 2968.848 4.517660e+11 2021-02 \n", "3368 000001.XSHE 2021-04-21 2934.415 4.465264e+11 2021-04 \n", "3451 000001.XSHE 2021-08-20 2496.066 3.768598e+11 2021-08 \n", "3488 000001.XSHE 2021-10-21 2570.613 3.881151e+11 2021-10 \n", "3582 000001.XSHE 2022-03-10 1872.692 2.827385e+11 2022-03 \n", "3614 000001.XSHE 2022-04-27 2011.505 3.036964e+11 2022-04 \n", "3691 000001.XSHE 2022-08-18 1602.586 2.377180e+11 2022-08 \n", "3733 000001.XSHE 2022-10-25 1393.269 2.066691e+11 2022-10 \n", "\n", " publishDate endDate book \n", "11048078 2007-04-26 2007-03-31 7.106094e+09 \n", "41 2007-08-16 2007-06-30 7.698478e+09 \n", "84 2007-10-23 2007-09-30 8.363553e+09 \n", "184 2008-03-20 2007-12-31 1.300606e+10 \n", "208 2008-04-24 2008-03-31 1.404138e+10 \n", "290 2008-08-21 2008-06-30 1.694330e+10 \n", "330 2008-10-24 2008-09-30 1.837466e+10 \n", "428 2009-03-20 2008-12-31 1.640079e+10 \n", "... ... ... ... \n", "3318 2021-02-02 2020-12-31 3.641310e+11 \n", "3368 2021-04-21 2021-03-31 3.726170e+11 \n", "3451 2021-08-20 2021-06-30 3.771930e+11 \n", "3488 2021-10-21 2021-09-30 3.888580e+11 \n", "3582 2022-03-10 2021-12-31 3.954480e+11 \n", "3614 2022-04-27 2022-03-31 4.061750e+11 \n", "3691 2022-08-18 2022-06-30 4.120980e+11 \n", "3733 2022-10-25 2022-09-30 4.253840e+11 \n", "\n", "[63 rows x 8 columns]" ] }, "execution_count": 140, "metadata": {}, "output_type": "execute_result" } ], "source": [ "temp[~temp['book'].isna()]" ] }, { "cell_type": "code", "execution_count": 141, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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publishDateendDatebook
110480782007-04-262007-03-317.106094e+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
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52007-04-262007-03-317.106094e+09
62007-04-262007-03-317.106094e+09
............
110480702022-10-282022-09-305.558669e+08
110480712022-10-282022-09-305.558669e+08
110480722022-10-282022-09-305.558669e+08
110480732022-10-282022-09-305.558669e+08
110480742022-10-282022-09-305.558669e+08
110480752022-10-282022-09-305.558669e+08
110480762022-10-282022-09-305.558669e+08
110480772022-10-282022-09-305.558669e+08
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11105035 rows × 3 columns

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" ], "text/plain": [ " publishDate endDate book\n", "11048078 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", "11048070 2022-10-28 2022-09-30 5.558669e+08\n", "11048071 2022-10-28 2022-09-30 5.558669e+08\n", "11048072 2022-10-28 2022-09-30 5.558669e+08\n", "11048073 2022-10-28 2022-09-30 5.558669e+08\n", "11048074 2022-10-28 2022-09-30 5.558669e+08\n", "11048075 2022-10-28 2022-09-30 5.558669e+08\n", "11048076 2022-10-28 2022-09-30 5.558669e+08\n", "11048077 2022-10-28 2022-09-30 5.558669e+08\n", "\n", "[11105035 rows x 3 columns]" ] }, "execution_count": 141, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_fundmen_df[['secID','publishDate','endDate','book']].groupby('secID').fillna(method='ffill')" ] }, { "cell_type": "code", "execution_count": 142, "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": 143, "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": 144, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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closePricenegMarketValueympublishDateendDatebook
secIDtradeDate
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2010-03-12945.6956.702070e+102010-032010-03-122009-12-312.046961e+10
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2010-03-18951.0596.740083e+102010-032010-03-122009-12-312.046961e+10
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closePricenegMarketValueympublishDateendDatebook
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3000001.XSHE2007-06-221120.2335.487665e+102007-062007-04-262007-03-317.106094e+09
4000001.XSHE2007-06-251113.9045.456661e+102007-062007-04-262007-03-317.106094e+09
5000001.XSHE2007-06-261113.9045.456661e+102007-062007-04-262007-03-317.106094e+09
6000001.XSHE2007-06-271019.6024.994705e+102007-062007-04-262007-03-317.106094e+09
7000001.XSHE2007-06-28953.7804.672266e+102007-062007-04-262007-03-317.106094e+09
8000001.XSHE2007-06-29870.8704.266117e+102007-062007-04-262007-03-317.106094e+09
9000001.XSHE2007-07-02867.0734.247515e+102007-072007-04-262007-03-317.106094e+09
...........................
11105025900957.XSHG2023-02-200.5821.061680e+082023-022022-10-282022-09-305.558669e+08
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11105027900957.XSHG2023-02-220.5771.052480e+082023-022022-10-282022-09-305.558669e+08
11105028900957.XSHG2023-02-230.5831.063520e+082023-022022-10-282022-09-305.558669e+08
11105029900957.XSHG2023-02-240.5791.056160e+082023-022022-10-282022-09-305.558669e+08
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11105034900957.XSHG2023-03-030.5681.037760e+082023-032022-10-282022-09-305.558669e+08
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11105035 rows × 8 columns

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" ], "text/plain": [ " secID tradeDate closePrice negMarketValue ym \\\n", "0 000001.XSHE 2007-04-26 NaN NaN NaT \n", "1 000001.XSHE 2007-06-20 987.007 4.835036e+10 2007-06 \n", "2 000001.XSHE 2007-06-21 1085.740 5.318694e+10 2007-06 \n", "3 000001.XSHE 2007-06-22 1120.233 5.487665e+10 2007-06 \n", "4 000001.XSHE 2007-06-25 1113.904 5.456661e+10 2007-06 \n", "5 000001.XSHE 2007-06-26 1113.904 5.456661e+10 2007-06 \n", "6 000001.XSHE 2007-06-27 1019.602 4.994705e+10 2007-06 \n", "7 000001.XSHE 2007-06-28 953.780 4.672266e+10 2007-06 \n", "8 000001.XSHE 2007-06-29 870.870 4.266117e+10 2007-06 \n", "9 000001.XSHE 2007-07-02 867.073 4.247515e+10 2007-07 \n", "... ... ... ... ... ... \n", "11105025 900957.XSHG 2023-02-20 0.582 1.061680e+08 2023-02 \n", "11105026 900957.XSHG 2023-02-21 0.582 1.061680e+08 2023-02 \n", "11105027 900957.XSHG 2023-02-22 0.577 1.052480e+08 2023-02 \n", "11105028 900957.XSHG 2023-02-23 0.583 1.063520e+08 2023-02 \n", "11105029 900957.XSHG 2023-02-24 0.579 1.056160e+08 2023-02 \n", "11105030 900957.XSHG 2023-02-27 0.578 1.054320e+08 2023-02 \n", "11105031 900957.XSHG 2023-02-28 0.578 1.054320e+08 2023-02 \n", "11105032 900957.XSHG 2023-03-01 0.579 1.056160e+08 2023-03 \n", "11105033 900957.XSHG 2023-03-02 0.573 1.045120e+08 2023-03 \n", "11105034 900957.XSHG 2023-03-03 0.568 1.037760e+08 2023-03 \n", "\n", " publishDate endDate book \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", "7 2007-04-26 2007-03-31 7.106094e+09 \n", "8 2007-04-26 2007-03-31 7.106094e+09 \n", "9 2007-04-26 2007-03-31 7.106094e+09 \n", "... ... ... ... \n", "11105025 2022-10-28 2022-09-30 5.558669e+08 \n", "11105026 2022-10-28 2022-09-30 5.558669e+08 \n", "11105027 2022-10-28 2022-09-30 5.558669e+08 \n", "11105028 2022-10-28 2022-09-30 5.558669e+08 \n", "11105029 2022-10-28 2022-09-30 5.558669e+08 \n", "11105030 2022-10-28 2022-09-30 5.558669e+08 \n", "11105031 2022-10-28 2022-09-30 5.558669e+08 \n", "11105032 2022-10-28 2022-09-30 5.558669e+08 \n", "11105033 2022-10-28 2022-09-30 5.558669e+08 \n", "11105034 2022-10-28 2022-09-30 5.558669e+08 \n", "\n", "[11105035 rows x 8 columns]" ] }, "execution_count": 148, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_fundmen_df" ] }, { "cell_type": "code", "execution_count": 149, "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": 150, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDymtradeDateclosePricenegMarketValuepublishDateendDatebookret
0000001.XSHE2007-062007-06-29870.8704.266117e+102007-04-262007-03-317.106094e+09NaN
1000001.XSHE2007-072007-07-311146.4985.616330e+102007-04-262007-03-317.106094e+090.316497
2000001.XSHE2007-082007-08-311202.5105.890714e+102007-08-162007-06-307.698478e+090.048855
3000001.XSHE2007-092007-09-281265.1676.197651e+102007-08-162007-06-307.698478e+090.052105
4000001.XSHE2007-102007-10-311520.5427.448652e+102007-10-232007-09-308.363553e+090.201851
5000001.XSHE2007-112007-11-301141.7515.593078e+102007-10-232007-09-308.363553e+09-0.249116
6000001.XSHE2007-122007-12-281221.4976.574629e+102007-10-232007-09-308.363553e+090.069845
7000001.XSHE2008-012008-01-311053.7785.850212e+102007-10-232007-09-308.363553e+09-0.137306
8000001.XSHE2008-022008-02-291049.0325.823860e+102007-10-232007-09-308.363553e+09-0.004504
9000001.XSHE2008-032008-03-31892.3894.954234e+102008-03-202007-12-311.300606e+10-0.149321
..............................
552748900957.XSHG2022-062022-06-300.6031.100320e+082022-04-302022-03-315.333509e+080.080645
552749900957.XSHG2022-072022-07-290.5891.074560e+082022-04-302022-03-315.333509e+08-0.023217
552750900957.XSHG2022-082022-08-310.6441.175760e+082022-08-162022-06-305.476224e+080.093379
552751900957.XSHG2022-092022-09-300.5921.080080e+082022-08-162022-06-305.476224e+08-0.080745
552752900957.XSHG2022-102022-10-310.6031.100320e+082022-10-282022-09-305.558669e+080.018581
552753900957.XSHG2022-112022-11-300.6171.126080e+082022-10-282022-09-305.558669e+080.023217
552754900957.XSHG2022-122022-12-300.5681.037760e+082022-10-282022-09-305.558669e+08-0.079417
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552756900957.XSHG2023-022023-02-280.5781.054320e+082022-10-282022-09-305.558669e+08-0.020339
552757900957.XSHG2023-032023-03-030.5681.037760e+082022-10-282022-09-305.558669e+08-0.017301
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552758 rows × 9 columns

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" ], "text/plain": [ " secID ym tradeDate closePrice negMarketValue \\\n", "0 000001.XSHE 2007-06 2007-06-29 870.870 4.266117e+10 \n", "1 000001.XSHE 2007-07 2007-07-31 1146.498 5.616330e+10 \n", "2 000001.XSHE 2007-08 2007-08-31 1202.510 5.890714e+10 \n", "3 000001.XSHE 2007-09 2007-09-28 1265.167 6.197651e+10 \n", "4 000001.XSHE 2007-10 2007-10-31 1520.542 7.448652e+10 \n", "5 000001.XSHE 2007-11 2007-11-30 1141.751 5.593078e+10 \n", "6 000001.XSHE 2007-12 2007-12-28 1221.497 6.574629e+10 \n", "7 000001.XSHE 2008-01 2008-01-31 1053.778 5.850212e+10 \n", "8 000001.XSHE 2008-02 2008-02-29 1049.032 5.823860e+10 \n", "9 000001.XSHE 2008-03 2008-03-31 892.389 4.954234e+10 \n", "... ... ... ... ... ... \n", "552748 900957.XSHG 2022-06 2022-06-30 0.603 1.100320e+08 \n", "552749 900957.XSHG 2022-07 2022-07-29 0.589 1.074560e+08 \n", "552750 900957.XSHG 2022-08 2022-08-31 0.644 1.175760e+08 \n", "552751 900957.XSHG 2022-09 2022-09-30 0.592 1.080080e+08 \n", "552752 900957.XSHG 2022-10 2022-10-31 0.603 1.100320e+08 \n", "552753 900957.XSHG 2022-11 2022-11-30 0.617 1.126080e+08 \n", "552754 900957.XSHG 2022-12 2022-12-30 0.568 1.037760e+08 \n", "552755 900957.XSHG 2023-01 2023-01-31 0.590 1.076400e+08 \n", "552756 900957.XSHG 2023-02 2023-02-28 0.578 1.054320e+08 \n", "552757 900957.XSHG 2023-03 2023-03-03 0.568 1.037760e+08 \n", "\n", " publishDate endDate book ret \n", "0 2007-04-26 2007-03-31 7.106094e+09 NaN \n", "1 2007-04-26 2007-03-31 7.106094e+09 0.316497 \n", "2 2007-08-16 2007-06-30 7.698478e+09 0.048855 \n", "3 2007-08-16 2007-06-30 7.698478e+09 0.052105 \n", "4 2007-10-23 2007-09-30 8.363553e+09 0.201851 \n", "5 2007-10-23 2007-09-30 8.363553e+09 -0.249116 \n", "6 2007-10-23 2007-09-30 8.363553e+09 0.069845 \n", "7 2007-10-23 2007-09-30 8.363553e+09 -0.137306 \n", "8 2007-10-23 2007-09-30 8.363553e+09 -0.004504 \n", "9 2008-03-20 2007-12-31 1.300606e+10 -0.149321 \n", "... ... ... ... ... \n", "552748 2022-04-30 2022-03-31 5.333509e+08 0.080645 \n", "552749 2022-04-30 2022-03-31 5.333509e+08 -0.023217 \n", "552750 2022-08-16 2022-06-30 5.476224e+08 0.093379 \n", "552751 2022-08-16 2022-06-30 5.476224e+08 -0.080745 \n", "552752 2022-10-28 2022-09-30 5.558669e+08 0.018581 \n", "552753 2022-10-28 2022-09-30 5.558669e+08 0.023217 \n", "552754 2022-10-28 2022-09-30 5.558669e+08 -0.079417 \n", "552755 2022-10-28 2022-09-30 5.558669e+08 0.038732 \n", "552756 2022-10-28 2022-09-30 5.558669e+08 -0.020339 \n", "552757 2022-10-28 2022-09-30 5.558669e+08 -0.017301 \n", "\n", "[552758 rows x 9 columns]" ] }, "execution_count": 150, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_df_m" ] }, { "cell_type": "code", "execution_count": 151, "metadata": { "editable": true }, "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": 152, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDymtradeDateclosePricenegMarketValuepublishDateendDatebookretret_date
0000001.XSHE2007-062007-06-29870.8704.266117e+102007-04-262007-03-317.106094e+090.3164972007-07
1000001.XSHE2007-072007-07-311146.4985.616330e+102007-04-262007-03-317.106094e+090.0488552007-08
2000001.XSHE2007-082007-08-311202.5105.890714e+102007-08-162007-06-307.698478e+090.0521052007-09
3000001.XSHE2007-092007-09-281265.1676.197651e+102007-08-162007-06-307.698478e+090.2018512007-10
4000001.XSHE2007-102007-10-311520.5427.448652e+102007-10-232007-09-308.363553e+09-0.2491162007-11
5000001.XSHE2007-112007-11-301141.7515.593078e+102007-10-232007-09-308.363553e+090.0698452007-12
6000001.XSHE2007-122007-12-281221.4976.574629e+102007-10-232007-09-308.363553e+09-0.1373062008-01
7000001.XSHE2008-012008-01-311053.7785.850212e+102007-10-232007-09-308.363553e+09-0.0045042008-02
8000001.XSHE2008-022008-02-291049.0325.823860e+102007-10-232007-09-308.363553e+09-0.1493212008-03
9000001.XSHE2008-032008-03-31892.3894.954234e+102008-03-202007-12-311.300606e+100.0503552008-04
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552748900957.XSHG2022-062022-06-300.6031.100320e+082022-04-302022-03-315.333509e+08-0.0232172022-07
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552753900957.XSHG2022-112022-11-300.6171.126080e+082022-10-282022-09-305.558669e+08-0.0794172022-12
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552756900957.XSHG2023-022023-02-280.5781.054320e+082022-10-282022-09-305.558669e+08-0.0173012023-03
552757900957.XSHG2023-032023-03-030.5681.037760e+082022-10-282022-09-305.558669e+08NaNNaT
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552758 rows × 10 columns

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" ], "text/plain": [ " secID ym tradeDate closePrice negMarketValue \\\n", "0 000001.XSHE 2007-06 2007-06-29 870.870 4.266117e+10 \n", "1 000001.XSHE 2007-07 2007-07-31 1146.498 5.616330e+10 \n", "2 000001.XSHE 2007-08 2007-08-31 1202.510 5.890714e+10 \n", "3 000001.XSHE 2007-09 2007-09-28 1265.167 6.197651e+10 \n", "4 000001.XSHE 2007-10 2007-10-31 1520.542 7.448652e+10 \n", "5 000001.XSHE 2007-11 2007-11-30 1141.751 5.593078e+10 \n", "6 000001.XSHE 2007-12 2007-12-28 1221.497 6.574629e+10 \n", "7 000001.XSHE 2008-01 2008-01-31 1053.778 5.850212e+10 \n", "8 000001.XSHE 2008-02 2008-02-29 1049.032 5.823860e+10 \n", "9 000001.XSHE 2008-03 2008-03-31 892.389 4.954234e+10 \n", "... ... ... ... ... ... \n", "552748 900957.XSHG 2022-06 2022-06-30 0.603 1.100320e+08 \n", "552749 900957.XSHG 2022-07 2022-07-29 0.589 1.074560e+08 \n", "552750 900957.XSHG 2022-08 2022-08-31 0.644 1.175760e+08 \n", "552751 900957.XSHG 2022-09 2022-09-30 0.592 1.080080e+08 \n", "552752 900957.XSHG 2022-10 2022-10-31 0.603 1.100320e+08 \n", "552753 900957.XSHG 2022-11 2022-11-30 0.617 1.126080e+08 \n", "552754 900957.XSHG 2022-12 2022-12-30 0.568 1.037760e+08 \n", "552755 900957.XSHG 2023-01 2023-01-31 0.590 1.076400e+08 \n", "552756 900957.XSHG 2023-02 2023-02-28 0.578 1.054320e+08 \n", "552757 900957.XSHG 2023-03 2023-03-03 0.568 1.037760e+08 \n", "\n", " publishDate endDate book ret ret_date \n", "0 2007-04-26 2007-03-31 7.106094e+09 0.316497 2007-07 \n", "1 2007-04-26 2007-03-31 7.106094e+09 0.048855 2007-08 \n", "2 2007-08-16 2007-06-30 7.698478e+09 0.052105 2007-09 \n", "3 2007-08-16 2007-06-30 7.698478e+09 0.201851 2007-10 \n", "4 2007-10-23 2007-09-30 8.363553e+09 -0.249116 2007-11 \n", "5 2007-10-23 2007-09-30 8.363553e+09 0.069845 2007-12 \n", "6 2007-10-23 2007-09-30 8.363553e+09 -0.137306 2008-01 \n", "7 2007-10-23 2007-09-30 8.363553e+09 -0.004504 2008-02 \n", "8 2007-10-23 2007-09-30 8.363553e+09 -0.149321 2008-03 \n", "9 2008-03-20 2007-12-31 1.300606e+10 0.050355 2008-04 \n", "... ... ... ... ... ... \n", "552748 2022-04-30 2022-03-31 5.333509e+08 -0.023217 2022-07 \n", "552749 2022-04-30 2022-03-31 5.333509e+08 0.093379 2022-08 \n", "552750 2022-08-16 2022-06-30 5.476224e+08 -0.080745 2022-09 \n", "552751 2022-08-16 2022-06-30 5.476224e+08 0.018581 2022-10 \n", "552752 2022-10-28 2022-09-30 5.558669e+08 0.023217 2022-11 \n", "552753 2022-10-28 2022-09-30 5.558669e+08 -0.079417 2022-12 \n", "552754 2022-10-28 2022-09-30 5.558669e+08 0.038732 2023-01 \n", "552755 2022-10-28 2022-09-30 5.558669e+08 -0.020339 2023-02 \n", "552756 2022-10-28 2022-09-30 5.558669e+08 -0.017301 2023-03 \n", "552757 2022-10-28 2022-09-30 5.558669e+08 NaN NaT \n", "\n", "[552758 rows x 10 columns]" ] }, "execution_count": 152, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_df_m" ] }, { "cell_type": "code", "execution_count": 153, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDymtradeDateclosePricenegMarketValuepublishDateendDatebookretret_dateym_diff
189000001.XSHE2023-032023-03-031869.4662.773053e+112022-10-252022-09-304.253840e+11NaNNaT9223372036854775170
384000002.XSHE2023-032023-03-032656.1851.633521e+112022-10-292022-09-302.411070e+11NaNNaT9223372036854775170
516000004.XSHE2022-052022-05-0585.9711.463441e+092022-04-302022-03-319.351158e+08NaNNaT9223372036854775180
671000005.XSHE2021-042021-04-3021.8142.348641e+092021-04-302021-03-311.248325e+09NaNNaT9223372036854775193
866000006.XSHE2023-032023-03-03417.7297.924471e+092022-10-282022-09-307.683314e+09NaNNaT9223372036854775170
870000007.XSHE2007-042007-04-2048.2037.268053e+08NaTNaTNaN1.6123272012-0561
978000007.XSHE2021-042021-04-2947.4011.217255e+092021-04-292021-03-316.318241e+070.8705722022-0715
987000007.XSHE2023-032023-03-0393.3592.397436e+092022-10-262022-09-309.238106e+07NaNNaT9223372036854775170
990000008.XSHE2007-032007-03-2922.9373.197319e+08NaTNaTNaN0.8145792013-0473
1110000008.XSHE2023-032023-03-0369.6606.811892e+092022-10-292022-09-305.032390e+09NaNNaT9223372036854775170
....................................
551865900951.XSHG2018-072018-07-300.5375.370000e+072018-04-212018-03-311.635296e+08-0.9124772020-0724
551867900951.XSHG2020-082020-08-260.0696.900000e+062020-04-252020-03-31-2.115164e+08NaNNaT9223372036854775201
552062900952.XSHG2023-032023-03-030.6326.149473e+072022-10-292022-09-306.615382e+09NaNNaT9223372036854775170
552078900953.XSHG2008-042008-04-300.4301.000800e+082008-04-302008-03-315.505128e+080.5883722009-0715
552243900953.XSHG2023-032023-03-030.4571.063200e+082022-10-312022-09-304.893210e+08NaNNaT9223372036854775170
552320900955.XSHG2013-052013-05-021.2511.188000e+082013-04-272013-03-311.602086e+09-0.1942452014-0411
552393900955.XSHG2020-042020-04-300.5074.818000e+072020-04-302020-03-311.031370e+09-0.8500992022-0626
552395900955.XSHG2022-072022-07-120.0736.930000e+062022-05-072021-12-316.172171e+08NaNNaT9223372036854775178
552562900956.XSHG2020-112020-11-203.4243.530500e+082020-10-292020-09-301.430181e+09NaNNaT9223372036854775198
552757900957.XSHG2023-032023-03-030.5681.037760e+082022-10-282022-09-305.558669e+08NaNNaT9223372036854775170
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5831 rows × 11 columns

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" ], "text/plain": [ " secID ym tradeDate closePrice negMarketValue \\\n", "189 000001.XSHE 2023-03 2023-03-03 1869.466 2.773053e+11 \n", "384 000002.XSHE 2023-03 2023-03-03 2656.185 1.633521e+11 \n", "516 000004.XSHE 2022-05 2022-05-05 85.971 1.463441e+09 \n", "671 000005.XSHE 2021-04 2021-04-30 21.814 2.348641e+09 \n", "866 000006.XSHE 2023-03 2023-03-03 417.729 7.924471e+09 \n", "870 000007.XSHE 2007-04 2007-04-20 48.203 7.268053e+08 \n", "978 000007.XSHE 2021-04 2021-04-29 47.401 1.217255e+09 \n", "987 000007.XSHE 2023-03 2023-03-03 93.359 2.397436e+09 \n", "990 000008.XSHE 2007-03 2007-03-29 22.937 3.197319e+08 \n", "1110 000008.XSHE 2023-03 2023-03-03 69.660 6.811892e+09 \n", "... ... ... ... ... ... \n", "551865 900951.XSHG 2018-07 2018-07-30 0.537 5.370000e+07 \n", "551867 900951.XSHG 2020-08 2020-08-26 0.069 6.900000e+06 \n", "552062 900952.XSHG 2023-03 2023-03-03 0.632 6.149473e+07 \n", "552078 900953.XSHG 2008-04 2008-04-30 0.430 1.000800e+08 \n", "552243 900953.XSHG 2023-03 2023-03-03 0.457 1.063200e+08 \n", "552320 900955.XSHG 2013-05 2013-05-02 1.251 1.188000e+08 \n", "552393 900955.XSHG 2020-04 2020-04-30 0.507 4.818000e+07 \n", "552395 900955.XSHG 2022-07 2022-07-12 0.073 6.930000e+06 \n", "552562 900956.XSHG 2020-11 2020-11-20 3.424 3.530500e+08 \n", "552757 900957.XSHG 2023-03 2023-03-03 0.568 1.037760e+08 \n", "\n", " publishDate endDate book ret ret_date \\\n", "189 2022-10-25 2022-09-30 4.253840e+11 NaN NaT \n", "384 2022-10-29 2022-09-30 2.411070e+11 NaN NaT \n", "516 2022-04-30 2022-03-31 9.351158e+08 NaN NaT \n", "671 2021-04-30 2021-03-31 1.248325e+09 NaN NaT \n", "866 2022-10-28 2022-09-30 7.683314e+09 NaN NaT \n", "870 NaT NaT NaN 1.612327 2012-05 \n", "978 2021-04-29 2021-03-31 6.318241e+07 0.870572 2022-07 \n", "987 2022-10-26 2022-09-30 9.238106e+07 NaN NaT \n", "990 NaT NaT NaN 0.814579 2013-04 \n", "1110 2022-10-29 2022-09-30 5.032390e+09 NaN NaT \n", "... ... ... ... ... ... \n", "551865 2018-04-21 2018-03-31 1.635296e+08 -0.912477 2020-07 \n", "551867 2020-04-25 2020-03-31 -2.115164e+08 NaN NaT \n", "552062 2022-10-29 2022-09-30 6.615382e+09 NaN NaT \n", "552078 2008-04-30 2008-03-31 5.505128e+08 0.588372 2009-07 \n", "552243 2022-10-31 2022-09-30 4.893210e+08 NaN NaT \n", "552320 2013-04-27 2013-03-31 1.602086e+09 -0.194245 2014-04 \n", "552393 2020-04-30 2020-03-31 1.031370e+09 -0.850099 2022-06 \n", "552395 2022-05-07 2021-12-31 6.172171e+08 NaN NaT \n", "552562 2020-10-29 2020-09-30 1.430181e+09 NaN NaT \n", "552757 2022-10-28 2022-09-30 5.558669e+08 NaN NaT \n", "\n", " ym_diff \n", "189 9223372036854775170 \n", "384 9223372036854775170 \n", "516 9223372036854775180 \n", "671 9223372036854775193 \n", "866 9223372036854775170 \n", "870 61 \n", "978 15 \n", "987 9223372036854775170 \n", "990 73 \n", "1110 9223372036854775170 \n", "... ... \n", "551865 24 \n", "551867 9223372036854775201 \n", "552062 9223372036854775170 \n", "552078 15 \n", "552243 9223372036854775170 \n", "552320 11 \n", "552393 26 \n", "552395 9223372036854775178 \n", "552562 9223372036854775198 \n", "552757 9223372036854775170 \n", "\n", "[5831 rows x 11 columns]" ] }, "execution_count": 153, "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": 154, "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": 155, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDymtradeDateclosePricenegMarketValuepublishDateendDatebookretret_dateym_diff
68321000901.XSHE2007-012007-01-3119.9235.894784e+08NaTNaTNaN0.1588622007-021
68322000901.XSHE2007-022007-02-2823.0886.831014e+08NaTNaTNaN0.3527812007-031
68323000901.XSHE2007-032007-03-3031.2339.240941e+08NaTNaTNaN0.1632252007-041
68324000901.XSHE2007-042007-04-3036.3312.032149e+092007-04-252007-03-313.653547e+080.0475902007-051
68325000901.XSHE2007-052007-05-3138.0602.128840e+092007-04-252007-03-313.653547e+08-0.3317922007-061
68326000901.XSHE2007-062007-06-2925.4321.422504e+092007-04-252007-03-313.653547e+080.1658932007-071
68327000901.XSHE2007-072007-07-3129.6511.658495e+092007-04-252007-03-313.653547e+080.3853502007-081
68328000901.XSHE2007-082007-08-3141.0772.297639e+092007-08-072007-06-303.506422e+080.0877382007-091
68329000901.XSHE2007-092007-09-2844.6812.499215e+092007-08-072007-06-303.506422e+08-0.2773662007-101
68330000901.XSHE2007-102007-10-3132.2881.805990e+092007-10-302007-09-303.540876e+080.1551662007-111
....................................
68506000901.XSHE2022-062022-06-3059.4897.255651e+092022-04-292022-03-314.193378e+090.0550192022-071
68507000901.XSHE2022-072022-07-2962.7627.654751e+092022-04-292022-03-314.193378e+09-0.1188782022-081
68508000901.XSHE2022-082022-08-3155.3016.744802e+092022-08-292022-06-304.161902e+09-0.0816622022-091
68509000901.XSHE2022-092022-09-3050.7856.194043e+092022-08-292022-06-304.161902e+090.1018022022-101
68510000901.XSHE2022-102022-10-3155.9556.824622e+092022-10-312022-09-304.165991e+090.0117062022-111
68511000901.XSHE2022-112022-11-3056.6106.904442e+092022-10-312022-09-304.165991e+09-0.0566512022-121
68512000901.XSHE2022-122022-12-3053.4036.513323e+092022-10-312022-09-304.165991e+090.1593172023-011
68513000901.XSHE2023-012023-01-3161.9117.550985e+092022-10-312022-09-304.165991e+090.1522192023-021
68514000901.XSHE2023-022023-02-2871.3358.700395e+092022-10-312022-09-304.165991e+09-0.0220232023-031
68515000901.XSHE2023-032023-03-0369.7648.508827e+092022-10-312022-09-304.165991e+09NaNNaT9223372036854775170
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195 rows × 11 columns

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" ], "text/plain": [ " secID ym tradeDate closePrice negMarketValue \\\n", "68321 000901.XSHE 2007-01 2007-01-31 19.923 5.894784e+08 \n", "68322 000901.XSHE 2007-02 2007-02-28 23.088 6.831014e+08 \n", "68323 000901.XSHE 2007-03 2007-03-30 31.233 9.240941e+08 \n", "68324 000901.XSHE 2007-04 2007-04-30 36.331 2.032149e+09 \n", "68325 000901.XSHE 2007-05 2007-05-31 38.060 2.128840e+09 \n", "68326 000901.XSHE 2007-06 2007-06-29 25.432 1.422504e+09 \n", "68327 000901.XSHE 2007-07 2007-07-31 29.651 1.658495e+09 \n", "68328 000901.XSHE 2007-08 2007-08-31 41.077 2.297639e+09 \n", "68329 000901.XSHE 2007-09 2007-09-28 44.681 2.499215e+09 \n", "68330 000901.XSHE 2007-10 2007-10-31 32.288 1.805990e+09 \n", "... ... ... ... ... ... \n", "68506 000901.XSHE 2022-06 2022-06-30 59.489 7.255651e+09 \n", "68507 000901.XSHE 2022-07 2022-07-29 62.762 7.654751e+09 \n", "68508 000901.XSHE 2022-08 2022-08-31 55.301 6.744802e+09 \n", "68509 000901.XSHE 2022-09 2022-09-30 50.785 6.194043e+09 \n", "68510 000901.XSHE 2022-10 2022-10-31 55.955 6.824622e+09 \n", "68511 000901.XSHE 2022-11 2022-11-30 56.610 6.904442e+09 \n", "68512 000901.XSHE 2022-12 2022-12-30 53.403 6.513323e+09 \n", "68513 000901.XSHE 2023-01 2023-01-31 61.911 7.550985e+09 \n", "68514 000901.XSHE 2023-02 2023-02-28 71.335 8.700395e+09 \n", "68515 000901.XSHE 2023-03 2023-03-03 69.764 8.508827e+09 \n", "\n", " publishDate endDate book ret ret_date \\\n", "68321 NaT NaT NaN 0.158862 2007-02 \n", "68322 NaT NaT NaN 0.352781 2007-03 \n", "68323 NaT NaT NaN 0.163225 2007-04 \n", "68324 2007-04-25 2007-03-31 3.653547e+08 0.047590 2007-05 \n", "68325 2007-04-25 2007-03-31 3.653547e+08 -0.331792 2007-06 \n", "68326 2007-04-25 2007-03-31 3.653547e+08 0.165893 2007-07 \n", "68327 2007-04-25 2007-03-31 3.653547e+08 0.385350 2007-08 \n", "68328 2007-08-07 2007-06-30 3.506422e+08 0.087738 2007-09 \n", "68329 2007-08-07 2007-06-30 3.506422e+08 -0.277366 2007-10 \n", "68330 2007-10-30 2007-09-30 3.540876e+08 0.155166 2007-11 \n", "... ... ... ... ... ... \n", "68506 2022-04-29 2022-03-31 4.193378e+09 0.055019 2022-07 \n", "68507 2022-04-29 2022-03-31 4.193378e+09 -0.118878 2022-08 \n", "68508 2022-08-29 2022-06-30 4.161902e+09 -0.081662 2022-09 \n", "68509 2022-08-29 2022-06-30 4.161902e+09 0.101802 2022-10 \n", "68510 2022-10-31 2022-09-30 4.165991e+09 0.011706 2022-11 \n", "68511 2022-10-31 2022-09-30 4.165991e+09 -0.056651 2022-12 \n", "68512 2022-10-31 2022-09-30 4.165991e+09 0.159317 2023-01 \n", "68513 2022-10-31 2022-09-30 4.165991e+09 0.152219 2023-02 \n", "68514 2022-10-31 2022-09-30 4.165991e+09 -0.022023 2023-03 \n", "68515 2022-10-31 2022-09-30 4.165991e+09 NaN NaT \n", "\n", " ym_diff \n", "68321 1 \n", "68322 1 \n", "68323 1 \n", "68324 1 \n", "68325 1 \n", "68326 1 \n", "68327 1 \n", "68328 1 \n", "68329 1 \n", "68330 1 \n", "... ... \n", "68506 1 \n", "68507 1 \n", "68508 1 \n", "68509 1 \n", "68510 1 \n", "68511 1 \n", "68512 1 \n", "68513 1 \n", "68514 1 \n", "68515 9223372036854775170 \n", "\n", "[195 rows x 11 columns]" ] }, "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": 156, "metadata": { "editable": true }, "outputs": [], "source": [ "del temp" ] }, { "cell_type": "code", "execution_count": 157, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDmktcap_book_datemktcapbookretret_date
0000001.XSHE2007-064.266117e+107.106094e+090.3164972007-07
1000001.XSHE2007-075.616330e+107.106094e+090.0488552007-08
2000001.XSHE2007-085.890714e+107.698478e+090.0521052007-09
3000001.XSHE2007-096.197651e+107.698478e+090.2018512007-10
4000001.XSHE2007-107.448652e+108.363553e+09-0.2491162007-11
5000001.XSHE2007-115.593078e+108.363553e+090.0698452007-12
6000001.XSHE2007-126.574629e+108.363553e+09-0.1373062008-01
7000001.XSHE2008-015.850212e+108.363553e+09-0.0045042008-02
8000001.XSHE2008-025.823860e+108.363553e+09-0.1493212008-03
9000001.XSHE2008-034.954234e+101.300606e+100.0503552008-04
.....................
552747900957.XSHG2022-051.019360e+085.333509e+080.0806452022-06
552748900957.XSHG2022-061.100320e+085.333509e+08-0.0232172022-07
552749900957.XSHG2022-071.074560e+085.333509e+080.0933792022-08
552750900957.XSHG2022-081.175760e+085.476224e+08-0.0807452022-09
552751900957.XSHG2022-091.080080e+085.476224e+080.0185812022-10
552752900957.XSHG2022-101.100320e+085.558669e+080.0232172022-11
552753900957.XSHG2022-111.126080e+085.558669e+08-0.0794172022-12
552754900957.XSHG2022-121.037760e+085.558669e+080.0387322023-01
552755900957.XSHG2023-011.076400e+085.558669e+08-0.0203392023-02
552756900957.XSHG2023-021.054320e+085.558669e+08-0.0173012023-03
\n", "

542964 rows × 6 columns

\n", "
" ], "text/plain": [ " secID mktcap_book_date mktcap book ret \\\n", "0 000001.XSHE 2007-06 4.266117e+10 7.106094e+09 0.316497 \n", "1 000001.XSHE 2007-07 5.616330e+10 7.106094e+09 0.048855 \n", "2 000001.XSHE 2007-08 5.890714e+10 7.698478e+09 0.052105 \n", "3 000001.XSHE 2007-09 6.197651e+10 7.698478e+09 0.201851 \n", "4 000001.XSHE 2007-10 7.448652e+10 8.363553e+09 -0.249116 \n", "5 000001.XSHE 2007-11 5.593078e+10 8.363553e+09 0.069845 \n", "6 000001.XSHE 2007-12 6.574629e+10 8.363553e+09 -0.137306 \n", "7 000001.XSHE 2008-01 5.850212e+10 8.363553e+09 -0.004504 \n", "8 000001.XSHE 2008-02 5.823860e+10 8.363553e+09 -0.149321 \n", "9 000001.XSHE 2008-03 4.954234e+10 1.300606e+10 0.050355 \n", "... ... ... ... ... ... \n", "552747 900957.XSHG 2022-05 1.019360e+08 5.333509e+08 0.080645 \n", "552748 900957.XSHG 2022-06 1.100320e+08 5.333509e+08 -0.023217 \n", "552749 900957.XSHG 2022-07 1.074560e+08 5.333509e+08 0.093379 \n", "552750 900957.XSHG 2022-08 1.175760e+08 5.476224e+08 -0.080745 \n", "552751 900957.XSHG 2022-09 1.080080e+08 5.476224e+08 0.018581 \n", "552752 900957.XSHG 2022-10 1.100320e+08 5.558669e+08 0.023217 \n", "552753 900957.XSHG 2022-11 1.126080e+08 5.558669e+08 -0.079417 \n", "552754 900957.XSHG 2022-12 1.037760e+08 5.558669e+08 0.038732 \n", "552755 900957.XSHG 2023-01 1.076400e+08 5.558669e+08 -0.020339 \n", "552756 900957.XSHG 2023-02 1.054320e+08 5.558669e+08 -0.017301 \n", "\n", " ret_date \n", "0 2007-07 \n", "1 2007-08 \n", "2 2007-09 \n", "3 2007-10 \n", "4 2007-11 \n", "5 2007-12 \n", "6 2008-01 \n", "7 2008-02 \n", "8 2008-03 \n", "9 2008-04 \n", "... ... \n", "552747 2022-06 \n", "552748 2022-07 \n", "552749 2022-08 \n", "552750 2022-09 \n", "552751 2022-10 \n", "552752 2022-11 \n", "552753 2022-12 \n", "552754 2023-01 \n", "552755 2023-02 \n", "552756 2023-03 \n", "\n", "[542964 rows x 6 columns]" ] }, "execution_count": 157, "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": "markdown", "metadata": { "editable": true }, "source": [ "### Merge" ] }, { "cell_type": "code", "execution_count": 158, "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": 159, "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
...........................
519992689009.XSHG2022-0423.6635021.892031e+100.2259360.81082022-050.224708
519993689009.XSHG2022-0523.8676392.320511e+100.1869350.98442022-06-0.025790
519994689009.XSHG2022-0623.8432202.264534e+100.1915560.90712022-070.113436
519995689009.XSHG2022-0723.9521252.525082e+100.1717900.79872022-08-0.114022
519996689009.XSHG2022-0823.8326022.240616e+100.2051910.85892022-09-0.131253
519997689009.XSHG2022-0923.6934421.949535e+100.2358280.91062022-10-0.166122
519998689009.XSHG2022-1023.5189851.637440e+100.2921900.70832022-110.041449
519999689009.XSHG2022-1123.5612061.708055e+100.2801100.73632022-12-0.088510
520000689009.XSHG2022-1223.4706481.560173e+100.3066600.69192023-010.086541
520001689009.XSHG2023-0123.5554981.698332e+100.2817140.73792023-02-0.007738
\n", "

520002 rows × 8 columns

\n", "
" ], "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", "519992 689009.XSHG 2022-04 23.663502 1.892031e+10 0.225936 0.8108 \n", "519993 689009.XSHG 2022-05 23.867639 2.320511e+10 0.186935 0.9844 \n", "519994 689009.XSHG 2022-06 23.843220 2.264534e+10 0.191556 0.9071 \n", "519995 689009.XSHG 2022-07 23.952125 2.525082e+10 0.171790 0.7987 \n", "519996 689009.XSHG 2022-08 23.832602 2.240616e+10 0.205191 0.8589 \n", "519997 689009.XSHG 2022-09 23.693442 1.949535e+10 0.235828 0.9106 \n", "519998 689009.XSHG 2022-10 23.518985 1.637440e+10 0.292190 0.7083 \n", "519999 689009.XSHG 2022-11 23.561206 1.708055e+10 0.280110 0.7363 \n", "520000 689009.XSHG 2022-12 23.470648 1.560173e+10 0.306660 0.6919 \n", "520001 689009.XSHG 2023-01 23.555498 1.698332e+10 0.281714 0.7379 \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", "519992 2022-05 0.224708 \n", "519993 2022-06 -0.025790 \n", "519994 2022-07 0.113436 \n", "519995 2022-08 -0.114022 \n", "519996 2022-09 -0.131253 \n", "519997 2022-10 -0.166122 \n", "519998 2022-11 0.041449 \n", "519999 2022-12 -0.088510 \n", "520000 2023-01 0.086541 \n", "520001 2023-02 -0.007738 \n", "\n", "[520002 rows x 8 columns]" ] }, "execution_count": 159, "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": 160, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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p1p2p3p4p5p6p7p8p9p10p10-p1
mean0.0022670.0053050.0076950.0084140.0102440.0112460.0124520.0133100.0143570.0157460.013479
t-value0.3366780.7898321.1160081.2048081.4696451.6001401.7614971.8702861.9279622.0085633.448444
\n", "
" ], "text/plain": [ " p1 p2 p3 p4 p5 p6 p7 \\\n", "mean 0.002267 0.005305 0.007695 0.008414 0.010244 0.011246 0.012452 \n", "t-value 0.336678 0.789832 1.116008 1.204808 1.469645 1.600140 1.761497 \n", "\n", " p8 p9 p10 p10-p1 \n", "mean 0.013310 0.014357 0.015746 0.013479 \n", "t-value 1.870286 1.927962 2.008563 3.448444 " ] }, "execution_count": 160, "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，除了p10\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": 161, "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": 162, "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", "# while begin_ <= 2023:\n", "# if begin_ == 2023:\n", "# yesterday = dt.datetime.today() - dt.timedelta(days=1)\n", "# yesterday.strftime('%Y%m%d')\n", "# pb[begin_] = DataAPI.MktStockFactorsDateRangeProGet(secID=stk_id,\n", "# beginDate=f'{begin_}0101',\n", "# endDate=yesterday,\n", "# field=['secID','tradeDate','PB'],pandas=\"1\")\n", "# else:\n", "# pb[begin_] = 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", " \n", "# for i in range(len(pb)):\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": 164, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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secIDtradeDatePB
0000001.XSHE2007-01-044.5351
1000002.XSHE2007-01-046.7999
2000004.XSHE2007-01-044.2535
3000005.XSHE2007-01-042.7093
4000006.XSHE2007-01-042.8088
5000007.XSHE2007-01-046.8244
6000008.XSHE2007-01-044.2374
7000014.XSHE2007-01-042.7436
8000016.XSHE2007-01-040.7852
9000017.XSHE2007-01-04-0.717
............
11190970688787.XSHG2023-03-0310.0327
11190971688788.XSHG2023-03-031.7234
11190972688789.XSHG2023-03-039.6548
11190973688793.XSHG2023-03-035.9281
11190974688798.XSHG2023-03-034.785
11190975688799.XSHG2023-03-032.1816
11190976688800.XSHG2023-03-035.8383
11190977688819.XSHG2023-03-032.698
11190978688981.XSHG2023-03-032.5813
11190979689009.XSHG2023-03-035.1622
\n", "

11190980 rows × 3 columns

\n", "
" ], "text/plain": [ " secID tradeDate PB\n", "0 000001.XSHE 2007-01-04 4.5351\n", "1 000002.XSHE 2007-01-04 6.7999\n", "2 000004.XSHE 2007-01-04 4.2535\n", "3 000005.XSHE 2007-01-04 2.7093\n", "4 000006.XSHE 2007-01-04 2.8088\n", "5 000007.XSHE 2007-01-04 6.8244\n", "6 000008.XSHE 2007-01-04 4.2374\n", "7 000014.XSHE 2007-01-04 2.7436\n", "8 000016.XSHE 2007-01-04 0.7852\n", "9 000017.XSHE 2007-01-04 -0.717\n", "... ... ... ...\n", "11190970 688787.XSHG 2023-03-03 10.0327\n", "11190971 688788.XSHG 2023-03-03 1.7234\n", "11190972 688789.XSHG 2023-03-03 9.6548\n", "11190973 688793.XSHG 2023-03-03 5.9281\n", "11190974 688798.XSHG 2023-03-03 4.785\n", "11190975 688799.XSHG 2023-03-03 2.1816\n", "11190976 688800.XSHG 2023-03-03 5.8383\n", "11190977 688819.XSHG 2023-03-03 2.698\n", "11190978 688981.XSHG 2023-03-03 2.5813\n", "11190979 689009.XSHG 2023-03-03 5.1622\n", "\n", "[11190980 rows x 3 columns]" ] }, "execution_count": 164, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pb_df" ] }, { "cell_type": "code", "execution_count": 172, "metadata": { "editable": true }, "outputs": [], "source": [ "# pb_df = pd.read_pickle('./data/pb_df.pkl')" ] }, { "cell_type": "code", "execution_count": 165, "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": 166, "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
..............................
519992689009.XSHG2022-0423.6635021.892031e+100.2259360.81082022-050.2247080.162419
519993689009.XSHG2022-0523.8676392.320511e+100.1869350.98442022-06-0.0257900.134383
519994689009.XSHG2022-0623.8432202.264534e+100.1915560.90712022-070.1134360.137005
519995689009.XSHG2022-0723.9521252.525082e+100.1717900.79872022-08-0.1140220.122868
519996689009.XSHG2022-0823.8326022.240616e+100.2051910.85892022-09-0.1312530.146757
519997689009.XSHG2022-0923.6934421.949535e+100.2358280.91062022-10-0.1661220.168319
519998689009.XSHG2022-1023.5189851.637440e+100.2921900.70832022-110.0414490.209701
519999689009.XSHG2022-1123.5612061.708055e+100.2801100.73632022-12-0.0885100.201033
520000689009.XSHG2022-1223.4706481.560173e+100.3066600.69192023-010.0865410.220085
520001689009.XSHG2023-0123.5554981.698332e+100.2817140.73792023-02-0.0077380.201772
\n", "

520002 rows × 9 columns

\n", "
" ], "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", "519992 689009.XSHG 2022-04 23.663502 1.892031e+10 0.225936 0.8108 \n", "519993 689009.XSHG 2022-05 23.867639 2.320511e+10 0.186935 0.9844 \n", "519994 689009.XSHG 2022-06 23.843220 2.264534e+10 0.191556 0.9071 \n", "519995 689009.XSHG 2022-07 23.952125 2.525082e+10 0.171790 0.7987 \n", "519996 689009.XSHG 2022-08 23.832602 2.240616e+10 0.205191 0.8589 \n", "519997 689009.XSHG 2022-09 23.693442 1.949535e+10 0.235828 0.9106 \n", "519998 689009.XSHG 2022-10 23.518985 1.637440e+10 0.292190 0.7083 \n", "519999 689009.XSHG 2022-11 23.561206 1.708055e+10 0.280110 0.7363 \n", "520000 689009.XSHG 2022-12 23.470648 1.560173e+10 0.306660 0.6919 \n", "520001 689009.XSHG 2023-01 23.555498 1.698332e+10 0.281714 0.7379 \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", "519992 2022-05 0.224708 0.162419 \n", "519993 2022-06 -0.025790 0.134383 \n", "519994 2022-07 0.113436 0.137005 \n", "519995 2022-08 -0.114022 0.122868 \n", "519996 2022-09 -0.131253 0.146757 \n", "519997 2022-10 -0.166122 0.168319 \n", "519998 2022-11 0.041449 0.209701 \n", "519999 2022-12 -0.088510 0.201033 \n", "520000 2023-01 0.086541 0.220085 \n", "520001 2023-02 -0.007738 0.201772 \n", "\n", "[520002 rows x 9 columns]" ] }, "execution_count": 166, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ret_df" ] }, { "cell_type": "code", "execution_count": 173, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 173, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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grouping_datebmbm_uqer
1194752008-020.1864320.037286
1194762008-030.2596200.051924
1194772008-040.7750020.155000
1194782008-050.6130570.153264
1194792008-060.8164630.204115
1194802008-070.6754060.168850
1194812008-080.7461630.186539
1194822008-090.8569080.214229
1194832008-101.1159260.278979
1194842008-111.0063620.251591
............
1196452022-040.7887050.572082
1196462022-050.7232810.521621
1196472022-060.7237720.575738
1196482022-070.6860760.547345
1196492022-080.6675070.532538
1196502022-090.7589500.606465
1196512022-100.7791890.622665
1196522022-110.7259350.580080
1196532022-120.7658820.608643
1196542023-010.7194650.571755
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180 rows × 3 columns

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" ], "text/plain": [ " grouping_date bm bm_uqer\n", "119475 2008-02 0.186432 0.037286\n", "119476 2008-03 0.259620 0.051924\n", "119477 2008-04 0.775002 0.155000\n", "119478 2008-05 0.613057 0.153264\n", "119479 2008-06 0.816463 0.204115\n", "119480 2008-07 0.675406 0.168850\n", "119481 2008-08 0.746163 0.186539\n", "119482 2008-09 0.856908 0.214229\n", "119483 2008-10 1.115926 0.278979\n", "119484 2008-11 1.006362 0.251591\n", "... ... ... ...\n", "119645 2022-04 0.788705 0.572082\n", "119646 2022-05 0.723281 0.521621\n", "119647 2022-06 0.723772 0.575738\n", "119648 2022-07 0.686076 0.547345\n", "119649 2022-08 0.667507 0.532538\n", "119650 2022-09 0.758950 0.606465\n", "119651 2022-10 0.779189 0.622665\n", "119652 2022-11 0.725935 0.580080\n", "119653 2022-12 0.765882 0.608643\n", "119654 2023-01 0.719465 0.571755\n", "\n", "[180 rows x 3 columns]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 180, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\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": 181, "metadata": { "editable": true }, "outputs": [ { "data": { "text/plain": [ "array([,\n", " ],\n", " dtype=object)" ] }, "execution_count": 181, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ret_df.loc[ret_df['secID'].isin(sample_id),['grouping_date','bm','bm_uqer']].set_index('grouping_date').plot(subplots=True)" ] }, { "cell_type": "code", "execution_count": 182, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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p1p2p3p4p5p6p7p8p9p10p10-p1
mean0.0009300.0075600.0084760.0099930.0118390.0124900.0123240.0127050.0129210.0117160.010786
t-value0.1349591.0800741.1529861.4152921.6299821.7769331.7251701.7853091.8319831.6753582.872140
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" ], "text/plain": [ " p1 p2 p3 p4 p5 p6 p7 \\\n", "mean 0.000930 0.007560 0.008476 0.009993 0.011839 0.012490 0.012324 \n", "t-value 0.134959 1.080074 1.152986 1.415292 1.629982 1.776933 1.725170 \n", "\n", " p8 p9 p10 p10-p1 \n", "mean 0.012705 0.012921 0.011716 0.010786 \n", "t-value 1.785309 1.831983 1.675358 2.872140 " ] }, "execution_count": 182, "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": 183, "metadata": { "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(190,)\n", "(190,)\n", "(190,)\n", "(190,)\n", "(190,)\n", "(190,)\n" ] }, { "data": { "text/html": [ "
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bm1_size1bm1_size2bm2_size1bm2_size2bm3_size1bm3_size2
ret_mean0.0101390.002160.0159180.0050020.0205090.005087
t_values1.3896540.321722.1674100.7324162.5474210.757503
\n", "
" ], "text/plain": [ " bm1_size1 bm1_size2 bm2_size1 bm2_size2 bm3_size1 bm3_size2\n", "ret_mean 0.010139 0.00216 0.015918 0.005002 0.020509 0.005087\n", "t_values 1.389654 0.32172 2.167410 0.732416 2.547421 0.757503" ] }, "execution_count": 183, "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": 184, "metadata": { "editable": true }, "outputs": [], "source": [ "# ret_df[(ret_df['ret_date'] >= '2008-02') & (ret_df['secID'] == '000001.XSHE')]" ] }, { "cell_type": "code", "execution_count": 185, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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interceptbeta_coefsize_coefbm_coef
ret_mean9.2127390.405394-0.3991750.039288
t_values2.6197631.133055-2.6792840.714466
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" ], "text/plain": [ " intercept beta_coef size_coef bm_coef\n", "ret_mean 9.212739 0.405394 -0.399175 0.039288\n", "t_values 2.619763 1.133055 -2.679284 0.714466" ] }, "execution_count": 185, "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": "code", "execution_count": 186, "metadata": { "editable": true }, "outputs": [ { "data": { "text/plain": [ "{'bm1_size1': secID grouping_date ret_date exret size mktcap \\\n", " 0 000014.XSHE 2007-06 2007-07 0.547459 21.108547 1.470028e+09 \n", " 1 000025.XSHE 2007-06 2007-07 -0.201773 20.485442 7.883413e+08 \n", " 2 000033.XSHE 2007-06 2007-07 0.126208 20.845732 1.130281e+09 \n", " 3 000049.XSHE 2007-06 2007-07 0.471941 20.141541 5.589333e+08 \n", " 4 000056.XSHE 2007-06 2007-07 0.156088 20.566809 8.551683e+08 \n", " 5 000411.XSHE 2007-06 2007-07 0.430651 19.585247 3.204531e+08 \n", " 6 000415.XSHE 2007-06 2007-07 0.165811 21.115524 1.480319e+09 \n", " 7 000421.XSHE 2007-06 2007-07 0.146840 21.013886 1.337256e+09 \n", " 8 000515.XSHE 2007-06 2007-07 0.252712 20.838220 1.121822e+09 \n", " 9 000532.XSHE 2007-06 2007-07 0.321315 21.090814 1.444189e+09 \n", " ... ... ... ... ... ... ... \n", " 58370 600817.XSHG 2007-05 2007-06 -0.259142 20.932604 1.232862e+09 \n", " 58371 600842.XSHG 2007-05 2007-06 -0.203433 20.925434 1.224054e+09 \n", " 58372 600846.XSHG 2007-05 2007-06 -0.330308 21.306837 1.792429e+09 \n", " 58373 600848.XSHG 2007-05 2007-06 -0.029055 20.366862 7.001897e+08 \n", " 58374 600850.XSHG 2007-05 2007-06 -0.410932 20.656770 9.356667e+08 \n", " 58375 600856.XSHG 2007-05 2007-06 -0.368356 20.756396 1.033685e+09 \n", " 58376 600869.XSHG 2007-05 2007-06 -0.292351 21.137896 1.513811e+09 \n", " 58377 600870.XSHG 2007-05 2007-06 -0.292416 21.209669 1.626455e+09 \n", " 58378 600883.XSHG 2007-05 2007-06 -0.122108 20.870365 1.158468e+09 \n", " 58379 600885.XSHG 2007-05 2007-06 -0.306797 20.460659 7.690443e+08 \n", " \n", " bm \n", " 0 0.231605 \n", " 1 0.171424 \n", " 2 0.276225 \n", " 3 0.276072 \n", " 4 0.191719 \n", " 5 0.270130 \n", " 6 0.269020 \n", " 7 0.317755 \n", " 8 0.309840 \n", " 9 0.323877 \n", " ... ... \n", " 58370 0.250712 \n", " 58371 0.208030 \n", " 58372 0.273707 \n", " 58373 0.204301 \n", " 58374 0.231505 \n", " 58375 0.173433 \n", " 58376 0.097180 \n", " 58377 0.202155 \n", " 58378 0.258326 \n", " 58379 0.184045 \n", " \n", " [58380 rows x 7 columns],\n", " 'bm1_size2': secID grouping_date ret_date exret size mktcap \\\n", " 0 000001.XSHE 2007-06 2007-07 0.313877 24.476555 4.266117e+10 \n", " 1 000002.XSHE 2007-06 2007-07 0.477505 25.259434 9.333248e+10 \n", " 2 000006.XSHE 2007-06 2007-07 0.282520 22.417635 5.443212e+09 \n", " 3 000024.XSHE 2007-06 2007-07 0.221415 23.468754 1.557222e+10 \n", " 4 000031.XSHE 2007-06 2007-07 0.500025 22.876698 8.614372e+09 \n", " 5 000040.XSHE 2007-06 2007-07 0.255868 21.707787 2.676529e+09 \n", " 6 000043.XSHE 2007-06 2007-07 0.353304 21.331385 1.836973e+09 \n", " 7 000060.XSHE 2007-06 2007-07 0.393288 23.262121 1.266516e+10 \n", " 8 000061.XSHE 2007-06 2007-07 -0.002620 22.475253 5.766049e+09 \n", " 9 000069.XSHE 2007-06 2007-07 0.220995 23.693526 1.949699e+10 \n", " ... ... ... ... ... ... ... \n", " 97693 600879.XSHG 2007-05 2007-06 0.026076 22.975368 9.507705e+09 \n", " 97694 600880.XSHG 2007-05 2007-06 -0.123901 21.893247 3.221933e+09 \n", " 97695 600881.XSHG 2007-05 2007-06 0.002865 23.759889 2.083477e+10 \n", " 97696 600887.XSHG 2007-05 2007-06 -0.117072 23.471224 1.561072e+10 \n", " 97697 600888.XSHG 2007-05 2007-06 0.077841 21.528878 2.238067e+09 \n", " 97698 600895.XSHG 2007-05 2007-06 -0.118853 23.162926 1.146914e+10 \n", " 97699 600896.XSHG 2007-05 2007-06 -0.019787 22.244590 4.578283e+09 \n", " 97700 600962.XSHG 2007-05 2007-06 -0.162432 21.543806 2.271728e+09 \n", " 97701 600970.XSHG 2007-05 2007-06 -0.068962 22.173919 4.265900e+09 \n", " 97702 600981.XSHG 2007-05 2007-06 0.099843 21.754868 2.805558e+09 \n", " \n", " bm \n", " 0 0.166571 \n", " 1 0.167751 \n", " 2 0.243786 \n", " 3 0.236311 \n", " 4 0.175485 \n", " 5 0.329985 \n", " 6 0.235918 \n", " 7 0.270314 \n", " 8 0.250229 \n", " 9 0.155310 \n", " ... ... \n", " 97693 0.142725 \n", " 97694 0.142852 \n", " 97695 0.127255 \n", " 97696 0.176896 \n", " 97697 0.237113 \n", " 97698 0.235866 \n", " 97699 0.248476 \n", " 97700 0.224796 \n", " 97701 0.199396 \n", " 97702 0.251969 \n", " \n", " [97703 rows x 7 columns],\n", " 'bm2_size1': secID grouping_date ret_date exret size mktcap \\\n", " 0 000019.XSHE 2007-06 2007-07 -0.002620 20.290546 6.487424e+08 \n", " 1 000023.XSHE 2007-06 2007-07 0.348274 20.128646 5.517721e+08 \n", " 2 000028.XSHE 2007-06 2007-07 0.618998 20.868838 1.156702e+09 \n", " 3 000055.XSHE 2007-06 2007-07 0.230079 20.441209 7.542311e+08 \n", " 4 000065.XSHE 2007-06 2007-07 0.315912 20.563070 8.519768e+08 \n", " 5 000159.XSHE 2007-06 2007-07 0.317826 21.009003 1.330742e+09 \n", " 6 000404.XSHE 2007-06 2007-07 0.464207 20.603151 8.868187e+08 \n", " 7 000502.XSHE 2007-06 2007-07 0.959733 20.244751 6.197031e+08 \n", " 8 000504.XSHE 2007-06 2007-07 0.374538 20.710027 9.868487e+08 \n", " 9 000516.XSHE 2007-06 2007-07 0.141541 20.788909 1.067846e+09 \n", " ... ... ... ... ... ... ... \n", " 107477 600967.XSHG 2007-05 2007-06 -0.271018 20.856611 1.142644e+09 \n", " 107478 600976.XSHG 2007-05 2007-06 -0.307428 21.175786 1.572269e+09 \n", " 107479 600979.XSHG 2007-05 2007-06 -0.303603 20.689486 9.667840e+08 \n", " 107480 600980.XSHG 2007-05 2007-06 -0.366915 20.430975 7.465511e+08 \n", " 107481 600982.XSHG 2007-05 2007-06 0.004987 20.497738 7.980946e+08 \n", " 107482 600983.XSHG 2007-05 2007-06 -0.318684 20.749450 1.026530e+09 \n", " 107483 600985.XSHG 2007-05 2007-06 -0.335514 20.341439 6.826134e+08 \n", " 107484 600993.XSHG 2007-05 2007-06 -0.124224 21.216830 1.638144e+09 \n", " 107485 600995.XSHG 2007-05 2007-06 -0.245821 21.113707 1.477632e+09 \n", " 107486 601008.XSHG 2007-05 2007-06 -0.018060 21.299654 1.779600e+09 \n", " \n", " bm \n", " 0 0.427137 \n", " 1 0.545605 \n", " 2 0.369999 \n", " 3 0.661388 \n", " 4 0.564491 \n", " 5 0.435299 \n", " 6 0.446288 \n", " 7 0.351514 \n", " 8 0.452700 \n", " 9 0.464874 \n", " ... ... \n", " 107477 0.426070 \n", " 107478 0.453182 \n", " 107479 0.452036 \n", " 107480 0.487037 \n", " 107481 0.464610 \n", " 107482 0.529070 \n", " 107483 0.426462 \n", " 107484 0.393184 \n", " 107485 0.401229 \n", " 107486 0.426846 \n", " \n", " [107487 rows x 7 columns],\n", " 'bm2_size2': secID grouping_date ret_date exret size mktcap \\\n", " 0 000012.XSHE 2007-06 2007-07 0.253637 22.351744 5.096114e+09 \n", " 1 000027.XSHE 2007-06 2007-07 0.123433 23.328501 1.353441e+10 \n", " 2 000029.XSHE 2007-06 2007-07 0.213347 21.363237 1.896427e+09 \n", " 3 000036.XSHE 2007-06 2007-07 0.148847 22.376371 5.223174e+09 \n", " 4 000039.XSHE 2007-06 2007-07 -0.006713 23.878072 2.344849e+10 \n", " 5 000046.XSHE 2007-06 2007-07 0.519563 22.760060 7.665997e+09 \n", " 6 000063.XSHE 2007-06 2007-07 0.047591 24.000356 2.649856e+10 \n", " 7 000070.XSHE 2007-06 2007-07 -0.277638 21.328446 1.831582e+09 \n", " 8 000088.XSHE 2007-06 2007-07 0.048321 22.642563 6.816168e+09 \n", " 9 000089.XSHE 2007-06 2007-07 0.117510 22.598489 6.522272e+09 \n", " ... ... ... ... ... ... ... \n", " 100491 600867.XSHG 2007-05 2007-06 -0.354046 22.170913 4.253096e+09 \n", " 100492 600875.XSHG 2007-05 2007-06 0.219101 22.364401 5.161023e+09 \n", " 100493 600884.XSHG 2007-05 2007-06 0.111874 22.126345 4.067705e+09 \n", " 100494 600886.XSHG 2007-05 2007-06 0.007688 22.675347 7.043329e+09 \n", " 100495 600900.XSHG 2007-05 2007-06 0.082105 24.871565 6.332622e+10 \n", " 100496 600971.XSHG 2007-05 2007-06 -0.124636 21.570288 2.332689e+09 \n", " 100497 600973.XSHG 2007-05 2007-06 0.034460 21.483241 2.138222e+09 \n", " 100498 600978.XSHG 2007-05 2007-06 0.037508 22.245342 4.581730e+09 \n", " 100499 600997.XSHG 2007-05 2007-06 -0.072709 22.484789 5.821295e+09 \n", " 100500 601001.XSHG 2007-05 2007-06 0.000842 22.683079 7.098000e+09 \n", " \n", " bm \n", " 0 0.527283 \n", " 1 0.369063 \n", " 2 0.589420 \n", " 3 0.361454 \n", " 4 0.541703 \n", " 5 0.447062 \n", " 6 0.406505 \n", " 7 0.337101 \n", " 8 0.485723 \n", " 9 0.522970 \n", " ... ... \n", " 100491 0.300867 \n", " 100492 0.494309 \n", " 100493 0.361535 \n", " 100494 0.380774 \n", " 100495 0.455506 \n", " 100496 0.448427 \n", " 100497 0.341000 \n", " 100498 0.477266 \n", " 100499 0.473105 \n", " 100500 0.505101 \n", " \n", " [100501 rows x 7 columns],\n", " 'bm3_size1': secID grouping_date ret_date exret size mktcap \\\n", " 0 000018.XSHE 2007-06 2007-07 0.276978 19.237899 2.264193e+08 \n", " 1 000032.XSHE 2007-06 2007-07 0.275006 20.558908 8.484381e+08 \n", " 2 000037.XSHE 2007-06 2007-07 0.228398 20.788640 1.067558e+09 \n", " 3 000045.XSHE 2007-06 2007-07 0.331251 19.445612 2.786903e+08 \n", " 4 000050.XSHE 2007-06 2007-07 0.197368 20.984007 1.297892e+09 \n", " 5 000062.XSHE 2007-06 2007-07 0.256482 21.249745 1.692961e+09 \n", " 6 000096.XSHE 2007-06 2007-07 0.547056 20.818688 1.100124e+09 \n", " 7 000151.XSHE 2007-06 2007-07 0.384384 20.913814 1.209912e+09 \n", " 8 000153.XSHE 2007-06 2007-07 0.207465 20.542752 8.348414e+08 \n", " 9 000155.XSHE 2007-06 2007-07 0.178528 21.155886 1.541290e+09 \n", " ... ... ... ... ... ... ... \n", " 94197 600960.XSHG 2007-05 2007-06 -0.236967 20.426873 7.434956e+08 \n", " 94198 600966.XSHG 2007-05 2007-06 -0.187255 21.425218 2.017687e+09 \n", " 94199 600969.XSHG 2007-05 2007-06 -0.239106 20.687282 9.646560e+08 \n", " 94200 600975.XSHG 2007-05 2007-06 0.107920 20.166645 5.731425e+08 \n", " 94201 600986.XSHG 2007-05 2007-06 0.106797 20.640347 9.204254e+08 \n", " 94202 600987.XSHG 2007-05 2007-06 -0.264676 21.128655 1.499886e+09 \n", " 94203 600990.XSHG 2007-05 2007-06 -0.219190 20.034617 5.022540e+08 \n", " 94204 600991.XSHG 2007-05 2007-06 -0.231495 21.073762 1.419772e+09 \n", " 94205 600992.XSHG 2007-05 2007-06 -0.244937 20.633606 9.142420e+08 \n", " 94206 601007.XSHG 2007-05 2007-06 -0.039934 20.999552 1.318225e+09 \n", " \n", " bm \n", " 0 1.214773 \n", " 1 0.746448 \n", " 2 1.487667 \n", " 3 1.241401 \n", " 4 0.685461 \n", " 5 0.764958 \n", " 6 1.225683 \n", " 7 0.740278 \n", " 8 0.748933 \n", " 9 1.093114 \n", " ... ... \n", " 94197 0.792550 \n", " 94198 0.718100 \n", " 94199 0.626084 \n", " 94200 0.811844 \n", " 94201 0.622055 \n", " 94202 0.699624 \n", " 94203 0.627999 \n", " 94204 1.418501 \n", " 94205 0.814164 \n", " 94206 0.629433 \n", " \n", " [94207 rows x 7 columns],\n", " 'bm3_size2': secID grouping_date ret_date exret size mktcap \\\n", " 0 000016.XSHE 2007-06 2007-07 0.160653 21.518036 2.213931e+09 \n", " 1 000021.XSHE 2007-06 2007-07 0.136982 22.393984 5.315985e+09 \n", " 2 000022.XSHE 2007-06 2007-07 0.095368 21.633381 2.484608e+09 \n", " 3 000042.XSHE 2007-06 2007-07 0.319595 21.542105 2.267866e+09 \n", " 4 000059.XSHE 2007-06 2007-07 0.195433 21.712219 2.688418e+09 \n", " 5 000066.XSHE 2007-06 2007-07 0.280551 21.604846 2.414713e+09 \n", " 6 000090.XSHE 2007-06 2007-07 0.152888 21.858640 3.112339e+09 \n", " 7 000420.XSHE 2007-06 2007-07 0.430264 21.313973 1.805264e+09 \n", " 8 000429.XSHE 2007-06 2007-07 0.250411 21.895636 3.229638e+09 \n", " 9 000488.XSHE 2007-06 2007-07 0.109754 22.670157 7.006869e+09 \n", " ... ... ... ... ... ... ... \n", " 61860 601318.XSHG 2007-05 2007-06 0.174394 24.279770 3.504050e+10 \n", " 61861 601333.XSHG 2007-05 2007-06 -0.172301 23.538647 1.669954e+10 \n", " 61862 601398.XSHG 2007-05 2007-06 -0.085398 24.641483 5.031067e+10 \n", " 61863 601588.XSHG 2007-05 2007-06 -0.327006 23.388652 1.437350e+10 \n", " 61864 601628.XSHG 2007-05 2007-06 0.108530 24.228823 3.330000e+10 \n", " 61865 601666.XSHG 2007-05 2007-06 -0.063740 22.691706 7.159500e+09 \n", " 61866 601699.XSHG 2007-05 2007-06 0.106236 22.615594 6.634800e+09 \n", " 61867 601872.XSHG 2007-05 2007-06 0.073035 22.892914 8.755200e+09 \n", " 61868 601988.XSHG 2007-05 2007-06 -0.126460 24.119138 2.984066e+10 \n", " 61869 601991.XSHG 2007-05 2007-06 0.470307 22.689538 7.143993e+09 \n", " \n", " bm \n", " 0 1.533636 \n", " 1 0.673904 \n", " 2 0.967896 \n", " 3 0.776067 \n", " 4 0.714057 \n", " 5 0.715789 \n", " 6 0.899783 \n", " 7 0.669211 \n", " 8 0.918247 \n", " 9 0.960996 \n", " ... ... \n", " 61860 2.508041 \n", " 61861 1.269615 \n", " 61862 9.647098 \n", " 61863 0.564303 \n", " 61864 3.816847 \n", " 61865 0.747574 \n", " 61866 0.597250 \n", " 61867 0.996823 \n", " 61868 13.359792 \n", " 61869 3.589068 \n", " \n", " [61870 rows x 7 columns]}" ] }, "execution_count": 186, "metadata": {}, "output_type": "execute_result" } ], "source": [ "portfolios" ] }, { "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": 187, "metadata": { "editable": true }, "outputs": [], "source": [ "portfolios_vwret = {}\n", "for pf in portfolios.keys():\n", " temp = portfolios[pf].groupby('ret_date')['mktcap'].agg({'mktcapsum':np.sum})\n", " portfolios[pf] = pd.merge(portfolios[pf], temp, on='ret_date')\n", " portfolios[pf]['weight'] = portfolios[pf]['mktcap'] / portfolios[pf]['mktcapsum']\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) # vw: value weighted" ] }, { "cell_type": "code", "execution_count": 188, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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SLBLSMBMSHBH
ret_date
2007-050.0713620.1110800.0809490.0990960.0534380.082722
2007-06-0.201392-0.054136-0.176249-0.084690-0.182697-0.075832
2007-070.2355630.1860600.2569530.1899000.2807290.192456
2007-080.0708020.1499300.1029630.1519110.1091160.181585
2007-090.0224620.0192540.0182860.0427210.0471190.103958
2007-10-0.105863-0.000321-0.115498-0.037586-0.1169330.017789
2007-11-0.031662-0.179535-0.046609-0.136686-0.036438-0.148290
2007-120.2058970.1371310.1972960.1652480.1916100.106343
2008-01-0.082404-0.103462-0.060240-0.103317-0.042012-0.169955
2008-020.0860330.0163890.1191680.0330180.1041490.014488
.....................
2022-050.1419310.0611830.1180540.0647280.1123640.010173
2022-060.0783730.1286860.0837060.0695740.0850630.038331
2022-070.046719-0.0462370.054177-0.0180710.040918-0.035799
2022-08-0.045451-0.042070-0.031431-0.021325-0.0275250.008635
2022-09-0.091620-0.073494-0.094059-0.071770-0.091325-0.044946
2022-100.058868-0.0554790.063400-0.0210310.038831-0.049913
2022-110.0733830.0511190.0780230.0953160.0842930.122252
2022-12-0.0355190.000507-0.036035-0.035794-0.053285-0.027306
2023-010.0713970.0701890.0910620.0649480.0853360.045333
2023-020.033873-0.0206760.0426640.0287670.0392530.002038
\n", "

190 rows × 6 columns

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" ], "text/plain": [ " SL BL SM BM SH BH\n", "ret_date \n", "2007-05 0.071362 0.111080 0.080949 0.099096 0.053438 0.082722\n", "2007-06 -0.201392 -0.054136 -0.176249 -0.084690 -0.182697 -0.075832\n", "2007-07 0.235563 0.186060 0.256953 0.189900 0.280729 0.192456\n", "2007-08 0.070802 0.149930 0.102963 0.151911 0.109116 0.181585\n", "2007-09 0.022462 0.019254 0.018286 0.042721 0.047119 0.103958\n", "2007-10 -0.105863 -0.000321 -0.115498 -0.037586 -0.116933 0.017789\n", "2007-11 -0.031662 -0.179535 -0.046609 -0.136686 -0.036438 -0.148290\n", "2007-12 0.205897 0.137131 0.197296 0.165248 0.191610 0.106343\n", "2008-01 -0.082404 -0.103462 -0.060240 -0.103317 -0.042012 -0.169955\n", "2008-02 0.086033 0.016389 0.119168 0.033018 0.104149 0.014488\n", "... ... ... ... ... ... ...\n", "2022-05 0.141931 0.061183 0.118054 0.064728 0.112364 0.010173\n", "2022-06 0.078373 0.128686 0.083706 0.069574 0.085063 0.038331\n", "2022-07 0.046719 -0.046237 0.054177 -0.018071 0.040918 -0.035799\n", "2022-08 -0.045451 -0.042070 -0.031431 -0.021325 -0.027525 0.008635\n", "2022-09 -0.091620 -0.073494 -0.094059 -0.071770 -0.091325 -0.044946\n", "2022-10 0.058868 -0.055479 0.063400 -0.021031 0.038831 -0.049913\n", "2022-11 0.073383 0.051119 0.078023 0.095316 0.084293 0.122252\n", "2022-12 -0.035519 0.000507 -0.036035 -0.035794 -0.053285 -0.027306\n", "2023-01 0.071397 0.070189 0.091062 0.064948 0.085336 0.045333\n", "2023-02 0.033873 -0.020676 0.042664 0.028767 0.039253 0.002038\n", "\n", "[190 rows x 6 columns]" ] }, "execution_count": 188, "metadata": {}, "output_type": "execute_result" } ], "source": [ "portfolios_vwret_df" ] }, { "cell_type": "code", "execution_count": 189, "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": 190, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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SMBHML
ret_date
2007-05-0.029050-0.023142
2007-06-0.115226-0.001500
2007-070.0682760.025781
2007-08-0.0668480.034985
2007-09-0.0260220.054680
2007-10-0.1060580.003520
2007-110.1166010.013235
2007-120.062027-0.022538
2008-010.064026-0.013051
2008-020.0818180.008108
.........
2022-050.078755-0.040289
2022-060.003517-0.041832
2022-070.0806400.002318
2022-08-0.0165490.034316
2022-09-0.0289320.014421
2022-100.095840-0.007235
2022-11-0.0109960.041022
2022-12-0.020749-0.022789
2023-010.022442-0.005458
2023-020.0352200.014046
\n", "

190 rows × 2 columns

\n", "
" ], "text/plain": [ " SMB HML\n", "ret_date \n", "2007-05 -0.029050 -0.023142\n", "2007-06 -0.115226 -0.001500\n", "2007-07 0.068276 0.025781\n", "2007-08 -0.066848 0.034985\n", "2007-09 -0.026022 0.054680\n", "2007-10 -0.106058 0.003520\n", "2007-11 0.116601 0.013235\n", "2007-12 0.062027 -0.022538\n", "2008-01 0.064026 -0.013051\n", "2008-02 0.081818 0.008108\n", "... ... ...\n", "2022-05 0.078755 -0.040289\n", "2022-06 0.003517 -0.041832\n", "2022-07 0.080640 0.002318\n", "2022-08 -0.016549 0.034316\n", "2022-09 -0.028932 0.014421\n", "2022-10 0.095840 -0.007235\n", "2022-11 -0.010996 0.041022\n", "2022-12 -0.020749 -0.022789\n", "2023-01 0.022442 -0.005458\n", "2023-02 0.035220 0.014046\n", "\n", "[190 rows x 2 columns]" ] }, "execution_count": 190, "metadata": {}, "output_type": "execute_result" } ], "source": [ "factors_df" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "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": 191, "metadata": { "editable": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 191, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "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": 192, "metadata": { "editable": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 192, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "((1 + factors_df.loc['2018':]).cumprod()*100).plot()" ] }, { "cell_type": "code", "execution_count": 193, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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exmktretSMBHML
ret_date
2007-05-310.098693-0.029050-0.023142
2007-06-30-0.074622-0.115226-0.001500
2007-07-310.1922400.0682760.025781
2007-08-310.167193-0.0668480.034985
2007-09-300.047263-0.0260220.054680
2007-10-31-0.010382-0.1060580.003520
2007-11-30-0.1573890.1166010.013235
2007-12-310.1373660.062027-0.022538
2008-01-31-0.1232540.064026-0.013051
2008-02-290.0240100.0818180.008108
............
2022-05-310.0288960.078755-0.040289
2022-06-300.0883800.003517-0.041832
2022-07-31-0.0609740.0806400.002318
2022-08-31-0.023276-0.0165490.034316
2022-09-30-0.069642-0.0289320.014421
2022-10-31-0.0560370.095840-0.007235
2022-11-300.086374-0.0109960.041022
2022-12-31-0.010731-0.020749-0.022789
2023-01-310.0713390.022442-0.005458
2023-02-28-0.0149300.0352200.014046
\n", "

190 rows × 3 columns

\n", "
" ], "text/plain": [ " exmktret SMB HML\n", "ret_date \n", "2007-05-31 0.098693 -0.029050 -0.023142\n", "2007-06-30 -0.074622 -0.115226 -0.001500\n", "2007-07-31 0.192240 0.068276 0.025781\n", "2007-08-31 0.167193 -0.066848 0.034985\n", "2007-09-30 0.047263 -0.026022 0.054680\n", "2007-10-31 -0.010382 -0.106058 0.003520\n", "2007-11-30 -0.157389 0.116601 0.013235\n", "2007-12-31 0.137366 0.062027 -0.022538\n", "2008-01-31 -0.123254 0.064026 -0.013051\n", "2008-02-29 0.024010 0.081818 0.008108\n", "... ... ... ...\n", "2022-05-31 0.028896 0.078755 -0.040289\n", "2022-06-30 0.088380 0.003517 -0.041832\n", "2022-07-31 -0.060974 0.080640 0.002318\n", "2022-08-31 -0.023276 -0.016549 0.034316\n", "2022-09-30 -0.069642 -0.028932 0.014421\n", "2022-10-31 -0.056037 0.095840 -0.007235\n", "2022-11-30 0.086374 -0.010996 0.041022\n", "2022-12-31 -0.010731 -0.020749 -0.022789\n", "2023-01-31 0.071339 0.022442 -0.005458\n", "2023-02-28 -0.014930 0.035220 0.014046\n", "\n", "[190 rows x 3 columns]" ] }, "execution_count": 193, "metadata": {}, "output_type": "execute_result" } ], "source": [ "factors_df" ] }, { "cell_type": "code", "execution_count": 194, "metadata": { "editable": true }, "outputs": [], "source": [ "factors_df.to_csv('./output_data/factors/ff3.csv')" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "## Long-only factors" ] }, { "cell_type": "code", "execution_count": 195, "metadata": { "editable": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 195, "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": [ "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": 196, "metadata": { "editable": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 196, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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exmktretsmall_onlyhigh_only
ret_date
2007-05-310.0986930.0685830.068080
2007-06-30-0.074622-0.186779-0.129264
2007-07-310.1922400.2577480.236593
2007-08-310.1671930.0942940.145350
2007-09-300.0472630.0292890.075538
2007-10-31-0.010382-0.112765-0.049572
2007-11-30-0.157389-0.038236-0.092364
2007-12-310.1373660.1982680.148977
2008-01-31-0.123254-0.061552-0.105984
2008-02-290.0240100.1031170.059319
............
2022-05-310.0288960.1241160.061268
2022-06-300.0883800.0823810.061697
2022-07-31-0.0609740.0472710.002560
2022-08-31-0.023276-0.034803-0.009445
2022-09-30-0.069642-0.092335-0.068136
2022-10-31-0.0560370.053699-0.005541
2022-11-300.0863740.0785660.103273
2022-12-31-0.010731-0.041613-0.040295
2023-01-310.0713390.0825980.065335
2023-02-28-0.0149300.0385970.020645
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190 rows × 3 columns

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