{ "cells": [ { "cell_type": "code", "execution_count": 201, "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": 202, "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": 203, "metadata": { "editable": true }, "outputs": [], "source": [ "START = '2007-01-01'\n", "END = '2024-03-31'" ] }, { "cell_type": "code", "execution_count": 204, "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": 205, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDtickersecShortNamecnSpellexchangeCDassetClasslistStatusCDlistDatetransCurrCDISINpartyIDdelistDate
0000001.XSHE000001平安银行PAYHXSHEEL1991-04-03CNYCNE0000000402.0NaN
1000002.XSHE000002万科AWKAXSHEEL1991-01-29CNYCNE0000000T23.0NaN
2000003.XSHE000003PT金田APTJTAXSHEEDE1991-07-03CNYCNE1000031Y54.02002-06-14
3000004.XSHE000004国华网安GHWAXSHEEL1990-12-01CNYCNE0000000Y25.0NaN
4000005.XSHE000005ST星源STXYXSHEEL1990-12-10CNYCNE0000001L76.0NaN
5000006.XSHE000006深振业ASZYAXSHEEL1992-04-27CNYCNE0000001647.0NaN
6000007.XSHE000007*ST全新*STQXXSHEEL1992-04-13CNYCNE0000000P08.0NaN
7000008.XSHE000008神州高铁SZGTXSHEEL1992-05-07CNYCNE0000001C69.0NaN
.......................................
26406900950.XSHG900950新城B股XCBGXSHGEDE1997-10-16USDCNE000000TH11429.02015-11-23
26407900951.XSHG900951退市大化TSDHXSHGEDE1997-10-21USDCNE000000TJ71430.02020-08-27
26408900952.XSHG900952锦港B股JGBGXSHGEL1998-05-19USDCNE000000W88763.0NaN
26409900953.XSHG900953凯马BKMBXSHGEL1998-06-24USDCNE000000WP81431.0NaN
26410900955.XSHG900955退市海BTSHBXSHGEDE1999-01-18USDCNE000000YC21063.02022-07-13
26411900956.XSHG900956东贝B股DBBGXSHGEDE1999-07-15USDCNE000000ZS51432.02020-11-23
26412900957.XSHG900957凌云B股LYBGXSHGEL2000-07-28USDCNE0000013W91433.0NaN
31160DY600018.XSHGDY600018上港集箱SGJXXSHGEDE2000-07-19CNYNaN618.02006-10-20
\n", "

5473 rows × 12 columns

\n", "
" ], "text/plain": [ " secID ticker secShortName cnSpell exchangeCD assetClass \\\n", "0 000001.XSHE 000001 平安银行 PAYH XSHE E \n", "1 000002.XSHE 000002 万科A WKA XSHE E \n", "2 000003.XSHE 000003 PT金田A PTJTA XSHE E \n", "3 000004.XSHE 000004 国华网安 GHWA XSHE E \n", "4 000005.XSHE 000005 ST星源 STXY XSHE E \n", "5 000006.XSHE 000006 深振业A SZYA XSHE E \n", "6 000007.XSHE 000007 *ST全新 *STQX XSHE E \n", "7 000008.XSHE 000008 神州高铁 SZGT XSHE E \n", "... ... ... ... ... ... ... \n", "26406 900950.XSHG 900950 新城B股 XCBG XSHG E \n", "26407 900951.XSHG 900951 退市大化 TSDH XSHG E \n", "26408 900952.XSHG 900952 锦港B股 JGBG XSHG E \n", "26409 900953.XSHG 900953 凯马B KMB XSHG E \n", "26410 900955.XSHG 900955 退市海B TSHB XSHG E \n", "26411 900956.XSHG 900956 东贝B股 DBBG XSHG E \n", "26412 900957.XSHG 900957 凌云B股 LYBG XSHG E \n", "31160 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 1990-12-01 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", "26406 DE 1997-10-16 USD CNE000000TH1 1429.0 2015-11-23 \n", "26407 DE 1997-10-21 USD CNE000000TJ7 1430.0 2020-08-27 \n", "26408 L 1998-05-19 USD CNE000000W88 763.0 NaN \n", "26409 L 1998-06-24 USD CNE000000WP8 1431.0 NaN \n", "26410 DE 1999-01-18 USD CNE000000YC2 1063.0 2022-07-13 \n", "26411 DE 1999-07-15 USD CNE000000ZS5 1432.0 2020-11-23 \n", "26412 L 2000-07-28 USD CNE0000013W9 1433.0 NaN \n", "31160 DE 2000-07-19 CNY NaN 618.0 2006-10-20 \n", "\n", "[5473 rows x 12 columns]" ] }, "execution_count": 205, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_info" ] }, { "cell_type": "code", "execution_count": 206, "metadata": { "editable": true }, "outputs": [ { "data": { "text/plain": [ "5473" ] }, "execution_count": 206, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(stk_id)" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "## ST" ] }, { "cell_type": "code", "execution_count": 207, "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": 208, "metadata": { "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 586133 entries, 0 to 586132\n", "Data columns (total 3 columns):\n", "secID 586133 non-null object\n", "tradeDate 586133 non-null object\n", "STflg 586133 non-null object\n", "dtypes: object(3)\n", "memory usage: 13.4+ MB\n" ] } ], "source": [ "st_df.info()" ] }, { "cell_type": "code", "execution_count": 209, "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
............
586125900955.XSHG2022-06-06*ST
586126900955.XSHG2022-06-07*ST
586127900955.XSHG2022-06-08*ST
586128900955.XSHG2022-06-09*ST
586129900955.XSHG2022-06-10*ST
586130900955.XSHG2022-06-13*ST
586131900955.XSHG2022-06-14*ST
586132900955.XSHG2022-06-15*ST
\n", "

586133 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", "586125 900955.XSHG 2022-06-06 *ST\n", "586126 900955.XSHG 2022-06-07 *ST\n", "586127 900955.XSHG 2022-06-08 *ST\n", "586128 900955.XSHG 2022-06-09 *ST\n", "586129 900955.XSHG 2022-06-10 *ST\n", "586130 900955.XSHG 2022-06-13 *ST\n", "586131 900955.XSHG 2022-06-14 *ST\n", "586132 900955.XSHG 2022-06-15 *ST\n", "\n", "[586133 rows x 3 columns]" ] }, "execution_count": 209, "metadata": {}, "output_type": "execute_result" } ], "source": [ "st_df" ] }, { "cell_type": "code", "execution_count": 210, "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": 211, "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": 212, "metadata": { "editable": true }, "outputs": [], "source": [ "fundmen_df = pd.read_pickle('./data/fundmen_df.pkl')" ] }, { "cell_type": "code", "execution_count": 213, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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secIDpublishDateendDateendDateRepactPubtimefiscalPeriodTShEquityTEquityAttrPminorityInt
0000001.XSHE2024-03-152023-12-312023-12-312024-03-14 18:46:58124.723280e+114.723280e+11NaN
1000001.XSHE2024-03-152022-12-312023-12-312024-03-14 18:46:58124.346800e+114.346800e+11NaN
2000001.XSHE2023-10-252022-12-312023-09-302023-10-24 17:52:46124.346800e+114.346800e+11NaN
3000001.XSHE2023-08-242022-12-312023-06-302023-08-23 18:10:28124.346800e+114.346800e+11NaN
4000001.XSHE2023-04-252022-12-312023-03-312023-04-24 18:00:27124.346800e+114.346800e+11NaN
5000001.XSHE2023-03-092022-12-312022-12-312023-03-08 17:56:33124.346800e+114.346800e+11NaN
6000001.XSHE2023-03-092021-12-312022-12-312023-03-08 17:56:33123.954480e+113.954480e+11NaN
7000001.XSHE2022-10-252021-12-312022-09-302022-10-24 20:52:23123.954480e+113.954480e+11NaN
..............................
307485900957.XSHG2009-08-012008-12-312009-06-302009-07-31 18:00:00124.902596e+084.369354e+0853324231.94
307486900957.XSHG2009-04-182008-12-312009-03-312009-04-17 18:00:00124.902596e+084.369354e+0853324231.94
307487900957.XSHG2009-03-262008-12-312008-12-312009-03-25 18:00:00124.902596e+084.369354e+0853324231.94
307488900957.XSHG2009-03-262007-12-312008-12-312009-03-25 18:00:00124.363166e+083.769447e+0859371874.07
307489900957.XSHG2008-10-242007-12-312008-09-302008-10-23 18:00:00124.363166e+083.769447e+0859371874.07
307490900957.XSHG2008-08-252007-12-312008-06-302008-08-24 18:00:00124.363166e+083.769447e+0859371874.07
307491900957.XSHG2008-04-242007-12-312008-03-312008-04-23 18:00:00124.363166e+083.769447e+0859371874.07
307492900957.XSHG2008-04-082007-12-312007-12-312008-04-07 18:00:00124.363166e+083.769447e+0859371874.07
\n", "

307493 rows × 9 columns

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" ], "text/plain": [ " secID publishDate endDate endDateRep actPubtime \\\n", "161053 300720.XSHE 2020-04-24 2018-12-31 2019-12-31 2020-04-23 21:04:35 \n", "161054 300720.XSHE 2019-10-30 2018-12-31 2019-09-30 2019-10-29 19:22:34 \n", "161055 300720.XSHE 2019-08-28 2018-12-31 2019-06-30 2019-08-27 19:42:06 \n", "161056 300720.XSHE 2019-04-26 2018-12-31 2019-03-31 2019-04-25 23:27:06 \n", "161057 300720.XSHE 2019-04-26 2018-12-31 2018-12-31 2019-04-25 23:27:06 \n", "\n", " fiscalPeriod TShEquity TEquityAttrP minorityInt \n", "161053 12 4.555515e+08 4.555515e+08 NaN \n", "161054 12 4.555515e+08 4.555515e+08 NaN \n", "161055 12 4.555515e+08 4.555515e+08 NaN \n", "161056 12 4.555515e+08 4.555515e+08 NaN \n", "161057 12 4.555515e+08 4.555515e+08 NaN " ] }, "execution_count": 218, "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": 219, "metadata": { "editable": true }, "outputs": [], "source": [ "fundmen_df[['publishDate','endDate']] = fundmen_df[['publishDate','endDate']].apply(pd.to_datetime)" ] }, { "cell_type": "code", "execution_count": 220, "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": 221, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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1000001.XSHE2024-03-152022-12-312023-12-314.346800e+114.346800e+11NaN320242022
2000001.XSHE2023-10-252022-12-312023-09-304.346800e+114.346800e+11NaN1020232022
3000001.XSHE2023-08-242022-12-312023-06-304.346800e+114.346800e+11NaN820232022
4000001.XSHE2023-04-252022-12-312023-03-314.346800e+114.346800e+11NaN420232022
5000001.XSHE2023-03-092022-12-312022-12-314.346800e+114.346800e+11NaN320232022
6000001.XSHE2023-03-092021-12-312022-12-313.954480e+113.954480e+11NaN320232021
7000001.XSHE2022-10-252021-12-312022-09-303.954480e+113.954480e+11NaN1020222021
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307493 rows × 10 columns

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" ], "text/plain": [ " secID publishDate endDate endDateRep TShEquity \\\n", "0 000001.XSHE 2024-03-15 2023-12-31 2023-12-31 4.723280e+11 \n", "1 000001.XSHE 2024-03-15 2022-12-31 2023-12-31 4.346800e+11 \n", "2 000001.XSHE 2023-10-25 2022-12-31 2023-09-30 4.346800e+11 \n", "3 000001.XSHE 2023-08-24 2022-12-31 2023-06-30 4.346800e+11 \n", "4 000001.XSHE 2023-04-25 2022-12-31 2023-03-31 4.346800e+11 \n", "5 000001.XSHE 2023-03-09 2022-12-31 2022-12-31 4.346800e+11 \n", "6 000001.XSHE 2023-03-09 2021-12-31 2022-12-31 3.954480e+11 \n", "7 000001.XSHE 2022-10-25 2021-12-31 2022-09-30 3.954480e+11 \n", "... ... ... ... ... ... \n", "307485 900957.XSHG 2009-08-01 2008-12-31 2009-06-30 4.902596e+08 \n", "307486 900957.XSHG 2009-04-18 2008-12-31 2009-03-31 4.902596e+08 \n", "307487 900957.XSHG 2009-03-26 2008-12-31 2008-12-31 4.902596e+08 \n", "307488 900957.XSHG 2009-03-26 2007-12-31 2008-12-31 4.363166e+08 \n", "307489 900957.XSHG 2008-10-24 2007-12-31 2008-09-30 4.363166e+08 \n", "307490 900957.XSHG 2008-08-25 2007-12-31 2008-06-30 4.363166e+08 \n", "307491 900957.XSHG 2008-04-24 2007-12-31 2008-03-31 4.363166e+08 \n", "307492 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 4.723280e+11 NaN 3 2024 2023 \n", "1 4.346800e+11 NaN 3 2024 2022 \n", "2 4.346800e+11 NaN 10 2023 2022 \n", "3 4.346800e+11 NaN 8 2023 2022 \n", "4 4.346800e+11 NaN 4 2023 2022 \n", "5 4.346800e+11 NaN 3 2023 2022 \n", "6 3.954480e+11 NaN 3 2023 2021 \n", "7 3.954480e+11 NaN 10 2022 2021 \n", "... ... ... ... ... ... \n", "307485 4.369354e+08 53324231.94 8 2009 2008 \n", "307486 4.369354e+08 53324231.94 4 2009 2008 \n", "307487 4.369354e+08 53324231.94 3 2009 2008 \n", "307488 3.769447e+08 59371874.07 3 2009 2007 \n", "307489 3.769447e+08 59371874.07 10 2008 2007 \n", "307490 3.769447e+08 59371874.07 8 2008 2007 \n", "307491 3.769447e+08 59371874.07 4 2008 2007 \n", "307492 3.769447e+08 59371874.07 4 2008 2007 \n", "\n", "[307493 rows x 10 columns]" ] }, "execution_count": 228, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fundmen_df" ] }, { "cell_type": "code", "execution_count": 229, "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(最近的报告)\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": 230, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDpublishDateendDateendDateRepTShEquityTEquityAttrPminorityIntpub_monthpub_yeardata_year
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161052300720.XSHE2020-04-242019-12-312019-12-314.783596e+084.783596e+08NaN420202019
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49000001.XSHE2014-04-242013-12-312014-03-311.120810e+111.120810e+11NaN420142013
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307449900957.XSHG2016-04-232015-12-312016-03-314.106786e+083.973929e+081.328570e+07420162015
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3000001.XSHE2010-12-312011-04-272011-03-313.351288e+103.351288e+10NaN420112010
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6000001.XSHE2013-12-312014-04-242014-03-311.120810e+111.120810e+11NaN420142013
7000001.XSHE2014-12-312015-04-242015-03-311.309490e+111.309490e+11NaN420152014
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54386900957.XSHG2015-12-312016-04-232016-03-314.106786e+083.973929e+081.328570e+07420162015
54387900957.XSHG2016-12-312017-04-262017-03-313.938268e+083.930721e+087.546643e+05420172016
54388900957.XSHG2017-12-312018-04-262018-03-314.238426e+084.231040e+087.386715e+05420182017
54389900957.XSHG2018-12-312019-04-252019-03-314.515278e+084.508051e+087.226781e+05420192018
54390900957.XSHG2019-12-312020-04-292020-03-314.768689e+084.761021e+087.667705e+05420202019
54391900957.XSHG2020-12-312021-04-272021-03-314.987276e+084.979110e+088.165551e+05420212020
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" ], "text/plain": [ " secID endDate publishDate endDateRep TShEquity \\\n", "0 000001.XSHE 2007-12-31 2008-04-24 2008-03-31 1.300606e+10 \n", "1 000001.XSHE 2008-12-31 2009-04-24 2009-03-31 1.640079e+10 \n", "2 000001.XSHE 2009-12-31 2010-04-29 2010-03-31 2.046961e+10 \n", "3 000001.XSHE 2010-12-31 2011-04-27 2011-03-31 3.351288e+10 \n", "4 000001.XSHE 2011-12-31 2012-04-26 2012-03-31 7.538058e+10 \n", "5 000001.XSHE 2012-12-31 2013-04-24 2013-03-31 8.479900e+10 \n", "6 000001.XSHE 2013-12-31 2014-04-24 2014-03-31 1.120810e+11 \n", "7 000001.XSHE 2014-12-31 2015-04-24 2015-03-31 1.309490e+11 \n", "... ... ... ... ... ... \n", "54386 900957.XSHG 2015-12-31 2016-04-23 2016-03-31 4.106786e+08 \n", "54387 900957.XSHG 2016-12-31 2017-04-26 2017-03-31 3.938268e+08 \n", "54388 900957.XSHG 2017-12-31 2018-04-26 2018-03-31 4.238426e+08 \n", "54389 900957.XSHG 2018-12-31 2019-04-25 2019-03-31 4.515278e+08 \n", "54390 900957.XSHG 2019-12-31 2020-04-29 2020-03-31 4.768689e+08 \n", "54391 900957.XSHG 2020-12-31 2021-04-27 2021-03-31 4.987276e+08 \n", "54392 900957.XSHG 2021-12-31 2022-04-30 2022-03-31 5.263733e+08 \n", "54393 900957.XSHG 2022-12-31 2023-04-27 2023-03-31 5.669258e+08 \n", "\n", " TEquityAttrP minorityInt pub_month pub_year data_year \n", "0 1.300606e+10 NaN 4 2008 2007 \n", "1 1.640079e+10 NaN 4 2009 2008 \n", "2 2.046961e+10 NaN 4 2010 2009 \n", "3 3.351288e+10 NaN 4 2011 2010 \n", "4 7.331084e+10 2.069747e+09 4 2012 2011 \n", "5 8.479900e+10 NaN 4 2013 2012 \n", "6 1.120810e+11 NaN 4 2014 2013 \n", "7 1.309490e+11 NaN 4 2015 2014 \n", "... ... ... ... ... ... \n", "54386 3.973929e+08 1.328570e+07 4 2016 2015 \n", "54387 3.930721e+08 7.546643e+05 4 2017 2016 \n", "54388 4.231040e+08 7.386715e+05 4 2018 2017 \n", "54389 4.508051e+08 7.226781e+05 4 2019 2018 \n", "54390 4.761021e+08 7.667705e+05 4 2020 2019 \n", "54391 4.979110e+08 8.165551e+05 4 2021 2020 \n", "54392 5.255741e+08 7.991940e+05 4 2022 2021 \n", "54393 5.660700e+08 8.557882e+05 4 2023 2022 \n", "\n", "[54394 rows x 10 columns]" ] }, "execution_count": 236, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fundmen_df.groupby(['secID','endDate'],as_index=False).last()" ] }, { "cell_type": "code", "execution_count": 237, "metadata": { "editable": true }, "outputs": [], "source": [ "# fundmen_df = fundmen_df.groupby(['secID','endDate'],as_index=False).first()" ] }, { "cell_type": "code", "execution_count": 238, "metadata": {}, "outputs": [], "source": [ "fundmen_df.drop_duplicates(['secID','endDate'],keep='last', inplace=True)" ] }, { "cell_type": "code", "execution_count": 239, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(54394, 10)" ] }, "execution_count": 239, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fundmen_df.shape" ] }, { "cell_type": "code", "execution_count": 240, "metadata": { "editable": true }, "outputs": [], "source": [ "fundmen_df['bm_date'] = fundmen_df['endDate'].dt.to_period('M')" ] }, { "cell_type": "code", "execution_count": 241, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDpublishDateendDateendDateRepTShEquityTEquityAttrPminorityIntpub_monthpub_yeardata_yearbm_date
79000001.XSHE2008-04-242007-12-312008-03-311.300606e+101.300606e+10NaN4200820072007-12
74000001.XSHE2009-04-242008-12-312009-03-311.640079e+101.640079e+10NaN4200920082008-12
69000001.XSHE2010-04-292009-12-312010-03-312.046961e+102.046961e+10NaN4201020092009-12
64000001.XSHE2011-04-272010-12-312011-03-313.351288e+103.351288e+10NaN4201120102010-12
59000001.XSHE2012-04-262011-12-312012-03-317.538058e+107.331084e+102.069747e+094201220112011-12
54000001.XSHE2013-04-242012-12-312013-03-318.479900e+108.479900e+10NaN4201320122012-12
49000001.XSHE2014-04-242013-12-312014-03-311.120810e+111.120810e+11NaN4201420132013-12
44000001.XSHE2015-04-242014-12-312015-03-311.309490e+111.309490e+11NaN4201520142014-12
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307449900957.XSHG2016-04-232015-12-312016-03-314.106786e+083.973929e+081.328570e+074201620152015-12
307444900957.XSHG2017-04-262016-12-312017-03-313.938268e+083.930721e+087.546643e+054201720162016-12
307439900957.XSHG2018-04-262017-12-312018-03-314.238426e+084.231040e+087.386715e+054201820172017-12
307434900957.XSHG2019-04-252018-12-312019-03-314.515278e+084.508051e+087.226781e+054201920182018-12
307429900957.XSHG2020-04-292019-12-312020-03-314.768689e+084.761021e+087.667705e+054202020192019-12
307424900957.XSHG2021-04-272020-12-312021-03-314.987276e+084.979110e+088.165551e+054202120202020-12
307419900957.XSHG2022-04-302021-12-312022-03-315.263733e+085.255741e+087.991940e+054202220212021-12
307414900957.XSHG2023-04-272022-12-312023-03-315.669258e+085.660700e+088.557882e+054202320222022-12
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54394 rows × 11 columns

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" ], "text/plain": [ " secID publishDate endDate endDateRep TShEquity \\\n", "79 000001.XSHE 2008-04-24 2007-12-31 2008-03-31 1.300606e+10 \n", "74 000001.XSHE 2009-04-24 2008-12-31 2009-03-31 1.640079e+10 \n", "69 000001.XSHE 2010-04-29 2009-12-31 2010-03-31 2.046961e+10 \n", "64 000001.XSHE 2011-04-27 2010-12-31 2011-03-31 3.351288e+10 \n", "59 000001.XSHE 2012-04-26 2011-12-31 2012-03-31 7.538058e+10 \n", "54 000001.XSHE 2013-04-24 2012-12-31 2013-03-31 8.479900e+10 \n", "49 000001.XSHE 2014-04-24 2013-12-31 2014-03-31 1.120810e+11 \n", "44 000001.XSHE 2015-04-24 2014-12-31 2015-03-31 1.309490e+11 \n", "... ... ... ... ... ... \n", "307449 900957.XSHG 2016-04-23 2015-12-31 2016-03-31 4.106786e+08 \n", "307444 900957.XSHG 2017-04-26 2016-12-31 2017-03-31 3.938268e+08 \n", "307439 900957.XSHG 2018-04-26 2017-12-31 2018-03-31 4.238426e+08 \n", "307434 900957.XSHG 2019-04-25 2018-12-31 2019-03-31 4.515278e+08 \n", "307429 900957.XSHG 2020-04-29 2019-12-31 2020-03-31 4.768689e+08 \n", "307424 900957.XSHG 2021-04-27 2020-12-31 2021-03-31 4.987276e+08 \n", "307419 900957.XSHG 2022-04-30 2021-12-31 2022-03-31 5.263733e+08 \n", "307414 900957.XSHG 2023-04-27 2022-12-31 2023-03-31 5.669258e+08 \n", "\n", " TEquityAttrP minorityInt pub_month pub_year data_year bm_date \n", "79 1.300606e+10 NaN 4 2008 2007 2007-12 \n", "74 1.640079e+10 NaN 4 2009 2008 2008-12 \n", "69 2.046961e+10 NaN 4 2010 2009 2009-12 \n", "64 3.351288e+10 NaN 4 2011 2010 2010-12 \n", "59 7.331084e+10 2.069747e+09 4 2012 2011 2011-12 \n", "54 8.479900e+10 NaN 4 2013 2012 2012-12 \n", "49 1.120810e+11 NaN 4 2014 2013 2013-12 \n", "44 1.309490e+11 NaN 4 2015 2014 2014-12 \n", "... ... ... ... ... ... ... \n", "307449 3.973929e+08 1.328570e+07 4 2016 2015 2015-12 \n", "307444 3.930721e+08 7.546643e+05 4 2017 2016 2016-12 \n", "307439 4.231040e+08 7.386715e+05 4 2018 2017 2017-12 \n", "307434 4.508051e+08 7.226781e+05 4 2019 2018 2018-12 \n", "307429 4.761021e+08 7.667705e+05 4 2020 2019 2019-12 \n", "307424 4.979110e+08 8.165551e+05 4 2021 2020 2020-12 \n", "307419 5.255741e+08 7.991940e+05 4 2022 2021 2021-12 \n", "307414 5.660700e+08 8.557882e+05 4 2023 2022 2022-12 \n", "\n", "[54394 rows x 11 columns]" ] }, "execution_count": 241, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fundmen_df" ] }, { "cell_type": "code", "execution_count": 242, "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": 243, "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": 244, "metadata": { "editable": true }, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 244, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.allclose(fundmen_df['book'],fundmen_df['TEquityAttrP'])" ] }, { "cell_type": "code", "execution_count": 245, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDpublishDateendDateendDateRepTShEquityTEquityAttrPminorityIntpub_monthpub_yeardata_yearbm_datebook
198000003.XSHE2008-04-302007-12-312007-12-31-2.969044e+09-2.889290e+09-7.975398e+074200820072007-12-2.889290e+09
195000003.XSHE2009-04-302008-12-312008-12-31-3.063484e+09-2.983208e+09-8.027613e+074200920082008-12-2.983208e+09
191000003.XSHE2011-04-282010-12-312010-12-31-2.994943e+09-2.994943e+09NaN4201120102010-12-2.994943e+09
188000003.XSHE2012-04-262011-12-312011-12-31-3.277899e+09-3.277899e+09NaN4201220112011-12-3.277899e+09
185000003.XSHE2013-04-262012-12-312012-12-31-3.292266e+09-3.292266e+09NaN4201320122012-12-3.292266e+09
182000003.XSHE2014-04-292013-12-312013-12-31-3.286909e+09-3.286909e+09NaN4201420132013-12-3.286909e+09
179000003.XSHE2015-04-292014-12-312014-12-31-3.299867e+09-3.299867e+09NaN4201520142014-12-3.299867e+09
564000007.XSHE2008-04-292007-12-312008-03-31-8.355160e+06-2.334433e+071.498917e+074200820072007-12-2.334433e+07
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753 rows × 12 columns

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secIDpublishDateendDateendDateRepTShEquityTEquityAttrPminorityIntpub_monthpub_yeardata_yearbm_datebook
198000003.XSHE2008-04-302007-12-312007-12-31-2.969044e+09-2.889290e+09-7.975398e+074200820072007-12-2.889290e+09
195000003.XSHE2009-04-302008-12-312008-12-31-3.063484e+09-2.983208e+09-8.027613e+074200920082008-12-2.983208e+09
191000003.XSHE2011-04-282010-12-312010-12-31-2.994943e+09-2.994943e+09NaN4201120102010-12-2.994943e+09
188000003.XSHE2012-04-262011-12-312011-12-31-3.277899e+09-3.277899e+09NaN4201220112011-12-3.277899e+09
185000003.XSHE2013-04-262012-12-312012-12-31-3.292266e+09-3.292266e+09NaN4201320122012-12-3.292266e+09
182000003.XSHE2014-04-292013-12-312013-12-31-3.286909e+09-3.286909e+09NaN4201420132013-12-3.286909e+09
179000003.XSHE2015-04-292014-12-312014-12-31-3.299867e+09-3.299867e+09NaN4201520142014-12-3.299867e+09
564000007.XSHE2008-04-292007-12-312008-03-31-8.355160e+06-2.334433e+071.498917e+074200820072007-12-2.334433e+07
.......................................
306222900938.XSHG2008-04-302007-12-312007-12-31-1.941231e+08-2.039037e+089.780564e+064200820072007-12-2.039037e+08
306216900938.XSHG2009-04-302008-12-312009-03-31-2.074566e+08-2.091595e+081.702871e+064200920082008-12-2.091595e+08
306211900938.XSHG2010-04-282009-12-312010-03-31-4.359970e+08-4.376105e+081.613520e+064201020092009-12-4.376105e+08
306206900938.XSHG2011-04-272010-12-312011-03-31-4.040346e+08-3.947695e+08-9.265102e+064201120102010-12-3.947695e+08
306201900938.XSHG2012-04-272011-12-312012-03-31-5.609697e+08-5.515713e+08-9.398335e+064201220112011-12-5.515713e+08
306388900940.XSHG2008-04-302007-12-312007-12-31-1.278218e+09-1.330081e+095.186286e+074200820072007-12-1.330081e+09
306644900945.XSHG2021-04-302020-12-312020-12-31-2.225391e+10-2.837151e+106.117605e+094202120202020-12-2.837151e+10
307040900951.XSHG2020-04-252019-12-312019-12-31-1.835457e+08-1.835457e+08NaN4202020192019-12-1.835457e+08
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760 rows × 12 columns

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" ], "text/plain": [ " secID publishDate endDate endDateRep TShEquity \\\n", "198 000003.XSHE 2008-04-30 2007-12-31 2007-12-31 -2.969044e+09 \n", "195 000003.XSHE 2009-04-30 2008-12-31 2008-12-31 -3.063484e+09 \n", "191 000003.XSHE 2011-04-28 2010-12-31 2010-12-31 -2.994943e+09 \n", "188 000003.XSHE 2012-04-26 2011-12-31 2011-12-31 -3.277899e+09 \n", "185 000003.XSHE 2013-04-26 2012-12-31 2012-12-31 -3.292266e+09 \n", "182 000003.XSHE 2014-04-29 2013-12-31 2013-12-31 -3.286909e+09 \n", "179 000003.XSHE 2015-04-29 2014-12-31 2014-12-31 -3.299867e+09 \n", "564 000007.XSHE 2008-04-29 2007-12-31 2008-03-31 -8.355160e+06 \n", "... ... ... ... ... ... \n", "306222 900938.XSHG 2008-04-30 2007-12-31 2007-12-31 -1.941231e+08 \n", "306216 900938.XSHG 2009-04-30 2008-12-31 2009-03-31 -2.074566e+08 \n", "306211 900938.XSHG 2010-04-28 2009-12-31 2010-03-31 -4.359970e+08 \n", "306206 900938.XSHG 2011-04-27 2010-12-31 2011-03-31 -4.040346e+08 \n", "306201 900938.XSHG 2012-04-27 2011-12-31 2012-03-31 -5.609697e+08 \n", "306388 900940.XSHG 2008-04-30 2007-12-31 2007-12-31 -1.278218e+09 \n", "306644 900945.XSHG 2021-04-30 2020-12-31 2020-12-31 -2.225391e+10 \n", "307040 900951.XSHG 2020-04-25 2019-12-31 2019-12-31 -1.835457e+08 \n", "\n", " TEquityAttrP minorityInt pub_month pub_year data_year bm_date \\\n", "198 -2.889290e+09 -7.975398e+07 4 2008 2007 2007-12 \n", "195 -2.983208e+09 -8.027613e+07 4 2009 2008 2008-12 \n", "191 -2.994943e+09 NaN 4 2011 2010 2010-12 \n", "188 -3.277899e+09 NaN 4 2012 2011 2011-12 \n", "185 -3.292266e+09 NaN 4 2013 2012 2012-12 \n", "182 -3.286909e+09 NaN 4 2014 2013 2013-12 \n", "179 -3.299867e+09 NaN 4 2015 2014 2014-12 \n", "564 -2.334433e+07 1.498917e+07 4 2008 2007 2007-12 \n", "... ... ... ... ... ... ... \n", "306222 -2.039037e+08 9.780564e+06 4 2008 2007 2007-12 \n", "306216 -2.091595e+08 1.702871e+06 4 2009 2008 2008-12 \n", "306211 -4.376105e+08 1.613520e+06 4 2010 2009 2009-12 \n", "306206 -3.947695e+08 -9.265102e+06 4 2011 2010 2010-12 \n", "306201 -5.515713e+08 -9.398335e+06 4 2012 2011 2011-12 \n", "306388 -1.330081e+09 5.186286e+07 4 2008 2007 2007-12 \n", "306644 -2.837151e+10 6.117605e+09 4 2021 2020 2020-12 \n", "307040 -1.835457e+08 NaN 4 2020 2019 2019-12 \n", "\n", " book \n", "198 -2.889290e+09 \n", "195 -2.983208e+09 \n", "191 -2.994943e+09 \n", "188 -3.277899e+09 \n", "185 -3.292266e+09 \n", "182 -3.286909e+09 \n", "179 -3.299867e+09 \n", "564 -2.334433e+07 \n", "... ... \n", "306222 -2.039037e+08 \n", "306216 -2.091595e+08 \n", "306211 -4.376105e+08 \n", "306206 -3.947695e+08 \n", "306201 -5.515713e+08 \n", "306388 -1.330081e+09 \n", "306644 -2.837151e+10 \n", "307040 -1.835457e+08 \n", "\n", "[760 rows x 12 columns]" ] }, "execution_count": 247, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fundmen_df.loc[fundmen_df['book'] < 0]" ] }, { "cell_type": "code", "execution_count": 248, "metadata": { "editable": true }, "outputs": [], "source": [ "# fundmen_df = fundmen_df[fundmen_df['book'] > 0]" ] }, { "cell_type": "code", "execution_count": 249, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDpublishDateendDateendDateRepTShEquityTEquityAttrPminorityIntpub_monthpub_yeardata_yearbm_datebook
79000001.XSHE2008-04-242007-12-312008-03-311.300606e+101.300606e+10NaN4200820072007-121.300606e+10
74000001.XSHE2009-04-242008-12-312009-03-311.640079e+101.640079e+10NaN4200920082008-121.640079e+10
69000001.XSHE2010-04-292009-12-312010-03-312.046961e+102.046961e+10NaN4201020092009-122.046961e+10
64000001.XSHE2011-04-272010-12-312011-03-313.351288e+103.351288e+10NaN4201120102010-123.351288e+10
59000001.XSHE2012-04-262011-12-312012-03-317.538058e+107.331084e+102.069747e+094201220112011-127.331084e+10
54000001.XSHE2013-04-242012-12-312013-03-318.479900e+108.479900e+10NaN4201320122012-128.479900e+10
49000001.XSHE2014-04-242013-12-312014-03-311.120810e+111.120810e+11NaN4201420132013-121.120810e+11
44000001.XSHE2015-04-242014-12-312015-03-311.309490e+111.309490e+11NaN4201520142014-121.309490e+11
.......................................
307449900957.XSHG2016-04-232015-12-312016-03-314.106786e+083.973929e+081.328570e+074201620152015-123.973929e+08
307444900957.XSHG2017-04-262016-12-312017-03-313.938268e+083.930721e+087.546643e+054201720162016-123.930721e+08
307439900957.XSHG2018-04-262017-12-312018-03-314.238426e+084.231040e+087.386715e+054201820172017-124.231040e+08
307434900957.XSHG2019-04-252018-12-312019-03-314.515278e+084.508051e+087.226781e+054201920182018-124.508051e+08
307429900957.XSHG2020-04-292019-12-312020-03-314.768689e+084.761021e+087.667705e+054202020192019-124.761021e+08
307424900957.XSHG2021-04-272020-12-312021-03-314.987276e+084.979110e+088.165551e+054202120202020-124.979110e+08
307419900957.XSHG2022-04-302021-12-312022-03-315.263733e+085.255741e+087.991940e+054202220212021-125.255741e+08
307414900957.XSHG2023-04-272022-12-312023-03-315.669258e+085.660700e+088.557882e+054202320222022-125.660700e+08
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54394 rows × 12 columns

\n", "
" ], "text/plain": [ " secID publishDate endDate endDateRep TShEquity \\\n", "79 000001.XSHE 2008-04-24 2007-12-31 2008-03-31 1.300606e+10 \n", "74 000001.XSHE 2009-04-24 2008-12-31 2009-03-31 1.640079e+10 \n", "69 000001.XSHE 2010-04-29 2009-12-31 2010-03-31 2.046961e+10 \n", "64 000001.XSHE 2011-04-27 2010-12-31 2011-03-31 3.351288e+10 \n", "59 000001.XSHE 2012-04-26 2011-12-31 2012-03-31 7.538058e+10 \n", "54 000001.XSHE 2013-04-24 2012-12-31 2013-03-31 8.479900e+10 \n", "49 000001.XSHE 2014-04-24 2013-12-31 2014-03-31 1.120810e+11 \n", "44 000001.XSHE 2015-04-24 2014-12-31 2015-03-31 1.309490e+11 \n", "... ... ... ... ... ... \n", "307449 900957.XSHG 2016-04-23 2015-12-31 2016-03-31 4.106786e+08 \n", "307444 900957.XSHG 2017-04-26 2016-12-31 2017-03-31 3.938268e+08 \n", "307439 900957.XSHG 2018-04-26 2017-12-31 2018-03-31 4.238426e+08 \n", "307434 900957.XSHG 2019-04-25 2018-12-31 2019-03-31 4.515278e+08 \n", "307429 900957.XSHG 2020-04-29 2019-12-31 2020-03-31 4.768689e+08 \n", "307424 900957.XSHG 2021-04-27 2020-12-31 2021-03-31 4.987276e+08 \n", "307419 900957.XSHG 2022-04-30 2021-12-31 2022-03-31 5.263733e+08 \n", "307414 900957.XSHG 2023-04-27 2022-12-31 2023-03-31 5.669258e+08 \n", "\n", " TEquityAttrP minorityInt pub_month pub_year data_year bm_date \\\n", "79 1.300606e+10 NaN 4 2008 2007 2007-12 \n", "74 1.640079e+10 NaN 4 2009 2008 2008-12 \n", "69 2.046961e+10 NaN 4 2010 2009 2009-12 \n", "64 3.351288e+10 NaN 4 2011 2010 2010-12 \n", "59 7.331084e+10 2.069747e+09 4 2012 2011 2011-12 \n", "54 8.479900e+10 NaN 4 2013 2012 2012-12 \n", "49 1.120810e+11 NaN 4 2014 2013 2013-12 \n", "44 1.309490e+11 NaN 4 2015 2014 2014-12 \n", "... ... ... ... ... ... ... \n", "307449 3.973929e+08 1.328570e+07 4 2016 2015 2015-12 \n", "307444 3.930721e+08 7.546643e+05 4 2017 2016 2016-12 \n", "307439 4.231040e+08 7.386715e+05 4 2018 2017 2017-12 \n", "307434 4.508051e+08 7.226781e+05 4 2019 2018 2018-12 \n", "307429 4.761021e+08 7.667705e+05 4 2020 2019 2019-12 \n", "307424 4.979110e+08 8.165551e+05 4 2021 2020 2020-12 \n", "307419 5.255741e+08 7.991940e+05 4 2022 2021 2021-12 \n", "307414 5.660700e+08 8.557882e+05 4 2023 2022 2022-12 \n", "\n", " book \n", "79 1.300606e+10 \n", "74 1.640079e+10 \n", "69 2.046961e+10 \n", "64 3.351288e+10 \n", "59 7.331084e+10 \n", "54 8.479900e+10 \n", "49 1.120810e+11 \n", "44 1.309490e+11 \n", "... ... \n", "307449 3.973929e+08 \n", "307444 3.930721e+08 \n", "307439 4.231040e+08 \n", "307434 4.508051e+08 \n", "307429 4.761021e+08 \n", "307424 4.979110e+08 \n", "307419 5.255741e+08 \n", "307414 5.660700e+08 \n", "\n", "[54394 rows x 12 columns]" ] }, "execution_count": 249, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fundmen_df" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "## Risk free rate" ] }, { "cell_type": "code", "execution_count": 250, "metadata": { "editable": true }, "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
..................
4142023-08-01202380.001708NaN
4152023-09-01202390.001807NaN
4162023-10-012023100.001945NaN
4172023-11-012023110.002044NaN
4182023-12-012023120.002068NaN
4192024-01-01202410.002068NaN
4202024-02-01202420.002068NaN
4212024-03-01202430.002068NaN
\n", "

422 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", "414 2023-08-01 2023 8 0.001708 NaN\n", "415 2023-09-01 2023 9 0.001807 NaN\n", "416 2023-10-01 2023 10 0.001945 NaN\n", "417 2023-11-01 2023 11 0.002044 NaN\n", "418 2023-12-01 2023 12 0.002068 NaN\n", "419 2024-01-01 2024 1 0.002068 NaN\n", "420 2024-02-01 2024 2 0.002068 NaN\n", "421 2024-03-01 2024 3 0.002068 NaN\n", "\n", "[422 rows x 5 columns]" ] }, "execution_count": 250, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.read_csv(\"./data/rf-monthly-2024.csv\", encoding='GBK')" ] }, { "cell_type": "code", "execution_count": 251, "metadata": { "editable": true }, "outputs": [], "source": [ "rf = pd.read_csv(\"./data/rf-monthly-2024.csv\", encoding='GBK').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": 252, "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", "
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
.........
4142023-080.001708
4152023-090.001807
4162023-100.001945
4172023-110.002044
4182023-120.002068
4192024-010.002068
4202024-020.002068
4212024-030.002068
\n", "

422 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", "414 2023-08 0.001708\n", "415 2023-09 0.001807\n", "416 2023-10 0.001945\n", "417 2023-11 0.002044\n", "418 2023-12 0.002068\n", "419 2024-01 0.002068\n", "420 2024-02 0.002068\n", "421 2024-03 0.002068\n", "\n", "[422 rows x 2 columns]" ] }, "execution_count": 252, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rf" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "## Beta" ] }, { "cell_type": "code", "execution_count": 253, "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": 254, "metadata": {}, "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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
secIDtradeDateBeta60Beta120Beta252ym
0000001.XSHE2007-01-040.94181.03990.87562007-01
1000002.XSHE2007-01-041.51911.36411.31702007-01
2000004.XSHE2007-01-040.72240.85210.66022007-01
3000006.XSHE2007-01-041.41361.32651.28972007-01
4000007.XSHE2007-01-040.94900.75090.74402007-01
5000008.XSHE2007-01-040.62540.83380.78912007-01
6000005.XSHE2007-01-040.98811.11830.75922007-01
7000009.XSHE2007-01-04NaNNaNNaN2007-01
.....................
12507330688776.XSHG2024-03-292.01161.59491.51642024-03
12507331688777.XSHG2024-03-291.71481.57611.44622024-03
12507332688778.XSHG2024-03-292.40061.95881.93992024-03
12507333688779.XSHG2024-03-292.24941.92981.83342024-03
12507334688786.XSHG2024-03-291.12051.12880.99242024-03
12507335688787.XSHG2024-03-292.19722.00892.00472024-03
12507336688788.XSHG2024-03-292.00911.72291.73132024-03
12507337688789.XSHG2024-03-291.64311.44371.47792024-03
\n", "

12507338 rows × 6 columns

\n", "
" ], "text/plain": [ " secID tradeDate Beta60 Beta120 Beta252 ym\n", "0 000001.XSHE 2007-01-04 0.9418 1.0399 0.8756 2007-01\n", "1 000002.XSHE 2007-01-04 1.5191 1.3641 1.3170 2007-01\n", "2 000004.XSHE 2007-01-04 0.7224 0.8521 0.6602 2007-01\n", "3 000006.XSHE 2007-01-04 1.4136 1.3265 1.2897 2007-01\n", "4 000007.XSHE 2007-01-04 0.9490 0.7509 0.7440 2007-01\n", "5 000008.XSHE 2007-01-04 0.6254 0.8338 0.7891 2007-01\n", "6 000005.XSHE 2007-01-04 0.9881 1.1183 0.7592 2007-01\n", "7 000009.XSHE 2007-01-04 NaN NaN NaN 2007-01\n", "... ... ... ... ... ... ...\n", "12507330 688776.XSHG 2024-03-29 2.0116 1.5949 1.5164 2024-03\n", "12507331 688777.XSHG 2024-03-29 1.7148 1.5761 1.4462 2024-03\n", "12507332 688778.XSHG 2024-03-29 2.4006 1.9588 1.9399 2024-03\n", "12507333 688779.XSHG 2024-03-29 2.2494 1.9298 1.8334 2024-03\n", "12507334 688786.XSHG 2024-03-29 1.1205 1.1288 0.9924 2024-03\n", "12507335 688787.XSHG 2024-03-29 2.1972 2.0089 2.0047 2024-03\n", "12507336 688788.XSHG 2024-03-29 2.0091 1.7229 1.7313 2024-03\n", "12507337 688789.XSHG 2024-03-29 1.6431 1.4437 1.4779 2024-03\n", "\n", "[12507338 rows x 6 columns]" ] }, "execution_count": 254, "metadata": {}, "output_type": "execute_result" } ], "source": [ "beta_df" ] }, { "cell_type": "code", "execution_count": 255, "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": 256, "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", "
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
............
619996689009.XSHG2023-080.8234
619997689009.XSHG2023-090.9152
619998689009.XSHG2023-100.9247
619999689009.XSHG2023-110.9541
620000689009.XSHG2023-121.0448
620001689009.XSHG2024-011.2314
620002689009.XSHG2024-021.4905
620003689009.XSHG2024-031.5477
\n", "

620004 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", "619996 689009.XSHG 2023-08 0.8234\n", "619997 689009.XSHG 2023-09 0.9152\n", "619998 689009.XSHG 2023-10 0.9247\n", "619999 689009.XSHG 2023-11 0.9541\n", "620000 689009.XSHG 2023-12 1.0448\n", "620001 689009.XSHG 2024-01 1.2314\n", "620002 689009.XSHG 2024-02 1.4905\n", "620003 689009.XSHG 2024-03 1.5477\n", "\n", "[620004 rows x 3 columns]" ] }, "execution_count": 256, "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": 257, "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": 258, "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": 259, "metadata": { "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Int64Index: 12939054 entries, 0 to 12939053\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: 592.3+ MB\n" ] } ], "source": [ "stk_df.info()" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "### Exclude ST" ] }, { "cell_type": "code", "execution_count": 260, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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secIDtradeDateclosePricenegMarketValueym
0000001.XSHE2007-01-04405.3451.988610e+102007-01
1000001.XSHE2007-01-05375.1821.840627e+102007-01
2000001.XSHE2007-01-08384.0871.884317e+102007-01
3000001.XSHE2007-01-09396.4401.944920e+102007-01
4000001.XSHE2007-01-10404.4841.984382e+102007-01
5000001.XSHE2007-01-11424.5932.083037e+102007-01
6000001.XSHE2007-01-12445.8512.187330e+102007-01
7000001.XSHE2007-01-15468.2592.297260e+102007-01
..................
12939046900957.XSHG2024-03-200.4237.728000e+072024-03
12939047900957.XSHG2024-03-210.4197.654400e+072024-03
12939048900957.XSHG2024-03-220.4187.636000e+072024-03
12939049900957.XSHG2024-03-250.4107.488800e+072024-03
12939050900957.XSHG2024-03-260.4147.562400e+072024-03
12939051900957.XSHG2024-03-270.4117.507200e+072024-03
12939052900957.XSHG2024-03-280.4187.636000e+072024-03
12939053900957.XSHG2024-03-290.4217.691200e+072024-03
\n", "

12939054 rows × 5 columns

\n", "
" ], "text/plain": [ " secID tradeDate closePrice negMarketValue ym\n", "0 000001.XSHE 2007-01-04 405.345 1.988610e+10 2007-01\n", "1 000001.XSHE 2007-01-05 375.182 1.840627e+10 2007-01\n", "2 000001.XSHE 2007-01-08 384.087 1.884317e+10 2007-01\n", "3 000001.XSHE 2007-01-09 396.440 1.944920e+10 2007-01\n", "4 000001.XSHE 2007-01-10 404.484 1.984382e+10 2007-01\n", "5 000001.XSHE 2007-01-11 424.593 2.083037e+10 2007-01\n", "6 000001.XSHE 2007-01-12 445.851 2.187330e+10 2007-01\n", "7 000001.XSHE 2007-01-15 468.259 2.297260e+10 2007-01\n", "... ... ... ... ... ...\n", "12939046 900957.XSHG 2024-03-20 0.423 7.728000e+07 2024-03\n", "12939047 900957.XSHG 2024-03-21 0.419 7.654400e+07 2024-03\n", "12939048 900957.XSHG 2024-03-22 0.418 7.636000e+07 2024-03\n", "12939049 900957.XSHG 2024-03-25 0.410 7.488800e+07 2024-03\n", "12939050 900957.XSHG 2024-03-26 0.414 7.562400e+07 2024-03\n", "12939051 900957.XSHG 2024-03-27 0.411 7.507200e+07 2024-03\n", "12939052 900957.XSHG 2024-03-28 0.418 7.636000e+07 2024-03\n", "12939053 900957.XSHG 2024-03-29 0.421 7.691200e+07 2024-03\n", "\n", "[12939054 rows x 5 columns]" ] }, "execution_count": 260, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_df" ] }, { "cell_type": "code", "execution_count": 261, "metadata": { "editable": true }, "outputs": [ { "data": { "text/plain": [ "(12939054, 5)" ] }, "execution_count": 261, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_df.dropna().shape" ] }, { "cell_type": "code", "execution_count": 262, "metadata": { "editable": true }, "outputs": [ { "data": { "text/plain": [ "(12939054, 5)" ] }, "execution_count": 262, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_df.shape" ] }, { "cell_type": "code", "execution_count": 263, "metadata": { "editable": true }, "outputs": [], "source": [ "stk_df = pd.merge(stk_df, st_df, on=['secID','tradeDate'],how='left')" ] }, { "cell_type": "code", "execution_count": 264, "metadata": { "editable": true }, "outputs": [], "source": [ "stk_df = stk_df[stk_df['STflg'].isna()].copy()" ] }, { "cell_type": "code", "execution_count": 265, "metadata": { "editable": true }, "outputs": [], "source": [ "stk_df.drop('STflg',axis=1,inplace=True)" ] }, { "cell_type": "code", "execution_count": 266, "metadata": { "editable": true }, "outputs": [ { "data": { "text/plain": [ "(12353284, 5)" ] }, "execution_count": 266, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_df.shape" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "### Monthly trading df" ] }, { "cell_type": "code", "execution_count": 267, "metadata": { "editable": true }, "outputs": [], "source": [ "stk_df_m = stk_df.groupby(['secID','ym'],as_index=False).tail(1)" ] }, { "cell_type": "code", "execution_count": 268, "metadata": { "editable": true }, "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
..................
12938915900957.XSHG2023-08-310.4337.912000e+072023-08
12938935900957.XSHG2023-09-280.4047.378400e+072023-09
12938952900957.XSHG2023-10-310.4027.341600e+072023-10
12938974900957.XSHG2023-11-300.4177.617600e+072023-11
12938995900957.XSHG2023-12-290.4167.599200e+072023-12
12939017900957.XSHG2024-01-310.4157.580800e+072024-01
12939032900957.XSHG2024-02-290.4498.188000e+072024-02
12939053900957.XSHG2024-03-290.4217.691200e+072024-03
\n", "

613079 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", "12938915 900957.XSHG 2023-08-31 0.433 7.912000e+07 2023-08\n", "12938935 900957.XSHG 2023-09-28 0.404 7.378400e+07 2023-09\n", "12938952 900957.XSHG 2023-10-31 0.402 7.341600e+07 2023-10\n", "12938974 900957.XSHG 2023-11-30 0.417 7.617600e+07 2023-11\n", "12938995 900957.XSHG 2023-12-29 0.416 7.599200e+07 2023-12\n", "12939017 900957.XSHG 2024-01-31 0.415 7.580800e+07 2024-01\n", "12939032 900957.XSHG 2024-02-29 0.449 8.188000e+07 2024-02\n", "12939053 900957.XSHG 2024-03-29 0.421 7.691200e+07 2024-03\n", "\n", "[613079 rows x 5 columns]" ] }, "execution_count": 268, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_df_m" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "#### Fill na months" ] }, { "cell_type": "code", "execution_count": 269, "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": 270, "metadata": { "editable": true }, "outputs": [], "source": [ "full_dates = np.sort(stk_df['ym'].unique())" ] }, { "cell_type": "code", "execution_count": 271, "metadata": { "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 37.8 s, sys: 104 ms, total: 37.9 s\n", "Wall time: 37.9 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": 272, "metadata": { "editable": true }, "outputs": [], "source": [ "stk_df_m.reset_index(drop=True, inplace=True)" ] }, { "cell_type": "code", "execution_count": 273, "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
..................
629696900957.XSHG2023-082023-08-310.4337.912000e+07
629697900957.XSHG2023-092023-09-280.4047.378400e+07
629698900957.XSHG2023-102023-10-310.4027.341600e+07
629699900957.XSHG2023-112023-11-300.4177.617600e+07
629700900957.XSHG2023-122023-12-290.4167.599200e+07
629701900957.XSHG2024-012024-01-310.4157.580800e+07
629702900957.XSHG2024-022024-02-290.4498.188000e+07
629703900957.XSHG2024-032024-03-290.4217.691200e+07
\n", "

629704 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", "629696 900957.XSHG 2023-08 2023-08-31 0.433 7.912000e+07\n", "629697 900957.XSHG 2023-09 2023-09-28 0.404 7.378400e+07\n", "629698 900957.XSHG 2023-10 2023-10-31 0.402 7.341600e+07\n", "629699 900957.XSHG 2023-11 2023-11-30 0.417 7.617600e+07\n", "629700 900957.XSHG 2023-12 2023-12-29 0.416 7.599200e+07\n", "629701 900957.XSHG 2024-01 2024-01-31 0.415 7.580800e+07\n", "629702 900957.XSHG 2024-02 2024-02-29 0.449 8.188000e+07\n", "629703 900957.XSHG 2024-03 2024-03-29 0.421 7.691200e+07\n", "\n", "[629704 rows x 5 columns]" ] }, "execution_count": 273, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_df_m" ] }, { "cell_type": "code", "execution_count": 274, "metadata": { "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 629704 entries, 0 to 629703\n", "Data columns (total 5 columns):\n", "secID 629704 non-null object\n", "ym 629704 non-null period[M]\n", "tradeDate 613079 non-null datetime64[ns]\n", "closePrice 613079 non-null float64\n", "negMarketValue 613079 non-null float64\n", "dtypes: datetime64[ns](1), float64(2), object(1), period[M](1)\n", "memory usage: 24.0+ MB\n" ] } ], "source": [ "stk_df_m.info()" ] }, { "cell_type": "code", "execution_count": 275, "metadata": { "editable": true }, "outputs": [], "source": [ "stk_df_m.drop('tradeDate',axis=1,inplace=True)" ] }, { "cell_type": "code", "execution_count": 276, "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
..................
629696900957.XSHG2023-08-0.1608539.420800e+072023-07
629697900957.XSHG2023-09-0.0669757.912000e+072023-08
629698900957.XSHG2023-10-0.0049507.378400e+072023-09
629699900957.XSHG2023-110.0373137.341600e+072023-10
629700900957.XSHG2023-12-0.0023987.617600e+072023-11
629701900957.XSHG2024-01-0.0024047.599200e+072023-12
629702900957.XSHG2024-020.0819287.580800e+072024-01
629703900957.XSHG2024-03-0.0623618.188000e+072024-02
\n", "

629704 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", "629696 900957.XSHG 2023-08 -0.160853 9.420800e+07 2023-07\n", "629697 900957.XSHG 2023-09 -0.066975 7.912000e+07 2023-08\n", "629698 900957.XSHG 2023-10 -0.004950 7.378400e+07 2023-09\n", "629699 900957.XSHG 2023-11 0.037313 7.341600e+07 2023-10\n", "629700 900957.XSHG 2023-12 -0.002398 7.617600e+07 2023-11\n", "629701 900957.XSHG 2024-01 -0.002404 7.599200e+07 2023-12\n", "629702 900957.XSHG 2024-02 0.081928 7.580800e+07 2024-01\n", "629703 900957.XSHG 2024-03 -0.062361 8.188000e+07 2024-02\n", "\n", "[629704 rows x 5 columns]" ] }, "execution_count": 276, "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": 277, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDymretmkt_capmkt_cap_date
1095000007.XSHE2021-03-0.0468011.254329e+092021-02
1096000007.XSHE2021-040.0180851.195629e+092021-03
1097000007.XSHE2021-05NaN1.217255e+092021-04
1098000007.XSHE2021-06NaNNaN2021-05
1099000007.XSHE2021-07NaNNaN2021-06
1100000007.XSHE2021-08NaNNaN2021-07
1101000007.XSHE2021-09NaNNaN2021-08
1102000007.XSHE2021-10NaNNaN2021-09
..................
1105000007.XSHE2022-01NaNNaN2021-12
1106000007.XSHE2022-02NaNNaN2022-01
1107000007.XSHE2022-03NaNNaN2022-02
1108000007.XSHE2022-04NaNNaN2022-03
1109000007.XSHE2022-05NaNNaN2022-04
1110000007.XSHE2022-06NaNNaN2022-05
1111000007.XSHE2022-07NaNNaN2022-06
1112000007.XSHE2022-080.0909022.276947e+092022-07
\n", "

18 rows × 5 columns

\n", "
" ], "text/plain": [ " secID ym ret mkt_cap mkt_cap_date\n", "1095 000007.XSHE 2021-03 -0.046801 1.254329e+09 2021-02\n", "1096 000007.XSHE 2021-04 0.018085 1.195629e+09 2021-03\n", "1097 000007.XSHE 2021-05 NaN 1.217255e+09 2021-04\n", "1098 000007.XSHE 2021-06 NaN NaN 2021-05\n", "1099 000007.XSHE 2021-07 NaN NaN 2021-06\n", "1100 000007.XSHE 2021-08 NaN NaN 2021-07\n", "1101 000007.XSHE 2021-09 NaN NaN 2021-08\n", "1102 000007.XSHE 2021-10 NaN NaN 2021-09\n", "... ... ... ... ... ...\n", "1105 000007.XSHE 2022-01 NaN NaN 2021-12\n", "1106 000007.XSHE 2022-02 NaN NaN 2022-01\n", "1107 000007.XSHE 2022-03 NaN NaN 2022-02\n", "1108 000007.XSHE 2022-04 NaN NaN 2022-03\n", "1109 000007.XSHE 2022-05 NaN NaN 2022-04\n", "1110 000007.XSHE 2022-06 NaN NaN 2022-05\n", "1111 000007.XSHE 2022-07 NaN NaN 2022-06\n", "1112 000007.XSHE 2022-08 0.090902 2.276947e+09 2022-07\n", "\n", "[18 rows x 5 columns]" ] }, "execution_count": 277, "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": 278, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDymretmkt_capmkt_cap_date
0000001.XSHE2007-06NaNNaNNaT
202000002.XSHE2007-01NaNNaNNaT
409000004.XSHE2011-06NaNNaNNaT
541000004.XSHE2022-06NaN1.463441e+092022-05
542000004.XSHE2022-07NaNNaN2022-06
543000004.XSHE2022-08NaNNaN2022-07
544000004.XSHE2022-09NaNNaN2022-08
545000004.XSHE2022-10NaNNaN2022-09
..................
629323900955.XSHG2022-01NaNNaN2021-12
629324900955.XSHG2022-02NaNNaN2022-01
629325900955.XSHG2022-03NaNNaN2022-02
629326900955.XSHG2022-04NaNNaN2022-03
629327900955.XSHG2022-05NaNNaN2022-04
629328900955.XSHG2022-06NaNNaN2022-05
629330900956.XSHG2007-01NaNNaNNaT
629497900957.XSHG2007-01NaNNaNNaT
\n", "

22754 rows × 5 columns

\n", "
" ], "text/plain": [ " secID ym ret mkt_cap mkt_cap_date\n", "0 000001.XSHE 2007-06 NaN NaN NaT\n", "202 000002.XSHE 2007-01 NaN NaN NaT\n", "409 000004.XSHE 2011-06 NaN NaN NaT\n", "541 000004.XSHE 2022-06 NaN 1.463441e+09 2022-05\n", "542 000004.XSHE 2022-07 NaN NaN 2022-06\n", "543 000004.XSHE 2022-08 NaN NaN 2022-07\n", "544 000004.XSHE 2022-09 NaN NaN 2022-08\n", "545 000004.XSHE 2022-10 NaN NaN 2022-09\n", "... ... ... ... ... ...\n", "629323 900955.XSHG 2022-01 NaN NaN 2021-12\n", "629324 900955.XSHG 2022-02 NaN NaN 2022-01\n", "629325 900955.XSHG 2022-03 NaN NaN 2022-02\n", "629326 900955.XSHG 2022-04 NaN NaN 2022-03\n", "629327 900955.XSHG 2022-05 NaN NaN 2022-04\n", "629328 900955.XSHG 2022-06 NaN NaN 2022-05\n", "629330 900956.XSHG 2007-01 NaN NaN NaT\n", "629497 900957.XSHG 2007-01 NaN NaN NaT\n", "\n", "[22754 rows x 5 columns]" ] }, "execution_count": 278, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_df_m[stk_df_m['ret'].isna()]" ] }, { "cell_type": "code", "execution_count": 279, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDymretmkt_capmkt_cap_date
0000001.XSHE2007-06NaNNaNNaT
202000002.XSHE2007-01NaNNaNNaT
409000004.XSHE2011-06NaNNaNNaT
542000004.XSHE2022-07NaNNaN2022-06
543000004.XSHE2022-08NaNNaN2022-07
544000004.XSHE2022-09NaNNaN2022-08
545000004.XSHE2022-10NaNNaN2022-09
546000004.XSHE2022-11NaNNaN2022-10
..................
629323900955.XSHG2022-01NaNNaN2021-12
629324900955.XSHG2022-02NaNNaN2022-01
629325900955.XSHG2022-03NaNNaN2022-02
629326900955.XSHG2022-04NaNNaN2022-03
629327900955.XSHG2022-05NaNNaN2022-04
629328900955.XSHG2022-06NaNNaN2022-05
629330900956.XSHG2007-01NaNNaNNaT
629497900957.XSHG2007-01NaNNaNNaT
\n", "

22029 rows × 5 columns

\n", "
" ], "text/plain": [ " secID ym ret mkt_cap mkt_cap_date\n", "0 000001.XSHE 2007-06 NaN NaN NaT\n", "202 000002.XSHE 2007-01 NaN NaN NaT\n", "409 000004.XSHE 2011-06 NaN NaN NaT\n", "542 000004.XSHE 2022-07 NaN NaN 2022-06\n", "543 000004.XSHE 2022-08 NaN NaN 2022-07\n", "544 000004.XSHE 2022-09 NaN NaN 2022-08\n", "545 000004.XSHE 2022-10 NaN NaN 2022-09\n", "546 000004.XSHE 2022-11 NaN NaN 2022-10\n", "... ... ... ... ... ...\n", "629323 900955.XSHG 2022-01 NaN NaN 2021-12\n", "629324 900955.XSHG 2022-02 NaN NaN 2022-01\n", "629325 900955.XSHG 2022-03 NaN NaN 2022-02\n", "629326 900955.XSHG 2022-04 NaN NaN 2022-03\n", "629327 900955.XSHG 2022-05 NaN NaN 2022-04\n", "629328 900955.XSHG 2022-06 NaN NaN 2022-05\n", "629330 900956.XSHG 2007-01 NaN NaN NaT\n", "629497 900957.XSHG 2007-01 NaN NaN NaT\n", "\n", "[22029 rows x 5 columns]" ] }, "execution_count": 279, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_df_m[stk_df_m['mkt_cap'].isna()]" ] }, { "cell_type": "code", "execution_count": 280, "metadata": { "editable": true }, "outputs": [], "source": [ "stk_df_m.dropna(inplace=True)" ] }, { "cell_type": "code", "execution_count": 281, "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
..................
629696900957.XSHG2023-08-0.1608539.420800e+072023-07
629697900957.XSHG2023-09-0.0669757.912000e+072023-08
629698900957.XSHG2023-10-0.0049507.378400e+072023-09
629699900957.XSHG2023-110.0373137.341600e+072023-10
629700900957.XSHG2023-12-0.0023987.617600e+072023-11
629701900957.XSHG2024-01-0.0024047.599200e+072023-12
629702900957.XSHG2024-020.0819287.580800e+072024-01
629703900957.XSHG2024-03-0.0623618.188000e+072024-02
\n", "

606950 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", "629696 900957.XSHG 2023-08 -0.160853 9.420800e+07 2023-07\n", "629697 900957.XSHG 2023-09 -0.066975 7.912000e+07 2023-08\n", "629698 900957.XSHG 2023-10 -0.004950 7.378400e+07 2023-09\n", "629699 900957.XSHG 2023-11 0.037313 7.341600e+07 2023-10\n", "629700 900957.XSHG 2023-12 -0.002398 7.617600e+07 2023-11\n", "629701 900957.XSHG 2024-01 -0.002404 7.599200e+07 2023-12\n", "629702 900957.XSHG 2024-02 0.081928 7.580800e+07 2024-01\n", "629703 900957.XSHG 2024-03 -0.062361 8.188000e+07 2024-02\n", "\n", "[606950 rows x 5 columns]" ] }, "execution_count": 281, "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": 282, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDpublishDateendDateendDateRepTShEquityTEquityAttrPminorityIntpub_monthpub_yeardata_yearbm_datebook
79000001.XSHE2008-04-242007-12-312008-03-311.300606e+101.300606e+10NaN4200820072007-121.300606e+10
74000001.XSHE2009-04-242008-12-312009-03-311.640079e+101.640079e+10NaN4200920082008-121.640079e+10
69000001.XSHE2010-04-292009-12-312010-03-312.046961e+102.046961e+10NaN4201020092009-122.046961e+10
64000001.XSHE2011-04-272010-12-312011-03-313.351288e+103.351288e+10NaN4201120102010-123.351288e+10
59000001.XSHE2012-04-262011-12-312012-03-317.538058e+107.331084e+102.069747e+094201220112011-127.331084e+10
54000001.XSHE2013-04-242012-12-312013-03-318.479900e+108.479900e+10NaN4201320122012-128.479900e+10
49000001.XSHE2014-04-242013-12-312014-03-311.120810e+111.120810e+11NaN4201420132013-121.120810e+11
44000001.XSHE2015-04-242014-12-312015-03-311.309490e+111.309490e+11NaN4201520142014-121.309490e+11
.......................................
307449900957.XSHG2016-04-232015-12-312016-03-314.106786e+083.973929e+081.328570e+074201620152015-123.973929e+08
307444900957.XSHG2017-04-262016-12-312017-03-313.938268e+083.930721e+087.546643e+054201720162016-123.930721e+08
307439900957.XSHG2018-04-262017-12-312018-03-314.238426e+084.231040e+087.386715e+054201820172017-124.231040e+08
307434900957.XSHG2019-04-252018-12-312019-03-314.515278e+084.508051e+087.226781e+054201920182018-124.508051e+08
307429900957.XSHG2020-04-292019-12-312020-03-314.768689e+084.761021e+087.667705e+054202020192019-124.761021e+08
307424900957.XSHG2021-04-272020-12-312021-03-314.987276e+084.979110e+088.165551e+054202120202020-124.979110e+08
307419900957.XSHG2022-04-302021-12-312022-03-315.263733e+085.255741e+087.991940e+054202220212021-125.255741e+08
307414900957.XSHG2023-04-272022-12-312023-03-315.669258e+085.660700e+088.557882e+054202320222022-125.660700e+08
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54394 rows × 12 columns

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" ], "text/plain": [ " secID publishDate endDate endDateRep TShEquity \\\n", "79 000001.XSHE 2008-04-24 2007-12-31 2008-03-31 1.300606e+10 \n", "74 000001.XSHE 2009-04-24 2008-12-31 2009-03-31 1.640079e+10 \n", "69 000001.XSHE 2010-04-29 2009-12-31 2010-03-31 2.046961e+10 \n", "64 000001.XSHE 2011-04-27 2010-12-31 2011-03-31 3.351288e+10 \n", "59 000001.XSHE 2012-04-26 2011-12-31 2012-03-31 7.538058e+10 \n", "54 000001.XSHE 2013-04-24 2012-12-31 2013-03-31 8.479900e+10 \n", "49 000001.XSHE 2014-04-24 2013-12-31 2014-03-31 1.120810e+11 \n", "44 000001.XSHE 2015-04-24 2014-12-31 2015-03-31 1.309490e+11 \n", "... ... ... ... ... ... \n", "307449 900957.XSHG 2016-04-23 2015-12-31 2016-03-31 4.106786e+08 \n", "307444 900957.XSHG 2017-04-26 2016-12-31 2017-03-31 3.938268e+08 \n", "307439 900957.XSHG 2018-04-26 2017-12-31 2018-03-31 4.238426e+08 \n", "307434 900957.XSHG 2019-04-25 2018-12-31 2019-03-31 4.515278e+08 \n", "307429 900957.XSHG 2020-04-29 2019-12-31 2020-03-31 4.768689e+08 \n", "307424 900957.XSHG 2021-04-27 2020-12-31 2021-03-31 4.987276e+08 \n", "307419 900957.XSHG 2022-04-30 2021-12-31 2022-03-31 5.263733e+08 \n", "307414 900957.XSHG 2023-04-27 2022-12-31 2023-03-31 5.669258e+08 \n", "\n", " TEquityAttrP minorityInt pub_month pub_year data_year bm_date \\\n", "79 1.300606e+10 NaN 4 2008 2007 2007-12 \n", "74 1.640079e+10 NaN 4 2009 2008 2008-12 \n", "69 2.046961e+10 NaN 4 2010 2009 2009-12 \n", "64 3.351288e+10 NaN 4 2011 2010 2010-12 \n", "59 7.331084e+10 2.069747e+09 4 2012 2011 2011-12 \n", "54 8.479900e+10 NaN 4 2013 2012 2012-12 \n", "49 1.120810e+11 NaN 4 2014 2013 2013-12 \n", "44 1.309490e+11 NaN 4 2015 2014 2014-12 \n", "... ... ... ... ... ... ... \n", "307449 3.973929e+08 1.328570e+07 4 2016 2015 2015-12 \n", "307444 3.930721e+08 7.546643e+05 4 2017 2016 2016-12 \n", "307439 4.231040e+08 7.386715e+05 4 2018 2017 2017-12 \n", "307434 4.508051e+08 7.226781e+05 4 2019 2018 2018-12 \n", "307429 4.761021e+08 7.667705e+05 4 2020 2019 2019-12 \n", "307424 4.979110e+08 8.165551e+05 4 2021 2020 2020-12 \n", "307419 5.255741e+08 7.991940e+05 4 2022 2021 2021-12 \n", "307414 5.660700e+08 8.557882e+05 4 2023 2022 2022-12 \n", "\n", " book \n", "79 1.300606e+10 \n", "74 1.640079e+10 \n", "69 2.046961e+10 \n", "64 3.351288e+10 \n", "59 7.331084e+10 \n", "54 8.479900e+10 \n", "49 1.120810e+11 \n", "44 1.309490e+11 \n", "... ... \n", "307449 3.973929e+08 \n", "307444 3.930721e+08 \n", "307439 4.231040e+08 \n", "307434 4.508051e+08 \n", "307429 4.761021e+08 \n", "307424 4.979110e+08 \n", "307419 5.255741e+08 \n", "307414 5.660700e+08 \n", "\n", "[54394 rows x 12 columns]" ] }, "execution_count": 282, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fundmen_df" ] }, { "cell_type": "code", "execution_count": 283, "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
..................
629696900957.XSHG2023-08-0.1608539.420800e+072023-07
629697900957.XSHG2023-09-0.0669757.912000e+072023-08
629698900957.XSHG2023-10-0.0049507.378400e+072023-09
629699900957.XSHG2023-110.0373137.341600e+072023-10
629700900957.XSHG2023-12-0.0023987.617600e+072023-11
629701900957.XSHG2024-01-0.0024047.599200e+072023-12
629702900957.XSHG2024-020.0819287.580800e+072024-01
629703900957.XSHG2024-03-0.0623618.188000e+072024-02
\n", "

606950 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", "629696 900957.XSHG 2023-08 -0.160853 9.420800e+07 2023-07\n", "629697 900957.XSHG 2023-09 -0.066975 7.912000e+07 2023-08\n", "629698 900957.XSHG 2023-10 -0.004950 7.378400e+07 2023-09\n", "629699 900957.XSHG 2023-11 0.037313 7.341600e+07 2023-10\n", "629700 900957.XSHG 2023-12 -0.002398 7.617600e+07 2023-11\n", "629701 900957.XSHG 2024-01 -0.002404 7.599200e+07 2023-12\n", "629702 900957.XSHG 2024-02 0.081928 7.580800e+07 2024-01\n", "629703 900957.XSHG 2024-03 -0.062361 8.188000e+07 2024-02\n", "\n", "[606950 rows x 5 columns]" ] }, "execution_count": 283, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_df_m" ] }, { "cell_type": "code", "execution_count": 284, "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": 285, "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.XSHE2012-121.704577
6000001.XSHE2013-121.640895
7000001.XSHE2014-120.840421
............
47668900957.XSHG2015-121.465227
47669900957.XSHG2016-121.893849
47670900957.XSHG2017-122.373042
47671900957.XSHG2018-123.977318
47672900957.XSHG2019-124.653798
47673900957.XSHG2020-125.379798
47674900957.XSHG2021-124.526753
47675900957.XSHG2022-125.454730
\n", "

47676 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 1.704577\n", "6 000001.XSHE 2013-12 1.640895\n", "7 000001.XSHE 2014-12 0.840421\n", "... ... ... ...\n", "47668 900957.XSHG 2015-12 1.465227\n", "47669 900957.XSHG 2016-12 1.893849\n", "47670 900957.XSHG 2017-12 2.373042\n", "47671 900957.XSHG 2018-12 3.977318\n", "47672 900957.XSHG 2019-12 4.653798\n", "47673 900957.XSHG 2020-12 5.379798\n", "47674 900957.XSHG 2021-12 4.526753\n", "47675 900957.XSHG 2022-12 5.454730\n", "\n", "[47676 rows x 3 columns]" ] }, "execution_count": 285, "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": 286, "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
........................
606942900957.XSHG2023-08-0.1608539.420800e+072023-070.001708-0.162561
606943900957.XSHG2023-09-0.0669757.912000e+072023-080.001807-0.068782
606944900957.XSHG2023-10-0.0049507.378400e+072023-090.001945-0.006895
606945900957.XSHG2023-110.0373137.341600e+072023-100.0020440.035269
606946900957.XSHG2023-12-0.0023987.617600e+072023-110.002068-0.004466
606947900957.XSHG2024-01-0.0024047.599200e+072023-120.002068-0.004472
606948900957.XSHG2024-020.0819287.580800e+072024-010.0020680.079860
606949900957.XSHG2024-03-0.0623618.188000e+072024-020.002068-0.064429
\n", "

606950 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", "606942 900957.XSHG 2023-08 -0.160853 9.420800e+07 2023-07 0.001708 \n", "606943 900957.XSHG 2023-09 -0.066975 7.912000e+07 2023-08 0.001807 \n", "606944 900957.XSHG 2023-10 -0.004950 7.378400e+07 2023-09 0.001945 \n", "606945 900957.XSHG 2023-11 0.037313 7.341600e+07 2023-10 0.002044 \n", "606946 900957.XSHG 2023-12 -0.002398 7.617600e+07 2023-11 0.002068 \n", "606947 900957.XSHG 2024-01 -0.002404 7.599200e+07 2023-12 0.002068 \n", "606948 900957.XSHG 2024-02 0.081928 7.580800e+07 2024-01 0.002068 \n", "606949 900957.XSHG 2024-03 -0.062361 8.188000e+07 2024-02 0.002068 \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", "606942 -0.162561 \n", "606943 -0.068782 \n", "606944 -0.006895 \n", "606945 0.035269 \n", "606946 -0.004466 \n", "606947 -0.004472 \n", "606948 0.079860 \n", "606949 -0.064429 \n", "\n", "[606950 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": 287, "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
..............................
587460689009.XSHG2023-08-0.0393901.779693e+102023-070.001708-0.0410982023-070.8702
587461689009.XSHG2023-090.0425021.709590e+102023-080.0018070.0406952023-080.8234
587462689009.XSHG2023-10-0.0585701.785208e+102023-090.001945-0.0605152023-090.9152
587463689009.XSHG2023-110.0094541.716478e+102023-100.0020440.0074102023-100.9247
587464689009.XSHG2023-12-0.1039271.732706e+102023-110.002068-0.1059952023-110.9541
587465689009.XSHG2024-01-0.2130821.552630e+102023-120.002068-0.2151502023-121.0448
587466689009.XSHG2024-020.2982011.221793e+102024-010.0020680.2961332024-011.2314
587467689009.XSHG2024-03-0.0115511.586132e+102024-020.002068-0.0136192024-021.4905
\n", "

587468 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", "587460 689009.XSHG 2023-08 -0.039390 1.779693e+10 2023-07 0.001708 \n", "587461 689009.XSHG 2023-09 0.042502 1.709590e+10 2023-08 0.001807 \n", "587462 689009.XSHG 2023-10 -0.058570 1.785208e+10 2023-09 0.001945 \n", "587463 689009.XSHG 2023-11 0.009454 1.716478e+10 2023-10 0.002044 \n", "587464 689009.XSHG 2023-12 -0.103927 1.732706e+10 2023-11 0.002068 \n", "587465 689009.XSHG 2024-01 -0.213082 1.552630e+10 2023-12 0.002068 \n", "587466 689009.XSHG 2024-02 0.298201 1.221793e+10 2024-01 0.002068 \n", "587467 689009.XSHG 2024-03 -0.011551 1.586132e+10 2024-02 0.002068 \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", "587460 -0.041098 2023-07 0.8702 \n", "587461 0.040695 2023-08 0.8234 \n", "587462 -0.060515 2023-09 0.9152 \n", "587463 0.007410 2023-10 0.9247 \n", "587464 -0.105995 2023-11 0.9541 \n", "587465 -0.215150 2023-12 1.0448 \n", "587466 0.296133 2024-01 1.2314 \n", "587467 -0.013619 2024-02 1.4905 \n", "\n", "[587468 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": 288, "metadata": { "editable": true }, "outputs": [], "source": [ "ret_df.drop(['ym_y'],axis=1,inplace=True)" ] }, { "cell_type": "code", "execution_count": 289, "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": 290, "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
...........................
587460689009.XSHG2023-08-0.0393901.779693e+102023-070.001708-0.0410980.8702
587461689009.XSHG2023-090.0425021.709590e+102023-080.0018070.0406950.8234
587462689009.XSHG2023-10-0.0585701.785208e+102023-090.001945-0.0605150.9152
587463689009.XSHG2023-110.0094541.716478e+102023-100.0020440.0074100.9247
587464689009.XSHG2023-12-0.1039271.732706e+102023-110.002068-0.1059950.9541
587465689009.XSHG2024-01-0.2130821.552630e+102023-120.002068-0.2151501.0448
587466689009.XSHG2024-020.2982011.221793e+102024-010.0020680.2961331.2314
587467689009.XSHG2024-03-0.0115511.586132e+102024-020.002068-0.0136191.4905
\n", "

587468 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", "587460 689009.XSHG 2023-08 -0.039390 1.779693e+10 2023-07 \n", "587461 689009.XSHG 2023-09 0.042502 1.709590e+10 2023-08 \n", "587462 689009.XSHG 2023-10 -0.058570 1.785208e+10 2023-09 \n", "587463 689009.XSHG 2023-11 0.009454 1.716478e+10 2023-10 \n", "587464 689009.XSHG 2023-12 -0.103927 1.732706e+10 2023-11 \n", "587465 689009.XSHG 2024-01 -0.213082 1.552630e+10 2023-12 \n", "587466 689009.XSHG 2024-02 0.298201 1.221793e+10 2024-01 \n", "587467 689009.XSHG 2024-03 -0.011551 1.586132e+10 2024-02 \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", "587460 0.001708 -0.041098 0.8702 \n", "587461 0.001807 0.040695 0.8234 \n", "587462 0.001945 -0.060515 0.9152 \n", "587463 0.002044 0.007410 0.9247 \n", "587464 0.002068 -0.105995 0.9541 \n", "587465 0.002068 -0.215150 1.0448 \n", "587466 0.002068 0.296133 1.2314 \n", "587467 0.002068 -0.013619 1.4905 \n", "\n", "[587468 rows x 8 columns]" ] }, "execution_count": 290, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ret_df" ] }, { "cell_type": "code", "execution_count": 291, "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": 292, "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
...........................
587460689009.XSHG2023-08-0.0393900.001708-0.0410982023-071.779693e+100.8702
587461689009.XSHG2023-090.0425020.0018070.0406952023-081.709590e+100.8234
587462689009.XSHG2023-10-0.0585700.001945-0.0605152023-091.785208e+100.9152
587463689009.XSHG2023-110.0094540.0020440.0074102023-101.716478e+100.9247
587464689009.XSHG2023-12-0.1039270.002068-0.1059952023-111.732706e+100.9541
587465689009.XSHG2024-01-0.2130820.002068-0.2151502023-121.552630e+101.0448
587466689009.XSHG2024-020.2982010.0020680.2961332024-011.221793e+101.2314
587467689009.XSHG2024-03-0.0115510.002068-0.0136192024-021.586132e+101.4905
\n", "

587468 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", "587460 689009.XSHG 2023-08 -0.039390 0.001708 -0.041098 2023-07 \n", "587461 689009.XSHG 2023-09 0.042502 0.001807 0.040695 2023-08 \n", "587462 689009.XSHG 2023-10 -0.058570 0.001945 -0.060515 2023-09 \n", "587463 689009.XSHG 2023-11 0.009454 0.002044 0.007410 2023-10 \n", "587464 689009.XSHG 2023-12 -0.103927 0.002068 -0.105995 2023-11 \n", "587465 689009.XSHG 2024-01 -0.213082 0.002068 -0.215150 2023-12 \n", "587466 689009.XSHG 2024-02 0.298201 0.002068 0.296133 2024-01 \n", "587467 689009.XSHG 2024-03 -0.011551 0.002068 -0.013619 2024-02 \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", "587460 1.779693e+10 0.8702 \n", "587461 1.709590e+10 0.8234 \n", "587462 1.785208e+10 0.9152 \n", "587463 1.716478e+10 0.9247 \n", "587464 1.732706e+10 0.9541 \n", "587465 1.552630e+10 1.0448 \n", "587466 1.221793e+10 1.2314 \n", "587467 1.586132e+10 1.4905 \n", "\n", "[587468 rows x 8 columns]" ] }, "execution_count": 292, "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": 293, "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": 294, "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
....................................
587460689009.XSHG2023-08-0.0393900.001708-0.0410982023-071.779693e+100.8702202382022
587461689009.XSHG2023-090.0425020.0018070.0406952023-081.709590e+100.8234202392022
587462689009.XSHG2023-10-0.0585700.001945-0.0605152023-091.785208e+100.91522023102022
587463689009.XSHG2023-110.0094540.0020440.0074102023-101.716478e+100.92472023112022
587464689009.XSHG2023-12-0.1039270.002068-0.1059952023-111.732706e+100.95412023122022
587465689009.XSHG2024-01-0.2130820.002068-0.2151502023-121.552630e+101.0448202412022
587466689009.XSHG2024-020.2982010.0020680.2961332024-011.221793e+101.2314202422022
587467689009.XSHG2024-03-0.0115510.002068-0.0136192024-021.586132e+101.4905202432022
\n", "

587468 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", "587460 689009.XSHG 2023-08 -0.039390 0.001708 -0.041098 2023-07 \n", "587461 689009.XSHG 2023-09 0.042502 0.001807 0.040695 2023-08 \n", "587462 689009.XSHG 2023-10 -0.058570 0.001945 -0.060515 2023-09 \n", "587463 689009.XSHG 2023-11 0.009454 0.002044 0.007410 2023-10 \n", "587464 689009.XSHG 2023-12 -0.103927 0.002068 -0.105995 2023-11 \n", "587465 689009.XSHG 2024-01 -0.213082 0.002068 -0.215150 2023-12 \n", "587466 689009.XSHG 2024-02 0.298201 0.002068 0.296133 2024-01 \n", "587467 689009.XSHG 2024-03 -0.011551 0.002068 -0.013619 2024-02 \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", "587460 1.779693e+10 0.8702 2023 8 2022 \n", "587461 1.709590e+10 0.8234 2023 9 2022 \n", "587462 1.785208e+10 0.9152 2023 10 2022 \n", "587463 1.716478e+10 0.9247 2023 11 2022 \n", "587464 1.732706e+10 0.9541 2023 12 2022 \n", "587465 1.552630e+10 1.0448 2024 1 2022 \n", "587466 1.221793e+10 1.2314 2024 2 2022 \n", "587467 1.586132e+10 1.4905 2024 3 2022 \n", "\n", "[587468 rows x 11 columns]" ] }, "execution_count": 294, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ret_df" ] }, { "cell_type": "code", "execution_count": 295, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDret_dateretrfexretmktcap_beta_datemkt_capbetayearmonthbm_date
284320300349.XSHE2013-01-0.0351970.003246-0.0384432012-127.245000e+080.6363201312011
284321300349.XSHE2013-020.0257510.0032400.0225112013-016.990000e+080.5292201322011
284322300349.XSHE2013-03-0.0736400.003236-0.0768762013-027.170000e+080.6351201332011
284323300349.XSHE2013-040.0255190.0032350.0222842013-036.642000e+080.7784201342011
284324300349.XSHE2013-050.3515750.0032350.3483402013-046.811500e+080.8078201352011
284325300349.XSHE2013-060.0002440.004241-0.0039972013-059.180000e+080.7089201362011
284326300349.XSHE2013-070.1725200.0039720.1685482013-069.182250e+080.5040201372012
284327300349.XSHE2013-080.0282020.0038800.0243222013-071.076625e+090.5452201382012
284328300349.XSHE2013-09-0.0873920.003884-0.0912762013-081.107000e+090.5464201392012
284329300349.XSHE2013-10-0.0022210.003897-0.0061182013-091.010250e+090.46692013102012
284330300349.XSHE2013-110.1667380.0039200.1628182013-101.008000e+090.65222013112012
284331300349.XSHE2013-12-0.0545340.004417-0.0589512013-111.176075e+090.64512013122012
\n", "
" ], "text/plain": [ " secID ret_date ret rf exret mktcap_beta_date \\\n", "284320 300349.XSHE 2013-01 -0.035197 0.003246 -0.038443 2012-12 \n", "284321 300349.XSHE 2013-02 0.025751 0.003240 0.022511 2013-01 \n", "284322 300349.XSHE 2013-03 -0.073640 0.003236 -0.076876 2013-02 \n", "284323 300349.XSHE 2013-04 0.025519 0.003235 0.022284 2013-03 \n", "284324 300349.XSHE 2013-05 0.351575 0.003235 0.348340 2013-04 \n", "284325 300349.XSHE 2013-06 0.000244 0.004241 -0.003997 2013-05 \n", "284326 300349.XSHE 2013-07 0.172520 0.003972 0.168548 2013-06 \n", "284327 300349.XSHE 2013-08 0.028202 0.003880 0.024322 2013-07 \n", "284328 300349.XSHE 2013-09 -0.087392 0.003884 -0.091276 2013-08 \n", "284329 300349.XSHE 2013-10 -0.002221 0.003897 -0.006118 2013-09 \n", "284330 300349.XSHE 2013-11 0.166738 0.003920 0.162818 2013-10 \n", "284331 300349.XSHE 2013-12 -0.054534 0.004417 -0.058951 2013-11 \n", "\n", " mkt_cap beta year month bm_date \n", "284320 7.245000e+08 0.6363 2013 1 2011 \n", "284321 6.990000e+08 0.5292 2013 2 2011 \n", "284322 7.170000e+08 0.6351 2013 3 2011 \n", "284323 6.642000e+08 0.7784 2013 4 2011 \n", "284324 6.811500e+08 0.8078 2013 5 2011 \n", "284325 9.180000e+08 0.7089 2013 6 2011 \n", "284326 9.182250e+08 0.5040 2013 7 2012 \n", "284327 1.076625e+09 0.5452 2013 8 2012 \n", "284328 1.107000e+09 0.5464 2013 9 2012 \n", "284329 1.010250e+09 0.4669 2013 10 2012 \n", "284330 1.008000e+09 0.6522 2013 11 2012 \n", "284331 1.176075e+09 0.6451 2013 12 2012 " ] }, "execution_count": 295, "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": 296, "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", "587460 2022-12-31\n", "587461 2022-12-31\n", "587462 2022-12-31\n", "587463 2022-12-31\n", "587464 2022-12-31\n", "587465 2022-12-31\n", "587466 2022-12-31\n", "587467 2022-12-31\n", "Name: bm_date, Length: 587468, dtype: datetime64[ns]" ] }, "execution_count": 296, "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": 297, "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": 298, "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
..............................
587460689009.XSHG2023-08-0.0393900.001708-0.0410982023-071.779693e+100.87022022-12
587461689009.XSHG2023-090.0425020.0018070.0406952023-081.709590e+100.82342022-12
587462689009.XSHG2023-10-0.0585700.001945-0.0605152023-091.785208e+100.91522022-12
587463689009.XSHG2023-110.0094540.0020440.0074102023-101.716478e+100.92472022-12
587464689009.XSHG2023-12-0.1039270.002068-0.1059952023-111.732706e+100.95412022-12
587465689009.XSHG2024-01-0.2130820.002068-0.2151502023-121.552630e+101.04482022-12
587466689009.XSHG2024-020.2982010.0020680.2961332024-011.221793e+101.23142022-12
587467689009.XSHG2024-03-0.0115510.002068-0.0136192024-021.586132e+101.49052022-12
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587468 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", "587460 689009.XSHG 2023-08 -0.039390 0.001708 -0.041098 2023-07 \n", "587461 689009.XSHG 2023-09 0.042502 0.001807 0.040695 2023-08 \n", "587462 689009.XSHG 2023-10 -0.058570 0.001945 -0.060515 2023-09 \n", "587463 689009.XSHG 2023-11 0.009454 0.002044 0.007410 2023-10 \n", "587464 689009.XSHG 2023-12 -0.103927 0.002068 -0.105995 2023-11 \n", "587465 689009.XSHG 2024-01 -0.213082 0.002068 -0.215150 2023-12 \n", "587466 689009.XSHG 2024-02 0.298201 0.002068 0.296133 2024-01 \n", "587467 689009.XSHG 2024-03 -0.011551 0.002068 -0.013619 2024-02 \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", "587460 1.779693e+10 0.8702 2022-12 \n", "587461 1.709590e+10 0.8234 2022-12 \n", "587462 1.785208e+10 0.9152 2022-12 \n", "587463 1.716478e+10 0.9247 2022-12 \n", "587464 1.732706e+10 0.9541 2022-12 \n", "587465 1.552630e+10 1.0448 2022-12 \n", "587466 1.221793e+10 1.2314 2022-12 \n", "587467 1.586132e+10 1.4905 2022-12 \n", "\n", "[587468 rows x 9 columns]" ] }, "execution_count": 298, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ret_df" ] }, { "cell_type": "code", "execution_count": 299, "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.XSHE2012-121.704577
6000001.XSHE2013-121.640895
7000001.XSHE2014-120.840421
............
47668900957.XSHG2015-121.465227
47669900957.XSHG2016-121.893849
47670900957.XSHG2017-122.373042
47671900957.XSHG2018-123.977318
47672900957.XSHG2019-124.653798
47673900957.XSHG2020-125.379798
47674900957.XSHG2021-124.526753
47675900957.XSHG2022-125.454730
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47676 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 1.704577\n", "6 000001.XSHE 2013-12 1.640895\n", "7 000001.XSHE 2014-12 0.840421\n", "... ... ... ...\n", "47668 900957.XSHG 2015-12 1.465227\n", "47669 900957.XSHG 2016-12 1.893849\n", "47670 900957.XSHG 2017-12 2.373042\n", "47671 900957.XSHG 2018-12 3.977318\n", "47672 900957.XSHG 2019-12 4.653798\n", "47673 900957.XSHG 2020-12 5.379798\n", "47674 900957.XSHG 2021-12 4.526753\n", "47675 900957.XSHG 2022-12 5.454730\n", "\n", "[47676 rows x 3 columns]" ] }, "execution_count": 299, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bm_df" ] }, { "cell_type": "code", "execution_count": 300, "metadata": { "editable": true }, "outputs": [], "source": [ "ret_df = pd.merge(ret_df,bm_df,on=['secID','bm_date'])" ] }, { "cell_type": "code", "execution_count": 301, "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
.................................
461614601999.XSHG2009-12-0.0250750.001516-0.0265912009-111.709400e+091.14282008-121.413367
461615601999.XSHG2010-010.1456600.0015530.1441072009-121.666480e+091.15622008-121.413367
461616601999.XSHG2010-020.1124050.0016040.1108012010-011.909200e+091.06572008-121.413367
461617601999.XSHG2010-030.0459810.0016190.0443622010-022.123800e+091.03072008-121.413367
461618601999.XSHG2010-04-0.1059540.001616-0.1075702010-032.221480e+090.98312008-121.413367
461619601999.XSHG2010-05-0.0924060.001646-0.0940522010-041.986160e+090.98182008-121.413367
461620601999.XSHG2010-06-0.1625270.002004-0.1645312010-051.802640e+090.88132008-121.413367
461621601999.XSHG2010-070.1619670.0021340.1598332010-061.509600e+090.92912009-120.880399
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33947 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", "461614 601999.XSHG 2009-12 -0.025075 0.001516 -0.026591 2009-11 \n", "461615 601999.XSHG 2010-01 0.145660 0.001553 0.144107 2009-12 \n", "461616 601999.XSHG 2010-02 0.112405 0.001604 0.110801 2010-01 \n", "461617 601999.XSHG 2010-03 0.045981 0.001619 0.044362 2010-02 \n", "461618 601999.XSHG 2010-04 -0.105954 0.001616 -0.107570 2010-03 \n", "461619 601999.XSHG 2010-05 -0.092406 0.001646 -0.094052 2010-04 \n", "461620 601999.XSHG 2010-06 -0.162527 0.002004 -0.164531 2010-05 \n", "461621 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", "461614 1.709400e+09 1.1428 2008-12 1.413367 \n", "461615 1.666480e+09 1.1562 2008-12 1.413367 \n", "461616 1.909200e+09 1.0657 2008-12 1.413367 \n", "461617 2.123800e+09 1.0307 2008-12 1.413367 \n", "461618 2.221480e+09 0.9831 2008-12 1.413367 \n", "461619 1.986160e+09 0.9818 2008-12 1.413367 \n", "461620 1.802640e+09 0.8813 2008-12 1.413367 \n", "461621 1.509600e+09 0.9291 2009-12 0.880399 \n", "\n", "[33947 rows x 10 columns]" ] }, "execution_count": 301, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ret_df[ret_df['ret_date']<='2010-07']" ] }, { "cell_type": "code", "execution_count": 302, "metadata": { "editable": true }, "outputs": [ { "data": { "text/plain": [ "22" ] }, "execution_count": 302, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gc.collect()" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "# Sorting on BM" ] }, { "cell_type": "code", "execution_count": 303, "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": 304, "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": 305, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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q1q2q3q4q5q6q7q8q9
bm_date
2007-120.1650970.2160190.2706200.3214110.3795830.4459260.5418770.6426730.832327
2008-120.4073720.5314450.6430240.7595320.9043431.0761601.2740611.5561172.032450
2009-120.1553730.2096030.2545850.3083230.3640620.4158710.5233840.6910870.935711
2010-120.1456520.2106400.2663470.3264320.4092530.5174690.6798490.9179261.268752
2011-120.2478970.3477760.4462470.5515990.6859180.8457181.0937221.4305951.936163
2012-120.2672560.3693600.4679710.5704650.6959790.8612051.0766221.3854931.837972
2013-120.2099530.3001500.3917350.4690510.5610020.6638630.7957650.9912081.332001
2014-120.1764420.2460770.3078780.3694680.4277290.4985450.5906460.7144670.907696
2015-120.1147310.1639130.2040800.2500640.2970290.3534770.4165200.5186310.713222
2016-120.1649370.2279400.2860510.3414880.3957740.4647470.5436220.6640820.880151
2017-120.2198480.3040610.3877090.4610240.5386730.6312920.7438040.8780971.140541
2018-120.3168980.4379540.5434790.6454810.7619190.8677691.0300491.2242851.565795
2019-120.2343020.3387460.4305560.5220320.6160380.7272970.8745741.0715911.393222
2020-120.1891480.2847970.3688750.4562950.5549920.6641320.8121821.0110781.380007
2021-120.1679100.2504950.3286880.4022210.4962030.6051550.7493330.9771411.324875
2022-120.2120850.3118760.4047730.4998490.5967500.7226540.9077491.1623811.620813
\n", "
" ], "text/plain": [ " q1 q2 q3 q4 q5 q6 q7 \\\n", "bm_date \n", "2007-12 0.165097 0.216019 0.270620 0.321411 0.379583 0.445926 0.541877 \n", "2008-12 0.407372 0.531445 0.643024 0.759532 0.904343 1.076160 1.274061 \n", "2009-12 0.155373 0.209603 0.254585 0.308323 0.364062 0.415871 0.523384 \n", "2010-12 0.145652 0.210640 0.266347 0.326432 0.409253 0.517469 0.679849 \n", "2011-12 0.247897 0.347776 0.446247 0.551599 0.685918 0.845718 1.093722 \n", "2012-12 0.267256 0.369360 0.467971 0.570465 0.695979 0.861205 1.076622 \n", "2013-12 0.209953 0.300150 0.391735 0.469051 0.561002 0.663863 0.795765 \n", "2014-12 0.176442 0.246077 0.307878 0.369468 0.427729 0.498545 0.590646 \n", "2015-12 0.114731 0.163913 0.204080 0.250064 0.297029 0.353477 0.416520 \n", "2016-12 0.164937 0.227940 0.286051 0.341488 0.395774 0.464747 0.543622 \n", "2017-12 0.219848 0.304061 0.387709 0.461024 0.538673 0.631292 0.743804 \n", "2018-12 0.316898 0.437954 0.543479 0.645481 0.761919 0.867769 1.030049 \n", "2019-12 0.234302 0.338746 0.430556 0.522032 0.616038 0.727297 0.874574 \n", "2020-12 0.189148 0.284797 0.368875 0.456295 0.554992 0.664132 0.812182 \n", "2021-12 0.167910 0.250495 0.328688 0.402221 0.496203 0.605155 0.749333 \n", "2022-12 0.212085 0.311876 0.404773 0.499849 0.596750 0.722654 0.907749 \n", "\n", " q8 q9 \n", "bm_date \n", "2007-12 0.642673 0.832327 \n", "2008-12 1.556117 2.032450 \n", "2009-12 0.691087 0.935711 \n", "2010-12 0.917926 1.268752 \n", "2011-12 1.430595 1.936163 \n", "2012-12 1.385493 1.837972 \n", "2013-12 0.991208 1.332001 \n", "2014-12 0.714467 0.907696 \n", "2015-12 0.518631 0.713222 \n", "2016-12 0.664082 0.880151 \n", "2017-12 0.878097 1.140541 \n", "2018-12 1.224285 1.565795 \n", "2019-12 1.071591 1.393222 \n", "2020-12 1.011078 1.380007 \n", "2021-12 0.977141 1.324875 \n", "2022-12 1.162381 1.620813 " ] }, "execution_count": 305, "metadata": {}, "output_type": "execute_result" } ], "source": [ "quantile_df" ] }, { "cell_type": "code", "execution_count": 306, "metadata": { "editable": true }, "outputs": [], "source": [ "ret_df_q = pd.merge(ret_df, quantile_df, on='bm_date')" ] }, { "cell_type": "code", "execution_count": 307, "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+101.06722007-120.1978220.1650970.2160190.2706200.3214110.3795830.4459260.5418770.6426730.832327
1000001.XSHE2008-08-0.0288460.003604-0.0324502008-074.455369e+101.09662007-120.1978220.1650970.2160190.2706200.3214110.3795830.4459260.5418770.6426730.832327
2000001.XSHE2008-09-0.2579220.003591-0.2615132008-084.326849e+101.03862007-120.1978220.1650970.2160190.2706200.3214110.3795830.4459260.5418770.6426730.832327
3000001.XSHE2008-10-0.2719590.003522-0.2754812008-093.210865e+101.11842007-120.1978220.1650970.2160190.2706200.3214110.3795830.4459260.5418770.6426730.832327
4000001.XSHE2008-110.0740750.0030630.0710122008-102.330715e+101.19912007-120.1978220.1650970.2160190.2706200.3214110.3795830.4459260.5418770.6426730.832327
5000001.XSHE2008-120.0522790.0019080.0503712008-112.503361e+101.21922007-120.1978220.1650970.2160190.2706200.3214110.3795830.4459260.5418770.6426730.832327
6000001.XSHE2009-010.2304460.0012560.2291902008-122.634237e+101.22062007-120.1978220.1650970.2160190.2706200.3214110.3795830.4459260.5418770.6426730.832327
7000001.XSHE2009-020.1855670.0010880.1844792009-013.241281e+101.25142007-120.1978220.1650970.2160190.2706200.3214110.3795830.4459260.5418770.6426730.832327
............................................................
515291689009.XSHG2023-08-0.0393900.001708-0.0410982023-071.779693e+100.87022022-120.3138710.2120850.3118760.4047730.4998490.5967500.7226540.9077491.1623811.620813
515292689009.XSHG2023-090.0425020.0018070.0406952023-081.709590e+100.82342022-120.3138710.2120850.3118760.4047730.4998490.5967500.7226540.9077491.1623811.620813
515293689009.XSHG2023-10-0.0585700.001945-0.0605152023-091.785208e+100.91522022-120.3138710.2120850.3118760.4047730.4998490.5967500.7226540.9077491.1623811.620813
515294689009.XSHG2023-110.0094540.0020440.0074102023-101.716478e+100.92472022-120.3138710.2120850.3118760.4047730.4998490.5967500.7226540.9077491.1623811.620813
515295689009.XSHG2023-12-0.1039270.002068-0.1059952023-111.732706e+100.95412022-120.3138710.2120850.3118760.4047730.4998490.5967500.7226540.9077491.1623811.620813
515296689009.XSHG2024-01-0.2130820.002068-0.2151502023-121.552630e+101.04482022-120.3138710.2120850.3118760.4047730.4998490.5967500.7226540.9077491.1623811.620813
515297689009.XSHG2024-020.2982010.0020680.2961332024-011.221793e+101.23142022-120.3138710.2120850.3118760.4047730.4998490.5967500.7226540.9077491.1623811.620813
515298689009.XSHG2024-03-0.0115510.002068-0.0136192024-021.586132e+101.49052022-120.3138710.2120850.3118760.4047730.4998490.5967500.7226540.9077491.1623811.620813
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515299 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", "515291 689009.XSHG 2023-08 -0.039390 0.001708 -0.041098 2023-07 \n", "515292 689009.XSHG 2023-09 0.042502 0.001807 0.040695 2023-08 \n", "515293 689009.XSHG 2023-10 -0.058570 0.001945 -0.060515 2023-09 \n", "515294 689009.XSHG 2023-11 0.009454 0.002044 0.007410 2023-10 \n", "515295 689009.XSHG 2023-12 -0.103927 0.002068 -0.105995 2023-11 \n", "515296 689009.XSHG 2024-01 -0.213082 0.002068 -0.215150 2023-12 \n", "515297 689009.XSHG 2024-02 0.298201 0.002068 0.296133 2024-01 \n", "515298 689009.XSHG 2024-03 -0.011551 0.002068 -0.013619 2024-02 \n", "\n", " mkt_cap beta bm_date bm q1 q2 q3 \\\n", "0 4.140495e+10 1.0672 2007-12 0.197822 0.165097 0.216019 0.270620 \n", "1 4.455369e+10 1.0966 2007-12 0.197822 0.165097 0.216019 0.270620 \n", "2 4.326849e+10 1.0386 2007-12 0.197822 0.165097 0.216019 0.270620 \n", "3 3.210865e+10 1.1184 2007-12 0.197822 0.165097 0.216019 0.270620 \n", "4 2.330715e+10 1.1991 2007-12 0.197822 0.165097 0.216019 0.270620 \n", "5 2.503361e+10 1.2192 2007-12 0.197822 0.165097 0.216019 0.270620 \n", "6 2.634237e+10 1.2206 2007-12 0.197822 0.165097 0.216019 0.270620 \n", "7 3.241281e+10 1.2514 2007-12 0.197822 0.165097 0.216019 0.270620 \n", "... ... ... ... ... ... ... ... \n", "515291 1.779693e+10 0.8702 2022-12 0.313871 0.212085 0.311876 0.404773 \n", "515292 1.709590e+10 0.8234 2022-12 0.313871 0.212085 0.311876 0.404773 \n", "515293 1.785208e+10 0.9152 2022-12 0.313871 0.212085 0.311876 0.404773 \n", "515294 1.716478e+10 0.9247 2022-12 0.313871 0.212085 0.311876 0.404773 \n", "515295 1.732706e+10 0.9541 2022-12 0.313871 0.212085 0.311876 0.404773 \n", "515296 1.552630e+10 1.0448 2022-12 0.313871 0.212085 0.311876 0.404773 \n", "515297 1.221793e+10 1.2314 2022-12 0.313871 0.212085 0.311876 0.404773 \n", "515298 1.586132e+10 1.4905 2022-12 0.313871 0.212085 0.311876 0.404773 \n", "\n", " q4 q5 q6 q7 q8 q9 \n", "0 0.321411 0.379583 0.445926 0.541877 0.642673 0.832327 \n", "1 0.321411 0.379583 0.445926 0.541877 0.642673 0.832327 \n", "2 0.321411 0.379583 0.445926 0.541877 0.642673 0.832327 \n", "3 0.321411 0.379583 0.445926 0.541877 0.642673 0.832327 \n", "4 0.321411 0.379583 0.445926 0.541877 0.642673 0.832327 \n", "5 0.321411 0.379583 0.445926 0.541877 0.642673 0.832327 \n", "6 0.321411 0.379583 0.445926 0.541877 0.642673 0.832327 \n", "7 0.321411 0.379583 0.445926 0.541877 0.642673 0.832327 \n", "... ... ... ... ... ... ... \n", "515291 0.499849 0.596750 0.722654 0.907749 1.162381 1.620813 \n", "515292 0.499849 0.596750 0.722654 0.907749 1.162381 1.620813 \n", "515293 0.499849 0.596750 0.722654 0.907749 1.162381 1.620813 \n", "515294 0.499849 0.596750 0.722654 0.907749 1.162381 1.620813 \n", "515295 0.499849 0.596750 0.722654 0.907749 1.162381 1.620813 \n", "515296 0.499849 0.596750 0.722654 0.907749 1.162381 1.620813 \n", "515297 0.499849 0.596750 0.722654 0.907749 1.162381 1.620813 \n", "515298 0.499849 0.596750 0.722654 0.907749 1.162381 1.620813 \n", "\n", "[515299 rows x 19 columns]" ] }, "execution_count": 307, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ret_df_q" ] }, { "cell_type": "code", "execution_count": 308, "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": 309, "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
.................................
515264688800.XSHG2023-08-0.1786230.001708-0.1803312023-075.532434e+090.86602022-120.249248
515265688800.XSHG2023-09-0.0659000.001807-0.0677072023-084.544189e+090.99532022-120.249248
515266688800.XSHG2023-100.0061440.0019450.0041992023-094.244787e+091.04642022-120.249248
515267688800.XSHG2023-110.1112490.0020440.1092052023-104.270822e+090.79322022-120.249248
515268688800.XSHG2023-12-0.0816050.002068-0.0836732023-114.745961e+090.91712022-120.249248
515269688800.XSHG2024-01-0.3392170.002068-0.3412852023-124.358690e+090.94732022-120.249248
515270688800.XSHG2024-020.0828560.0020680.0807882024-012.880120e+091.26422022-120.249248
515271688800.XSHG2024-030.0727040.0020680.0706362024-023.118774e+091.64282022-120.249248
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51694 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", "515264 688800.XSHG 2023-08 -0.178623 0.001708 -0.180331 2023-07 \n", "515265 688800.XSHG 2023-09 -0.065900 0.001807 -0.067707 2023-08 \n", "515266 688800.XSHG 2023-10 0.006144 0.001945 0.004199 2023-09 \n", "515267 688800.XSHG 2023-11 0.111249 0.002044 0.109205 2023-10 \n", "515268 688800.XSHG 2023-12 -0.081605 0.002068 -0.083673 2023-11 \n", "515269 688800.XSHG 2024-01 -0.339217 0.002068 -0.341285 2023-12 \n", "515270 688800.XSHG 2024-02 0.082856 0.002068 0.080788 2024-01 \n", "515271 688800.XSHG 2024-03 0.072704 0.002068 0.070636 2024-02 \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", "515264 5.532434e+09 0.8660 2022-12 0.249248 \n", "515265 4.544189e+09 0.9953 2022-12 0.249248 \n", "515266 4.244787e+09 1.0464 2022-12 0.249248 \n", "515267 4.270822e+09 0.7932 2022-12 0.249248 \n", "515268 4.745961e+09 0.9171 2022-12 0.249248 \n", "515269 4.358690e+09 0.9473 2022-12 0.249248 \n", "515270 2.880120e+09 1.2642 2022-12 0.249248 \n", "515271 3.118774e+09 1.6428 2022-12 0.249248 \n", "\n", "[51694 rows x 10 columns]" ] }, "execution_count": 309, "metadata": {}, "output_type": "execute_result" } ], "source": [ "portfolios['p2']" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "## return by portfolios" ] }, { "cell_type": "code", "execution_count": 310, "metadata": { "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Int64Index: 51694 entries, 0 to 515271\n", "Data columns (total 10 columns):\n", "secID 51694 non-null object\n", "ret_date 51694 non-null period[M]\n", "ret 51694 non-null float64\n", "rf 51694 non-null float64\n", "exret 51694 non-null float64\n", "mktcap_beta_date 51694 non-null period[M]\n", "mkt_cap 51694 non-null float64\n", "beta 50599 non-null float64\n", "bm_date 51694 non-null period[M]\n", "bm 51694 non-null float64\n", "dtypes: float64(6), object(1), period[M](3)\n", "memory usage: 4.3+ MB\n" ] } ], "source": [ "portfolios['p2'].info()" ] }, { "cell_type": "code", "execution_count": 311, "metadata": { "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.005184876450845155\n", "0.007280429013998528\n", "0.009327430490390221\n", "0.00982277988046715\n", "0.010623475374778825\n", "0.010964110079221058\n", "0.011234456863697532\n", "0.011441985154973293\n", "0.01041495408499976\n", "0.008091993160715343\n" ] } ], "source": [ "for k in portfolios.keys():\n", " print(portfolios[k].groupby(['ret_date'])['exret'].mean().mean())" ] }, { "cell_type": "code", "execution_count": 312, "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": 313, "metadata": { "editable": true }, "outputs": [ { "data": { "text/plain": [ "ret_date\n", "2008-07 0.067153\n", "2008-08 -0.260092\n", "2008-09 -0.077001\n", "2008-10 -0.280612\n", "2008-11 0.213463\n", "2008-12 0.064230\n", "2009-01 0.161588\n", "2009-02 0.084984\n", " ... \n", "2023-08 -0.057541\n", "2023-09 -0.014099\n", "2023-10 -0.015751\n", "2023-11 0.027949\n", "2023-12 -0.018744\n", "2024-01 -0.215710\n", "2024-02 0.087800\n", "2024-03 0.017890\n", "Freq: M, Name: exret, Length: 189, dtype: float64" ] }, "execution_count": 313, "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": 314, "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": 315, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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p1p2p3p4p5p6p7p8p9p10p10-p1
mean0.0051850.0072800.0093270.0098230.0106230.0109640.0112340.0114420.0104150.0080920.002907
t-value0.8321971.1879211.5249871.6213361.7538791.7283451.7958261.8595511.6766601.3611141.129565
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" ], "text/plain": [ " p1 p2 p3 p4 p5 p6 p7 \\\n", "mean 0.005185 0.007280 0.009327 0.009823 0.010623 0.010964 0.011234 \n", "t-value 0.832197 1.187921 1.524987 1.621336 1.753879 1.728345 1.795826 \n", "\n", " p8 p9 p10 p10-p1 \n", "mean 0.011442 0.010415 0.008092 0.002907 \n", "t-value 1.859551 1.676660 1.361114 1.129565 " ] }, "execution_count": 315, "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": 316, "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
.................................
515174688777.XSHG2023-08-0.0691920.001708-0.0709002023-072.898015e+100.31282022-120.176378
515175688777.XSHG2023-09-0.0523390.001807-0.0541462023-082.697470e+100.44902022-120.176378
515176688777.XSHG2023-10-0.0878730.001945-0.0898182023-092.556286e+100.56692022-120.176378
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515180688777.XSHG2024-020.3025560.0020680.3004882024-012.667083e+101.16012022-120.176378
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51614 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", "515174 688777.XSHG 2023-08 -0.069192 0.001708 -0.070900 2023-07 \n", "515175 688777.XSHG 2023-09 -0.052339 0.001807 -0.054146 2023-08 \n", "515176 688777.XSHG 2023-10 -0.087873 0.001945 -0.089818 2023-09 \n", "515177 688777.XSHG 2023-11 0.004594 0.002044 0.002550 2023-10 \n", "515178 688777.XSHG 2023-12 0.035377 0.002068 0.033309 2023-11 \n", "515179 688777.XSHG 2024-01 -0.221612 0.002068 -0.223680 2023-12 \n", "515180 688777.XSHG 2024-02 0.302556 0.002068 0.300488 2024-01 \n", "515181 688777.XSHG 2024-03 0.011962 0.002068 0.009894 2024-02 \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", "515174 2.898015e+10 0.3128 2022-12 0.176378 \n", "515175 2.697470e+10 0.4490 2022-12 0.176378 \n", "515176 2.556286e+10 0.5669 2022-12 0.176378 \n", "515177 2.331675e+10 0.7040 2022-12 0.176378 \n", "515178 3.291554e+10 0.9070 2022-12 0.176378 \n", "515179 3.408036e+10 0.8799 2022-12 0.176378 \n", "515180 2.667083e+10 1.1601 2022-12 0.176378 \n", "515181 3.474008e+10 1.4353 2022-12 0.176378 \n", "\n", "[51614 rows x 10 columns]" ] }, "execution_count": 316, "metadata": {}, "output_type": "execute_result" } ], "source": [ "portfolios['p1']" ] }, { "cell_type": "code", "execution_count": 317, "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": 318, "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
.......................................
515282688981.XSHG2023-080.0560750.0017080.0543672023-071.005322e+110.87582022-121.6562271.0560751.001708
515283688981.XSHG2023-09-0.0569690.001807-0.0587762023-081.061695e+110.95342022-121.6562270.9430311.001807
515284688981.XSHG2023-100.1192570.0019450.1173122023-091.009501e+111.12032022-121.6562271.1192571.001945
515285688981.XSHG2023-11-0.0620090.002044-0.0640532023-101.129891e+110.97432022-121.6562270.9379911.002044
515286688981.XSHG2023-12-0.0126630.002068-0.0147312023-111.059828e+110.98502022-121.6562270.9873371.002068
515287688981.XSHG2024-01-0.1835160.002068-0.1855842023-121.046408e+110.92912022-121.6562270.8164841.002068
515288688981.XSHG2024-020.1168860.0020680.1148182024-018.543754e+101.16412022-121.6562271.1168861.002068
515289688981.XSHG2024-03-0.0970010.002068-0.0990692024-029.542400e+101.30322022-121.6562270.9029991.002068
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51636 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", "515282 688981.XSHG 2023-08 0.056075 0.001708 0.054367 2023-07 \n", "515283 688981.XSHG 2023-09 -0.056969 0.001807 -0.058776 2023-08 \n", "515284 688981.XSHG 2023-10 0.119257 0.001945 0.117312 2023-09 \n", "515285 688981.XSHG 2023-11 -0.062009 0.002044 -0.064053 2023-10 \n", "515286 688981.XSHG 2023-12 -0.012663 0.002068 -0.014731 2023-11 \n", "515287 688981.XSHG 2024-01 -0.183516 0.002068 -0.185584 2023-12 \n", "515288 688981.XSHG 2024-02 0.116886 0.002068 0.114818 2024-01 \n", "515289 688981.XSHG 2024-03 -0.097001 0.002068 -0.099069 2024-02 \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", "515282 1.005322e+11 0.8758 2022-12 1.656227 1.056075 1.001708 \n", "515283 1.061695e+11 0.9534 2022-12 1.656227 0.943031 1.001807 \n", "515284 1.009501e+11 1.1203 2022-12 1.656227 1.119257 1.001945 \n", "515285 1.129891e+11 0.9743 2022-12 1.656227 0.937991 1.002044 \n", "515286 1.059828e+11 0.9850 2022-12 1.656227 0.987337 1.002068 \n", "515287 1.046408e+11 0.9291 2022-12 1.656227 0.816484 1.002068 \n", "515288 8.543754e+10 1.1641 2022-12 1.656227 1.116886 1.002068 \n", "515289 9.542400e+10 1.3032 2022-12 1.656227 0.902999 1.002068 \n", "\n", "[51636 rows x 12 columns]" ] }, "execution_count": 318, "metadata": {}, "output_type": "execute_result" } ], "source": [ "portfolios[k]" ] }, { "cell_type": "code", "execution_count": 319, "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
3000001.XSHE2022-120.936776
4000011.XSHE2014-120.904518
5000011.XSHE2015-121.508421
6000011.XSHE2018-121.744011
7000016.XSHE2007-121.311983
............
4429688739.XSHG2021-120.668023
4430688739.XSHG2022-120.854647
4431688767.XSHG2021-120.495825
4432688799.XSHG2021-121.263452
4433688819.XSHG2021-121.083487
4434688819.XSHG2022-120.773621
4435688981.XSHG2020-120.730670
4436688981.XSHG2022-120.864212
\n", "

4437 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 000001.XSHE 2022-12 0.936776\n", "4 000011.XSHE 2014-12 0.904518\n", "5 000011.XSHE 2015-12 1.508421\n", "6 000011.XSHE 2018-12 1.744011\n", "7 000016.XSHE 2007-12 1.311983\n", "... ... ... ...\n", "4429 688739.XSHG 2021-12 0.668023\n", "4430 688739.XSHG 2022-12 0.854647\n", "4431 688767.XSHG 2021-12 0.495825\n", "4432 688799.XSHG 2021-12 1.263452\n", "4433 688819.XSHG 2021-12 1.083487\n", "4434 688819.XSHG 2022-12 0.773621\n", "4435 688981.XSHG 2020-12 0.730670\n", "4436 688981.XSHG 2022-12 0.864212\n", "\n", "[4437 rows x 3 columns]" ] }, "execution_count": 319, "metadata": {}, "output_type": "execute_result" } ], "source": [ "portfolios[k].groupby(['secID','bm_date'],as_index=False)['1+ret'].prod()" ] }, { "cell_type": "code", "execution_count": 320, "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": 321, "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
........................
4553688639.XSHG2022-121.2376271.0176770.2376270.0176770.219950
4554688677.XSHG2022-120.9834361.017677-0.0165640.017677-0.034241
4555688690.XSHG2022-120.5243201.017677-0.4756800.017677-0.493357
4556688700.XSHG2022-120.3529551.017677-0.6470450.017677-0.664722
4557688711.XSHG2022-120.5111401.017677-0.4888600.017677-0.506537
4558688768.XSHG2022-120.3694781.017677-0.6305220.017677-0.648199
4559688777.XSHG2022-120.7411581.017677-0.2588420.017677-0.276519
4560689009.XSHG2021-120.8280901.021321-0.1719100.021321-0.193231
\n", "

4561 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", "4553 688639.XSHG 2022-12 1.237627 1.017677 0.237627 0.017677 0.219950\n", "4554 688677.XSHG 2022-12 0.983436 1.017677 -0.016564 0.017677 -0.034241\n", "4555 688690.XSHG 2022-12 0.524320 1.017677 -0.475680 0.017677 -0.493357\n", "4556 688700.XSHG 2022-12 0.352955 1.017677 -0.647045 0.017677 -0.664722\n", "4557 688711.XSHG 2022-12 0.511140 1.017677 -0.488860 0.017677 -0.506537\n", "4558 688768.XSHG 2022-12 0.369478 1.017677 -0.630522 0.017677 -0.648199\n", "4559 688777.XSHG 2022-12 0.741158 1.017677 -0.258842 0.017677 -0.276519\n", "4560 689009.XSHG 2021-12 0.828090 1.021321 -0.171910 0.021321 -0.193231\n", "\n", "[4561 rows x 7 columns]" ] }, "execution_count": 321, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pf_year_ret['p1']" ] }, { "cell_type": "code", "execution_count": 322, "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": 323, "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": 324, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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p1p2p3p4p5p6p7p8p9p10p10-p1
mean0.0460710.0710460.1001820.1101330.1190500.1342130.1334570.1421900.1262360.0997470.053676
t-value0.7695561.2995271.7727141.9648792.3792791.9559252.1180162.0162611.7601521.4875741.498356
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" ], "text/plain": [ " p1 p2 p3 p4 p5 p6 p7 \\\n", "mean 0.046071 0.071046 0.100182 0.110133 0.119050 0.134213 0.133457 \n", "t-value 0.769556 1.299527 1.772714 1.964879 2.379279 1.955925 2.118016 \n", "\n", " p8 p9 p10 p10-p1 \n", "mean 0.142190 0.126236 0.099747 0.053676 \n", "t-value 2.016261 1.760152 1.487574 1.498356 " ] }, "execution_count": 324, "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": 325, "metadata": { "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.14392137034662614\n", "0.25635419135256954\n", "0.33651688825561543\n", "0.41398356741103115\n", "0.4973170627775123\n", "0.5941588082807973\n", "0.7161155550812974\n", "0.8839632809099515\n", "1.1355658764464016\n", "2.1790106351713265\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": 326, "metadata": { "editable": true }, "outputs": [ { "data": { "text/plain": [ "bm_date\n", "2007-12 134\n", "2008-12 144\n", "2009-12 153\n", "2010-12 189\n", "2011-12 223\n", "2012-12 238\n", "2013-12 245\n", "2014-12 267\n", "2015-12 293\n", "2016-12 303\n", "2017-12 341\n", "2018-12 347\n", "2019-12 372\n", "2020-12 397\n", "2021-12 444\n", "2022-12 471\n", "Freq: M, Name: secID, dtype: int64" ] }, "execution_count": 326, "metadata": {}, "output_type": "execute_result" } ], "source": [ "portfolios['p1'].groupby('bm_date')['secID'].nunique()" ] }, { "cell_type": "code", "execution_count": 327, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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p1p2p3p4p5p6p7p8p9p10
bm_date
2007-12134132133131132133132132131132
2008-12144142144143144141142142142141
2009-12153152151151151151150152150150
2010-12189186186186187185186185185185
2011-12223219216216215215216215216214
2012-12238240234236237235235234234235
2013-12245243243240241239241240239240
2014-12267254253253254251253254252251
2015-12293275273274273271274272274271
2016-12303294294293296295293294293292
2017-12341338339338338335335336335335
2018-12347344341341343343342341341342
2019-12372359359354355356354355354354
2020-12397389388389385390387387387386
2021-12444440438440439438438439437437
2022-12471472472472472472472472471472
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" ], "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", "2008-12 144 142 144 143 144 141 142 142 142 141\n", "2009-12 153 152 151 151 151 151 150 152 150 150\n", "2010-12 189 186 186 186 187 185 186 185 185 185\n", "2011-12 223 219 216 216 215 215 216 215 216 214\n", "2012-12 238 240 234 236 237 235 235 234 234 235\n", "2013-12 245 243 243 240 241 239 241 240 239 240\n", "2014-12 267 254 253 253 254 251 253 254 252 251\n", "2015-12 293 275 273 274 273 271 274 272 274 271\n", "2016-12 303 294 294 293 296 295 293 294 293 292\n", "2017-12 341 338 339 338 338 335 335 336 335 335\n", "2018-12 347 344 341 341 343 343 342 341 341 342\n", "2019-12 372 359 359 354 355 356 354 355 354 354\n", "2020-12 397 389 388 389 385 390 387 387 387 386\n", "2021-12 444 440 438 440 439 438 438 439 437 437\n", "2022-12 471 472 472 472 472 472 472 472 471 472" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 327, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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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": 328, "metadata": { "editable": true }, "outputs": [ { "data": { "text/plain": [ "bm_date\n", "2007-12 9.542726\n", "2008-12 24.894830\n", "2009-12 23.400401\n", "2010-12 7.330638\n", "2011-12 4.288909\n", "2012-12 6.852996\n", "2013-12 24.818758\n", "2014-12 30.313610\n", "2015-12 46.562980\n", "2016-12 49.633862\n", "2017-12 25.174232\n", "2018-12 23.576350\n", "2019-12 25.416282\n", "2020-12 27.492293\n", "2021-12 24.716043\n", "2022-12 25.729374\n", "Freq: M, Name: mkt_cap, dtype: float64" ] }, "execution_count": 328, "metadata": {}, "output_type": "execute_result" } ], "source": [ "portfolios['p10'].groupby('bm_date')['mkt_cap'].mean()/1e9" ] }, { "cell_type": "code", "execution_count": 329, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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p1p2p3p4p5p6p7p8p9p10
bm_date
2007-125.630655e+094.798264e+092.876730e+093.355280e+093.951063e+093.269568e+092.655424e+092.311952e+091.676053e+099.542726e+09
2008-128.178973e+099.146321e+097.128349e+096.812269e+097.259373e+095.630777e+096.089142e+095.187530e+097.724494e+092.489483e+10
2009-121.071026e+108.369074e+097.495947e+098.559172e+099.185138e+091.346080e+101.394263e+101.182861e+104.310919e+092.340040e+10
2010-129.810361e+096.731424e+095.541175e+095.788917e+095.701781e+091.164625e+101.966112e+101.477238e+107.540653e+097.330638e+09
2011-128.587196e+095.192321e+095.859148e+098.239202e+091.323264e+108.510640e+091.434685e+107.458483e+093.779696e+094.288909e+09
2012-127.933644e+097.147617e+095.764682e+096.531256e+098.286704e+091.406319e+101.345871e+104.797786e+096.148487e+096.852996e+09
2013-121.040011e+109.921432e+098.797037e+098.957331e+098.985043e+097.210376e+091.400447e+101.697149e+101.815386e+102.481876e+10
2014-121.110414e+101.253855e+101.251423e+101.087585e+101.278176e+101.062642e+101.064701e+101.912427e+101.357927e+103.031361e+10
2015-129.342241e+099.029194e+099.919987e+091.014272e+109.218084e+091.074967e+109.973294e+091.022611e+101.547728e+104.656298e+10
2016-128.672172e+091.300864e+108.145392e+099.267191e+091.054148e+108.595530e+091.158117e+101.130542e+101.626216e+104.963386e+10
2017-121.780426e+109.119886e+097.239314e+099.046567e+098.909767e+098.501073e+098.094228e+091.126554e+101.213115e+102.517423e+10
2018-122.191151e+101.316527e+108.365672e+098.509259e+091.022018e+109.067076e+091.208629e+101.469209e+101.134925e+102.357635e+10
2019-124.058968e+101.920928e+101.227957e+108.884326e+091.358683e+101.566599e+101.317303e+101.111390e+101.180776e+102.541628e+10
2020-125.050199e+101.585111e+101.316088e+101.043343e+108.433095e+091.186422e+101.355960e+101.104882e+101.148145e+102.749229e+10
2021-123.727386e+101.558099e+101.228374e+101.056891e+109.971603e+098.428058e+098.990826e+091.343816e+101.207105e+102.471604e+10
2022-122.710490e+101.483812e+101.265372e+109.901386e+091.096142e+108.908659e+099.592421e+091.074510e+101.272501e+102.572937e+10
\n", "
" ], "text/plain": [ " p1 p2 p3 p4 p5 \\\n", "bm_date \n", "2007-12 5.630655e+09 4.798264e+09 2.876730e+09 3.355280e+09 3.951063e+09 \n", "2008-12 8.178973e+09 9.146321e+09 7.128349e+09 6.812269e+09 7.259373e+09 \n", "2009-12 1.071026e+10 8.369074e+09 7.495947e+09 8.559172e+09 9.185138e+09 \n", "2010-12 9.810361e+09 6.731424e+09 5.541175e+09 5.788917e+09 5.701781e+09 \n", "2011-12 8.587196e+09 5.192321e+09 5.859148e+09 8.239202e+09 1.323264e+10 \n", "2012-12 7.933644e+09 7.147617e+09 5.764682e+09 6.531256e+09 8.286704e+09 \n", "2013-12 1.040011e+10 9.921432e+09 8.797037e+09 8.957331e+09 8.985043e+09 \n", "2014-12 1.110414e+10 1.253855e+10 1.251423e+10 1.087585e+10 1.278176e+10 \n", "2015-12 9.342241e+09 9.029194e+09 9.919987e+09 1.014272e+10 9.218084e+09 \n", "2016-12 8.672172e+09 1.300864e+10 8.145392e+09 9.267191e+09 1.054148e+10 \n", "2017-12 1.780426e+10 9.119886e+09 7.239314e+09 9.046567e+09 8.909767e+09 \n", "2018-12 2.191151e+10 1.316527e+10 8.365672e+09 8.509259e+09 1.022018e+10 \n", "2019-12 4.058968e+10 1.920928e+10 1.227957e+10 8.884326e+09 1.358683e+10 \n", "2020-12 5.050199e+10 1.585111e+10 1.316088e+10 1.043343e+10 8.433095e+09 \n", "2021-12 3.727386e+10 1.558099e+10 1.228374e+10 1.056891e+10 9.971603e+09 \n", "2022-12 2.710490e+10 1.483812e+10 1.265372e+10 9.901386e+09 1.096142e+10 \n", "\n", " p6 p7 p8 p9 p10 \n", "bm_date \n", "2007-12 3.269568e+09 2.655424e+09 2.311952e+09 1.676053e+09 9.542726e+09 \n", "2008-12 5.630777e+09 6.089142e+09 5.187530e+09 7.724494e+09 2.489483e+10 \n", "2009-12 1.346080e+10 1.394263e+10 1.182861e+10 4.310919e+09 2.340040e+10 \n", "2010-12 1.164625e+10 1.966112e+10 1.477238e+10 7.540653e+09 7.330638e+09 \n", "2011-12 8.510640e+09 1.434685e+10 7.458483e+09 3.779696e+09 4.288909e+09 \n", "2012-12 1.406319e+10 1.345871e+10 4.797786e+09 6.148487e+09 6.852996e+09 \n", "2013-12 7.210376e+09 1.400447e+10 1.697149e+10 1.815386e+10 2.481876e+10 \n", "2014-12 1.062642e+10 1.064701e+10 1.912427e+10 1.357927e+10 3.031361e+10 \n", "2015-12 1.074967e+10 9.973294e+09 1.022611e+10 1.547728e+10 4.656298e+10 \n", "2016-12 8.595530e+09 1.158117e+10 1.130542e+10 1.626216e+10 4.963386e+10 \n", "2017-12 8.501073e+09 8.094228e+09 1.126554e+10 1.213115e+10 2.517423e+10 \n", "2018-12 9.067076e+09 1.208629e+10 1.469209e+10 1.134925e+10 2.357635e+10 \n", "2019-12 1.566599e+10 1.317303e+10 1.111390e+10 1.180776e+10 2.541628e+10 \n", "2020-12 1.186422e+10 1.355960e+10 1.104882e+10 1.148145e+10 2.749229e+10 \n", "2021-12 8.428058e+09 8.990826e+09 1.343816e+10 1.207105e+10 2.471604e+10 \n", "2022-12 8.908659e+09 9.592421e+09 1.074510e+10 1.272501e+10 2.572937e+10 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 329, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "pf_mktcap = pd.DataFrame()\n", "for key, value in portfolios.items():\n", " pf_mktcap[key] = portfolios[key].groupby('bm_date')['mkt_cap'].mean()\n", "\n", "display(pf_mktcap)\n", "\n", "pf_mktcap.plot()" ] }, { "cell_type": "code", "execution_count": 330, "metadata": { "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.7847247100142434\n", "1.0852969091438325\n", "0.8751598397387613\n", "0.8492066351781058\n", "0.9451622740075204\n", "0.9762393338086365\n", "1.1366012783674107\n", "1.1017977763666085\n", "1.0388658587264499\n", "2.37340177025748\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": 331, "metadata": { "editable": true }, "outputs": [], "source": [ "del portfolios, portfolios_crs_mean" ] }, { "cell_type": "code", "execution_count": 332, "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": 333, "metadata": { "editable": true }, "outputs": [], "source": [ "fundmen_df = pd.read_pickle('./data/fundmen_df_pit.pkl')" ] }, { "cell_type": "code", "execution_count": 334, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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3000001.XSHE2023-04-252023-03-312023-03-312023-04-24 18:00:2734.467450e+114.467450e+11NaN
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5000001.XSHE2023-10-252022-12-312023-09-302023-10-24 17:52:46124.346800e+114.346800e+11NaN
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7000001.XSHE2023-04-252022-12-312023-03-312023-04-24 18:00:27124.346800e+114.346800e+11NaN
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482037900957.XSHG2009-03-262007-12-312008-12-312009-03-25 18:00:00124.363166e+083.769447e+0859371874.07
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482041900957.XSHG2008-04-082007-12-312007-12-312008-04-07 18:00:00124.363166e+083.769447e+0859371874.07
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482045 rows × 9 columns

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" ], "text/plain": [ " secID publishDate endDate endDateRep actPubtime \\\n", "0 000001.XSHE 2024-03-15 2023-12-31 2023-12-31 2024-03-14 18:46:58 \n", "1 000001.XSHE 2023-10-25 2023-09-30 2023-09-30 2023-10-24 17:52:46 \n", "2 000001.XSHE 2023-08-24 2023-06-30 2023-06-30 2023-08-23 18:10:28 \n", "3 000001.XSHE 2023-04-25 2023-03-31 2023-03-31 2023-04-24 18:00:27 \n", "4 000001.XSHE 2024-03-15 2022-12-31 2023-12-31 2024-03-14 18:46:58 \n", "5 000001.XSHE 2023-10-25 2022-12-31 2023-09-30 2023-10-24 17:52:46 \n", "6 000001.XSHE 2023-08-24 2022-12-31 2023-06-30 2023-08-23 18:10:28 \n", "7 000001.XSHE 2023-04-25 2022-12-31 2023-03-31 2023-04-24 18:00:27 \n", "... ... ... ... ... ... \n", "482037 900957.XSHG 2009-03-26 2007-12-31 2008-12-31 2009-03-25 18:00:00 \n", "482038 900957.XSHG 2008-10-24 2007-12-31 2008-09-30 2008-10-23 18:00:00 \n", "482039 900957.XSHG 2008-08-25 2007-12-31 2008-06-30 2008-08-24 18:00:00 \n", "482040 900957.XSHG 2008-04-24 2007-12-31 2008-03-31 2008-04-23 18:00:00 \n", "482041 900957.XSHG 2008-04-08 2007-12-31 2007-12-31 2008-04-07 18:00:00 \n", "482042 900957.XSHG 2007-10-23 2007-09-30 2007-09-30 2007-10-22 18:00:00 \n", "482043 900957.XSHG 2007-08-31 2007-06-30 2007-06-30 2007-08-30 18:00:00 \n", "482044 900957.XSHG 2007-04-26 2007-03-31 2007-03-31 2007-04-25 18:00:00 \n", "\n", " fiscalPeriod TShEquity TEquityAttrP minorityInt \n", "0 12 4.723280e+11 4.723280e+11 NaN \n", "1 9 4.658600e+11 4.658600e+11 NaN \n", "2 6 4.520730e+11 4.520730e+11 NaN \n", "3 3 4.467450e+11 4.467450e+11 NaN \n", "4 12 4.346800e+11 4.346800e+11 NaN \n", "5 12 4.346800e+11 4.346800e+11 NaN \n", "6 12 4.346800e+11 4.346800e+11 NaN \n", "7 12 4.346800e+11 4.346800e+11 NaN \n", "... ... ... ... ... \n", "482037 12 4.363166e+08 3.769447e+08 59371874.07 \n", "482038 12 4.363166e+08 3.769447e+08 59371874.07 \n", "482039 12 4.363166e+08 3.769447e+08 59371874.07 \n", "482040 12 4.363166e+08 3.769447e+08 59371874.07 \n", "482041 12 4.363166e+08 3.769447e+08 59371874.07 \n", "482042 9 4.328222e+08 3.774350e+08 55387111.58 \n", "482043 6 4.287524e+08 3.755800e+08 53172311.54 \n", "482044 3 4.312767e+08 3.687691e+08 62507665.37 \n", "\n", "[482045 rows x 9 columns]" ] }, "execution_count": 334, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fundmen_df" ] }, { "cell_type": "code", "execution_count": 335, "metadata": { "editable": true }, "outputs": [], "source": [ "fundmen_df[['publishDate','endDate']] = fundmen_df[['publishDate','endDate']].apply(pd.to_datetime)" ] }, { "cell_type": "code", "execution_count": 336, "metadata": { "editable": true }, "outputs": [], "source": [ "fundmen_df.sort_values(['secID','publishDate','endDate'],inplace=True)" ] }, { "cell_type": "code", "execution_count": 337, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDpublishDateendDateendDateRepactPubtimefiscalPeriodTShEquityTEquityAttrPminorityInt
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129000001.XSHE2007-10-232007-09-302007-09-302007-10-22 18:00:0098.363553e+098.363553e+09NaN
128000001.XSHE2008-03-202007-12-312007-12-312008-03-19 18:00:00121.300606e+101.300606e+10NaN
127000001.XSHE2008-04-242007-12-312008-03-312008-04-23 18:00:00121.300606e+101.300606e+10NaN
123000001.XSHE2008-04-242008-03-312008-03-312008-04-23 18:00:0031.404138e+101.404138e+10NaN
126000001.XSHE2008-08-212007-12-312008-06-302008-08-20 18:00:00121.300606e+101.300606e+10NaN
122000001.XSHE2008-08-212008-06-302008-06-302008-08-20 18:00:0061.694330e+101.694330e+10NaN
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481917900957.XSHG2023-04-082022-12-312022-12-312023-04-07 15:38:50125.669258e+085.660700e+08855788.18
481916900957.XSHG2023-04-272022-12-312023-03-312023-04-26 18:14:09125.669258e+085.660700e+08855788.18
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481915900957.XSHG2023-08-082022-12-312023-06-302023-08-07 15:32:40125.669258e+085.660700e+08855788.18
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482045 rows × 9 columns

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" ], "text/plain": [ " secID publishDate endDate endDateRep actPubtime \\\n", "131 000001.XSHE 2007-04-26 2007-03-31 2007-03-31 2007-04-25 18:00:00 \n", "130 000001.XSHE 2007-08-16 2007-06-30 2007-06-30 2007-08-15 18:00:00 \n", "129 000001.XSHE 2007-10-23 2007-09-30 2007-09-30 2007-10-22 18:00:00 \n", "128 000001.XSHE 2008-03-20 2007-12-31 2007-12-31 2008-03-19 18:00:00 \n", "127 000001.XSHE 2008-04-24 2007-12-31 2008-03-31 2008-04-23 18:00:00 \n", "123 000001.XSHE 2008-04-24 2008-03-31 2008-03-31 2008-04-23 18:00:00 \n", "126 000001.XSHE 2008-08-21 2007-12-31 2008-06-30 2008-08-20 18:00:00 \n", "122 000001.XSHE 2008-08-21 2008-06-30 2008-06-30 2008-08-20 18:00:00 \n", "... ... ... ... ... ... \n", "481921 900957.XSHG 2023-04-08 2021-12-31 2022-12-31 2023-04-07 15:38:50 \n", "481917 900957.XSHG 2023-04-08 2022-12-31 2022-12-31 2023-04-07 15:38:50 \n", "481916 900957.XSHG 2023-04-27 2022-12-31 2023-03-31 2023-04-26 18:14:09 \n", "481913 900957.XSHG 2023-04-27 2023-03-31 2023-03-31 2023-04-26 18:14:09 \n", "481915 900957.XSHG 2023-08-08 2022-12-31 2023-06-30 2023-08-07 15:32:40 \n", "481912 900957.XSHG 2023-08-08 2023-06-30 2023-06-30 2023-08-07 15:32:40 \n", "481914 900957.XSHG 2023-10-28 2022-12-31 2023-09-30 2023-10-27 15:36:39 \n", "481911 900957.XSHG 2023-10-28 2023-09-30 2023-09-30 2023-10-27 15:36:39 \n", "\n", " fiscalPeriod TShEquity TEquityAttrP minorityInt \n", "131 3 7.106094e+09 7.106094e+09 NaN \n", "130 6 7.698478e+09 7.698478e+09 NaN \n", "129 9 8.363553e+09 8.363553e+09 NaN \n", "128 12 1.300606e+10 1.300606e+10 NaN \n", "127 12 1.300606e+10 1.300606e+10 NaN \n", "123 3 1.404138e+10 1.404138e+10 NaN \n", "126 12 1.300606e+10 1.300606e+10 NaN \n", "122 6 1.694330e+10 1.694330e+10 NaN \n", "... ... ... ... ... \n", "481921 12 5.263733e+08 5.255741e+08 799194.04 \n", "481917 12 5.669258e+08 5.660700e+08 855788.18 \n", "481916 12 5.669258e+08 5.660700e+08 855788.18 \n", "481913 3 5.756460e+08 5.747912e+08 854765.57 \n", "481915 12 5.669258e+08 5.660700e+08 855788.18 \n", "481912 6 5.862225e+08 5.853687e+08 853798.86 \n", "481914 12 5.669258e+08 5.660700e+08 855788.18 \n", "481911 9 5.983664e+08 5.975140e+08 852427.19 \n", "\n", "[482045 rows x 9 columns]" ] }, "execution_count": 337, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fundmen_df" ] }, { "cell_type": "code", "execution_count": 338, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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secIDpublishDateendDateendDateRepactPubtimefiscalPeriodTShEquityTEquityAttrPminorityInt
127000001.XSHE2008-04-242007-12-312008-03-312008-04-23 18:00:00121.300606e+101.300606e+10NaN
123000001.XSHE2008-04-242008-03-312008-03-312008-04-23 18:00:0031.404138e+101.404138e+10NaN
126000001.XSHE2008-08-212007-12-312008-06-302008-08-20 18:00:00121.300606e+101.300606e+10NaN
122000001.XSHE2008-08-212008-06-302008-06-302008-08-20 18:00:0061.694330e+101.694330e+10NaN
125000001.XSHE2008-10-242007-12-312008-09-302008-10-23 18:00:00121.300606e+101.300606e+10NaN
121000001.XSHE2008-10-242008-09-302008-09-302008-10-23 18:00:0091.837466e+101.837466e+10NaN
124000001.XSHE2009-03-202007-12-312008-12-312009-03-19 18:00:00121.300606e+101.300606e+10NaN
120000001.XSHE2009-03-202008-12-312008-12-312009-03-19 18:00:00121.640079e+101.640079e+10NaN
..............................
481921900957.XSHG2023-04-082021-12-312022-12-312023-04-07 15:38:50125.263733e+085.255741e+08799194.04
481917900957.XSHG2023-04-082022-12-312022-12-312023-04-07 15:38:50125.669258e+085.660700e+08855788.18
481916900957.XSHG2023-04-272022-12-312023-03-312023-04-26 18:14:09125.669258e+085.660700e+08855788.18
481913900957.XSHG2023-04-272023-03-312023-03-312023-04-26 18:14:0935.756460e+085.747912e+08854765.57
481915900957.XSHG2023-08-082022-12-312023-06-302023-08-07 15:32:40125.669258e+085.660700e+08855788.18
481912900957.XSHG2023-08-082023-06-302023-06-302023-08-07 15:32:4065.862225e+085.853687e+08853798.86
481914900957.XSHG2023-10-282022-12-312023-09-302023-10-27 15:36:39125.669258e+085.660700e+08855788.18
481911900957.XSHG2023-10-282023-09-302023-09-302023-10-27 15:36:3995.983664e+085.975140e+08852427.19
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474076 rows × 9 columns

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216437 rows × 9 columns

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2586000043.XSHE22008-04-112007-12-312007-12-312008-04-10 18:00:00121.753205e+091.499986e+092.532189e+08
2587000043.XSHE22008-04-222008-03-312008-03-312008-04-21 18:00:0031.754624e+091.497749e+092.568750e+08
2588000043.XSHE22008-08-192008-06-302008-06-302008-08-18 18:00:0061.660061e+091.441032e+092.190291e+08
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2643000043.XSHE22022-04-282022-03-312022-03-312022-04-27 18:00:4938.722855e+098.805963e+09-8.310777e+07
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secIDsecShortNamepublishDateendDateendDateRepactPubtimefiscalPeriodTShEquityTEquityAttrPminorityInt
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1000043.XSHE2中航善达2023-10-272023-09-302023-09-302023-10-26 18:24:5999.809555e+099.618211e+091.913436e+08
2000043.XSHE2中航善达2023-08-252023-06-302023-06-302023-08-24 16:04:5769.746350e+099.569769e+091.765801e+08
3000043.XSHE2中航善达2023-04-222023-03-312023-03-312023-04-21 18:09:3139.503130e+099.334366e+091.687640e+08
4000043.XSHE2中航善达2024-03-162022-12-312023-12-312024-03-15 20:29:31129.311151e+099.149839e+091.613115e+08
5000043.XSHE2中航善达2023-10-272022-12-312023-09-302023-10-26 18:24:59129.311151e+099.149839e+091.613115e+08
6000043.XSHE2中航善达2023-08-252022-12-312023-06-302023-08-24 16:04:57129.311151e+099.149839e+091.613115e+08
7000043.XSHE2中航善达2023-04-222022-12-312023-03-312023-04-21 18:09:31129.311151e+099.149839e+091.613115e+08
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126000043.XSHE2中航善达2008-08-192007-12-312008-06-302008-08-18 18:00:00121.753205e+091.499986e+092.532189e+08
127000043.XSHE2中航善达2008-04-222007-12-312008-03-312008-04-21 18:00:00121.753205e+091.499986e+092.532189e+08
128000043.XSHE2中航善达2008-04-112007-12-312007-12-312008-04-10 18:00:00121.753205e+091.499986e+092.532189e+08
129000043.XSHE2中航善达2007-10-302007-09-302007-09-302007-10-29 18:00:0091.494239e+091.360025e+091.342143e+08
130000043.XSHE2中航善达2007-08-202007-06-302007-06-302007-08-19 18:00:0066.094195e+084.395480e+081.698716e+08
131000043.XSHE2中航善达2007-08-182007-06-302007-06-302007-08-17 18:00:0066.094195e+084.395480e+081.698716e+08
132000043.XSHE2中航善达2007-04-302007-03-312007-03-312007-04-29 18:00:0036.000622e+084.333743e+081.666879e+08
133000043.XSHE2中航善达2007-04-282007-03-312007-03-312007-04-27 18:00:0036.000622e+084.333743e+081.666879e+08
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134 rows × 10 columns

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" ], "text/plain": [ " secID secShortName publishDate endDate endDateRep \\\n", "0 000043.XSHE2 中航善达 2024-03-16 2023-12-31 2023-12-31 \n", "1 000043.XSHE2 中航善达 2023-10-27 2023-09-30 2023-09-30 \n", "2 000043.XSHE2 中航善达 2023-08-25 2023-06-30 2023-06-30 \n", "3 000043.XSHE2 中航善达 2023-04-22 2023-03-31 2023-03-31 \n", "4 000043.XSHE2 中航善达 2024-03-16 2022-12-31 2023-12-31 \n", "5 000043.XSHE2 中航善达 2023-10-27 2022-12-31 2023-09-30 \n", "6 000043.XSHE2 中航善达 2023-08-25 2022-12-31 2023-06-30 \n", "7 000043.XSHE2 中航善达 2023-04-22 2022-12-31 2023-03-31 \n", ".. ... ... ... ... ... \n", "126 000043.XSHE2 中航善达 2008-08-19 2007-12-31 2008-06-30 \n", "127 000043.XSHE2 中航善达 2008-04-22 2007-12-31 2008-03-31 \n", "128 000043.XSHE2 中航善达 2008-04-11 2007-12-31 2007-12-31 \n", "129 000043.XSHE2 中航善达 2007-10-30 2007-09-30 2007-09-30 \n", "130 000043.XSHE2 中航善达 2007-08-20 2007-06-30 2007-06-30 \n", "131 000043.XSHE2 中航善达 2007-08-18 2007-06-30 2007-06-30 \n", "132 000043.XSHE2 中航善达 2007-04-30 2007-03-31 2007-03-31 \n", "133 000043.XSHE2 中航善达 2007-04-28 2007-03-31 2007-03-31 \n", "\n", " actPubtime fiscalPeriod TShEquity TEquityAttrP \\\n", "0 2024-03-15 20:29:31 12 9.910374e+09 9.759359e+09 \n", "1 2023-10-26 18:24:59 9 9.809555e+09 9.618211e+09 \n", "2 2023-08-24 16:04:57 6 9.746350e+09 9.569769e+09 \n", "3 2023-04-21 18:09:31 3 9.503130e+09 9.334366e+09 \n", "4 2024-03-15 20:29:31 12 9.311151e+09 9.149839e+09 \n", "5 2023-10-26 18:24:59 12 9.311151e+09 9.149839e+09 \n", "6 2023-08-24 16:04:57 12 9.311151e+09 9.149839e+09 \n", "7 2023-04-21 18:09:31 12 9.311151e+09 9.149839e+09 \n", ".. ... ... ... ... \n", "126 2008-08-18 18:00:00 12 1.753205e+09 1.499986e+09 \n", "127 2008-04-21 18:00:00 12 1.753205e+09 1.499986e+09 \n", "128 2008-04-10 18:00:00 12 1.753205e+09 1.499986e+09 \n", "129 2007-10-29 18:00:00 9 1.494239e+09 1.360025e+09 \n", "130 2007-08-19 18:00:00 6 6.094195e+08 4.395480e+08 \n", "131 2007-08-17 18:00:00 6 6.094195e+08 4.395480e+08 \n", "132 2007-04-29 18:00:00 3 6.000622e+08 4.333743e+08 \n", "133 2007-04-27 18:00:00 3 6.000622e+08 4.333743e+08 \n", "\n", " minorityInt \n", "0 1.510153e+08 \n", "1 1.913436e+08 \n", "2 1.765801e+08 \n", "3 1.687640e+08 \n", "4 1.613115e+08 \n", "5 1.613115e+08 \n", "6 1.613115e+08 \n", "7 1.613115e+08 \n", ".. ... \n", "126 2.532189e+08 \n", "127 2.532189e+08 \n", "128 2.532189e+08 \n", "129 1.342143e+08 \n", "130 1.698716e+08 \n", "131 1.698716e+08 \n", "132 1.666879e+08 \n", "133 1.666879e+08 \n", "\n", "[134 rows x 10 columns]" ] }, "execution_count": 344, "metadata": {}, "output_type": "execute_result" } ], "source": [ "DataAPI.FdmtBSGet(secID='000043.XSHE2',beginDate=START,endDate=END,publishDateEnd=u\"\",publishDateBegin=u\"\",endDateRep=\"\",beginDateRep=\"\",beginYear=\"\",endYear=\"\",fiscalPeriod=\"\",field=[\"secID\",\"secShortName\",\"publishDate\",\"endDate\",\"endDateRep\",\"actPubtime\",\"fiscalPeriod\",\"TShEquity\",\"TEquityAttrP\",\"minorityInt\"],pandas=\"1\")" ] }, { "cell_type": "code", "execution_count": 345, "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": 346, "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": 347, "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": 348, "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
.................................
216429900957.XSHG2022-04-202021-12-312021-12-312022-04-19 17:15:56125.263733e+085.255741e+08799194.045.255741e+08
216430900957.XSHG2022-04-302022-03-312022-03-312022-04-29 15:36:3835.341491e+085.333509e+08798170.285.333509e+08
216431900957.XSHG2022-08-162022-06-302022-06-302022-08-15 16:24:2465.483870e+085.476224e+08764620.525.476224e+08
216432900957.XSHG2022-10-282022-09-302022-09-302022-10-27 16:41:2895.566301e+085.558669e+08763140.905.558669e+08
216433900957.XSHG2023-04-082022-12-312022-12-312023-04-07 15:38:50125.669258e+085.660700e+08855788.185.660700e+08
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216435900957.XSHG2023-08-082023-06-302023-06-302023-08-07 15:32:4065.862225e+085.853687e+08853798.865.853687e+08
216436900957.XSHG2023-10-282023-09-302023-09-302023-10-27 15:36:3995.983664e+085.975140e+08852427.195.975140e+08
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216367 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", "216429 900957.XSHG 2022-04-20 2021-12-31 2021-12-31 2022-04-19 17:15:56 \n", "216430 900957.XSHG 2022-04-30 2022-03-31 2022-03-31 2022-04-29 15:36:38 \n", "216431 900957.XSHG 2022-08-16 2022-06-30 2022-06-30 2022-08-15 16:24:24 \n", "216432 900957.XSHG 2022-10-28 2022-09-30 2022-09-30 2022-10-27 16:41:28 \n", "216433 900957.XSHG 2023-04-08 2022-12-31 2022-12-31 2023-04-07 15:38:50 \n", "216434 900957.XSHG 2023-04-27 2023-03-31 2023-03-31 2023-04-26 18:14:09 \n", "216435 900957.XSHG 2023-08-08 2023-06-30 2023-06-30 2023-08-07 15:32:40 \n", "216436 900957.XSHG 2023-10-28 2023-09-30 2023-09-30 2023-10-27 15:36:39 \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", "216429 12 5.263733e+08 5.255741e+08 799194.04 5.255741e+08 \n", "216430 3 5.341491e+08 5.333509e+08 798170.28 5.333509e+08 \n", "216431 6 5.483870e+08 5.476224e+08 764620.52 5.476224e+08 \n", "216432 9 5.566301e+08 5.558669e+08 763140.90 5.558669e+08 \n", "216433 12 5.669258e+08 5.660700e+08 855788.18 5.660700e+08 \n", "216434 3 5.756460e+08 5.747912e+08 854765.57 5.747912e+08 \n", "216435 6 5.862225e+08 5.853687e+08 853798.86 5.853687e+08 \n", "216436 9 5.983664e+08 5.975140e+08 852427.19 5.975140e+08 \n", "\n", "[216367 rows x 10 columns]" ] }, "execution_count": 348, "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": 349, "metadata": { "editable": true }, "outputs": [], "source": [ "# fundmen_df['publishDate+1'] = fundmen_df['publishDate'] + dt.timedelta(days=1)" ] }, { "cell_type": "code", "execution_count": 350, "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": 351, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDtradeDateclosePricenegMarketValueympublishDateendDatebook
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
...........................
12416343900957.XSHGNaTNaNNaNNaT2016-08-062016-06-303.906354e+08
12416344900957.XSHGNaTNaNNaNNaT2017-03-252016-12-313.930721e+08
12416345900957.XSHGNaTNaNNaNNaT2019-03-302018-12-314.508051e+08
12416346900957.XSHGNaTNaNNaNNaT2019-08-102019-06-304.618426e+08
12416347900957.XSHGNaTNaNNaNNaT2020-04-252019-12-314.761021e+08
12416348900957.XSHGNaTNaNNaNNaT2022-04-302022-03-315.333509e+08
12416349900957.XSHGNaTNaNNaNNaT2023-04-082022-12-315.660700e+08
12416350900957.XSHGNaTNaNNaNNaT2023-10-282023-09-305.975140e+08
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12416351 rows × 8 columns

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63067 rows × 8 columns

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12416351 rows × 8 columns

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6000001.XSHE2007-06-28953.7804.672266e+102007-06NaTNaTNaN
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12416351 rows × 8 columns

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6000001.XSHE2007-06-28953.7804.672266e+102007-06NaTNaTNaN
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4074000001.XSHE2024-03-201401.0292.027880e+112024-03NaTNaTNaN
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secIDtradeDateclosePricenegMarketValueympublishDateendDatebook
12353284000001.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
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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
3824000001.XSHE2023-03-091726.8682.561532e+112023-032023-03-092022-12-314.346800e+11
3856000001.XSHE2023-04-251606.5112.383001e+112023-042023-04-252023-03-314.467450e+11
3938000001.XSHE2023-08-241492.1962.159837e+112023-082023-08-242023-06-304.520730e+11
3976000001.XSHE2023-10-251391.6442.014296e+112023-102023-10-252023-09-304.658600e+11
4071000001.XSHE2024-03-151421.1392.056988e+112024-032024-03-152023-12-314.723280e+11
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68 rows × 8 columns

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" ], "text/plain": [ " secID tradeDate closePrice negMarketValue ym \\\n", "12353284 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", "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", "3824 000001.XSHE 2023-03-09 1726.868 2.561532e+11 2023-03 \n", "3856 000001.XSHE 2023-04-25 1606.511 2.383001e+11 2023-04 \n", "3938 000001.XSHE 2023-08-24 1492.196 2.159837e+11 2023-08 \n", "3976 000001.XSHE 2023-10-25 1391.644 2.014296e+11 2023-10 \n", "4071 000001.XSHE 2024-03-15 1421.139 2.056988e+11 2024-03 \n", "\n", " publishDate endDate book \n", "12353284 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", "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", "3824 2023-03-09 2022-12-31 4.346800e+11 \n", "3856 2023-04-25 2023-03-31 4.467450e+11 \n", "3938 2023-08-24 2023-06-30 4.520730e+11 \n", "3976 2023-10-25 2023-09-30 4.658600e+11 \n", "4071 2024-03-15 2023-12-31 4.723280e+11 \n", "\n", "[68 rows x 8 columns]" ] }, "execution_count": 361, "metadata": {}, "output_type": "execute_result" } ], "source": [ "temp[~temp['book'].isna()]" ] }, { "cell_type": "code", "execution_count": 362, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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publishDateendDatebook
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............
123532762023-10-282023-09-305.975140e+08
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12416351 rows × 3 columns

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" ], "text/plain": [ " publishDate endDate book\n", "12353284 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", "12353276 2023-10-28 2023-09-30 5.975140e+08\n", "12353277 2023-10-28 2023-09-30 5.975140e+08\n", "12353278 2023-10-28 2023-09-30 5.975140e+08\n", "12353279 2023-10-28 2023-09-30 5.975140e+08\n", "12353280 2023-10-28 2023-09-30 5.975140e+08\n", "12353281 2023-10-28 2023-09-30 5.975140e+08\n", "12353282 2023-10-28 2023-09-30 5.975140e+08\n", "12353283 2023-10-28 2023-09-30 5.975140e+08\n", "\n", "[12416351 rows x 3 columns]" ] }, "execution_count": 362, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_fundmen_df[['secID','publishDate','endDate','book']].groupby('secID').fillna(method='ffill')" ] }, { "cell_type": "code", "execution_count": 363, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(12416351, 8)" ] }, "execution_count": 363, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_fundmen_df.shape" ] }, { "cell_type": "code", "execution_count": 364, "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": 365, "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": 366, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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closePricenegMarketValueympublishDateendDatebook
secIDtradeDate
000001.XSHE2010-03-01926.3036.564637e+102010-032009-10-292009-09-301.908844e+10
2010-03-02953.5356.757628e+102010-032009-10-292009-09-301.908844e+10
2010-03-03961.3746.813186e+102010-032009-10-292009-09-301.908844e+10
2010-03-04953.1226.754704e+102010-032009-10-292009-09-301.908844e+10
2010-03-05960.1376.804414e+102010-032009-10-292009-09-301.908844e+10
2010-03-08984.0686.974013e+102010-032009-10-292009-09-301.908844e+10
2010-03-09982.8306.965240e+102010-032009-10-292009-09-301.908844e+10
2010-03-10965.5006.842427e+102010-032009-10-292009-09-301.908844e+10
2010-03-11976.2286.918454e+102010-032009-10-292009-09-301.908844e+10
2010-03-12945.6956.702070e+102010-032010-03-122009-12-312.046961e+10
.....................
2010-03-18951.0596.740083e+102010-032010-03-122009-12-312.046961e+10
2010-03-19950.6476.737159e+102010-032010-03-122009-12-312.046961e+10
2010-03-22955.1856.769325e+102010-032010-03-122009-12-312.046961e+10
2010-03-23941.9826.675753e+102010-032010-03-122009-12-312.046961e+10
2010-03-24940.7446.666981e+102010-032010-03-122009-12-312.046961e+10
2010-03-25920.1146.520775e+102010-032010-03-122009-12-312.046961e+10
2010-03-26941.1576.669905e+102010-032010-03-122009-12-312.046961e+10
2010-03-29972.1026.889213e+102010-032010-03-122009-12-312.046961e+10
2010-03-30975.8166.915530e+102010-032010-03-122009-12-312.046961e+10
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23 rows × 6 columns

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" ], "text/plain": [ " closePrice negMarketValue ym publishDate \\\n", "secID tradeDate \n", "000001.XSHE 2010-03-01 926.303 6.564637e+10 2010-03 2009-10-29 \n", " 2010-03-02 953.535 6.757628e+10 2010-03 2009-10-29 \n", " 2010-03-03 961.374 6.813186e+10 2010-03 2009-10-29 \n", " 2010-03-04 953.122 6.754704e+10 2010-03 2009-10-29 \n", " 2010-03-05 960.137 6.804414e+10 2010-03 2009-10-29 \n", " 2010-03-08 984.068 6.974013e+10 2010-03 2009-10-29 \n", " 2010-03-09 982.830 6.965240e+10 2010-03 2009-10-29 \n", " 2010-03-10 965.500 6.842427e+10 2010-03 2009-10-29 \n", " 2010-03-11 976.228 6.918454e+10 2010-03 2009-10-29 \n", " 2010-03-12 945.695 6.702070e+10 2010-03 2010-03-12 \n", "... ... ... ... ... \n", " 2010-03-18 951.059 6.740083e+10 2010-03 2010-03-12 \n", " 2010-03-19 950.647 6.737159e+10 2010-03 2010-03-12 \n", " 2010-03-22 955.185 6.769325e+10 2010-03 2010-03-12 \n", " 2010-03-23 941.982 6.675753e+10 2010-03 2010-03-12 \n", " 2010-03-24 940.744 6.666981e+10 2010-03 2010-03-12 \n", " 2010-03-25 920.114 6.520775e+10 2010-03 2010-03-12 \n", " 2010-03-26 941.157 6.669905e+10 2010-03 2010-03-12 \n", " 2010-03-29 972.102 6.889213e+10 2010-03 2010-03-12 \n", " 2010-03-30 975.816 6.915530e+10 2010-03 2010-03-12 \n", " 2010-03-31 957.248 6.783945e+10 2010-03 2010-03-12 \n", "\n", " endDate book \n", "secID tradeDate \n", "000001.XSHE 2010-03-01 2009-09-30 1.908844e+10 \n", " 2010-03-02 2009-09-30 1.908844e+10 \n", " 2010-03-03 2009-09-30 1.908844e+10 \n", " 2010-03-04 2009-09-30 1.908844e+10 \n", " 2010-03-05 2009-09-30 1.908844e+10 \n", " 2010-03-08 2009-09-30 1.908844e+10 \n", " 2010-03-09 2009-09-30 1.908844e+10 \n", " 2010-03-10 2009-09-30 1.908844e+10 \n", " 2010-03-11 2009-09-30 1.908844e+10 \n", " 2010-03-12 2009-12-31 2.046961e+10 \n", "... ... ... \n", " 2010-03-18 2009-12-31 2.046961e+10 \n", " 2010-03-19 2009-12-31 2.046961e+10 \n", " 2010-03-22 2009-12-31 2.046961e+10 \n", " 2010-03-23 2009-12-31 2.046961e+10 \n", " 2010-03-24 2009-12-31 2.046961e+10 \n", " 2010-03-25 2009-12-31 2.046961e+10 \n", " 2010-03-26 2009-12-31 2.046961e+10 \n", " 2010-03-29 2009-12-31 2.046961e+10 \n", " 2010-03-30 2009-12-31 2.046961e+10 \n", " 2010-03-31 2009-12-31 2.046961e+10 \n", "\n", "[23 rows x 6 columns]" ] }, "execution_count": 366, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_fundmen_df.loc[idx['000001.XSHE','2010-03'],:]" ] }, { "cell_type": "code", "execution_count": 367, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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closePricenegMarketValueympublishDateendDatebook
secIDtradeDate
000001.XSHE2007-04-26NaNNaNNaT2007-04-262007-03-317.106094e+09
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" ], "text/plain": [ " closePrice negMarketValue ym publishDate \\\n", "secID tradeDate \n", "000001.XSHE 2007-04-26 NaN NaN NaT 2007-04-26 \n", "\n", " endDate book \n", "secID tradeDate \n", "000001.XSHE 2007-04-26 2007-03-31 7.106094e+09 " ] }, "execution_count": 367, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_fundmen_df.loc[idx['000001.XSHE','2007-04'],:]" ] }, { "cell_type": "code", "execution_count": 368, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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closePricenegMarketValueympublishDateendDatebook
secIDtradeDate
000001.XSHE2007-04-26NaNNaNNaT2007-04-262007-03-317.106094e+09
2007-06-20987.0074.835036e+102007-062007-04-262007-03-317.106094e+09
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2007-06-221120.2335.487665e+102007-062007-04-262007-03-317.106094e+09
2007-06-251113.9045.456661e+102007-062007-04-262007-03-317.106094e+09
2007-06-261113.9045.456661e+102007-062007-04-262007-03-317.106094e+09
2007-06-271019.6024.994705e+102007-062007-04-262007-03-317.106094e+09
2007-06-28953.7804.672266e+102007-062007-04-262007-03-317.106094e+09
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" ], "text/plain": [ " closePrice negMarketValue ym publishDate \\\n", "secID tradeDate \n", "000001.XSHE 2007-04-26 NaN NaN NaT 2007-04-26 \n", " 2007-06-20 987.007 4.835036e+10 2007-06 2007-04-26 \n", " 2007-06-21 1085.740 5.318694e+10 2007-06 2007-04-26 \n", " 2007-06-22 1120.233 5.487665e+10 2007-06 2007-04-26 \n", " 2007-06-25 1113.904 5.456661e+10 2007-06 2007-04-26 \n", " 2007-06-26 1113.904 5.456661e+10 2007-06 2007-04-26 \n", " 2007-06-27 1019.602 4.994705e+10 2007-06 2007-04-26 \n", " 2007-06-28 953.780 4.672266e+10 2007-06 2007-04-26 \n", " 2007-06-29 870.870 4.266117e+10 2007-06 2007-04-26 \n", "\n", " endDate book \n", "secID tradeDate \n", "000001.XSHE 2007-04-26 2007-03-31 7.106094e+09 \n", " 2007-06-20 2007-03-31 7.106094e+09 \n", " 2007-06-21 2007-03-31 7.106094e+09 \n", " 2007-06-22 2007-03-31 7.106094e+09 \n", " 2007-06-25 2007-03-31 7.106094e+09 \n", " 2007-06-26 2007-03-31 7.106094e+09 \n", " 2007-06-27 2007-03-31 7.106094e+09 \n", " 2007-06-28 2007-03-31 7.106094e+09 \n", " 2007-06-29 2007-03-31 7.106094e+09 " ] }, "execution_count": 368, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_fundmen_df.loc[idx['000001.XSHE','2007-04':'2007-06'],:]" ] }, { "cell_type": "code", "execution_count": 369, "metadata": { "editable": true }, "outputs": [], "source": [ "stk_fundmen_df.reset_index(inplace=True)" ] }, { "cell_type": "code", "execution_count": 370, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDtradeDateclosePricenegMarketValueympublishDateendDatebook
0000001.XSHE2007-04-26NaNNaNNaT2007-04-262007-03-317.106094e+09
1000001.XSHE2007-06-20987.0074.835036e+102007-062007-04-262007-03-317.106094e+09
2000001.XSHE2007-06-211085.7405.318694e+102007-062007-04-262007-03-317.106094e+09
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
...........................
12416341900957.XSHG2024-03-180.4367.967200e+072024-032023-10-282023-09-305.975140e+08
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12416343900957.XSHG2024-03-200.4237.728000e+072024-032023-10-282023-09-305.975140e+08
12416344900957.XSHG2024-03-210.4197.654400e+072024-032023-10-282023-09-305.975140e+08
12416345900957.XSHG2024-03-220.4187.636000e+072024-032023-10-282023-09-305.975140e+08
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12416351 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", "12416341 900957.XSHG 2024-03-18 0.436 7.967200e+07 2024-03 \n", "12416342 900957.XSHG 2024-03-19 0.429 7.838400e+07 2024-03 \n", "12416343 900957.XSHG 2024-03-20 0.423 7.728000e+07 2024-03 \n", "12416344 900957.XSHG 2024-03-21 0.419 7.654400e+07 2024-03 \n", "12416345 900957.XSHG 2024-03-22 0.418 7.636000e+07 2024-03 \n", "12416346 900957.XSHG 2024-03-25 0.410 7.488800e+07 2024-03 \n", "12416347 900957.XSHG 2024-03-26 0.414 7.562400e+07 2024-03 \n", "12416348 900957.XSHG 2024-03-27 0.411 7.507200e+07 2024-03 \n", "12416349 900957.XSHG 2024-03-28 0.418 7.636000e+07 2024-03 \n", "12416350 900957.XSHG 2024-03-29 0.421 7.691200e+07 2024-03 \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", "12416341 2023-10-28 2023-09-30 5.975140e+08 \n", "12416342 2023-10-28 2023-09-30 5.975140e+08 \n", "12416343 2023-10-28 2023-09-30 5.975140e+08 \n", "12416344 2023-10-28 2023-09-30 5.975140e+08 \n", "12416345 2023-10-28 2023-09-30 5.975140e+08 \n", "12416346 2023-10-28 2023-09-30 5.975140e+08 \n", "12416347 2023-10-28 2023-09-30 5.975140e+08 \n", "12416348 2023-10-28 2023-09-30 5.975140e+08 \n", "12416349 2023-10-28 2023-09-30 5.975140e+08 \n", "12416350 2023-10-28 2023-09-30 5.975140e+08 \n", "\n", "[12416351 rows x 8 columns]" ] }, "execution_count": 370, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_fundmen_df" ] }, { "cell_type": "code", "execution_count": 371, "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": 372, "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
..............................
613069900957.XSHG2023-062023-06-300.4969.052800e+072023-04-272023-03-315.747912e+08-0.033138
613070900957.XSHG2023-072023-07-310.5169.420800e+072023-04-272023-03-315.747912e+080.040323
613071900957.XSHG2023-082023-08-310.4337.912000e+072023-08-082023-06-305.853687e+08-0.160853
613072900957.XSHG2023-092023-09-280.4047.378400e+072023-08-082023-06-305.853687e+08-0.066975
613073900957.XSHG2023-102023-10-310.4027.341600e+072023-10-282023-09-305.975140e+08-0.004950
613074900957.XSHG2023-112023-11-300.4177.617600e+072023-10-282023-09-305.975140e+080.037313
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613077900957.XSHG2024-022024-02-290.4498.188000e+072023-10-282023-09-305.975140e+080.081928
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613079 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", "613069 900957.XSHG 2023-06 2023-06-30 0.496 9.052800e+07 \n", "613070 900957.XSHG 2023-07 2023-07-31 0.516 9.420800e+07 \n", "613071 900957.XSHG 2023-08 2023-08-31 0.433 7.912000e+07 \n", "613072 900957.XSHG 2023-09 2023-09-28 0.404 7.378400e+07 \n", "613073 900957.XSHG 2023-10 2023-10-31 0.402 7.341600e+07 \n", "613074 900957.XSHG 2023-11 2023-11-30 0.417 7.617600e+07 \n", "613075 900957.XSHG 2023-12 2023-12-29 0.416 7.599200e+07 \n", "613076 900957.XSHG 2024-01 2024-01-31 0.415 7.580800e+07 \n", "613077 900957.XSHG 2024-02 2024-02-29 0.449 8.188000e+07 \n", "613078 900957.XSHG 2024-03 2024-03-29 0.421 7.691200e+07 \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", "613069 2023-04-27 2023-03-31 5.747912e+08 -0.033138 \n", "613070 2023-04-27 2023-03-31 5.747912e+08 0.040323 \n", "613071 2023-08-08 2023-06-30 5.853687e+08 -0.160853 \n", "613072 2023-08-08 2023-06-30 5.853687e+08 -0.066975 \n", "613073 2023-10-28 2023-09-30 5.975140e+08 -0.004950 \n", "613074 2023-10-28 2023-09-30 5.975140e+08 0.037313 \n", "613075 2023-10-28 2023-09-30 5.975140e+08 -0.002398 \n", "613076 2023-10-28 2023-09-30 5.975140e+08 -0.002404 \n", "613077 2023-10-28 2023-09-30 5.975140e+08 0.081928 \n", "613078 2023-10-28 2023-09-30 5.975140e+08 -0.062361 \n", "\n", "[613079 rows x 9 columns]" ] }, "execution_count": 372, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_df_m" ] }, { "cell_type": "code", "execution_count": 373, "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) # 上一期的BM影响下一期" ] }, { "cell_type": "code", "execution_count": 374, "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
.................................
613069900957.XSHG2023-062023-06-300.4969.052800e+072023-04-272023-03-315.747912e+080.0403232023-07
613070900957.XSHG2023-072023-07-310.5169.420800e+072023-04-272023-03-315.747912e+08-0.1608532023-08
613071900957.XSHG2023-082023-08-310.4337.912000e+072023-08-082023-06-305.853687e+08-0.0669752023-09
613072900957.XSHG2023-092023-09-280.4047.378400e+072023-08-082023-06-305.853687e+08-0.0049502023-10
613073900957.XSHG2023-102023-10-310.4027.341600e+072023-10-282023-09-305.975140e+080.0373132023-11
613074900957.XSHG2023-112023-11-300.4177.617600e+072023-10-282023-09-305.975140e+08-0.0023982023-12
613075900957.XSHG2023-122023-12-290.4167.599200e+072023-10-282023-09-305.975140e+08-0.0024042024-01
613076900957.XSHG2024-012024-01-310.4157.580800e+072023-10-282023-09-305.975140e+080.0819282024-02
613077900957.XSHG2024-022024-02-290.4498.188000e+072023-10-282023-09-305.975140e+08-0.0623612024-03
613078900957.XSHG2024-032024-03-290.4217.691200e+072023-10-282023-09-305.975140e+08NaNNaT
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613079 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", "613069 900957.XSHG 2023-06 2023-06-30 0.496 9.052800e+07 \n", "613070 900957.XSHG 2023-07 2023-07-31 0.516 9.420800e+07 \n", "613071 900957.XSHG 2023-08 2023-08-31 0.433 7.912000e+07 \n", "613072 900957.XSHG 2023-09 2023-09-28 0.404 7.378400e+07 \n", "613073 900957.XSHG 2023-10 2023-10-31 0.402 7.341600e+07 \n", "613074 900957.XSHG 2023-11 2023-11-30 0.417 7.617600e+07 \n", "613075 900957.XSHG 2023-12 2023-12-29 0.416 7.599200e+07 \n", "613076 900957.XSHG 2024-01 2024-01-31 0.415 7.580800e+07 \n", "613077 900957.XSHG 2024-02 2024-02-29 0.449 8.188000e+07 \n", "613078 900957.XSHG 2024-03 2024-03-29 0.421 7.691200e+07 \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", "613069 2023-04-27 2023-03-31 5.747912e+08 0.040323 2023-07 \n", "613070 2023-04-27 2023-03-31 5.747912e+08 -0.160853 2023-08 \n", "613071 2023-08-08 2023-06-30 5.853687e+08 -0.066975 2023-09 \n", "613072 2023-08-08 2023-06-30 5.853687e+08 -0.004950 2023-10 \n", "613073 2023-10-28 2023-09-30 5.975140e+08 0.037313 2023-11 \n", "613074 2023-10-28 2023-09-30 5.975140e+08 -0.002398 2023-12 \n", "613075 2023-10-28 2023-09-30 5.975140e+08 -0.002404 2024-01 \n", "613076 2023-10-28 2023-09-30 5.975140e+08 0.081928 2024-02 \n", "613077 2023-10-28 2023-09-30 5.975140e+08 -0.062361 2024-03 \n", "613078 2023-10-28 2023-09-30 5.975140e+08 NaN NaT \n", "\n", "[613079 rows x 10 columns]" ] }, "execution_count": 374, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_df_m" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "这里处理停牌仍然可以用“填充NA日期”的办法，但需要在日度数据先填。日度数据填充可能会使数据变得很大，但应该更稳妥。" ] }, { "cell_type": "code", "execution_count": 375, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDymtradeDateclosePricenegMarketValuepublishDateendDatebookretret_dateym_diff
201000001.XSHE2024-032024-03-291410.4132.041464e+112024-03-152023-12-314.723280e+11NaNNaT9223372036854775158
408000002.XSHE2024-032024-03-291495.1788.745242e+102024-03-292023-12-312.507846e+11NaNNaT9223372036854775158
540000004.XSHE2022-052022-05-0585.9711.463441e+092022-04-302022-03-319.351158e+080.2122462023-0613
550000004.XSHE2024-032024-03-29102.8511.900636e+092023-10-282023-09-303.173013e+08NaNNaT9223372036854775158
705000005.XSHE2021-042021-04-3021.8142.348641e+092021-04-302021-03-311.248325e+09NaNNaT9223372036854775193
912000006.XSHE2024-032024-03-29287.1335.332450e+092023-10-272023-09-307.773724e+09NaNNaT9223372036854775158
916000007.XSHE2007-042007-04-2048.2037.268053e+08NaTNaTNaN1.6123272012-0561
1024000007.XSHE2021-042021-04-2947.4011.217255e+092021-04-292021-03-316.318241e+070.8705722022-0715
1035000007.XSHE2023-052023-05-0465.5681.683767e+092023-04-292023-03-319.028576e+07NaNNaT9223372036854775168
1038000008.XSHE2007-032007-03-2922.9373.197319e+08NaTNaTNaN0.8145792013-0473
....................................
612150900951.XSHG2018-072018-07-300.5375.370000e+072018-04-212018-03-311.635296e+08-0.9124772020-0724
612152900951.XSHG2020-082020-08-260.0696.900000e+062020-04-252020-03-31-2.115164e+08NaNNaT9223372036854775201
612359900952.XSHG2024-032024-03-290.4214.099649e+072023-10-282023-09-306.712409e+09NaNNaT9223372036854775158
612375900953.XSHG2008-042008-04-300.4301.000800e+082008-04-302008-03-315.505128e+080.5883722009-0715
612552900953.XSHG2024-032024-03-290.3006.984000e+072023-10-282023-09-303.711410e+08NaNNaT9223372036854775158
612629900955.XSHG2013-052013-05-021.2511.188000e+082013-04-272013-03-311.602086e+09-0.1942452014-0411
612702900955.XSHG2020-042020-04-300.5074.818000e+072020-04-302020-03-311.031370e+09-0.8500992022-0626
612704900955.XSHG2022-072022-07-120.0736.930000e+062022-05-072021-12-316.172171e+08NaNNaT9223372036854775178
612871900956.XSHG2020-112020-11-203.4243.530500e+082020-10-292020-09-301.430181e+09NaNNaT9223372036854775198
613078900957.XSHG2024-032024-03-290.4217.691200e+072023-10-282023-09-305.975140e+08NaNNaT9223372036854775158
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6129 rows × 11 columns

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" ], "text/plain": [ " secID ym tradeDate closePrice negMarketValue \\\n", "201 000001.XSHE 2024-03 2024-03-29 1410.413 2.041464e+11 \n", "408 000002.XSHE 2024-03 2024-03-29 1495.178 8.745242e+10 \n", "540 000004.XSHE 2022-05 2022-05-05 85.971 1.463441e+09 \n", "550 000004.XSHE 2024-03 2024-03-29 102.851 1.900636e+09 \n", "705 000005.XSHE 2021-04 2021-04-30 21.814 2.348641e+09 \n", "912 000006.XSHE 2024-03 2024-03-29 287.133 5.332450e+09 \n", "916 000007.XSHE 2007-04 2007-04-20 48.203 7.268053e+08 \n", "1024 000007.XSHE 2021-04 2021-04-29 47.401 1.217255e+09 \n", "1035 000007.XSHE 2023-05 2023-05-04 65.568 1.683767e+09 \n", "1038 000008.XSHE 2007-03 2007-03-29 22.937 3.197319e+08 \n", "... ... ... ... ... ... \n", "612150 900951.XSHG 2018-07 2018-07-30 0.537 5.370000e+07 \n", "612152 900951.XSHG 2020-08 2020-08-26 0.069 6.900000e+06 \n", "612359 900952.XSHG 2024-03 2024-03-29 0.421 4.099649e+07 \n", "612375 900953.XSHG 2008-04 2008-04-30 0.430 1.000800e+08 \n", "612552 900953.XSHG 2024-03 2024-03-29 0.300 6.984000e+07 \n", "612629 900955.XSHG 2013-05 2013-05-02 1.251 1.188000e+08 \n", "612702 900955.XSHG 2020-04 2020-04-30 0.507 4.818000e+07 \n", "612704 900955.XSHG 2022-07 2022-07-12 0.073 6.930000e+06 \n", "612871 900956.XSHG 2020-11 2020-11-20 3.424 3.530500e+08 \n", "613078 900957.XSHG 2024-03 2024-03-29 0.421 7.691200e+07 \n", "\n", " publishDate endDate book ret ret_date \\\n", "201 2024-03-15 2023-12-31 4.723280e+11 NaN NaT \n", "408 2024-03-29 2023-12-31 2.507846e+11 NaN NaT \n", "540 2022-04-30 2022-03-31 9.351158e+08 0.212246 2023-06 \n", "550 2023-10-28 2023-09-30 3.173013e+08 NaN NaT \n", "705 2021-04-30 2021-03-31 1.248325e+09 NaN NaT \n", "912 2023-10-27 2023-09-30 7.773724e+09 NaN NaT \n", "916 NaT NaT NaN 1.612327 2012-05 \n", "1024 2021-04-29 2021-03-31 6.318241e+07 0.870572 2022-07 \n", "1035 2023-04-29 2023-03-31 9.028576e+07 NaN NaT \n", "1038 NaT NaT NaN 0.814579 2013-04 \n", "... ... ... ... ... ... \n", "612150 2018-04-21 2018-03-31 1.635296e+08 -0.912477 2020-07 \n", "612152 2020-04-25 2020-03-31 -2.115164e+08 NaN NaT \n", "612359 2023-10-28 2023-09-30 6.712409e+09 NaN NaT \n", "612375 2008-04-30 2008-03-31 5.505128e+08 0.588372 2009-07 \n", "612552 2023-10-28 2023-09-30 3.711410e+08 NaN NaT \n", "612629 2013-04-27 2013-03-31 1.602086e+09 -0.194245 2014-04 \n", "612702 2020-04-30 2020-03-31 1.031370e+09 -0.850099 2022-06 \n", "612704 2022-05-07 2021-12-31 6.172171e+08 NaN NaT \n", "612871 2020-10-29 2020-09-30 1.430181e+09 NaN NaT \n", "613078 2023-10-28 2023-09-30 5.975140e+08 NaN NaT \n", "\n", " ym_diff \n", "201 9223372036854775158 \n", "408 9223372036854775158 \n", "540 13 \n", "550 9223372036854775158 \n", "705 9223372036854775193 \n", "912 9223372036854775158 \n", "916 61 \n", "1024 15 \n", "1035 9223372036854775168 \n", "1038 73 \n", "... ... \n", "612150 24 \n", "612152 9223372036854775201 \n", "612359 9223372036854775158 \n", "612375 15 \n", "612552 9223372036854775158 \n", "612629 11 \n", "612702 26 \n", "612704 9223372036854775178 \n", "612871 9223372036854775198 \n", "613078 9223372036854775158 \n", "\n", "[6129 rows x 11 columns]" ] }, "execution_count": 375, "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": 378, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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secIDtradeDateclosePricenegMarketValueym
12108000004.XSHE2022-05-0585.9711.463441e+092022-05
12388000004.XSHE2023-06-28103.4661.800402e+092023-06
12389000004.XSHE2023-06-29109.2751.901481e+092023-06
12390000004.XSHE2023-06-30104.2181.813483e+092023-06
12391000004.XSHE2023-07-0394.5131.644621e+092023-07
12392000004.XSHE2023-07-04101.0741.758781e+092023-07
12393000004.XSHE2023-07-05111.1881.934778e+092023-07
12394000004.XSHE2023-07-06122.3282.128613e+092023-07
12395000004.XSHE2023-07-07116.0402.019209e+092023-07
12396000004.XSHE2023-07-10104.4231.817050e+092023-07
..................
12563000004.XSHE2024-03-18106.7461.972620e+092024-03
12564000004.XSHE2024-03-19103.7391.917053e+092024-03
12565000004.XSHE2024-03-20103.1931.906950e+092024-03
12566000004.XSHE2024-03-21101.6891.879167e+092024-03
12567000004.XSHE2024-03-2299.5021.838755e+092024-03
12568000004.XSHE2024-03-2599.7761.843806e+092024-03
12569000004.XSHE2024-03-26101.1421.869064e+092024-03
12570000004.XSHE2024-03-27100.2541.852646e+092024-03
12571000004.XSHE2024-03-28105.5841.951151e+092024-03
12572000004.XSHE2024-03-29102.8511.900636e+092024-03
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186 rows × 5 columns

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" ], "text/plain": [ " secID tradeDate closePrice negMarketValue ym\n", "12108 000004.XSHE 2022-05-05 85.971 1.463441e+09 2022-05\n", "12388 000004.XSHE 2023-06-28 103.466 1.800402e+09 2023-06\n", "12389 000004.XSHE 2023-06-29 109.275 1.901481e+09 2023-06\n", "12390 000004.XSHE 2023-06-30 104.218 1.813483e+09 2023-06\n", "12391 000004.XSHE 2023-07-03 94.513 1.644621e+09 2023-07\n", "12392 000004.XSHE 2023-07-04 101.074 1.758781e+09 2023-07\n", "12393 000004.XSHE 2023-07-05 111.188 1.934778e+09 2023-07\n", "12394 000004.XSHE 2023-07-06 122.328 2.128613e+09 2023-07\n", "12395 000004.XSHE 2023-07-07 116.040 2.019209e+09 2023-07\n", "12396 000004.XSHE 2023-07-10 104.423 1.817050e+09 2023-07\n", "... ... ... ... ... ...\n", "12563 000004.XSHE 2024-03-18 106.746 1.972620e+09 2024-03\n", "12564 000004.XSHE 2024-03-19 103.739 1.917053e+09 2024-03\n", "12565 000004.XSHE 2024-03-20 103.193 1.906950e+09 2024-03\n", "12566 000004.XSHE 2024-03-21 101.689 1.879167e+09 2024-03\n", "12567 000004.XSHE 2024-03-22 99.502 1.838755e+09 2024-03\n", "12568 000004.XSHE 2024-03-25 99.776 1.843806e+09 2024-03\n", "12569 000004.XSHE 2024-03-26 101.142 1.869064e+09 2024-03\n", "12570 000004.XSHE 2024-03-27 100.254 1.852646e+09 2024-03\n", "12571 000004.XSHE 2024-03-28 105.584 1.951151e+09 2024-03\n", "12572 000004.XSHE 2024-03-29 102.851 1.900636e+09 2024-03\n", "\n", "[186 rows x 5 columns]" ] }, "execution_count": 378, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_df.loc[(stk_df['secID']=='000004.XSHE')&(stk_df['ym']>='2022-05')]" ] }, { "cell_type": "code", "execution_count": 379, "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": 384, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDymtradeDateclosePricenegMarketValuepublishDateendDatebookretret_dateym_diff
272043300183.XSHE2011-022011-02-2844.8108.962000e+082011-02-092010-06-301.207288e+08-0.0198622011-031
272044300183.XSHE2011-032011-03-3143.9208.784000e+082011-03-312010-12-311.874104e+080.0849272011-041
272045300183.XSHE2011-042011-04-2947.6509.530000e+082011-04-182011-03-311.197714e+09-0.1334732011-051
272046300183.XSHE2011-052011-05-3141.2901.032250e+092011-04-182011-03-311.197714e+090.0511022011-061
272047300183.XSHE2011-062011-06-3043.4001.085000e+092011-04-182011-03-311.197714e+090.1868662011-071
272048300183.XSHE2011-072011-07-2951.5101.287750e+092011-04-182011-03-311.197714e+09-0.0112602011-081
272049300183.XSHE2011-082011-08-3150.9301.273250e+092011-08-242011-06-301.234990e+09-0.0720602011-091
272050300183.XSHE2011-092011-09-3047.2601.181500e+092011-08-242011-06-301.234990e+090.0928902011-101
272051300183.XSHE2011-102011-10-3151.6501.291250e+092011-10-182011-09-301.282289e+090.1229432011-111
272052300183.XSHE2011-112011-11-3058.0001.450000e+092011-10-182011-09-301.282289e+090.1768972011-121
....................................
272191300183.XSHE2023-062023-06-3084.9265.538294e+092023-04-242023-03-313.158659e+09-0.0666342023-071
272192300183.XSHE2023-072023-07-3179.2675.169298e+092023-04-242023-03-313.158659e+09-0.0220522023-081
272193300183.XSHE2023-082023-08-3177.5195.055245e+092023-08-152023-06-303.122131e+090.0152612023-091
272194300183.XSHE2023-092023-09-2878.7025.132399e+092023-08-152023-06-303.122131e+09-0.0496812023-101
272195300183.XSHE2023-102023-10-3174.7924.877456e+092023-10-272023-09-303.141132e+090.0756632023-111
272196300183.XSHE2023-112023-11-3080.4515.246452e+092023-10-272023-09-303.141132e+09-0.0351772023-121
272197300183.XSHE2023-122023-12-2977.6215.061954e+092023-10-272023-09-303.141132e+09-0.2723622024-011
272198300183.XSHE2024-012024-01-3156.4803.683251e+092023-10-272023-09-303.141132e+090.1193172024-021
272199300183.XSHE2024-022024-02-2963.2194.122692e+092023-10-272023-09-303.141132e+090.0097602024-031
272200300183.XSHE2024-032024-03-2963.8364.162946e+092023-10-272023-09-303.141132e+09NaNNaT9223372036854775158
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158 rows × 11 columns

\n", "
" ], "text/plain": [ " secID ym tradeDate closePrice negMarketValue \\\n", "272043 300183.XSHE 2011-02 2011-02-28 44.810 8.962000e+08 \n", "272044 300183.XSHE 2011-03 2011-03-31 43.920 8.784000e+08 \n", "272045 300183.XSHE 2011-04 2011-04-29 47.650 9.530000e+08 \n", "272046 300183.XSHE 2011-05 2011-05-31 41.290 1.032250e+09 \n", "272047 300183.XSHE 2011-06 2011-06-30 43.400 1.085000e+09 \n", "272048 300183.XSHE 2011-07 2011-07-29 51.510 1.287750e+09 \n", "272049 300183.XSHE 2011-08 2011-08-31 50.930 1.273250e+09 \n", "272050 300183.XSHE 2011-09 2011-09-30 47.260 1.181500e+09 \n", "272051 300183.XSHE 2011-10 2011-10-31 51.650 1.291250e+09 \n", "272052 300183.XSHE 2011-11 2011-11-30 58.000 1.450000e+09 \n", "... ... ... ... ... ... \n", "272191 300183.XSHE 2023-06 2023-06-30 84.926 5.538294e+09 \n", "272192 300183.XSHE 2023-07 2023-07-31 79.267 5.169298e+09 \n", "272193 300183.XSHE 2023-08 2023-08-31 77.519 5.055245e+09 \n", "272194 300183.XSHE 2023-09 2023-09-28 78.702 5.132399e+09 \n", "272195 300183.XSHE 2023-10 2023-10-31 74.792 4.877456e+09 \n", "272196 300183.XSHE 2023-11 2023-11-30 80.451 5.246452e+09 \n", "272197 300183.XSHE 2023-12 2023-12-29 77.621 5.061954e+09 \n", "272198 300183.XSHE 2024-01 2024-01-31 56.480 3.683251e+09 \n", "272199 300183.XSHE 2024-02 2024-02-29 63.219 4.122692e+09 \n", "272200 300183.XSHE 2024-03 2024-03-29 63.836 4.162946e+09 \n", "\n", " publishDate endDate book ret ret_date \\\n", "272043 2011-02-09 2010-06-30 1.207288e+08 -0.019862 2011-03 \n", "272044 2011-03-31 2010-12-31 1.874104e+08 0.084927 2011-04 \n", "272045 2011-04-18 2011-03-31 1.197714e+09 -0.133473 2011-05 \n", "272046 2011-04-18 2011-03-31 1.197714e+09 0.051102 2011-06 \n", "272047 2011-04-18 2011-03-31 1.197714e+09 0.186866 2011-07 \n", "272048 2011-04-18 2011-03-31 1.197714e+09 -0.011260 2011-08 \n", "272049 2011-08-24 2011-06-30 1.234990e+09 -0.072060 2011-09 \n", "272050 2011-08-24 2011-06-30 1.234990e+09 0.092890 2011-10 \n", "272051 2011-10-18 2011-09-30 1.282289e+09 0.122943 2011-11 \n", "272052 2011-10-18 2011-09-30 1.282289e+09 0.176897 2011-12 \n", "... ... ... ... ... ... \n", "272191 2023-04-24 2023-03-31 3.158659e+09 -0.066634 2023-07 \n", "272192 2023-04-24 2023-03-31 3.158659e+09 -0.022052 2023-08 \n", "272193 2023-08-15 2023-06-30 3.122131e+09 0.015261 2023-09 \n", "272194 2023-08-15 2023-06-30 3.122131e+09 -0.049681 2023-10 \n", "272195 2023-10-27 2023-09-30 3.141132e+09 0.075663 2023-11 \n", "272196 2023-10-27 2023-09-30 3.141132e+09 -0.035177 2023-12 \n", "272197 2023-10-27 2023-09-30 3.141132e+09 -0.272362 2024-01 \n", "272198 2023-10-27 2023-09-30 3.141132e+09 0.119317 2024-02 \n", "272199 2023-10-27 2023-09-30 3.141132e+09 0.009760 2024-03 \n", "272200 2023-10-27 2023-09-30 3.141132e+09 NaN NaT \n", "\n", " ym_diff \n", "272043 1 \n", "272044 1 \n", "272045 1 \n", "272046 1 \n", "272047 1 \n", "272048 1 \n", "272049 1 \n", "272050 1 \n", "272051 1 \n", "272052 1 \n", "... ... \n", "272191 1 \n", "272192 1 \n", "272193 1 \n", "272194 1 \n", "272195 1 \n", "272196 1 \n", "272197 1 \n", "272198 1 \n", "272199 1 \n", "272200 9223372036854775158 \n", "\n", "[158 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": 385, "metadata": { "editable": true }, "outputs": [], "source": [ "del temp" ] }, { "cell_type": "code", "execution_count": 386, "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
.....................
613068900957.XSHG2023-059.365600e+075.747912e+08-0.0331382023-06
613069900957.XSHG2023-069.052800e+075.747912e+080.0403232023-07
613070900957.XSHG2023-079.420800e+075.747912e+08-0.1608532023-08
613071900957.XSHG2023-087.912000e+075.853687e+08-0.0669752023-09
613072900957.XSHG2023-097.378400e+075.853687e+08-0.0049502023-10
613073900957.XSHG2023-107.341600e+075.975140e+080.0373132023-11
613074900957.XSHG2023-117.617600e+075.975140e+08-0.0023982023-12
613075900957.XSHG2023-127.599200e+075.975140e+08-0.0024042024-01
613076900957.XSHG2024-017.580800e+075.975140e+080.0819282024-02
613077900957.XSHG2024-028.188000e+075.975140e+08-0.0623612024-03
\n", "

602985 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", "613068 900957.XSHG 2023-05 9.365600e+07 5.747912e+08 -0.033138 \n", "613069 900957.XSHG 2023-06 9.052800e+07 5.747912e+08 0.040323 \n", "613070 900957.XSHG 2023-07 9.420800e+07 5.747912e+08 -0.160853 \n", "613071 900957.XSHG 2023-08 7.912000e+07 5.853687e+08 -0.066975 \n", "613072 900957.XSHG 2023-09 7.378400e+07 5.853687e+08 -0.004950 \n", "613073 900957.XSHG 2023-10 7.341600e+07 5.975140e+08 0.037313 \n", "613074 900957.XSHG 2023-11 7.617600e+07 5.975140e+08 -0.002398 \n", "613075 900957.XSHG 2023-12 7.599200e+07 5.975140e+08 -0.002404 \n", "613076 900957.XSHG 2024-01 7.580800e+07 5.975140e+08 0.081928 \n", "613077 900957.XSHG 2024-02 8.188000e+07 5.975140e+08 -0.062361 \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", "613068 2023-06 \n", "613069 2023-07 \n", "613070 2023-08 \n", "613071 2023-09 \n", "613072 2023-10 \n", "613073 2023-11 \n", "613074 2023-12 \n", "613075 2024-01 \n", "613076 2024-02 \n", "613077 2024-03 \n", "\n", "[602985 rows x 6 columns]" ] }, "execution_count": 386, "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": 387, "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": 388, "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
...........................
583787689009.XSHG2023-0523.5400121.672236e+100.2986290.85952023-060.125807
583788689009.XSHG2023-0623.6601051.885614e+100.2648360.88332023-07-0.057938
583789689009.XSHG2023-0723.6022921.779693e+100.2805980.87022023-08-0.041098
583790689009.XSHG2023-0823.5621041.709590e+100.3059610.82342023-090.040695
583791689009.XSHG2023-0923.6053861.785208e+100.2930010.91522023-10-0.060515
583792689009.XSHG2023-1023.5661251.716478e+100.3082560.92472023-110.007410
583793689009.XSHG2023-1123.5755351.732706e+100.3053690.95412023-12-0.105995
583794689009.XSHG2023-1223.4658011.552630e+100.3407861.04482024-01-0.215150
583795689009.XSHG2024-0123.2261701.221793e+100.4330641.23142024-020.296133
583796689009.XSHG2024-0223.4871491.586132e+100.3335881.49052024-03-0.013619
\n", "

583797 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", "583787 689009.XSHG 2023-05 23.540012 1.672236e+10 0.298629 0.8595 \n", "583788 689009.XSHG 2023-06 23.660105 1.885614e+10 0.264836 0.8833 \n", "583789 689009.XSHG 2023-07 23.602292 1.779693e+10 0.280598 0.8702 \n", "583790 689009.XSHG 2023-08 23.562104 1.709590e+10 0.305961 0.8234 \n", "583791 689009.XSHG 2023-09 23.605386 1.785208e+10 0.293001 0.9152 \n", "583792 689009.XSHG 2023-10 23.566125 1.716478e+10 0.308256 0.9247 \n", "583793 689009.XSHG 2023-11 23.575535 1.732706e+10 0.305369 0.9541 \n", "583794 689009.XSHG 2023-12 23.465801 1.552630e+10 0.340786 1.0448 \n", "583795 689009.XSHG 2024-01 23.226170 1.221793e+10 0.433064 1.2314 \n", "583796 689009.XSHG 2024-02 23.487149 1.586132e+10 0.333588 1.4905 \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", "583787 2023-06 0.125807 \n", "583788 2023-07 -0.057938 \n", "583789 2023-08 -0.041098 \n", "583790 2023-09 0.040695 \n", "583791 2023-10 -0.060515 \n", "583792 2023-11 0.007410 \n", "583793 2023-12 -0.105995 \n", "583794 2024-01 -0.215150 \n", "583795 2024-02 0.296133 \n", "583796 2024-03 -0.013619 \n", "\n", "[583797 rows x 8 columns]" ] }, "execution_count": 388, "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": 389, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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p1p2p3p4p5p6p7p8p9p10p10-p1
mean0.0008100.0038940.0064860.0070950.0089850.0100790.0111180.0119370.0127880.0143240.013514
t-value0.1268740.6119100.9955401.0762161.3624701.5181071.6655621.7738981.8139591.9328713.656579
\n", "
" ], "text/plain": [ " p1 p2 p3 p4 p5 p6 p7 \\\n", "mean 0.000810 0.003894 0.006486 0.007095 0.008985 0.010079 0.011118 \n", "t-value 0.126874 0.611910 0.995540 1.076216 1.362470 1.518107 1.665562 \n", "\n", " p8 p9 p10 p10-p1 \n", "mean 0.011937 0.012788 0.014324 0.013514 \n", "t-value 1.773898 1.813959 1.932871 3.656579 " ] }, "execution_count": 389, "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": 165, "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": 390, "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", "# %%time\n", "# pb = {}\n", "# begin_ = 2007\n", "# end_ = 2010\n", "# while begin_ <= 2024:\n", "# if begin_ == 2024:\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')\n", "\n", "# # About 5mins" ] }, { "cell_type": "code", "execution_count": 391, "metadata": { "editable": true }, "outputs": [], "source": [ "pb_df = pd.read_pickle('./data/pb_df.pkl')" ] }, { "cell_type": "code", "execution_count": 392, "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": 393, "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
..............................
583787689009.XSHG2023-0523.5400121.672236e+100.2986290.85952023-060.1258070.213288
583788689009.XSHG2023-0623.6601051.885614e+100.2648360.88332023-07-0.0579380.189150
583789689009.XSHG2023-0723.6022921.779693e+100.2805980.87022023-08-0.0410980.200409
583790689009.XSHG2023-0823.5621041.709590e+100.3059610.82342023-090.0406950.218522
583791689009.XSHG2023-0923.6053861.785208e+100.2930010.91522023-10-0.0605150.209367
583792689009.XSHG2023-1023.5661251.716478e+100.3082560.92472023-110.0074100.223899
583793689009.XSHG2023-1123.5755351.732706e+100.3053690.95412023-12-0.1059950.221803
583794689009.XSHG2023-1223.4658011.552630e+100.3407861.04482024-01-0.2151500.247525
583795689009.XSHG2024-0123.2261701.221793e+100.4330641.23142024-020.2961330.313607
583796689009.XSHG2024-0223.4871491.586132e+100.3335881.49052024-03-0.0136190.241569
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583797 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", "583787 689009.XSHG 2023-05 23.540012 1.672236e+10 0.298629 0.8595 \n", "583788 689009.XSHG 2023-06 23.660105 1.885614e+10 0.264836 0.8833 \n", "583789 689009.XSHG 2023-07 23.602292 1.779693e+10 0.280598 0.8702 \n", "583790 689009.XSHG 2023-08 23.562104 1.709590e+10 0.305961 0.8234 \n", "583791 689009.XSHG 2023-09 23.605386 1.785208e+10 0.293001 0.9152 \n", "583792 689009.XSHG 2023-10 23.566125 1.716478e+10 0.308256 0.9247 \n", "583793 689009.XSHG 2023-11 23.575535 1.732706e+10 0.305369 0.9541 \n", "583794 689009.XSHG 2023-12 23.465801 1.552630e+10 0.340786 1.0448 \n", "583795 689009.XSHG 2024-01 23.226170 1.221793e+10 0.433064 1.2314 \n", "583796 689009.XSHG 2024-02 23.487149 1.586132e+10 0.333588 1.4905 \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", "583787 2023-06 0.125807 0.213288 \n", "583788 2023-07 -0.057938 0.189150 \n", "583789 2023-08 -0.041098 0.200409 \n", "583790 2023-09 0.040695 0.218522 \n", "583791 2023-10 -0.060515 0.209367 \n", "583792 2023-11 0.007410 0.223899 \n", "583793 2023-12 -0.105995 0.221803 \n", "583794 2024-01 -0.215150 0.247525 \n", "583795 2024-02 0.296133 0.313607 \n", "583796 2024-03 -0.013619 0.241569 \n", "\n", "[583797 rows x 9 columns]" ] }, "execution_count": 393, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ret_df" ] }, { "cell_type": "code", "execution_count": 394, "metadata": { "editable": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 394, "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": [ "ret_df.loc[ret_df['secID']=='000001.XSHE',['grouping_date','bm','bm_uqer']].set_index('grouping_date').plot()" ] }, { "cell_type": "code", "execution_count": 400, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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grouping_datebmbm_uqer
3055282016-120.6369560.159238
3055292017-010.4783120.119579
3055302017-020.4438680.110967
3055312017-030.4510450.112761
3055322017-040.6334150.158353
3055332017-050.7358170.183955
3055342017-060.7436670.185919
3055352017-070.8871210.221779
3055362017-080.8289230.207232
3055372017-090.8032580.200815
............
3056052023-050.1250950.096100
3056062023-060.1085710.083406
3056072023-070.1286020.098794
3056082023-080.1272490.097756
3056092023-090.1109020.085196
3056102023-100.1029440.079083
3056112023-110.1083830.083385
3056122023-120.0912510.070205
3056132024-010.1151340.088579
3056142024-020.1101120.084716
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87 rows × 3 columns

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" ], "text/plain": [ " grouping_date bm bm_uqer\n", "305528 2016-12 0.636956 0.159238\n", "305529 2017-01 0.478312 0.119579\n", "305530 2017-02 0.443868 0.110967\n", "305531 2017-03 0.451045 0.112761\n", "305532 2017-04 0.633415 0.158353\n", "305533 2017-05 0.735817 0.183955\n", "305534 2017-06 0.743667 0.185919\n", "305535 2017-07 0.887121 0.221779\n", "305536 2017-08 0.828923 0.207232\n", "305537 2017-09 0.803258 0.200815\n", "... ... ... ...\n", "305605 2023-05 0.125095 0.096100\n", "305606 2023-06 0.108571 0.083406\n", "305607 2023-07 0.128602 0.098794\n", "305608 2023-08 0.127249 0.097756\n", "305609 2023-09 0.110902 0.085196\n", "305610 2023-10 0.102944 0.079083\n", "305611 2023-11 0.108383 0.083385\n", "305612 2023-12 0.091251 0.070205\n", "305613 2024-01 0.115134 0.088579\n", "305614 2024-02 0.110112 0.084716\n", "\n", "[87 rows x 3 columns]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 400, "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": 404, "metadata": { "editable": true }, "outputs": [ { "data": { "text/plain": [ "array([,\n", " ],\n", " dtype=object)" ] }, "execution_count": 404, "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": [ "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": 405, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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p1p2p3p4p5p6p7p8p9p10p10-p1
mean-0.0007310.0058760.0069340.0087640.0105010.0111270.0110300.0115400.011790.0106920.011424
t-value-0.1115860.8828730.9947021.3133691.5296241.6716501.6328971.7198811.773871.6238123.187656
\n", "
" ], "text/plain": [ " p1 p2 p3 p4 p5 p6 p7 \\\n", "mean -0.000731 0.005876 0.006934 0.008764 0.010501 0.011127 0.011030 \n", "t-value -0.111586 0.882873 0.994702 1.313369 1.529624 1.671650 1.632897 \n", "\n", " p8 p9 p10 p10-p1 \n", "mean 0.011540 0.01179 0.010692 0.011424 \n", "t-value 1.719881 1.77387 1.623812 3.187656 " ] }, "execution_count": 405, "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": 406, "metadata": { "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(203,)\n", "(203,)\n", "(203,)\n", "(203,)\n", "(203,)\n", "(203,)\n" ] }, { "data": { "text/html": [ "
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bm1_size1bm1_size2bm2_size1bm2_size2bm3_size1bm3_size2
ret_mean0.0090900.0006650.0144750.0039050.0185400.004349
t_values1.3212030.1044602.0822720.6065302.4240120.689819
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" ], "text/plain": [ " bm1_size1 bm1_size2 bm2_size1 bm2_size2 bm3_size1 bm3_size2\n", "ret_mean 0.009090 0.000665 0.014475 0.003905 0.018540 0.004349\n", "t_values 1.321203 0.104460 2.082272 0.606530 2.424012 0.689819" ] }, "execution_count": 406, "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": 407, "metadata": { "editable": true }, "outputs": [], "source": [ "# ret_df[(ret_df['ret_date'] >= '2008-02') & (ret_df['secID'] == '000001.XSHE')]" ] }, { "cell_type": "code", "execution_count": 408, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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interceptbeta_coefsize_coefbm_coef
ret_mean8.7034390.326045-0.3787940.044544
t_values2.5923150.959551-2.6625170.855783
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" ], "text/plain": [ " intercept beta_coef size_coef bm_coef\n", "ret_mean 8.703439 0.326045 -0.378794 0.044544\n", "t_values 2.592315 0.959551 -2.662517 0.855783" ] }, "execution_count": 408, "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": 409, "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", " 65461 600817.XSHG 2007-05 2007-06 -0.259142 20.932604 1.232862e+09 \n", " 65462 600842.XSHG 2007-05 2007-06 -0.203433 20.925434 1.224054e+09 \n", " 65463 600846.XSHG 2007-05 2007-06 -0.330308 21.306837 1.792429e+09 \n", " 65464 600848.XSHG 2007-05 2007-06 -0.029055 20.366862 7.001897e+08 \n", " 65465 600850.XSHG 2007-05 2007-06 -0.410932 20.656770 9.356667e+08 \n", " 65466 600856.XSHG 2007-05 2007-06 -0.368356 20.756396 1.033685e+09 \n", " 65467 600869.XSHG 2007-05 2007-06 -0.292351 21.137896 1.513811e+09 \n", " 65468 600870.XSHG 2007-05 2007-06 -0.292416 21.209669 1.626455e+09 \n", " 65469 600883.XSHG 2007-05 2007-06 -0.122108 20.870365 1.158468e+09 \n", " 65470 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", " 65461 0.250712 \n", " 65462 0.208030 \n", " 65463 0.273707 \n", " 65464 0.204301 \n", " 65465 0.231505 \n", " 65466 0.173433 \n", " 65467 0.097180 \n", " 65468 0.202155 \n", " 65469 0.258326 \n", " 65470 0.184045 \n", " \n", " [65471 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", " 109749 600879.XSHG 2007-05 2007-06 0.026076 22.975368 9.507705e+09 \n", " 109750 600880.XSHG 2007-05 2007-06 -0.123901 21.893247 3.221933e+09 \n", " 109751 600881.XSHG 2007-05 2007-06 0.002865 23.759889 2.083477e+10 \n", " 109752 600887.XSHG 2007-05 2007-06 -0.117072 23.471224 1.561072e+10 \n", " 109753 600888.XSHG 2007-05 2007-06 0.077841 21.528878 2.238067e+09 \n", " 109754 600895.XSHG 2007-05 2007-06 -0.118853 23.162926 1.146914e+10 \n", " 109755 600896.XSHG 2007-05 2007-06 -0.019787 22.244590 4.578283e+09 \n", " 109756 600962.XSHG 2007-05 2007-06 -0.162432 21.543806 2.271728e+09 \n", " 109757 600970.XSHG 2007-05 2007-06 -0.068962 22.173919 4.265900e+09 \n", " 109758 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", " 109749 0.142725 \n", " 109750 0.142852 \n", " 109751 0.127255 \n", " 109752 0.176896 \n", " 109753 0.237113 \n", " 109754 0.235866 \n", " 109755 0.248476 \n", " 109756 0.224796 \n", " 109757 0.199396 \n", " 109758 0.251969 \n", " \n", " [109759 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", " 120806 600967.XSHG 2007-05 2007-06 -0.271018 20.856611 1.142644e+09 \n", " 120807 600976.XSHG 2007-05 2007-06 -0.307428 21.175786 1.572269e+09 \n", " 120808 600979.XSHG 2007-05 2007-06 -0.303603 20.689486 9.667840e+08 \n", " 120809 600980.XSHG 2007-05 2007-06 -0.366915 20.430975 7.465511e+08 \n", " 120810 600982.XSHG 2007-05 2007-06 0.004987 20.497738 7.980946e+08 \n", " 120811 600983.XSHG 2007-05 2007-06 -0.318684 20.749450 1.026530e+09 \n", " 120812 600985.XSHG 2007-05 2007-06 -0.335514 20.341439 6.826134e+08 \n", " 120813 600993.XSHG 2007-05 2007-06 -0.124224 21.216830 1.638144e+09 \n", " 120814 600995.XSHG 2007-05 2007-06 -0.245821 21.113707 1.477632e+09 \n", " 120815 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", " 120806 0.426070 \n", " 120807 0.453182 \n", " 120808 0.452036 \n", " 120809 0.487037 \n", " 120810 0.464610 \n", " 120811 0.529070 \n", " 120812 0.426462 \n", " 120813 0.393184 \n", " 120814 0.401229 \n", " 120815 0.426846 \n", " \n", " [120816 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", " 112677 600867.XSHG 2007-05 2007-06 -0.354046 22.170913 4.253096e+09 \n", " 112678 600875.XSHG 2007-05 2007-06 0.219101 22.364401 5.161023e+09 \n", " 112679 600884.XSHG 2007-05 2007-06 0.111874 22.126345 4.067705e+09 \n", " 112680 600886.XSHG 2007-05 2007-06 0.007688 22.675347 7.043329e+09 \n", " 112681 600900.XSHG 2007-05 2007-06 0.082105 24.871565 6.332622e+10 \n", " 112682 600971.XSHG 2007-05 2007-06 -0.124636 21.570288 2.332689e+09 \n", " 112683 600973.XSHG 2007-05 2007-06 0.034460 21.483241 2.138222e+09 \n", " 112684 600978.XSHG 2007-05 2007-06 0.037508 22.245342 4.581730e+09 \n", " 112685 600997.XSHG 2007-05 2007-06 -0.072709 22.484789 5.821295e+09 \n", " 112686 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", " 112677 0.300867 \n", " 112678 0.494309 \n", " 112679 0.361535 \n", " 112680 0.380774 \n", " 112681 0.455506 \n", " 112682 0.448427 \n", " 112683 0.341000 \n", " 112684 0.477266 \n", " 112685 0.473105 \n", " 112686 0.505101 \n", " \n", " [112687 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", " 105680 600960.XSHG 2007-05 2007-06 -0.236967 20.426873 7.434956e+08 \n", " 105681 600966.XSHG 2007-05 2007-06 -0.187255 21.425218 2.017687e+09 \n", " 105682 600969.XSHG 2007-05 2007-06 -0.239106 20.687282 9.646560e+08 \n", " 105683 600975.XSHG 2007-05 2007-06 0.107920 20.166645 5.731425e+08 \n", " 105684 600986.XSHG 2007-05 2007-06 0.106797 20.640347 9.204254e+08 \n", " 105685 600987.XSHG 2007-05 2007-06 -0.264676 21.128655 1.499886e+09 \n", " 105686 600990.XSHG 2007-05 2007-06 -0.219190 20.034617 5.022540e+08 \n", " 105687 600991.XSHG 2007-05 2007-06 -0.231495 21.073762 1.419772e+09 \n", " 105688 600992.XSHG 2007-05 2007-06 -0.244937 20.633606 9.142420e+08 \n", " 105689 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", " 105680 0.792550 \n", " 105681 0.718100 \n", " 105682 0.626084 \n", " 105683 0.811844 \n", " 105684 0.622055 \n", " 105685 0.699624 \n", " 105686 0.627999 \n", " 105687 1.418501 \n", " 105688 0.814164 \n", " 105689 0.629433 \n", " \n", " [105690 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", " 69521 601318.XSHG 2007-05 2007-06 0.174394 24.279770 3.504050e+10 \n", " 69522 601333.XSHG 2007-05 2007-06 -0.172301 23.538647 1.669954e+10 \n", " 69523 601398.XSHG 2007-05 2007-06 -0.085398 24.641483 5.031067e+10 \n", " 69524 601588.XSHG 2007-05 2007-06 -0.327006 23.388652 1.437350e+10 \n", " 69525 601628.XSHG 2007-05 2007-06 0.108530 24.228823 3.330000e+10 \n", " 69526 601666.XSHG 2007-05 2007-06 -0.063740 22.691706 7.159500e+09 \n", " 69527 601699.XSHG 2007-05 2007-06 0.106236 22.615594 6.634800e+09 \n", " 69528 601872.XSHG 2007-05 2007-06 0.073035 22.892914 8.755200e+09 \n", " 69529 601988.XSHG 2007-05 2007-06 -0.126460 24.119138 2.984066e+10 \n", " 69530 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", " 69521 2.508041 \n", " 69522 1.269615 \n", " 69523 9.647098 \n", " 69524 0.564303 \n", " 69525 3.816847 \n", " 69526 0.747574 \n", " 69527 0.597250 \n", " 69528 0.996823 \n", " 69529 13.359792 \n", " 69530 3.589068 \n", " \n", " [69531 rows x 7 columns]}" ] }, "execution_count": 409, "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": 410, "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": 411, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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SLBLSMBMSHBH
ret_date
2007-050.0713620.1120770.0819120.0987830.0534380.081964
2007-06-0.201392-0.054659-0.178662-0.084690-0.182697-0.075787
2007-070.2349730.1853680.2572580.1899000.2807290.192456
2007-080.0708020.1499340.1029630.1523490.1091160.181313
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
.....................
2023-060.0256660.0194940.0415830.0182620.016999-0.001084
2023-07-0.0102640.002281-0.0016900.0253730.0288850.075968
2023-08-0.033373-0.055725-0.032530-0.060224-0.033911-0.059155
2023-090.006949-0.0296660.001824-0.001621-0.0084800.007229
2023-10-0.001610-0.0281030.004487-0.022098-0.012435-0.037685
2023-110.0582390.0002340.057021-0.0011250.031522-0.010649
2023-12-0.013533-0.027493-0.012145-0.021259-0.020581-0.017663
2024-01-0.221299-0.161830-0.220379-0.108842-0.1883850.019751
2024-020.0343950.1277380.0223740.1019090.0189300.051783
2024-030.0768670.0128100.0732330.0121890.058665-0.002009
\n", "

203 rows × 6 columns

\n", "
" ], "text/plain": [ " SL BL SM BM SH BH\n", "ret_date \n", "2007-05 0.071362 0.112077 0.081912 0.098783 0.053438 0.081964\n", "2007-06 -0.201392 -0.054659 -0.178662 -0.084690 -0.182697 -0.075787\n", "2007-07 0.234973 0.185368 0.257258 0.189900 0.280729 0.192456\n", "2007-08 0.070802 0.149934 0.102963 0.152349 0.109116 0.181313\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", "2023-06 0.025666 0.019494 0.041583 0.018262 0.016999 -0.001084\n", "2023-07 -0.010264 0.002281 -0.001690 0.025373 0.028885 0.075968\n", "2023-08 -0.033373 -0.055725 -0.032530 -0.060224 -0.033911 -0.059155\n", "2023-09 0.006949 -0.029666 0.001824 -0.001621 -0.008480 0.007229\n", "2023-10 -0.001610 -0.028103 0.004487 -0.022098 -0.012435 -0.037685\n", "2023-11 0.058239 0.000234 0.057021 -0.001125 0.031522 -0.010649\n", "2023-12 -0.013533 -0.027493 -0.012145 -0.021259 -0.020581 -0.017663\n", "2024-01 -0.221299 -0.161830 -0.220379 -0.108842 -0.188385 0.019751\n", "2024-02 0.034395 0.127738 0.022374 0.101909 0.018930 0.051783\n", "2024-03 0.076867 0.012810 0.073233 0.012189 0.058665 -0.002009\n", "\n", "[203 rows x 6 columns]" ] }, "execution_count": 411, "metadata": {}, "output_type": "execute_result" } ], "source": [ "portfolios_vwret_df" ] }, { "cell_type": "code", "execution_count": 412, "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": 413, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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SMBHML
ret_date
2007-05-0.028704-0.024019
2007-06-0.115872-0.001216
2007-070.0684120.026422
2007-08-0.0669050.034846
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
.........
2023-060.015859-0.014623
2023-07-0.0288970.056418
2023-080.025097-0.001984
2023-090.0081170.010733
2023-100.026110-0.010204
2023-110.052774-0.018800
2023-120.0067190.001391
2024-01-0.1263800.107248
2024-02-0.068577-0.045710
2024-030.061925-0.016510
\n", "

203 rows × 2 columns

\n", "
" ], "text/plain": [ " SMB HML\n", "ret_date \n", "2007-05 -0.028704 -0.024019\n", "2007-06 -0.115872 -0.001216\n", "2007-07 0.068412 0.026422\n", "2007-08 -0.066905 0.034846\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", "2023-06 0.015859 -0.014623\n", "2023-07 -0.028897 0.056418\n", "2023-08 0.025097 -0.001984\n", "2023-09 0.008117 0.010733\n", "2023-10 0.026110 -0.010204\n", "2023-11 0.052774 -0.018800\n", "2023-12 0.006719 0.001391\n", "2024-01 -0.126380 0.107248\n", "2024-02 -0.068577 -0.045710\n", "2024-03 0.061925 -0.016510\n", "\n", "[203 rows x 2 columns]" ] }, "execution_count": 413, "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市值公司的整体状况。" ] }, { "cell_type": "code", "execution_count": 414, "metadata": { "editable": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 414, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# index_info = DataAPI.SecIDGet(assetClass=\"IDX\",pandas=\"1\")\n", "\n", "# 用中证800作为market return\n", "sec_id = ['000906.ZICN']\n", "index_df = DataAPI.MktIdxdGet(indexID=sec_id,beginDate=START,endDate=END,field=['indexID','secShortName','tradeDate','closeIndex','CHGPct'],pandas=\"1\")\n", "index_df['tradeDate'] = pd.to_datetime(index_df['tradeDate'])\n", "index_df['ret_date'] = index_df['tradeDate'].dt.to_period('M')\n", "\n", "index_df.sort_values('tradeDate',inplace=True)\n", "index_df = index_df.groupby('ret_date',as_index=False).last()\n", "index_df['mktret'] = index_df['closeIndex'] / index_df['closeIndex'].shift() - 1\n", "\n", "index_df = pd.merge(index_df,rf,left_on=['ret_date'],right_on=['ym'])\n", "index_df['exmktret'] = index_df['mktret'] - index_df['rf']\n", "\n", "index_df.drop(['ym','mktret','rf','indexID','secShortName','tradeDate',\n", " 'closeIndex','CHGPct'],axis=1,inplace=True)\n", "\n", "index_df.dropna(inplace=True)\n", "\n", "factors_df = pd.merge(index_df, factors_df, on='ret_date')\n", "\n", "factors_df['ret_date'] = factors_df['ret_date'].dt.to_timestamp(how='end').dt.normalize()\n", "\n", "factors_df.set_index('ret_date',inplace=True)\n", "\n", "((1 + factors_df).cumprod()*100).plot()" ] }, { "cell_type": "code", "execution_count": 415, "metadata": { "editable": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 415, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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ret_date
2007-05-310.098693-0.028704-0.024019
2007-06-30-0.074622-0.115872-0.001216
2007-07-310.1922400.0684120.026422
2007-08-310.167193-0.0669050.034846
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
............
2023-06-300.0045450.015859-0.014623
2023-07-310.035149-0.0288970.056418
2023-08-31-0.0625760.025097-0.001984
2023-09-30-0.0189040.0081170.010733
2023-10-31-0.0328350.026110-0.010204
2023-11-30-0.0170060.052774-0.018800
2023-12-31-0.0213420.0067190.001391
2024-01-31-0.084298-0.1263800.107248
2024-02-290.102804-0.068577-0.045710
2024-03-31-0.0005260.061925-0.016510
\n", "

203 rows × 3 columns

\n", "
" ], "text/plain": [ " exmktret SMB HML\n", "ret_date \n", "2007-05-31 0.098693 -0.028704 -0.024019\n", "2007-06-30 -0.074622 -0.115872 -0.001216\n", "2007-07-31 0.192240 0.068412 0.026422\n", "2007-08-31 0.167193 -0.066905 0.034846\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", "2023-06-30 0.004545 0.015859 -0.014623\n", "2023-07-31 0.035149 -0.028897 0.056418\n", "2023-08-31 -0.062576 0.025097 -0.001984\n", "2023-09-30 -0.018904 0.008117 0.010733\n", "2023-10-31 -0.032835 0.026110 -0.010204\n", "2023-11-30 -0.017006 0.052774 -0.018800\n", "2023-12-31 -0.021342 0.006719 0.001391\n", "2024-01-31 -0.084298 -0.126380 0.107248\n", "2024-02-29 0.102804 -0.068577 -0.045710\n", "2024-03-31 -0.000526 0.061925 -0.016510\n", "\n", "[203 rows x 3 columns]" ] }, "execution_count": 416, "metadata": {}, "output_type": "execute_result" } ], "source": [ "factors_df" ] }, { "cell_type": "code", "execution_count": 417, "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": 418, "metadata": { "editable": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 418, "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": 419, "metadata": { "editable": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 419, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ret_date
2007-05-310.0986930.0689040.067701
2007-06-30-0.074622-0.187584-0.129242
2007-07-310.1922400.2576530.236593
2007-08-310.1671930.0942940.145215
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
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2023-06-300.0045450.0280830.007957
2023-07-310.0351490.0056440.052426
2023-08-31-0.062576-0.033271-0.046533
2023-09-30-0.0189040.000098-0.000625
2023-10-31-0.032835-0.003186-0.025060
2023-11-30-0.0170060.0489270.010436
2023-12-31-0.021342-0.015419-0.019122
2024-01-31-0.084298-0.210021-0.084317
2024-02-290.1028040.0252330.035357
2024-03-31-0.0005260.0695880.028328
\n", "

203 rows × 3 columns

\n", "
" ], "text/plain": [ " exmktret small_only high_only\n", "ret_date \n", "2007-05-31 0.098693 0.068904 0.067701\n", "2007-06-30 -0.074622 -0.187584 -0.129242\n", "2007-07-31 0.192240 0.257653 0.236593\n", "2007-08-31 0.167193 0.094294 0.145215\n", "2007-09-30 0.047263 0.029289 0.075538\n", "2007-10-31 -0.010382 -0.112765 -0.049572\n", "2007-11-30 -0.157389 -0.038236 -0.092364\n", "2007-12-31 0.137366 0.198268 0.148977\n", "2008-01-31 -0.123254 -0.061552 -0.105984\n", "2008-02-29 0.024010 0.103117 0.059319\n", "... ... ... ...\n", "2023-06-30 0.004545 0.028083 0.007957\n", "2023-07-31 0.035149 0.005644 0.052426\n", "2023-08-31 -0.062576 -0.033271 -0.046533\n", "2023-09-30 -0.018904 0.000098 -0.000625\n", "2023-10-31 -0.032835 -0.003186 -0.025060\n", "2023-11-30 -0.017006 0.048927 0.010436\n", "2023-12-31 -0.021342 -0.015419 -0.019122\n", "2024-01-31 -0.084298 -0.210021 -0.084317\n", "2024-02-29 0.102804 0.025233 0.035357\n", "2024-03-31 -0.000526 0.069588 0.028328\n", "\n", "[203 rows x 3 columns]" ] }, "execution_count": 420, "metadata": {}, "output_type": "execute_result" } ], "source": [ "factors_long_df" ] }, { "cell_type": "code", "execution_count": 421, "metadata": { "editable": true }, "outputs": [], "source": [ "factors_long_df.to_csv('./output_data/factors/ff3_long_only.csv')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "editable": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "editable": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.5" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": {}, "version_major": 2, "version_minor": 0 } } }, "nbformat": 4, "nbformat_minor": 4 }