{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "editable": true }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import datetime as dt\n", "\n", "pd.set_option('display.max_rows', 16)\n", "\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "plt.rcParams['figure.figsize'] = (16.0, 9.0)\n", "import seaborn as sns\n", "\n", "import statsmodels.api as sm\n", "from sklearn.linear_model import LinearRegression\n", "\n", "import gc" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "plt.rcParams['figure.figsize'] = (16.0, 9.0)" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "# Data" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "editable": true }, "outputs": [], "source": [ "START = '2007-01-01'\n", "END = '2022-03-31'" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "editable": true }, "outputs": [], "source": [ "# Security Id\n", "stk_info = DataAPI.SecIDGet(assetClass=\"E\",pandas=\"1\")\n", "cond1 = (stk_info['exchangeCD'] == 'XSHE') | (stk_info['exchangeCD'] == 'XSHG')\n", "cond2 = (stk_info['listStatusCD'] == 'L') | (stk_info['listStatusCD'] == 'DE')\n", "stk_info = stk_info[cond1 & cond2].copy()\n", "stk_id = stk_info['secID']" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDtickersecShortNamecnSpellexchangeCDassetClasslistStatusCDlistDatetransCurrCDISINpartyIDdelistDate
0000001.XSHE000001平安银行PAYHXSHEEL1991-04-03CNYCNE0000000402.0NaN
1000002.XSHE000002万科AWKAXSHEEL1991-01-29CNYCNE0000000T23.0NaN
2000003.XSHE000003PT金田APTJTAXSHEEDE1991-07-03CNYCNE1000031Y54.02002-06-14
3000004.XSHE000004国华网安GHWAXSHEEL1991-01-14CNYCNE0000000Y25.0NaN
4000005.XSHE000005ST星源STXYXSHEEL1990-12-10CNYCNE0000001L76.0NaN
5000006.XSHE000006深振业ASZYAXSHEEL1992-04-27CNYCNE0000001647.0NaN
6000007.XSHE000007*ST全新*STQXXSHEEL1992-04-13CNYCNE0000000P08.0NaN
7000008.XSHE000008神州高铁SZGTXSHEEL1992-05-07CNYCNE0000001C69.0NaN
.......................................
24126900950.XSHG900950新城B股XCBGXSHGEDE1997-10-16USDCNE000000TH11429.02015-11-23
24127900951.XSHG900951退市大化TSDHXSHGEDE1997-10-21USDCNE000000TJ71430.02020-08-27
24128900952.XSHG900952锦港B股JGBGXSHGEL1998-05-19USDCNE000000W88763.0NaN
24129900953.XSHG900953凯马BKMBXSHGEL1998-06-24USDCNE000000WP81431.0NaN
24130900955.XSHG900955*ST海创B*STHCBXSHGEL1999-01-18USDCNE000000YC21063.0NaN
24131900956.XSHG900956东贝B股DBBGXSHGEDE1999-07-15USDCNE000000ZS51432.02020-11-23
24132900957.XSHG900957凌云B股LYBGXSHGEL2000-07-28USDCNE0000013W91433.0NaN
28065DY600018.XSHGDY600018上港集箱SGJXXSHGEDE2000-07-19CNYNaN618.02006-10-20
\n", "

4923 rows × 12 columns

\n", "
" ], "text/plain": [ " secID ticker secShortName cnSpell exchangeCD assetClass \\\n", "0 000001.XSHE 000001 平安银行 PAYH XSHE E \n", "1 000002.XSHE 000002 万科A WKA XSHE E \n", "2 000003.XSHE 000003 PT金田A PTJTA XSHE E \n", "3 000004.XSHE 000004 国华网安 GHWA XSHE E \n", "4 000005.XSHE 000005 ST星源 STXY XSHE E \n", "5 000006.XSHE 000006 深振业A SZYA XSHE E \n", "6 000007.XSHE 000007 *ST全新 *STQX XSHE E \n", "7 000008.XSHE 000008 神州高铁 SZGT XSHE E \n", "... ... ... ... ... ... ... \n", "24126 900950.XSHG 900950 新城B股 XCBG XSHG E \n", "24127 900951.XSHG 900951 退市大化 TSDH XSHG E \n", "24128 900952.XSHG 900952 锦港B股 JGBG XSHG E \n", "24129 900953.XSHG 900953 凯马B KMB XSHG E \n", "24130 900955.XSHG 900955 *ST海创B *STHCB XSHG E \n", "24131 900956.XSHG 900956 东贝B股 DBBG XSHG E \n", "24132 900957.XSHG 900957 凌云B股 LYBG XSHG E \n", "28065 DY600018.XSHG DY600018 上港集箱 SGJX XSHG E \n", "\n", " listStatusCD listDate transCurrCD ISIN partyID delistDate \n", "0 L 1991-04-03 CNY CNE000000040 2.0 NaN \n", "1 L 1991-01-29 CNY CNE0000000T2 3.0 NaN \n", "2 DE 1991-07-03 CNY CNE1000031Y5 4.0 2002-06-14 \n", "3 L 1991-01-14 CNY CNE0000000Y2 5.0 NaN \n", "4 L 1990-12-10 CNY CNE0000001L7 6.0 NaN \n", "5 L 1992-04-27 CNY CNE000000164 7.0 NaN \n", "6 L 1992-04-13 CNY CNE0000000P0 8.0 NaN \n", "7 L 1992-05-07 CNY CNE0000001C6 9.0 NaN \n", "... ... ... ... ... ... ... \n", "24126 DE 1997-10-16 USD CNE000000TH1 1429.0 2015-11-23 \n", "24127 DE 1997-10-21 USD CNE000000TJ7 1430.0 2020-08-27 \n", "24128 L 1998-05-19 USD CNE000000W88 763.0 NaN \n", "24129 L 1998-06-24 USD CNE000000WP8 1431.0 NaN \n", "24130 L 1999-01-18 USD CNE000000YC2 1063.0 NaN \n", "24131 DE 1999-07-15 USD CNE000000ZS5 1432.0 2020-11-23 \n", "24132 L 2000-07-28 USD CNE0000013W9 1433.0 NaN \n", "28065 DE 2000-07-19 CNY NaN 618.0 2006-10-20 \n", "\n", "[4923 rows x 12 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_info" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "## ST" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "editable": true }, "outputs": [], "source": [ "st_df = DataAPI.SecSTGet(beginDate=START,endDate=END,secID=stk_id,field=['secID','tradeDate','STflg'],pandas=\"1\")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "st_df['tradeDate'] = pd.to_datetime(st_df['tradeDate'],format=\"%Y-%m-%d\")" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "## Risk free rate" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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ymrf
02007-010.002100
12007-020.002110
22007-030.002234
32007-040.002125
42007-050.002527
52007-060.002542
62007-070.002481
72007-080.002404
.........
1752021-080.001941
1762021-090.002033
1772021-100.001998
1782021-110.001963
1792021-120.002026
1802022-010.002014
1812022-020.001921
1822022-030.001918
\n", "

183 rows × 2 columns

\n", "
" ], "text/plain": [ " ym rf\n", "0 2007-01 0.002100\n", "1 2007-02 0.002110\n", "2 2007-03 0.002234\n", "3 2007-04 0.002125\n", "4 2007-05 0.002527\n", "5 2007-06 0.002542\n", "6 2007-07 0.002481\n", "7 2007-08 0.002404\n", ".. ... ...\n", "175 2021-08 0.001941\n", "176 2021-09 0.002033\n", "177 2021-10 0.001998\n", "178 2021-11 0.001963\n", "179 2021-12 0.002026\n", "180 2022-01 0.002014\n", "181 2022-02 0.001921\n", "182 2022-03 0.001918\n", "\n", "[183 rows x 2 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "shibor_df = DataAPI.MktIborGet(secID=\"Shibor1M.IRCN\",beginDate=START,endDate=END,field=['secID','tradeDate','rate'],pandas=\"1\")\n", "shibor_df['rate'] = shibor_df['rate']*0.01/12\n", "shibor_df['tradeDate'] = pd.to_datetime(shibor_df['tradeDate'])\n", "shibor_df.drop('secID',axis=1,inplace=True)\n", "shibor_df.rename(columns={'rate':'rf'},inplace=True)\n", "shibor_df['ym'] = shibor_df['tradeDate'].dt.to_period('M')\n", "shibor_df.sort_values('tradeDate',inplace=True)\n", "shibor_df_m = shibor_df.groupby('ym',as_index=False).last()\n", "shibor_df_m.drop('tradeDate',axis=1,inplace=True)\n", "shibor_df_m" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "## Beta" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDymbeta
0000001.XSHE2007-010.7949
1000001.XSHE2007-020.7880
2000001.XSHE2007-030.8512
3000001.XSHE2007-040.8642
4000001.XSHE2007-050.7715
5000001.XSHE2007-060.4614
6000001.XSHE2007-070.6423
7000001.XSHE2007-080.7722
............
501234689009.XSHG2021-081.0727
501235689009.XSHG2021-091.0100
501236689009.XSHG2021-100.8570
501237689009.XSHG2021-110.7546
501238689009.XSHG2021-120.5898
501239689009.XSHG2022-010.5326
501240689009.XSHG2022-020.5294
501241689009.XSHG2022-030.5710
\n", "

501242 rows × 3 columns

\n", "
" ], "text/plain": [ " secID ym beta\n", "0 000001.XSHE 2007-01 0.7949\n", "1 000001.XSHE 2007-02 0.7880\n", "2 000001.XSHE 2007-03 0.8512\n", "3 000001.XSHE 2007-04 0.8642\n", "4 000001.XSHE 2007-05 0.7715\n", "5 000001.XSHE 2007-06 0.4614\n", "6 000001.XSHE 2007-07 0.6423\n", "7 000001.XSHE 2007-08 0.7722\n", "... ... ... ...\n", "501234 689009.XSHG 2021-08 1.0727\n", "501235 689009.XSHG 2021-09 1.0100\n", "501236 689009.XSHG 2021-10 0.8570\n", "501237 689009.XSHG 2021-11 0.7546\n", "501238 689009.XSHG 2021-12 0.5898\n", "501239 689009.XSHG 2022-01 0.5326\n", "501240 689009.XSHG 2022-02 0.5294\n", "501241 689009.XSHG 2022-03 0.5710\n", "\n", "[501242 rows x 3 columns]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "beta_df = pd.read_pickle('./data/beta_df.pkl')\n", "beta_df['tradeDate'] = pd.to_datetime(beta_df['tradeDate'], format=\"%Y-%m-%d\")\n", "beta_df['ym'] = beta_df['tradeDate'].dt.to_period('M')\n", "beta_df.drop(['Beta60','Beta120'],axis=1,inplace=True)\n", "beta_df['Beta252'] = pd.to_numeric(beta_df['Beta252'])\n", "# Winsorization\n", "# up_q = 0.99999\n", "# lower_q = 0.00001\n", "# beta_df['Beta252_winsor'] = beta_df['Beta252'].clip(lower=beta_df['Beta252'].quantile(lower_q),upper=beta_df['Beta252'].quantile(up_q))\n", "# Monthly\n", "beta_df_m = beta_df.groupby(['secID','ym'],as_index=False)['Beta252'].last()\n", "beta_df_m.rename(columns={'Beta252':'beta'},inplace=True)\n", "beta_df_m" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "## Trading data" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(9934190, 8)\n", "(9535739, 8)\n" ] } ], "source": [ "stk_df = pd.read_pickle('./data/stk_df.pkl')\n", "stk_df['tradeDate'] = pd.to_datetime(stk_df['tradeDate'], format='%Y-%m-%d')\n", "stk_df['ym'] = stk_df['tradeDate'].dt.to_period('M')\n", "stk_df.sort_values(['secID','tradeDate'],inplace=True)\n", "# drop ST stocks\n", "print(stk_df.shape)\n", "stk_df = pd.merge(stk_df, st_df, on=['secID','tradeDate'],how='left')\n", "stk_df = stk_df[stk_df['STflg'].isna()].copy()\n", "stk_df.drop('STflg',axis=1,inplace=True)\n", "print(stk_df.shape)\n", "# Monthly\n", "stk_df_m = stk_df.groupby(['secID','ym'],as_index=False).last()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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secIDymtradeDatepreClosePriceclosePricenegMarketValueturnoverValueturnoverRate
0000001.XSHE2007-062007-06-29953.780870.8704.266117e+101.410758e+090.0316
1000001.XSHE2007-072007-07-311082.2591146.4985.616330e+101.479466e+090.0270
2000001.XSHE2007-082007-08-311193.0161202.5105.890714e+106.552881e+080.0112
3000001.XSHE2007-092007-09-281228.1421265.1676.197651e+101.408136e+090.0228
4000001.XSHE2007-102007-10-311427.1891520.5427.448652e+101.440425e+090.0200
5000001.XSHE2007-112007-11-301172.4471141.7515.593078e+105.452159e+080.0096
6000001.XSHE2007-122007-12-281234.1551221.4976.574629e+101.019671e+090.0154
7000001.XSHE2008-012008-01-311074.3471053.7785.850212e+105.328429e+080.0089
...........................
484445900957.XSHG2021-082021-08-310.6260.6121.116880e+083.033640e+050.0027
484446900957.XSHG2021-092021-09-300.6550.6671.218080e+082.086830e+050.0017
484447900957.XSHG2021-102021-10-290.6360.6401.168400e+086.162200e+040.0005
484448900957.XSHG2021-112021-11-300.6230.6141.120560e+081.161060e+050.0010
484449900957.XSHG2021-122021-12-310.6350.6361.161040e+081.059960e+050.0009
484450900957.XSHG2022-012022-01-280.6170.6221.135280e+081.319240e+050.0012
484451900957.XSHG2022-022022-02-280.6160.6151.122400e+089.851400e+040.0009
484452900957.XSHG2022-032022-03-140.6060.5941.083760e+081.005700e+050.0009
\n", "

484453 rows × 8 columns

\n", "
" ], "text/plain": [ " secID ym tradeDate preClosePrice closePrice \\\n", "0 000001.XSHE 2007-06 2007-06-29 953.780 870.870 \n", "1 000001.XSHE 2007-07 2007-07-31 1082.259 1146.498 \n", "2 000001.XSHE 2007-08 2007-08-31 1193.016 1202.510 \n", "3 000001.XSHE 2007-09 2007-09-28 1228.142 1265.167 \n", "4 000001.XSHE 2007-10 2007-10-31 1427.189 1520.542 \n", "5 000001.XSHE 2007-11 2007-11-30 1172.447 1141.751 \n", "6 000001.XSHE 2007-12 2007-12-28 1234.155 1221.497 \n", "7 000001.XSHE 2008-01 2008-01-31 1074.347 1053.778 \n", "... ... ... ... ... ... \n", "484445 900957.XSHG 2021-08 2021-08-31 0.626 0.612 \n", "484446 900957.XSHG 2021-09 2021-09-30 0.655 0.667 \n", "484447 900957.XSHG 2021-10 2021-10-29 0.636 0.640 \n", "484448 900957.XSHG 2021-11 2021-11-30 0.623 0.614 \n", "484449 900957.XSHG 2021-12 2021-12-31 0.635 0.636 \n", "484450 900957.XSHG 2022-01 2022-01-28 0.617 0.622 \n", "484451 900957.XSHG 2022-02 2022-02-28 0.616 0.615 \n", "484452 900957.XSHG 2022-03 2022-03-14 0.606 0.594 \n", "\n", " negMarketValue turnoverValue turnoverRate \n", "0 4.266117e+10 1.410758e+09 0.0316 \n", "1 5.616330e+10 1.479466e+09 0.0270 \n", "2 5.890714e+10 6.552881e+08 0.0112 \n", "3 6.197651e+10 1.408136e+09 0.0228 \n", "4 7.448652e+10 1.440425e+09 0.0200 \n", "5 5.593078e+10 5.452159e+08 0.0096 \n", "6 6.574629e+10 1.019671e+09 0.0154 \n", "7 5.850212e+10 5.328429e+08 0.0089 \n", "... ... ... ... \n", "484445 1.116880e+08 3.033640e+05 0.0027 \n", "484446 1.218080e+08 2.086830e+05 0.0017 \n", "484447 1.168400e+08 6.162200e+04 0.0005 \n", "484448 1.120560e+08 1.161060e+05 0.0010 \n", "484449 1.161040e+08 1.059960e+05 0.0009 \n", "484450 1.135280e+08 1.319240e+05 0.0012 \n", "484451 1.122400e+08 9.851400e+04 0.0009 \n", "484452 1.083760e+08 1.005700e+05 0.0009 \n", "\n", "[484453 rows x 8 columns]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_df_m" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "## Momentum" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Momentum 介绍：" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Momentum即动量，指的是过去一段时间表现较好的股票，在未来一段时间内也会较好。“过去”和“未来”有很多定义，比较常见的：\n", "- 过去：t月前的t-12:t-2个月的累积收益率\n", "- 未来: t月的收益\n", "\n", "也即，中间跳过了一个月，t-1。\n", "这是因为，短期发现了反转，reversal：上个月表现好的，这个月表现差" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "Momentum 的计算涉及到pandas groupby rolling。如果是自定义的函数，apply会比较慢。但新版本(pandas version >= 1)中，apply中可以指定用numba作为计算引擎，速度会快非常多。由于优矿的pandas版本很低, 没有这个选项。\n", "\n", "另外，按照月来做rolling时，pandas rolling的选项不可以用\"MonthEnd\"这样长度不固定的时间作为window size。因此，如果想做得很精确的话，需要用一些其他的办法。一种比较容易的思路是把停牌的日期(用MultiIndex)填上，对应的ret值留空。窗口长度就可以固定（通过指定observation个数，而不是月份数）。\n", "\n", "- 注意：应当先计算收益率，再填充空值。原因：\n", " - 如果先填充空值，刚恢复交易时的第一个月，ret会是NaN。若用0填充来得到1+ret==1，会有一定程度的失真。\n", " - 先计算ret，则刚恢复交易时的第一个月，ret是从刚停牌时的价格和这个月的价格相除计算得到的，较真实。\n", " \n", "例：" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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secIDtradeDatepreClosePriceclosePricenegMarketValueturnoverValueturnoverRateym
799000001.XSHE2010-06-21714.635755.0715.351129e+101.063389e+090.02022010-06
800000001.XSHE2010-06-22755.071764.5615.418384e+107.704887e+080.01432010-06
801000001.XSHE2010-06-23764.561757.1345.365750e+105.972744e+080.01112010-06
802000001.XSHE2010-06-24757.134764.5615.418384e+104.329053e+080.00802010-06
803000001.XSHE2010-06-25764.561747.2315.295571e+104.151940e+080.00782010-06
804000001.XSHE2010-06-28747.231762.9105.741829e+104.487918e+080.00792010-06
805000001.XSHE2010-06-29762.910722.4755.437499e+105.491348e+080.00992010-06
806000001.XSHE2010-09-02722.475750.5325.648664e+102.984709e+090.05262010-09
807000001.XSHE2010-09-03750.532732.7905.515133e+101.110874e+090.02012010-09
808000001.XSHE2010-09-06732.790750.9455.651769e+101.106419e+090.01962010-09
809000001.XSHE2010-09-07750.945744.3435.602083e+107.112746e+080.01272010-09
810000001.XSHE2010-09-08744.343728.2515.480974e+108.656646e+080.01582010-09
811000001.XSHE2010-09-09728.251710.5095.347443e+108.169379e+080.01512010-09
812000001.XSHE2010-09-10710.509709.6845.341232e+104.325192e+080.00812010-09
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" ], "text/plain": [ " secID tradeDate preClosePrice closePrice negMarketValue \\\n", "799 000001.XSHE 2010-06-21 714.635 755.071 5.351129e+10 \n", "800 000001.XSHE 2010-06-22 755.071 764.561 5.418384e+10 \n", "801 000001.XSHE 2010-06-23 764.561 757.134 5.365750e+10 \n", "802 000001.XSHE 2010-06-24 757.134 764.561 5.418384e+10 \n", "803 000001.XSHE 2010-06-25 764.561 747.231 5.295571e+10 \n", "804 000001.XSHE 2010-06-28 747.231 762.910 5.741829e+10 \n", "805 000001.XSHE 2010-06-29 762.910 722.475 5.437499e+10 \n", "806 000001.XSHE 2010-09-02 722.475 750.532 5.648664e+10 \n", "807 000001.XSHE 2010-09-03 750.532 732.790 5.515133e+10 \n", "808 000001.XSHE 2010-09-06 732.790 750.945 5.651769e+10 \n", "809 000001.XSHE 2010-09-07 750.945 744.343 5.602083e+10 \n", "810 000001.XSHE 2010-09-08 744.343 728.251 5.480974e+10 \n", "811 000001.XSHE 2010-09-09 728.251 710.509 5.347443e+10 \n", "812 000001.XSHE 2010-09-10 710.509 709.684 5.341232e+10 \n", "\n", " turnoverValue turnoverRate ym \n", "799 1.063389e+09 0.0202 2010-06 \n", "800 7.704887e+08 0.0143 2010-06 \n", "801 5.972744e+08 0.0111 2010-06 \n", "802 4.329053e+08 0.0080 2010-06 \n", "803 4.151940e+08 0.0078 2010-06 \n", "804 4.487918e+08 0.0079 2010-06 \n", "805 5.491348e+08 0.0099 2010-06 \n", "806 2.984709e+09 0.0526 2010-09 \n", "807 1.110874e+09 0.0201 2010-09 \n", "808 1.106419e+09 0.0196 2010-09 \n", "809 7.112746e+08 0.0127 2010-09 \n", "810 8.656646e+08 0.0158 2010-09 \n", "811 8.169379e+08 0.0151 2010-09 \n", "812 4.325192e+08 0.0081 2010-09 " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_df.loc[(stk_df['secID']=='000001.XSHE') & (stk_df['tradeDate']>='2010-06-20') & (stk_df['tradeDate']<='2010-09-10')]" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "stk_df_m['ret_mom'] = stk_df_m.groupby('secID')['closePrice'].apply(lambda x: x / x.shift() - 1) #这个ret_mom不用作后面ret的计算，后面仍保留monthly ret" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "stk_df_m.sort_values(['secID','ym'],inplace=True)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDymtradeDatepreClosePriceclosePricenegMarketValueturnoverValueturnoverRateret_mom
0000001.XSHE2007-062007-06-29953.780870.8704.266117e+101.410758e+090.0316NaN
1000001.XSHE2007-072007-07-311082.2591146.4985.616330e+101.479466e+090.02700.316497
2000001.XSHE2007-082007-08-311193.0161202.5105.890714e+106.552881e+080.01120.048855
3000001.XSHE2007-092007-09-281228.1421265.1676.197651e+101.408136e+090.02280.052105
4000001.XSHE2007-102007-10-311427.1891520.5427.448652e+101.440425e+090.02000.201851
5000001.XSHE2007-112007-11-301172.4471141.7515.593078e+105.452159e+080.0096-0.249116
6000001.XSHE2007-122007-12-281234.1551221.4976.574629e+101.019671e+090.01540.069845
7000001.XSHE2008-012008-01-311074.3471053.7785.850212e+105.328429e+080.0089-0.137306
..............................
484445900957.XSHG2021-082021-08-310.6260.6121.116880e+083.033640e+050.0027-0.058462
484446900957.XSHG2021-092021-09-300.6550.6671.218080e+082.086830e+050.00170.089869
484447900957.XSHG2021-102021-10-290.6360.6401.168400e+086.162200e+040.0005-0.040480
484448900957.XSHG2021-112021-11-300.6230.6141.120560e+081.161060e+050.0010-0.040625
484449900957.XSHG2021-122021-12-310.6350.6361.161040e+081.059960e+050.00090.035831
484450900957.XSHG2022-012022-01-280.6170.6221.135280e+081.319240e+050.0012-0.022013
484451900957.XSHG2022-022022-02-280.6160.6151.122400e+089.851400e+040.0009-0.011254
484452900957.XSHG2022-032022-03-140.6060.5941.083760e+081.005700e+050.0009-0.034146
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484453 rows × 9 columns

\n", "
" ], "text/plain": [ " secID ym tradeDate preClosePrice closePrice \\\n", "0 000001.XSHE 2007-06 2007-06-29 953.780 870.870 \n", "1 000001.XSHE 2007-07 2007-07-31 1082.259 1146.498 \n", "2 000001.XSHE 2007-08 2007-08-31 1193.016 1202.510 \n", "3 000001.XSHE 2007-09 2007-09-28 1228.142 1265.167 \n", "4 000001.XSHE 2007-10 2007-10-31 1427.189 1520.542 \n", "5 000001.XSHE 2007-11 2007-11-30 1172.447 1141.751 \n", "6 000001.XSHE 2007-12 2007-12-28 1234.155 1221.497 \n", "7 000001.XSHE 2008-01 2008-01-31 1074.347 1053.778 \n", "... ... ... ... ... ... \n", "484445 900957.XSHG 2021-08 2021-08-31 0.626 0.612 \n", "484446 900957.XSHG 2021-09 2021-09-30 0.655 0.667 \n", "484447 900957.XSHG 2021-10 2021-10-29 0.636 0.640 \n", "484448 900957.XSHG 2021-11 2021-11-30 0.623 0.614 \n", "484449 900957.XSHG 2021-12 2021-12-31 0.635 0.636 \n", "484450 900957.XSHG 2022-01 2022-01-28 0.617 0.622 \n", "484451 900957.XSHG 2022-02 2022-02-28 0.616 0.615 \n", "484452 900957.XSHG 2022-03 2022-03-14 0.606 0.594 \n", "\n", " negMarketValue turnoverValue turnoverRate ret_mom \n", "0 4.266117e+10 1.410758e+09 0.0316 NaN \n", "1 5.616330e+10 1.479466e+09 0.0270 0.316497 \n", "2 5.890714e+10 6.552881e+08 0.0112 0.048855 \n", "3 6.197651e+10 1.408136e+09 0.0228 0.052105 \n", "4 7.448652e+10 1.440425e+09 0.0200 0.201851 \n", "5 5.593078e+10 5.452159e+08 0.0096 -0.249116 \n", "6 6.574629e+10 1.019671e+09 0.0154 0.069845 \n", "7 5.850212e+10 5.328429e+08 0.0089 -0.137306 \n", "... ... ... ... ... \n", "484445 1.116880e+08 3.033640e+05 0.0027 -0.058462 \n", "484446 1.218080e+08 2.086830e+05 0.0017 0.089869 \n", "484447 1.168400e+08 6.162200e+04 0.0005 -0.040480 \n", "484448 1.120560e+08 1.161060e+05 0.0010 -0.040625 \n", "484449 1.161040e+08 1.059960e+05 0.0009 0.035831 \n", "484450 1.135280e+08 1.319240e+05 0.0012 -0.022013 \n", "484451 1.122400e+08 9.851400e+04 0.0009 -0.011254 \n", "484452 1.083760e+08 1.005700e+05 0.0009 -0.034146 \n", "\n", "[484453 rows x 9 columns]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_df_m" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "stk_df_m['1+ret_mom'] = stk_df_m['ret_mom'] + 1" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "#### Fill na months" ] }, { "cell_type": "code", "execution_count": 17, "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": 18, "metadata": { "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 38.4 s, sys: 252 ms, total: 38.6 s\n", "Wall time: 38.6 s\n" ] } ], "source": [ "%%time\n", "full_dates = np.sort(stk_df['ym'].unique())\n", "stk_df_m = stk_df_m.groupby('secID').apply(fill_missing, full_dates=full_dates)\n", "stk_df_m.reset_index(drop=True, inplace=True)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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secIDymtradeDatepreClosePriceclosePricenegMarketValueturnoverValueturnoverRateret_mom1+ret_mom
0000001.XSHE2007-062007-06-29953.780870.8704.266117e+101.410758e+090.0316NaNNaN
37000001.XSHE2010-07NaTNaNNaNNaNNaNNaNNaNNaN
38000001.XSHE2010-08NaTNaNNaNNaNNaNNaNNaNNaN
178000002.XSHE2007-012007-01-31701.344635.3604.982264e+101.400388e+090.0269NaNNaN
286000002.XSHE2016-01NaTNaNNaNNaNNaNNaNNaNNaN
287000002.XSHE2016-02NaTNaNNaNNaNNaNNaNNaNNaN
288000002.XSHE2016-03NaTNaNNaNNaNNaNNaNNaNNaN
289000002.XSHE2016-04NaTNaNNaNNaNNaNNaNNaNNaN
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507059900955.XSHG2015-07NaTNaNNaNNaNNaNNaNNaNNaN
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507229900956.XSHG2016-05NaTNaNNaNNaNNaNNaNNaNNaN
507284900957.XSHG2007-012007-01-310.4300.4197.654400e+071.380447e+060.0173NaNNaN
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27867 rows × 10 columns

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" ], "text/plain": [ " secID ym tradeDate preClosePrice closePrice \\\n", "0 000001.XSHE 2007-06 2007-06-29 953.780 870.870 \n", "37 000001.XSHE 2010-07 NaT NaN NaN \n", "38 000001.XSHE 2010-08 NaT NaN NaN \n", "178 000002.XSHE 2007-01 2007-01-31 701.344 635.360 \n", "286 000002.XSHE 2016-01 NaT NaN NaN \n", "287 000002.XSHE 2016-02 NaT NaN NaN \n", "288 000002.XSHE 2016-03 NaT NaN NaN \n", "289 000002.XSHE 2016-04 NaT NaN NaN \n", "... ... ... ... ... ... \n", "507059 900955.XSHG 2015-07 NaT NaN NaN \n", "507060 900955.XSHG 2015-08 NaT NaN NaN \n", "507061 900955.XSHG 2015-09 NaT NaN NaN \n", "507062 900955.XSHG 2015-10 NaT NaN NaN \n", "507117 900956.XSHG 2007-01 2007-01-31 0.629 0.594 \n", "507228 900956.XSHG 2016-04 NaT NaN NaN \n", "507229 900956.XSHG 2016-05 NaT NaN NaN \n", "507284 900957.XSHG 2007-01 2007-01-31 0.430 0.419 \n", "\n", " negMarketValue turnoverValue turnoverRate ret_mom 1+ret_mom \n", "0 4.266117e+10 1.410758e+09 0.0316 NaN NaN \n", "37 NaN NaN NaN NaN NaN \n", "38 NaN NaN NaN NaN NaN \n", "178 4.982264e+10 1.400388e+09 0.0269 NaN NaN \n", "286 NaN NaN NaN NaN NaN \n", "287 NaN NaN NaN NaN NaN \n", "288 NaN NaN NaN NaN NaN \n", "289 NaN NaN NaN NaN NaN \n", "... ... ... ... ... ... \n", "507059 NaN NaN NaN NaN NaN \n", "507060 NaN NaN NaN NaN NaN \n", "507061 NaN NaN NaN NaN NaN \n", "507062 NaN NaN NaN NaN NaN \n", "507117 6.796500e+07 8.597120e+05 0.0123 NaN NaN \n", "507228 NaN NaN NaN NaN NaN \n", "507229 NaN NaN NaN NaN NaN \n", "507284 7.654400e+07 1.380447e+06 0.0173 NaN NaN \n", "\n", "[27867 rows x 10 columns]" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_df_m.loc[stk_df_m['1+ret_mom'].isna()]" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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secIDymtradeDatepreClosePriceclosePricenegMarketValueturnoverValueturnoverRateret_mom1+ret_mom
36000001.XSHE2010-062010-06-29762.910722.4755.437499e+10549134850.00.00990.0000001.000000
37000001.XSHE2010-07NaTNaNNaNNaNNaNNaNNaNNaN
38000001.XSHE2010-08NaTNaNNaNNaNNaNNaNNaNNaN
39000001.XSHE2010-092010-09-29670.899669.2495.036906e+10347260768.00.0069-0.0736720.926328
40000001.XSHE2010-102010-10-29774.463759.6105.716982e+10663509380.00.01150.1350191.135019
41000001.XSHE2010-112010-11-30689.879676.2635.089697e+10464292192.00.0091-0.1097230.890277
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" ], "text/plain": [ " secID ym tradeDate preClosePrice closePrice \\\n", "36 000001.XSHE 2010-06 2010-06-29 762.910 722.475 \n", "37 000001.XSHE 2010-07 NaT NaN NaN \n", "38 000001.XSHE 2010-08 NaT NaN NaN \n", "39 000001.XSHE 2010-09 2010-09-29 670.899 669.249 \n", "40 000001.XSHE 2010-10 2010-10-29 774.463 759.610 \n", "41 000001.XSHE 2010-11 2010-11-30 689.879 676.263 \n", "\n", " negMarketValue turnoverValue turnoverRate ret_mom 1+ret_mom \n", "36 5.437499e+10 549134850.0 0.0099 0.000000 1.000000 \n", "37 NaN NaN NaN NaN NaN \n", "38 NaN NaN NaN NaN NaN \n", "39 5.036906e+10 347260768.0 0.0069 -0.073672 0.926328 \n", "40 5.716982e+10 663509380.0 0.0115 0.135019 1.135019 \n", "41 5.089697e+10 464292192.0 0.0091 -0.109723 0.890277 " ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_df_m.loc[(stk_df_m['secID']=='000001.XSHE') & (stk_df_m['ym']>='2010-06') & (stk_df_m['ym']<='2010-11')]" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "stk_df_m.loc[stk_df_m['1+ret_mom'].isna(),'1+ret_mom'] = 1 # 缺失位置填充为1，以便连乘。" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "editable": true }, "outputs": [], "source": [ "stk_df_m['mom'] = stk_df_m.groupby('secID').rolling(11,min_periods=11)['1+ret_mom'].apply(np.prod, raw=True).values - 1 \n", "stk_df_m['mom_6m'] = stk_df_m.groupby('secID').rolling(6,min_periods=6)['1+ret_mom'].apply(np.prod, raw=True).values - 1 \n", "# 当只用numpy function时可以选raw=True，只用ndarray格式的数据，运算速度快很多。" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDymtradeDatepreClosePriceclosePricenegMarketValueturnoverValueturnoverRateret_mom1+ret_mommommom_6m
0000001.XSHE2007-062007-06-29953.780870.8704.266117e+101.410758e+090.0316NaN1.000000NaNNaN
1000001.XSHE2007-072007-07-311082.2591146.4985.616330e+101.479466e+090.02700.3164971.316497NaNNaN
2000001.XSHE2007-082007-08-311193.0161202.5105.890714e+106.552881e+080.01120.0488551.048855NaNNaN
3000001.XSHE2007-092007-09-281228.1421265.1676.197651e+101.408136e+090.02280.0521051.052105NaNNaN
4000001.XSHE2007-102007-10-311427.1891520.5427.448652e+101.440425e+090.02000.2018511.201851NaNNaN
5000001.XSHE2007-112007-11-301172.4471141.7515.593078e+105.452159e+080.0096-0.2491160.750884NaN0.311046
6000001.XSHE2007-122007-12-281234.1551221.4976.574629e+101.019671e+090.01540.0698451.069845NaN0.402617
7000001.XSHE2008-012008-01-311074.3471053.7785.850212e+105.328429e+080.0089-0.1373060.862694NaN-0.080872
.......................................
507459900957.XSHG2021-082021-08-310.6260.6121.116880e+083.033640e+050.0027-0.0584620.9415380.186047-0.170732
507460900957.XSHG2021-092021-09-300.6550.6671.218080e+082.086830e+050.00170.0898691.0898690.285164-0.043042
507461900957.XSHG2021-102021-10-290.6360.6401.168400e+086.162200e+040.0005-0.0404800.9595200.2167300.019108
507462900957.XSHG2021-112021-11-300.6230.6141.120560e+081.161060e+050.0010-0.0406250.9593750.211045-0.012862
507463900957.XSHG2021-122021-12-310.6350.6361.161040e+081.059960e+050.00090.0358311.035831-0.059172-0.004695
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507465900957.XSHG2022-022022-02-280.6160.6151.122400e+089.851400e+040.0009-0.0112540.988746-0.1176470.004902
507466900957.XSHG2022-032022-03-140.6060.5941.083760e+081.005700e+050.0009-0.0341460.965854-0.054140-0.109445
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507467 rows × 12 columns

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" ], "text/plain": [ " secID ym tradeDate preClosePrice closePrice \\\n", "0 000001.XSHE 2007-06 2007-06-29 953.780 870.870 \n", "1 000001.XSHE 2007-07 2007-07-31 1082.259 1146.498 \n", "2 000001.XSHE 2007-08 2007-08-31 1193.016 1202.510 \n", "3 000001.XSHE 2007-09 2007-09-28 1228.142 1265.167 \n", "4 000001.XSHE 2007-10 2007-10-31 1427.189 1520.542 \n", "5 000001.XSHE 2007-11 2007-11-30 1172.447 1141.751 \n", "6 000001.XSHE 2007-12 2007-12-28 1234.155 1221.497 \n", "7 000001.XSHE 2008-01 2008-01-31 1074.347 1053.778 \n", "... ... ... ... ... ... \n", "507459 900957.XSHG 2021-08 2021-08-31 0.626 0.612 \n", "507460 900957.XSHG 2021-09 2021-09-30 0.655 0.667 \n", "507461 900957.XSHG 2021-10 2021-10-29 0.636 0.640 \n", "507462 900957.XSHG 2021-11 2021-11-30 0.623 0.614 \n", "507463 900957.XSHG 2021-12 2021-12-31 0.635 0.636 \n", "507464 900957.XSHG 2022-01 2022-01-28 0.617 0.622 \n", "507465 900957.XSHG 2022-02 2022-02-28 0.616 0.615 \n", "507466 900957.XSHG 2022-03 2022-03-14 0.606 0.594 \n", "\n", " negMarketValue turnoverValue turnoverRate ret_mom 1+ret_mom \\\n", "0 4.266117e+10 1.410758e+09 0.0316 NaN 1.000000 \n", "1 5.616330e+10 1.479466e+09 0.0270 0.316497 1.316497 \n", "2 5.890714e+10 6.552881e+08 0.0112 0.048855 1.048855 \n", "3 6.197651e+10 1.408136e+09 0.0228 0.052105 1.052105 \n", "4 7.448652e+10 1.440425e+09 0.0200 0.201851 1.201851 \n", "5 5.593078e+10 5.452159e+08 0.0096 -0.249116 0.750884 \n", "6 6.574629e+10 1.019671e+09 0.0154 0.069845 1.069845 \n", "7 5.850212e+10 5.328429e+08 0.0089 -0.137306 0.862694 \n", "... ... ... ... ... ... \n", "507459 1.116880e+08 3.033640e+05 0.0027 -0.058462 0.941538 \n", "507460 1.218080e+08 2.086830e+05 0.0017 0.089869 1.089869 \n", "507461 1.168400e+08 6.162200e+04 0.0005 -0.040480 0.959520 \n", "507462 1.120560e+08 1.161060e+05 0.0010 -0.040625 0.959375 \n", "507463 1.161040e+08 1.059960e+05 0.0009 0.035831 1.035831 \n", "507464 1.135280e+08 1.319240e+05 0.0012 -0.022013 0.977987 \n", "507465 1.122400e+08 9.851400e+04 0.0009 -0.011254 0.988746 \n", "507466 1.083760e+08 1.005700e+05 0.0009 -0.034146 0.965854 \n", "\n", " mom mom_6m \n", "0 NaN NaN \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", "4 NaN NaN \n", "5 NaN 0.311046 \n", "6 NaN 0.402617 \n", "7 NaN -0.080872 \n", "... ... ... \n", "507459 0.186047 -0.170732 \n", "507460 0.285164 -0.043042 \n", "507461 0.216730 0.019108 \n", "507462 0.211045 -0.012862 \n", "507463 -0.059172 -0.004695 \n", "507464 -0.157182 -0.043077 \n", "507465 -0.117647 0.004902 \n", "507466 -0.054140 -0.109445 \n", "\n", "[507467 rows x 12 columns]" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_df_m" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "stk_df_m['ret'] = stk_df_m.groupby('secID')['closePrice'].apply(lambda x: x / x.shift() - 1)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "stk_df_m['size'] = np.log(stk_df_m['negMarketValue'])\n", "stk_df_m.drop(['tradeDate','closePrice'],axis=1,inplace=True)\n", "stk_df_m = pd.merge(stk_df_m, shibor_df_m, on='ym')\n", "stk_df_m['exret'] = stk_df_m['ret'] - stk_df_m['rf']" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "editable": true }, "outputs": [], "source": [ "# 把日期对齐。\n", "# 例：\n", "# ret_date == 2020.03\n", "# size_date == 2020.02\n", "# cumret_date == 2020.01\n", "stk_df_m['exret'] = stk_df_m.groupby(['secID'])['exret'].shift(-1)\n", "stk_df_m['ret_date'] = stk_df_m.groupby('secID')['ym'].shift(-1)\n", "\n", "stk_df_m['mom'] = stk_df_m.groupby(['secID'])['mom'].shift()\n", "stk_df_m['mom_6m'] = stk_df_m.groupby(['secID'])['mom_6m'].shift()\n", "stk_df_m['mom_date'] = stk_df_m.groupby('secID')['ym'].shift()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "editable": true }, "outputs": [], "source": [ "stk_df_m.dropna(inplace=True)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "editable": true }, "outputs": [], "source": [ "stk_df_m = stk_df_m[['secID','ret_date','exret','mom_date','mom','mom_6m','ym','negMarketValue','size']]" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "editable": true }, "outputs": [], "source": [ "stk_df_m.rename(columns={'negMarketValue':'mktcap'},inplace=True)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "stk_df_m.sort_values(['secID','ym'],inplace=True)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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secIDret_dateexretmom_datemommom_6mymmktcapsize
16100000001.XSHE2008-06-0.2369612008-040.076309-0.3835592008-054.432458e+1024.514805
17658000001.XSHE2008-070.0732412008-05-0.083212-0.3007212008-064.140495e+1024.446666
19238000001.XSHE2008-08-0.0315272008-06-0.466464-0.4992232008-074.455369e+1024.519961
20825000001.XSHE2008-09-0.2608112008-07-0.452632-0.3753752008-084.326849e+1024.490690
22418000001.XSHE2008-10-0.2744682008-08-0.494747-0.3906492008-093.210865e+1024.192391
24017000001.XSHE2008-110.0721722008-09-0.688034-0.4684402008-102.330715e+1023.872026
25616000001.XSHE2008-120.0512572008-10-0.697524-0.6315562008-112.503361e+1023.943485
27216000001.XSHE2009-010.2295232008-11-0.696328-0.5354052008-122.634237e+1023.994445
..............................
464995900957.XSHG2021-08-0.0604022021-060.0941780.2603552021-071.186800e+0818.591941
469360900957.XSHG2021-090.0878372021-070.183971-0.0384622021-081.116880e+0818.531220
473762900957.XSHG2021-10-0.0424782021-080.186047-0.1707322021-091.218080e+0818.617957
478193900957.XSHG2021-11-0.0425882021-090.285164-0.0430422021-101.168400e+0818.576316
482656900957.XSHG2021-120.0338052021-100.2167300.0191082021-111.120560e+0818.534509
487164900957.XSHG2022-01-0.0240272021-110.211045-0.0128622021-121.161040e+0818.569997
491702900957.XSHG2022-02-0.0131752021-12-0.059172-0.0046952022-011.135280e+0818.547560
496253900957.XSHG2022-03-0.0360642022-01-0.157182-0.0430772022-021.122400e+0818.536150
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421413 rows × 9 columns

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" ], "text/plain": [ " secID ret_date exret mom_date mom mom_6m ym \\\n", "16100 000001.XSHE 2008-06 -0.236961 2008-04 0.076309 -0.383559 2008-05 \n", "17658 000001.XSHE 2008-07 0.073241 2008-05 -0.083212 -0.300721 2008-06 \n", "19238 000001.XSHE 2008-08 -0.031527 2008-06 -0.466464 -0.499223 2008-07 \n", "20825 000001.XSHE 2008-09 -0.260811 2008-07 -0.452632 -0.375375 2008-08 \n", "22418 000001.XSHE 2008-10 -0.274468 2008-08 -0.494747 -0.390649 2008-09 \n", "24017 000001.XSHE 2008-11 0.072172 2008-09 -0.688034 -0.468440 2008-10 \n", "25616 000001.XSHE 2008-12 0.051257 2008-10 -0.697524 -0.631556 2008-11 \n", "27216 000001.XSHE 2009-01 0.229523 2008-11 -0.696328 -0.535405 2008-12 \n", "... ... ... ... ... ... ... ... \n", "464995 900957.XSHG 2021-08 -0.060402 2021-06 0.094178 0.260355 2021-07 \n", "469360 900957.XSHG 2021-09 0.087837 2021-07 0.183971 -0.038462 2021-08 \n", "473762 900957.XSHG 2021-10 -0.042478 2021-08 0.186047 -0.170732 2021-09 \n", "478193 900957.XSHG 2021-11 -0.042588 2021-09 0.285164 -0.043042 2021-10 \n", "482656 900957.XSHG 2021-12 0.033805 2021-10 0.216730 0.019108 2021-11 \n", "487164 900957.XSHG 2022-01 -0.024027 2021-11 0.211045 -0.012862 2021-12 \n", "491702 900957.XSHG 2022-02 -0.013175 2021-12 -0.059172 -0.004695 2022-01 \n", "496253 900957.XSHG 2022-03 -0.036064 2022-01 -0.157182 -0.043077 2022-02 \n", "\n", " mktcap size \n", "16100 4.432458e+10 24.514805 \n", "17658 4.140495e+10 24.446666 \n", "19238 4.455369e+10 24.519961 \n", "20825 4.326849e+10 24.490690 \n", "22418 3.210865e+10 24.192391 \n", "24017 2.330715e+10 23.872026 \n", "25616 2.503361e+10 23.943485 \n", "27216 2.634237e+10 23.994445 \n", "... ... ... \n", "464995 1.186800e+08 18.591941 \n", "469360 1.116880e+08 18.531220 \n", "473762 1.218080e+08 18.617957 \n", "478193 1.168400e+08 18.576316 \n", "482656 1.120560e+08 18.534509 \n", "487164 1.161040e+08 18.569997 \n", "491702 1.135280e+08 18.547560 \n", "496253 1.122400e+08 18.536150 \n", "\n", "[421413 rows x 9 columns]" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_df_m" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "## BM" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDymbm
0000001.XSHE2007-010.162639
1000001.XSHE2007-020.163321
2000001.XSHE2007-030.176236
3000001.XSHE2007-040.140732
4000001.XSHE2007-050.127291
5000001.XSHE2007-060.123739
6000001.XSHE2007-070.093992
7000001.XSHE2007-080.097085
............
500888689009.XSHG2021-080.076097
500889689009.XSHG2021-090.070547
500890689009.XSHG2021-100.092734
500891689009.XSHG2021-110.097551
500892689009.XSHG2021-120.084318
500893689009.XSHG2022-010.097158
500894689009.XSHG2022-020.103561
500895689009.XSHG2022-030.108407
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494110 rows × 3 columns

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" ], "text/plain": [ " secID ym bm\n", "0 000001.XSHE 2007-01 0.162639\n", "1 000001.XSHE 2007-02 0.163321\n", "2 000001.XSHE 2007-03 0.176236\n", "3 000001.XSHE 2007-04 0.140732\n", "4 000001.XSHE 2007-05 0.127291\n", "5 000001.XSHE 2007-06 0.123739\n", "6 000001.XSHE 2007-07 0.093992\n", "7 000001.XSHE 2007-08 0.097085\n", "... ... ... ...\n", "500888 689009.XSHG 2021-08 0.076097\n", "500889 689009.XSHG 2021-09 0.070547\n", "500890 689009.XSHG 2021-10 0.092734\n", "500891 689009.XSHG 2021-11 0.097551\n", "500892 689009.XSHG 2021-12 0.084318\n", "500893 689009.XSHG 2022-01 0.097158\n", "500894 689009.XSHG 2022-02 0.103561\n", "500895 689009.XSHG 2022-03 0.108407\n", "\n", "[494110 rows x 3 columns]" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pb_df = pd.read_pickle('./data/pb_df.pkl')\n", "pb_df['tradeDate'] = pd.to_datetime(pb_df['tradeDate'])\n", "pb_df['PB'] = pd.to_numeric(pb_df['PB'])\n", "pb_df['ym'] = pb_df['tradeDate'].dt.to_period('M')\n", "pb_df.sort_values(['secID','tradeDate'],inplace=True)\n", "pb_df = pb_df.groupby(['secID','ym'],as_index=False).last()\n", "pb_df['bm'] = 1 / pb_df['PB']\n", "pb_df.drop(['tradeDate','PB'],axis=1,inplace=True)\n", "pb_df = pb_df[pb_df['bm'] >= 0]\n", "pb_df" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "## Merge " ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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secIDret_dateexretmom_datemommom_6mymmktcapsize
16100000001.XSHE2008-06-0.2369612008-040.076309-0.3835592008-054.432458e+1024.514805
17658000001.XSHE2008-070.0732412008-05-0.083212-0.3007212008-064.140495e+1024.446666
19238000001.XSHE2008-08-0.0315272008-06-0.466464-0.4992232008-074.455369e+1024.519961
20825000001.XSHE2008-09-0.2608112008-07-0.452632-0.3753752008-084.326849e+1024.490690
22418000001.XSHE2008-10-0.2744682008-08-0.494747-0.3906492008-093.210865e+1024.192391
24017000001.XSHE2008-110.0721722008-09-0.688034-0.4684402008-102.330715e+1023.872026
25616000001.XSHE2008-120.0512572008-10-0.697524-0.6315562008-112.503361e+1023.943485
27216000001.XSHE2009-010.2295232008-11-0.696328-0.5354052008-122.634237e+1023.994445
..............................
464995900957.XSHG2021-08-0.0604022021-060.0941780.2603552021-071.186800e+0818.591941
469360900957.XSHG2021-090.0878372021-070.183971-0.0384622021-081.116880e+0818.531220
473762900957.XSHG2021-10-0.0424782021-080.186047-0.1707322021-091.218080e+0818.617957
478193900957.XSHG2021-11-0.0425882021-090.285164-0.0430422021-101.168400e+0818.576316
482656900957.XSHG2021-120.0338052021-100.2167300.0191082021-111.120560e+0818.534509
487164900957.XSHG2022-01-0.0240272021-110.211045-0.0128622021-121.161040e+0818.569997
491702900957.XSHG2022-02-0.0131752021-12-0.059172-0.0046952022-011.135280e+0818.547560
496253900957.XSHG2022-03-0.0360642022-01-0.157182-0.0430772022-021.122400e+0818.536150
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421413 rows × 9 columns

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" ], "text/plain": [ " secID ret_date exret mom_date mom mom_6m ym \\\n", "16100 000001.XSHE 2008-06 -0.236961 2008-04 0.076309 -0.383559 2008-05 \n", "17658 000001.XSHE 2008-07 0.073241 2008-05 -0.083212 -0.300721 2008-06 \n", "19238 000001.XSHE 2008-08 -0.031527 2008-06 -0.466464 -0.499223 2008-07 \n", "20825 000001.XSHE 2008-09 -0.260811 2008-07 -0.452632 -0.375375 2008-08 \n", "22418 000001.XSHE 2008-10 -0.274468 2008-08 -0.494747 -0.390649 2008-09 \n", "24017 000001.XSHE 2008-11 0.072172 2008-09 -0.688034 -0.468440 2008-10 \n", "25616 000001.XSHE 2008-12 0.051257 2008-10 -0.697524 -0.631556 2008-11 \n", "27216 000001.XSHE 2009-01 0.229523 2008-11 -0.696328 -0.535405 2008-12 \n", "... ... ... ... ... ... ... ... \n", "464995 900957.XSHG 2021-08 -0.060402 2021-06 0.094178 0.260355 2021-07 \n", "469360 900957.XSHG 2021-09 0.087837 2021-07 0.183971 -0.038462 2021-08 \n", "473762 900957.XSHG 2021-10 -0.042478 2021-08 0.186047 -0.170732 2021-09 \n", "478193 900957.XSHG 2021-11 -0.042588 2021-09 0.285164 -0.043042 2021-10 \n", "482656 900957.XSHG 2021-12 0.033805 2021-10 0.216730 0.019108 2021-11 \n", "487164 900957.XSHG 2022-01 -0.024027 2021-11 0.211045 -0.012862 2021-12 \n", "491702 900957.XSHG 2022-02 -0.013175 2021-12 -0.059172 -0.004695 2022-01 \n", "496253 900957.XSHG 2022-03 -0.036064 2022-01 -0.157182 -0.043077 2022-02 \n", "\n", " mktcap size \n", "16100 4.432458e+10 24.514805 \n", "17658 4.140495e+10 24.446666 \n", "19238 4.455369e+10 24.519961 \n", "20825 4.326849e+10 24.490690 \n", "22418 3.210865e+10 24.192391 \n", "24017 2.330715e+10 23.872026 \n", "25616 2.503361e+10 23.943485 \n", "27216 2.634237e+10 23.994445 \n", "... ... ... \n", "464995 1.186800e+08 18.591941 \n", "469360 1.116880e+08 18.531220 \n", "473762 1.218080e+08 18.617957 \n", "478193 1.168400e+08 18.576316 \n", "482656 1.120560e+08 18.534509 \n", "487164 1.161040e+08 18.569997 \n", "491702 1.135280e+08 18.547560 \n", "496253 1.122400e+08 18.536150 \n", "\n", "[421413 rows x 9 columns]" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stk_df_m" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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secIDymbeta
0000001.XSHE2007-010.7949
1000001.XSHE2007-020.7880
2000001.XSHE2007-030.8512
3000001.XSHE2007-040.8642
4000001.XSHE2007-050.7715
5000001.XSHE2007-060.4614
6000001.XSHE2007-070.6423
7000001.XSHE2007-080.7722
............
501234689009.XSHG2021-081.0727
501235689009.XSHG2021-091.0100
501236689009.XSHG2021-100.8570
501237689009.XSHG2021-110.7546
501238689009.XSHG2021-120.5898
501239689009.XSHG2022-010.5326
501240689009.XSHG2022-020.5294
501241689009.XSHG2022-030.5710
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501242 rows × 3 columns

\n", "
" ], "text/plain": [ " secID ym beta\n", "0 000001.XSHE 2007-01 0.7949\n", "1 000001.XSHE 2007-02 0.7880\n", "2 000001.XSHE 2007-03 0.8512\n", "3 000001.XSHE 2007-04 0.8642\n", "4 000001.XSHE 2007-05 0.7715\n", "5 000001.XSHE 2007-06 0.4614\n", "6 000001.XSHE 2007-07 0.6423\n", "7 000001.XSHE 2007-08 0.7722\n", "... ... ... ...\n", "501234 689009.XSHG 2021-08 1.0727\n", "501235 689009.XSHG 2021-09 1.0100\n", "501236 689009.XSHG 2021-10 0.8570\n", "501237 689009.XSHG 2021-11 0.7546\n", "501238 689009.XSHG 2021-12 0.5898\n", "501239 689009.XSHG 2022-01 0.5326\n", "501240 689009.XSHG 2022-02 0.5294\n", "501241 689009.XSHG 2022-03 0.5710\n", "\n", "[501242 rows x 3 columns]" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "beta_df_m" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "editable": true }, "outputs": [], "source": [ "ret_df = pd.merge(stk_df_m, beta_df_m, on=['secID','ym'],how='left') # beta 的 na 值不管它，不是重点，保留左边的dataframe" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "editable": true }, "outputs": [], "source": [ "ret_df = pd.merge(ret_df, pb_df,on=['secID','ym'] ,how='left')" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDret_dateexretmom_datemommom_6mymmktcapsizebetabm
0000001.XSHE2008-06-0.2369612008-040.076309-0.3835592008-054.432458e+1024.5148051.06970.242665
1000001.XSHE2008-070.0732412008-05-0.083212-0.3007212008-064.140495e+1024.4466661.06720.304090
2000001.XSHE2008-08-0.0315272008-06-0.466464-0.4992232008-074.455369e+1024.5199611.09660.282598
3000001.XSHE2008-09-0.2608112008-07-0.452632-0.3753752008-084.326849e+1024.4906901.03860.351136
4000001.XSHE2008-10-0.2744682008-08-0.494747-0.3906492008-093.210865e+1024.1923911.11840.473171
5000001.XSHE2008-110.0721722008-09-0.688034-0.4684402008-102.330715e+1023.8720261.19910.706914
6000001.XSHE2008-120.0512572008-10-0.697524-0.6315562008-112.503361e+1023.9434851.21920.658155
7000001.XSHE2009-010.2295232008-11-0.696328-0.5354052008-122.634237e+1023.9944451.22060.625469
....................................
421405900957.XSHG2021-08-0.0604022021-060.0941780.2603552021-071.186800e+0818.591941NaNNaN
421406900957.XSHG2021-090.0878372021-070.183971-0.0384622021-081.116880e+0818.531220NaNNaN
421407900957.XSHG2021-10-0.0424782021-080.186047-0.1707322021-091.218080e+0818.617957NaNNaN
421408900957.XSHG2021-11-0.0425882021-090.285164-0.0430422021-101.168400e+0818.576316NaNNaN
421409900957.XSHG2021-120.0338052021-100.2167300.0191082021-111.120560e+0818.534509NaNNaN
421410900957.XSHG2022-01-0.0240272021-110.211045-0.0128622021-121.161040e+0818.569997NaNNaN
421411900957.XSHG2022-02-0.0131752021-12-0.059172-0.0046952022-011.135280e+0818.547560NaNNaN
421412900957.XSHG2022-03-0.0360642022-01-0.157182-0.0430772022-021.122400e+0818.536150NaNNaN
\n", "

421413 rows × 11 columns

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" ], "text/plain": [ " secID ret_date exret mom_date mom mom_6m ym \\\n", "0 000001.XSHE 2008-06 -0.236961 2008-04 0.076309 -0.383559 2008-05 \n", "1 000001.XSHE 2008-07 0.073241 2008-05 -0.083212 -0.300721 2008-06 \n", "2 000001.XSHE 2008-08 -0.031527 2008-06 -0.466464 -0.499223 2008-07 \n", "3 000001.XSHE 2008-09 -0.260811 2008-07 -0.452632 -0.375375 2008-08 \n", "4 000001.XSHE 2008-10 -0.274468 2008-08 -0.494747 -0.390649 2008-09 \n", "5 000001.XSHE 2008-11 0.072172 2008-09 -0.688034 -0.468440 2008-10 \n", "6 000001.XSHE 2008-12 0.051257 2008-10 -0.697524 -0.631556 2008-11 \n", "7 000001.XSHE 2009-01 0.229523 2008-11 -0.696328 -0.535405 2008-12 \n", "... ... ... ... ... ... ... ... \n", "421405 900957.XSHG 2021-08 -0.060402 2021-06 0.094178 0.260355 2021-07 \n", "421406 900957.XSHG 2021-09 0.087837 2021-07 0.183971 -0.038462 2021-08 \n", "421407 900957.XSHG 2021-10 -0.042478 2021-08 0.186047 -0.170732 2021-09 \n", "421408 900957.XSHG 2021-11 -0.042588 2021-09 0.285164 -0.043042 2021-10 \n", "421409 900957.XSHG 2021-12 0.033805 2021-10 0.216730 0.019108 2021-11 \n", "421410 900957.XSHG 2022-01 -0.024027 2021-11 0.211045 -0.012862 2021-12 \n", "421411 900957.XSHG 2022-02 -0.013175 2021-12 -0.059172 -0.004695 2022-01 \n", "421412 900957.XSHG 2022-03 -0.036064 2022-01 -0.157182 -0.043077 2022-02 \n", "\n", " mktcap size beta bm \n", "0 4.432458e+10 24.514805 1.0697 0.242665 \n", "1 4.140495e+10 24.446666 1.0672 0.304090 \n", "2 4.455369e+10 24.519961 1.0966 0.282598 \n", "3 4.326849e+10 24.490690 1.0386 0.351136 \n", "4 3.210865e+10 24.192391 1.1184 0.473171 \n", "5 2.330715e+10 23.872026 1.1991 0.706914 \n", "6 2.503361e+10 23.943485 1.2192 0.658155 \n", "7 2.634237e+10 23.994445 1.2206 0.625469 \n", "... ... ... ... ... \n", "421405 1.186800e+08 18.591941 NaN NaN \n", "421406 1.116880e+08 18.531220 NaN NaN \n", "421407 1.218080e+08 18.617957 NaN NaN \n", "421408 1.168400e+08 18.576316 NaN NaN \n", "421409 1.120560e+08 18.534509 NaN NaN \n", "421410 1.161040e+08 18.569997 NaN NaN \n", "421411 1.135280e+08 18.547560 NaN NaN \n", "421412 1.122400e+08 18.536150 NaN NaN \n", "\n", "[421413 rows x 11 columns]" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ret_df" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "# ret_df_full = ret_df.copy()\n", "# ret_df = ret_df[ret_df['ret_date']>='2015'].copy()\n", "# ret_df = ret_df_full.copy()" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "# Momentum single sort" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "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))\n", "\n", "quantile_df = pd.DataFrame()\n", "for key, value in q.items():\n", " quantile_df[key] = ret_df.groupby(['mom_date'])['mom'].quantile(value)\n", "\n", "ret_df_q = pd.merge(ret_df, quantile_df, on='mom_date')" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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secIDret_dateexretmom_datemommom_6mymmktcapsizebetabmq1q2q3q4q5q6q7q8q9
0000001.XSHE2008-06-0.2369612008-040.076309-0.3835592008-054.432458e+1024.5148051.06970.242665-0.408563-0.349779-0.297711-0.236348-0.171563-0.0954820.0177870.1678640.425902
1000002.XSHE2008-06-0.2687382008-040.255538-0.3740052008-051.161977e+1125.4785591.01580.222539-0.408563-0.349779-0.297711-0.236348-0.171563-0.0954820.0177870.1678640.425902
2000006.XSHE2008-06-0.3370222008-040.031918-0.2656392008-055.347648e+0922.3999231.16680.333045-0.408563-0.349779-0.297711-0.236348-0.171563-0.0954820.0177870.1678640.425902
3000012.XSHE2008-06-0.2069912008-040.352915-0.1189532008-058.225671e+0922.8305261.09910.188466-0.408563-0.349779-0.297711-0.236348-0.171563-0.0954820.0177870.1678640.425902
4000014.XSHE2008-06-0.1870982008-040.012457-0.2559642008-051.813492e+0921.3185200.87480.133397-0.408563-0.349779-0.297711-0.236348-0.171563-0.0954820.0177870.1678640.425902
5000016.XSHE2008-06-0.3041372008-040.1907280.4492172008-053.218501e+0921.8921820.90410.552456-0.408563-0.349779-0.297711-0.236348-0.171563-0.0954820.0177870.1678640.425902
6000019.XSHE2008-06-0.2328372008-040.296517-0.3095052008-051.011833e+0920.7350290.89150.127740-0.408563-0.349779-0.297711-0.236348-0.171563-0.0954820.0177870.1678640.425902
7000021.XSHE2008-06-0.3168202008-04-0.333955-0.3816652008-053.763882e+0922.0487171.06520.396369-0.408563-0.349779-0.297711-0.236348-0.171563-0.0954820.0177870.1678640.425902
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421405900949.XSHG2010-100.0600352010-080.135945-0.1117122010-094.008900e+0819.809198NaNNaN-0.1087750.0079640.1138600.1898550.2947920.3990630.5199690.6758070.958518
421406900950.XSHG2010-10-0.0006172010-08-0.061590-0.1333712010-092.785411e+0819.445076NaNNaN-0.1087750.0079640.1138600.1898550.2947920.3990630.5199690.6758070.958518
421407900951.XSHG2010-10-0.0368852010-080.2684090.0308882010-095.480000e+0717.819201NaNNaN-0.1087750.0079640.1138600.1898550.2947920.3990630.5199690.6758070.958518
421408900952.XSHG2010-100.0351982010-080.166467-0.0837252010-091.071702e+0818.489928NaNNaN-0.1087750.0079640.1138600.1898550.2947920.3990630.5199690.6758070.958518
421409900953.XSHG2010-10-0.0008292010-080.293796-0.0166442010-091.680000e+0818.939475NaNNaN-0.1087750.0079640.1138600.1898550.2947920.3990630.5199690.6758070.958518
421410900955.XSHG2010-100.0840262010-080.142754-0.0087992010-091.498200e+0818.824945NaNNaN-0.1087750.0079640.1138600.1898550.2947920.3990630.5199690.6758070.958518
421411900956.XSHG2010-100.0623742010-080.8057850.0282352010-091.003950e+0818.424623NaNNaN-0.1087750.0079640.1138600.1898550.2947920.3990630.5199690.6758070.958518
421412900957.XSHG2010-100.0460522010-080.7321940.1851852010-091.210720e+0818.611896NaNNaN-0.1087750.0079640.1138600.1898550.2947920.3990630.5199690.6758070.958518
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421413 rows × 20 columns

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" ], "text/plain": [ " secID ret_date exret mom_date mom mom_6m ym \\\n", "0 000001.XSHE 2008-06 -0.236961 2008-04 0.076309 -0.383559 2008-05 \n", "1 000002.XSHE 2008-06 -0.268738 2008-04 0.255538 -0.374005 2008-05 \n", "2 000006.XSHE 2008-06 -0.337022 2008-04 0.031918 -0.265639 2008-05 \n", "3 000012.XSHE 2008-06 -0.206991 2008-04 0.352915 -0.118953 2008-05 \n", "4 000014.XSHE 2008-06 -0.187098 2008-04 0.012457 -0.255964 2008-05 \n", "5 000016.XSHE 2008-06 -0.304137 2008-04 0.190728 0.449217 2008-05 \n", "6 000019.XSHE 2008-06 -0.232837 2008-04 0.296517 -0.309505 2008-05 \n", "7 000021.XSHE 2008-06 -0.316820 2008-04 -0.333955 -0.381665 2008-05 \n", "... ... ... ... ... ... ... ... \n", "421405 900949.XSHG 2010-10 0.060035 2010-08 0.135945 -0.111712 2010-09 \n", "421406 900950.XSHG 2010-10 -0.000617 2010-08 -0.061590 -0.133371 2010-09 \n", "421407 900951.XSHG 2010-10 -0.036885 2010-08 0.268409 0.030888 2010-09 \n", "421408 900952.XSHG 2010-10 0.035198 2010-08 0.166467 -0.083725 2010-09 \n", "421409 900953.XSHG 2010-10 -0.000829 2010-08 0.293796 -0.016644 2010-09 \n", "421410 900955.XSHG 2010-10 0.084026 2010-08 0.142754 -0.008799 2010-09 \n", "421411 900956.XSHG 2010-10 0.062374 2010-08 0.805785 0.028235 2010-09 \n", "421412 900957.XSHG 2010-10 0.046052 2010-08 0.732194 0.185185 2010-09 \n", "\n", " mktcap size beta bm q1 q2 \\\n", "0 4.432458e+10 24.514805 1.0697 0.242665 -0.408563 -0.349779 \n", "1 1.161977e+11 25.478559 1.0158 0.222539 -0.408563 -0.349779 \n", "2 5.347648e+09 22.399923 1.1668 0.333045 -0.408563 -0.349779 \n", "3 8.225671e+09 22.830526 1.0991 0.188466 -0.408563 -0.349779 \n", "4 1.813492e+09 21.318520 0.8748 0.133397 -0.408563 -0.349779 \n", "5 3.218501e+09 21.892182 0.9041 0.552456 -0.408563 -0.349779 \n", "6 1.011833e+09 20.735029 0.8915 0.127740 -0.408563 -0.349779 \n", "7 3.763882e+09 22.048717 1.0652 0.396369 -0.408563 -0.349779 \n", "... ... ... ... ... ... ... \n", "421405 4.008900e+08 19.809198 NaN NaN -0.108775 0.007964 \n", "421406 2.785411e+08 19.445076 NaN NaN -0.108775 0.007964 \n", "421407 5.480000e+07 17.819201 NaN NaN -0.108775 0.007964 \n", "421408 1.071702e+08 18.489928 NaN NaN -0.108775 0.007964 \n", "421409 1.680000e+08 18.939475 NaN NaN -0.108775 0.007964 \n", "421410 1.498200e+08 18.824945 NaN NaN -0.108775 0.007964 \n", "421411 1.003950e+08 18.424623 NaN NaN -0.108775 0.007964 \n", "421412 1.210720e+08 18.611896 NaN NaN -0.108775 0.007964 \n", "\n", " q3 q4 q5 q6 q7 q8 q9 \n", "0 -0.297711 -0.236348 -0.171563 -0.095482 0.017787 0.167864 0.425902 \n", "1 -0.297711 -0.236348 -0.171563 -0.095482 0.017787 0.167864 0.425902 \n", "2 -0.297711 -0.236348 -0.171563 -0.095482 0.017787 0.167864 0.425902 \n", "3 -0.297711 -0.236348 -0.171563 -0.095482 0.017787 0.167864 0.425902 \n", "4 -0.297711 -0.236348 -0.171563 -0.095482 0.017787 0.167864 0.425902 \n", "5 -0.297711 -0.236348 -0.171563 -0.095482 0.017787 0.167864 0.425902 \n", "6 -0.297711 -0.236348 -0.171563 -0.095482 0.017787 0.167864 0.425902 \n", "7 -0.297711 -0.236348 -0.171563 -0.095482 0.017787 0.167864 0.425902 \n", "... ... ... ... ... ... ... ... \n", "421405 0.113860 0.189855 0.294792 0.399063 0.519969 0.675807 0.958518 \n", "421406 0.113860 0.189855 0.294792 0.399063 0.519969 0.675807 0.958518 \n", "421407 0.113860 0.189855 0.294792 0.399063 0.519969 0.675807 0.958518 \n", "421408 0.113860 0.189855 0.294792 0.399063 0.519969 0.675807 0.958518 \n", "421409 0.113860 0.189855 0.294792 0.399063 0.519969 0.675807 0.958518 \n", "421410 0.113860 0.189855 0.294792 0.399063 0.519969 0.675807 0.958518 \n", "421411 0.113860 0.189855 0.294792 0.399063 0.519969 0.675807 0.958518 \n", "421412 0.113860 0.189855 0.294792 0.399063 0.519969 0.675807 0.958518 \n", "\n", "[421413 rows x 20 columns]" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ret_df_q" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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p1p2p3p4p5p6p7p8p9p10p10-p1
mean0.0047720.0072780.0077420.0083230.0098490.0091650.0078170.0070990.0054090.004262-0.000509
t-value0.5927400.9331271.0037441.1107971.3079991.2396491.0291160.9356580.7302280.589883-0.118755
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" ], "text/plain": [ " p1 p2 p3 p4 p5 p6 p7 \\\n", "mean 0.004772 0.007278 0.007742 0.008323 0.009849 0.009165 0.007817 \n", "t-value 0.592740 0.933127 1.003744 1.110797 1.307999 1.239649 1.029116 \n", "\n", " p8 p9 p10 p10-p1 \n", "mean 0.007099 0.005409 0.004262 -0.000509 \n", "t-value 0.935658 0.730228 0.589883 -0.118755 " ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "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['mom'] <= 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['mom']) & (ret_df_q['mom'] <= 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['mom'] >= 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": [ "# Sort on size and mom" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(171,)\n", "(171,)\n", "(171,)\n", "(171,)\n", "(171,)\n", "(171,)\n" ] }, { "data": { "text/html": [ "
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mom1_size1mom1_size2mom2_size1mom2_size2mom3_size1mom3_size2
ret_mean0.0112660.0007350.0133410.0031740.0091510.002499
t_values1.4289150.0942981.7036240.4451351.1336610.352932
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" ], "text/plain": [ " mom1_size1 mom1_size2 mom2_size1 mom2_size2 mom3_size1 \\\n", "ret_mean 0.011266 0.000735 0.013341 0.003174 0.009151 \n", "t_values 1.428915 0.094298 1.703624 0.445135 1.133661 \n", "\n", " mom3_size2 \n", "ret_mean 0.002499 \n", "t_values 0.352932 " ] }, "execution_count": 43, "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_mom = dict()\n", "keys = ['q_mom_1','q_mom_2']\n", "values = [0.3, 0.7]\n", "q_mom.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(['ym'])['size'].quantile(value)\n", "\n", "q_mom_df = pd.DataFrame()\n", "for key, value in q_mom.items():\n", " q_mom_df[key] = ret_df.groupby(['mom_date'])['mom'].quantile(value)\n", "\n", "ret_df_q = pd.merge(ret_df, q_size_df, on='ym')\n", "ret_df_q = pd.merge(ret_df_q, q_mom_df, on='mom_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','ym','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','ym','ret_date','exret','size','mktcap']]\n", "\n", "portfolios_mom = dict()\n", "portfolios_mom['mom1'] = ret_df_q.loc[ret_df_q['mom'] <= ret_df_q['q_mom_1'],\n", " ['secID','ym','ret_date','exret','mom']]\n", "portfolios_mom['mom2'] = ret_df_q.loc[(ret_df_q['mom'] >= ret_df_q['q_mom_1']) & \\\n", " (ret_df_q['mom'] <= ret_df_q['q_mom_2']),\n", " ['secID','ym','ret_date','exret','mom']]\n", "portfolios_mom['mom3'] = ret_df_q.loc[ret_df_q['mom'] >= ret_df_q['q_mom_2'],\n", " ['secID','ym','ret_date','exret','mom']]\n", "\n", "portfolios = dict()\n", "for mom_group in portfolios_mom.keys():\n", " for size_group in portfolios_size.keys():\n", " portfolios[f'{mom_group}_{size_group}'] = pd.merge(portfolios_size[size_group],\n", " portfolios_mom[mom_group][['secID','ret_date','mom']],\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mom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['mom1_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": 44, "metadata": { "editable": true }, "outputs": [], "source": [ "ret_df['exret100'] = ret_df['exret'] * 100\n", "\n", "def fm_reg(df,cols):\n", " df_ = df.dropna()\n", " if df_.shape[0] < 15:\n", " return [None]*(len(cols)+1)\n", " reg = LinearRegression(fit_intercept=True).fit(y=df_.loc[:,'exret100'], X=df_.loc[:,cols])\n", " return np.insert(reg.coef_, 0, reg.intercept_)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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interceptsize
ret_mean11.119108-0.471948
t_values2.911662-2.981927
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" ], "text/plain": [ " intercept size\n", "ret_mean 11.119108 -0.471948\n", "t_values 2.911662 -2.981927" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cols = ['size']\n", "temp = ret_df.groupby('ret_date').apply(fm_reg, cols=cols)\n", "reg_result_df = pd.DataFrame(temp.values.tolist())\n", "reg_result_df.index=temp.index\n", "reg_result_df.columns = ['intercept'] + cols\n", "reg_result_df.dropna(inplace=True)\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": 46, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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interceptbetasizebmmom
ret_mean12.151013-0.011070-0.5368651.109133-0.018742
t_values3.268864-0.028461-3.5184522.390261-0.072048
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" ], "text/plain": [ " intercept beta size bm mom\n", "ret_mean 12.151013 -0.011070 -0.536865 1.109133 -0.018742\n", "t_values 3.268864 -0.028461 -3.518452 2.390261 -0.072048" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cols = ['beta','size','bm','mom']\n", "temp = ret_df.groupby('ret_date').apply(fm_reg, cols=cols)\n", "reg_result_df = pd.DataFrame(temp.values.tolist())\n", "reg_result_df.index=temp.index\n", "reg_result_df.columns = ['intercept'] + cols\n", "reg_result_df.dropna(inplace=True)\n", "# Mean of coefs with NW adjustment\n", "mean_values = {}\n", "t_values = {}\n", "for k in reg_result_df.columns:\n", " y = reg_result_df[k]\n", " const = np.full(shape=len(y),fill_value=1)\n", " reg = sm.OLS(y, const).fit().get_robustcov_results(cov_type='HAC', maxlags=6)\n", " mean_values[k] = reg.params[0]\n", " t_values[k] = reg.tvalues[0]\n", "pd.DataFrame([mean_values.values(),t_values.values()],index=['ret_mean','t_values'],columns=mean_values.keys())" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "# Reversal" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "editable": true }, "outputs": [], "source": [ "ret_df['rev'] = ret_df.groupby('secID')['exret'].shift()" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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secIDret_dateexretmom_datemommom_6mymmktcapsizebetabmexret100rev
0000001.XSHE2008-06-0.2369612008-040.076309-0.3835592008-054.432458e+1024.5148051.06970.242665-23.696074NaN
1000001.XSHE2008-070.0732412008-05-0.083212-0.3007212008-064.140495e+1024.4466661.06720.3040907.324091-0.236961
2000001.XSHE2008-08-0.0315272008-06-0.466464-0.4992232008-074.455369e+1024.5199611.09660.282598-3.1526740.073241
3000001.XSHE2008-09-0.2608112008-07-0.452632-0.3753752008-084.326849e+1024.4906901.03860.351136-26.081082-0.031527
4000001.XSHE2008-10-0.2744682008-08-0.494747-0.3906492008-093.210865e+1024.1923911.11840.473171-27.446815-0.260811
5000001.XSHE2008-110.0721722008-09-0.688034-0.4684402008-102.330715e+1023.8720261.19910.7069147.217240-0.274468
6000001.XSHE2008-120.0512572008-10-0.697524-0.6315562008-112.503361e+1023.9434851.21920.6581555.1257340.072172
7000001.XSHE2009-010.2295232008-11-0.696328-0.5354052008-122.634237e+1023.9944451.22060.62546922.9523330.051257
..........................................
421405900957.XSHG2021-08-0.0604022021-060.0941780.2603552021-071.186800e+0818.591941NaNNaN-6.0402370.015272
421406900957.XSHG2021-090.0878372021-070.183971-0.0384622021-081.116880e+0818.531220NaNNaN8.783678-0.060402
421407900957.XSHG2021-10-0.0424782021-080.186047-0.1707322021-091.218080e+0818.617957NaNNaN-4.2478090.087837
421408900957.XSHG2021-11-0.0425882021-090.285164-0.0430422021-101.168400e+0818.576316NaNNaN-4.258833-0.042478
421409900957.XSHG2021-120.0338052021-100.2167300.0191082021-111.120560e+0818.534509NaNNaN3.380479-0.042588
421410900957.XSHG2022-01-0.0240272021-110.211045-0.0128622021-121.161040e+0818.569997NaNNaN-2.4026750.033805
421411900957.XSHG2022-02-0.0131752021-12-0.059172-0.0046952022-011.135280e+0818.547560NaNNaN-1.317485-0.024027
421412900957.XSHG2022-03-0.0360642022-01-0.157182-0.0430772022-021.122400e+0818.536150NaNNaN-3.606384-0.013175
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421413 rows × 13 columns

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" ], "text/plain": [ " secID ret_date exret mom_date mom mom_6m ym \\\n", "0 000001.XSHE 2008-06 -0.236961 2008-04 0.076309 -0.383559 2008-05 \n", "1 000001.XSHE 2008-07 0.073241 2008-05 -0.083212 -0.300721 2008-06 \n", "2 000001.XSHE 2008-08 -0.031527 2008-06 -0.466464 -0.499223 2008-07 \n", "3 000001.XSHE 2008-09 -0.260811 2008-07 -0.452632 -0.375375 2008-08 \n", "4 000001.XSHE 2008-10 -0.274468 2008-08 -0.494747 -0.390649 2008-09 \n", "5 000001.XSHE 2008-11 0.072172 2008-09 -0.688034 -0.468440 2008-10 \n", "6 000001.XSHE 2008-12 0.051257 2008-10 -0.697524 -0.631556 2008-11 \n", "7 000001.XSHE 2009-01 0.229523 2008-11 -0.696328 -0.535405 2008-12 \n", "... ... ... ... ... ... ... ... \n", "421405 900957.XSHG 2021-08 -0.060402 2021-06 0.094178 0.260355 2021-07 \n", "421406 900957.XSHG 2021-09 0.087837 2021-07 0.183971 -0.038462 2021-08 \n", "421407 900957.XSHG 2021-10 -0.042478 2021-08 0.186047 -0.170732 2021-09 \n", "421408 900957.XSHG 2021-11 -0.042588 2021-09 0.285164 -0.043042 2021-10 \n", "421409 900957.XSHG 2021-12 0.033805 2021-10 0.216730 0.019108 2021-11 \n", "421410 900957.XSHG 2022-01 -0.024027 2021-11 0.211045 -0.012862 2021-12 \n", "421411 900957.XSHG 2022-02 -0.013175 2021-12 -0.059172 -0.004695 2022-01 \n", "421412 900957.XSHG 2022-03 -0.036064 2022-01 -0.157182 -0.043077 2022-02 \n", "\n", " mktcap size beta bm exret100 rev \n", "0 4.432458e+10 24.514805 1.0697 0.242665 -23.696074 NaN \n", "1 4.140495e+10 24.446666 1.0672 0.304090 7.324091 -0.236961 \n", "2 4.455369e+10 24.519961 1.0966 0.282598 -3.152674 0.073241 \n", "3 4.326849e+10 24.490690 1.0386 0.351136 -26.081082 -0.031527 \n", "4 3.210865e+10 24.192391 1.1184 0.473171 -27.446815 -0.260811 \n", "5 2.330715e+10 23.872026 1.1991 0.706914 7.217240 -0.274468 \n", "6 2.503361e+10 23.943485 1.2192 0.658155 5.125734 0.072172 \n", "7 2.634237e+10 23.994445 1.2206 0.625469 22.952333 0.051257 \n", "... ... ... ... ... ... ... \n", "421405 1.186800e+08 18.591941 NaN NaN -6.040237 0.015272 \n", "421406 1.116880e+08 18.531220 NaN NaN 8.783678 -0.060402 \n", "421407 1.218080e+08 18.617957 NaN NaN -4.247809 0.087837 \n", "421408 1.168400e+08 18.576316 NaN NaN -4.258833 -0.042478 \n", "421409 1.120560e+08 18.534509 NaN NaN 3.380479 -0.042588 \n", "421410 1.161040e+08 18.569997 NaN NaN -2.402675 0.033805 \n", "421411 1.135280e+08 18.547560 NaN NaN -1.317485 -0.024027 \n", "421412 1.122400e+08 18.536150 NaN NaN -3.606384 -0.013175 \n", "\n", "[421413 rows x 13 columns]" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ret_df" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "# Reversal single sort" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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p1p2p3p4p5p6p7p8p9p10p10-p1
mean0.0111530.0118730.0115400.0102140.0103230.0113930.0080360.0064060.001786-0.004663-0.015816
t-value1.4705201.5799281.5554091.3338021.3571021.5258341.0583280.8832040.237767-0.613183-3.962279
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" ], "text/plain": [ " p1 p2 p3 p4 p5 p6 p7 \\\n", "mean 0.011153 0.011873 0.011540 0.010214 0.010323 0.011393 0.008036 \n", "t-value 1.470520 1.579928 1.555409 1.333802 1.357102 1.525834 1.058328 \n", "\n", " p8 p9 p10 p10-p1 \n", "mean 0.006406 0.001786 -0.004663 -0.015816 \n", "t-value 0.883204 0.237767 -0.613183 -3.962279 " ] }, "execution_count": 49, "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(['ym'])['rev'].quantile(value)\n", "\n", "ret_df_q = pd.merge(ret_df, quantile_df, on='ym')\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['rev'] <= 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['rev']) & (ret_df_q['rev'] <= 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['rev'] >= 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 Reversal" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(170,)\n", "(170,)\n", "(170,)\n", "(170,)\n", "(170,)\n", "(170,)\n" ] }, { "data": { "text/html": [ "
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rev1_size1rev1_size2rev2_size1rev2_size2rev3_size1rev3_size2
ret_mean0.016740.0067650.0146950.0046490.003711-0.001491
t_values2.119810.9125801.8637670.6299310.470534-0.205014
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" ], "text/plain": [ " rev1_size1 rev1_size2 rev2_size1 rev2_size2 rev3_size1 \\\n", "ret_mean 0.01674 0.006765 0.014695 0.004649 0.003711 \n", "t_values 2.11981 0.912580 1.863767 0.629931 0.470534 \n", "\n", " rev3_size2 \n", "ret_mean -0.001491 \n", "t_values -0.205014 " ] }, "execution_count": 50, "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_rev = dict()\n", "keys = ['q_rev_1','q_rev_2']\n", "values = [0.3, 0.7]\n", "q_rev.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(['ym'])['size'].quantile(value)\n", "\n", "q_rev_df = pd.DataFrame()\n", "for key, value in q_rev.items():\n", " q_rev_df[key] = ret_df.groupby(['ym'])['rev'].quantile(value)\n", "\n", "ret_df_q = pd.merge(ret_df, q_size_df, on='ym')\n", "ret_df_q = pd.merge(ret_df_q, q_rev_df, on='ym')\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','ym','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','ym','ret_date','exret','size','mktcap']]\n", "\n", "portfolios_rev = dict()\n", "portfolios_rev['rev1'] = ret_df_q.loc[ret_df_q['rev'] <= ret_df_q['q_rev_1'],\n", " ['secID','ym','ret_date','exret','rev']]\n", "portfolios_rev['rev2'] = ret_df_q.loc[(ret_df_q['rev'] >= ret_df_q['q_rev_1']) & \\\n", " (ret_df_q['rev'] <= ret_df_q['q_rev_2']),\n", " ['secID','ym','ret_date','exret','rev']]\n", "portfolios_rev['rev3'] = ret_df_q.loc[ret_df_q['rev'] >= ret_df_q['q_rev_2'],\n", " ['secID','ym','ret_date','exret','rev']]\n", "\n", "portfolios = dict()\n", "for rev_group in portfolios_rev.keys():\n", " for size_group in portfolios_size.keys():\n", " portfolios[f'{rev_group}_{size_group}'] = pd.merge(portfolios_size[size_group],\n", " portfolios_rev[rev_group][['secID','ret_date','rev']],\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rev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['rev1_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": [ "## FM regression on Reversal" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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interceptbetasizebmmomrev
ret_mean11.807590-0.156102-0.5126450.723984-0.142003-4.558951
t_values3.055111-0.389382-3.2782631.626673-0.459694-5.587708
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" ], "text/plain": [ " intercept beta size bm mom rev\n", "ret_mean 11.807590 -0.156102 -0.512645 0.723984 -0.142003 -4.558951\n", "t_values 3.055111 -0.389382 -3.278263 1.626673 -0.459694 -5.587708" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cols = ['beta','size','bm','mom','rev']\n", "temp = ret_df.groupby('ret_date').apply(fm_reg, cols=cols)\n", "reg_result_df = pd.DataFrame(temp.values.tolist())\n", "reg_result_df.index=temp.index\n", "reg_result_df.columns = ['intercept'] + cols\n", "reg_result_df.dropna(inplace=True)\n", "# Mean of coefs with NW adjustment\n", "mean_values = {}\n", "t_values = {}\n", "for k in reg_result_df.columns:\n", " y = reg_result_df[k]\n", " const = np.full(shape=len(y),fill_value=1)\n", " reg = sm.OLS(y, const).fit().get_robustcov_results(cov_type='HAC', maxlags=6)\n", " mean_values[k] = reg.params[0]\n", " t_values[k] = reg.tvalues[0]\n", "pd.DataFrame([mean_values.values(),t_values.values()],index=['ret_mean','t_values'],columns=mean_values.keys())" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "# Reversal factor" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "editable": true }, "outputs": [], "source": [ "portfolios_vwret = {}\n", "for pf in portfolios.keys():\n", " temp = portfolios[pf].groupby('ret_date')['mktcap'].agg({'mktcap_sum':np.sum})\n", " portfolios[pf] = pd.merge(portfolios[pf], temp, on='ret_date')\n", " portfolios[pf]['weight'] = portfolios[pf]['mktcap'] / portfolios[pf]['mktcap_sum']\n", " portfolios[pf]['weighted_exret'] = portfolios[pf]['exret'] * portfolios[pf]['weight']\n", " portfolios_vwret[pf] = portfolios[pf].groupby('ret_date')['weighted_exret'].sum()\n", "\n", "portfolios_vwret_df = pd.DataFrame(np.vstack([pf for pf in portfolios_vwret.values()])).T\n", "portfolios_vwret_df.index = portfolios_vwret['rev1_size1'].index\n", "portfolios_vwret_df.columns = portfolios_vwret.keys()\n", "portfolios_vwret_df.rename(columns={\"rev1_size1\": \"Small_LowRet\",\n", " \"rev2_size1\": \"Small_MedRet\",\n", " \"rev3_size1\": \"Small_HighRet\",\n", " \"rev1_size2\": \"Big_LowRet\",\n", " \"rev2_size2\": \"Big_MedRet\",\n", " \"rev3_size2\": \"Big_HighRet\"},\n", " inplace=True)" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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Small_LowRetBig_LowRetSmall_MedRetBig_MedRetSmall_HighRetBig_HighRet
ret_date
2008-020.0946650.0245580.1088720.0238690.1157050.030757
2008-03-0.192331-0.163018-0.178676-0.224631-0.183230-0.216635
2008-04-0.0230340.075035-0.0682480.055380-0.100413-0.018084
2008-050.001173-0.078373-0.023021-0.066636-0.041251-0.095612
2008-06-0.265260-0.233824-0.248027-0.225978-0.262296-0.217338
2008-070.1305340.0383270.1102680.0410780.064990-0.003460
2008-08-0.234575-0.176245-0.217874-0.150069-0.224829-0.195775
2008-09-0.077498-0.049067-0.078070-0.031726-0.111573-0.092973
.....................
2021-080.046775-0.0071110.0726280.0407290.0724180.074190
2021-09-0.0167320.035371-0.0018480.016471-0.041209-0.055238
2021-10-0.0138760.030062-0.0324840.001466-0.041415-0.017156
2021-110.1357800.0059680.1168500.0141780.1306540.007129
2021-120.0603850.0302050.0692630.0045810.015113-0.035031
2022-01-0.095986-0.125671-0.061284-0.071161-0.087645-0.087708
2022-020.0480100.0529660.0509130.0121230.0478770.014711
2022-03-0.062453-0.089342-0.044583-0.079938-0.052765-0.072547
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170 rows × 6 columns

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" ], "text/plain": [ " Small_LowRet Big_LowRet Small_MedRet Big_MedRet Small_HighRet \\\n", "ret_date \n", "2008-02 0.094665 0.024558 0.108872 0.023869 0.115705 \n", "2008-03 -0.192331 -0.163018 -0.178676 -0.224631 -0.183230 \n", "2008-04 -0.023034 0.075035 -0.068248 0.055380 -0.100413 \n", "2008-05 0.001173 -0.078373 -0.023021 -0.066636 -0.041251 \n", "2008-06 -0.265260 -0.233824 -0.248027 -0.225978 -0.262296 \n", "2008-07 0.130534 0.038327 0.110268 0.041078 0.064990 \n", "2008-08 -0.234575 -0.176245 -0.217874 -0.150069 -0.224829 \n", "2008-09 -0.077498 -0.049067 -0.078070 -0.031726 -0.111573 \n", "... ... ... ... ... ... \n", "2021-08 0.046775 -0.007111 0.072628 0.040729 0.072418 \n", "2021-09 -0.016732 0.035371 -0.001848 0.016471 -0.041209 \n", "2021-10 -0.013876 0.030062 -0.032484 0.001466 -0.041415 \n", "2021-11 0.135780 0.005968 0.116850 0.014178 0.130654 \n", "2021-12 0.060385 0.030205 0.069263 0.004581 0.015113 \n", "2022-01 -0.095986 -0.125671 -0.061284 -0.071161 -0.087645 \n", "2022-02 0.048010 0.052966 0.050913 0.012123 0.047877 \n", "2022-03 -0.062453 -0.089342 -0.044583 -0.079938 -0.052765 \n", "\n", " Big_HighRet \n", "ret_date \n", "2008-02 0.030757 \n", "2008-03 -0.216635 \n", "2008-04 -0.018084 \n", "2008-05 -0.095612 \n", "2008-06 -0.217338 \n", "2008-07 -0.003460 \n", "2008-08 -0.195775 \n", "2008-09 -0.092973 \n", "... ... \n", "2021-08 0.074190 \n", "2021-09 -0.055238 \n", "2021-10 -0.017156 \n", "2021-11 0.007129 \n", "2021-12 -0.035031 \n", "2022-01 -0.087708 \n", "2022-02 0.014711 \n", "2022-03 -0.072547 \n", "\n", "[170 rows x 6 columns]" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "portfolios_vwret_df" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "editable": true }, "outputs": [], "source": [ "rev_df = (portfolios_vwret_df['Small_LowRet'] + portfolios_vwret_df['Big_LowRet']) / 2 - \\\n", " (portfolios_vwret_df['Small_HighRet'] + portfolios_vwret_df['Big_HighRet']) / 2 " ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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ret_daterev
02008-02-0.013619
12008-030.022258
22008-040.085249
32008-050.029831
42008-06-0.009725
52008-070.053666
62008-080.004892
72008-090.038991
.........
1622021-08-0.053472
1632021-090.057543
1642021-100.037378
1652021-110.001982
1662021-120.055254
1672022-01-0.023152
1682022-020.019194
1692022-03-0.013241
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170 rows × 2 columns

\n", "
" ], "text/plain": [ " ret_date rev\n", "0 2008-02 -0.013619\n", "1 2008-03 0.022258\n", "2 2008-04 0.085249\n", "3 2008-05 0.029831\n", "4 2008-06 -0.009725\n", "5 2008-07 0.053666\n", "6 2008-08 0.004892\n", "7 2008-09 0.038991\n", ".. ... ...\n", "162 2021-08 -0.053472\n", "163 2021-09 0.057543\n", "164 2021-10 0.037378\n", "165 2021-11 0.001982\n", "166 2021-12 0.055254\n", "167 2022-01 -0.023152\n", "168 2022-02 0.019194\n", "169 2022-03 -0.013241\n", "\n", "[170 rows x 2 columns]" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rev_df = rev_df.reset_index()\n", "rev_df.columns = ['ret_date','rev']\n", "rev_df" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "## Combine with FF3" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "editable": true }, "outputs": [], "source": [ "factors_df = pd.read_csv('./data/factors/ff3.csv')" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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ret_dateexmktretSMBHML
02007-05-310.098693-0.031591-0.021360
12007-06-30-0.074622-0.117856-0.000611
22007-07-310.1922400.0713170.021021
32007-08-310.167193-0.0654460.030286
42007-09-300.047263-0.0209480.048817
52007-10-31-0.010382-0.1105600.005402
62007-11-30-0.1573890.1139380.017011
72007-12-310.1373660.065398-0.022715
...............
1702021-07-31-0.0647760.043955-0.032335
1712021-08-310.0141990.0109600.052207
1722021-09-300.002272-0.0299210.046108
1732021-10-310.001777-0.014233-0.053258
1742021-11-30-0.0060470.123285-0.031466
1752021-12-310.0181850.0155070.051330
1762022-01-31-0.085436-0.0037980.069385
1772022-02-280.0106330.023980-0.009554
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178 rows × 4 columns

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" ], "text/plain": [ " ret_date exmktret SMB HML\n", "0 2007-05-31 0.098693 -0.031591 -0.021360\n", "1 2007-06-30 -0.074622 -0.117856 -0.000611\n", "2 2007-07-31 0.192240 0.071317 0.021021\n", "3 2007-08-31 0.167193 -0.065446 0.030286\n", "4 2007-09-30 0.047263 -0.020948 0.048817\n", "5 2007-10-31 -0.010382 -0.110560 0.005402\n", "6 2007-11-30 -0.157389 0.113938 0.017011\n", "7 2007-12-31 0.137366 0.065398 -0.022715\n", ".. ... ... ... ...\n", "170 2021-07-31 -0.064776 0.043955 -0.032335\n", "171 2021-08-31 0.014199 0.010960 0.052207\n", "172 2021-09-30 0.002272 -0.029921 0.046108\n", "173 2021-10-31 0.001777 -0.014233 -0.053258\n", "174 2021-11-30 -0.006047 0.123285 -0.031466\n", "175 2021-12-31 0.018185 0.015507 0.051330\n", "176 2022-01-31 -0.085436 -0.003798 0.069385\n", "177 2022-02-28 0.010633 0.023980 -0.009554\n", "\n", "[178 rows x 4 columns]" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "factors_df" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "editable": true }, "outputs": [], "source": [ "factors_df['ret_date'] = pd.to_datetime(factors_df['ret_date']) \n", "\n", "factors_df['ret_date'] = factors_df['ret_date'].dt.to_period('M')\n", "\n", "factors_df = pd.merge(factors_df, rev_df, on='ret_date')\n", "\n", "factors_df['ret_date'] = factors_df['ret_date'].dt.to_timestamp(freq='day',how='end').dt.normalize()\n", "\n", "factors_df.set_index('ret_date',inplace=True)" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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exmktretSMBHMLrev
ret_date
2008-02-290.0240100.0849020.007286-0.013619
2008-03-31-0.1954800.013640-0.0220600.022258
2008-04-300.022519-0.1185340.0246710.085249
2008-05-31-0.0807980.063246-0.0011050.029831
2008-06-30-0.236014-0.029915-0.003586-0.009725
2008-07-310.0149720.0908900.0087730.053666
2008-08-31-0.170063-0.0660460.0328370.004892
2008-09-30-0.067913-0.0330790.0321290.038991
...............
2021-07-31-0.0647760.043955-0.032335-0.093907
2021-08-310.0141990.0109600.052207-0.053472
2021-09-300.002272-0.0299210.0461080.057543
2021-10-310.001777-0.014233-0.0532580.037378
2021-11-30-0.0060470.123285-0.0314660.001982
2021-12-310.0181850.0155070.0513300.055254
2022-01-31-0.085436-0.0037980.069385-0.023152
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169 rows × 4 columns

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" ], "text/plain": [ " exmktret SMB HML rev\n", "ret_date \n", "2008-02-29 0.024010 0.084902 0.007286 -0.013619\n", "2008-03-31 -0.195480 0.013640 -0.022060 0.022258\n", "2008-04-30 0.022519 -0.118534 0.024671 0.085249\n", "2008-05-31 -0.080798 0.063246 -0.001105 0.029831\n", "2008-06-30 -0.236014 -0.029915 -0.003586 -0.009725\n", "2008-07-31 0.014972 0.090890 0.008773 0.053666\n", "2008-08-31 -0.170063 -0.066046 0.032837 0.004892\n", "2008-09-30 -0.067913 -0.033079 0.032129 0.038991\n", "... ... ... ... ...\n", "2021-07-31 -0.064776 0.043955 -0.032335 -0.093907\n", "2021-08-31 0.014199 0.010960 0.052207 -0.053472\n", "2021-09-30 0.002272 -0.029921 0.046108 0.057543\n", "2021-10-31 0.001777 -0.014233 -0.053258 0.037378\n", "2021-11-30 -0.006047 0.123285 -0.031466 0.001982\n", "2021-12-31 0.018185 0.015507 0.051330 0.055254\n", "2022-01-31 -0.085436 -0.003798 0.069385 -0.023152\n", "2022-02-28 0.010633 0.023980 -0.009554 0.019194\n", "\n", "[169 rows x 4 columns]" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "factors_df" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "editable": true }, "outputs": [], "source": [ "factors_df.to_csv('./data/factors/ff3_rev.csv')" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [], "source": [ "factors_df.to_pickle('./data/factors/ff3_rev.pkl')" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "editable": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "((1 + factors_df['2018':]).cumprod()*100).plot()" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "## Long-only factor" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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ret_daterev_long
02008-020.059612
12008-03-0.177674
22008-040.026001
32008-05-0.038600
42008-06-0.249542
52008-070.084431
62008-08-0.205410
72008-09-0.063282
.........
1622021-080.019832
1632021-090.009320
1642021-100.008093
1652021-110.070874
1662021-120.045295
1672022-01-0.110829
1682022-020.050488
1692022-03-0.075897
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170 rows × 2 columns

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" ], "text/plain": [ " ret_date rev_long\n", "0 2008-02 0.059612\n", "1 2008-03 -0.177674\n", "2 2008-04 0.026001\n", "3 2008-05 -0.038600\n", "4 2008-06 -0.249542\n", "5 2008-07 0.084431\n", "6 2008-08 -0.205410\n", "7 2008-09 -0.063282\n", ".. ... ...\n", "162 2021-08 0.019832\n", "163 2021-09 0.009320\n", "164 2021-10 0.008093\n", "165 2021-11 0.070874\n", "166 2021-12 0.045295\n", "167 2022-01 -0.110829\n", "168 2022-02 0.050488\n", "169 2022-03 -0.075897\n", "\n", "[170 rows x 2 columns]" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rev_long_df = (portfolios_vwret_df['Small_LowRet'] + portfolios_vwret_df['Big_LowRet']) / 2\n", "\n", "rev_long_df = rev_long_df.reset_index()\n", "\n", "rev_long_df.columns=['ret_date','rev_long']\n", "\n", "rev_long_df" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "editable": true }, "outputs": [], "source": [ "factors_long_df = pd.read_csv('./data/factors/ff3_long_only.csv')\n", "\n", "factors_long_df['ret_date'] = pd.to_datetime(factors_long_df['ret_date']) \n", "\n", "factors_long_df['ret_date'] = factors_long_df['ret_date'].dt.to_period('M')\n", "\n", "factors_long_df = pd.merge(factors_long_df, rev_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)" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "editable": true }, "outputs": [ { "data": { "text/html": [ "
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exmktretsmall_onlyhigh_onlyrev_long
ret_date
2008-02-290.0240100.1063500.0607890.059612
2008-03-31-0.195480-0.185605-0.204875-0.177674
2008-04-300.022519-0.073984-0.0081380.026001
2008-05-31-0.080798-0.020726-0.056888-0.038600
2008-06-30-0.236014-0.258596-0.240880-0.249542
2008-07-310.0149720.1103890.0721200.084431
2008-08-31-0.170063-0.234635-0.187086-0.205410
2008-09-30-0.067913-0.088250-0.068698-0.063282
...............
2021-07-31-0.064776-0.001619-0.044277-0.064251
2021-08-310.0141990.0570720.0682520.019832
2021-09-300.002272-0.0246470.0136580.009320
2021-10-310.001777-0.025143-0.0410590.008093
2021-11-30-0.0060470.1303580.0438550.070874
2021-12-310.0181850.0376710.0502230.045295
2022-01-31-0.085436-0.086267-0.045570-0.110829
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169 rows × 4 columns

\n", "
" ], "text/plain": [ " exmktret small_only high_only rev_long\n", "ret_date \n", "2008-02-29 0.024010 0.106350 0.060789 0.059612\n", "2008-03-31 -0.195480 -0.185605 -0.204875 -0.177674\n", "2008-04-30 0.022519 -0.073984 -0.008138 0.026001\n", "2008-05-31 -0.080798 -0.020726 -0.056888 -0.038600\n", "2008-06-30 -0.236014 -0.258596 -0.240880 -0.249542\n", "2008-07-31 0.014972 0.110389 0.072120 0.084431\n", "2008-08-31 -0.170063 -0.234635 -0.187086 -0.205410\n", "2008-09-30 -0.067913 -0.088250 -0.068698 -0.063282\n", "... ... ... ... ...\n", "2021-07-31 -0.064776 -0.001619 -0.044277 -0.064251\n", "2021-08-31 0.014199 0.057072 0.068252 0.019832\n", "2021-09-30 0.002272 -0.024647 0.013658 0.009320\n", "2021-10-31 0.001777 -0.025143 -0.041059 0.008093\n", "2021-11-30 -0.006047 0.130358 0.043855 0.070874\n", "2021-12-31 0.018185 0.037671 0.050223 0.045295\n", "2022-01-31 -0.085436 -0.086267 -0.045570 -0.110829\n", "2022-02-28 0.010633 0.049548 0.034011 0.050488\n", "\n", "[169 rows x 4 columns]" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "factors_long_df" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "editable": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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\n", 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