数据获取及因子筛选.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "548f2de5",
   "metadata": {},
   "source": [
    "（一）数据获取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d5c4addf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>factor</th>\n",
       "      <th>factor_intro</th>\n",
       "      <th>category</th>\n",
       "      <th>category_intro</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>administration_expense_ttm</td>\n",
       "      <td>管理费用TTM</td>\n",
       "      <td>basics</td>\n",
       "      <td>基础科目及衍生类因子</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>asset_impairment_loss_ttm</td>\n",
       "      <td>资产减值损失TTM</td>\n",
       "      <td>basics</td>\n",
       "      <td>基础科目及衍生类因子</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>cash_flow_to_price_ratio</td>\n",
       "      <td>现金流市值比</td>\n",
       "      <td>basics</td>\n",
       "      <td>基础科目及衍生类因子</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>circulating_market_cap</td>\n",
       "      <td>流通市值</td>\n",
       "      <td>basics</td>\n",
       "      <td>基础科目及衍生类因子</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EBIT</td>\n",
       "      <td>息税前利润</td>\n",
       "      <td>basics</td>\n",
       "      <td>基础科目及衍生类因子</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>MAC5</td>\n",
       "      <td>5日移动均线</td>\n",
       "      <td>technical</td>\n",
       "      <td>技术指标因子</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>MAC60</td>\n",
       "      <td>60日移动均线</td>\n",
       "      <td>technical</td>\n",
       "      <td>技术指标因子</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>MACDC</td>\n",
       "      <td>平滑异同移动平均线</td>\n",
       "      <td>technical</td>\n",
       "      <td>技术指标因子</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>MFI14</td>\n",
       "      <td>资金流量指标</td>\n",
       "      <td>technical</td>\n",
       "      <td>技术指标因子</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>price_no_fq</td>\n",
       "      <td>不复权价格因子</td>\n",
       "      <td>technical</td>\n",
       "      <td>技术指标因子</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>260 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         factor factor_intro   category category_intro\n",
       "0    administration_expense_ttm      管理费用TTM     basics     基础科目及衍生类因子\n",
       "1     asset_impairment_loss_ttm    资产减值损失TTM     basics     基础科目及衍生类因子\n",
       "2      cash_flow_to_price_ratio       现金流市值比     basics     基础科目及衍生类因子\n",
       "3        circulating_market_cap         流通市值     basics     基础科目及衍生类因子\n",
       "4                          EBIT        息税前利润     basics     基础科目及衍生类因子\n",
       "..                          ...          ...        ...            ...\n",
       "255                        MAC5       5日移动均线  technical         技术指标因子\n",
       "256                       MAC60      60日移动均线  technical         技术指标因子\n",
       "257                       MACDC    平滑异同移动平均线  technical         技术指标因子\n",
       "258                       MFI14       资金流量指标  technical         技术指标因子\n",
       "259                 price_no_fq      不复权价格因子  technical         技术指标因子\n",
       "\n",
       "[260 rows x 4 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import jqdatasdk\n",
    "jqdatasdk.auth('18811386107', 'PClkjh123456')\n",
    "factors_list=jqdatasdk.get_all_factors()\n",
    "factors_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "58fd22f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "factors_list=['net_debt','VEMA5','VEMA26','debt_to_assets','EMAC120','EMA5','book_to_price_ratio','size','growth','beta','momentum','MAC5','MAC120']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "1e6baa30",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>display_name</th>\n",
       "      <th>name</th>\n",
       "      <th>start_date</th>\n",
       "      <th>end_date</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>000001.XSHE</th>\n",
       "      <td>平安银行</td>\n",
       "      <td>PAYH</td>\n",
       "      <td>1991-04-03</td>\n",
       "      <td>2200-01-01</td>\n",
       "      <td>stock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000002.XSHE</th>\n",
       "      <td>万科A</td>\n",
       "      <td>WKA</td>\n",
       "      <td>1991-01-29</td>\n",
       "      <td>2200-01-01</td>\n",
       "      <td>stock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000004.XSHE</th>\n",
       "      <td>ST国华</td>\n",
       "      <td>STGH</td>\n",
       "      <td>1990-12-01</td>\n",
       "      <td>2200-01-01</td>\n",
       "      <td>stock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000005.XSHE</th>\n",
       "      <td>ST星源</td>\n",
       "      <td>STXY</td>\n",
       "      <td>1990-12-10</td>\n",
       "      <td>2200-01-01</td>\n",
       "      <td>stock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000006.XSHE</th>\n",
       "      <td>深振业A</td>\n",
       "      <td>SZYA</td>\n",
       "      <td>1992-04-27</td>\n",
       "      <td>2200-01-01</td>\n",
       "      <td>stock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688799.XSHG</th>\n",
       "      <td>华纳药厂</td>\n",
       "      <td>HNYC</td>\n",
       "      <td>2021-07-13</td>\n",
       "      <td>2200-01-01</td>\n",
       "      <td>stock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688800.XSHG</th>\n",
       "      <td>瑞可达</td>\n",
       "      <td>RKD</td>\n",
       "      <td>2021-07-22</td>\n",
       "      <td>2200-01-01</td>\n",
       "      <td>stock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688819.XSHG</th>\n",
       "      <td>天能股份</td>\n",
       "      <td>TNGF</td>\n",
       "      <td>2021-01-18</td>\n",
       "      <td>2200-01-01</td>\n",
       "      <td>stock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688981.XSHG</th>\n",
       "      <td>中芯国际</td>\n",
       "      <td>ZXGJ</td>\n",
       "      <td>2020-07-16</td>\n",
       "      <td>2200-01-01</td>\n",
       "      <td>stock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>689009.XSHG</th>\n",
       "      <td>九号公司</td>\n",
       "      <td>JHGS</td>\n",
       "      <td>2020-10-29</td>\n",
       "      <td>2200-01-01</td>\n",
       "      <td>stock</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4842 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            display_name  name start_date   end_date   type\n",
       "000001.XSHE         平安银行  PAYH 1991-04-03 2200-01-01  stock\n",
       "000002.XSHE          万科A   WKA 1991-01-29 2200-01-01  stock\n",
       "000004.XSHE         ST国华  STGH 1990-12-01 2200-01-01  stock\n",
       "000005.XSHE         ST星源  STXY 1990-12-10 2200-01-01  stock\n",
       "000006.XSHE         深振业A  SZYA 1992-04-27 2200-01-01  stock\n",
       "...                  ...   ...        ...        ...    ...\n",
       "688799.XSHG         华纳药厂  HNYC 2021-07-13 2200-01-01  stock\n",
       "688800.XSHG          瑞可达   RKD 2021-07-22 2200-01-01  stock\n",
       "688819.XSHG         天能股份  TNGF 2021-01-18 2200-01-01  stock\n",
       "688981.XSHG         中芯国际  ZXGJ 2020-07-16 2200-01-01  stock\n",
       "689009.XSHG         九号公司  JHGS 2020-10-29 2200-01-01  stock\n",
       "\n",
       "[4842 rows x 5 columns]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd=get_all_securities(types=['stock'], date=None)\n",
    "pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "6a9212f5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\anaconda3\\lib\\site-packages\\jqdatasdk\\api.py:34: PanelObsoleteWarning: 当前环境 pandas 版本高于 0.25，get_price 与 get_fundamentals_continuously 接口的 panel 参数将固定为 False（0.25 及以上版本的 pandas 不再支持 panel，如使用该数据结构和相关函数请注意修改）\n",
      "  warnings.warn(PandasChecker.VERSION_NOTICE_MESSAGE, PanelObsoleteWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2017-05-08', '2017-05-09', '2017-05-10', '2017-05-11',\n",
       "               '2017-05-12', '2017-05-15', '2017-05-16', '2017-05-17',\n",
       "               '2017-05-18', '2017-05-19',\n",
       "               ...\n",
       "               '2022-04-20', '2022-04-21', '2022-04-22', '2022-04-25',\n",
       "               '2022-04-26', '2022-04-27', '2022-04-28', '2022-04-29',\n",
       "               '2022-05-05', '2022-05-06'],\n",
       "              dtype='datetime64[ns]', length=1215, freq=None)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d1=jqdatasdk.get_price('603986.XSHG', start_date='2017-05-06', end_date='2022-05-06', frequency='daily', fields=None, skip_paused=False, fq='pre', panel=True)\n",
    "d1.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "43b72028",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>net_debt</th>\n",
       "      <th>VEMA5</th>\n",
       "      <th>VEMA26</th>\n",
       "      <th>debt_to_assets</th>\n",
       "      <th>EMAC120</th>\n",
       "      <th>EMA5</th>\n",
       "      <th>book_to_price_ratio</th>\n",
       "      <th>size</th>\n",
       "      <th>growth</th>\n",
       "      <th>beta</th>\n",
       "      <th>momentum</th>\n",
       "      <th>MAC5</th>\n",
       "      <th>MAC120</th>\n",
       "      <th>stock_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-05-08</th>\n",
       "      <td>5.360259e+12</td>\n",
       "      <td>3.303641e+06</td>\n",
       "      <td>2.529913e+06</td>\n",
       "      <td>0.934465</td>\n",
       "      <td>1.078371</td>\n",
       "      <td>1.006817</td>\n",
       "      <td>1.798656</td>\n",
       "      <td>1.185397</td>\n",
       "      <td>0.154027</td>\n",
       "      <td>-1.234032</td>\n",
       "      <td>-0.077985</td>\n",
       "      <td>1.009123</td>\n",
       "      <td>1.098069</td>\n",
       "      <td>600000.XSHG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-09</th>\n",
       "      <td>5.360259e+12</td>\n",
       "      <td>2.884328e+06</td>\n",
       "      <td>2.494045e+06</td>\n",
       "      <td>0.934465</td>\n",
       "      <td>1.084735</td>\n",
       "      <td>1.009509</td>\n",
       "      <td>1.797925</td>\n",
       "      <td>1.194864</td>\n",
       "      <td>0.154498</td>\n",
       "      <td>-1.238216</td>\n",
       "      <td>-0.079929</td>\n",
       "      <td>1.010991</td>\n",
       "      <td>1.105023</td>\n",
       "      <td>600000.XSHG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-10</th>\n",
       "      <td>5.360259e+12</td>\n",
       "      <td>2.928752e+06</td>\n",
       "      <td>2.532827e+06</td>\n",
       "      <td>0.934465</td>\n",
       "      <td>1.094033</td>\n",
       "      <td>1.013299</td>\n",
       "      <td>1.796608</td>\n",
       "      <td>1.182904</td>\n",
       "      <td>0.151232</td>\n",
       "      <td>-1.203526</td>\n",
       "      <td>-0.085852</td>\n",
       "      <td>1.014741</td>\n",
       "      <td>1.114968</td>\n",
       "      <td>600000.XSHG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-11</th>\n",
       "      <td>5.360259e+12</td>\n",
       "      <td>2.921250e+06</td>\n",
       "      <td>2.560488e+06</td>\n",
       "      <td>0.934465</td>\n",
       "      <td>1.081833</td>\n",
       "      <td>1.002520</td>\n",
       "      <td>1.802597</td>\n",
       "      <td>1.172766</td>\n",
       "      <td>0.152181</td>\n",
       "      <td>-1.196008</td>\n",
       "      <td>-0.098102</td>\n",
       "      <td>1.001622</td>\n",
       "      <td>1.102883</td>\n",
       "      <td>600000.XSHG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-12</th>\n",
       "      <td>5.360259e+12</td>\n",
       "      <td>3.569598e+06</td>\n",
       "      <td>2.731288e+06</td>\n",
       "      <td>0.934465</td>\n",
       "      <td>1.048879</td>\n",
       "      <td>0.981916</td>\n",
       "      <td>1.793379</td>\n",
       "      <td>1.163653</td>\n",
       "      <td>0.150869</td>\n",
       "      <td>-1.146725</td>\n",
       "      <td>-0.094155</td>\n",
       "      <td>0.975699</td>\n",
       "      <td>1.069340</td>\n",
       "      <td>600000.XSHG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-27</th>\n",
       "      <td>7.102886e+12</td>\n",
       "      <td>2.613059e+06</td>\n",
       "      <td>2.177368e+06</td>\n",
       "      <td>0.915993</td>\n",
       "      <td>1.073462</td>\n",
       "      <td>1.010862</td>\n",
       "      <td>2.717079</td>\n",
       "      <td>0.917164</td>\n",
       "      <td>-0.515747</td>\n",
       "      <td>-1.857665</td>\n",
       "      <td>-0.421772</td>\n",
       "      <td>1.013538</td>\n",
       "      <td>1.073265</td>\n",
       "      <td>600000.XSHG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-28</th>\n",
       "      <td>7.563470e+12</td>\n",
       "      <td>2.810983e+06</td>\n",
       "      <td>2.253625e+06</td>\n",
       "      <td>0.916347</td>\n",
       "      <td>1.051107</td>\n",
       "      <td>0.993747</td>\n",
       "      <td>2.727166</td>\n",
       "      <td>0.903018</td>\n",
       "      <td>-0.485820</td>\n",
       "      <td>-1.844839</td>\n",
       "      <td>-0.365742</td>\n",
       "      <td>0.991239</td>\n",
       "      <td>1.051012</td>\n",
       "      <td>600000.XSHG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-29</th>\n",
       "      <td>7.563470e+12</td>\n",
       "      <td>2.927260e+06</td>\n",
       "      <td>2.320750e+06</td>\n",
       "      <td>0.916347</td>\n",
       "      <td>1.045113</td>\n",
       "      <td>0.992531</td>\n",
       "      <td>2.700036</td>\n",
       "      <td>0.914330</td>\n",
       "      <td>-0.530054</td>\n",
       "      <td>-1.913852</td>\n",
       "      <td>-0.366103</td>\n",
       "      <td>0.984807</td>\n",
       "      <td>1.045247</td>\n",
       "      <td>600000.XSHG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-05</th>\n",
       "      <td>7.563470e+12</td>\n",
       "      <td>2.640860e+06</td>\n",
       "      <td>2.302032e+06</td>\n",
       "      <td>0.916347</td>\n",
       "      <td>1.049513</td>\n",
       "      <td>0.998333</td>\n",
       "      <td>2.654081</td>\n",
       "      <td>0.923653</td>\n",
       "      <td>-0.632104</td>\n",
       "      <td>-1.943735</td>\n",
       "      <td>-0.383453</td>\n",
       "      <td>0.993992</td>\n",
       "      <td>1.049917</td>\n",
       "      <td>600000.XSHG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-06</th>\n",
       "      <td>7.563470e+12</td>\n",
       "      <td>2.512927e+06</td>\n",
       "      <td>2.298701e+06</td>\n",
       "      <td>0.916347</td>\n",
       "      <td>1.067103</td>\n",
       "      <td>1.010759</td>\n",
       "      <td>2.642075</td>\n",
       "      <td>0.932633</td>\n",
       "      <td>-0.630537</td>\n",
       "      <td>-1.955272</td>\n",
       "      <td>-0.342224</td>\n",
       "      <td>1.011210</td>\n",
       "      <td>1.067941</td>\n",
       "      <td>600000.XSHG</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1215 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                net_debt         VEMA5        VEMA26  debt_to_assets  \\\n",
       "2017-05-08  5.360259e+12  3.303641e+06  2.529913e+06        0.934465   \n",
       "2017-05-09  5.360259e+12  2.884328e+06  2.494045e+06        0.934465   \n",
       "2017-05-10  5.360259e+12  2.928752e+06  2.532827e+06        0.934465   \n",
       "2017-05-11  5.360259e+12  2.921250e+06  2.560488e+06        0.934465   \n",
       "2017-05-12  5.360259e+12  3.569598e+06  2.731288e+06        0.934465   \n",
       "...                  ...           ...           ...             ...   \n",
       "2022-04-27  7.102886e+12  2.613059e+06  2.177368e+06        0.915993   \n",
       "2022-04-28  7.563470e+12  2.810983e+06  2.253625e+06        0.916347   \n",
       "2022-04-29  7.563470e+12  2.927260e+06  2.320750e+06        0.916347   \n",
       "2022-05-05  7.563470e+12  2.640860e+06  2.302032e+06        0.916347   \n",
       "2022-05-06  7.563470e+12  2.512927e+06  2.298701e+06        0.916347   \n",
       "\n",
       "             EMAC120      EMA5  book_to_price_ratio      size    growth  \\\n",
       "2017-05-08  1.078371  1.006817             1.798656  1.185397  0.154027   \n",
       "2017-05-09  1.084735  1.009509             1.797925  1.194864  0.154498   \n",
       "2017-05-10  1.094033  1.013299             1.796608  1.182904  0.151232   \n",
       "2017-05-11  1.081833  1.002520             1.802597  1.172766  0.152181   \n",
       "2017-05-12  1.048879  0.981916             1.793379  1.163653  0.150869   \n",
       "...              ...       ...                  ...       ...       ...   \n",
       "2022-04-27  1.073462  1.010862             2.717079  0.917164 -0.515747   \n",
       "2022-04-28  1.051107  0.993747             2.727166  0.903018 -0.485820   \n",
       "2022-04-29  1.045113  0.992531             2.700036  0.914330 -0.530054   \n",
       "2022-05-05  1.049513  0.998333             2.654081  0.923653 -0.632104   \n",
       "2022-05-06  1.067103  1.010759             2.642075  0.932633 -0.630537   \n",
       "\n",
       "                beta  momentum      MAC5    MAC120   stock_code  \n",
       "2017-05-08 -1.234032 -0.077985  1.009123  1.098069  600000.XSHG  \n",
       "2017-05-09 -1.238216 -0.079929  1.010991  1.105023  600000.XSHG  \n",
       "2017-05-10 -1.203526 -0.085852  1.014741  1.114968  600000.XSHG  \n",
       "2017-05-11 -1.196008 -0.098102  1.001622  1.102883  600000.XSHG  \n",
       "2017-05-12 -1.146725 -0.094155  0.975699  1.069340  600000.XSHG  \n",
       "...              ...       ...       ...       ...          ...  \n",
       "2022-04-27 -1.857665 -0.421772  1.013538  1.073265  600000.XSHG  \n",
       "2022-04-28 -1.844839 -0.365742  0.991239  1.051012  600000.XSHG  \n",
       "2022-04-29 -1.913852 -0.366103  0.984807  1.045247  600000.XSHG  \n",
       "2022-05-05 -1.943735 -0.383453  0.993992  1.049917  600000.XSHG  \n",
       "2022-05-06 -1.955272 -0.342224  1.011210  1.067941  600000.XSHG  \n",
       "\n",
       "[1215 rows x 14 columns]"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np,pandas as pd\n",
    "df=pd.DataFrame()\n",
    "for i in factors_list:\n",
    "    data=jqdatasdk.get_factor_values(\n",
    "    securities='600000.XSHG',\n",
    "    factors=i,\n",
    "    start_date='2017-05-06',\n",
    "    end_date='2022-05-06')\n",
    "    data=np.array(list(data.values()))\n",
    "    d=list(data[0,:,0])\n",
    "    df[i]=d\n",
    "df.index=d1.index\n",
    "df['stock_code']='600000.XSHG'\n",
    "df   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "d1043a05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>net_debt</th>\n",
       "      <th>VEMA5</th>\n",
       "      <th>VEMA26</th>\n",
       "      <th>debt_to_assets</th>\n",
       "      <th>EMAC120</th>\n",
       "      <th>EMA5</th>\n",
       "      <th>book_to_price_ratio</th>\n",
       "      <th>size</th>\n",
       "      <th>growth</th>\n",
       "      <th>beta</th>\n",
       "      <th>momentum</th>\n",
       "      <th>MAC5</th>\n",
       "      <th>MAC120</th>\n",
       "      <th>stock_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-05-08</th>\n",
       "      <td>5.360259e+12</td>\n",
       "      <td>3.303641e+06</td>\n",
       "      <td>2.529913e+06</td>\n",
       "      <td>0.934465</td>\n",
       "      <td>1.078371</td>\n",
       "      <td>1.006817</td>\n",
       "      <td>1.798656</td>\n",
       "      <td>1.185397</td>\n",
       "      <td>0.154027</td>\n",
       "      <td>-1.234032</td>\n",
       "      <td>-0.077985</td>\n",
       "      <td>1.009123</td>\n",
       "      <td>1.098069</td>\n",
       "      <td>600000.XSHG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-09</th>\n",
       "      <td>5.360259e+12</td>\n",
       "      <td>2.884328e+06</td>\n",
       "      <td>2.494045e+06</td>\n",
       "      <td>0.934465</td>\n",
       "      <td>1.084735</td>\n",
       "      <td>1.009509</td>\n",
       "      <td>1.797925</td>\n",
       "      <td>1.194864</td>\n",
       "      <td>0.154498</td>\n",
       "      <td>-1.238216</td>\n",
       "      <td>-0.079929</td>\n",
       "      <td>1.010991</td>\n",
       "      <td>1.105023</td>\n",
       "      <td>600000.XSHG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-10</th>\n",
       "      <td>5.360259e+12</td>\n",
       "      <td>2.928752e+06</td>\n",
       "      <td>2.532827e+06</td>\n",
       "      <td>0.934465</td>\n",
       "      <td>1.094033</td>\n",
       "      <td>1.013299</td>\n",
       "      <td>1.796608</td>\n",
       "      <td>1.182904</td>\n",
       "      <td>0.151232</td>\n",
       "      <td>-1.203526</td>\n",
       "      <td>-0.085852</td>\n",
       "      <td>1.014741</td>\n",
       "      <td>1.114968</td>\n",
       "      <td>600000.XSHG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-11</th>\n",
       "      <td>5.360259e+12</td>\n",
       "      <td>2.921250e+06</td>\n",
       "      <td>2.560488e+06</td>\n",
       "      <td>0.934465</td>\n",
       "      <td>1.081833</td>\n",
       "      <td>1.002520</td>\n",
       "      <td>1.802597</td>\n",
       "      <td>1.172766</td>\n",
       "      <td>0.152181</td>\n",
       "      <td>-1.196008</td>\n",
       "      <td>-0.098102</td>\n",
       "      <td>1.001622</td>\n",
       "      <td>1.102883</td>\n",
       "      <td>600000.XSHG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-12</th>\n",
       "      <td>5.360259e+12</td>\n",
       "      <td>3.569598e+06</td>\n",
       "      <td>2.731288e+06</td>\n",
       "      <td>0.934465</td>\n",
       "      <td>1.048879</td>\n",
       "      <td>0.981916</td>\n",
       "      <td>1.793379</td>\n",
       "      <td>1.163653</td>\n",
       "      <td>0.150869</td>\n",
       "      <td>-1.146725</td>\n",
       "      <td>-0.094155</td>\n",
       "      <td>0.975699</td>\n",
       "      <td>1.069340</td>\n",
       "      <td>600000.XSHG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-27</th>\n",
       "      <td>-4.547849e+09</td>\n",
       "      <td>1.902895e+06</td>\n",
       "      <td>1.859752e+06</td>\n",
       "      <td>0.130359</td>\n",
       "      <td>1.197721</td>\n",
       "      <td>0.998885</td>\n",
       "      <td>-1.312936</td>\n",
       "      <td>0.729439</td>\n",
       "      <td>-1.224873</td>\n",
       "      <td>1.439302</td>\n",
       "      <td>0.020656</td>\n",
       "      <td>1.002688</td>\n",
       "      <td>1.237855</td>\n",
       "      <td>603986.XSHG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-28</th>\n",
       "      <td>-5.316578e+09</td>\n",
       "      <td>2.167021e+06</td>\n",
       "      <td>1.921643e+06</td>\n",
       "      <td>0.095052</td>\n",
       "      <td>1.258436</td>\n",
       "      <td>1.035430</td>\n",
       "      <td>-1.261586</td>\n",
       "      <td>0.661125</td>\n",
       "      <td>-1.198403</td>\n",
       "      <td>1.431825</td>\n",
       "      <td>-0.030034</td>\n",
       "      <td>1.033266</td>\n",
       "      <td>1.301401</td>\n",
       "      <td>603986.XSHG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-29</th>\n",
       "      <td>-5.316578e+09</td>\n",
       "      <td>2.231323e+06</td>\n",
       "      <td>1.954108e+06</td>\n",
       "      <td>0.095052</td>\n",
       "      <td>1.192188</td>\n",
       "      <td>0.989053</td>\n",
       "      <td>-1.273941</td>\n",
       "      <td>0.688646</td>\n",
       "      <td>0.035819</td>\n",
       "      <td>1.466256</td>\n",
       "      <td>-0.182159</td>\n",
       "      <td>0.973207</td>\n",
       "      <td>1.233081</td>\n",
       "      <td>603986.XSHG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-05</th>\n",
       "      <td>-5.316578e+09</td>\n",
       "      <td>2.051008e+06</td>\n",
       "      <td>1.934573e+06</td>\n",
       "      <td>0.095052</td>\n",
       "      <td>1.187510</td>\n",
       "      <td>0.991858</td>\n",
       "      <td>-1.284961</td>\n",
       "      <td>0.687589</td>\n",
       "      <td>0.043367</td>\n",
       "      <td>1.468769</td>\n",
       "      <td>-0.129138</td>\n",
       "      <td>0.975114</td>\n",
       "      <td>1.228580</td>\n",
       "      <td>603986.XSHG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-06</th>\n",
       "      <td>-5.316578e+09</td>\n",
       "      <td>1.798137e+06</td>\n",
       "      <td>1.887004e+06</td>\n",
       "      <td>0.095052</td>\n",
       "      <td>1.239917</td>\n",
       "      <td>1.025999</td>\n",
       "      <td>-1.265014</td>\n",
       "      <td>0.674482</td>\n",
       "      <td>0.047831</td>\n",
       "      <td>1.566263</td>\n",
       "      <td>-0.232001</td>\n",
       "      <td>1.027058</td>\n",
       "      <td>1.283639</td>\n",
       "      <td>603986.XSHG</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>60750 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                net_debt         VEMA5        VEMA26  debt_to_assets  \\\n",
       "2017-05-08  5.360259e+12  3.303641e+06  2.529913e+06        0.934465   \n",
       "2017-05-09  5.360259e+12  2.884328e+06  2.494045e+06        0.934465   \n",
       "2017-05-10  5.360259e+12  2.928752e+06  2.532827e+06        0.934465   \n",
       "2017-05-11  5.360259e+12  2.921250e+06  2.560488e+06        0.934465   \n",
       "2017-05-12  5.360259e+12  3.569598e+06  2.731288e+06        0.934465   \n",
       "...                  ...           ...           ...             ...   \n",
       "2022-04-27 -4.547849e+09  1.902895e+06  1.859752e+06        0.130359   \n",
       "2022-04-28 -5.316578e+09  2.167021e+06  1.921643e+06        0.095052   \n",
       "2022-04-29 -5.316578e+09  2.231323e+06  1.954108e+06        0.095052   \n",
       "2022-05-05 -5.316578e+09  2.051008e+06  1.934573e+06        0.095052   \n",
       "2022-05-06 -5.316578e+09  1.798137e+06  1.887004e+06        0.095052   \n",
       "\n",
       "             EMAC120      EMA5  book_to_price_ratio      size    growth  \\\n",
       "2017-05-08  1.078371  1.006817             1.798656  1.185397  0.154027   \n",
       "2017-05-09  1.084735  1.009509             1.797925  1.194864  0.154498   \n",
       "2017-05-10  1.094033  1.013299             1.796608  1.182904  0.151232   \n",
       "2017-05-11  1.081833  1.002520             1.802597  1.172766  0.152181   \n",
       "2017-05-12  1.048879  0.981916             1.793379  1.163653  0.150869   \n",
       "...              ...       ...                  ...       ...       ...   \n",
       "2022-04-27  1.197721  0.998885            -1.312936  0.729439 -1.224873   \n",
       "2022-04-28  1.258436  1.035430            -1.261586  0.661125 -1.198403   \n",
       "2022-04-29  1.192188  0.989053            -1.273941  0.688646  0.035819   \n",
       "2022-05-05  1.187510  0.991858            -1.284961  0.687589  0.043367   \n",
       "2022-05-06  1.239917  1.025999            -1.265014  0.674482  0.047831   \n",
       "\n",
       "                beta  momentum      MAC5    MAC120   stock_code  \n",
       "2017-05-08 -1.234032 -0.077985  1.009123  1.098069  600000.XSHG  \n",
       "2017-05-09 -1.238216 -0.079929  1.010991  1.105023  600000.XSHG  \n",
       "2017-05-10 -1.203526 -0.085852  1.014741  1.114968  600000.XSHG  \n",
       "2017-05-11 -1.196008 -0.098102  1.001622  1.102883  600000.XSHG  \n",
       "2017-05-12 -1.146725 -0.094155  0.975699  1.069340  600000.XSHG  \n",
       "...              ...       ...       ...       ...          ...  \n",
       "2022-04-27  1.439302  0.020656  1.002688  1.237855  603986.XSHG  \n",
       "2022-04-28  1.431825 -0.030034  1.033266  1.301401  603986.XSHG  \n",
       "2022-04-29  1.466256 -0.182159  0.973207  1.233081  603986.XSHG  \n",
       "2022-05-05  1.468769 -0.129138  0.975114  1.228580  603986.XSHG  \n",
       "2022-05-06  1.566263 -0.232001  1.027058  1.283639  603986.XSHG  \n",
       "\n",
       "[60750 rows x 14 columns]"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np,pandas as pd\n",
    "stock_data=pd.DataFrame()\n",
    "for j in stocks:\n",
    "    df=pd.DataFrame()\n",
    "    for i in factors_list:\n",
    "        data=jqdatasdk.get_factor_values(\n",
    "        securities=j,\n",
    "        factors=i,\n",
    "        start_date='2017-05-06',\n",
    "        end_date='2022-05-06')\n",
    "        data=np.array(list(data.values()))\n",
    "        d=list(data[0,:,0])\n",
    "        df[i]=d\n",
    "    df.index=d1.index\n",
    "    df['stock_code']=j\n",
    "    stock_data=stock_data.append(df)\n",
    "stock_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "c376bd66",
   "metadata": {},
   "outputs": [],
   "source": [
    "stock_data.to_csv('stock_data.csv', sep=',', header=True, index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43448865",
   "metadata": {},
   "source": [
    "（二）因子筛选\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe60ee8b",
   "metadata": {},
   "source": [
    "（1）查看因子相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4896a7c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns,matplotlib.pyplot as plt,pandas as pd\n",
    "f,ax = plt.subplots(figsize=(11, 9))\n",
    "stock_data=pd.read_csv('stock_data.csv')\n",
    "sns.heatmap(stock_data.corr(), annot=True,  cmap=plt.get_cmap('Blues'),linewidths=.5, fmt= '.1f',ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0251e4ca",
   "metadata": {},
   "source": [
    "从图像可以看出，VEMA5与VEMA26相关性较高，MAC5与EMA5相关性较高，MAC120与EMAC120相关性较高。\n",
    "故三对因子中各去除一个"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ff79751e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>net_debt</th>\n",
       "      <th>VEMA5</th>\n",
       "      <th>debt_to_assets</th>\n",
       "      <th>book_to_price_ratio</th>\n",
       "      <th>size</th>\n",
       "      <th>growth</th>\n",
       "      <th>beta</th>\n",
       "      <th>momentum</th>\n",
       "      <th>MAC5</th>\n",
       "      <th>MAC120</th>\n",
       "      <th>stock_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017/5/8</td>\n",
       "      <td>5.360260e+12</td>\n",
       "      <td>3303641.390</td>\n",
       "      <td>0.934465</td>\n",
       "      <td>1.798656</td>\n",
       "      <td>1.185397</td>\n",
       "      <td>0.154027</td>\n",
       "      <td>-1.234032</td>\n",
       "      <td>-0.077985</td>\n",
       "      <td>1.009123</td>\n",
       "      <td>1.098069</td>\n",
       "      <td>600000.XSHG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017/5/9</td>\n",
       "      <td>5.360260e+12</td>\n",
       "      <td>2884327.593</td>\n",
       "      <td>0.934465</td>\n",
       "      <td>1.797925</td>\n",
       "      <td>1.194864</td>\n",
       "      <td>0.154498</td>\n",
       "      <td>-1.238216</td>\n",
       "      <td>-0.079929</td>\n",
       "      <td>1.010991</td>\n",
       "      <td>1.105023</td>\n",
       "      <td>600000.XSHG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017/5/10</td>\n",
       "      <td>5.360260e+12</td>\n",
       "      <td>2928752.062</td>\n",
       "      <td>0.934465</td>\n",
       "      <td>1.796608</td>\n",
       "      <td>1.182904</td>\n",
       "      <td>0.151232</td>\n",
       "      <td>-1.203526</td>\n",
       "      <td>-0.085852</td>\n",
       "      <td>1.014741</td>\n",
       "      <td>1.114968</td>\n",
       "      <td>600000.XSHG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017/5/11</td>\n",
       "      <td>5.360260e+12</td>\n",
       "      <td>2921250.375</td>\n",
       "      <td>0.934465</td>\n",
       "      <td>1.802597</td>\n",
       "      <td>1.172766</td>\n",
       "      <td>0.152181</td>\n",
       "      <td>-1.196008</td>\n",
       "      <td>-0.098102</td>\n",
       "      <td>1.001622</td>\n",
       "      <td>1.102883</td>\n",
       "      <td>600000.XSHG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017/5/12</td>\n",
       "      <td>5.360260e+12</td>\n",
       "      <td>3569597.917</td>\n",
       "      <td>0.934465</td>\n",
       "      <td>1.793379</td>\n",
       "      <td>1.163653</td>\n",
       "      <td>0.150869</td>\n",
       "      <td>-1.146725</td>\n",
       "      <td>-0.094155</td>\n",
       "      <td>0.975699</td>\n",
       "      <td>1.069340</td>\n",
       "      <td>600000.XSHG</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Unnamed: 0      net_debt        VEMA5  debt_to_assets  book_to_price_ratio  \\\n",
       "0   2017/5/8  5.360260e+12  3303641.390        0.934465             1.798656   \n",
       "1   2017/5/9  5.360260e+12  2884327.593        0.934465             1.797925   \n",
       "2  2017/5/10  5.360260e+12  2928752.062        0.934465             1.796608   \n",
       "3  2017/5/11  5.360260e+12  2921250.375        0.934465             1.802597   \n",
       "4  2017/5/12  5.360260e+12  3569597.917        0.934465             1.793379   \n",
       "\n",
       "       size    growth      beta  momentum      MAC5    MAC120   stock_code  \n",
       "0  1.185397  0.154027 -1.234032 -0.077985  1.009123  1.098069  600000.XSHG  \n",
       "1  1.194864  0.154498 -1.238216 -0.079929  1.010991  1.105023  600000.XSHG  \n",
       "2  1.182904  0.151232 -1.203526 -0.085852  1.014741  1.114968  600000.XSHG  \n",
       "3  1.172766  0.152181 -1.196008 -0.098102  1.001622  1.102883  600000.XSHG  \n",
       "4  1.163653  0.150869 -1.146725 -0.094155  0.975699  1.069340  600000.XSHG  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "stock_data=pd.read_csv('stock_data.csv')\n",
    "drop_list=['VEMA26','EMA5','EMAC120']\n",
    "stock_data=stock_data.drop(drop_list,axis=1)\n",
    "stock_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "58c88a30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns,matplotlib.pyplot as plt\n",
    "f,ax = plt.subplots(figsize=(11, 9))\n",
    "sns.heatmap(stock_data.corr(), annot=True,  cmap=plt.get_cmap('Blues'),linewidths=.5, fmt= '.1f',ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "390c3296",
   "metadata": {},
   "source": [
    "（2）随机森林筛选特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d803de58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "auth success \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['600000.XSHG',\n",
       " '600028.XSHG',\n",
       " '600030.XSHG',\n",
       " '600031.XSHG',\n",
       " '600036.XSHG',\n",
       " '600048.XSHG',\n",
       " '600050.XSHG',\n",
       " '600104.XSHG',\n",
       " '600196.XSHG',\n",
       " '600276.XSHG',\n",
       " '600309.XSHG',\n",
       " '600436.XSHG',\n",
       " '600438.XSHG',\n",
       " '600519.XSHG',\n",
       " '600547.XSHG',\n",
       " '600570.XSHG',\n",
       " '600585.XSHG',\n",
       " '600588.XSHG',\n",
       " '600690.XSHG',\n",
       " '600745.XSHG',\n",
       " '600809.XSHG',\n",
       " '600837.XSHG',\n",
       " '600887.XSHG',\n",
       " '600893.XSHG',\n",
       " '600900.XSHG',\n",
       " '601012.XSHG',\n",
       " '601066.XSHG',\n",
       " '601088.XSHG',\n",
       " '601138.XSHG',\n",
       " '601166.XSHG',\n",
       " '601211.XSHG',\n",
       " '601288.XSHG',\n",
       " '601318.XSHG',\n",
       " '601336.XSHG',\n",
       " '601398.XSHG',\n",
       " '601601.XSHG',\n",
       " '601628.XSHG',\n",
       " '601633.XSHG',\n",
       " '601668.XSHG',\n",
       " '601688.XSHG',\n",
       " '601728.XSHG',\n",
       " '601857.XSHG',\n",
       " '601888.XSHG',\n",
       " '601899.XSHG',\n",
       " '601919.XSHG',\n",
       " '601995.XSHG',\n",
       " '603259.XSHG',\n",
       " '603288.XSHG',\n",
       " '603501.XSHG',\n",
       " '603986.XSHG']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import jqdatasdk\n",
    "jqdatasdk.auth('18811386107', 'PClkjh123456')\n",
    "stocks =jqdatasdk.get_index_stocks('000050.XSHG')\n",
    "stocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "686b300f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\anaconda3\\lib\\site-packages\\jqdatasdk\\api.py:34: PanelObsoleteWarning: 当前环境 pandas 版本高于 0.25，get_price 与 get_fundamentals_continuously 接口的 panel 参数将固定为 False（0.25 及以上版本的 pandas 不再支持 panel，如使用该数据结构和相关函数请注意修改）\n",
      "  warnings.warn(PandasChecker.VERSION_NOTICE_MESSAGE, PanelObsoleteWarning)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>daily_return</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-05-08</th>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>2017-05-09</th>\n",
       "      <td>-0.007157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-10</th>\n",
       "      <td>-0.010299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-11</th>\n",
       "      <td>0.010406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-12</th>\n",
       "      <td>0.030896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-27</th>\n",
       "      <td>0.077958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-28</th>\n",
       "      <td>-0.051520</td>\n",
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       "    <tr>\n",
       "      <th>2022-04-29</th>\n",
       "      <td>0.052716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-05</th>\n",
       "      <td>0.001282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-06</th>\n",
       "      <td>-0.045371</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>60750 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            daily_return\n",
       "2017-05-08      0.000000\n",
       "2017-05-09     -0.007157\n",
       "2017-05-10     -0.010299\n",
       "2017-05-11      0.010406\n",
       "2017-05-12      0.030896\n",
       "...                  ...\n",
       "2022-04-27      0.077958\n",
       "2022-04-28     -0.051520\n",
       "2022-04-29      0.052716\n",
       "2022-05-05      0.001282\n",
       "2022-05-06     -0.045371\n",
       "\n",
       "[60750 rows x 1 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd,numpy as np\n",
    "df=pd.DataFrame()\n",
    "for j in stocks:\n",
    "    data=jqdatasdk.get_price(j, start_date='2017-05-06', end_date='2022-05-06', frequency='daily', fields='close', skip_paused=False, fq='pre',panel=True)\n",
    "    data['daily_return']=data.pct_change().fillna(0)\n",
    "    df=df.append(data)\n",
    "df=df.drop('close',axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4406475a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        , -0.00715746, -0.01029866, ...,  0.05271592,\n",
       "        0.00128195, -0.04537089])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_return=np.array(df['daily_return'].values.tolist())\n",
    "daily_return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9235358b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>net_debt</th>\n",
       "      <th>VEMA5</th>\n",
       "      <th>debt_to_assets</th>\n",
       "      <th>book_to_price_ratio</th>\n",
       "      <th>size</th>\n",
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       "      <th>momentum</th>\n",
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       "      <th>MAC120</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-05-08</th>\n",
       "      <td>5.360260e+12</td>\n",
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       "      <td>1.009123</td>\n",
       "      <td>1.098069</td>\n",
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       "      <th>2017-05-09</th>\n",
       "      <td>5.360260e+12</td>\n",
       "      <td>2884327.593</td>\n",
       "      <td>0.934465</td>\n",
       "      <td>1.797925</td>\n",
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       "      <td>0.154498</td>\n",
       "      <td>-1.238216</td>\n",
       "      <td>-0.079929</td>\n",
       "      <td>1.010991</td>\n",
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       "    <tr>\n",
       "      <th>2017-05-10</th>\n",
       "      <td>5.360260e+12</td>\n",
       "      <td>2928752.062</td>\n",
       "      <td>0.934465</td>\n",
       "      <td>1.796608</td>\n",
       "      <td>1.182904</td>\n",
       "      <td>0.151232</td>\n",
       "      <td>-1.203526</td>\n",
       "      <td>-0.085852</td>\n",
       "      <td>1.014741</td>\n",
       "      <td>1.114968</td>\n",
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       "    <tr>\n",
       "      <th>2017-05-11</th>\n",
       "      <td>5.360260e+12</td>\n",
       "      <td>2921250.375</td>\n",
       "      <td>0.934465</td>\n",
       "      <td>1.802597</td>\n",
       "      <td>1.172766</td>\n",
       "      <td>0.152181</td>\n",
       "      <td>-1.196008</td>\n",
       "      <td>-0.098102</td>\n",
       "      <td>1.001622</td>\n",
       "      <td>1.102883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-12</th>\n",
       "      <td>5.360260e+12</td>\n",
       "      <td>3569597.917</td>\n",
       "      <td>0.934465</td>\n",
       "      <td>1.793379</td>\n",
       "      <td>1.163653</td>\n",
       "      <td>0.150869</td>\n",
       "      <td>-1.146725</td>\n",
       "      <td>-0.094155</td>\n",
       "      <td>0.975699</td>\n",
       "      <td>1.069340</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-27</th>\n",
       "      <td>-4.547849e+09</td>\n",
       "      <td>1902894.715</td>\n",
       "      <td>0.130359</td>\n",
       "      <td>-1.312936</td>\n",
       "      <td>0.729439</td>\n",
       "      <td>-1.224873</td>\n",
       "      <td>1.439302</td>\n",
       "      <td>0.020656</td>\n",
       "      <td>1.002688</td>\n",
       "      <td>1.237855</td>\n",
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       "    <tr>\n",
       "      <th>2022-04-28</th>\n",
       "      <td>-5.316578e+09</td>\n",
       "      <td>2167020.810</td>\n",
       "      <td>0.095052</td>\n",
       "      <td>-1.261586</td>\n",
       "      <td>0.661125</td>\n",
       "      <td>-1.198403</td>\n",
       "      <td>1.431825</td>\n",
       "      <td>-0.030034</td>\n",
       "      <td>1.033266</td>\n",
       "      <td>1.301401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-29</th>\n",
       "      <td>-5.316578e+09</td>\n",
       "      <td>2231323.207</td>\n",
       "      <td>0.095052</td>\n",
       "      <td>-1.273941</td>\n",
       "      <td>0.688646</td>\n",
       "      <td>0.035819</td>\n",
       "      <td>1.466256</td>\n",
       "      <td>-0.182159</td>\n",
       "      <td>0.973207</td>\n",
       "      <td>1.233081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-05</th>\n",
       "      <td>-5.316578e+09</td>\n",
       "      <td>2051007.804</td>\n",
       "      <td>0.095052</td>\n",
       "      <td>-1.284961</td>\n",
       "      <td>0.687589</td>\n",
       "      <td>0.043367</td>\n",
       "      <td>1.468769</td>\n",
       "      <td>-0.129138</td>\n",
       "      <td>0.975114</td>\n",
       "      <td>1.228580</td>\n",
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       "    <tr>\n",
       "      <th>2022-05-06</th>\n",
       "      <td>-5.316578e+09</td>\n",
       "      <td>1798137.203</td>\n",
       "      <td>0.095052</td>\n",
       "      <td>-1.265014</td>\n",
       "      <td>0.674482</td>\n",
       "      <td>0.047831</td>\n",
       "      <td>1.566263</td>\n",
       "      <td>-0.232001</td>\n",
       "      <td>1.027058</td>\n",
       "      <td>1.283639</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>60750 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                net_debt        VEMA5  debt_to_assets  book_to_price_ratio  \\\n",
       "2017-05-08  5.360260e+12  3303641.390        0.934465             1.798656   \n",
       "2017-05-09  5.360260e+12  2884327.593        0.934465             1.797925   \n",
       "2017-05-10  5.360260e+12  2928752.062        0.934465             1.796608   \n",
       "2017-05-11  5.360260e+12  2921250.375        0.934465             1.802597   \n",
       "2017-05-12  5.360260e+12  3569597.917        0.934465             1.793379   \n",
       "...                  ...          ...             ...                  ...   \n",
       "2022-04-27 -4.547849e+09  1902894.715        0.130359            -1.312936   \n",
       "2022-04-28 -5.316578e+09  2167020.810        0.095052            -1.261586   \n",
       "2022-04-29 -5.316578e+09  2231323.207        0.095052            -1.273941   \n",
       "2022-05-05 -5.316578e+09  2051007.804        0.095052            -1.284961   \n",
       "2022-05-06 -5.316578e+09  1798137.203        0.095052            -1.265014   \n",
       "\n",
       "                size    growth      beta  momentum      MAC5    MAC120  \n",
       "2017-05-08  1.185397  0.154027 -1.234032 -0.077985  1.009123  1.098069  \n",
       "2017-05-09  1.194864  0.154498 -1.238216 -0.079929  1.010991  1.105023  \n",
       "2017-05-10  1.182904  0.151232 -1.203526 -0.085852  1.014741  1.114968  \n",
       "2017-05-11  1.172766  0.152181 -1.196008 -0.098102  1.001622  1.102883  \n",
       "2017-05-12  1.163653  0.150869 -1.146725 -0.094155  0.975699  1.069340  \n",
       "...              ...       ...       ...       ...       ...       ...  \n",
       "2022-04-27  0.729439 -1.224873  1.439302  0.020656  1.002688  1.237855  \n",
       "2022-04-28  0.661125 -1.198403  1.431825 -0.030034  1.033266  1.301401  \n",
       "2022-04-29  0.688646  0.035819  1.466256 -0.182159  0.973207  1.233081  \n",
       "2022-05-05  0.687589  0.043367  1.468769 -0.129138  0.975114  1.228580  \n",
       "2022-05-06  0.674482  0.047831  1.566263 -0.232001  1.027058  1.283639  \n",
       "\n",
       "[60750 rows x 10 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "stock_data=pd.read_csv('stock_data.csv')\n",
    "drop_list=['VEMA26','EMA5','EMAC120','Unnamed: 0','stock_code']\n",
    "stock_data=stock_data.drop(drop_list,axis=1)\n",
    "stock_data.index=df.index\n",
    "stock_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6cf8191",
   "metadata": {},
   "source": [
    "缺失值处理\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0f19d3a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_data.isnull().any().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2fd1b72f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_data=stock_data.fillna(0)\n",
    "stock_data.isnull().any().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a92171eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5.36026000e+12,  3.30364139e+06,  9.34465000e-01, ...,\n",
       "        -7.79850000e-02,  1.00912300e+00,  1.09806900e+00],\n",
       "       [ 5.36026000e+12,  2.88432759e+06,  9.34465000e-01, ...,\n",
       "        -7.99290000e-02,  1.01099100e+00,  1.10502300e+00],\n",
       "       [ 5.36026000e+12,  2.92875206e+06,  9.34465000e-01, ...,\n",
       "        -8.58520000e-02,  1.01474100e+00,  1.11496800e+00],\n",
       "       ...,\n",
       "       [-5.31657818e+09,  2.23132321e+06,  9.50520000e-02, ...,\n",
       "        -1.82159000e-01,  9.73207000e-01,  1.23308100e+00],\n",
       "       [-5.31657818e+09,  2.05100780e+06,  9.50520000e-02, ...,\n",
       "        -1.29138000e-01,  9.75114000e-01,  1.22858000e+00],\n",
       "       [-5.31657818e+09,  1.79813720e+06,  9.50520000e-02, ...,\n",
       "        -2.32001000e-01,  1.02705800e+00,  1.28363900e+00]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(stock_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e5082bc9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal number of features : 1\n",
      "Best features : Index(['MAC120'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.feature_selection import RFECV\n",
    "x_train, x_test, y_train, y_test = train_test_split(stock_data,daily_return, test_size=0.3, random_state=42)\n",
    "clf = RandomForestClassifier(n_estimators =100,min_samples_leaf=5)      \n",
    "rfecv = RFECV(estimator=clf, step=1,cv=5,scoring='accuracy')  \n",
    "rf= rfecv.fit(x_train, y_train.astype('int'))\n",
    "print('Optimal number of features :', rf.n_features_)\n",
    "print('Best features :', x_train.columns[rf.support_])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f03820fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "factors_list=['liquidity','sharpe_ratio_20','roa_ttm','quick_ratio','MLEV','GMI','cash_to_current_liability','ACCA','single_day_VPT','BIAS10','BBIC']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f08ef816",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\anaconda3\\lib\\site-packages\\jqdatasdk\\api.py:34: PanelObsoleteWarning: 当前环境 pandas 版本高于 0.25，get_price 与 get_fundamentals_continuously 接口的 panel 参数将固定为 False（0.25 及以上版本的 pandas 不再支持 panel，如使用该数据结构和相关函数请注意修改）\n",
      "  warnings.warn(PandasChecker.VERSION_NOTICE_MESSAGE, PanelObsoleteWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2017-05-08', '2017-05-09', '2017-05-10', '2017-05-11',\n",
       "               '2017-05-12', '2017-05-15', '2017-05-16', '2017-05-17',\n",
       "               '2017-05-18', '2017-05-19',\n",
       "               ...\n",
       "               '2022-04-20', '2022-04-21', '2022-04-22', '2022-04-25',\n",
       "               '2022-04-26', '2022-04-27', '2022-04-28', '2022-04-29',\n",
       "               '2022-05-05', '2022-05-06'],\n",
       "              dtype='datetime64[ns]', length=1215, freq=None)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import jqdatasdk\n",
    "jqdatasdk.auth('18811386107', 'PClkjh123456')\n",
    "d1=jqdatasdk.get_price('603986.XSHG', start_date='2017-05-06', end_date='2022-05-06', frequency='daily', fields=None, skip_paused=False, fq='pre', panel=True)\n",
    "d1.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b6931f52",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>liquidity</th>\n",
       "      <th>sharpe_ratio_20</th>\n",
       "      <th>roa_ttm</th>\n",
       "      <th>quick_ratio</th>\n",
       "      <th>MLEV</th>\n",
       "      <th>GMI</th>\n",
       "      <th>cash_to_current_liability</th>\n",
       "      <th>ACCA</th>\n",
       "      <th>single_day_VPT</th>\n",
       "      <th>BIAS10</th>\n",
       "      <th>BBIC</th>\n",
       "      <th>stock_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-05-08</th>\n",
       "      <td>-0.791590</td>\n",
       "      <td>-5.747721</td>\n",
       "      <td>0.009218</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.036616</td>\n",
       "      <td>-2064.770053</td>\n",
       "      <td>-1.245473</td>\n",
       "      <td>1.015998</td>\n",
       "      <td>600000.XSHG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-09</th>\n",
       "      <td>-0.817750</td>\n",
       "      <td>-5.720025</td>\n",
       "      <td>0.009218</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.036616</td>\n",
       "      <td>-1828.758497</td>\n",
       "      <td>-1.795983</td>\n",
       "      <td>1.019660</td>\n",
       "      <td>600000.XSHG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-10</th>\n",
       "      <td>-0.827541</td>\n",
       "      <td>-5.817354</td>\n",
       "      <td>0.009218</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.036616</td>\n",
       "      <td>-3735.927027</td>\n",
       "      <td>-2.484472</td>\n",
       "      <td>1.024833</td>\n",
       "      <td>600000.XSHG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-11</th>\n",
       "      <td>-0.846744</td>\n",
       "      <td>-5.059783</td>\n",
       "      <td>0.009218</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.036616</td>\n",
       "      <td>3634.073703</td>\n",
       "      <td>-1.315789</td>\n",
       "      <td>1.011890</td>\n",
       "      <td>600000.XSHG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-12</th>\n",
       "      <td>-0.842516</td>\n",
       "      <td>-2.241047</td>\n",
       "      <td>0.009218</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.036616</td>\n",
       "      <td>18621.769369</td>\n",
       "      <td>1.706970</td>\n",
       "      <td>0.984521</td>\n",
       "      <td>600000.XSHG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-27</th>\n",
       "      <td>1.074871</td>\n",
       "      <td>-1.382414</td>\n",
       "      <td>0.126993</td>\n",
       "      <td>5.465675</td>\n",
       "      <td>0.004217</td>\n",
       "      <td>1.084031</td>\n",
       "      <td>5.216915</td>\n",
       "      <td>0.000897</td>\n",
       "      <td>9968.011038</td>\n",
       "      <td>-3.716541</td>\n",
       "      <td>1.025762</td>\n",
       "      <td>603986.XSHG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-28</th>\n",
       "      <td>1.078710</td>\n",
       "      <td>-1.381964</td>\n",
       "      <td>0.171810</td>\n",
       "      <td>7.542089</td>\n",
       "      <td>0.004203</td>\n",
       "      <td>1.084031</td>\n",
       "      <td>4.680714</td>\n",
       "      <td>-0.043941</td>\n",
       "      <td>-7723.878400</td>\n",
       "      <td>-7.822206</td>\n",
       "      <td>1.068153</td>\n",
       "      <td>603986.XSHG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-29</th>\n",
       "      <td>1.068820</td>\n",
       "      <td>-1.304333</td>\n",
       "      <td>0.171810</td>\n",
       "      <td>7.542089</td>\n",
       "      <td>0.003994</td>\n",
       "      <td>0.803127</td>\n",
       "      <td>4.680714</td>\n",
       "      <td>-0.043941</td>\n",
       "      <td>6919.861252</td>\n",
       "      <td>-2.386948</td>\n",
       "      <td>1.015714</td>\n",
       "      <td>603986.XSHG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-05</th>\n",
       "      <td>1.058341</td>\n",
       "      <td>-1.195184</td>\n",
       "      <td>0.171810</td>\n",
       "      <td>7.542089</td>\n",
       "      <td>0.003989</td>\n",
       "      <td>0.803127</td>\n",
       "      <td>4.680714</td>\n",
       "      <td>-0.043941</td>\n",
       "      <td>120.533932</td>\n",
       "      <td>-1.114900</td>\n",
       "      <td>1.009439</td>\n",
       "      <td>603986.XSHG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-06</th>\n",
       "      <td>1.033173</td>\n",
       "      <td>-1.299617</td>\n",
       "      <td>0.171810</td>\n",
       "      <td>7.542089</td>\n",
       "      <td>0.004177</td>\n",
       "      <td>0.803127</td>\n",
       "      <td>4.680714</td>\n",
       "      <td>-0.043941</td>\n",
       "      <td>-3261.577099</td>\n",
       "      <td>-4.393262</td>\n",
       "      <td>1.051687</td>\n",
       "      <td>603986.XSHG</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>60750 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            liquidity  sharpe_ratio_20   roa_ttm  quick_ratio      MLEV  \\\n",
       "2017-05-08  -0.791590        -5.747721  0.009218          NaN  0.000000   \n",
       "2017-05-09  -0.817750        -5.720025  0.009218          NaN  0.000000   \n",
       "2017-05-10  -0.827541        -5.817354  0.009218          NaN  0.000000   \n",
       "2017-05-11  -0.846744        -5.059783  0.009218          NaN  0.000000   \n",
       "2017-05-12  -0.842516        -2.241047  0.009218          NaN  0.000000   \n",
       "...               ...              ...       ...          ...       ...   \n",
       "2022-04-27   1.074871        -1.382414  0.126993     5.465675  0.004217   \n",
       "2022-04-28   1.078710        -1.381964  0.171810     7.542089  0.004203   \n",
       "2022-04-29   1.068820        -1.304333  0.171810     7.542089  0.003994   \n",
       "2022-05-05   1.058341        -1.195184  0.171810     7.542089  0.003989   \n",
       "2022-05-06   1.033173        -1.299617  0.171810     7.542089  0.004177   \n",
       "\n",
       "                 GMI  cash_to_current_liability      ACCA  single_day_VPT  \\\n",
       "2017-05-08       NaN                        NaN -0.036616    -2064.770053   \n",
       "2017-05-09       NaN                        NaN -0.036616    -1828.758497   \n",
       "2017-05-10       NaN                        NaN -0.036616    -3735.927027   \n",
       "2017-05-11       NaN                        NaN -0.036616     3634.073703   \n",
       "2017-05-12       NaN                        NaN -0.036616    18621.769369   \n",
       "...              ...                        ...       ...             ...   \n",
       "2022-04-27  1.084031                   5.216915  0.000897     9968.011038   \n",
       "2022-04-28  1.084031                   4.680714 -0.043941    -7723.878400   \n",
       "2022-04-29  0.803127                   4.680714 -0.043941     6919.861252   \n",
       "2022-05-05  0.803127                   4.680714 -0.043941      120.533932   \n",
       "2022-05-06  0.803127                   4.680714 -0.043941    -3261.577099   \n",
       "\n",
       "              BIAS10      BBIC   stock_code  \n",
       "2017-05-08 -1.245473  1.015998  600000.XSHG  \n",
       "2017-05-09 -1.795983  1.019660  600000.XSHG  \n",
       "2017-05-10 -2.484472  1.024833  600000.XSHG  \n",
       "2017-05-11 -1.315789  1.011890  600000.XSHG  \n",
       "2017-05-12  1.706970  0.984521  600000.XSHG  \n",
       "...              ...       ...          ...  \n",
       "2022-04-27 -3.716541  1.025762  603986.XSHG  \n",
       "2022-04-28 -7.822206  1.068153  603986.XSHG  \n",
       "2022-04-29 -2.386948  1.015714  603986.XSHG  \n",
       "2022-05-05 -1.114900  1.009439  603986.XSHG  \n",
       "2022-05-06 -4.393262  1.051687  603986.XSHG  \n",
       "\n",
       "[60750 rows x 12 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np,pandas as pd\n",
    "stock_data=pd.DataFrame()\n",
    "for j in stocks:\n",
    "    df=pd.DataFrame()\n",
    "    for i in factors_list:\n",
    "        data=jqdatasdk.get_factor_values(\n",
    "        securities=j,\n",
    "        factors=i,\n",
    "        start_date='2017-05-06',\n",
    "        end_date='2022-05-06')\n",
    "        data=np.array(list(data.values()))\n",
    "        d=list(data[0,:,0])\n",
    "        df[i]=d\n",
    "    df.index=d1.index\n",
    "    df['stock_code']=j\n",
    "    stock_data=stock_data.append(df)\n",
    "stock_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "de55ff34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns,matplotlib.pyplot as plt,pandas as pd\n",
    "f,ax = plt.subplots(figsize=(11, 9))\n",
    "sns.heatmap(stock_data.corr(), annot=True,  cmap=plt.get_cmap('Blues'),linewidths=.5, fmt= '.1f',ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0b3b7cef",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\anaconda3\\lib\\site-packages\\jqdatasdk\\api.py:34: PanelObsoleteWarning: 当前环境 pandas 版本高于 0.25，get_price 与 get_fundamentals_continuously 接口的 panel 参数将固定为 False（0.25 及以上版本的 pandas 不再支持 panel，如使用该数据结构和相关函数请注意修改）\n",
      "  warnings.warn(PandasChecker.VERSION_NOTICE_MESSAGE, PanelObsoleteWarning)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>daily_return</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-05-08</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-09</th>\n",
       "      <td>-0.007157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-10</th>\n",
       "      <td>-0.010299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-11</th>\n",
       "      <td>0.010406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-12</th>\n",
       "      <td>0.030896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-27</th>\n",
       "      <td>0.077958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-28</th>\n",
       "      <td>-0.051520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-29</th>\n",
       "      <td>0.052716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-05</th>\n",
       "      <td>0.001282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-06</th>\n",
       "      <td>-0.045371</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>60750 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            daily_return\n",
       "2017-05-08      0.000000\n",
       "2017-05-09     -0.007157\n",
       "2017-05-10     -0.010299\n",
       "2017-05-11      0.010406\n",
       "2017-05-12      0.030896\n",
       "...                  ...\n",
       "2022-04-27      0.077958\n",
       "2022-04-28     -0.051520\n",
       "2022-04-29      0.052716\n",
       "2022-05-05      0.001282\n",
       "2022-05-06     -0.045371\n",
       "\n",
       "[60750 rows x 1 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd,numpy as np\n",
    "df=pd.DataFrame()\n",
    "for j in stocks:\n",
    "    data=jqdatasdk.get_price(j, start_date='2017-05-06', end_date='2022-05-06', frequency='daily', fields='close', skip_paused=False, fq='pre',panel=True)\n",
    "    data['daily_return']=data.pct_change().fillna(0)\n",
    "    df=df.append(data)\n",
    "df=df.drop('close',axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "69176fbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "daily_return=np.array(df['daily_return'].values.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ff6992c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>liquidity</th>\n",
       "      <th>sharpe_ratio_20</th>\n",
       "      <th>roa_ttm</th>\n",
       "      <th>quick_ratio</th>\n",
       "      <th>MLEV</th>\n",
       "      <th>GMI</th>\n",
       "      <th>cash_to_current_liability</th>\n",
       "      <th>ACCA</th>\n",
       "      <th>single_day_VPT</th>\n",
       "      <th>BIAS10</th>\n",
       "      <th>BBIC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-05-08</th>\n",
       "      <td>-0.791590</td>\n",
       "      <td>-5.747721</td>\n",
       "      <td>0.009218</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>-0.036616</td>\n",
       "      <td>-2064.770053</td>\n",
       "      <td>-1.245473</td>\n",
       "      <td>1.015998</td>\n",
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       "    <tr>\n",
       "      <th>2017-05-09</th>\n",
       "      <td>-0.817750</td>\n",
       "      <td>-5.720025</td>\n",
       "      <td>0.009218</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.036616</td>\n",
       "      <td>-1828.758497</td>\n",
       "      <td>-1.795983</td>\n",
       "      <td>1.019660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-10</th>\n",
       "      <td>-0.827541</td>\n",
       "      <td>-5.817354</td>\n",
       "      <td>0.009218</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.036616</td>\n",
       "      <td>-3735.927027</td>\n",
       "      <td>-2.484472</td>\n",
       "      <td>1.024833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-11</th>\n",
       "      <td>-0.846744</td>\n",
       "      <td>-5.059783</td>\n",
       "      <td>0.009218</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.036616</td>\n",
       "      <td>3634.073703</td>\n",
       "      <td>-1.315789</td>\n",
       "      <td>1.011890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-12</th>\n",
       "      <td>-0.842516</td>\n",
       "      <td>-2.241047</td>\n",
       "      <td>0.009218</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.036616</td>\n",
       "      <td>18621.769369</td>\n",
       "      <td>1.706970</td>\n",
       "      <td>0.984521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-27</th>\n",
       "      <td>1.074871</td>\n",
       "      <td>-1.382414</td>\n",
       "      <td>0.126993</td>\n",
       "      <td>5.465675</td>\n",
       "      <td>0.004217</td>\n",
       "      <td>1.084031</td>\n",
       "      <td>5.216915</td>\n",
       "      <td>0.000897</td>\n",
       "      <td>9968.011038</td>\n",
       "      <td>-3.716541</td>\n",
       "      <td>1.025762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-28</th>\n",
       "      <td>1.078710</td>\n",
       "      <td>-1.381964</td>\n",
       "      <td>0.171810</td>\n",
       "      <td>7.542089</td>\n",
       "      <td>0.004203</td>\n",
       "      <td>1.084031</td>\n",
       "      <td>4.680714</td>\n",
       "      <td>-0.043941</td>\n",
       "      <td>-7723.878400</td>\n",
       "      <td>-7.822206</td>\n",
       "      <td>1.068153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-29</th>\n",
       "      <td>1.068820</td>\n",
       "      <td>-1.304333</td>\n",
       "      <td>0.171810</td>\n",
       "      <td>7.542089</td>\n",
       "      <td>0.003994</td>\n",
       "      <td>0.803127</td>\n",
       "      <td>4.680714</td>\n",
       "      <td>-0.043941</td>\n",
       "      <td>6919.861252</td>\n",
       "      <td>-2.386948</td>\n",
       "      <td>1.015714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-05</th>\n",
       "      <td>1.058341</td>\n",
       "      <td>-1.195184</td>\n",
       "      <td>0.171810</td>\n",
       "      <td>7.542089</td>\n",
       "      <td>0.003989</td>\n",
       "      <td>0.803127</td>\n",
       "      <td>4.680714</td>\n",
       "      <td>-0.043941</td>\n",
       "      <td>120.533932</td>\n",
       "      <td>-1.114900</td>\n",
       "      <td>1.009439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-06</th>\n",
       "      <td>1.033173</td>\n",
       "      <td>-1.299617</td>\n",
       "      <td>0.171810</td>\n",
       "      <td>7.542089</td>\n",
       "      <td>0.004177</td>\n",
       "      <td>0.803127</td>\n",
       "      <td>4.680714</td>\n",
       "      <td>-0.043941</td>\n",
       "      <td>-3261.577099</td>\n",
       "      <td>-4.393262</td>\n",
       "      <td>1.051687</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>60750 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            liquidity  sharpe_ratio_20   roa_ttm  quick_ratio      MLEV  \\\n",
       "2017-05-08  -0.791590        -5.747721  0.009218          NaN  0.000000   \n",
       "2017-05-09  -0.817750        -5.720025  0.009218          NaN  0.000000   \n",
       "2017-05-10  -0.827541        -5.817354  0.009218          NaN  0.000000   \n",
       "2017-05-11  -0.846744        -5.059783  0.009218          NaN  0.000000   \n",
       "2017-05-12  -0.842516        -2.241047  0.009218          NaN  0.000000   \n",
       "...               ...              ...       ...          ...       ...   \n",
       "2022-04-27   1.074871        -1.382414  0.126993     5.465675  0.004217   \n",
       "2022-04-28   1.078710        -1.381964  0.171810     7.542089  0.004203   \n",
       "2022-04-29   1.068820        -1.304333  0.171810     7.542089  0.003994   \n",
       "2022-05-05   1.058341        -1.195184  0.171810     7.542089  0.003989   \n",
       "2022-05-06   1.033173        -1.299617  0.171810     7.542089  0.004177   \n",
       "\n",
       "                 GMI  cash_to_current_liability      ACCA  single_day_VPT  \\\n",
       "2017-05-08       NaN                        NaN -0.036616    -2064.770053   \n",
       "2017-05-09       NaN                        NaN -0.036616    -1828.758497   \n",
       "2017-05-10       NaN                        NaN -0.036616    -3735.927027   \n",
       "2017-05-11       NaN                        NaN -0.036616     3634.073703   \n",
       "2017-05-12       NaN                        NaN -0.036616    18621.769369   \n",
       "...              ...                        ...       ...             ...   \n",
       "2022-04-27  1.084031                   5.216915  0.000897     9968.011038   \n",
       "2022-04-28  1.084031                   4.680714 -0.043941    -7723.878400   \n",
       "2022-04-29  0.803127                   4.680714 -0.043941     6919.861252   \n",
       "2022-05-05  0.803127                   4.680714 -0.043941      120.533932   \n",
       "2022-05-06  0.803127                   4.680714 -0.043941    -3261.577099   \n",
       "\n",
       "              BIAS10      BBIC  \n",
       "2017-05-08 -1.245473  1.015998  \n",
       "2017-05-09 -1.795983  1.019660  \n",
       "2017-05-10 -2.484472  1.024833  \n",
       "2017-05-11 -1.315789  1.011890  \n",
       "2017-05-12  1.706970  0.984521  \n",
       "...              ...       ...  \n",
       "2022-04-27 -3.716541  1.025762  \n",
       "2022-04-28 -7.822206  1.068153  \n",
       "2022-04-29 -2.386948  1.015714  \n",
       "2022-05-05 -1.114900  1.009439  \n",
       "2022-05-06 -4.393262  1.051687  \n",
       "\n",
       "[60750 rows x 11 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "stock_data=stock_data.drop('stock_code',axis=1)\n",
    "stock_data.index=df.index\n",
    "stock_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "5dcb5e3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_data.isnull().any().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2bdfa8c5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_data=stock_data.fillna(0)\n",
    "stock_data.isnull().any().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "dd86f5e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal number of features : 1\n",
      "Best features : Index(['BBIC'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.feature_selection import RFECV\n",
    "x_train, x_test, y_train, y_test = train_test_split(stock_data,daily_return, test_size=0.3, random_state=42)\n",
    "clf = RandomForestClassifier(n_estimators =100,min_samples_leaf=5)      \n",
    "rfecv = RFECV(estimator=clf, step=1,cv=5,scoring='accuracy')  \n",
    "rf= rfecv.fit(x_train, y_train.astype('int'))\n",
    "print('Optimal number of features :', rf.n_features_)\n",
    "print('Best features :', x_train.columns[rf.support_])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d7ec773f",
   "metadata": {},
   "outputs": [],
   "source": [
    "factors_list=['bull_power','CCI20','cube_of_size','earnings_growth','raw_beta','boll_down','boll_up','MFI14','Kurtosis60','SGI','quick_ratio']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "0b45feb3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\anaconda3\\lib\\site-packages\\jqdatasdk\\api.py:34: PanelObsoleteWarning: 当前环境 pandas 版本高于 0.25，get_price 与 get_fundamentals_continuously 接口的 panel 参数将固定为 False（0.25 及以上版本的 pandas 不再支持 panel，如使用该数据结构和相关函数请注意修改）\n",
      "  warnings.warn(PandasChecker.VERSION_NOTICE_MESSAGE, PanelObsoleteWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2017-05-08', '2017-05-09', '2017-05-10', '2017-05-11',\n",
       "               '2017-05-12', '2017-05-15', '2017-05-16', '2017-05-17',\n",
       "               '2017-05-18', '2017-05-19',\n",
       "               ...\n",
       "               '2022-04-20', '2022-04-21', '2022-04-22', '2022-04-25',\n",
       "               '2022-04-26', '2022-04-27', '2022-04-28', '2022-04-29',\n",
       "               '2022-05-05', '2022-05-06'],\n",
       "              dtype='datetime64[ns]', length=1215, freq=None)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import jqdatasdk\n",
    "jqdatasdk.auth('18811386107', 'PClkjh123456')\n",
    "d1=jqdatasdk.get_price('603986.XSHG', start_date='2017-05-06', end_date='2022-05-06', frequency='daily', fields=None, skip_paused=False, fq='pre', panel=True)\n",
    "d1.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "a9651513",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bull_power</th>\n",
       "      <th>CCI20</th>\n",
       "      <th>cube_of_size</th>\n",
       "      <th>earnings_growth</th>\n",
       "      <th>raw_beta</th>\n",
       "      <th>boll_down</th>\n",
       "      <th>boll_up</th>\n",
       "      <th>MFI14</th>\n",
       "      <th>Kurtosis60</th>\n",
       "      <th>SGI</th>\n",
       "      <th>quick_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-05-08</th>\n",
       "      <td>-0.015069</td>\n",
       "      <td>-109.211776</td>\n",
       "      <td>-8.709430</td>\n",
       "      <td>0.069897</td>\n",
       "      <td>0.265349</td>\n",
       "      <td>0.979462</td>\n",
       "      <td>1.075726</td>\n",
       "      <td>34.308188</td>\n",
       "      <td>0.069945</td>\n",
       "      <td>1.097182</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-09</th>\n",
       "      <td>-0.015681</td>\n",
       "      <td>-110.501989</td>\n",
       "      <td>-8.656458</td>\n",
       "      <td>0.069897</td>\n",
       "      <td>0.259832</td>\n",
       "      <td>0.985374</td>\n",
       "      <td>1.076427</td>\n",
       "      <td>34.815235</td>\n",
       "      <td>0.100013</td>\n",
       "      <td>1.097182</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-10</th>\n",
       "      <td>-0.010666</td>\n",
       "      <td>-128.993370</td>\n",
       "      <td>-8.680877</td>\n",
       "      <td>0.069897</td>\n",
       "      <td>0.273169</td>\n",
       "      <td>0.992764</td>\n",
       "      <td>1.080939</td>\n",
       "      <td>34.478735</td>\n",
       "      <td>0.099543</td>\n",
       "      <td>1.097182</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-11</th>\n",
       "      <td>-0.012127</td>\n",
       "      <td>-116.651556</td>\n",
       "      <td>-8.798553</td>\n",
       "      <td>0.069897</td>\n",
       "      <td>0.274100</td>\n",
       "      <td>0.984459</td>\n",
       "      <td>1.061036</td>\n",
       "      <td>45.143318</td>\n",
       "      <td>0.001057</td>\n",
       "      <td>1.097182</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-12</th>\n",
       "      <td>0.014875</td>\n",
       "      <td>2.298851</td>\n",
       "      <td>-8.836206</td>\n",
       "      <td>0.069897</td>\n",
       "      <td>0.297579</td>\n",
       "      <td>0.959242</td>\n",
       "      <td>1.021877</td>\n",
       "      <td>49.121890</td>\n",
       "      <td>3.201417</td>\n",
       "      <td>1.097182</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2022-04-27</th>\n",
       "      <td>-0.034220</td>\n",
       "      <td>-131.028216</td>\n",
       "      <td>-10.091557</td>\n",
       "      <td>-0.020570</td>\n",
       "      <td>1.354647</td>\n",
       "      <td>0.935154</td>\n",
       "      <td>1.180286</td>\n",
       "      <td>41.733864</td>\n",
       "      <td>0.448102</td>\n",
       "      <td>1.404000</td>\n",
       "      <td>5.465675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-28</th>\n",
       "      <td>-0.033765</td>\n",
       "      <td>-130.124797</td>\n",
       "      <td>-9.894273</td>\n",
       "      <td>-0.020570</td>\n",
       "      <td>1.350326</td>\n",
       "      <td>0.972346</td>\n",
       "      <td>1.238341</td>\n",
       "      <td>34.626351</td>\n",
       "      <td>0.428624</td>\n",
       "      <td>1.404000</td>\n",
       "      <td>7.542089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-29</th>\n",
       "      <td>-0.006624</td>\n",
       "      <td>-74.745707</td>\n",
       "      <td>-9.974772</td>\n",
       "      <td>0.163761</td>\n",
       "      <td>1.365028</td>\n",
       "      <td>0.927676</td>\n",
       "      <td>1.154858</td>\n",
       "      <td>43.365755</td>\n",
       "      <td>0.224111</td>\n",
       "      <td>1.892467</td>\n",
       "      <td>7.542089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-05</th>\n",
       "      <td>0.006087</td>\n",
       "      <td>-47.474994</td>\n",
       "      <td>-10.088015</td>\n",
       "      <td>0.163761</td>\n",
       "      <td>1.362977</td>\n",
       "      <td>0.926831</td>\n",
       "      <td>1.140185</td>\n",
       "      <td>49.682242</td>\n",
       "      <td>0.236983</td>\n",
       "      <td>1.892467</td>\n",
       "      <td>7.542089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-06</th>\n",
       "      <td>-0.033977</td>\n",
       "      <td>-102.353538</td>\n",
       "      <td>-9.828834</td>\n",
       "      <td>0.163761</td>\n",
       "      <td>1.384201</td>\n",
       "      <td>0.965549</td>\n",
       "      <td>1.181986</td>\n",
       "      <td>50.390877</td>\n",
       "      <td>0.253536</td>\n",
       "      <td>1.892467</td>\n",
       "      <td>7.542089</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>60750 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            bull_power       CCI20  cube_of_size  earnings_growth  raw_beta  \\\n",
       "2017-05-08   -0.015069 -109.211776     -8.709430         0.069897  0.265349   \n",
       "2017-05-09   -0.015681 -110.501989     -8.656458         0.069897  0.259832   \n",
       "2017-05-10   -0.010666 -128.993370     -8.680877         0.069897  0.273169   \n",
       "2017-05-11   -0.012127 -116.651556     -8.798553         0.069897  0.274100   \n",
       "2017-05-12    0.014875    2.298851     -8.836206         0.069897  0.297579   \n",
       "...                ...         ...           ...              ...       ...   \n",
       "2022-04-27   -0.034220 -131.028216    -10.091557        -0.020570  1.354647   \n",
       "2022-04-28   -0.033765 -130.124797     -9.894273        -0.020570  1.350326   \n",
       "2022-04-29   -0.006624  -74.745707     -9.974772         0.163761  1.365028   \n",
       "2022-05-05    0.006087  -47.474994    -10.088015         0.163761  1.362977   \n",
       "2022-05-06   -0.033977 -102.353538     -9.828834         0.163761  1.384201   \n",
       "\n",
       "            boll_down   boll_up      MFI14  Kurtosis60       SGI  quick_ratio  \n",
       "2017-05-08   0.979462  1.075726  34.308188    0.069945  1.097182          NaN  \n",
       "2017-05-09   0.985374  1.076427  34.815235    0.100013  1.097182          NaN  \n",
       "2017-05-10   0.992764  1.080939  34.478735    0.099543  1.097182          NaN  \n",
       "2017-05-11   0.984459  1.061036  45.143318    0.001057  1.097182          NaN  \n",
       "2017-05-12   0.959242  1.021877  49.121890    3.201417  1.097182          NaN  \n",
       "...               ...       ...        ...         ...       ...          ...  \n",
       "2022-04-27   0.935154  1.180286  41.733864    0.448102  1.404000     5.465675  \n",
       "2022-04-28   0.972346  1.238341  34.626351    0.428624  1.404000     7.542089  \n",
       "2022-04-29   0.927676  1.154858  43.365755    0.224111  1.892467     7.542089  \n",
       "2022-05-05   0.926831  1.140185  49.682242    0.236983  1.892467     7.542089  \n",
       "2022-05-06   0.965549  1.181986  50.390877    0.253536  1.892467     7.542089  \n",
       "\n",
       "[60750 rows x 11 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np,pandas as pd\n",
    "stock_data=pd.DataFrame()\n",
    "for j in stocks:\n",
    "    df=pd.DataFrame()\n",
    "    for i in factors_list:\n",
    "        data=jqdatasdk.get_factor_values(\n",
    "        securities=j,\n",
    "        factors=i,\n",
    "        start_date='2017-05-06',\n",
    "        end_date='2022-05-06')\n",
    "        data=np.array(list(data.values()))\n",
    "        d=list(data[0,:,0])\n",
    "        df[i]=d\n",
    "    df.index=d1.index\n",
    "    stock_data=stock_data.append(df)\n",
    "stock_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "dd043bf1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns,matplotlib.pyplot as plt,pandas as pd\n",
    "f,ax = plt.subplots(figsize=(11, 9))\n",
    "sns.heatmap(stock_data.corr(), annot=True,  cmap=plt.get_cmap('Blues'),linewidths=.5, fmt= '.1f',ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "c5701f9a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\anaconda3\\lib\\site-packages\\jqdatasdk\\api.py:34: PanelObsoleteWarning: 当前环境 pandas 版本高于 0.25，get_price 与 get_fundamentals_continuously 接口的 panel 参数将固定为 False（0.25 及以上版本的 pandas 不再支持 panel，如使用该数据结构和相关函数请注意修改）\n",
      "  warnings.warn(PandasChecker.VERSION_NOTICE_MESSAGE, PanelObsoleteWarning)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>daily_return</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-05-08</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-09</th>\n",
       "      <td>-0.007157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-10</th>\n",
       "      <td>-0.010299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-11</th>\n",
       "      <td>0.010406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-12</th>\n",
       "      <td>0.030896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-27</th>\n",
       "      <td>0.077958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-28</th>\n",
       "      <td>-0.051520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-29</th>\n",
       "      <td>0.052716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-05</th>\n",
       "      <td>0.001282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-06</th>\n",
       "      <td>-0.045371</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>60750 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            daily_return\n",
       "2017-05-08      0.000000\n",
       "2017-05-09     -0.007157\n",
       "2017-05-10     -0.010299\n",
       "2017-05-11      0.010406\n",
       "2017-05-12      0.030896\n",
       "...                  ...\n",
       "2022-04-27      0.077958\n",
       "2022-04-28     -0.051520\n",
       "2022-04-29      0.052716\n",
       "2022-05-05      0.001282\n",
       "2022-05-06     -0.045371\n",
       "\n",
       "[60750 rows x 1 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd,numpy as np\n",
    "df=pd.DataFrame()\n",
    "for j in stocks:\n",
    "    data=jqdatasdk.get_price(j, start_date='2017-05-06', end_date='2022-05-06', frequency='daily', fields='close', skip_paused=False, fq='pre',panel=True)\n",
    "    data['daily_return']=data.pct_change().fillna(0)\n",
    "    df=df.append(data)\n",
    "df=df.drop('close',axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "4d84d8e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "daily_return=np.array(df['daily_return'].values.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "334fc8ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_data.isnull().any().any()\n",
    "stock_data=stock_data.fillna(0)\n",
    "stock_data.isnull().any().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "c6880fa9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal number of features : 1\n",
      "Best features : Index(['quick_ratio'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.feature_selection import RFECV\n",
    "x_train, x_test, y_train, y_test = train_test_split(stock_data,daily_return, test_size=0.3, random_state=42)\n",
    "clf = RandomForestClassifier(n_estimators =100,min_samples_leaf=5)      \n",
    "rfecv = RFECV(estimator=clf, step=1,cv=5,scoring='accuracy')  \n",
    "rf= rfecv.fit(x_train, y_train.astype('int'))\n",
    "print('Optimal number of features :', rf.n_features_)\n",
    "print('Best features :', x_train.columns[rf.support_])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9c629161",
   "metadata": {},
   "outputs": [],
   "source": [
    "factors_list=['circulating_market_cap','EBIT','financial_expense_ttm','net_working_capital','ATR6','MAWVAD','money_flow_20','VDEA','TVMA20','TVSTD20']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c23503d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\anaconda3\\lib\\site-packages\\jqdatasdk\\api.py:34: PanelObsoleteWarning: 当前环境 pandas 版本高于 0.25，get_price 与 get_fundamentals_continuously 接口的 panel 参数将固定为 False（0.25 及以上版本的 pandas 不再支持 panel，如使用该数据结构和相关函数请注意修改）\n",
      "  warnings.warn(PandasChecker.VERSION_NOTICE_MESSAGE, PanelObsoleteWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2017-05-08', '2017-05-09', '2017-05-10', '2017-05-11',\n",
       "               '2017-05-12', '2017-05-15', '2017-05-16', '2017-05-17',\n",
       "               '2017-05-18', '2017-05-19',\n",
       "               ...\n",
       "               '2022-04-20', '2022-04-21', '2022-04-22', '2022-04-25',\n",
       "               '2022-04-26', '2022-04-27', '2022-04-28', '2022-04-29',\n",
       "               '2022-05-05', '2022-05-06'],\n",
       "              dtype='datetime64[ns]', length=1215, freq=None)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import jqdatasdk\n",
    "jqdatasdk.auth('18811386107', 'PClkjh123456')\n",
    "d1=jqdatasdk.get_price('603986.XSHG', start_date='2017-05-06', end_date='2022-05-06', frequency='daily', fields=None, skip_paused=False, fq='pre', panel=True)\n",
    "d1.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0528ccc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>circulating_market_cap</th>\n",
       "      <th>EBIT</th>\n",
       "      <th>financial_expense_ttm</th>\n",
       "      <th>net_working_capital</th>\n",
       "      <th>ATR6</th>\n",
       "      <th>MAWVAD</th>\n",
       "      <th>money_flow_20</th>\n",
       "      <th>VDEA</th>\n",
       "      <th>TVMA20</th>\n",
       "      <th>TVSTD20</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-05-08</th>\n",
       "      <td>3.212476e+11</td>\n",
       "      <td>1.901700e+10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.178333</td>\n",
       "      <td>5.169753e+05</td>\n",
       "      <td>7.355468e+09</td>\n",
       "      <td>23053.376988</td>\n",
       "      <td>3.671845e+08</td>\n",
       "      <td>1.534138e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-09</th>\n",
       "      <td>3.190858e+11</td>\n",
       "      <td>1.901700e+10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.271667</td>\n",
       "      <td>-3.428668e+05</td>\n",
       "      <td>7.380477e+09</td>\n",
       "      <td>45378.079900</td>\n",
       "      <td>3.684446e+08</td>\n",
       "      <td>1.525768e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-10</th>\n",
       "      <td>3.158431e+11</td>\n",
       "      <td>1.901700e+10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>-9.697763e+05</td>\n",
       "      <td>7.500651e+09</td>\n",
       "      <td>67446.962788</td>\n",
       "      <td>3.745856e+08</td>\n",
       "      <td>1.519517e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-11</th>\n",
       "      <td>3.188696e+11</td>\n",
       "      <td>1.901700e+10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.805000</td>\n",
       "      <td>-9.446547e+05</td>\n",
       "      <td>7.549659e+09</td>\n",
       "      <td>86268.302029</td>\n",
       "      <td>3.769834e+08</td>\n",
       "      <td>1.519608e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-12</th>\n",
       "      <td>3.288140e+11</td>\n",
       "      <td>1.901700e+10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.306667</td>\n",
       "      <td>2.555114e+05</td>\n",
       "      <td>7.868335e+09</td>\n",
       "      <td>133142.264386</td>\n",
       "      <td>3.928874e+08</td>\n",
       "      <td>1.670226e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-27</th>\n",
       "      <td>7.718257e+10</td>\n",
       "      <td>9.247419e+08</td>\n",
       "      <td>-6.239810e+07</td>\n",
       "      <td>8.650150e+09</td>\n",
       "      <td>42.078333</td>\n",
       "      <td>-5.144813e+05</td>\n",
       "      <td>2.683205e+10</td>\n",
       "      <td>-103884.489608</td>\n",
       "      <td>1.340882e+09</td>\n",
       "      <td>3.593279e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-28</th>\n",
       "      <td>7.320613e+10</td>\n",
       "      <td>7.317224e+08</td>\n",
       "      <td>-1.151183e+08</td>\n",
       "      <td>1.016336e+10</td>\n",
       "      <td>46.573333</td>\n",
       "      <td>-1.106110e+06</td>\n",
       "      <td>2.642080e+10</td>\n",
       "      <td>-78927.037335</td>\n",
       "      <td>1.320665e+09</td>\n",
       "      <td>3.174925e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-29</th>\n",
       "      <td>7.706525e+10</td>\n",
       "      <td>7.317224e+08</td>\n",
       "      <td>-1.151183e+08</td>\n",
       "      <td>1.016336e+10</td>\n",
       "      <td>49.708333</td>\n",
       "      <td>-1.702862e+06</td>\n",
       "      <td>2.643786e+10</td>\n",
       "      <td>-52611.652557</td>\n",
       "      <td>1.321480e+09</td>\n",
       "      <td>3.182612e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-05</th>\n",
       "      <td>7.716405e+10</td>\n",
       "      <td>7.317224e+08</td>\n",
       "      <td>-1.151183e+08</td>\n",
       "      <td>1.016336e+10</td>\n",
       "      <td>47.585000</td>\n",
       "      <td>-1.926714e+06</td>\n",
       "      <td>2.595677e+10</td>\n",
       "      <td>-37387.020507</td>\n",
       "      <td>1.297639e+09</td>\n",
       "      <td>3.093354e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-06</th>\n",
       "      <td>7.366305e+10</td>\n",
       "      <td>7.317224e+08</td>\n",
       "      <td>-1.151183e+08</td>\n",
       "      <td>1.016336e+10</td>\n",
       "      <td>46.166667</td>\n",
       "      <td>-1.768693e+06</td>\n",
       "      <td>2.576001e+10</td>\n",
       "      <td>-36176.299606</td>\n",
       "      <td>1.287881e+09</td>\n",
       "      <td>3.201516e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>60750 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            circulating_market_cap          EBIT  financial_expense_ttm  \\\n",
       "2017-05-08            3.212476e+11  1.901700e+10                    NaN   \n",
       "2017-05-09            3.190858e+11  1.901700e+10                    NaN   \n",
       "2017-05-10            3.158431e+11  1.901700e+10                    NaN   \n",
       "2017-05-11            3.188696e+11  1.901700e+10                    NaN   \n",
       "2017-05-12            3.288140e+11  1.901700e+10                    NaN   \n",
       "...                            ...           ...                    ...   \n",
       "2022-04-27            7.718257e+10  9.247419e+08          -6.239810e+07   \n",
       "2022-04-28            7.320613e+10  7.317224e+08          -1.151183e+08   \n",
       "2022-04-29            7.706525e+10  7.317224e+08          -1.151183e+08   \n",
       "2022-05-05            7.716405e+10  7.317224e+08          -1.151183e+08   \n",
       "2022-05-06            7.366305e+10  7.317224e+08          -1.151183e+08   \n",
       "\n",
       "            net_working_capital       ATR6        MAWVAD  money_flow_20  \\\n",
       "2017-05-08                  NaN   2.178333  5.169753e+05   7.355468e+09   \n",
       "2017-05-09                  NaN   2.271667 -3.428668e+05   7.380477e+09   \n",
       "2017-05-10                  NaN   2.600000 -9.697763e+05   7.500651e+09   \n",
       "2017-05-11                  NaN   2.805000 -9.446547e+05   7.549659e+09   \n",
       "2017-05-12                  NaN   3.306667  2.555114e+05   7.868335e+09   \n",
       "...                         ...        ...           ...            ...   \n",
       "2022-04-27         8.650150e+09  42.078333 -5.144813e+05   2.683205e+10   \n",
       "2022-04-28         1.016336e+10  46.573333 -1.106110e+06   2.642080e+10   \n",
       "2022-04-29         1.016336e+10  49.708333 -1.702862e+06   2.643786e+10   \n",
       "2022-05-05         1.016336e+10  47.585000 -1.926714e+06   2.595677e+10   \n",
       "2022-05-06         1.016336e+10  46.166667 -1.768693e+06   2.576001e+10   \n",
       "\n",
       "                     VDEA        TVMA20       TVSTD20  \n",
       "2017-05-08   23053.376988  3.671845e+08  1.534138e+08  \n",
       "2017-05-09   45378.079900  3.684446e+08  1.525768e+08  \n",
       "2017-05-10   67446.962788  3.745856e+08  1.519517e+08  \n",
       "2017-05-11   86268.302029  3.769834e+08  1.519608e+08  \n",
       "2017-05-12  133142.264386  3.928874e+08  1.670226e+08  \n",
       "...                   ...           ...           ...  \n",
       "2022-04-27 -103884.489608  1.340882e+09  3.593279e+08  \n",
       "2022-04-28  -78927.037335  1.320665e+09  3.174925e+08  \n",
       "2022-04-29  -52611.652557  1.321480e+09  3.182612e+08  \n",
       "2022-05-05  -37387.020507  1.297639e+09  3.093354e+08  \n",
       "2022-05-06  -36176.299606  1.287881e+09  3.201516e+08  \n",
       "\n",
       "[60750 rows x 10 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks =jqdatasdk.get_index_stocks('000050.XSHG')\n",
    "import numpy as np,pandas as pd\n",
    "stock_data=pd.DataFrame()\n",
    "for j in stocks:\n",
    "    df=pd.DataFrame()\n",
    "    for i in factors_list:\n",
    "        data=jqdatasdk.get_factor_values(\n",
    "        securities=j,\n",
    "        factors=i,\n",
    "        start_date='2017-05-06',\n",
    "        end_date='2022-05-06')\n",
    "        data=np.array(list(data.values()))\n",
    "        d=list(data[0,:,0])\n",
    "        df[i]=d\n",
    "    df.index=d1.index\n",
    "    stock_data=stock_data.append(df)\n",
    "stock_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c40157a1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns,matplotlib.pyplot as plt,pandas as pd\n",
    "f,ax = plt.subplots(figsize=(11, 9))\n",
    "sns.heatmap(stock_data.corr(), annot=True,  cmap=plt.get_cmap('Blues'),linewidths=.5, fmt= '.1f',ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2c8095a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns,matplotlib.pyplot as plt,pandas as pd\n",
    "f,ax = plt.subplots(figsize=(11, 9))\n",
    "stock_data=stock_data.drop(['TVSTD20'],axis=1)\n",
    "sns.heatmap(stock_data.corr(), annot=True,  cmap=plt.get_cmap('Blues'),linewidths=.5, fmt= '.1f',ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "18fa84d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_data.isnull().any().any()\n",
    "stock_data=stock_data.fillna(0)\n",
    "stock_data.isnull().any().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "375fbfc6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\anaconda3\\lib\\site-packages\\jqdatasdk\\api.py:34: PanelObsoleteWarning: 当前环境 pandas 版本高于 0.25，get_price 与 get_fundamentals_continuously 接口的 panel 参数将固定为 False（0.25 及以上版本的 pandas 不再支持 panel，如使用该数据结构和相关函数请注意修改）\n",
      "  warnings.warn(PandasChecker.VERSION_NOTICE_MESSAGE, PanelObsoleteWarning)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>daily_return</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-05-08</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-09</th>\n",
       "      <td>-0.007157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-10</th>\n",
       "      <td>-0.010299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-11</th>\n",
       "      <td>0.010406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-12</th>\n",
       "      <td>0.030896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-27</th>\n",
       "      <td>0.077958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-28</th>\n",
       "      <td>-0.051520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-29</th>\n",
       "      <td>0.052716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-05</th>\n",
       "      <td>0.001282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-06</th>\n",
       "      <td>-0.045371</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>60750 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            daily_return\n",
       "2017-05-08      0.000000\n",
       "2017-05-09     -0.007157\n",
       "2017-05-10     -0.010299\n",
       "2017-05-11      0.010406\n",
       "2017-05-12      0.030896\n",
       "...                  ...\n",
       "2022-04-27      0.077958\n",
       "2022-04-28     -0.051520\n",
       "2022-04-29      0.052716\n",
       "2022-05-05      0.001282\n",
       "2022-05-06     -0.045371\n",
       "\n",
       "[60750 rows x 1 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd,numpy as np\n",
    "df=pd.DataFrame()\n",
    "for j in stocks:\n",
    "    data=jqdatasdk.get_price(j, start_date='2017-05-06', end_date='2022-05-06', frequency='daily', fields='close', skip_paused=False, fq='pre',panel=True)\n",
    "    data['daily_return']=data.pct_change().fillna(0)\n",
    "    df=df.append(data)\n",
    "df=df.drop('close',axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9395f37a",
   "metadata": {},
   "outputs": [],
   "source": [
    "daily_return=np.array(df['daily_return'].values.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "74cc1cef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal number of features : 1\n",
      "Best features : Index(['TVMA20'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.feature_selection import RFECV\n",
    "x_train, x_test, y_train, y_test = train_test_split(stock_data,daily_return, test_size=0.3, random_state=42)\n",
    "clf = RandomForestClassifier(n_estimators =100,min_samples_leaf=5)      \n",
    "rfecv = RFECV(estimator=clf, step=1,cv=5,scoring='accuracy')  \n",
    "rf= rfecv.fit(x_train, y_train.astype('int'))\n",
    "print('Optimal number of features :', rf.n_features_)\n",
    "print('Best features :', x_train.columns[rf.support_])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e184bfc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "factors_list=['VOL60','VROC12','total_profit_growth_rate','bear_power','bull_power','CR20','MASS','PLRC12','ROC20','MLEV','maximum_margin','Skewness60','VMACD']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ebf162bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\anaconda3\\lib\\site-packages\\jqdatasdk\\api.py:34: PanelObsoleteWarning: 当前环境 pandas 版本高于 0.25，get_price 与 get_fundamentals_continuously 接口的 panel 参数将固定为 False（0.25 及以上版本的 pandas 不再支持 panel，如使用该数据结构和相关函数请注意修改）\n",
      "  warnings.warn(PandasChecker.VERSION_NOTICE_MESSAGE, PanelObsoleteWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2017-05-08', '2017-05-09', '2017-05-10', '2017-05-11',\n",
       "               '2017-05-12', '2017-05-15', '2017-05-16', '2017-05-17',\n",
       "               '2017-05-18', '2017-05-19',\n",
       "               ...\n",
       "               '2022-04-20', '2022-04-21', '2022-04-22', '2022-04-25',\n",
       "               '2022-04-26', '2022-04-27', '2022-04-28', '2022-04-29',\n",
       "               '2022-05-05', '2022-05-06'],\n",
       "              dtype='datetime64[ns]', length=1215, freq=None)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import jqdatasdk\n",
    "jqdatasdk.auth('18811386107', 'PClkjh123456')\n",
    "d1=jqdatasdk.get_price('603986.XSHG', start_date='2017-05-06', end_date='2022-05-06', frequency='daily', fields=None, skip_paused=False, fq='pre', panel=True)\n",
    "d1.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "48187b1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VOL60</th>\n",
       "      <th>VROC12</th>\n",
       "      <th>total_profit_growth_rate</th>\n",
       "      <th>bear_power</th>\n",
       "      <th>bull_power</th>\n",
       "      <th>CR20</th>\n",
       "      <th>MASS</th>\n",
       "      <th>PLRC12</th>\n",
       "      <th>ROC20</th>\n",
       "      <th>MLEV</th>\n",
       "      <th>maximum_margin</th>\n",
       "      <th>Skewness60</th>\n",
       "      <th>VMACD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-05-08</th>\n",
       "      <td>0.097167</td>\n",
       "      <td>6.105905</td>\n",
       "      <td>0.000099</td>\n",
       "      <td>-0.041349</td>\n",
       "      <td>-0.015069</td>\n",
       "      <td>30.245747</td>\n",
       "      <td>25.282888</td>\n",
       "      <td>-0.000321</td>\n",
       "      <td>-7.679604</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.227340</td>\n",
       "      <td>181759.928575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-09</th>\n",
       "      <td>0.097667</td>\n",
       "      <td>-15.262767</td>\n",
       "      <td>0.000099</td>\n",
       "      <td>-0.027937</td>\n",
       "      <td>-0.015681</td>\n",
       "      <td>33.918129</td>\n",
       "      <td>25.223625</td>\n",
       "      <td>-0.001433</td>\n",
       "      <td>-7.577019</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.288440</td>\n",
       "      <td>89298.811646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-10</th>\n",
       "      <td>0.098667</td>\n",
       "      <td>60.878916</td>\n",
       "      <td>0.000099</td>\n",
       "      <td>-0.031205</td>\n",
       "      <td>-0.010666</td>\n",
       "      <td>37.204724</td>\n",
       "      <td>25.216511</td>\n",
       "      <td>-0.002440</td>\n",
       "      <td>-8.033473</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.348938</td>\n",
       "      <td>88275.531552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-11</th>\n",
       "      <td>0.099167</td>\n",
       "      <td>110.489161</td>\n",
       "      <td>0.000099</td>\n",
       "      <td>-0.028430</td>\n",
       "      <td>-0.012127</td>\n",
       "      <td>37.992126</td>\n",
       "      <td>25.183663</td>\n",
       "      <td>-0.003033</td>\n",
       "      <td>-7.112971</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.362788</td>\n",
       "      <td>75285.356964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-12</th>\n",
       "      <td>0.101667</td>\n",
       "      <td>206.116617</td>\n",
       "      <td>0.000099</td>\n",
       "      <td>-0.018006</td>\n",
       "      <td>0.014875</td>\n",
       "      <td>55.942623</td>\n",
       "      <td>25.410880</td>\n",
       "      <td>-0.002171</td>\n",
       "      <td>-3.459916</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.165982</td>\n",
       "      <td>187495.849427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-27</th>\n",
       "      <td>1.779722</td>\n",
       "      <td>36.670117</td>\n",
       "      <td>1.401083</td>\n",
       "      <td>-0.136623</td>\n",
       "      <td>-0.034220</td>\n",
       "      <td>43.435656</td>\n",
       "      <td>24.394278</td>\n",
       "      <td>-0.004802</td>\n",
       "      <td>-16.566547</td>\n",
       "      <td>0.004217</td>\n",
       "      <td>0.919619</td>\n",
       "      <td>0.489358</td>\n",
       "      <td>49818.139988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-28</th>\n",
       "      <td>1.770153</td>\n",
       "      <td>66.618736</td>\n",
       "      <td>1.687635</td>\n",
       "      <td>-0.101244</td>\n",
       "      <td>-0.033765</td>\n",
       "      <td>52.381474</td>\n",
       "      <td>24.573026</td>\n",
       "      <td>-0.008669</td>\n",
       "      <td>-16.430535</td>\n",
       "      <td>0.004203</td>\n",
       "      <td>0.948893</td>\n",
       "      <td>0.476443</td>\n",
       "      <td>99829.809094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-29</th>\n",
       "      <td>1.775220</td>\n",
       "      <td>0.621570</td>\n",
       "      <td>1.687635</td>\n",
       "      <td>-0.063997</td>\n",
       "      <td>-0.006624</td>\n",
       "      <td>59.742968</td>\n",
       "      <td>24.732013</td>\n",
       "      <td>-0.011502</td>\n",
       "      <td>-14.857767</td>\n",
       "      <td>0.003994</td>\n",
       "      <td>0.947590</td>\n",
       "      <td>0.455686</td>\n",
       "      <td>105261.539111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-05</th>\n",
       "      <td>1.770782</td>\n",
       "      <td>-21.098208</td>\n",
       "      <td>1.687635</td>\n",
       "      <td>-0.028641</td>\n",
       "      <td>0.006087</td>\n",
       "      <td>68.776525</td>\n",
       "      <td>24.922531</td>\n",
       "      <td>-0.011839</td>\n",
       "      <td>-11.387648</td>\n",
       "      <td>0.003989</td>\n",
       "      <td>0.948254</td>\n",
       "      <td>0.427774</td>\n",
       "      <td>60898.528200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-06</th>\n",
       "      <td>1.750547</td>\n",
       "      <td>-40.598120</td>\n",
       "      <td>1.687635</td>\n",
       "      <td>-0.058194</td>\n",
       "      <td>-0.033977</td>\n",
       "      <td>66.126494</td>\n",
       "      <td>25.080147</td>\n",
       "      <td>-0.013001</td>\n",
       "      <td>-15.052692</td>\n",
       "      <td>0.004177</td>\n",
       "      <td>0.948577</td>\n",
       "      <td>0.439746</td>\n",
       "      <td>4842.883604</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>60750 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               VOL60      VROC12  total_profit_growth_rate  bear_power  \\\n",
       "2017-05-08  0.097167    6.105905                  0.000099   -0.041349   \n",
       "2017-05-09  0.097667  -15.262767                  0.000099   -0.027937   \n",
       "2017-05-10  0.098667   60.878916                  0.000099   -0.031205   \n",
       "2017-05-11  0.099167  110.489161                  0.000099   -0.028430   \n",
       "2017-05-12  0.101667  206.116617                  0.000099   -0.018006   \n",
       "...              ...         ...                       ...         ...   \n",
       "2022-04-27  1.779722   36.670117                  1.401083   -0.136623   \n",
       "2022-04-28  1.770153   66.618736                  1.687635   -0.101244   \n",
       "2022-04-29  1.775220    0.621570                  1.687635   -0.063997   \n",
       "2022-05-05  1.770782  -21.098208                  1.687635   -0.028641   \n",
       "2022-05-06  1.750547  -40.598120                  1.687635   -0.058194   \n",
       "\n",
       "            bull_power       CR20       MASS    PLRC12      ROC20      MLEV  \\\n",
       "2017-05-08   -0.015069  30.245747  25.282888 -0.000321  -7.679604  0.000000   \n",
       "2017-05-09   -0.015681  33.918129  25.223625 -0.001433  -7.577019  0.000000   \n",
       "2017-05-10   -0.010666  37.204724  25.216511 -0.002440  -8.033473  0.000000   \n",
       "2017-05-11   -0.012127  37.992126  25.183663 -0.003033  -7.112971  0.000000   \n",
       "2017-05-12    0.014875  55.942623  25.410880 -0.002171  -3.459916  0.000000   \n",
       "...                ...        ...        ...       ...        ...       ...   \n",
       "2022-04-27   -0.034220  43.435656  24.394278 -0.004802 -16.566547  0.004217   \n",
       "2022-04-28   -0.033765  52.381474  24.573026 -0.008669 -16.430535  0.004203   \n",
       "2022-04-29   -0.006624  59.742968  24.732013 -0.011502 -14.857767  0.003994   \n",
       "2022-05-05    0.006087  68.776525  24.922531 -0.011839 -11.387648  0.003989   \n",
       "2022-05-06   -0.033977  66.126494  25.080147 -0.013001 -15.052692  0.004177   \n",
       "\n",
       "            maximum_margin  Skewness60          VMACD  \n",
       "2017-05-08             NaN    0.227340  181759.928575  \n",
       "2017-05-09             NaN    0.288440   89298.811646  \n",
       "2017-05-10             NaN    0.348938   88275.531552  \n",
       "2017-05-11             NaN    0.362788   75285.356964  \n",
       "2017-05-12             NaN    1.165982  187495.849427  \n",
       "...                    ...         ...            ...  \n",
       "2022-04-27        0.919619    0.489358   49818.139988  \n",
       "2022-04-28        0.948893    0.476443   99829.809094  \n",
       "2022-04-29        0.947590    0.455686  105261.539111  \n",
       "2022-05-05        0.948254    0.427774   60898.528200  \n",
       "2022-05-06        0.948577    0.439746    4842.883604  \n",
       "\n",
       "[60750 rows x 13 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks =jqdatasdk.get_index_stocks('000050.XSHG')\n",
    "import numpy as np,pandas as pd\n",
    "stock_data=pd.DataFrame()\n",
    "for j in stocks:\n",
    "    df=pd.DataFrame()\n",
    "    for i in factors_list:\n",
    "        data=jqdatasdk.get_factor_values(\n",
    "        securities=j,\n",
    "        factors=i,\n",
    "        start_date='2017-05-06',\n",
    "        end_date='2022-05-06')\n",
    "        data=np.array(list(data.values()))\n",
    "        d=list(data[0,:,0])\n",
    "        df[i]=d\n",
    "    df.index=d1.index\n",
    "    stock_data=stock_data.append(df)\n",
    "stock_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9ea753c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns,matplotlib.pyplot as plt,pandas as pd\n",
    "f,ax = plt.subplots(figsize=(11, 9))\n",
    "stock_data=stock_data.drop('bull_power',axis=1)\n",
    "sns.heatmap(stock_data.corr(), annot=True,  cmap=plt.get_cmap('Blues'),linewidths=.5, fmt= '.1f',ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8c804b9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_data.isnull().any().any()\n",
    "stock_data=stock_data.fillna(0)\n",
    "stock_data.isnull().any().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e548717d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\anaconda3\\lib\\site-packages\\jqdatasdk\\api.py:34: PanelObsoleteWarning: 当前环境 pandas 版本高于 0.25，get_price 与 get_fundamentals_continuously 接口的 panel 参数将固定为 False（0.25 及以上版本的 pandas 不再支持 panel，如使用该数据结构和相关函数请注意修改）\n",
      "  warnings.warn(PandasChecker.VERSION_NOTICE_MESSAGE, PanelObsoleteWarning)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>daily_return</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-05-08</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-09</th>\n",
       "      <td>-0.007157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-10</th>\n",
       "      <td>-0.010299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-11</th>\n",
       "      <td>0.010406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-12</th>\n",
       "      <td>0.030896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-27</th>\n",
       "      <td>0.077958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-28</th>\n",
       "      <td>-0.051520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-29</th>\n",
       "      <td>0.052716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-05</th>\n",
       "      <td>0.001282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-06</th>\n",
       "      <td>-0.045371</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>60750 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            daily_return\n",
       "2017-05-08      0.000000\n",
       "2017-05-09     -0.007157\n",
       "2017-05-10     -0.010299\n",
       "2017-05-11      0.010406\n",
       "2017-05-12      0.030896\n",
       "...                  ...\n",
       "2022-04-27      0.077958\n",
       "2022-04-28     -0.051520\n",
       "2022-04-29      0.052716\n",
       "2022-05-05      0.001282\n",
       "2022-05-06     -0.045371\n",
       "\n",
       "[60750 rows x 1 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd,numpy as np\n",
    "df=pd.DataFrame()\n",
    "for j in stocks:\n",
    "    data=jqdatasdk.get_price(j, start_date='2017-05-06', end_date='2022-05-06', frequency='daily', fields='close', skip_paused=False, fq='pre',panel=True)\n",
    "    data['daily_return']=data.pct_change().fillna(0)\n",
    "    df=df.append(data)\n",
    "df=df.drop('close',axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b59e2097",
   "metadata": {},
   "outputs": [],
   "source": [
    "daily_return=np.array(df['daily_return'].values.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "64b11c1f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal number of features : 1\n",
      "Best features : Index(['VMACD'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.feature_selection import RFECV\n",
    "x_train, x_test, y_train, y_test = train_test_split(stock_data,daily_return, test_size=0.3, random_state=42)\n",
    "clf = RandomForestClassifier(n_estimators =100,min_samples_leaf=5)      \n",
    "rfecv = RFECV(estimator=clf, step=1,cv=5,scoring='accuracy')  \n",
    "rf= rfecv.fit(x_train, y_train.astype('int'))\n",
    "print('Optimal number of features :', rf.n_features_)\n",
    "print('Best features :', x_train.columns[rf.support_])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eadfc623",
   "metadata": {},
   "source": [
    "因子筛选结果：<br>\n",
    "MAC120(120日移动均线,技术指标因子),<br>\n",
    "BBIC(BBI 动量,动量类因子),<br>\n",
    "quick_ratio(速动比率,质量类因子),<br>\n",
    "TVMA20(20日成交金额的移动平均值,情绪类因子),<br>\n",
    "VMACD(成交量指数平滑异同移动平均线,情绪类因子)"
   ]
  }
 ],
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