input_data_preparation 1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.io as sio\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime\n",
    "import idx_w_stock_basic_filter\n",
    "from sklearn.preprocessing import normalize\n",
    "\n",
    "return_threshold = 0.097 # 涨停bar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Helper Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_date_interval_idx(time,start,end):\n",
    "    '''\n",
    "    得到时间区间开头和结尾的idx\n",
    "    '''\n",
    "    time = np.array(time)\n",
    "    start_idx = np.where(time>=start)\n",
    "    end_idx   = np.where(time<=end)\n",
    "    return start_idx[0][0],end_idx[0][-1]+1\n",
    "\n",
    "def complete_all_datelist_info(stock_data5,stock_data10,datelist):\n",
    "    '''\n",
    "    个股某一天某些数据有nan则这一天的个股数据都是nan\n",
    "    '''\n",
    "    idx5  = np.unique((stock_data5 != stock_data5).nonzero()[0])\n",
    "    idx10 = np.unique((stock_data10 != stock_data10).nonzero()[0])\n",
    "    stock_data5[idx5,1:]   = np.nan\n",
    "    stock_data10[idx10,1:] = np.nan\n",
    "\n",
    "    complete_stock_data_5  = np.empty((len(datelist),stock_data5.shape[1]))\n",
    "    complete_stock_data_10 = np.empty((len(datelist),stock_data10.shape[1]))\n",
    "    iter_ = 0\n",
    "    for date in datelist:\n",
    "        if date in stock_data5[:,0]:\n",
    "            idx = np.where(stock_data5[:,0] == date)\n",
    "            idx = idx[0][0]\n",
    "            complete_stock_data_5[iter_,:] = stock_data5[idx,:]\n",
    "            complete_stock_data_10[iter_,:] = stock_data10[idx,:]\n",
    "        else:\n",
    "            complete_stock_data_5[iter_,0]  = date\n",
    "            complete_stock_data_5[iter_,1:] = np.nan\n",
    "            complete_stock_data_10[iter_,0]  = date\n",
    "            complete_stock_data_10[iter_,:] = np.nan\n",
    "        iter_ += 1\n",
    "    return complete_stock_data_5,complete_stock_data_10\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_data(ts_code):\n",
    "    mat_daily       = sio.loadmat('../daily/' + ts_code + '.mat')\n",
    "    mat_daily_basic = sio.loadmat('../daily_basic/'+ ts_code + '.mat')\n",
    "    daily           = mat_daily['daily']\n",
    "    daily_basic     = mat_daily_basic['daily_basic']\n",
    "    start_date      = 20160101\n",
    "    end_date        = 20200801\n",
    "    '''\n",
    "    daily\n",
    "    '''\n",
    "    # read data starting from 2016/01 to 2020/08/01\n",
    "    time_daily   = daily[0,0]['time']\n",
    "    start_index_daily,end_index_daily  = get_date_interval_idx(time_daily,start_date,end_date)\n",
    "    open_        = daily[0,0]['open'][start_index_daily:end_index_daily]\n",
    "    high         = daily[0,0]['high'][start_index_daily:end_index_daily]\n",
    "    low          = daily[0,0]['low'][start_index_daily:end_index_daily]\n",
    "    close        = daily[0,0]['close'][start_index_daily:end_index_daily]\n",
    "    volume       = daily[0,0]['volume'][start_index_daily:end_index_daily] # 成交量\n",
    "    turnover     = daily[0,0]['turnover'][start_index_daily:end_index_daily] # 成交额\n",
    "    adj_factor   = daily[0,0]['adj_factor'][start_index_daily:end_index_daily] # 复权因子\n",
    "    time_daily   = time_daily[start_index_daily:end_index_daily]\n",
    "    matrix_daily = np.concatenate((time_daily,open_,high,low,close,volume,turnover,adj_factor),axis=1)\n",
    "    matrix_daily = matrix_daily[np.lexsort((matrix_daily[:,-1],matrix_daily[:,0]))] # sort the matrix by time then by adj_factor\n",
    "    # remove duplicate daily: duplicate rows of same time\n",
    "    _,de_duplicate_index = np.unique(matrix_daily[:,0],return_index=True)\n",
    "    matrix_daily = matrix_daily[de_duplicate_index]\n",
    "\n",
    "    '''\n",
    "    daily_basic\n",
    "    '''\n",
    "    # read data starting from 2016/01 to 2020/08/01\n",
    "    time_daily_basic  = daily_basic[0,0]['time'] # obtain time in the format yyyymmdd as integer\n",
    "    start_index_daily_basic,end_index_daily_basic = get_date_interval_idx(time_daily_basic,start_date,end_date)\n",
    "    turnover_rate      = daily_basic[0,0]['turnover_rate'][start_index_daily_basic:end_index_daily_basic]\n",
    "    turnover_rate_free = daily_basic[0,0]['turnover_rate_free'][start_index_daily_basic:end_index_daily_basic]\n",
    "    float_share        = daily_basic[0,0]['float_share'][start_index_daily_basic:end_index_daily_basic]\n",
    "    free_share         = daily_basic[0,0]['free_share'][start_index_daily_basic:end_index_daily_basic]\n",
    "    time_daily_basic   = time_daily_basic[start_index_daily_basic:end_index_daily_basic]\n",
    "    matrix_daily_basic = np.concatenate((time_daily_basic,turnover_rate,turnover_rate_free,float_share,free_share),axis=1)\n",
    "    matrix_daily_basic = matrix_daily_basic[matrix_daily_basic[:,0].argsort()] # sort the matrix by time   \n",
    "    # remove duplicate daily_basic: duplicate rows of same time\n",
    "    _,de_duplicate_index = np.unique(matrix_daily_basic[:,0],return_index=True)\n",
    "    matrix_daily_basic = matrix_daily_basic[de_duplicate_index]\n",
    "    return matrix_daily, matrix_daily_basic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 前复权; vwap, turn, free turn; 得第二交易日是否涨跌停; Label: return5 and return10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_adjust(daily):\n",
    "    last_adj = daily[:,-1][-1]\n",
    "    adj = (daily[:,-1] / last_adj).reshape(daily.shape[0],1)\n",
    "    daily[:,1:5] = daily[:,1:5]*adj\n",
    "    daily = daily[:,:7]\n",
    "    return daily\n",
    "\n",
    "def get_return_vwap_turnover(daily,daily_basic):\n",
    "    # get return\n",
    "    close          = daily[:,4].reshape(len(daily),1)\n",
    "    close_shift    = np.roll(close,-1)\n",
    "    close_shift[-1] = np.nan\n",
    "    return1        = (close_shift - close) / close\n",
    "    # get vwap\n",
    "    volume         = daily[:,5].reshape(len(daily),1)\n",
    "    turnover       = daily[:,6].reshape(len(daily),1)\n",
    "    vwap           = turnover / volume\n",
    "    # get turnover \n",
    "    float_share    = daily_basic[:,3].reshape(len(daily_basic),1)\n",
    "    free_share     = daily_basic[:,4].reshape(len(daily_basic),1)\n",
    "    turn           = volume / float_share\n",
    "    free_turn      = volume / free_share\n",
    "    daily      = daily[:,:5]\n",
    "    stock_data = np.concatenate((daily,vwap,volume,return1,turn,free_turn),axis=1)\n",
    "    return stock_data\n",
    "\n",
    "def get_reach_limit(stock_data):\n",
    "    return1     = stock_data[:,7].reshape(len(stock_data),1)\n",
    "    limit_up    = (return1 > return_threshold)*1  #涨停\n",
    "    limit_down  = (return1 < -return_threshold)*1 #跌停\n",
    "    limit       = limit_up+limit_down             #涨跌停的交易日\n",
    "    # 每个交易日得到下一个交易日是否涨跌停\n",
    "    limit       = np.roll(limit,-1).reshape(len(stock_data),1)\n",
    "    return np.concatenate((stock_data,limit),axis=1)\n",
    "\n",
    "def get_return(stock_data):\n",
    "    close   = stock_data[:,4].reshape(len(stock_data),1)\n",
    "    close5  = np.roll(close,-5).reshape(len(stock_data),1)\n",
    "    close5[-5:,:]  = np.nan\n",
    "    close10 = np.roll(close,-10).reshape(len(stock_data),1)\n",
    "    close10[-10:,:] = np.nan\n",
    "    return5  = (close5 - close) / close\n",
    "    return10 = (close10 - close) / close\n",
    "    stock_data5  = np.concatenate((stock_data,return5),axis=1)\n",
    "    stock_data10 = np.concatenate((stock_data,return10),axis=1)\n",
    "    return stock_data5, stock_data10\n",
    "\n",
    "def get_datelist(ts_code_list):\n",
    "    datelist = np.array([])\n",
    "    for ts_code in ts_code_list:\n",
    "        daily, daily_basic = load_data(ts_code)\n",
    "        date_daily       = daily[:,0]\n",
    "        date_daily_basic = daily_basic[:,0]\n",
    "        datelist = np.concatenate((datelist,date_daily,date_daily_basic),axis=0)\n",
    "        datelist = np.unique(np.sort(datelist,axis=0))\n",
    "    return datelist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 处理原始数据，为生成数据图片准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_raw_data(ts_code:str,datelist:list):\n",
    "    daily,daily_basic = load_data(ts_code)\n",
    "    daily = split_adjust(daily) # 前复权，从矩阵中去除adj_factor\n",
    "    stock_data = get_return_vwap_turnover(daily,daily_basic) # 计算vwap, turn, free turn, 从矩阵去除turnover\n",
    "    stock_data = get_reach_limit(stock_data) # 得到下一交易日是否涨跌停, (0 or 1)\n",
    "    stock_data5, stock_data10 = get_return(stock_data) # 计算个股5天后收益和个股10天后收益\n",
    "    # 有所有交易日数据，如果一个交易日有nan则整个交易日所有数据都是nan\n",
    "    stock_data5, stock_data10 = complete_all_datelist_info(stock_data5,stock_data10,datelist)\n",
    "    return stock_data5, stock_data10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## debug"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "daily,daily_basic = load_data('002266.SZ')\n",
    "ts_code_list = idx_w_stock_basic_filter.get_filtered_ts_code()[:,0]\n",
    "datelist = get_datelist(ts_code_list)\n",
    "# datelist = [datetime.strptime(str(int(item)),\"%Y%m%d\") for item in datelist]\n",
    "\n",
    "df_daily       = pd.DataFrame(daily,columns=['date','open','high','low','close','volume','turnover','adj_factor'])\n",
    "df_daily_basic = pd.DataFrame(daily_basic,columns=['date','turnover_rate','turnover_rate_free','float_share','free_share'])\n",
    "\n",
    "daily = split_adjust(daily) # 前复权，从矩阵中去除adj_factor\n",
    "df_daily       = pd.DataFrame(daily,columns=['date','open','high','low','close','volume','turnover'])\n",
    "stock_data = get_return_vwap_turnover(daily,daily_basic) # 从矩阵去除turnover\n",
    "df_stock       = pd.DataFrame(stock_data,columns=['date','open','high','low','close','vwap','volume','return','turn','free turn'])\n",
    "# print(df_stock)\n",
    "\n",
    "stock_data = get_reach_limit(stock_data) # 得到下一交易日是否涨跌停, (0 or 1)\n",
    "st = pd.DataFrame(stock_data,columns=['date','open','high','low','close','vwap', 'volume','return','turn','free turn','limit'])\n",
    "# print(st)\n",
    "stock_data5, stock_data10 = get_return(stock_data)\n",
    "\n",
    "# print(stock_data5)\n",
    "# 有所有交易日数据，如果一个交易日有nan则整个交易日所有数据都是nan\n",
    "# stock_data5, stock_data10 = complete_all_datelist_info(stock_data5,stock_data10,datelist)\n",
    "df_stock = pd.DataFrame(stock_data5,columns=['date','open','high','low','close','vwap','volume','return','turn','free turn','limit','return5'])\n",
    "# df_stock['return1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
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       "      <td>0.007389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>983</th>\n",
       "      <td>20200615.0</td>\n",
       "      <td>4.08</td>\n",
       "      <td>4.10</td>\n",
       "      <td>4.02</td>\n",
       "      <td>4.02</td>\n",
       "      <td>4.044300</td>\n",
       "      <td>9006903.0</td>\n",
       "      <td>0.012438</td>\n",
       "      <td>0.005500</td>\n",
       "      <td>0.005881</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.004975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>984</th>\n",
       "      <td>20200616.0</td>\n",
       "      <td>4.05</td>\n",
       "      <td>4.09</td>\n",
       "      <td>4.05</td>\n",
       "      <td>4.07</td>\n",
       "      <td>4.066762</td>\n",
       "      <td>9414171.0</td>\n",
       "      <td>-0.004914</td>\n",
       "      <td>0.005749</td>\n",
       "      <td>0.006147</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.017199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>985</th>\n",
       "      <td>20200617.0</td>\n",
       "      <td>4.05</td>\n",
       "      <td>4.07</td>\n",
       "      <td>3.98</td>\n",
       "      <td>4.05</td>\n",
       "      <td>4.030943</td>\n",
       "      <td>11796314.0</td>\n",
       "      <td>0.002469</td>\n",
       "      <td>0.007204</td>\n",
       "      <td>0.007702</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.022222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>986</th>\n",
       "      <td>20200618.0</td>\n",
       "      <td>4.06</td>\n",
       "      <td>4.08</td>\n",
       "      <td>4.02</td>\n",
       "      <td>4.06</td>\n",
       "      <td>4.050609</td>\n",
       "      <td>8644420.0</td>\n",
       "      <td>0.007389</td>\n",
       "      <td>0.005279</td>\n",
       "      <td>0.005644</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.046798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>987</th>\n",
       "      <td>20200619.0</td>\n",
       "      <td>4.04</td>\n",
       "      <td>4.09</td>\n",
       "      <td>4.04</td>\n",
       "      <td>4.09</td>\n",
       "      <td>4.066936</td>\n",
       "      <td>12609606.0</td>\n",
       "      <td>-0.022005</td>\n",
       "      <td>0.007700</td>\n",
       "      <td>0.008233</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.053790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>988</th>\n",
       "      <td>20200622.0</td>\n",
       "      <td>4.09</td>\n",
       "      <td>4.13</td>\n",
       "      <td>3.99</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.052745</td>\n",
       "      <td>16522118.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010090</td>\n",
       "      <td>0.010788</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.037500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>989</th>\n",
       "      <td>20200623.0</td>\n",
       "      <td>4.03</td>\n",
       "      <td>4.05</td>\n",
       "      <td>3.97</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.999123</td>\n",
       "      <td>11422048.0</td>\n",
       "      <td>-0.010000</td>\n",
       "      <td>0.006975</td>\n",
       "      <td>0.007458</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>990</th>\n",
       "      <td>20200624.0</td>\n",
       "      <td>4.01</td>\n",
       "      <td>4.03</td>\n",
       "      <td>3.96</td>\n",
       "      <td>3.96</td>\n",
       "      <td>3.984874</td>\n",
       "      <td>8106967.0</td>\n",
       "      <td>-0.022727</td>\n",
       "      <td>0.004951</td>\n",
       "      <td>0.005293</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.101010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991</th>\n",
       "      <td>20200629.0</td>\n",
       "      <td>3.96</td>\n",
       "      <td>3.97</td>\n",
       "      <td>3.84</td>\n",
       "      <td>3.87</td>\n",
       "      <td>3.895048</td>\n",
       "      <td>12934839.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007899</td>\n",
       "      <td>0.008446</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.196382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>992</th>\n",
       "      <td>20200630.0</td>\n",
       "      <td>3.90</td>\n",
       "      <td>3.90</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.87</td>\n",
       "      <td>3.868815</td>\n",
       "      <td>12901040.0</td>\n",
       "      <td>-0.005168</td>\n",
       "      <td>0.007878</td>\n",
       "      <td>0.008424</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.217054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>993</th>\n",
       "      <td>20200701.0</td>\n",
       "      <td>3.90</td>\n",
       "      <td>3.90</td>\n",
       "      <td>3.83</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.858038</td>\n",
       "      <td>14102873.0</td>\n",
       "      <td>0.028571</td>\n",
       "      <td>0.008612</td>\n",
       "      <td>0.009208</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.264935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>994</th>\n",
       "      <td>20200702.0</td>\n",
       "      <td>3.86</td>\n",
       "      <td>3.96</td>\n",
       "      <td>3.83</td>\n",
       "      <td>3.96</td>\n",
       "      <td>3.899034</td>\n",
       "      <td>20678911.0</td>\n",
       "      <td>0.101010</td>\n",
       "      <td>0.012628</td>\n",
       "      <td>0.013502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.229798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>20200703.0</td>\n",
       "      <td>4.05</td>\n",
       "      <td>4.36</td>\n",
       "      <td>4.01</td>\n",
       "      <td>4.36</td>\n",
       "      <td>4.232035</td>\n",
       "      <td>78833016.0</td>\n",
       "      <td>0.061927</td>\n",
       "      <td>0.048141</td>\n",
       "      <td>0.051473</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.116972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>20200706.0</td>\n",
       "      <td>4.55</td>\n",
       "      <td>4.80</td>\n",
       "      <td>4.31</td>\n",
       "      <td>4.63</td>\n",
       "      <td>4.595914</td>\n",
       "      <td>98854089.0</td>\n",
       "      <td>0.017279</td>\n",
       "      <td>0.060367</td>\n",
       "      <td>0.064545</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.092873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>20200707.0</td>\n",
       "      <td>4.63</td>\n",
       "      <td>4.80</td>\n",
       "      <td>4.51</td>\n",
       "      <td>4.71</td>\n",
       "      <td>4.689977</td>\n",
       "      <td>70581512.0</td>\n",
       "      <td>0.033970</td>\n",
       "      <td>0.043102</td>\n",
       "      <td>0.046085</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.065817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>20200708.0</td>\n",
       "      <td>4.67</td>\n",
       "      <td>4.94</td>\n",
       "      <td>4.65</td>\n",
       "      <td>4.87</td>\n",
       "      <td>4.849767</td>\n",
       "      <td>59216367.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.036162</td>\n",
       "      <td>0.038664</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.008214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>20200709.0</td>\n",
       "      <td>4.84</td>\n",
       "      <td>4.95</td>\n",
       "      <td>4.81</td>\n",
       "      <td>4.87</td>\n",
       "      <td>4.879802</td>\n",
       "      <td>46811962.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.028587</td>\n",
       "      <td>0.030565</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.020534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>20200710.0</td>\n",
       "      <td>4.89</td>\n",
       "      <td>5.13</td>\n",
       "      <td>4.82</td>\n",
       "      <td>4.87</td>\n",
       "      <td>4.941834</td>\n",
       "      <td>62807030.0</td>\n",
       "      <td>0.039014</td>\n",
       "      <td>0.038354</td>\n",
       "      <td>0.041009</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.045175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1001</th>\n",
       "      <td>20200713.0</td>\n",
       "      <td>4.89</td>\n",
       "      <td>5.10</td>\n",
       "      <td>4.88</td>\n",
       "      <td>5.06</td>\n",
       "      <td>5.000056</td>\n",
       "      <td>46884257.0</td>\n",
       "      <td>-0.007905</td>\n",
       "      <td>0.028631</td>\n",
       "      <td>0.030612</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.009881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1002</th>\n",
       "      <td>20200714.0</td>\n",
       "      <td>5.11</td>\n",
       "      <td>5.19</td>\n",
       "      <td>4.95</td>\n",
       "      <td>5.02</td>\n",
       "      <td>5.054991</td>\n",
       "      <td>49111477.0</td>\n",
       "      <td>-0.037849</td>\n",
       "      <td>0.029991</td>\n",
       "      <td>0.032067</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.017928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003</th>\n",
       "      <td>20200715.0</td>\n",
       "      <td>5.08</td>\n",
       "      <td>5.25</td>\n",
       "      <td>4.82</td>\n",
       "      <td>4.83</td>\n",
       "      <td>5.027431</td>\n",
       "      <td>79908379.0</td>\n",
       "      <td>-0.012422</td>\n",
       "      <td>0.048798</td>\n",
       "      <td>0.052175</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.089027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1004</th>\n",
       "      <td>20200716.0</td>\n",
       "      <td>4.92</td>\n",
       "      <td>5.15</td>\n",
       "      <td>4.73</td>\n",
       "      <td>4.77</td>\n",
       "      <td>4.967387</td>\n",
       "      <td>72655536.0</td>\n",
       "      <td>-0.025157</td>\n",
       "      <td>0.044368</td>\n",
       "      <td>0.047439</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.098532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1005</th>\n",
       "      <td>20200717.0</td>\n",
       "      <td>4.76</td>\n",
       "      <td>4.82</td>\n",
       "      <td>4.56</td>\n",
       "      <td>4.65</td>\n",
       "      <td>4.650452</td>\n",
       "      <td>51867642.0</td>\n",
       "      <td>0.098925</td>\n",
       "      <td>0.031674</td>\n",
       "      <td>0.033866</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.083871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1006</th>\n",
       "      <td>20200720.0</td>\n",
       "      <td>4.69</td>\n",
       "      <td>5.12</td>\n",
       "      <td>4.69</td>\n",
       "      <td>5.11</td>\n",
       "      <td>5.026958</td>\n",
       "      <td>87075347.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.053174</td>\n",
       "      <td>0.056855</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.027397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1007</th>\n",
       "      <td>20200721.0</td>\n",
       "      <td>5.13</td>\n",
       "      <td>5.15</td>\n",
       "      <td>5.02</td>\n",
       "      <td>5.11</td>\n",
       "      <td>5.091945</td>\n",
       "      <td>43741822.0</td>\n",
       "      <td>0.029354</td>\n",
       "      <td>0.026712</td>\n",
       "      <td>0.028561</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.131115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1008</th>\n",
       "      <td>20200722.0</td>\n",
       "      <td>5.15</td>\n",
       "      <td>5.42</td>\n",
       "      <td>5.04</td>\n",
       "      <td>5.26</td>\n",
       "      <td>5.252883</td>\n",
       "      <td>67321603.0</td>\n",
       "      <td>-0.003802</td>\n",
       "      <td>0.041111</td>\n",
       "      <td>0.043957</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.083650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1009</th>\n",
       "      <td>20200723.0</td>\n",
       "      <td>5.30</td>\n",
       "      <td>5.37</td>\n",
       "      <td>5.06</td>\n",
       "      <td>5.24</td>\n",
       "      <td>5.201109</td>\n",
       "      <td>51309226.0</td>\n",
       "      <td>-0.038168</td>\n",
       "      <td>0.031333</td>\n",
       "      <td>0.033502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.082061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1010</th>\n",
       "      <td>20200724.0</td>\n",
       "      <td>5.25</td>\n",
       "      <td>5.32</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.04</td>\n",
       "      <td>5.118924</td>\n",
       "      <td>40506325.0</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.024736</td>\n",
       "      <td>0.026448</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.113095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1011</th>\n",
       "      <td>20200727.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.34</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.25</td>\n",
       "      <td>5.191994</td>\n",
       "      <td>53290177.0</td>\n",
       "      <td>0.100952</td>\n",
       "      <td>0.032543</td>\n",
       "      <td>0.034795</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1012</th>\n",
       "      <td>20200728.0</td>\n",
       "      <td>5.30</td>\n",
       "      <td>5.78</td>\n",
       "      <td>5.28</td>\n",
       "      <td>5.78</td>\n",
       "      <td>5.699649</td>\n",
       "      <td>149777409.0</td>\n",
       "      <td>-0.013841</td>\n",
       "      <td>0.091464</td>\n",
       "      <td>0.097795</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1013</th>\n",
       "      <td>20200729.0</td>\n",
       "      <td>5.77</td>\n",
       "      <td>5.77</td>\n",
       "      <td>5.56</td>\n",
       "      <td>5.70</td>\n",
       "      <td>5.655087</td>\n",
       "      <td>98634804.0</td>\n",
       "      <td>-0.005263</td>\n",
       "      <td>0.060233</td>\n",
       "      <td>0.064402</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1014</th>\n",
       "      <td>20200730.0</td>\n",
       "      <td>5.64</td>\n",
       "      <td>5.77</td>\n",
       "      <td>5.53</td>\n",
       "      <td>5.67</td>\n",
       "      <td>5.654590</td>\n",
       "      <td>72872677.0</td>\n",
       "      <td>-0.010582</td>\n",
       "      <td>0.044501</td>\n",
       "      <td>0.047581</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1015</th>\n",
       "      <td>20200731.0</td>\n",
       "      <td>5.66</td>\n",
       "      <td>5.75</td>\n",
       "      <td>5.55</td>\n",
       "      <td>5.61</td>\n",
       "      <td>5.630112</td>\n",
       "      <td>44686774.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.027289</td>\n",
       "      <td>0.029178</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            date  open  high   low  close      vwap       volume    return  \\\n",
       "971   20200528.0  3.67  3.67  3.61   3.63  3.639446    7247810.0  0.002755   \n",
       "972   20200529.0  3.63  3.66  3.62   3.64  3.638521    8071120.0  0.071429   \n",
       "973   20200601.0  3.66  4.00  3.66   3.90  3.872923   40626801.0  0.005128   \n",
       "974   20200602.0  3.90  3.97  3.89   3.92  3.919855   21821896.0  0.022959   \n",
       "975   20200603.0  3.94  4.04  3.91   4.01  3.961288   22450247.0  0.014963   \n",
       "976   20200604.0  4.07  4.22  3.94   4.07  4.070979   39053017.0  0.014742   \n",
       "977   20200605.0  4.07  4.20  4.05   4.13  4.142647   24354796.0 -0.002421   \n",
       "978   20200608.0  4.18  4.19  4.10   4.12  4.130878   13844584.0 -0.014563   \n",
       "979   20200609.0  4.11  4.14  4.04   4.06  4.077769   17872080.0  0.000000   \n",
       "980   20200610.0  4.10  4.15  4.05   4.06  4.081959    9543113.0  0.000000   \n",
       "981   20200611.0  4.10  4.13  4.04   4.06  4.079227   14494916.0  0.000000   \n",
       "982   20200612.0  4.02  4.10  3.98   4.06  4.063407   13239127.0 -0.009852   \n",
       "983   20200615.0  4.08  4.10  4.02   4.02  4.044300    9006903.0  0.012438   \n",
       "984   20200616.0  4.05  4.09  4.05   4.07  4.066762    9414171.0 -0.004914   \n",
       "985   20200617.0  4.05  4.07  3.98   4.05  4.030943   11796314.0  0.002469   \n",
       "986   20200618.0  4.06  4.08  4.02   4.06  4.050609    8644420.0  0.007389   \n",
       "987   20200619.0  4.04  4.09  4.04   4.09  4.066936   12609606.0 -0.022005   \n",
       "988   20200622.0  4.09  4.13  3.99   4.00  4.052745   16522118.0  0.000000   \n",
       "989   20200623.0  4.03  4.05  3.97   4.00  3.999123   11422048.0 -0.010000   \n",
       "990   20200624.0  4.01  4.03  3.96   3.96  3.984874    8106967.0 -0.022727   \n",
       "991   20200629.0  3.96  3.97  3.84   3.87  3.895048   12934839.0  0.000000   \n",
       "992   20200630.0  3.90  3.90  3.85   3.87  3.868815   12901040.0 -0.005168   \n",
       "993   20200701.0  3.90  3.90  3.83   3.85  3.858038   14102873.0  0.028571   \n",
       "994   20200702.0  3.86  3.96  3.83   3.96  3.899034   20678911.0  0.101010   \n",
       "995   20200703.0  4.05  4.36  4.01   4.36  4.232035   78833016.0  0.061927   \n",
       "996   20200706.0  4.55  4.80  4.31   4.63  4.595914   98854089.0  0.017279   \n",
       "997   20200707.0  4.63  4.80  4.51   4.71  4.689977   70581512.0  0.033970   \n",
       "998   20200708.0  4.67  4.94  4.65   4.87  4.849767   59216367.0  0.000000   \n",
       "999   20200709.0  4.84  4.95  4.81   4.87  4.879802   46811962.0  0.000000   \n",
       "1000  20200710.0  4.89  5.13  4.82   4.87  4.941834   62807030.0  0.039014   \n",
       "1001  20200713.0  4.89  5.10  4.88   5.06  5.000056   46884257.0 -0.007905   \n",
       "1002  20200714.0  5.11  5.19  4.95   5.02  5.054991   49111477.0 -0.037849   \n",
       "1003  20200715.0  5.08  5.25  4.82   4.83  5.027431   79908379.0 -0.012422   \n",
       "1004  20200716.0  4.92  5.15  4.73   4.77  4.967387   72655536.0 -0.025157   \n",
       "1005  20200717.0  4.76  4.82  4.56   4.65  4.650452   51867642.0  0.098925   \n",
       "1006  20200720.0  4.69  5.12  4.69   5.11  5.026958   87075347.0  0.000000   \n",
       "1007  20200721.0  5.13  5.15  5.02   5.11  5.091945   43741822.0  0.029354   \n",
       "1008  20200722.0  5.15  5.42  5.04   5.26  5.252883   67321603.0 -0.003802   \n",
       "1009  20200723.0  5.30  5.37  5.06   5.24  5.201109   51309226.0 -0.038168   \n",
       "1010  20200724.0  5.25  5.32  5.00   5.04  5.118924   40506325.0  0.041667   \n",
       "1011  20200727.0  5.00  5.34  5.00   5.25  5.191994   53290177.0  0.100952   \n",
       "1012  20200728.0  5.30  5.78  5.28   5.78  5.699649  149777409.0 -0.013841   \n",
       "1013  20200729.0  5.77  5.77  5.56   5.70  5.655087   98634804.0 -0.005263   \n",
       "1014  20200730.0  5.64  5.77  5.53   5.67  5.654590   72872677.0 -0.010582   \n",
       "1015  20200731.0  5.66  5.75  5.55   5.61  5.630112   44686774.0       NaN   \n",
       "\n",
       "          turn  free turn  limit   return5  \n",
       "971   0.004426   0.004732    0.0  0.121212  \n",
       "972   0.004929   0.005270    0.0  0.134615  \n",
       "973   0.024810   0.026527    0.0  0.056410  \n",
       "974   0.013326   0.014248    0.0  0.035714  \n",
       "975   0.013710   0.014659    0.0  0.012469  \n",
       "976   0.023848   0.025499    0.0 -0.002457  \n",
       "977   0.014873   0.015902    0.0 -0.016949  \n",
       "978   0.008454   0.009040    0.0 -0.024272  \n",
       "979   0.010914   0.011669    0.0  0.002463  \n",
       "980   0.005828   0.006231    0.0 -0.002463  \n",
       "981   0.008852   0.009464    0.0  0.000000  \n",
       "982   0.008085   0.008644    0.0  0.007389  \n",
       "983   0.005500   0.005881    0.0 -0.004975  \n",
       "984   0.005749   0.006147    0.0 -0.017199  \n",
       "985   0.007204   0.007702    0.0 -0.022222  \n",
       "986   0.005279   0.005644    0.0 -0.046798  \n",
       "987   0.007700   0.008233    0.0 -0.053790  \n",
       "988   0.010090   0.010788    0.0 -0.037500  \n",
       "989   0.006975   0.007458    0.0 -0.010000  \n",
       "990   0.004951   0.005293    0.0  0.101010  \n",
       "991   0.007899   0.008446    0.0  0.196382  \n",
       "992   0.007878   0.008424    0.0  0.217054  \n",
       "993   0.008612   0.009208    1.0  0.264935  \n",
       "994   0.012628   0.013502    0.0  0.229798  \n",
       "995   0.048141   0.051473    0.0  0.116972  \n",
       "996   0.060367   0.064545    0.0  0.092873  \n",
       "997   0.043102   0.046085    0.0  0.065817  \n",
       "998   0.036162   0.038664    0.0 -0.008214  \n",
       "999   0.028587   0.030565    0.0 -0.020534  \n",
       "1000  0.038354   0.041009    0.0 -0.045175  \n",
       "1001  0.028631   0.030612    0.0  0.009881  \n",
       "1002  0.029991   0.032067    0.0  0.017928  \n",
       "1003  0.048798   0.052175    0.0  0.089027  \n",
       "1004  0.044368   0.047439    1.0  0.098532  \n",
       "1005  0.031674   0.033866    0.0  0.083871  \n",
       "1006  0.053174   0.056855    0.0  0.027397  \n",
       "1007  0.026712   0.028561    0.0  0.131115  \n",
       "1008  0.041111   0.043957    0.0  0.083650  \n",
       "1009  0.031333   0.033502    0.0  0.082061  \n",
       "1010  0.024736   0.026448    1.0  0.113095  \n",
       "1011  0.032543   0.034795    0.0       NaN  \n",
       "1012  0.091464   0.097795    0.0       NaN  \n",
       "1013  0.060233   0.064402    0.0       NaN  \n",
       "1014  0.044501   0.047581    0.0       NaN  \n",
       "1015  0.027289   0.029178    0.0       NaN  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# with pd.option_context('display.max_rows', None, 'display.max_columns', None):  # more options can be specified also\n",
    "#     display(df_stock)\n",
    "# df_stock.to_string()\n",
    "df_stock[-45:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 生成处理数据图片,预测目标"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_single_data_matrix(data,pass_date,sample_size,sample_interval):\n",
    "    data_img = np.empty((sample_size,pass_date))\n",
    "    for i in range(sample_size):\n",
    "        data_img[i,:] = data[:,i*sample_interval:pass_date+i*sample_interval]\n",
    "    return data_img\n",
    "\n",
    "def get_img(stock_data,pass_date:int,sample_interval:int):\n",
    "    '''\n",
    "    pass_date: 过去30天的个股量价数据作为数据图片\n",
    "    sample_interval: 每隔两天采样一次\n",
    "    '''\n",
    "    date_size      = len(stock_data)\n",
    "    sample_size    = int((date_size-pass_date)/2) + 1 # 采样天数\n",
    "    col_size       = stock_data.shape[1]\n",
    "    stock_data_img = np.empty((col_size,sample_size,pass_date))\n",
    "    for i in range(col_size):\n",
    "        data     = stock_data[:,i].reshape(1,date_size)\n",
    "        data_img = get_single_data_matrix(data,pass_date,sample_size,sample_interval)\n",
    "        stock_data_img[i,...] = data_img\n",
    "    stock_data_img = np.transpose(stock_data_img,(1,0,2))\n",
    "    return stock_data_img\n",
    "\n",
    "def handle_nan_limit_up_down(stock_data):\n",
    "    '''\n",
    "    handle_nan: 当图片中有nan, 整个图片都是nan\n",
    "    limit_up_down: 截面期下一交易日涨跌停时, 整个图片都是nan\n",
    "    '''\n",
    "    sample_size = len(stock_data)\n",
    "    limit = stock_data[:,-2,-1]\n",
    "    nan_img_idx = np.unique((stock_data != stock_data).nonzero()[0])\n",
    "    limit_idx   = np.where(limit == 1)\n",
    "    stock_data[nan_img_idx,1:] = np.nan\n",
    "    stock_data[limit_idx,1:]   = np.nan  \n",
    "    return stock_data\n",
    "\n",
    "def process_data_img(stock_data5,stock_data10,pass_date=30,sample_interval=2):\n",
    "    stock_data5  = get_img(stock_data5,pass_date,sample_interval)\n",
    "    stock_data10 = get_img(stock_data10,pass_date,sample_interval)\n",
    "    display(pd.DataFrame(stock_data5[-1].T))\n",
    "    stock_data5  = handle_nan_limit_up_down(stock_data5)\n",
    "    print(stock_data5[-3])\n",
    "\n",
    "    stock_data10 = handle_nan_limit_up_down(stock_data10)\n",
    "    return5      = stock_data5[:,-1,-1].reshape(len(stock_data5),1)\n",
    "    return10     = stock_data10[:,-1,-1].reshape(len(stock_data10),1)\n",
    "    stock_data5  = stock_data5[:,0:10,:]\n",
    "    stock_data10 = stock_data10[:,0:10,:]\n",
    "    return stock_data5,stock_data10,return5,return10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20200617.0</td>\n",
       "      <td>4.05</td>\n",
       "      <td>4.07</td>\n",
       "      <td>3.98</td>\n",
       "      <td>4.05</td>\n",
       "      <td>4.030943</td>\n",
       "      <td>11796314.0</td>\n",
       "      <td>0.002469</td>\n",
       "      <td>0.007204</td>\n",
       "      <td>0.007702</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.022222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20200618.0</td>\n",
       "      <td>4.06</td>\n",
       "      <td>4.08</td>\n",
       "      <td>4.02</td>\n",
       "      <td>4.06</td>\n",
       "      <td>4.050609</td>\n",
       "      <td>8644420.0</td>\n",
       "      <td>0.007389</td>\n",
       "      <td>0.005279</td>\n",
       "      <td>0.005644</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.046798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20200619.0</td>\n",
       "      <td>4.04</td>\n",
       "      <td>4.09</td>\n",
       "      <td>4.04</td>\n",
       "      <td>4.09</td>\n",
       "      <td>4.066936</td>\n",
       "      <td>12609606.0</td>\n",
       "      <td>-0.022005</td>\n",
       "      <td>0.007700</td>\n",
       "      <td>0.008233</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.053790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20200622.0</td>\n",
       "      <td>4.09</td>\n",
       "      <td>4.13</td>\n",
       "      <td>3.99</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.052745</td>\n",
       "      <td>16522118.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010090</td>\n",
       "      <td>0.010788</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.037500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20200623.0</td>\n",
       "      <td>4.03</td>\n",
       "      <td>4.05</td>\n",
       "      <td>3.97</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.999123</td>\n",
       "      <td>11422048.0</td>\n",
       "      <td>-0.010000</td>\n",
       "      <td>0.006975</td>\n",
       "      <td>0.007458</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>20200624.0</td>\n",
       "      <td>4.01</td>\n",
       "      <td>4.03</td>\n",
       "      <td>3.96</td>\n",
       "      <td>3.96</td>\n",
       "      <td>3.984874</td>\n",
       "      <td>8106967.0</td>\n",
       "      <td>-0.022727</td>\n",
       "      <td>0.004951</td>\n",
       "      <td>0.005293</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.101010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>20200629.0</td>\n",
       "      <td>3.96</td>\n",
       "      <td>3.97</td>\n",
       "      <td>3.84</td>\n",
       "      <td>3.87</td>\n",
       "      <td>3.895048</td>\n",
       "      <td>12934839.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007899</td>\n",
       "      <td>0.008446</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.196382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>20200630.0</td>\n",
       "      <td>3.90</td>\n",
       "      <td>3.90</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.87</td>\n",
       "      <td>3.868815</td>\n",
       "      <td>12901040.0</td>\n",
       "      <td>-0.005168</td>\n",
       "      <td>0.007878</td>\n",
       "      <td>0.008424</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.217054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>20200701.0</td>\n",
       "      <td>3.90</td>\n",
       "      <td>3.90</td>\n",
       "      <td>3.83</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.858038</td>\n",
       "      <td>14102873.0</td>\n",
       "      <td>0.028571</td>\n",
       "      <td>0.008612</td>\n",
       "      <td>0.009208</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.264935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>20200702.0</td>\n",
       "      <td>3.86</td>\n",
       "      <td>3.96</td>\n",
       "      <td>3.83</td>\n",
       "      <td>3.96</td>\n",
       "      <td>3.899034</td>\n",
       "      <td>20678911.0</td>\n",
       "      <td>0.101010</td>\n",
       "      <td>0.012628</td>\n",
       "      <td>0.013502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.229798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>20200703.0</td>\n",
       "      <td>4.05</td>\n",
       "      <td>4.36</td>\n",
       "      <td>4.01</td>\n",
       "      <td>4.36</td>\n",
       "      <td>4.232035</td>\n",
       "      <td>78833016.0</td>\n",
       "      <td>0.061927</td>\n",
       "      <td>0.048141</td>\n",
       "      <td>0.051473</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.116972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>20200706.0</td>\n",
       "      <td>4.55</td>\n",
       "      <td>4.80</td>\n",
       "      <td>4.31</td>\n",
       "      <td>4.63</td>\n",
       "      <td>4.595914</td>\n",
       "      <td>98854089.0</td>\n",
       "      <td>0.017279</td>\n",
       "      <td>0.060367</td>\n",
       "      <td>0.064545</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.092873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>20200707.0</td>\n",
       "      <td>4.63</td>\n",
       "      <td>4.80</td>\n",
       "      <td>4.51</td>\n",
       "      <td>4.71</td>\n",
       "      <td>4.689977</td>\n",
       "      <td>70581512.0</td>\n",
       "      <td>0.033970</td>\n",
       "      <td>0.043102</td>\n",
       "      <td>0.046085</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.065817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>20200708.0</td>\n",
       "      <td>4.67</td>\n",
       "      <td>4.94</td>\n",
       "      <td>4.65</td>\n",
       "      <td>4.87</td>\n",
       "      <td>4.849767</td>\n",
       "      <td>59216367.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.036162</td>\n",
       "      <td>0.038664</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.008214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>20200709.0</td>\n",
       "      <td>4.84</td>\n",
       "      <td>4.95</td>\n",
       "      <td>4.81</td>\n",
       "      <td>4.87</td>\n",
       "      <td>4.879802</td>\n",
       "      <td>46811962.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.028587</td>\n",
       "      <td>0.030565</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.020534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>20200710.0</td>\n",
       "      <td>4.89</td>\n",
       "      <td>5.13</td>\n",
       "      <td>4.82</td>\n",
       "      <td>4.87</td>\n",
       "      <td>4.941834</td>\n",
       "      <td>62807030.0</td>\n",
       "      <td>0.039014</td>\n",
       "      <td>0.038354</td>\n",
       "      <td>0.041009</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.045175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>20200713.0</td>\n",
       "      <td>4.89</td>\n",
       "      <td>5.10</td>\n",
       "      <td>4.88</td>\n",
       "      <td>5.06</td>\n",
       "      <td>5.000056</td>\n",
       "      <td>46884257.0</td>\n",
       "      <td>-0.007905</td>\n",
       "      <td>0.028631</td>\n",
       "      <td>0.030612</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.009881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>20200714.0</td>\n",
       "      <td>5.11</td>\n",
       "      <td>5.19</td>\n",
       "      <td>4.95</td>\n",
       "      <td>5.02</td>\n",
       "      <td>5.054991</td>\n",
       "      <td>49111477.0</td>\n",
       "      <td>-0.037849</td>\n",
       "      <td>0.029991</td>\n",
       "      <td>0.032067</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.017928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>20200715.0</td>\n",
       "      <td>5.08</td>\n",
       "      <td>5.25</td>\n",
       "      <td>4.82</td>\n",
       "      <td>4.83</td>\n",
       "      <td>5.027431</td>\n",
       "      <td>79908379.0</td>\n",
       "      <td>-0.012422</td>\n",
       "      <td>0.048798</td>\n",
       "      <td>0.052175</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.089027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20200716.0</td>\n",
       "      <td>4.92</td>\n",
       "      <td>5.15</td>\n",
       "      <td>4.73</td>\n",
       "      <td>4.77</td>\n",
       "      <td>4.967387</td>\n",
       "      <td>72655536.0</td>\n",
       "      <td>-0.025157</td>\n",
       "      <td>0.044368</td>\n",
       "      <td>0.047439</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.098532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20200717.0</td>\n",
       "      <td>4.76</td>\n",
       "      <td>4.82</td>\n",
       "      <td>4.56</td>\n",
       "      <td>4.65</td>\n",
       "      <td>4.650452</td>\n",
       "      <td>51867642.0</td>\n",
       "      <td>0.098925</td>\n",
       "      <td>0.031674</td>\n",
       "      <td>0.033866</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.083871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>20200720.0</td>\n",
       "      <td>4.69</td>\n",
       "      <td>5.12</td>\n",
       "      <td>4.69</td>\n",
       "      <td>5.11</td>\n",
       "      <td>5.026958</td>\n",
       "      <td>87075347.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.053174</td>\n",
       "      <td>0.056855</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.027397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>20200721.0</td>\n",
       "      <td>5.13</td>\n",
       "      <td>5.15</td>\n",
       "      <td>5.02</td>\n",
       "      <td>5.11</td>\n",
       "      <td>5.091945</td>\n",
       "      <td>43741822.0</td>\n",
       "      <td>0.029354</td>\n",
       "      <td>0.026712</td>\n",
       "      <td>0.028561</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.131115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>20200722.0</td>\n",
       "      <td>5.15</td>\n",
       "      <td>5.42</td>\n",
       "      <td>5.04</td>\n",
       "      <td>5.26</td>\n",
       "      <td>5.252883</td>\n",
       "      <td>67321603.0</td>\n",
       "      <td>-0.003802</td>\n",
       "      <td>0.041111</td>\n",
       "      <td>0.043957</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.083650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>20200723.0</td>\n",
       "      <td>5.30</td>\n",
       "      <td>5.37</td>\n",
       "      <td>5.06</td>\n",
       "      <td>5.24</td>\n",
       "      <td>5.201109</td>\n",
       "      <td>51309226.0</td>\n",
       "      <td>-0.038168</td>\n",
       "      <td>0.031333</td>\n",
       "      <td>0.033502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.082061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>20200724.0</td>\n",
       "      <td>5.25</td>\n",
       "      <td>5.32</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.04</td>\n",
       "      <td>5.118924</td>\n",
       "      <td>40506325.0</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.024736</td>\n",
       "      <td>0.026448</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.113095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>20200727.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>20200728.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>20200729.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>20200730.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            0     1     2     3     4         5           6         7   \\\n",
       "0   20200617.0  4.05  4.07  3.98  4.05  4.030943  11796314.0  0.002469   \n",
       "1   20200618.0  4.06  4.08  4.02  4.06  4.050609   8644420.0  0.007389   \n",
       "2   20200619.0  4.04  4.09  4.04  4.09  4.066936  12609606.0 -0.022005   \n",
       "3   20200622.0  4.09  4.13  3.99  4.00  4.052745  16522118.0  0.000000   \n",
       "4   20200623.0  4.03  4.05  3.97  4.00  3.999123  11422048.0 -0.010000   \n",
       "5   20200624.0  4.01  4.03  3.96  3.96  3.984874   8106967.0 -0.022727   \n",
       "6   20200629.0  3.96  3.97  3.84  3.87  3.895048  12934839.0  0.000000   \n",
       "7   20200630.0  3.90  3.90  3.85  3.87  3.868815  12901040.0 -0.005168   \n",
       "8   20200701.0  3.90  3.90  3.83  3.85  3.858038  14102873.0  0.028571   \n",
       "9   20200702.0  3.86  3.96  3.83  3.96  3.899034  20678911.0  0.101010   \n",
       "10  20200703.0  4.05  4.36  4.01  4.36  4.232035  78833016.0  0.061927   \n",
       "11  20200706.0  4.55  4.80  4.31  4.63  4.595914  98854089.0  0.017279   \n",
       "12  20200707.0  4.63  4.80  4.51  4.71  4.689977  70581512.0  0.033970   \n",
       "13  20200708.0  4.67  4.94  4.65  4.87  4.849767  59216367.0  0.000000   \n",
       "14  20200709.0  4.84  4.95  4.81  4.87  4.879802  46811962.0  0.000000   \n",
       "15  20200710.0  4.89  5.13  4.82  4.87  4.941834  62807030.0  0.039014   \n",
       "16  20200713.0  4.89  5.10  4.88  5.06  5.000056  46884257.0 -0.007905   \n",
       "17  20200714.0  5.11  5.19  4.95  5.02  5.054991  49111477.0 -0.037849   \n",
       "18  20200715.0  5.08  5.25  4.82  4.83  5.027431  79908379.0 -0.012422   \n",
       "19  20200716.0  4.92  5.15  4.73  4.77  4.967387  72655536.0 -0.025157   \n",
       "20  20200717.0  4.76  4.82  4.56  4.65  4.650452  51867642.0  0.098925   \n",
       "21  20200720.0  4.69  5.12  4.69  5.11  5.026958  87075347.0  0.000000   \n",
       "22  20200721.0  5.13  5.15  5.02  5.11  5.091945  43741822.0  0.029354   \n",
       "23  20200722.0  5.15  5.42  5.04  5.26  5.252883  67321603.0 -0.003802   \n",
       "24  20200723.0  5.30  5.37  5.06  5.24  5.201109  51309226.0 -0.038168   \n",
       "25  20200724.0  5.25  5.32  5.00  5.04  5.118924  40506325.0  0.041667   \n",
       "26  20200727.0   NaN   NaN   NaN   NaN       NaN         NaN       NaN   \n",
       "27  20200728.0   NaN   NaN   NaN   NaN       NaN         NaN       NaN   \n",
       "28  20200729.0   NaN   NaN   NaN   NaN       NaN         NaN       NaN   \n",
       "29  20200730.0   NaN   NaN   NaN   NaN       NaN         NaN       NaN   \n",
       "\n",
       "          8         9    10        11  \n",
       "0   0.007204  0.007702  0.0 -0.022222  \n",
       "1   0.005279  0.005644  0.0 -0.046798  \n",
       "2   0.007700  0.008233  0.0 -0.053790  \n",
       "3   0.010090  0.010788  0.0 -0.037500  \n",
       "4   0.006975  0.007458  0.0 -0.010000  \n",
       "5   0.004951  0.005293  0.0  0.101010  \n",
       "6   0.007899  0.008446  0.0  0.196382  \n",
       "7   0.007878  0.008424  0.0  0.217054  \n",
       "8   0.008612  0.009208  1.0  0.264935  \n",
       "9   0.012628  0.013502  0.0  0.229798  \n",
       "10  0.048141  0.051473  0.0  0.116972  \n",
       "11  0.060367  0.064545  0.0  0.092873  \n",
       "12  0.043102  0.046085  0.0  0.065817  \n",
       "13  0.036162  0.038664  0.0 -0.008214  \n",
       "14  0.028587  0.030565  0.0 -0.020534  \n",
       "15  0.038354  0.041009  0.0 -0.045175  \n",
       "16  0.028631  0.030612  0.0  0.009881  \n",
       "17  0.029991  0.032067  0.0  0.017928  \n",
       "18  0.048798  0.052175  0.0  0.089027  \n",
       "19  0.044368  0.047439  1.0  0.098532  \n",
       "20  0.031674  0.033866  0.0  0.083871  \n",
       "21  0.053174  0.056855  0.0  0.027397  \n",
       "22  0.026712  0.028561  0.0  0.131115  \n",
       "23  0.041111  0.043957  0.0  0.083650  \n",
       "24  0.031333  0.033502  0.0  0.082061  \n",
       "25  0.024736  0.026448  1.0  0.113095  \n",
       "26       NaN       NaN  NaN       NaN  \n",
       "27       NaN       NaN  NaN       NaN  \n",
       "28       NaN       NaN  NaN       NaN  \n",
       "29       NaN       NaN  NaN       NaN  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[20200611. 20200612. 20200615. 20200616. 20200617. 20200618. 20200619.\n",
      "  20200622. 20200623. 20200624. 20200629. 20200630. 20200701. 20200702.\n",
      "  20200703. 20200706. 20200707. 20200708. 20200709. 20200710. 20200713.\n",
      "  20200714. 20200715. 20200716. 20200717. 20200720. 20200721. 20200722.\n",
      "  20200723. 20200724.]\n",
      " [      nan       nan       nan       nan       nan       nan       nan\n",
      "        nan       nan       nan       nan       nan       nan       nan\n",
      "        nan       nan       nan       nan       nan       nan       nan\n",
      "        nan       nan       nan       nan       nan       nan       nan\n",
      "        nan       nan]\n",
      " [      nan       nan       nan       nan       nan       nan       nan\n",
      "        nan       nan       nan       nan       nan       nan       nan\n",
      "        nan       nan       nan       nan       nan       nan       nan\n",
      "        nan       nan       nan       nan       nan       nan       nan\n",
      "        nan       nan]\n",
      " [      nan       nan       nan       nan       nan       nan       nan\n",
      "        nan       nan       nan       nan       nan       nan       nan\n",
      "        nan       nan       nan       nan       nan       nan       nan\n",
      "        nan       nan       nan       nan       nan       nan       nan\n",
      "        nan       nan]\n",
      " [      nan       nan       nan       nan       nan       nan       nan\n",
      "        nan       nan       nan       nan       nan       nan       nan\n",
      "        nan       nan       nan       nan       nan       nan       nan\n",
      "        nan       nan       nan       nan       nan       nan       nan\n",
      "        nan       nan]\n",
      " [      nan       nan       nan       nan       nan       nan       nan\n",
      "        nan       nan       nan       nan       nan       nan       nan\n",
      "        nan       nan       nan       nan       nan       nan       nan\n",
      "        nan       nan       nan       nan       nan       nan       nan\n",
      "        nan       nan]\n",
      " [      nan       nan       nan       nan       nan       nan       nan\n",
      "        nan       nan       nan       nan       nan       nan       nan\n",
      "        nan       nan       nan       nan       nan       nan       nan\n",
      "        nan       nan       nan       nan       nan       nan       nan\n",
      "        nan       nan]\n",
      " [      nan       nan       nan       nan       nan       nan       nan\n",
      "        nan       nan       nan       nan       nan       nan       nan\n",
      "        nan       nan       nan       nan       nan       nan       nan\n",
      "        nan       nan       nan       nan       nan       nan       nan\n",
      "        nan       nan]\n",
      " [      nan       nan       nan       nan       nan       nan       nan\n",
      "        nan       nan       nan       nan       nan       nan       nan\n",
      "        nan       nan       nan       nan       nan       nan       nan\n",
      "        nan       nan       nan       nan       nan       nan       nan\n",
      "        nan       nan]\n",
      " [      nan       nan       nan       nan       nan       nan       nan\n",
      "        nan       nan       nan       nan       nan       nan       nan\n",
      "        nan       nan       nan       nan       nan       nan       nan\n",
      "        nan       nan       nan       nan       nan       nan       nan\n",
      "        nan       nan]\n",
      " [      nan       nan       nan       nan       nan       nan       nan\n",
      "        nan       nan       nan       nan       nan       nan       nan\n",
      "        nan       nan       nan       nan       nan       nan       nan\n",
      "        nan       nan       nan       nan       nan       nan       nan\n",
      "        nan       nan]\n",
      " [      nan       nan       nan       nan       nan       nan       nan\n",
      "        nan       nan       nan       nan       nan       nan       nan\n",
      "        nan       nan       nan       nan       nan       nan       nan\n",
      "        nan       nan       nan       nan       nan       nan       nan\n",
      "        nan       nan]]\n"
     ]
    }
   ],
   "source": [
    "s5,s10,return5,return10 = process_data_img(stock_data5,stock_data10)\n",
    "# for i in range(len(s5)):\n",
    "#     print(s5[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 生成dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def construct_dataset():\n",
    "    ts_code_list = idx_w_stock_basic_filter.get_filtered_ts_code()[:,0]\n",
    "    datelist = get_datelist(ts_code_list)\n",
    "    ts_code_size = len(ts_code_list) \n",
    "    sample_size  = int((len(datelist)-30)/2) + 1 # 采样天数\n",
    "    x5  = np.empty((ts_code_size,sample_size,10,30))\n",
    "    x10 = np.empty((ts_code_size,sample_size,10,30))\n",
    "    y5  = np.empty((ts_code_size,sample_size,1))\n",
    "    y10 = np.empty((ts_code_size,sample_size,1))\n",
    "    iter_ = 0\n",
    "    for ts_code in ts_code_list:\n",
    "        stock_data5,stock_data10 = process_raw_data(ts_code,datelist)\n",
    "        stock_data5,stock_data10,return5,return10 = process_data_img(stock_data5,stock_data10)\n",
    "        x5[iter_,:]  = stock_data5\n",
    "        x10[iter_,:] = stock_data10\n",
    "        y5[iter_,:]  = return5\n",
    "        y10[iter_,:] = return10\n",
    "        iter_ += 1\n",
    "    x5  = np.transpose(x5,(1,0,2,3)) \n",
    "    x10 = np.transpose(x10,(1,0,2,3))\n",
    "    y5  = np.transpose(y5,(1,0,2)) \n",
    "    y10 = np.transpose(y10,(1,0,2)) \n",
    "    x5  = x5[:,:,np.newaxis,...]\n",
    "    x10 = x10[:,:,np.newaxis,...]\n",
    "    return x5,x10,y5,y10,ts_code_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "x5,x10,y5,y10,ts_code_list = construct_dataset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('./x5.npy',x5)\n",
    "np.save('./x10.npy',x10)\n",
    "np.save('./y5.npy',y5)\n",
    "np.save('./y10.npy',y10)\n",
    "np.save('./ts_code_list.npy',ts_code_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
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       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20200611.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>1</th>\n",
       "      <td>20200612.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
       "      <td>20200615.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20200616.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20200617.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>20200618.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>20200619.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>20200622.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>20200623.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>20200624.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>20200629.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>20200630.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>20200701.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>20200702.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>20200703.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>20200706.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>20200707.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>20200708.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>20200709.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20200710.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20200713.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>20200714.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>20200715.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>20200716.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>20200717.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>20200720.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>20200721.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>20200722.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>20200723.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>20200724.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             0   1   2   3   4   5   6   7   8   9\n",
       "0   20200611.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
       "1   20200612.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
       "2   20200615.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
       "3   20200616.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
       "4   20200617.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
       "5   20200618.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
       "6   20200619.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
       "7   20200622.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
       "8   20200623.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
       "9   20200624.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
       "10  20200629.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
       "11  20200630.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
       "12  20200701.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
       "13  20200702.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
       "14  20200703.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
       "15  20200706.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
       "16  20200707.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
       "17  20200708.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
       "18  20200709.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
       "19  20200710.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
       "20  20200713.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
       "21  20200714.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
       "22  20200715.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
       "23  20200716.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
       "24  20200717.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
       "25  20200720.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
       "26  20200721.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
       "27  20200722.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
       "28  20200723.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
       "29  20200724.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(np.squeeze(x5[-3,145].T))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
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