{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-01-13T06:27:21.717853Z",
     "start_time": "2025-01-13T06:27:21.714567Z"
    }
   },
   "source": [
    "import pandas as pd\n",
    "import tushare as ts\n",
    "\n",
    "ts_pro = ts.pro_api(token=\"64ebff4fa679167600b905ee45dd88e76f3963c0ff39157f3f085f0e\")"
   ],
   "outputs": [],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-13T07:07:47.242076Z",
     "start_time": "2025-01-13T07:07:46.498970Z"
    }
   },
   "cell_type": "code",
   "source": [
    "sse_df = ts_pro.trade_cal(exchange=\"SSE\")[[\"cal_date\", \"is_open\"]]\n",
    "szse_df = ts_pro.trade_cal(exchange=\"SZSE\")[[\"cal_date\", \"is_open\"]]\n",
    "merge_df = pd.merge(sse_df, szse_df, on=\"cal_date\", how=\"left\")\n",
    "merge_df = merge_df.fillna(value=0)\n",
    "merge_df[\"is_open\"] = (merge_df[\"is_open_x\"] + merge_df[\"is_open_y\"]) > 0\n",
    "trade_date_df = merge_df[[\"cal_date\", \"is_open\"]]\n",
    "trade_date_df"
   ],
   "id": "fe5b7f88c1739a13",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       cal_date  is_open\n",
       "0      20251231     True\n",
       "1      20251230     True\n",
       "2      20251229     True\n",
       "3      20251228    False\n",
       "4      20251227    False\n",
       "...         ...      ...\n",
       "12792  19901223    False\n",
       "12793  19901222    False\n",
       "12794  19901221     True\n",
       "12795  19901220     True\n",
       "12796  19901219     True\n",
       "\n",
       "[12797 rows x 2 columns]"
      ],
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cal_date</th>\n",
       "      <th>is_open</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
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       "      <td>20251229</td>\n",
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       "      <th>3</th>\n",
       "      <td>20251228</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20251227</td>\n",
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       "      <th>12793</th>\n",
       "      <td>19901222</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
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       "      <th>12794</th>\n",
       "      <td>19901221</td>\n",
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       "      <th>12795</th>\n",
       "      <td>19901220</td>\n",
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       "      <th>12796</th>\n",
       "      <td>19901219</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12797 rows × 2 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 41
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "import os\n",
    "import time\n",
    "\n",
    "for year in range(1990, 2025):\n",
    "    target_path = f\"../temp/stk_factor/{year}.csv\"\n",
    "    # 判断target_path文件是否存在\n",
    "    if os.path.exists(target_path):\n",
    "        continue\n",
    "    result_df = pd.DataFrame()\n",
    "    for trade_date in trade_date_df[(trade_date_df[\"cal_date\"].str.startswith(f\"{year}\")) & trade_date_df[\"is_open\"]][\n",
    "        \"cal_date\"].values:\n",
    "        time.sleep(1)\n",
    "        print(f\"{trade_date} downloading...\")\n",
    "        stk_df = ts_pro.stk_factor(trade_date=trade_date)\n",
    "        result_df = pd.concat([result_df, stk_df])\n",
    "    result_df.sort_values(by=[\"trade_date\", \"ts_code\"], ascending=True, inplace=True)\n",
    "    result_df.to_csv(f\"/Users/lanyuanxiaoyao/SynologyDrive/data/Tushare/日线行情/{year}.csv\", index=False)"
   ],
   "id": "bde839364048fb89",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mKeyboardInterrupt\u001B[0m                         Traceback (most recent call last)",
      "Cell \u001B[0;32mIn[82], line 12\u001B[0m\n\u001B[1;32m      9\u001B[0m result_df \u001B[38;5;241m=\u001B[39m pd\u001B[38;5;241m.\u001B[39mDataFrame()\n\u001B[1;32m     10\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m trade_date \u001B[38;5;129;01min\u001B[39;00m trade_date_df[(trade_date_df[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mcal_date\u001B[39m\u001B[38;5;124m\"\u001B[39m]\u001B[38;5;241m.\u001B[39mstr\u001B[38;5;241m.\u001B[39mstartswith(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;132;01m{\u001B[39;00myear\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m)) \u001B[38;5;241m&\u001B[39m trade_date_df[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mis_open\u001B[39m\u001B[38;5;124m\"\u001B[39m]][\n\u001B[1;32m     11\u001B[0m     \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mcal_date\u001B[39m\u001B[38;5;124m\"\u001B[39m]\u001B[38;5;241m.\u001B[39mvalues:\n\u001B[0;32m---> 12\u001B[0m     \u001B[43mtime\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msleep\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m1\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[1;32m     13\u001B[0m     \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mtrade_date\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m downloading...\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m     14\u001B[0m     stk_df \u001B[38;5;241m=\u001B[39m ts_pro\u001B[38;5;241m.\u001B[39mstk_factor(trade_date\u001B[38;5;241m=\u001B[39mtrade_date)\n",
      "\u001B[0;31mKeyboardInterrupt\u001B[0m: "
     ]
    }
   ],
   "execution_count": 82
  }
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