NewStock/main/data/update/update_money_flow.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b94bb1f2-5332-485e-ae1b-eea01f938106",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-09T14:57:40.184418Z",
     "start_time": "2025-04-09T14:57:39.137312Z"
    }
   },
   "outputs": [],
   "source": [
    "import tushare as ts\n",
    "\n",
    "ts.set_token('3a0741c702ee7e5e5f2bf1f0846bafaafe4e320833240b2a7e4a685f')\n",
    "pro = ts.pro_api()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "742c29d453b9bb38",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-09T14:58:10.515830Z",
     "start_time": "2025-04-09T14:57:40.190466Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 9387872 entries, 0 to 25863\n",
      "Data columns (total 2 columns):\n",
      " #   Column      Dtype \n",
      "---  ------      ----- \n",
      " 0   ts_code     object\n",
      " 1   trade_date  object\n",
      "dtypes: object(2)\n",
      "memory usage: 214.9+ MB\n",
      "None\n",
      "20260130\n",
      "start_date: 20260202\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import time\n",
    "\n",
    "h5_filename = '/mnt/d/PyProject/NewStock/data/money_flow.h5'\n",
    "key = '/money_flow'\n",
    "max_date = None\n",
    "with pd.HDFStore(h5_filename, mode='r') as store:\n",
    "    df = store[key][['ts_code', 'trade_date']]\n",
    "    print(df.info())\n",
    "    max_date = df['trade_date'].max()\n",
    "\n",
    "print(max_date)\n",
    "trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20260310')\n",
    "trade_cal = trade_cal[trade_cal['is_open'] == 1]  # 只保留交易日\n",
    "trade_dates = trade_cal[trade_cal['cal_date'] > max_date]['cal_date'].tolist()\n",
    "start_date = min(trade_dates)\n",
    "print(f'start_date: {start_date}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "679ce40e-8d62-4887-970c-e1d8cbdeee6b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-09T14:58:17.197319Z",
     "start_time": "2025-04-09T14:58:10.724923Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "任务 20260310 完成\n",
      "任务 20260309 完成\n",
      "任务 20260306 完成\n",
      "任务 20260305 完成\n",
      "任务 20260304 完成\n",
      "任务 20260303 完成\n",
      "任务 20260302 完成\n",
      "任务 20260227 完成\n",
      "任务 20260226 完成\n",
      "任务 20260225 完成\n",
      "任务 20260224 完成\n",
      "任务 20260213 完成\n",
      "任务 20260212 完成\n",
      "任务 20260211 完成\n",
      "任务 20260210 完成\n",
      "任务 20260209 完成\n",
      "任务 20260206 完成\n",
      "任务 20260205 完成\n",
      "任务 20260204 完成\n",
      "任务 20260203 完成\n",
      "任务 20260202 完成\n"
     ]
    }
   ],
   "source": [
    "from concurrent.futures import ThreadPoolExecutor, as_completed\n",
    "\n",
    "all_daily_data = []\n",
    "\n",
    "# API 调用计数和时间控制变量\n",
    "api_call_count = 0\n",
    "batch_start_time = time.time()\n",
    "\n",
    "\n",
    "def get_data(trade_date):\n",
    "    time.sleep(0.1)\n",
    "    money_flow_data = pro.moneyflow(trade_date=trade_date)\n",
    "    if money_flow_data is not None and not money_flow_data.empty:\n",
    "        return money_flow_data\n",
    "\n",
    "\n",
    "with ThreadPoolExecutor(max_workers=2) as executor:\n",
    "    future_to_date = {executor.submit(get_data, td): td for td in trade_dates}\n",
    "\n",
    "    for future in as_completed(future_to_date):\n",
    "        trade_date = future_to_date[future]  # 获取对应的交易日期\n",
    "        try:\n",
    "            result = future.result()  # 获取任务执行的结果\n",
    "            all_daily_data.append(result)\n",
    "            print(f\"任务 {trade_date} 完成\")\n",
    "        except Exception as e:\n",
    "            print(f\"获取 {trade_date} 数据时出错: {e}\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9af80516849d4e80",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-09T14:58:17.214168Z",
     "start_time": "2025-04-09T14:58:17.210734Z"
    }
   },
   "outputs": [],
   "source": [
    "all_daily_data_df = pd.concat(all_daily_data, ignore_index=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a2b05187-437f-4053-bc43-bd80d4cf8b0e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-09T14:58:19.633456Z",
     "start_time": "2025-04-09T14:58:17.229837Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "所有每日基础数据获取并保存完毕！\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# 将所有数据合并为一个 DataFrame\n",
    "\n",
    "# 将数据保存为 HDF5 文件（table 格式）\n",
    "all_daily_data_df.to_hdf(h5_filename, key='money_flow', mode='a', format='table', append=True, data_columns=True)\n",
    "\n",
    "print(\"所有每日基础数据获取并保存完毕！\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e6f2a2fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         ts_code trade_date  buy_sm_vol  buy_sm_amount  sell_sm_vol  \\\n",
      "0      002397.SZ   20260206      143016        6566.04       142640   \n",
      "1      603882.SH   20260206       32234       10009.25        31819   \n",
      "2      002824.SZ   20260206       58407       13856.33        47164   \n",
      "3      603379.SH   20260206       31545       21744.99        30955   \n",
      "4      600223.SH   20260206       73778        5582.50        67552   \n",
      "...          ...        ...         ...            ...          ...   \n",
      "25871  002774.SZ   20260202       32854        3675.61        20566   \n",
      "25872  300188.SZ   20260202       54005        7416.58        48176   \n",
      "25873  688173.SH   20260202       64149       10373.20        68049   \n",
      "25874  300145.SZ   20260202      132990        6043.37       133529   \n",
      "25875  300205.SZ   20260202        8361         364.75         6236   \n",
      "\n",
      "       sell_sm_amount  buy_md_vol  buy_md_amount  sell_md_vol  sell_md_amount  \\\n",
      "0             6550.08      114346        5247.29       121777         5580.76   \n",
      "1             9880.13       15560        4828.56        14519         4506.56   \n",
      "2            11195.49       27988        6622.95        30015         7118.27   \n",
      "3            21323.49       21708       14968.66        21044        14503.09   \n",
      "4             5110.61       53914        4075.02        53354         4037.48   \n",
      "...               ...         ...            ...          ...             ...   \n",
      "25871         2300.99       20761        2320.61        21785         2437.00   \n",
      "25872         6623.07       52821        7250.13        53449         7347.52   \n",
      "25873        11000.03       35320        5706.28        30270         4895.56   \n",
      "25874         6066.65      114646        5208.71        97260         4422.03   \n",
      "25875          272.25        9374         408.60         9827          427.93   \n",
      "\n",
      "       buy_lg_vol  buy_lg_amount  sell_lg_vol  sell_lg_amount  buy_elg_vol  \\\n",
      "0           85477        3926.44        87204         3989.37        28400   \n",
      "1            5474        1697.68         6390         1982.08          721   \n",
      "2           11723        2778.17        15330         3630.71         3939   \n",
      "3           13069        9000.74        15789        10882.14         7583   \n",
      "4           27279        2062.59        22583         1709.48        10118   \n",
      "...           ...            ...          ...             ...          ...   \n",
      "25871        8975        1002.99        18793         2100.36            0   \n",
      "25872       31050        4271.36        35903         4923.97         7655   \n",
      "25873       11829        1915.52        12687         2050.68         2000   \n",
      "25874       81164        3682.66       113414         5149.25        41421   \n",
      "25875        6887         302.35         8559          375.51            0   \n",
      "\n",
      "       buy_elg_amount  sell_elg_vol  sell_elg_amount  net_mf_vol  \\\n",
      "0             1293.83         19618           913.39       42201   \n",
      "1              223.34          1261           390.07        -823   \n",
      "2              935.53          9548          2248.51        2997   \n",
      "3             5221.72          6117          4227.39        6215   \n",
      "4              766.07         21600          1628.62       -9839   \n",
      "...               ...           ...              ...         ...   \n",
      "25871            0.00          1446           160.86      -20182   \n",
      "25872         1053.35          8003          1096.85      -12903   \n",
      "25873          319.00          2291           367.73       -5670   \n",
      "25874         1881.32         26018          1178.15      -70082   \n",
      "25875            0.00             0             0.00       -1678   \n",
      "\n",
      "       net_mf_amount  \n",
      "0            1943.09  \n",
      "1            -246.33  \n",
      "2             705.13  \n",
      "3            4367.94  \n",
      "4            -741.40  \n",
      "...              ...  \n",
      "25871       -2251.19  \n",
      "25872       -1769.73  \n",
      "25873        -918.58  \n",
      "25874       -3146.31  \n",
      "25875         -68.42  \n",
      "\n",
      "[25876 rows x 20 columns]\n"
     ]
    }
   ],
   "source": [
    "print(all_daily_data_df)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "stock",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}