liaozhaorun
7bb0a0537b
- 新增 /api/messages API 接口，支持从 Redis Stream 读取消息 - 支持按策略筛选消息和分页展示 - 前端新增消息列表卡片，展示时间、策略、股票代码、动作、价格和状态 - 自动判断消息处理状态（已处理/待处理） - 消息列表每30秒自动刷新，支持手动刷新
282 lines
10 KiB
Plaintext
282 lines
10 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "b94bb1f2-5332-485e-ae1b-eea01f938106",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-04-09T14:57:40.184418Z",
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"start_time": "2025-04-09T14:57:39.137312Z"
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}
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},
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"outputs": [],
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"source": [
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"import tushare as ts\n",
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"\n",
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"ts.set_token('3a0741c702ee7e5e5f2bf1f0846bafaafe4e320833240b2a7e4a685f')\n",
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"pro = ts.pro_api()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "742c29d453b9bb38",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-04-09T14:58:10.515830Z",
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"start_time": "2025-04-09T14:57:40.190466Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"<class 'pandas.core.frame.DataFrame'>\n",
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"Index: 9439640 entries, 0 to 25891\n",
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"Data columns (total 2 columns):\n",
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" # Column Dtype \n",
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"--- ------ ----- \n",
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" 0 ts_code object\n",
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" 1 trade_date object\n",
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"dtypes: object(2)\n",
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"memory usage: 216.1+ MB\n",
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"None\n",
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"20260213\n",
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"start_date: 20260224\n"
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]
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}
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],
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"source": [
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"import pandas as pd\n",
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"import time\n",
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"\n",
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"h5_filename = '/mnt/d/PyProject/NewStock/data/money_flow.h5'\n",
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"key = '/money_flow'\n",
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"max_date = None\n",
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"with pd.HDFStore(h5_filename, mode='r') as store:\n",
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" df = store[key][['ts_code', 'trade_date']]\n",
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" print(df.info())\n",
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" max_date = df['trade_date'].max()\n",
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"\n",
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"print(max_date)\n",
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"trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20260310')\n",
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"trade_cal = trade_cal[trade_cal['is_open'] == 1] # 只保留交易日\n",
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"trade_dates = trade_cal[trade_cal['cal_date'] > max_date]['cal_date'].tolist()\n",
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"start_date = min(trade_dates)\n",
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"print(f'start_date: {start_date}')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "679ce40e-8d62-4887-970c-e1d8cbdeee6b",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-04-09T14:58:17.197319Z",
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"start_time": "2025-04-09T14:58:10.724923Z"
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},
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"scrolled": true
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"任务 20260310 完成\n",
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"任务 20260309 完成\n",
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"任务 20260306 完成\n",
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"任务 20260305 完成\n",
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"任务 20260304 完成\n",
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"任务 20260303 完成\n",
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"任务 20260302 完成\n",
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"任务 20260227 完成\n",
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"任务 20260226 完成\n",
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"任务 20260225 完成\n",
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"任务 20260224 完成\n"
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]
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}
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],
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"source": [
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"from concurrent.futures import ThreadPoolExecutor, as_completed\n",
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"\n",
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"all_daily_data = []\n",
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"\n",
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"# API 调用计数和时间控制变量\n",
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"api_call_count = 0\n",
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"batch_start_time = time.time()\n",
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"\n",
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"\n",
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"def get_data(trade_date):\n",
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" time.sleep(0.1)\n",
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" money_flow_data = pro.moneyflow(trade_date=trade_date)\n",
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" if money_flow_data is not None and not money_flow_data.empty:\n",
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" return money_flow_data\n",
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"\n",
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"\n",
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"with ThreadPoolExecutor(max_workers=2) as executor:\n",
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" future_to_date = {executor.submit(get_data, td): td for td in trade_dates}\n",
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"\n",
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" for future in as_completed(future_to_date):\n",
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" trade_date = future_to_date[future] # 获取对应的交易日期\n",
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" try:\n",
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" result = future.result() # 获取任务执行的结果\n",
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" all_daily_data.append(result)\n",
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" print(f\"任务 {trade_date} 完成\")\n",
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" except Exception as e:\n",
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" print(f\"获取 {trade_date} 数据时出错: {e}\")\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "9af80516849d4e80",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-04-09T14:58:17.214168Z",
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"start_time": "2025-04-09T14:58:17.210734Z"
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}
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},
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"outputs": [],
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"source": [
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"all_daily_data_df = pd.concat(all_daily_data, ignore_index=True)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "a2b05187-437f-4053-bc43-bd80d4cf8b0e",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-04-09T14:58:19.633456Z",
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"start_time": "2025-04-09T14:58:17.229837Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"所有每日基础数据获取并保存完毕！\n"
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]
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}
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],
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"source": [
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"\n",
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"# 将所有数据合并为一个 DataFrame\n",
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"\n",
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"# 将数据保存为 HDF5 文件（table 格式）\n",
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"all_daily_data_df.to_hdf(h5_filename, key='money_flow', mode='a', format='table', append=True, data_columns=True)\n",
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"\n",
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"print(\"所有每日基础数据获取并保存完毕！\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "e6f2a2fe",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" ts_code trade_date buy_sm_vol buy_sm_amount sell_sm_vol \\\n",
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"0 002968.SZ 20260227 21302 2803.57 22552 \n",
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"1 600822.SH 20260227 20371 2267.76 18994 \n",
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"2 688003.SH 20260227 10527 8037.01 11183 \n",
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"3 300894.SZ 20260227 15100 1751.27 16491 \n",
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"4 603026.SH 20260227 35310 23574.59 36976 \n",
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"... ... ... ... ... ... \n",
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"20704 002587.SZ 20260224 114009 8175.31 115996 \n",
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"20705 002474.SZ 20260224 132136 11662.67 104912 \n",
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"20706 002742.SZ 20260224 9048 381.92 6158 \n",
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"20707 601298.SH 20260224 95226 8736.97 102361 \n",
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"20708 301413.SZ 20260224 11648 17813.73 7614 \n",
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"\n",
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" sell_sm_amount buy_md_vol buy_md_amount sell_md_vol sell_md_amount \\\n",
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"0 2971.99 12991 1715.40 11827 1560.91 \n",
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"1 2114.45 11869 1321.48 13242 1474.72 \n",
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"2 8535.14 6168 4707.52 5857 4472.05 \n",
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"3 1914.17 12204 1416.49 10014 1161.98 \n",
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"4 24680.03 25979 17356.95 23472 15689.34 \n",
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"... ... ... ... ... ... \n",
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"20704 8318.63 70581 5059.80 77960 5594.40 \n",
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"20705 9262.36 65247 5760.34 70730 6242.71 \n",
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"20706 259.97 12268 517.35 10575 446.72 \n",
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"20707 9387.51 17890 1640.43 21755 1995.51 \n",
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"20708 11690.25 18481 28263.21 16404 25116.12 \n",
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"\n",
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" buy_lg_vol buy_lg_amount sell_lg_vol sell_lg_amount buy_elg_vol \\\n",
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"0 4640 616.17 5428 716.74 873 \n",
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"1 7412 824.96 7836 871.98 420 \n",
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"2 5007 3817.18 4258 3252.13 792 \n",
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"3 5426 629.63 6225 721.24 0 \n",
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"4 14372 9614.17 12027 8050.76 2859 \n",
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"... ... ... ... ... ... \n",
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"20704 59178 4247.14 57492 4122.70 13717 \n",
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"20705 28382 2504.08 47970 4231.98 8727 \n",
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"20706 8183 345.88 10348 436.35 2869 \n",
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"20707 15594 1429.59 14207 1303.96 12463 \n",
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"20708 11245 17223.41 14418 22047.20 2604 \n",
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"\n",
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" buy_elg_amount sell_elg_vol sell_elg_amount net_mf_vol \\\n",
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"0 114.50 0 0.00 8754 \n",
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"1 46.96 0 0.00 -2636 \n",
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"2 604.59 1196 906.98 -2186 \n",
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"3 0.00 0 0.00 -2514 \n",
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"4 1908.59 6045 4034.17 -2211 \n",
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"... ... ... ... ... \n",
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"20704 987.82 6038 434.35 32850 \n",
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"20705 770.48 10880 960.53 -24208 \n",
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"20706 120.50 5287 222.60 -5084 \n",
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"20707 1140.48 2850 260.49 4058 \n",
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"20708 3986.99 5543 8433.77 -7520 \n",
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"\n",
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" net_mf_amount \n",
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"0 1160.00 \n",
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"1 -291.18 \n",
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"2 -1656.29 \n",
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"3 -289.80 \n",
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"4 -1416.49 \n",
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"... ... \n",
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"20704 2367.64 \n",
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"20705 -2127.88 \n",
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"20706 -213.09 \n",
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"20707 379.12 \n",
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"20708 -11475.67 \n",
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"\n",
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"[20709 rows x 20 columns]\n"
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]
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}
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],
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"source": [
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"print(all_daily_data_df)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "stock",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.11"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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