467 lines
18 KiB
Plaintext
467 lines
18 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": "18d1d622-b083-4cc4-a6f8-7c1ed2d0edd2",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-04-09T14:57:36.913044Z",
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"start_time": "2025-04-09T14:57:36.159612Z"
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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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"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": "14671a7f72de2564",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-04-09T14:57:39.128278Z",
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"start_time": "2025-04-09T14:57:36.918051Z"
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}
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},
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"outputs": [],
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"source": [
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"from datetime import datetime\n",
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"import pandas as pd\n",
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"import warnings\n",
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"\n",
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"warnings.filterwarnings(\"ignore\")\n",
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"def filter_rows(df):\n",
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" # 按照 name 和 start_date 分组\n",
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" def select_row(group):\n",
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" # 如果有 end_date 不为 NaT 的行，优先保留这些行\n",
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" valid_rows = group[group['end_date'].notna()]\n",
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" if not valid_rows.empty:\n",
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" return valid_rows.iloc[0] # 返回第一个有效行\n",
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" else:\n",
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" return group.iloc[0] # 如果没有有效行，返回第一行\n",
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"\n",
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" filtered_df = df.groupby(['name', 'start_date'], group_keys=False).apply(select_row)\n",
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" filtered_df = filtered_df.reset_index(drop=True)\n",
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" return filtered_df\n",
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"\n",
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"def is_st(name_change_dict, stock_code, target_date):\n",
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" target_date = datetime.strptime(target_date, '%Y%m%d')\n",
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" if stock_code not in name_change_dict.keys():\n",
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" return False\n",
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" df = name_change_dict[stock_code]\n",
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" for i in range(len(df)):\n",
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" sds = df.iloc[i, 2]\n",
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" eds = df.iloc[i, 3]\n",
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" if eds is None or eds is pd.NaT:\n",
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" eds = datetime.now()\n",
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" if (target_date - sds).days >= 0 and (target_date - eds).days <= 0:\n",
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" return True\n",
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" return False\n",
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"\n",
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"name_change_df = pd.read_hdf('../../../data/name_change.h5', key='name_change')\n",
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"name_change_df = name_change_df.drop_duplicates(keep='first')\n",
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"\n",
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"# 确保 name_change_df 的日期格式正确\n",
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"name_change_df['start_date'] = pd.to_datetime(name_change_df['start_date'], format='%Y%m%d')\n",
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"name_change_df['end_date'] = pd.to_datetime(name_change_df['end_date'], format='%Y%m%d', errors='coerce')\n",
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"# name_change_df = name_change_df[name_change_df.name.str.contains('ST') ]\n",
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"name_change_dict = {}\n",
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"for ts_code, group in name_change_df.groupby('ts_code'):\n",
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" # 只保留 'ST' 和 '*ST' 的记录\n",
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" # st_data = group[(group['change_reason'] == 'ST') | (group['change_reason'] == '*ST')]\n",
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" st_data = group[(group['name'].str.contains('ST')) | (group['name'].str.contains('退'))]\n",
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" if not st_data.empty:\n",
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" name_change_dict[ts_code] = filter_rows(st_data)"
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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": "e7f8cce2f80e2f20",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-04-09T14:58:09.296046Z",
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"start_time": "2025-04-09T14:57:39.339423Z"
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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: 8647642 entries, 0 to 26951\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: 197.9+ MB\n",
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"None\n",
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"20250516\n",
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"20250519\n"
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]
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}
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],
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"source": [
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"import time\n",
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"from concurrent.futures import ThreadPoolExecutor, as_completed\n",
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"\n",
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"h5_filename = '../../../data/daily_basic.h5'\n",
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"key = '/daily_basic'\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='20250720')\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(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": 4,
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"id": "553cfb36-f560-4cc4-b2bc-68323ccc5072",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-04-09T14:58:16.817010Z",
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"start_time": "2025-04-09T14:58:09.326485Z"
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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 20250717 完成\n",
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"任务 20250718 完成\n",
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"任务 20250715 完成\n",
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"任务 20250716 完成\n",
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"任务 20250714 完成\n",
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"任务 20250711 完成\n",
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"任务 20250709 完成\n",
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"任务 20250710 完成\n",
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"任务 20250708 完成\n",
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"任务 20250707 完成\n",
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"任务 20250704 完成\n",
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"任务 20250703 完成\n",
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"任务 20250702 完成\n",
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"任务 20250701 完成\n",
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"任务 20250630 完成\n",
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"任务 20250627 完成\n",
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"任务 20250625 完成\n",
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"任务 20250626 完成\n",
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"任务 20250624 完成\n",
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"任务 20250623 完成\n",
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"任务 20250619 完成\n",
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"任务 20250620 完成\n",
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"任务 20250618 完成\n",
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"任务 20250617 完成\n",
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"任务 20250616 完成\n",
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"任务 20250613 完成\n",
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"任务 20250612 完成\n",
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"任务 20250611 完成\n",
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"任务 20250610 完成\n",
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"任务 20250609 完成\n",
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"任务 20250606 完成\n",
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"任务 20250605 完成\n",
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"任务 20250604 完成\n",
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"任务 20250603 完成\n",
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"任务 20250529 完成\n",
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"任务 20250530 完成\n",
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"任务 20250527 完成\n",
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"任务 20250528 完成\n",
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"任务 20250526 完成\n",
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"任务 20250523 完成\n",
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"任务 20250522 完成\n",
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"任务 20250521 完成\n",
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"任务 20250520 完成\n",
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"任务 20250519 完成\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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"\n",
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"# 使用 HDFStore 存储数据\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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" daily_basic_data = pro.daily_basic(ts_code='', trade_date=trade_date)\n",
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" if daily_basic_data is not None and not daily_basic_data.empty:\n",
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" # 添加交易日期列标识\n",
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" daily_basic_data['trade_date'] = trade_date\n",
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" daily_basic_data['is_st'] = daily_basic_data.apply(\n",
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" lambda row: is_st(name_change_dict, row['ts_code'], row['trade_date']), axis=1\n",
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" )\n",
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" time.sleep(0.2)\n",
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" # print(f\"成功获取并保存 {trade_date} 的每日基础数据\")\n",
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" return daily_basic_data\n",
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"\n",
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"\n",
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"# 遍历每个交易日期并获取数据\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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" # 计数一次 API 调用\n",
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" api_call_count += 1\n",
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"\n",
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" # 每调用 300 次，检查时间是否少于 1 分钟，如果少于则等待剩余时间\n",
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" if api_call_count % 150 == 0:\n",
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" elapsed = time.time() - batch_start_time\n",
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" if elapsed < 60:\n",
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" sleep_time = 60 - elapsed\n",
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" print(f\"已调用 150 次 API，等待 {sleep_time:.2f} 秒以满足速率限制...\")\n",
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" time.sleep(sleep_time)\n",
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" # 重置批次起始时间\n",
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" batch_start_time = time.time()\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": 5,
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"id": "919023c693d7a47a",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-04-09T14:58:16.864178Z",
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"start_time": "2025-04-09T14:58:16.855084Z"
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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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" ts_code trade_date close turnover_rate turnover_rate_f \\\n",
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"0 000839.SZ 20250523 2.67 0.8124 1.2782 \n",
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"1 300274.SZ 20250523 60.60 3.2852 3.7071 \n",
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"2 301356.SZ 20250523 17.59 5.0050 5.0698 \n",
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"3 600152.SH 20250523 5.73 1.3359 2.0988 \n",
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"4 300049.SZ 20250523 29.91 1.6066 1.7292 \n",
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"... ... ... ... ... ... \n",
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"26941 002458.SZ 20250519 8.36 2.1950 2.5416 \n",
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"26942 600882.SH 20250519 27.18 2.2244 4.6853 \n",
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"26943 001283.SZ 20250519 54.51 3.0453 3.0453 \n",
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"26944 000718.SZ 20250519 2.20 1.4790 2.2404 \n",
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"26945 002141.SZ 20250519 3.09 4.9267 7.1872 \n",
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"\n",
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" volume_ratio pe pe_ttm pb ps ps_ttm dv_ratio \\\n",
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"0 0.62 NaN NaN 7.4695 3.0824 3.1095 0.0000 \n",
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"1 1.82 11.3840 9.8414 3.0807 1.6137 1.4907 1.1292 \n",
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"2 1.43 NaN 18055.4366 1.2789 4.2618 3.3028 0.0000 \n",
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"3 1.11 NaN NaN 1.7367 1.9844 2.0758 0.0000 \n",
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"4 1.05 70.3242 80.3071 4.4707 5.9056 5.8725 0.0000 \n",
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"... ... ... ... ... ... ... ... \n",
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"26941 1.47 18.3588 24.2570 2.1403 2.9497 3.0116 2.3923 \n",
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"26942 0.89 122.4919 89.9537 3.0986 2.8733 2.7144 0.0000 \n",
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"26943 0.92 48.1520 36.6481 2.1043 0.8602 0.8229 0.8691 \n",
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"26944 1.76 40.4178 55.0402 0.7058 3.1476 3.2425 3.6364 \n",
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"26945 1.51 NaN NaN 3.8214 7.2461 4.4422 0.0000 \n",
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"\n",
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" dv_ttm total_share float_share free_share total_mv \\\n",
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"0 NaN 391982.6352 391982.6352 249133.8007 1.046594e+06 \n",
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"1 1.1292 207321.1424 158970.9449 140880.3307 1.256366e+07 \n",
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"2 NaN 21600.0000 5481.0000 5410.9920 3.799440e+05 \n",
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"3 NaN 52907.9375 52907.9375 33676.4965 3.031625e+05 \n",
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"4 NaN 26635.6100 23351.5217 21696.0562 7.966711e+05 \n",
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"... ... ... ... ... ... \n",
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"26941 2.3577 110641.2915 74886.8285 64675.1303 9.249612e+05 \n",
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"26942 NaN 51205.3647 51205.3647 24310.0793 1.391762e+06 \n",
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"26943 0.8691 8061.0011 5785.5721 5785.5721 4.394052e+05 \n",
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"26944 3.6364 303463.6384 228209.3122 150654.2061 6.676200e+05 \n",
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"26945 NaN 103293.5798 103159.2875 70714.2228 3.191772e+05 \n",
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"\n",
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" circ_mv is_st \n",
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"0 1.046594e+06 False \n",
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"1 9.633639e+06 False \n",
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"2 9.641079e+04 False \n",
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"3 3.031625e+05 False \n",
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"4 6.984440e+05 False \n",
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"... ... ... \n",
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"26941 6.260539e+05 False \n",
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"26942 1.391762e+06 False \n",
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"26943 3.153715e+05 False \n",
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"26944 5.020605e+05 False \n",
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"26945 3.187622e+05 True \n",
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"\n",
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"[26946 rows x 19 columns]\n"
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]
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}
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],
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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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"print(all_daily_data_df)"
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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": "28cb78d032671b20",
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||
"metadata": {
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||
"ExecuteTime": {
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||
"end_time": "2025-04-09T14:58:16.881685Z",
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"start_time": "2025-04-09T14:58:16.871184Z"
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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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" ts_code trade_date close turnover_rate turnover_rate_f \\\n",
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"23 002898.SZ 20250523 10.20 22.8874 36.4442 \n",
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"35 000889.SZ 20250523 2.76 1.6609 2.2443 \n",
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"53 300379.SZ 20250523 6.12 9.3935 9.5800 \n",
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"58 300268.SZ 20250523 10.27 1.8178 2.5956 \n",
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"155 000615.SZ 20250523 3.15 1.1640 1.7189 \n",
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"... ... ... ... ... ... \n",
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"26880 300147.SZ 20250519 8.80 6.8409 8.8527 \n",
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"26891 002501.SZ 20250519 2.17 4.4260 5.7136 \n",
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"26910 600421.SH 20250519 6.39 3.4329 7.3909 \n",
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"26938 600289.SH 20250519 5.90 1.1380 1.6532 \n",
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"26945 002141.SZ 20250519 3.09 4.9267 7.1872 \n",
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"\n",
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" volume_ratio pe pe_ttm pb ps ps_ttm dv_ratio dv_ttm \\\n",
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"23 10.43 NaN NaN 3.6011 6.8112 7.2338 0.1961 0.1961 \n",
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"35 0.52 NaN NaN 27.2957 1.7661 1.7554 0.0000 NaN \n",
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"53 0.89 NaN NaN 1.0993 4.5062 4.1828 0.0000 NaN \n",
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"58 0.99 NaN NaN NaN 0.5235 0.5833 0.0000 NaN \n",
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"155 0.99 NaN NaN NaN 2.1957 2.2727 0.0000 NaN \n",
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"... ... .. ... ... ... ... ... ... \n",
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"26880 1.55 NaN NaN 6.0171 3.1309 3.4015 0.0000 NaN \n",
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"26891 1.83 NaN NaN 23.5587 23.0948 27.1516 0.0000 NaN \n",
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"26910 0.92 NaN NaN 173.6254 10.6672 10.8459 0.0000 NaN \n",
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"26938 0.46 NaN NaN 3.0370 11.6255 11.9049 0.0000 NaN \n",
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"26945 1.51 NaN NaN 3.8214 7.2461 4.4422 0.0000 NaN \n",
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"\n",
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" total_share float_share free_share total_mv circ_mv is_st \n",
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"23 17600.0000 10126.2561 6359.4096 179520.0000 103287.8122 True \n",
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"35 93629.1116 86984.9676 64375.7658 258416.3480 240078.5106 True \n",
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"53 55792.2828 52663.7564 51638.5483 341448.7707 322302.1892 True \n",
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"58 17420.0000 13370.7500 9364.1581 178903.4000 137317.6025 True \n",
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"155 76297.9719 76250.0287 51632.2709 240338.6115 240187.5904 True \n",
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"... ... ... ... ... ... ... \n",
|
||
"26880 66127.9045 65745.9042 50804.9121 581925.5596 578563.9570 True \n",
|
||
"26891 355000.0000 354999.9006 274999.9006 770350.0000 770349.7843 True \n",
|
||
"26910 19560.0000 19560.0000 9085.2748 124988.4000 124988.4000 True \n",
|
||
"26938 63105.2069 56592.2684 38956.2787 372320.7207 333894.3836 True \n",
|
||
"26945 103293.5798 103159.2875 70714.2228 319177.1616 318762.1984 True \n",
|
||
"\n",
|
||
"[944 rows x 19 columns]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(all_daily_data_df[all_daily_data_df['is_st']])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"id": "692b58674b7462c9",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-04-09T14:58:17.773453Z",
|
||
"start_time": "2025-04-09T14:58:16.903459Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"所有每日基础数据获取并保存完毕！\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# 将数据保存为 HDF5 文件（table 格式）\n",
|
||
"all_daily_data_df.to_hdf(h5_filename, key='daily_basic', 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！\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "d7a773fc20293477",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-04-09T14:58:24.305403Z",
|
||
"start_time": "2025-04-09T14:58:17.816332Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"<class 'pandas.core.frame.DataFrame'>\n",
|
||
"Index: 8674588 entries, 0 to 26945\n",
|
||
"Data columns (total 3 columns):\n",
|
||
" # Column Dtype \n",
|
||
"--- ------ ----- \n",
|
||
" 0 ts_code object\n",
|
||
" 1 trade_date object\n",
|
||
" 2 is_st bool \n",
|
||
"dtypes: bool(1), object(2)\n",
|
||
"memory usage: 206.8+ MB\n",
|
||
"None\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"with pd.HDFStore(h5_filename, mode='r') as store:\n",
|
||
" df = store[key][['ts_code', 'trade_date', 'is_st']]\n",
|
||
" print(df.info())"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "new_trader",
|
||
"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": "3.11.11"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|