From 21bbc9cd1e3d1e550bc952ea291fbf5de08ffbf0 Mon Sep 17 00:00:00 2001 From: hubian <908234780@qq.com> Date: Tue, 14 Apr 2026 09:29:57 +0800 Subject: [PATCH] =?UTF-8?q?feat:=20=E4=BC=98=E5=8C=96=E6=AF=8F=E6=97=A5?= =?UTF-8?q?=E6=95=B0=E6=8D=AE=E6=8B=89=E5=8F=96=EF=BC=8C=E6=94=B9=E4=B8=BA?= =?UTF-8?q?=E6=8C=89=E6=97=A5=E6=9C=9F=E6=89=B9=E9=87=8F=E8=8E=B7=E5=8F=96?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- cron_daily_fetch.py | 224 +++ data/completed_stocks.txt | 2749 +++++++++++++++++++++++++++++++++++++ fetch_daily_update.py | 264 ++++ fetch_history.py | 97 +- 4 files changed, 3323 insertions(+), 11 deletions(-) create mode 100644 cron_daily_fetch.py create mode 100644 data/completed_stocks.txt create mode 100644 fetch_daily_update.py diff --git a/cron_daily_fetch.py b/cron_daily_fetch.py new file mode 100644 index 0000000..0e18497 --- /dev/null +++ b/cron_daily_fetch.py @@ -0,0 +1,224 @@ +""" +A股每日数据自动拉取脚本 (Cron专用) +每个交易日17:00自动运行，拉取当日所有股票数据 + +优化版本： +- 使用 trade_date 参数一次性获取当天所有股票数据 +- 大幅减少 API 请求次数（从 5000+ 次减少到每天 1 次） + +用法： +python3 cron_daily_fetch.py # 获取今日数据 +python3 cron_daily_fetch.py --days 5 # 获取最近5天数据 +python3 cron_daily_fetch.py --date 20260408 # 获取指定日期数据 + +Cron配置： +0 17 * * 1-5 python3 /home/xian/.openclaw/common/stock_system/cron_daily_fetch.py +""" + +import tushare as ts +import pandas as pd +import os +import time +from datetime import datetime, timedelta +from pathlib import Path +import argparse + +# 配置 +BASE_DIR = Path(__file__).parent +DATA_DIR = BASE_DIR / 'data' +DB_FILE = DATA_DIR / 'progress.db' +LOG_FILE = BASE_DIR / 'logs' / 'daily_fetch.log' +MAIN_FILE = DATA_DIR / 'stock_daily_data.parquet' + +REQUEST_INTERVAL = 0.5 # 每次请求间隔（按日期获取次数少，可以慢一点也没关系） + + +def log(msg): + """写入日志""" + timestamp = datetime.now().strftime('%Y-%m-%d %H:%M:%S') + log_msg = f"[{timestamp}] {msg}" + print(log_msg) + + LOG_FILE.parent.mkdir(exist_ok=True) + with open(LOG_FILE, 'a') as f: + f.write(log_msg + '\n') + + +def setup_tushare(): + """初始化tushare""" + token = os.environ.get('TUSHARE_TOKEN', '') + if not token: + config_file = BASE_DIR / 'config.txt' + if config_file.exists(): + token = config_file.read_text().strip() + if not token: + raise ValueError("缺少 Tushare Token") + ts.set_token(token) + return ts.pro_api() + + +def is_weekday(date_str): + """判断是否为工作日（周一到周五）""" + d = datetime.strptime(date_str, '%Y%m%d') + return d.weekday() < 5 + + +def get_trade_dates_in_range(start_date, end_date): + """获取日期范围内的所有工作日""" + start = datetime.strptime(start_date, '%Y%m%d') + end = datetime.strptime(end_date, '%Y%m%d') + + trade_dates = [] + current = start + while current <= end: + if current.weekday() < 5: # 周一到周五 + trade_dates.append(current.strftime('%Y%m%d')) + current += timedelta(days=1) + + return trade_dates + + +def get_latest_trade_date(): + """获取数据中最新的交易日期""" + if not MAIN_FILE.exists(): + return None + + df = pd.read_parquet(MAIN_FILE) + if len(df) == 0: + return None + + return df['trade_date'].max() + + +def fetch_data_by_date(pro, trade_date): + """ + 按日期获取当日所有股票数据 + 这是优化版本：一次请求获取当天所有股票 + """ + log(f"获取 {trade_date} 的数据...") + + try: + # 一次性获取当天所有股票数据 + df = pro.daily(trade_date=trade_date) + + if df is None or len(df) == 0: + log(f" {trade_date} 无数据（可能是非交易日）") + return 0, None + + log(f" {trade_date} 获取到 {len(df)} 条记录") + return len(df), df + + except Exception as e: + log(f" {trade_date} 获取失败: {str(e)[:100]}") + return 0, None + + +def merge_data(new_df): + """合并新数据到主文件""" + if new_df is None or len(new_df) == 0: + return + + if MAIN_FILE.exists(): + existing_df = pd.read_parquet(MAIN_FILE) + # 合并并去重 + combined = pd.concat([existing_df, new_df], ignore_index=True) + combined = combined.sort_values(['ts_code', 'trade_date']).reset_index(drop=True) + combined = combined.drop_duplicates(subset=['ts_code', 'trade_date'], keep='last') + else: + combined = new_df + + combined.to_parquet(MAIN_FILE, index=False, compression='snappy') + return len(combined) + + +def fetch_dates(pro, dates): + """获取多个日期的数据""" + if not dates: + log("没有需要获取的日期") + return 0 + + total_new = 0 + all_new_data = [] + + log(f"需要获取 {len(dates)} 个交易日的数据") + + for i, trade_date in enumerate(dates): + count, df = fetch_data_by_date(pro, trade_date) + + if df is not None and len(df) > 0: + all_new_data.append(df) + total_new += count + + # 间隔 + if i < len(dates) - 1: + time.sleep(REQUEST_INTERVAL) + + # 一次性合并所有数据 + if all_new_data: + combined_new = pd.concat(all_new_data, ignore_index=True) + total_records = merge_data(combined_new) + log(f"数据合并完成，总记录数: {total_records}") + + return total_new + + +def main(): + """主函数""" + parser = argparse.ArgumentParser(description='A股每日数据拉取（按日期批量获取）') + parser.add_argument('--date', type=str, help='获取指定日期数据 (格式: 20260408)') + parser.add_argument('--days', type=int, default=0, help='获取最近N天数据') + args = parser.parse_args() + + log("=" * 60) + log(f"A股每日数据拉取开始") + log("=" * 60) + + try: + # 初始化 + pro = setup_tushare() + + # 确定要获取的日期 + if args.date: + # 指定日期 + dates = [args.date] + log(f"模式: 获取指定日期 {args.date}") + elif args.days > 0: + # 最近N天 + end_date = datetime.now().strftime('%Y%m%d') + start_date = (datetime.now() - timedelta(days=args.days-1)).strftime('%Y%m%d') + dates = get_trade_dates_in_range(start_date, end_date) + log(f"模式: 获取最近 {args.days} 天数据 ({start_date} ~ {end_date})") + log(f"工作日: {dates}") + else: + # 默认：获取今日数据 + today = datetime.now().strftime('%Y%m%d') + + # 如果不是工作日，跳过 + if not is_weekday(today): + log(f"今天({datetime.now().strftime('%A')})不是工作日，跳过") + return + + dates = [today] + log(f"模式: 获取今日数据 ({today})") + + # 获取数据 + new_records = fetch_dates(pro, dates) + + # 显示文件信息 + if MAIN_FILE.exists(): + size_mb = MAIN_FILE.stat().st_size / 1024 / 1024 + log(f"数据文件大小: {size_mb:.2f} MB") + + log(f"本次新增数据: {new_records} 条") + log("=" * 60) + log("完成") + + except Exception as e: + log(f"错误: {e}") + import traceback + log(traceback.format_exc()) + raise + + +if __name__ == 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+ +# 配置 +BASE_DIR = Path(__file__).parent +DATA_DIR = BASE_DIR / 'data' +TEMP_DIR = DATA_DIR / 'temp' +DB_FILE = DATA_DIR / 'progress.db' +STOCK_LIST_FILE = BASE_DIR / 'A股股票列表.csv' + +REQUEST_INTERVAL = 0.3 # 增量数据请求间隔更短 + + +def setup_tushare(): + """初始化tushare""" + token = os.environ.get('TUSHARE_TOKEN', '') + if not token: + config_file = BASE_DIR / 'config.txt' + if config_file.exists(): + token = config_file.read_text().strip() + if not token: + raise ValueError("缺少 Tushare Token") + ts.set_token(token) + return ts.pro_api() + + +def get_latest_dates(): + """获取每只股票的最新数据日期""" + main_file = DATA_DIR / 'stock_daily_data.parquet' + if not main_file.exists(): + return {} + + df = pd.read_parquet(main_file) + latest = df.groupby('ts_code')['trade_date'].max() + return dict(latest) + + +def get_all_stock_codes(): + """获取所有股票代码""" + conn = sqlite3.connect(DB_FILE) + cursor = conn.cursor() + cursor.execute("SELECT ts_code FROM progress WHERE status = 'completed'") + codes = [row[0] for row in cursor.fetchall()] + conn.close() + return codes + + +def is_trading_day(date_str): + """判断是否为交易日（简单版：排除周末）""" + d = datetime.strptime(date_str, '%Y%m%d') + return d.weekday() < 5 # 周一到周五 + + +def get_trade_dates(start_date, end_date): + """获取日期范围内的可能交易日""" + start = datetime.strptime(start_date, '%Y%m%d') + end = datetime.strptime(end_date, '%Y%m%d') + + trade_dates = [] + current = start + while current <= end: + if current.weekday() < 5: # 周一到周五 + trade_dates.append(current.strftime('%Y%m%d')) + current += timedelta(days=1) + + return trade_dates + + +def get_missing_dates(pro, latest_dates, codes, days=30): + """找出需要更新的日期范围""" + today = datetime.now().strftime('%Y%m%d') + + # 使用简单交易日判断（排除周末） + start = (datetime.now() - timedelta(days=days)).strftime('%Y%m%d') + trade_dates = get_trade_dates(start, today) + + # 找出每个股票缺失的交易日 + missing_info = {} + for ts_code in codes: + latest = latest_dates.get(ts_code, '20100101') + missing = [d for d in trade_dates if d > latest and is_trading_day(d)] + if missing: + missing_info[ts_code] = { + 'latest': latest, + 'missing': missing, + 'missing_count': len(missing) + } + + return missing_info, trade_dates + + +def fetch_missing_data(pro, missing_info, batch_size=100): + """批量获取缺失数据""" + total_stocks = len(missing_info) + print(f"\n需要更新 {total_stocks} 只股票") + + # 计算需要获取的日期范围 + all_missing_dates = set() + for info in missing_info.values(): + all_missing_dates.update(info['missing']) + + if not all_missing_dates: + print("没有缺失数据") + return 0 + + start_date = min(all_missing_dates) + end_date = max(all_missing_dates) + print(f"数据范围: {start_date} ~ {end_date}") + + # 批量获取数据（tushare支持批量查询） + codes = list(missing_info.keys()) + total_records = 0 + batch_count = 0 + + main_file = DATA_DIR / 'stock_daily_data.parquet' + existing_df = pd.read_parquet(main_file) if main_file.exists() else None + + print(f"\n开始获取增量数据...") + print("-" * 50) + + for i, ts_code in enumerate(codes): + try: + latest = missing_info[ts_code]['latest'] + # 从最新日期的下一天开始获取 + start_fetch = latest + + df = pro.daily(ts_code=ts_code, start_date=start_fetch, end_date=end_date) + + if df is not None and len(df) > 0: + # 过滤掉已存在的数据 + df = df[df['trade_date'] > latest] + + if len(df) > 0: + # 合并到主文件 + if existing_df is not None: + combined = pd.concat([existing_df, df], ignore_index=True) + combined = combined.sort_values(['ts_code', 'trade_date']).reset_index(drop=True) + combined.to_parquet(main_file, index=False, compression='snappy') + existing_df = combined + else: + df.to_parquet(main_file, index=False, compression='snappy') + existing_df = df + + total_records += len(df) + print(f"[{i+1}/{total_stocks}] {ts_code} ✓ +{len(df)}条") + else: + print(f"[{i+1}/{total_stocks}] {ts_code} - 无新数据") + else: + print(f"[{i+1}/{total_stocks}] {ts_code} - 无数据") + + batch_count += 1 + + # 每批次保存一次，减少请求间隔 + if batch_count >= batch_size: + batch_count = 0 + + time.sleep(REQUEST_INTERVAL) + + except Exception as e: + print(f"[{i+1}/{total_stocks}] {ts_code} ✗ {str(e)[:50]}") + time.sleep(1) # 错误后多等一会 + + print("-" * 50) + print(f"增量更新完成，新增 {total_records} 条记录") + + return total_records + + +def check_missing_status(pro, days=30): + """检查缺失状态""" + print("=" * 60) + print("A股数据缺失状态检查") + print("=" * 60) + + # 获取现有数据最新日期 + latest_dates = get_latest_dates() + codes = get_all_stock_codes() + + print(f"已获取数据股票数: {len(codes)}") + + # 获取缺失信息 + missing_info, trade_dates = get_missing_dates(pro, latest_dates, codes, days) + + # 统计 + total_missing = len(missing_info) + if total_missing == 0: + print("\n✓ 所有股票数据都是最新的！") + return {} + + # 按缺失天数分组统计 + missing_stats = {} + for ts_code, info in missing_info.items(): + count = info['missing_count'] + if count not in missing_stats: + missing_stats[count] = 0 + missing_stats[count] += 1 + + print(f"\n需要更新的股票: {total_missing} 只") + print(f"最近交易日: {trade_dates[-5:] if len(trade_dates) >= 5 else trade_dates}") + + print("\n缺失天数分布:") + for days_count in sorted(missing_stats.keys()): + print(f" 缺{days_count}天: {missing_stats[days_count]} 只") + + # 显示缺失最多的股票 + print("\n缺失最多的10只股票:") + sorted_missing = sorted(missing_info.items(), key=lambda x: x[1]['missing_count'], reverse=True)[:10] + for ts_code, info in sorted_missing: + print(f" {ts_code}: 最新{info['latest']}, 缺{info['missing_count']}天") + + return missing_info + + +def main(): + parser = argparse.ArgumentParser(description='A股每日增量数据更新') + parser.add_argument('--check', action='store_true', help='只检查不更新') + parser.add_argument('--days', type=int, default=30, help='检查最近多少天') + args = parser.parse_args() + + print(f"运行时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}") + + # 初始化 + pro = setup_tushare() + + if args.check: + # 只检查 + check_missing_status(pro, args.days) + else: + # 检查并更新 + missing_info = check_missing_status(pro, args.days) + if missing_info: + fetch_missing_data(pro, missing_info) + # 再次检查确认 + print("\n更新后再次检查...") + check_missing_status(pro, args.days) + + # 显示文件大小 + main_file = DATA_DIR / 'stock_daily_data.parquet' + if main_file.exists(): + size_mb = main_file.stat().st_size / 1024 / 1024 + print(f"\n数据文件大小: {size_mb:.2f} MB") + + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/fetch_history.py b/fetch_history.py index 69486ba..25c347a 100644 --- a/fetch_history.py +++ b/fetch_history.py @@ -25,7 +25,7 @@ START_DATE = '20100101' END_DATE = datetime.now().strftime('%Y%m%d') # 请求间隔（秒）- tushare积分限制 -REQUEST_INTERVAL = 9 +REQUEST_INTERVAL = 5 def setup_tushare(token=None): @@ -91,34 +91,109 @@ def get_stock_codes_with_suffix(df): def fetch_daily_data(pro, codes, start_date, end_date): - """逐个获取日线数据（每次一支股票）""" - all_data = [] + """逐个获取日线数据（每次一支股票），支持断点续传""" total = len(codes) - print(f"\n共 {total} 只股票需要获取") - print(f"预计耗时: {total * REQUEST_INTERVAL / 60:.1f} 分钟") + # 加载已完成的股票列表 + completed_file = DATA_DIR / 'completed_stocks.txt' + completed_stocks = set() + if completed_file.exists(): + lines = completed_file.read_text().strip().split('\n') + completed_stocks = set(line.strip() for line in lines if line.strip()) + print(f"已完成: {len(completed_stocks)} 只股票") + + # 统计已有数据 + existing_data_file = DATA_DIR / 'stock_daily_data.parquet' + if existing_data_file.exists(): + existing_df = pd.read_parquet(existing_data_file) + print(f"已有数据: {len(existing_df)} 条记录") + + print(f"\n共 {total} 只股票，待处理: {total - len(completed_stocks)} 只") + print(f"预计耗时: {(total - len(completed_stocks)) * REQUEST_INTERVAL / 60:.1f} 分钟") print("-" * 50) for i, ts_code in enumerate(codes): + # 跳过已完成的 + if ts_code in completed_stocks: + print(f"[{i+1}/{total}] {ts_code} 已完成，跳过") + continue + try: - print(f"[{i+1}/{total}] 获取 {ts_code}...", end=' ') + print(f"[{i+1}/{total}] 获取 {ts_code}...", end=' ', flush=True) df = pro.daily(ts_code=ts_code, start_date=start_date, end_date=end_date) if df is not None and len(df) > 0: - all_data.append(df) print(f"成功，{len(df)} 条记录") + # 实时保存单只股票数据 + save_single_stock(df, ts_code, completed_file) else: print("无数据") + # 无数据也标记为完成 + mark_completed(ts_code, completed_file) except Exception as e: print(f"错误: {e}") + # 出错不标记完成，下次重试 - # 每次请求后休息9秒 - if i < total - 1: # 最后一个不需要等待 + # 每次请求后休息 + if i < total - 1: time.sleep(REQUEST_INTERVAL) - return all_data + # 最后合并所有数据 + return merge_all_data() + + +def save_single_stock(df, ts_code, completed_file): + """保存单只股票数据并标记完成""" + # 读取已有数据 + output_file = DATA_DIR / 'stock_daily_data.parquet' + + if output_file.exists(): + existing_df = pd.read_parquet(output_file) + # 删除该股票的旧数据（如果有） + existing_df = existing_df[existing_df['ts_code'] != ts_code] + # 合并新数据 + combined_df = pd.concat([existing_df, df], ignore_index=True) + else: + combined_df = df + + # 排序 + combined_df = combined_df.sort_values(['ts_code', 'trade_date']).reset_index(drop=True) + + # 保存 + combined_df.to_parquet(output_file, index=False) + + # 标记完成 + mark_completed(ts_code, completed_file) + + +def merge_all_data(): + """最后合并所有数据（用于返回）""" + output_file = DATA_DIR / 'stock_daily_data.parquet' + if output_file.exists(): + return [pd.read_parquet(output_file)] + return [] + + +def save_progress(all_data, ts_code, completed_file): + """实时保存进度（保留兼容性）""" + # 合并并保存数据 + combined_df = pd.concat(all_data, ignore_index=True) + combined_df = combined_df.sort_values(['ts_code', 'trade_date']).reset_index(drop=True) + + # 保存parquet + output_file = DATA_DIR / 'stock_daily_data.parquet' + combined_df.to_parquet(output_file, index=False) + + # 标记完成 + mark_completed(ts_code, completed_file) + + +def mark_completed(ts_code, completed_file): + """标记股票已完成""" + with open(completed_file, 'a') as f: + f.write(ts_code + '\n') def save_to_parquet(df, filename): @@ -197,4 +272,4 @@ def main(): if __name__ == '__main__': - main() \ No newline at end of file + main()