"""通达信「沪深京日线数据完整包」全量导入 candles 表。 用法(在 backend 目录下): uv run python scripts/import_tdx_day.py C:/Users/cirry/Downloads/hsjday [symbol ...] # symbol 为可选的 6 位代码过滤(如 000001 002671),只重导这些标的 uv run python scripts/import_tdx_day.py <目录> --no-clear # --no-clear:不清空任何行,纯 upsert(用于给已导入的底座回补 amount 成交额) - 解析 vipdoc 的 .day 二进制文件(每条 32 字节): 日期(YYYYMMDD) 开 高 低 收(×100) 成交额(元, float32) 成交量(股) 保留 - 只导入 stock_basic 里登记的股票(自动排除指数/基金/可转债/回购); sh000001(上证指数) 与 sz000001(平安银行) 这类代码冲突也由此化解。 - 价格为**不复权**:全量模式导入前清空已有的非 DEMO 行情;指定 symbol 过滤时 只清空这些标的(用于修复被复权口径污染的个别股票),其余不动。 - amount 为 TDX 原生 float32(元),精度 ~6 位有效数字,展示用途足够; ON CONFLICT 时仅更新 amount 列,不动 OHLCV/turnover(避免与换手率回补互相干扰)。 - 写入用 asyncpg execute_many + ON CONFLICT DO UPDATE,可重复执行(幂等)。 """ from __future__ import annotations import argparse import asyncio import struct import sys import time from pathlib import Path import asyncpg # .env 里的 DATABASE_URL 是 SQLAlchemy 格式,asyncpg 需要 libpq 格式 DEFAULT_URL = "postgresql://postgres:postgres@localhost:5432/stock" BATCH = 20_000 # 每批 upsert 行数 def load_db_url() -> str: env = Path(__file__).resolve().parent.parent / ".env" if env.exists(): for line in env.read_text(encoding="utf-8").splitlines(): line = line.strip() if line.startswith("DATABASE_URL=postgresql+asyncpg://"): return "postgresql://" + line.split("://", 1)[1] return DEFAULT_URL def parse_day_file(path: Path) -> list[tuple[int, float, float, float, float, float, float]]: """解析单个 .day 文件 -> [(date, open, high, low, close, volume(股), amount(元)), ...]""" raw = path.read_bytes() unpack = struct.Struct(" None: if not root.exists(): sys.exit(f"目录不存在: {root}") conn = await asyncpg.connect(load_db_url()) try: # 股票清单:ts_code 形如 000001.SZ,用于过滤指数/基金/转债 rows = await conn.fetch("SELECT ts_code, symbol FROM stock_basic WHERE list_status = 'L'") by_exchange: dict[str, set[str]] = {"sh": set(), "sz": set(), "bj": set()} for r in rows: suffix = r["ts_code"].split(".")[-1].lower() # SH/SZ/BJ -> sh/sz/bj if suffix in by_exchange: by_exchange[suffix].add(r["symbol"]) print(f"stock_basic 在市股票: " + ", ".join(f"{k}={len(v)}" for k, v in by_exchange.items())) files = sorted(root.glob("*/lday/*.day")) print(f"发现 .day 文件: {len(files)} 个") if no_clear: print("--no-clear:不清空任何行,纯 upsert 回补 amount") elif symbols: # 清空旧行情(保留 DEMO 合成数据),避免 qfq/不复权混用; # 带 symbol 过滤时只清空目标标的(修复个别被污染的股票,不动其余底座) deleted = await conn.execute( "DELETE FROM candles WHERE symbol = ANY($1)", symbols ) print(f"清空目标标的 {symbols}: {deleted}") keep = set(symbols) files = [p for p in files if p.name[2:8] in keep] print(f"过滤后待导入 .day 文件: {len(files)} 个") else: deleted = await conn.execute("DELETE FROM candles WHERE symbol <> 'DEMO'") print(f"清空旧行情: {deleted}") if no_clear: # 回填模式:只写 amount,不动 OHLCV/turnover(底座已就位,避免全表重写) upsert_sql = """ INSERT INTO candles (symbol, timeframe, ts, open, high, low, close, volume, amount) VALUES ($1, '1d', to_timestamp($2::text, 'YYYYMMDD')::timestamp, $3, $4, $5, $6, $7, $8) ON CONFLICT (symbol, timeframe, ts) DO UPDATE SET amount = EXCLUDED.amount """ else: upsert_sql = """ INSERT INTO candles (symbol, timeframe, ts, open, high, low, close, volume, amount) VALUES ($1, '1d', to_timestamp($2::text, 'YYYYMMDD')::timestamp, $3, $4, $5, $6, $7, $8) ON CONFLICT (symbol, timeframe, ts) DO UPDATE SET open = EXCLUDED.open, high = EXCLUDED.high, low = EXCLUDED.low, close = EXCLUDED.close, volume = EXCLUDED.volume, amount = EXCLUDED.amount """ t0 = time.time() total_stocks = 0 skipped = 0 batch: list[tuple] = [] rows_done = 0 async def flush() -> None: nonlocal batch, rows_done if batch: await conn.executemany(upsert_sql, batch) rows_done += len(batch) batch = [] for n, path in enumerate(files, 1): market = path.name[:2].lower() # sh / sz / bj code = path.name[2:8] if code not in by_exchange.get(market, set()): skipped += 1 continue for date, o, h, l, c, v, amount in parse_day_file(path): batch.append((code, str(date), o, h, l, c, v, amount)) total_stocks += 1 if len(batch) >= BATCH: await flush() if n % 500 == 0: elapsed = time.time() - t0 print(f" 进度 {n}/{len(files)} 文件, 已入库 {total_stocks} 只股票, " f"{rows_done + len(batch):,} 行, {elapsed:.0f}s") await flush() cnt = await conn.fetchval("SELECT count(*) FROM candles WHERE symbol <> 'DEMO'") span = await conn.fetchrow( "SELECT min(ts) AS lo, max(ts) AS hi FROM candles WHERE symbol <> 'DEMO'" ) with_amt = await conn.fetchval( "SELECT count(*) FROM candles WHERE symbol <> 'DEMO' AND amount IS NOT NULL" ) print(f"\n完成: {total_stocks} 只股票, {cnt:,} 行日线, " f"范围 {span['lo']:%Y-%m-%d} ~ {span['hi']:%Y-%m-%d}, " f"含成交额 {with_amt:,} 行, " f"跳过非股票文件 {skipped} 个, 耗时 {time.time() - t0:.0f}s") finally: await conn.close() if __name__ == "__main__": ap = argparse.ArgumentParser(description="TDX 沪深京日线全量导入 candles") ap.add_argument("root", help="hsjday 目录(其下 */lday/*.day)") ap.add_argument("symbols", nargs="*", help="可选的 6 位代码过滤") ap.add_argument("--no-clear", action="store_true", help="不清空任何行,纯 upsert(amount 回补模式)") a = ap.parse_args() asyncio.run(main(Path(a.root), a.symbols or None, a.no_clear))