"""同步任务：5 分钟 K 线（mairui）。 设计要点： 1) 启动时**一次 SQL** 拿全表 {stock_code: max(bar_time)} 快照 2) 内存里给每只股票算 (start, end, windows_needed) - DB 没数据 → start = mairui 历史深度起点 (2023-06-14) - DB 有数据 → start = max(2023-06-14, max(bar_time) - 2 天) - 兜底：start >= end → 跳过这只 3) 输出"计划"（全量/增量分类、预估请求数、预估耗时）再开始执行 4) 5 worker × 1 RPS/worker = 5 RPS 总（守住 mairui 上限） 5) 窗口大小 1 年（mairui 一次最多 ~11600 条） akshare 时代写法：start 写死 2020-01-01 + 4 天窗口，137 小时跑完全市场。 本版：增量时通常只补最近 1-2 天，~30 分钟。 """ from __future__ import annotations import os import time from concurrent.futures import ThreadPoolExecutor, as_completed, TimeoutError as FuturesTimeoutError from datetime import datetime, timedelta, timezone from typing import Any, Optional from app.core.db import ops as db_ops from app.core.datasource.base import registry as ds_registry from app.core.datasource.registry import is_source_ready from app.core.datasource.utils import is_a_share_code, to_code6, to_hermes from app.core.sync.base import SyncTask from app.core.sync.registry import mark_sync_blocked from app.core.utils.logging import get_logger logger = get_logger("sync.kline_5min") # mairui 5min K 实测历史深度起点（实测 2023-06-14 才有数据，更早就 0） MAIRUI_5MIN_FLOOR = datetime(2023, 6, 14, tzinfo=timezone.utc) # 单次请求窗口大小：1 年（mairui 单次最多 ~11640 条 5min） WINDOW_DAYS = 365 # 增量时往前多取的天数（防交易日历边界漏当天） INCREMENT_OVERLAP_DAYS = 2 # 2026-07-08 教训: 数据源层有 20s timeout,task 层再硬兜底, # 防 SDK 升级 / 网络层 bug 让单只股票卡住(7月7日 4.4只/秒 后突然 0 持续 24h) # 7月9日 mairui/雪球 间歇性 "服务器连接失败" + Broken pipe,拉慢。给 120s 容忍。 FETCH_HARD_TIMEOUT = 600.0 # 5min K 全市场 5200+ 只,按 RPS 预估约 17min,给 10min 硬超时 class SyncKline5Min(SyncTask): dataset_id = "kline_5min" def _plan( self, stock_codes: list[str], end: datetime, snapshots: dict[str, Optional[datetime]], ) -> list[tuple[str, datetime, datetime]]: """为每只股票算 (start, end) — 已排除 start >= end 的"无需同步"。 Returns: [(code6, start, end), ...] """ plans = [] for code6 in stock_codes: latest = snapshots.get(code6) if latest is None: # DB 里没数据 → 全量从 mairui 历史深度起点 start = MAIRUI_5MIN_FLOOR else: # DB 有数据 → 增量：从 max(bar_time) - overlap 到 end start = max(MAIRUI_5MIN_FLOOR, latest - timedelta(days=INCREMENT_OVERLAP_DAYS)) if start < end: plans.append((code6, start, end)) return plans def _run( self, *, trigger_source: str = "manual", codes: list[str] | None = None, max_workers: int = 5, **kwargs, ) -> dict[str, Any]: primary = ds_registry.get("datasource_mairui") if primary is None: return {"status": "error", "message": "5min 数据源 mairui 未注册"} ok, reason = is_source_ready(primary.key) if not ok: mark_sync_blocked(self.dataset_id, message=f"{primary.key} 未就绪: {reason}") return {"status": "blocked", "message": f"{primary.key} 未就绪: {reason}"} if codes: stock_codes = [to_code6(c) for c in codes] else: stock_codes = [c for c in db_ops.iter_stock_codes(active_only=True) if is_a_share_code(c)] limit_raw = os.environ.get("MARKET_DATA_STOCK_LIMIT", "").strip() if limit_raw.isdigit() and int(limit_raw) > 0: stock_codes = stock_codes[: int(limit_raw)] if not stock_codes: return {"status": "error", "message": "无股票代码"} # ── 1) 一次 SQL 拿全表快照 ── logger.info(f"[5min] 读取 DB 快照 ({len(stock_codes)} 只待查)…") snapshots = db_ops.get_kline_5min_snapshots() logger.info(f"[5min] DB 里已有 {len(snapshots)} 只的 kline_5min 数据") # ── 2) 内存算计划 ── # PG TIMESTAMPTZ 是 timezone-aware，所以 end 也必须 tz-aware 否则比较会炸 # 2026-07-12 修复: end 改为上海时区最近交易日 15:00，避免 UTC 与上海时间混用 from app.core.datasource.utils import effective_market_date end_date_str = effective_market_date() end = datetime.strptime(f"{end_date_str} 15:00:00", "%Y-%m-%d %H:%M:%S").replace( tzinfo=timezone(timedelta(hours=8)) ) plans = self._plan(stock_codes, end, snapshots) if not plans: msg = f"5min K线 — 全部 {len(stock_codes)} 只都已最新（无需同步）" logger.info(msg) return {"status": "ok", "message": msg, "ok": 0, "fail": 0, "rows": 0, "elapsed_sec": 0.0} # 分类统计 full_sync = [p for p in plans if snapshots.get(p[0]) is None] incr_sync = [p for p in plans if snapshots.get(p[0]) is not None] total_windows = sum( max(1, (p[2] - p[1]).days // WINDOW_DAYS + 1) for p in plans ) # mairui 5 RPS 上限（5 worker × 1 RPS） est_sec = total_windows / 5.0 logger.info( f"[5min] 计划: 总 {len(plans)} 只 (全量 {len(full_sync)} / 增量 {len(incr_sync)})，" f"总请求 {total_windows}，按 5 RPS 预估 {est_sec:.0f}s ({est_sec/60:.1f}min)" ) self._progress( message=f"计划: {len(plans)} 只 (全量 {len(full_sync)}/增量 {len(incr_sync)}) " f"约 {total_windows} 个请求, 预估 {est_sec/60:.1f}min", current=0, total=len(plans), current_step="planning", ) # ── 3) 执行 ── t0 = time.time() ok_cnt = fail_cnt = 0 rows_total = 0 # 不用 `with ThreadPoolExecutor(...) as pool:` — 它的 __exit__ 默认 wait=True, # 一旦某个 worker 卡在 xueqiu IO,主线程会在 as_completed 触发 60s timeout 后 # 仍被 __exit__ 阻塞等 worker 退出 → 进程挂死(2026-07-09 13:21 教训)。 # 改成手动管理 + shutdown(wait=False),主线程能立刻退出。 pool = ThreadPoolExecutor(max_workers=max_workers) futures = {pool.submit(self._sync_one, primary, p[0], p[1], p[2]): p[0] for p in plans} try: for i, future in enumerate(as_completed(futures, timeout=FETCH_HARD_TIMEOUT), 1): c6 = futures[future] try: res = future.result(timeout=0.1) # 已被 as_completed 释放 if res["status"] == "ok": ok_cnt += 1 rows_total += res["rows"] else: fail_cnt += 1 if res.get("error"): logger.warning(f"[5min {c6}] {res['error']}") except FuturesTimeoutError: fail_cnt += 1 logger.warning(f"[5min {c6}] 内部 race timeout, 记 fail") except Exception as e: fail_cnt += 1 logger.warning(f"[5min {c6}] {e}") if i % 20 == 0 or i == len(plans): self._progress( message=f"5min 进度 {i}/{len(plans)} OK:{ok_cnt} FAIL:{fail_cnt}", current=i, total=len(plans), current_step=c6, ) except FuturesTimeoutError: stuck = [c6 for _, c6 in futures.items() if not _.done()] logger.error( "[5min] 所有 fetcher 卡死 (>%ss), %d 只股票未完成", FETCH_HARD_TIMEOUT, len(stuck), ) for f, c6 in futures.items(): try: if not f.done() and hasattr(f, "cancel"): f.cancel() except Exception as e: logger.warning(f"[5min] cancel future for {c6} 失败: {e}") try: pool.shutdown(wait=False) except Exception as e: logger.warning(f"[5min] pool.shutdown(wait=False) 失败: {e}") # ── 补偿：冷却后重入队列 ── # mairui 偶发全 hang（中间件重启/网络抖动），等 30s 再试一次。 # 如果还 hang 就放弃，下次调度增量重拉。 if stuck: cooldown = 30 logger.warning(f"[5min] 冷却 {cooldown}s 后重试 {len(stuck)} 只…") time.sleep(cooldown) retry_ok = retry_fail = retry_rows = 0 retry_pool = ThreadPoolExecutor(max_workers=max_workers) plan_dict = {p[0]: (p[1], p[2]) for p in plans} retry_futs = {} for c6 in stuck: if c6 in plan_dict: s, e = plan_dict[c6] # 重试直接用雪球 fallback（Mairui 已全 hang） retry_futs[retry_pool.submit(self._fallback_xueqiu_5m_and_write, c6, s, e)] = c6 if retry_futs: for fut in as_completed(retry_futs, timeout=FETCH_HARD_TIMEOUT): c6 = retry_futs[fut] try: res = fut.result(timeout=0.1) if res["status"] == "ok": retry_ok += 1 retry_rows += res["rows"] else: retry_fail += 1 except Exception: retry_fail += 1 try: retry_pool.shutdown(wait=False) except Exception: pass ok_cnt += retry_ok fail_cnt -= retry_ok # 从 fail 挪到 ok rows_total += retry_rows logger.warning( f"[5min] 重试结果: {retry_ok}成 {retry_fail}败 " f"共{retry_rows}行, 救回 {retry_ok} 只" ) else: pool.shutdown(wait=True) elapsed = round(time.time() - t0, 1) msg = ( f"5min K线 {ok_cnt}成 {fail_cnt}败 共{rows_total}行, " f"全量{len(full_sync)}/增量{len(incr_sync)}, {elapsed}s" ) return { "status": "ok" if fail_cnt == 0 else "warning", "message": msg, "ok": ok_cnt, "fail": fail_cnt, "rows": rows_total, "full_sync": len(full_sync), "incr_sync": len(incr_sync), "total_requests": total_windows, "elapsed_sec": elapsed, } def _sync_one(self, primary, code6: str, start: datetime, end: datetime) -> dict: all_rows: list[dict] = [] try: cur = start while cur < end: w_end = min(cur + timedelta(days=WINDOW_DAYS), end) df = primary.fetch_kline_5min( code6, cur.strftime("%Y-%m-%d"), w_end.strftime("%Y-%m-%d"), ) if df is not None and not df.empty: hermes = to_hermes(code6) for _, r in df.iterrows(): bar_time = r["bar_time"] all_rows.append({ "stock_code": hermes, "bar_time": bar_time.strftime("%Y-%m-%d %H:%M:%S") if hasattr(bar_time, "strftime") else str(bar_time), "open": float(r.get("open") or 0), "high": float(r.get("high") or 0), "low": float(r.get("low") or 0), "close": float(r.get("close") or 0), "volume": float(r.get("volume") or 0), "amount": float(r.get("amount") or 0), "turnover_rate": float(r.get("turnover_rate") or 0), }) cur = w_end + timedelta(days=1) except Exception as e: logger.warning(f"[5min {code6}] mairui 失败, 尝试雪球 5m fallback: {e}") all_rows = self._fallback_xueqiu_5m(code6, start, end) if not all_rows: # Mairui 无数据, 尝试雪球 fallback logger.info(f"[5min {code6}] mairui 无数据, 尝试雪球 fallback") all_rows = self._fallback_xueqiu_5m(code6, start, end) if not all_rows: return {"status": "fail", "error": "no data (mairui + xueqiu fallback)"} try: CHUNK = 5000 for i in range(0, len(all_rows), CHUNK): db_ops.upsert_kline_5min(all_rows[i: i + CHUNK]) except Exception as e: return {"status": "fail", "error": f"db write: {e}"} return {"status": "ok", "rows": len(all_rows)} @staticmethod def _fallback_xueqiu_5m_and_write(code6: str, start: datetime, end: datetime) -> dict: """雪球 5m fallback + 直接写库，对齐 _sync_one 返回格式供 retry 逻辑使用。""" rows = SyncKline5Min._fallback_xueqiu_5m(code6, start, end) if not rows: return {"status": "fail", "error": "no data via xueqiu"} try: CHUNK = 5000 for i in range(0, len(rows), CHUNK): db_ops.upsert_kline_5min(rows[i: i + CHUNK]) except Exception as e: return {"status": "fail", "error": f"db write: {e}"} return {"status": "ok", "rows": len(rows)} @staticmethod def _fallback_xueqiu_5m(code6: str, start: datetime, end: datetime) -> list[dict]: """通过雪球 5m K线 fallback 拉取数据，对齐 _sync_one 返回格式。""" import os from datetime import datetime as dt_mod from app.core.datasource.utils import code6_to_xueqiu, to_hermes token_raw = os.environ.get("XUEQIU_TOKEN", "").strip() if not token_raw: # 从 .env 读 try: with open("/home/gao/Development/quant_home/market_sync/.env") as f: for line in f: if line.startswith("XUEQIU_TOKEN="): token_raw = line.strip().split("=", 1)[1] break except Exception: pass if not token_raw: logger.warning("[xueqiu_5m] 无 XUEQIU_TOKEN, 跳过 fallback") return [] import pysnowball as ball ball.set_token(token_raw) symbol = code6_to_xueqiu(code6) # 计算需要多少根 5m bar (每天 48 根, 加 buffer) days_needed = max((end - start).days + 2, 1) count = days_needed * 48 try: data = ball.kline(symbol, "5m", min(count, 5000)) except Exception as e: logger.warning(f"[xueqiu_5m {code6}] 请求失败: {e}") return [] items = data.get("data", {}).get("item", []) if not items: return [] columns = data["data"]["column"] # [timestamp, volume, open, high, low, close, ...] idx = {c: i for i, c in enumerate(columns)} hermes = to_hermes(code6) rows = [] for item in items: ts = item[idx["timestamp"]] / 1000 bar_time = dt_mod.fromtimestamp(ts).strftime("%Y-%m-%d %H:%M:%S") rows.append({ "stock_code": hermes, "bar_time": bar_time, "open": float(item[idx["open"]]), "high": float(item[idx["high"]]), "low": float(item[idx["low"]]), "close": float(item[idx["close"]]), "volume": float(item[idx["volume"]]), "amount": 0.0, "turnover_rate": 0.0, }) return rows