782 lines
36 KiB
Python
782 lines
36 KiB
Python
"""盘后管道 + 盘前维表同步。
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调度:
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09:10 盘前 — 同步个股维表 instruments (全量覆盖)
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15:30 盘后 — 日K同步 + 增量除权因子 + enriched 计算 + 刷新视图
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盘后同步策略:
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日 K: QuoteService 交易时段已实时落盘 → 有数据时跳过 batch,首次拉 1 年区间
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除权因子: 从已有数据最新日期的下一天开始增量获取,避免重复拉取和计算
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"""
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from __future__ import annotations
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import logging
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from collections.abc import Callable
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from pathlib import Path
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import polars as pl
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from apscheduler.schedulers.asyncio import AsyncIOScheduler
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from apscheduler.triggers.cron import CronTrigger
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from app.indicators.pipeline import run_pipeline
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from app.config import settings
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from app.services import index_sync, instrument_sync, kline_sync, preferences as _prefs
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from app.tickflow.capabilities import Cap, CapabilitySet
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from app.tickflow.pools import DEMO_SYMBOLS, get_pool
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from app.tickflow.repository import KlineRepository
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logger = logging.getLogger(__name__)
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ProgressCb = Callable[..., None]
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def _noop(stage: str, pct: int, msg: str, **kwargs) -> None: # noqa: ARG001
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pass
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def _invalidate(table: str | None = None) -> None:
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"""stage 写完调用,让 /api/data/status 只重算被影响的那张表。"""
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from app.api.data import invalidate_data_cache
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invalidate_data_cache(table)
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def _resolve_universe(capset: CapabilitySet) -> list[str]:
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"""解析标的池 — 以 CN_Equity_A (沪深京A股 ~5522只) 为主。
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有 batch 能力 → 直接拉 CN_Equity_A universe
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其他用户 → 用 instruments parquet 兜底
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"""
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if capset.has(Cap.KLINE_DAILY_BATCH):
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try:
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all_a = get_pool("CN_Equity_A", refresh=True)
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if all_a:
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return sorted(all_a)
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except Exception as e: # noqa: BLE001
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logger.warning("CN_Equity_A pool unavailable, fallback: %s", e)
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# Free 用户兜底: instruments parquet + demo
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base: set[str] = set(DEMO_SYMBOLS)
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d = Path(settings.data_dir)
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inst_path = d / "instruments" / "instruments.parquet"
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if inst_path.exists():
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try:
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inst = pl.read_parquet(inst_path, columns=["symbol"])
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base.update(inst["symbol"].to_list())
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except Exception as e: # noqa: BLE001
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logger.warning("instruments supplement failed: %s", e)
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return sorted(base)
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def run_instruments_sync(repo: KlineRepository) -> dict:
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"""盘前同步个股维表。"""
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rows = instrument_sync.sync_instruments(repo.store.data_dir)
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_refresh_instruments_view(repo)
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_invalidate("instruments")
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return {"instruments_rows": rows}
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def run_now(
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repo: KlineRepository,
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capset: CapabilitySet,
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on_progress: ProgressCb | None = None,
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) -> dict:
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"""立即执行一次盘后管道,支持进度回调。
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跳过的 stage **不 emit**,避免前端把"无 capability"的卡片错误标记为 active/done。
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result 里带 skipped_stages 列表供前端展示。
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"""
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emit = on_progress or _noop
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skipped: list[str] = []
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# Step 0: 先同步个股维表, 再解析标的池 — 确保标的池基于最新 instruments
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emit("sync_instruments", 2, "同步个股维表…")
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inst_rows = instrument_sync.sync_instruments(repo.store.data_dir)
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if inst_rows > 0:
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_refresh_instruments_view(repo)
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emit("sync_instruments", 8, f"个股维表同步完成,{inst_rows} 只标的")
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_invalidate("instruments")
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emit("resolve_universe", 9, "解析标的池…")
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universe = _resolve_universe(capset)
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emit("resolve_universe", 10, f"标的池规模:{len(universe)} 只")
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# Step 1: 日 K 同步
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# 付费档 + 今天有数据 → 实时行情接口拉一次覆写（1请求全市场）
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# 有历史数据 → batch K-line API 补齐缺口
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# 无任何数据 → batch K-line API 拉首次 1 年
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from datetime import date as _date, timedelta as _td, datetime as _dt
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latest_daily = repo.latest_daily_date()
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today = _date.today()
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today_exists = latest_daily and latest_daily >= today
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new_daily_days = 0
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# 日K范围拉取的起点(分支3补缺口/分支4首次); 实时增量/跳过时为 None。
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# 供 Step 1.5 除权因子回溯范围对齐: 范围拉取→用日K范围, 非范围→最近N天兜底。
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daily_range_start: _date | None = None
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# A 股日K拉取开关(默认开);关闭时跳过日K同步,保留已有数据
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pull_a_share = _prefs.get_pipeline_pull_a_share()
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if not pull_a_share:
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emit("sync_daily", 45, "已跳过 A 股日K同步(拉取内容未勾选)")
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logger.info("sync_daily: skipped (pipeline_pull_a_share=False)")
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elif today_exists and capset.has(Cap.QUOTE_POOL):
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# 付费档:今天有数据(QuoteService 已落盘)→ 实时行情覆写,确保最新。
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# free/none 档无 quote.pool 能力,即便今天已有数据(如从 expert 降级),
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# 也降级到下方 batch 路径刷新,避免调用无权限的实时行情接口。
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emit("sync_daily", 12, f"获取日K [{today} ~ {today}] 实时行情…")
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written_daily = kline_sync.sync_daily_by_quotes(repo)
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new_daily_days = 1
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emit("sync_daily", 45, f"日K 完成,{written_daily} 只标的")
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logger.info("sync_daily: [%s ~ %s] live quotes, %d symbols", today, today, written_daily)
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elif latest_daily:
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# 有历史 → batch 补齐缺口。
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# 也覆盖"今天已有数据但无实时行情权限(free/none)"的降级场景:
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# 此时 start_date = latest_daily = today,batch 刷新当天日K。
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start_date = latest_daily
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daily_range_start = start_date
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emit("sync_daily", 12, f"获取日K [{start_date} ~ {today}]…")
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logger.info("sync_daily: [%s ~ %s] %s", start_date, today,
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"refresh today" if today_exists else "gap fill")
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def _daily_chunk_progress(cur: int, tot: int) -> None:
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emit("sync_daily", 12 + int(33 * cur / tot),
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f"日K 批次 {cur}/{tot}", stage_pct=int(100 * cur / tot), skip_log=True)
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written_daily = kline_sync.sync_and_persist_daily_batch(
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universe, repo, capset,
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start_date=_dt.combine(start_date, _dt.min.time()),
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end_date=_dt.combine(today, _dt.min.time()),
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on_chunk_done=_daily_chunk_progress,
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)
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gap_days = (today - start_date).days
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new_daily_days = gap_days
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emit("sync_daily", 45, f"日K 完成,覆盖 {gap_days} 天")
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logger.info("sync_daily: [%s ~ %s] done, %d days", start_date, today, gap_days)
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else:
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# 首次：无任何数据 → batch 拉 1 年
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start_date = today - _td(days=365)
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daily_range_start = start_date
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emit("sync_daily", 12, f"获取日K [{start_date} ~ {today}]…")
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logger.info("sync_daily: [%s ~ %s] initial fetch", start_date, today)
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def _daily_chunk_progress(cur: int, tot: int) -> None:
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emit("sync_daily", 12 + int(33 * cur / tot),
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f"日K 批次 {cur}/{tot}", stage_pct=int(100 * cur / tot), skip_log=True)
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written_daily = kline_sync.sync_and_persist_daily_batch(
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universe, repo, capset,
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start_date=_dt.combine(start_date, _dt.min.time()),
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end_date=_dt.combine(today, _dt.min.time()),
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on_chunk_done=_daily_chunk_progress,
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)
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new_daily_days = 365
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emit("sync_daily", 45, "日K 完成")
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logger.info("sync_daily: [%s ~ %s] done", start_date, today)
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_invalidate("daily")
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# Step 1.5: 同步除权因子 — 范围与日K拉取方式对齐
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# 日K范围拉取(补缺口/首次) → 除权用日K范围 [daily_range_start, now]
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# 首次会覆盖整个日K区间内的历史除权事件; 补缺口天然只增量(起点=latest_daily≈昨天)
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# 日K实时增量/跳过(分支2/分支1) → 除权兜底拉最近 30 天, 补可能遗漏的新除权
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# (这两类分支不拉历史日K, 除权不能用日K范围, 只能兜底最近几日)
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written_adj = 0
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affected_symbols: list[str] = []
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if capset.has(Cap.ADJ_FACTOR):
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from datetime import datetime, timedelta
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adj_end = datetime.now()
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if daily_range_start is not None:
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adj_start = datetime.combine(daily_range_start, datetime.min.time())
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else:
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# 日K实时增量/跳过时, 除权兜底拉最近 N 天, 覆盖周末/长假/停机期间的新除权事件。
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# 15 天: 覆盖春节/国庆最长约10天长假 + 故障恢复缓冲; sync_adj_factor 内部 merge+unique 幂等, 多拉无副作用。
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adj_start = adj_end - timedelta(days=15)
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adj_start_str = adj_start.strftime("%Y-%m-%d")
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adj_end_str = adj_end.strftime("%Y-%m-%d")
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emit("sync_adj", 50, f"获取除权因子 [{adj_start_str} ~ {adj_end_str}]…")
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logger.info("sync_adj: [%s ~ %s] start", adj_start_str, adj_end_str)
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def _adj_chunk_progress(cur: int, tot: int) -> None:
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emit("sync_adj", 50 + int(10 * cur / tot),
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f"除权因子批次 {cur}/{tot}", stage_pct=int(100 * cur / tot), skip_log=True)
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written_adj, affected_symbols = kline_sync.sync_adj_factor(
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universe, repo, capset,
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start_time=adj_start, end_time=adj_end,
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on_chunk_done=_adj_chunk_progress,
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)
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if affected_symbols:
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_refresh_single_view(repo, "adj_factor")
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emit("sync_adj", 60, f"除权因子完成,新增 {len(affected_symbols)} 只个股")
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logger.info("sync_adj: [%s ~ %s] done, %d symbols", adj_start_str, adj_end_str, len(affected_symbols))
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else:
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emit("sync_adj", 60, "除权因子完成,无新增")
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logger.info("sync_adj: [%s ~ %s] no new factors", adj_start_str, adj_end_str)
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_invalidate("adj_factor")
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else:
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skipped.append("sync_adj")
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logger.info("sync_adj skipped: no ADJ_FACTOR capability")
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# Step 2: 计算 enriched
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# 判断策略:
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# - 首次 (enriched 目录不存在) → 全量
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# - 往前扩展历史 (新日期 < enriched 已有最早日期) → 全量
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# 前面的除权因子会改变累积因子链,影响后面所有日期的复权价格
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# - 往后新增日期 (新日期 > enriched 已有最晚日期)
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# → 增量补新区块(所有标的) + 受除权影响个股全日期重算
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# - 无新日期 + 有新除权因子 → 增量: 只重算受影响个股的全部日期
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# - 无新日期 + 无变化 → 跳过
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enriched_dir = repo.store.data_dir / "kline_daily_enriched"
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enriched_exists = enriched_dir.exists() and any(enriched_dir.glob("date=*"))
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daily_dir = repo.store.data_dir / "kline_daily"
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daily_days = len(list(daily_dir.glob("date=*"))) if daily_dir.exists() else 0
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prev_enriched_days = len(list(enriched_dir.glob("date=*"))) if enriched_exists else 0
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# 判断新日期方向: 找 daily 和 enriched 的日期集合做比较
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forward_incremental = False
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backward_extension = False
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if daily_days > prev_enriched_days and enriched_exists:
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daily_dates = sorted(d.stem.split("=")[1] for d in daily_dir.glob("date=*"))
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enriched_dates = sorted(d.stem.split("=")[1] for d in enriched_dir.glob("date=*"))
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earliest_enriched = enriched_dates[0]
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latest_enriched = enriched_dates[-1]
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new_dates = set(daily_dates) - set(enriched_dates)
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if new_dates:
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# 有新日期早于 enriched 最早日期 → 往前扩展
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if any(d < earliest_enriched for d in new_dates):
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backward_extension = True
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# 有新日期晚于 enriched 最晚日期 → 往后新增
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if any(d > latest_enriched for d in new_dates):
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forward_incremental = True
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def _enriched_batch_progress(cur: int, tot: int) -> None:
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emit("compute_enriched", 65 + int(23 * cur / tot),
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f"计算指标 批次 {cur}/{tot}", stage_pct=int(100 * cur / tot), skip_log=True)
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if not enriched_exists or backward_extension:
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# 首次 或 往前扩展 → 全量
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emit("compute_enriched", 65, "全量计算 enriched…")
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logger.info("compute_enriched: full rebuild (first=%s, backward=%s, daily=%d, enriched=%d)",
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not enriched_exists, backward_extension, daily_days, prev_enriched_days)
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written_enriched = run_pipeline(on_batch_done=_enriched_batch_progress)
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new_enriched_days = len(list(enriched_dir.glob("date=*")))
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emit("compute_enriched", 88, f"enriched 完成,覆盖 {new_enriched_days} 天")
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logger.info("compute_enriched: full rebuild done, %d days", new_enriched_days)
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elif forward_incremental:
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# 往后新增日期: 增量补新区块 + 受影响个股全日期重算
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symbols_to_recompute = list(set(affected_symbols)) if affected_symbols else []
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emit("compute_enriched", 65,
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f"增量计算 enriched (新日期 + {len(symbols_to_recompute)} 只个股重算)…"
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if symbols_to_recompute else "增量计算 enriched (新日期)…")
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logger.info("compute_enriched: forward incremental, %d symbols to recompute",
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len(symbols_to_recompute))
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written_enriched = run_pipeline(
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new_dates_only=True,
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symbols=symbols_to_recompute or None,
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on_batch_done=_enriched_batch_progress,
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)
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new_enriched_days = len(list(enriched_dir.glob("date=*")))
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emit("compute_enriched", 88, f"enriched 完成,覆盖 {new_enriched_days} 天")
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logger.info("compute_enriched: forward incremental done, %d days", new_enriched_days)
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elif affected_symbols:
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# 无新日期,仅除权因子变更 → 只重算受影响个股的全部日期
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emit("compute_enriched", 65, f"增量计算 enriched ({len(affected_symbols)} 只个股)…")
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logger.info("compute_enriched: adj_factor incremental, %d symbols", len(affected_symbols))
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written_enriched = run_pipeline(symbols=affected_symbols, on_batch_done=_enriched_batch_progress)
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emit("compute_enriched", 88, f"enriched 完成,{len(affected_symbols)} 只个股")
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else:
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written_enriched = 0
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logger.info("compute_enriched: skip (no new daily, no adj_factor changes)")
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_refresh_single_view(repo, "kline_enriched")
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_invalidate("enriched")
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# Step 2.3: 指数 / ETF 同步 — 物理分开存储；ETF 可复权，指数不复权。
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written_index_daily = 0
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written_etf_daily = 0
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index_count = 0
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etf_count = 0
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etf_adj_symbols = 0
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pull_index = _prefs.get_pipeline_pull_index()
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pull_etf = _prefs.get_pipeline_pull_etf()
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if capset.has(Cap.KLINE_DAILY_BATCH) and (pull_index or pull_etf):
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_types = []
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if pull_index:
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_types.append("指数")
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if pull_etf:
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_types.append("ETF")
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emit("sync_index", 88, f"同步{'+'.join(_types)}日K…")
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# 子阶段进度分配: 88.0(开始) → 89.0(完成), 指数占前半, ETF 占后半
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try:
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if pull_index:
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emit("sync_index", 88, "同步指数维表…")
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index_count = index_sync.sync_index_instruments(repo, pull_index=True, pull_etf=False)
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emit("sync_index", 88, f"指数维表完成,{index_count} 只")
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index_dir = repo.store.data_dir / "kline_index_enriched"
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index_dates = sorted(
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d.name[5:] for d in index_dir.glob("date=*")
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if d.is_dir() and d.name.startswith("date=")
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) if index_dir.exists() else []
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index_start = _date.fromisoformat(index_dates[-1]) if index_dates else today - _td(days=365)
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def _index_chunk(cur: int, tot: int) -> None:
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emit("sync_index", 88, f"指数日K批次 {cur}/{tot}",
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stage_pct=int(100 * cur / tot) if tot else 100, skip_log=cur < tot)
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written_index_daily = index_sync.sync_and_persist_index_daily(
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repo,
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capset,
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start_date=_dt.combine(index_start, _dt.min.time()),
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end_date=_dt.combine(today, _dt.min.time()),
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on_chunk_done=_index_chunk,
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)
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emit("sync_index", 88, f"指数日K完成,{written_index_daily} 行")
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_invalidate("index_instruments")
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_invalidate("index_daily")
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_invalidate("index_enriched")
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if pull_etf:
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emit("sync_index", 88, "同步 ETF 维表…")
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etf_count = index_sync.sync_etf_instruments(repo)
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emit("sync_index", 88, f"ETF 维表完成,{etf_count} 只")
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etf_symbols: list[str] = []
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etf_inst = repo.get_etf_instruments()
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if not etf_inst.is_empty() and "symbol" in etf_inst.columns:
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etf_symbols = sorted(set(etf_inst["symbol"].to_list()))
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if etf_symbols and capset.has(Cap.ADJ_FACTOR):
|
|
try:
|
|
emit("sync_index", 88, "同步 ETF 除权因子…")
|
|
from datetime import datetime, timedelta
|
|
adj_end = datetime.now()
|
|
adj_path = repo.store.data_dir / "adj_factor_etf" / "all.parquet"
|
|
fallback_start = adj_end - timedelta(days=30)
|
|
adj_start = fallback_start
|
|
if adj_path.exists():
|
|
max_date = pl.scan_parquet(adj_path).select(pl.col("trade_date").max()).collect().item()
|
|
if max_date is not None:
|
|
if isinstance(max_date, str):
|
|
adj_start = datetime.combine(_date.fromisoformat(max_date), datetime.min.time())
|
|
elif isinstance(max_date, datetime):
|
|
adj_start = datetime.combine(max_date.date(), datetime.min.time())
|
|
else:
|
|
adj_start = datetime.combine(max_date, datetime.min.time())
|
|
_, affected_etfs = index_sync.sync_etf_adj_factor(
|
|
etf_symbols,
|
|
repo,
|
|
capset,
|
|
start_time=adj_start,
|
|
end_time=adj_end,
|
|
)
|
|
etf_adj_symbols = len(affected_etfs)
|
|
emit("sync_index", 88, f"ETF 除权因子完成,{etf_adj_symbols} 只")
|
|
except Exception as e: # noqa: BLE001
|
|
logger.warning("ETF adj_factor skipped: %s", e)
|
|
etf_dir = repo.store.data_dir / "kline_etf_enriched"
|
|
etf_dates = sorted(
|
|
d.name[5:] for d in etf_dir.glob("date=*")
|
|
if d.is_dir() and d.name.startswith("date=")
|
|
) if etf_dir.exists() else []
|
|
etf_start = _date.fromisoformat(etf_dates[-1]) if etf_dates else today - _td(days=365)
|
|
|
|
def _etf_chunk(cur: int, tot: int) -> None:
|
|
emit("sync_index", 88, f"ETF 日K批次 {cur}/{tot}",
|
|
stage_pct=int(100 * cur / tot) if tot else 100, skip_log=cur < tot)
|
|
|
|
written_etf_daily = index_sync.sync_and_persist_etf_daily(
|
|
repo,
|
|
capset,
|
|
start_date=_dt.combine(etf_start, _dt.min.time()),
|
|
end_date=_dt.combine(today, _dt.min.time()),
|
|
on_chunk_done=_etf_chunk,
|
|
)
|
|
emit("sync_index", 88, f"ETF 日K完成,{written_etf_daily} 行")
|
|
_invalidate("etf_instruments")
|
|
_invalidate("etf_daily")
|
|
|
|
repo.refresh_index_views()
|
|
emit(
|
|
"sync_index",
|
|
89,
|
|
f"同步完成,指数 {index_count} 只/{written_index_daily} 行, ETF {etf_count} 只/{written_etf_daily} 行"
|
|
+ (f", ETF复权 {etf_adj_symbols} 只" if etf_adj_symbols else ""),
|
|
)
|
|
except Exception as e: # noqa: BLE001
|
|
logger.warning("sync_index/etf failed: %s", e)
|
|
emit("sync_index", 89, f"指数/ETF同步失败:{e}")
|
|
else:
|
|
skipped.append("sync_index")
|
|
|
|
# Step 2.5: 分钟 K 同步(可选) — 未启用或无 capability 时静默跳过(不 emit)
|
|
from app.services import preferences
|
|
minute_on = preferences.get_minute_sync_enabled()
|
|
minute_days = preferences.get_minute_sync_days()
|
|
written_minute = 0
|
|
if minute_on and capset.has(Cap.KLINE_MINUTE_BATCH):
|
|
minute_start = today - _td(days=minute_days)
|
|
emit("sync_minute", 90, f"获取分钟K [{minute_start} ~ {today}]…")
|
|
logger.info("sync_minute: [%s ~ %s] start", minute_start, today)
|
|
minute_symbols = _resolve_minute_symbols(capset)
|
|
def _minute_chunk_progress(cur: int, tot: int) -> None:
|
|
emit("sync_minute", 90 + int(3 * cur / tot),
|
|
f"分钟K 批次 {cur}/{tot}", stage_pct=int(100 * cur / tot), skip_log=True)
|
|
written_minute = kline_sync.sync_and_persist_minute(
|
|
minute_symbols, repo, capset, days=minute_days,
|
|
on_chunk_done=_minute_chunk_progress,
|
|
)
|
|
minute_dir = repo.store.data_dir / "kline_minute"
|
|
minute_cover_days = len(list(minute_dir.glob("date=*"))) if minute_dir.exists() else 0
|
|
emit("sync_minute", 93, f"分钟K完成,覆盖 {minute_cover_days} 天")
|
|
logger.info("sync_minute: [%s ~ %s] done, %d days", minute_start, today, minute_cover_days)
|
|
_invalidate("minute")
|
|
else:
|
|
skipped.append("sync_minute")
|
|
if minute_on:
|
|
logger.info("sync_minute skipped: no KLINE_MINUTE_BATCH capability")
|
|
else:
|
|
logger.info("sync_minute skipped: user disabled")
|
|
|
|
# Step 3: 刷新视图
|
|
emit("refresh_views", 95, "刷新 DuckDB 视图…")
|
|
_refresh_views(repo)
|
|
|
|
emit("done", 100, "完成")
|
|
_invalidate(None) # 兜底:全清
|
|
|
|
return {
|
|
"universe_size": len(universe),
|
|
"daily_days": new_daily_days,
|
|
"adj_factor_symbols": len(affected_symbols),
|
|
"enriched_days": written_enriched,
|
|
"index_count": index_count,
|
|
"index_daily_rows": written_index_daily,
|
|
"etf_count": etf_count,
|
|
"etf_daily_rows": written_etf_daily,
|
|
"etf_adj_factor_symbols": etf_adj_symbols,
|
|
"minute_rows": written_minute,
|
|
"skipped_stages": skipped,
|
|
}
|
|
|
|
|
|
def _refresh_views(repo: KlineRepository) -> None:
|
|
"""刷新所有 DuckDB 视图。"""
|
|
d = repo.store.data_dir.as_posix()
|
|
views = {
|
|
"kline_daily": f"{d}/kline_daily/**/*.parquet",
|
|
"kline_enriched": f"{d}/kline_daily_enriched/**/*.parquet",
|
|
"kline_index_daily": f"{d}/kline_index_daily/**/*.parquet",
|
|
"kline_index_enriched": f"{d}/kline_index_enriched/**/*.parquet",
|
|
"kline_etf_daily": f"{d}/kline_etf_daily/**/*.parquet",
|
|
"kline_etf_enriched": f"{d}/kline_etf_enriched/**/*.parquet",
|
|
"kline_etf_minute": f"{d}/kline_etf_minute/**/*.parquet",
|
|
"kline_minute": f"{d}/kline_minute/**/*.parquet",
|
|
"adj_factor": f"{d}/adj_factor/**/*.parquet",
|
|
"adj_factor_etf": f"{d}/adj_factor_etf/**/*.parquet",
|
|
"instruments": f"{d}/instruments/**/*.parquet",
|
|
"instruments_index": f"{d}/instruments_index/**/*.parquet",
|
|
"instruments_etf": f"{d}/instruments_etf/**/*.parquet",
|
|
}
|
|
for name, path in views.items():
|
|
try:
|
|
repo.db.execute(
|
|
f"CREATE OR REPLACE VIEW {name} AS "
|
|
f"SELECT * FROM read_parquet('{path}', union_by_name=true)"
|
|
)
|
|
except Exception as e: # noqa: BLE001
|
|
logger.warning("refresh view %s failed: %s", name, e)
|
|
repo.store._register_unified_views()
|
|
|
|
|
|
def _refresh_single_view(repo: KlineRepository, name: str) -> None:
|
|
"""刷新单个 DuckDB 视图。"""
|
|
d = repo.store.data_dir.as_posix()
|
|
paths = {
|
|
"kline_daily": f"{d}/kline_daily/**/*.parquet",
|
|
"kline_enriched": f"{d}/kline_daily_enriched/**/*.parquet",
|
|
"kline_index_daily": f"{d}/kline_index_daily/**/*.parquet",
|
|
"kline_index_enriched": f"{d}/kline_index_enriched/**/*.parquet",
|
|
"kline_etf_daily": f"{d}/kline_etf_daily/**/*.parquet",
|
|
"kline_etf_enriched": f"{d}/kline_etf_enriched/**/*.parquet",
|
|
"kline_etf_minute": f"{d}/kline_etf_minute/**/*.parquet",
|
|
"kline_minute": f"{d}/kline_minute/**/*.parquet",
|
|
"adj_factor": f"{d}/adj_factor/**/*.parquet",
|
|
"adj_factor_etf": f"{d}/adj_factor_etf/**/*.parquet",
|
|
"instruments": f"{d}/instruments/**/*.parquet",
|
|
"instruments_index": f"{d}/instruments_index/**/*.parquet",
|
|
"instruments_etf": f"{d}/instruments_etf/**/*.parquet",
|
|
}
|
|
path = paths.get(name)
|
|
if not path:
|
|
return
|
|
try:
|
|
repo.db.execute(
|
|
f"CREATE OR REPLACE VIEW {name} AS "
|
|
f"SELECT * FROM read_parquet('{path}', union_by_name=true)"
|
|
)
|
|
except Exception as e: # noqa: BLE001
|
|
logger.warning("refresh view %s failed: %s", name, e)
|
|
|
|
|
|
def _resolve_minute_symbols(capset: CapabilitySet) -> list[str]:
|
|
"""分钟 K 同步标的 — 与日K共用同一标的池。"""
|
|
return _resolve_universe(capset)
|
|
|
|
|
|
def _refresh_instruments_view(repo: KlineRepository) -> None:
|
|
"""单独刷新 instruments 视图。"""
|
|
d = repo.store.data_dir.as_posix()
|
|
try:
|
|
repo.db.execute(
|
|
f"CREATE OR REPLACE VIEW instruments AS "
|
|
f"SELECT * FROM read_parquet('{d}/instruments/**/*.parquet', union_by_name=true)"
|
|
)
|
|
except Exception as e: # noqa: BLE001
|
|
logger.warning("refresh instruments view failed: %s", e)
|
|
|
|
|
|
def _run_tracked(fn, job_label: str) -> None:
|
|
"""调度触发时包装 JobStore 跟踪，确保同步历史有记录。"""
|
|
from app.services.pipeline_jobs import job_store
|
|
|
|
job_id = job_store.create()
|
|
job_store.start(job_id)
|
|
|
|
def progress(stage: str, pct: int, msg: str, stage_pct: int | None = None,
|
|
skip_log: bool = False) -> None:
|
|
job_store.progress(job_id, stage, pct, msg, stage_pct=stage_pct, skip_log=skip_log)
|
|
|
|
try:
|
|
result = fn(on_progress=progress)
|
|
job_store.succeed(job_id, result)
|
|
logger.info("scheduled %s completed: job_id=%s", job_label, job_id)
|
|
except Exception:
|
|
logger.exception("scheduled %s failed: job_id=%s", job_label, job_id)
|
|
job_store.fail(job_id, f"scheduled {job_label} failed")
|
|
|
|
|
|
# ================================================================
|
|
# 定时复盘 (AI 大盘复盘报告)
|
|
# ================================================================
|
|
|
|
REVIEW_JOB_ID = "scheduled_review"
|
|
|
|
|
|
async def _run_scheduled_review(repo) -> None:
|
|
"""定时复盘 job: 流式生成复盘 → 落盘归档 → 推飞书。
|
|
|
|
与手动「生成复盘」体验一致。LLM 偶发断流(peer closed connection)时自动重试最多 2 次。
|
|
任何异常都吞掉只记日志, 绝不影响调度器主循环。
|
|
"""
|
|
|
|
try:
|
|
from app.services import market_recap_reports
|
|
from app import secrets_store as ss
|
|
|
|
# AI Key 未配置时跳过(避免每日报错刷日志)
|
|
if not ss.get_ai_key():
|
|
logger.info("scheduled review skipped: AI key not configured")
|
|
return
|
|
|
|
content, meta = await _stream_review_with_retry(repo)
|
|
if not content:
|
|
logger.warning("scheduled review produced no content (meta=%s)", meta)
|
|
return
|
|
|
|
# 落盘: 与手动生成完全相同的归档格式
|
|
market_recap_reports.save_report({
|
|
"as_of": meta.get("as_of"),
|
|
"focus": "",
|
|
"content": content,
|
|
"summary": meta.get("summary", ""),
|
|
"emotion_score": meta.get("emotion_score"),
|
|
"emotion_label": meta.get("emotion_label", ""),
|
|
})
|
|
logger.info("scheduled review saved: as_of=%s", meta.get("as_of"))
|
|
|
|
# 推送到飞书(可选): 运行时读取配置, 用户改设置下次触发即生效。
|
|
# 失败静默降级, 不影响已归档的报告。
|
|
_maybe_push_review(content, meta)
|
|
except Exception as e: # noqa: BLE001
|
|
logger.exception("scheduled review failed: %s", e)
|
|
|
|
|
|
async def _stream_review_with_retry(repo) -> tuple[str, dict]:
|
|
"""流式生成复盘, 累积内容。LLM 断流时最多重试 2 次。
|
|
|
|
返回 (content, meta)。成功(收到 done/无 error)或耗尽重试后返回。
|
|
"""
|
|
import asyncio
|
|
import json
|
|
from app.services.market_recap import recap_market_stream
|
|
|
|
max_attempts = 3 # 初次 + 2 次重试
|
|
last_meta: dict = {}
|
|
content_parts: list[str] = []
|
|
|
|
for attempt in range(1, max_attempts + 1):
|
|
content_parts = [] # 每次重试重新累积
|
|
failed = False
|
|
try:
|
|
async for evt_json in recap_market_stream(repo):
|
|
evt = json.loads(evt_json)
|
|
t = evt.get("type")
|
|
|
|
if t == "meta":
|
|
last_meta = evt
|
|
elif t == "delta" and evt.get("content"):
|
|
content_parts.append(evt["content"])
|
|
elif t == "error":
|
|
failed = True
|
|
logger.warning("scheduled review stream error (attempt %d/%d): %s",
|
|
attempt, max_attempts, evt.get("message"))
|
|
break # 触发重试
|
|
elif t == "done":
|
|
# 正常完成
|
|
return "".join(content_parts), last_meta
|
|
# 流自然结束(无 done 事件)且有内容, 视为成功
|
|
if content_parts and not failed:
|
|
return "".join(content_parts), last_meta
|
|
except Exception as e: # noqa: BLE001
|
|
# LLM 断流等异常(httpx.RemoteProtocolError)落到这里
|
|
failed = True
|
|
logger.warning("scheduled review stream exception (attempt %d/%d): %s",
|
|
attempt, max_attempts, e)
|
|
|
|
# 失败: 决定是否重试
|
|
if attempt < max_attempts:
|
|
logger.info("scheduled review retrying in 3s (attempt %d → %d)", attempt, attempt + 1)
|
|
await asyncio.sleep(3)
|
|
|
|
# 耗尽重试, 返回已累积内容(可能为空)和最后 meta
|
|
return "".join(content_parts), last_meta
|
|
|
|
|
|
def _maybe_push_review(content: str, meta: dict) -> None:
|
|
"""复盘报告归档后, 按 review_push_channels 选定的外部工具逐个推送完整报告。
|
|
|
|
定时生成与手动生成共用本函数 (手动归档端点 POST /api/market-recap/reports 也会调用)。
|
|
channels 为空则不推送; 'feishu' 复用监控中心的全局飞书 Webhook 通道。
|
|
推送失败静默降级 (Webhook 是辅助通道), 不影响已归档的报告。
|
|
"""
|
|
try:
|
|
from app.services import preferences, webhook_adapter
|
|
|
|
channels = preferences.get_review_push_channels()
|
|
if not channels:
|
|
return
|
|
|
|
emotion = f"{meta.get('emotion_label') or ''}".strip()
|
|
as_of = meta.get("as_of") or ""
|
|
subtitle = as_of + (f" · 情绪 {emotion}" if emotion else "")
|
|
|
|
for ch in channels:
|
|
if ch == "feishu":
|
|
url = preferences.get_feishu_webhook_url()
|
|
if not url:
|
|
logger.info("review push(feishu) skipped: webhook not configured")
|
|
continue
|
|
secret = preferences.get_feishu_webhook_secret()
|
|
ok = webhook_adapter.send_feishu_card(
|
|
url, "TickFlow · 每日复盘", subtitle, content, secret
|
|
)
|
|
logger.info("review push(feishu) %s", "sent" if ok else "failed")
|
|
# 未来更多渠道在此追加分支
|
|
except Exception as e: # noqa: BLE001
|
|
logger.warning("review push error: %s", e)
|
|
|
|
|
|
def _register_review_job(scheduler, repo, hour: int, minute: int) -> None:
|
|
"""注册/更新定时复盘 job(工作日 mon-fri, Asia/Shanghai)。
|
|
|
|
供 start_scheduler(启动时) 和 settings API(改时间时) 共用。
|
|
用 replace_existing=True, 重复注册只更新 trigger。
|
|
|
|
注意: _run_scheduled_review 是协程函数, 必须把函数对象本身(配合 args)传给
|
|
add_job, 而非用 lambda 包裹 —— 否则 APScheduler 会把 lambda 当同步函数在线程池
|
|
执行, 仅得到一个未 await 的协程对象, 复盘实际不会运行。
|
|
"""
|
|
scheduler.add_job(
|
|
_run_scheduled_review,
|
|
args=[repo],
|
|
trigger=CronTrigger(day_of_week="mon-fri",
|
|
hour=hour, minute=minute,
|
|
timezone="Asia/Shanghai"),
|
|
id=REVIEW_JOB_ID,
|
|
misfire_grace_time=7200, # 复盘非关键, 允许 2 小时内补跑
|
|
replace_existing=True,
|
|
)
|
|
|
|
|
|
def start_scheduler(repo: KlineRepository, capset: CapabilitySet) -> AsyncIOScheduler:
|
|
"""启动调度器。
|
|
|
|
工作日 09:10 — 同步个股维表
|
|
工作日 HH:MM — 盘后管道（时间由用户偏好决定，默认 15:30）
|
|
"""
|
|
from app.services import preferences
|
|
sched = preferences.get_pipeline_schedule()
|
|
inst_sched = preferences.get_instruments_schedule()
|
|
|
|
scheduler = AsyncIOScheduler(timezone="Asia/Shanghai")
|
|
|
|
# 盘前: 同步 instruments（时间由偏好决定）
|
|
def _instruments_task(on_progress=None):
|
|
emit = on_progress or _noop
|
|
emit("sync_instruments", 0, "同步个股维表…")
|
|
result = run_instruments_sync(repo)
|
|
emit("done", 100, f"个股维表同步完成,{result.get('instruments_rows', 0)} 只标的")
|
|
return result
|
|
|
|
scheduler.add_job(
|
|
lambda: _run_tracked(_instruments_task, "instruments_sync"),
|
|
trigger=CronTrigger(day_of_week="mon-fri",
|
|
hour=inst_sched["hour"], minute=inst_sched["minute"],
|
|
timezone="Asia/Shanghai"),
|
|
id="pre_market_instruments",
|
|
misfire_grace_time=1800,
|
|
replace_existing=True,
|
|
)
|
|
|
|
# 盘后: 日 K + enriched（时间由偏好决定）
|
|
def _pipeline_then_refresh(on_progress=None):
|
|
# 与手动触发 (/api/pipeline/run) 对齐: 管道落盘后重建 Polars 内存缓存,
|
|
# 否则 live_agg 的昨日连板数等基准列会停留在旧交易日, 次日开盘连板梯队
|
|
# 整体少算一档 (仅手动触发或重启才会刷缓存, cron 调度路径此前漏了这步)。
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|
result = run_now(repo, capset, on_progress=on_progress)
|
|
repo.refresh_cache()
|
|
return result
|
|
|
|
scheduler.add_job(
|
|
lambda: _run_tracked(_pipeline_then_refresh, "daily_pipeline"),
|
|
trigger=CronTrigger(day_of_week="mon-fri",
|
|
hour=sched["hour"], minute=sched["minute"],
|
|
timezone="Asia/Shanghai"),
|
|
id="daily_pipeline",
|
|
misfire_grace_time=3600,
|
|
replace_existing=True,
|
|
)
|
|
|
|
# 定时复盘 (AI 大盘复盘报告): 工作日到点自动生成并归档。
|
|
# 默认关闭 —— 仅当用户在复盘页开启时才注册 job。
|
|
# 复用 recap_market_once(非流式) + market_recap_reports.save_report(落盘)。
|
|
# quote_service / depth_service 通过 _get_app_state() 延迟取用。
|
|
review_sched = preferences.get_review_schedule()
|
|
if review_sched["enabled"]:
|
|
_register_review_job(scheduler, repo, review_sched["hour"], review_sched["minute"])
|
|
logger.info("scheduled_review enabled @%02d:%02d mon-fri",
|
|
review_sched["hour"], review_sched["minute"])
|
|
|
|
scheduler.start()
|
|
logger.info("scheduler started; instruments@%02d:%02d, pipeline@%02d:%02d mon-fri",
|
|
inst_sched["hour"], inst_sched["minute"], sched["hour"], sched["minute"])
|
|
return scheduler
|
|
|
|
|
|
# app_state 延迟引用(start_scheduler 在 lifespan 早期调用, app.state 可能还没就绪)
|
|
_app_state_ref = None
|
|
|
|
|
|
def set_app_state(app_state) -> None:
|
|
"""lifespan 注册 app.state 引用, 供 scheduled job 访问单例。"""
|
|
global _app_state_ref
|
|
_app_state_ref = app_state
|
|
|
|
|
|
def _get_app_state():
|
|
return _app_state_ref
|