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feat: ETF 支持(选股 / 回测 / 监控) (#61)
* feat(screener): 选股引擎支持 ETF - 12 个内置策略打 asset_types 白名单 + strategy_supports_asset;涨停类 (连板/断板反包)仅股票,其余 10 个技术类对 ETF 开放 - ScreenerService(repo, asset_type) 分流取数,ETF 复用 kline_etf_enriched, 跳过股票专用历史缓存与涨停信号;进程级 _history_cache key 含 asset_type - API /run、/run_preset 透传 asset_type;/strategies 按资产过滤; 股票专有策略在 ETF 下返回空 - 新增 enriched_dirname(asset_type) 共享 helper;get_enriched_latest_asset 增 refresh 参数(供轮询线程避免冷缓存同步重算) - 前端「策略」页加 股票/ETF 切换,ETF 走实时单跑(空日期→用 ETF 自身最新日); QK.screenerStrategies 按 asset_type keyed - 测试:test_screener_etf.py Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * feat(backtest): 回测支持 ETF(个股/因子/策略组合) - 三条回测路径 + 共用 BacktestEngine 面板加载按 asset_type 路由到 kline_etf_enriched(复用 enriched_dirname);PanelCache key 隔离资产; ETF 跳过股票专用 get_enriched_range 缓存 - 面板 compute_all/名称 JOIN 按 asset_type 取维表(get_instruments_asset), 修复 ETF 策略回测用错股票维表致名称为空/涨停信号算错 - BacktestConfig/FactorConfig/StrategyBacktestConfig 增 asset_type - 三个回测 API + SSE stream 透传 asset_type;_make_job_key 纳入 asset_type (修复 stream 与 cancel job_key 不对齐致取消失效的回归) - 前端策略组合页/因子页加 股票/ETF 切换,标的搜索与策略列表跟随资产; assetType 持久化 - 测试:test_backtest_etf.py(含 job_key 一致性回归);既有回测测试替身同步 Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * feat(monitor): 监控规则支持 ETF - engine.evaluate(df, asset_type) 按规则 asset_type 分轮评估;quote_service 增开 ETF 评估轮(用 ETF enriched 快照),股票轮不受影响、不重置其策略结果 - ETF 评估轮独立 try(异常不丢弃已算出的股票告警)+ refresh=False(不在轮询 线程触发 ETF 冷缓存同步重算) - ETF 版历史加载器(main.py 注入)+ 按规则 asset_type 选加载器 - _strategy_pools 按 (sid, asset_type) 键,避免同策略股票/ETF 规则互相覆盖 - name_map 仅在有 ETF 规则时补 ETF 维表, setdefault 保股票名优先 - RuleModel/normalize 增 asset_type(默认 stock,持久化往返) - 前端 RuleEditor 加 股票/ETF 选择,策略列表与标的搜索跟随资产 - 测试:test_monitor_etf.py Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * feat(etf): 前端 API 绑定透传 asset_type + 文档 - api.ts: screener/backtest 绑定加 assetType 参数,MonitorRule 类型加 asset_type - docs/features.md: 标注选股/回测/监控的 ETF 支持范围与前提 Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * fix(reliability): 管道并发/原子写/能力探测/监控告警多处加固 后端可靠性专项修复(均带回归测试, backend 全套 64 passed): 并发与数据完整性: - 盘后管道单飞: JobStore.create() 去重纳入 pending∨running, 关闭"两次快速点击" 并发双跑窗口; 新增 _heavy_run_lock 执行槽挡住 reap 后僵尸线程并发写 parquet - adj_factor/minute 全部改走原子写(tmp+replace), 消除 kill/断电致 all.parquet 损坏 - 分块拉取失败聚合 WARNING 可见化(不再静默当成功); 复权失败标的会保持旧价已提示 能力探测: - 周期重探(60min)热更新 app.state.capabilities, 付费 Key 过期/续费无需重启即可见 - 瞬时探测失败(超时/连接/5xx, 按 _is_transient 判定)不降级、保留旧付费档; 真 401/无权限仍正常降级回落 free-api 监控告警: - 评估仅在连续竞价(9:30-11:30/13:00-15:00)+ 快照当日新鲜度下进行, 避开集合竞价/ 收盘后陈旧价与节假日误告警 - scope=sector fail-closed(validate 拒绝新建 + _apply_scope 返回空), 修复板块规则 对全市场刷屏 - 飞书 webhook 加退避重试并移到独立线程池 fire-and-forget, 不再阻塞行情轮询线程 单标的新鲜度: 新增 repo.symbols_lagging() 检测掉队标的并 WARNING + 计入 job 结果 Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
This commit is contained in:
Jinfeng Sun
co-authored by
Claude Opus 4.8
Claude Opus 4.8
parent
9c730e4b69
commit
e5a94c42d5
@@ -134,8 +134,9 @@ class PanelCache:
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end: date,
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columns: list[str] | None,
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compute_fn,
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asset_type: str = "stock",
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) -> pl.DataFrame:
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key = self._make_key(symbols, start, end, columns)
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key = self._make_key(symbols, start, end, columns, asset_type)
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now = time.monotonic()
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if key in self._cache:
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@@ -145,7 +146,7 @@ class PanelCache:
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return entry.df
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del self._cache[key]
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df = compute_fn(symbols, start, end, columns)
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df = compute_fn(symbols, start, end, columns, asset_type)
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self._cache[key] = _CacheEntry(df=df, ts=now)
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if len(self._cache) > self._max_size:
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self._cache.popitem(last=False)
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@@ -155,13 +156,13 @@ class PanelCache:
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self._cache.clear()
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@staticmethod
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def _make_key(symbols: list[str] | None, start: date, end: date, columns: list[str] | None) -> str:
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def _make_key(symbols: list[str] | None, start: date, end: date, columns: list[str] | None, asset_type: str = "stock") -> str:
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if symbols is None:
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h = "all"
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else:
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h = hashlib.md5(",".join(sorted(symbols)).encode()).hexdigest()[:12]
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cols = "all" if columns is None else hashlib.md5(",".join(sorted(columns)).encode()).hexdigest()[:8]
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return f"{h}:{start}:{end}:{cols}"
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return f"{asset_type}:{h}:{start}:{end}:{cols}"
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# ================================================================
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@@ -183,9 +184,10 @@ class BacktestEngine:
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start: date,
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end: date,
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columns: list[str] | None = None,
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asset_type: str = "stock",
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) -> pl.DataFrame:
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"""加载 enriched 数据面板，带缓存。"""
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return self._cache.get_or_compute(symbols, start, end, columns, self._load_panel_inner)
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"""加载 enriched 数据面板，带缓存。asset_type='etf' 时读 ETF enriched。"""
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return self._cache.get_or_compute(symbols, start, end, columns, self._load_panel_inner, asset_type=asset_type)
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def _load_panel_inner(
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self,
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@@ -193,12 +195,13 @@ class BacktestEngine:
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start: date,
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end: date,
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columns: list[str] | None = None,
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asset_type: str = "stock",
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) -> pl.DataFrame:
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t0 = time.perf_counter()
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# 近期区间优先复用 repository 的预计算 enriched 历史缓存，避免重复 scan_parquet + compute_all。
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# 近期区间优先复用 repository 的预计算 enriched 历史缓存 (仅 stock: 该缓存为股票专用)。
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try:
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if self.repo is not None and hasattr(self.repo, "get_enriched_range"):
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if asset_type == "stock" and self.repo is not None and hasattr(self.repo, "get_enriched_range"):
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cached = self.repo.get_enriched_range(start, end, symbols=symbols, columns=columns)
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if cached is not None and not cached.is_empty():
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elapsed = (time.perf_counter() - t0) * 1000
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@@ -207,7 +210,8 @@ class BacktestEngine:
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except Exception as e: # noqa: BLE001
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logger.debug("backtest load panel cache miss: %s", e)
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enriched_glob = str(self.repo.store.data_dir / "kline_daily_enriched" / "**" / "*.parquet")
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from app.tickflow.repository import enriched_dirname
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enriched_glob = str(self.repo.store.data_dir / enriched_dirname(asset_type) / "**" / "*.parquet")
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try:
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lf = pl.scan_parquet(enriched_glob)
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@@ -242,7 +246,9 @@ class BacktestEngine:
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return df
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from app.indicators.pipeline import compute_all
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instruments = self.repo.get_instruments()
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# 按 asset_type 取维表: ETF 回测须用 ETF 维表, 否则名称 JOIN 失败(全 null)、
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# 涨停信号算在错误的 instruments 上。
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instruments = self.repo.get_instruments_asset(asset_type)
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df = compute_all(df, instruments=instruments)
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if not instruments.is_empty() and "name" not in df.columns:
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inst_cols = [c for c in ["symbol", "name"] if c in instruments.columns]
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