"""全局实时行情服务。 集中管理全市场行情拉取 + enriched 缓存，供盘中选股、自选股等所有模块复用。 架构: - 后台线程轮询 TickFlow get_by_universes(["CN_Equity_A", "CN_ETF"]) + 核心指数按码拉取 (自定义源走 provider.get_realtime() + 可选 get_realtime_indices() 指数补充) - 拉取行情 → 写 kline_daily (不复权) + 增量计算 enriched → 写盘 + 更新缓存 - _enriched_cache 是唯一的盘中数据源 (OHLCV + 全套技术指标) - _live_agg_cache 是递推状态 (只加载一次, 盘中不变) 数据流 (每轮 ~15s): 1. API 拉取 → raw_records (临时变量) 2. raw_records → 写 kline_daily (不复权原始价格) 3. raw_records → 更新 _enriched_cache 的 OHLCV 4. 增量计算 enriched 指标 (~50ms) 5. 写 kline_daily_enriched + 替换 _enriched_cache 6. 通知 SSE 生命周期: - 服务启动时读取 preferences，若 enabled 则自动启动线程 - 运行中可通过 API 切换开关 - 关闭时停止线程 """ from __future__ import annotations import logging import threading import time from concurrent.futures import ThreadPoolExecutor from contextlib import contextmanager from datetime import date, datetime, time as dt_time import polars as pl from app.market_time import CN_TZ, cn_now, cn_today from app.parquet import scan_daily_parquet from app.services.index_const import CORE_INDEX_SYMBOLS from app.strategy.intraday_signals import IntradaySignalEvaluator from app.strategy.monitor import format_alert_quote # 告警来源 → 中文标签 (webhook 标题 / 系统通知标题共用) SOURCE_LABELS = { "strategy": "策略", "signal": "信号", "price": "价格", "market": "异动", "ladder": "连板梯队", "sector": "板块", "volume_delta": "放量", "abnormal": "异动", "date": "日期提醒", } # final 定版确认容差: 快照时间戳允许早于边界 5s 内 (供应商时间戳精度不一) _FINAL_CONFIRM_SLACK_MS = 5_000 # final 定版边界与重试窗口终点 (北京时间)。收盘窗口终点 15:30, 恰与盘后管道 # 启动同时: 管道运行期间轮询本就被暂停, 此后未确认的定版不再写盘, 当日分区 # 由管道按官方日线值级校正 —— 避免定版重试与权威重建互相覆盖。 _FINAL_BOUNDARY = {"morning_final": dt_time(11, 30), "close_final": dt_time(15, 0)} _FINAL_DEADLINE = {"morning_final": dt_time(12, 10), "close_final": dt_time(15, 30)} def _body_with_quote(body: str, ev: dict) -> str: """推送正文尾部补上触发时的现价/涨跌幅 (日期提醒无行情, 自然为空)。 默认告警的 message 已由引擎拼过引语 (monitor._default_message), 这里仅在正文 尚未带引语时追加, 避免「现价」出现两遍 (自定义 message 的规则则补上这一句)。 """ quote_tail = format_alert_quote(ev.get("price"), ev.get("change_pct")) if not quote_tail or body.endswith(quote_tail): return body return f"{body} · {quote_tail}" logger = logging.getLogger(__name__) # Webhook(飞书等)投递专用线程池 —— 与行情轮询线程隔离。 # send_feishu 内置重试(最坏 ~3×5s 超时 + 退避), 若在 _poll_loop 上同步投递, # webhook 慢/宕机会逐条累加, 拖垮整条实时行情+告警轮询。这里 fire-and-forget, # 失败由 webhook_adapter 记 WARNING(可见), 但绝不阻塞热路径。 _WEBHOOK_EXECUTOR = ThreadPoolExecutor(max_workers=2, thread_name_prefix="feishu-webhook") class QuoteSubscriber: """一个 SSE 连接对应一个订阅者: 独立事件 + 独立队列。 此前四个通道共用服务级 Event + pending 列表, pop 是「取走」语义: 多客户端 (多标签页/多设备) 时告警只会被先醒来的连接消费, 其余永远 收不到; 共享 Event 的 clear/wait 也存在互相吞信号的竞态。 改为每连接独立订阅者后, 事件对所有客户端广播。 """ def __init__(self, max_alerts: int = 1000, max_reviews: int = 200) -> None: self._event = threading.Event() self._lock = threading.Lock() self._max_alerts = max_alerts self._max_reviews = max_reviews self._quote_updated = False self._strategy_results_updated = False self._depth_updated = False self._alerts: list[dict] = [] self._reviews: list[str] = [] # ── 消费侧 (SSE generator 线程) ────────────────────── def wait(self, timeout: float = 5.0) -> bool: """阻塞等待任一通道有新信号。""" return self._event.wait(timeout=timeout) def pop(self) -> dict: """原子取走全部待推送内容并复位事件。""" with self._lock: out = { "quote_updated": self._quote_updated, "strategy_results_updated": self._strategy_results_updated, "depth_updated": self._depth_updated, "alerts": self._alerts, "reviews": self._reviews, } self._quote_updated = False self._strategy_results_updated = False self._depth_updated = False self._alerts = [] self._reviews = [] self._event.clear() return out # ── 生产侧 (行情轮询 / depth / 复盘线程) ───────────── def push_alerts(self, alerts: list[dict]) -> None: with self._lock: self._alerts.extend(alerts) if len(self._alerts) > self._max_alerts: # 背压: 丢弃最旧 self._alerts = self._alerts[-self._max_alerts:] self._event.set() def push_review(self, event_json: str) -> None: with self._lock: self._reviews.append(event_json) if len(self._reviews) > self._max_reviews: self._reviews = self._reviews[-self._max_reviews:] self._event.set() def clear_alerts(self) -> None: with self._lock: self._alerts = [] if ( not self._quote_updated and not self._strategy_results_updated and not self._depth_updated and not self._reviews ): self._event.clear() def notify_quote(self) -> None: with self._lock: self._quote_updated = True self._event.set() def notify_strategy_results(self) -> None: with self._lock: self._strategy_results_updated = True self._event.set() def notify_depth(self) -> None: with self._lock: self._depth_updated = True self._event.set() # 落盘节流间隔: last_fetch_ms 仅在进程重启后用于显示"最后获取时间"(运行中读内存值), # 每 30s 持久化一次足够, 避免 expert 档每秒一轮的全量 preferences 重写磁盘。 _LAST_FETCH_WRITE_INTERVAL_MS = 30_000.0 _last_fetch_written_at_ms: float = 0.0 def _persist_last_fetch(fetched_at_ms: float) -> None: """把"最后获取"时间戳持久化到 preferences, 使进程重启后仍可显示。 放在锁外调用 (IO); 失败不影响主流程 (内存值已更新, 下次 fetch 再写)。 距上次成功落盘不足 30s 时跳过 (节流只影响落盘频率, 内存值不受影响)。 """ global _last_fetch_written_at_ms if (fetched_at_ms - _last_fetch_written_at_ms) < _LAST_FETCH_WRITE_INTERVAL_MS: return try: from app.services import preferences preferences.save({"last_fetch_ms": round(fetched_at_ms, 0)}) _last_fetch_written_at_ms = fetched_at_ms except Exception as e: # noqa: BLE001 logger.debug("last_fetch_ms 持久化失败 (不影响行情): %s", e) def _monitor_name_map(repo) -> dict[str, str]: """监控回填用的 symbol → name 映射 (股票 + ETF + 指数, 股票优先)。 走 repo.get_name_map() 的进程内 memo (三份 instruments 维表刷新时失效), 避免每轮监控对 ~7000 行维表 iter_rows 重建。过滤空名称与旧行为一致。 """ return {s: n for s, n in repo.get_name_map().items() if n} class QuoteService: """全局实时行情服务 — 单例。""" # 档位 → 最小轮询间隔 (秒) — TickFlow 档位限速保护, 仅实时源为 tickflow 时适用 TIER_MIN_INTERVAL = { "expert": 1.0, "pro": 3.0, "starter": 6.0, "free": 6.0, } # 插件/自定义源: 不受 TickFlow 档位保护约束, 通用下限 1s (默认间隔仍为 DEFAULT_INTERVAL) CUSTOM_PROVIDER_MIN_INTERVAL = 1.0 DEFAULT_INTERVAL = 6.0 MAX_INTERVAL = 60.0 def __init__(self) -> None: self._lock = threading.Lock() # 串行化行情拉取: 手动 POST /refresh 与后台轮询线程可能并发调用 # _fetch_quotes, 两者同时写同一批 parquet/缓存会互相覆盖 self._fetch_lock = threading.Lock() self._running = False self._enabled = False # 全局开关 (持久化到 preferences) # 暂停态: 盘后管道/数据修正运行期间临时暂停取数, 防止与管道写同一批 parquet 竞态。 # 与 _enabled 不同 — pause 不改 preferences、不 stop 线程, 仅让轮询循环跳过取数; # 进程重启后 _paused 归零, 从 preferences 恢复真实开关态, 无"假关闭"副作用。 self._paused = False self._interval = self.DEFAULT_INTERVAL self._thread: threading.Thread | None = None self._repo = None # 延迟注入, 避免循环导入 # SSE 订阅者集合: 每个 /stream 连接一个 QuoteSubscriber, 事件广播到所有订阅者 self._subscribers: set[QuoteSubscriber] = set() self._strategy_monitor = None # 延迟注入 self._app_state = None # 延迟注入 (FastAPI app.state) # 异动边缘规则上次评估时间戳 (秒)。异动快照历史部分有 60s 缓存, # 但每次构建仍有全市场循环, 轮询线程里限频到 30s 一次。 self._abnormal_last_eval = 0.0 # 拉取元信息 (给 SSE / status 用) self._fetch_time: float = 0.0 # perf_counter (用于计算 quote_age_ms) self._fetch_ms: float = 0.0 # 拉取耗时 (毫秒) # _fetched_at 持久化到 preferences: 进程重启后仍能显示"最后获取"时间, # 不因关闭开关/重启而归零 (数据页卡片常驻显示, 方便判断上次拉取时刻)。 try: from app.services import preferences as _prefs self._fetched_at: float = float(_prefs.load().get("last_fetch_ms", 0.0)) except Exception: # noqa: BLE001 self._fetched_at = 0.0 # 拉取完成的 Unix 时间戳 (毫秒) self._symbol_count: int = 0 self._index_symbol_count: int = 0 self._etf_symbol_count: int = 0 self._index_quotes_cache: pl.DataFrame | None = None self._intraday_signal_evaluator = IntradaySignalEvaluator() self._intraday_signal_bucket: dict[str, str] = {} # 午休/收盘最终同步状态: 到边界后必须成功拉取一版行情, 再进入休盘态。 self._final_sync_done: set[tuple[date, str]] = set() self._final_sync_failed: dict[tuple[date, str], str] = {} # 最近一次 final 定版拉取是否取得边界后快照 (None=非 final 拉取) self._last_final_confirmed: bool | None = None self._holiday_active = False # 交易日探针当前是否判休市 (日志去重) # 轮询放量 (volume_delta 规则): 上一轮全市场股票快照的 (累计成交量[手], 累计成交额[元])。 # 每轮全量快照后更新 (含非连续竞价时段, 保证 13:00 恢复时 prev 是 12:59 # 而非 11:30); 跨交易日清空; cur < prev (数据源重置) 时丢弃该轮差值。 self._prev_stock_volume: dict[str, tuple[float, float]] | None = None self._prev_volume_fetched_at: float | None = None # epoch 毫秒 self._prev_volume_date: date | None = None # 最近一轮的有效差值 (vol_delta[手], amt_delta[元]) - 仅连续竞价时段内、 # prev 不早于本时段开盘时计算 self._volume_delta: dict[str, tuple[float, float]] = {} self._volume_delta_span_s: float = 0.0 # ================================================================ # 生命周期 # ================================================================ def start(self, interval: float = 0.0) -> None: """启动后台行情轮询线程。""" if self._running: return if interval <= 0: from app.services import preferences interval = preferences.get_realtime_quote_interval() self._interval = self._clamp_interval(interval) self._running = True self._enabled = True self._thread = threading.Thread(target=self._poll_loop, daemon=True) self._thread.start() self._save_enabled(True) logger.info("行情服务已启动, 轮询间隔 %.1fs", self._interval) def stop(self) -> None: """停止后台行情轮询线程。""" self._running = False self._enabled = False if self._thread: self._thread.join(timeout=10) self._thread = None self._save_enabled(False) logger.info("行情服务已停止") def enable(self) -> bool: """开启自动行情 (不立即启动线程，等下一个交易时段)。 none 档无实时行情权限,拒绝开启并返回 False; free 档开启自选股实时,starter+ 开启全市场实时。返回值表示是否真正开启。 """ if not self.is_realtime_allowed(): logger.warning("实时行情开启被拒:当前档位(none)无实时行情权限") return False self._enabled = True self._save_enabled(True) if not self._running: from app.services import preferences self._interval = self._clamp_interval(preferences.get_realtime_quote_interval()) self._running = True self._thread = threading.Thread(target=self._poll_loop, daemon=True) self._thread.start() logger.info("行情服务已启用, 轮询间隔 %.1fs", self._interval) return True def disable(self) -> None: """关闭自动行情。""" self.stop() logger.info("行情服务已关闭") # ================================================================ # 临时暂停 (盘后管道/数据修正期间, 防止写盘竞态) # ================================================================ def pause(self) -> None: """临时暂停行情轮询取数 (不关闭线程、不改 preferences)。 用于盘后管道/数据修正运行期间, 防止实时行情覆写管道正在写的 parquet。 与 stop() 的区别: 线程继续存活但跳过 _fetch_quotes; preferences 开关态不变, 管道结束调用 resume() 即恢复。线程级检查, 即时生效, 无 join 等待。 """ self._paused = True logger.info("行情轮询已临时暂停 (管道/修正运行中)") def resume(self) -> None: """恢复暂停的行情轮询取数 (对应 pause)。""" self._paused = False logger.info("行情轮询已恢复") def is_paused(self) -> bool: """是否处于临时暂停态 (管道运行期间)。""" return self._paused @contextmanager def paused(self): """上下文管理器: 进入时暂停轮询取数, 退出时(含异常)自动恢复。 供盘后管道/数据修正复用: with quote_service.paused(): run_pipeline(...) 无论正常结束还是异常/crash, finally 都会 resume (除非进程直接被 kill)。 """ self.pause() try: yield finally: self.resume() def boot_check(self) -> None: """启动时检查 preferences，若 enabled 则自动启动。 none 档无实时行情权限:即使 preferences 标记为 enabled, 也不启动,并同步 preferences 为关闭(避免 UI 误显示已开启)。 """ from app.services import preferences if not self.is_realtime_allowed(): if preferences.get_realtime_quotes_enabled(): self._save_enabled(False) logger.info("实时行情未启动:当前档位(none)无实时行情权限") return if preferences.get_realtime_quotes_enabled(): self.start() def set_repo(self, repo) -> None: """注入 KlineRepository, 用于实时落盘。""" self._repo = repo def set_app_state(self, app_state) -> None: """注入 FastAPI app.state, 用于获取 strategy_monitor 等单例。""" self._app_state = app_state def set_interval(self, interval: float) -> float: """运行时更新轮询间隔（立即生效）。""" clamped = self._clamp_interval(interval) self._interval = clamped from app.services import preferences preferences.set_realtime_quote_interval(clamped) logger.info("轮询间隔已更新为 %.1fs", clamped) return clamped def get_min_interval(self) -> float: """返回当前档位允许的最小间隔。""" return self._tier_min_interval() # ================================================================ # SSE 订阅管理 — 每个 /stream 连接一个订阅者, 事件广播 # ================================================================ def subscribe(self) -> QuoteSubscriber: """注册一个 SSE 订阅者 (连接建立时调用)。""" sub = QuoteSubscriber() with self._lock: self._subscribers.add(sub) return sub def unsubscribe(self, sub: QuoteSubscriber) -> None: """注销订阅者 (连接断开时调用)。""" with self._lock: self._subscribers.discard(sub) def _snapshot_subscribers(self) -> list[QuoteSubscriber]: with self._lock: return list(self._subscribers) def _broadcast_quote_updated(self) -> None: # 实时行情刷新后清空总览聚合缓存, 使看板 (overview-market) 在 SSE 触发的 # 重取中拿到最新指数/聚合值。与 _broadcast 同时进行, 与侧栏 /intraday/indices # (无缓存, 直读实时缓存) 行为对齐, 避免看板落后于侧栏。 # 延迟导入规避 services <-> api 层循环依赖。 from app.api.overview import invalidate_overview_cache invalidate_overview_cache() for sub in self._snapshot_subscribers(): sub.notify_quote() def notify_strategy_results_updated(self) -> None: """策略监控完成实时结果更新后调用，仅刷新策略页结果缓存。""" for sub in self._snapshot_subscribers(): sub.notify_strategy_results() def notify_depth_updated(self) -> None: """五档盘口修正完成后调用: 通知 SSE 推送 depth_updated, 触发连板梯队刷新。 与行情/告警通道独立 — 只刷新连板梯队, 不连带刷新 watchlist 等。 """ for sub in self._snapshot_subscribers(): sub.notify_depth() def _broadcast_alerts(self, alerts: list[dict]) -> None: for sub in self._snapshot_subscribers(): sub.push_alerts(alerts) def push_alerts(self, alerts: list[dict]) -> None: self._broadcast_alerts(alerts) def clear_pending_alerts(self) -> None: for sub in self._snapshot_subscribers(): sub.clear_alerts() def push_review_event(self, event_json: str) -> None: """广播一条复盘进度事件(JSON 字符串), 唤醒所有 SSE generator。 事件格式与 recap_market_stream 的产出一致(meta/delta/error/done), 前端 reviewStore 直接消费。背压在订阅者队列内做 (丢弃最旧)。 """ for sub in self._snapshot_subscribers(): sub.push_review(event_json) # ================================================================ # 档位感知间隔限制 # ================================================================ @staticmethod def _current_tier() -> str: """获取当前档位名（小写）。""" from app.tickflow.policy import tier_label return tier_label().split()[0].split("+")[0].strip().lower() @classmethod def realtime_mode(cls) -> str: """当前实时行情模式: none / full_market。 TickFlow 免费档不再提供"自选前 5 只"降级实时(自定义源 fuyao 的全市场 快照已全面覆盖且免费); TickFlow 免费档 = 无实时, 接入自定义实时源 (如 fuyao)或升级 TickFlow 后恢复全市场模式。 """ from app.services import preferences if preferences.get_realtime_data_provider() != "tickflow": return "full_market" tier = cls._current_tier() if tier in ("none", "free"): return "none" return "full_market" @classmethod def is_realtime_allowed(cls) -> bool: """当前档位是否允许使用实时行情。""" return cls.realtime_mode() != "none" @classmethod def _tier_min_interval(cls) -> float: # 实时源路由到插件/自定义源时, TickFlow 档位限速不适用 (中立能力原则): # 下限放宽到通用 1s, 默认/已保存间隔不变 from app.services import preferences if preferences.get_realtime_data_provider() != "tickflow": return cls.CUSTOM_PROVIDER_MIN_INTERVAL tier = cls._current_tier() return cls.TIER_MIN_INTERVAL.get(tier, cls.DEFAULT_INTERVAL) def _clamp_interval(self, interval: float) -> float: return max(self._tier_min_interval(), min(self.MAX_INTERVAL, interval)) # ================================================================ # 行情数据访问 # ================================================================ def get_enriched_today(self) -> tuple[pl.DataFrame, date | None]: """返回今天 enriched 数据 + 日期 (线程安全)。 所有页面统一通过此方法获取实时行情 + 技术指标。 """ if not self._repo: return pl.DataFrame(), None return self._repo.get_enriched_latest() def get_quotes_compat(self) -> pl.DataFrame: """兼容接口: 返回行情 DataFrame (用于盘中选股等需要 last_price/prev_close 的场景)。 从 _enriched_cache 取 today 的数据, 只选行情基础列, 补上 last_price 别名。 不返回指标列, 避免 JOIN live_agg 时列名冲突。 """ df, _ = self.get_enriched_today() if df.is_empty(): return df # 只取盘中选股需要的行情基础列 keep = [c for c in [ "symbol", "close", "open", "high", "low", "volume", "amount", "prev_close", "change_pct", "change_amount", "amplitude", "turnover_rate", ] if c in df.columns] df = df.select(keep) # enriched 的 close 等价于 last_price if "close" in df.columns and "last_price" not in df.columns: df = df.with_columns(pl.col("close").alias("last_price")) return df def get_index_quotes(self, symbols: list[str] | None = None) -> pl.DataFrame: """返回实时指数行情缓存。不会触发 TickFlow 请求。""" with self._lock: df = self._index_quotes_cache.clone() if self._index_quotes_cache is not None else pl.DataFrame() if df.is_empty(): return df if symbols: return df.filter(pl.col("symbol").is_in(symbols)) return df def status(self) -> dict: """返回行情服务状态。""" age = (time.perf_counter() - self._fetch_time) * 1000 if self._fetch_time else -1 mode = self.realtime_mode() phase = self._market_phase() final_key = self._final_sync_key(phase) final_done = bool(final_key and final_key in self._final_sync_done) final_failed = self._final_sync_failed.get(final_key) if final_key else None return { "enabled": self._enabled, "running": self._running, "paused": self._paused, "mode": mode, "realtime_allowed": mode != "none", "interval_s": self._interval, "symbol_count": self._symbol_count, "index_symbol_count": self._index_symbol_count, "etf_symbol_count": self._etf_symbol_count, "quote_age_ms": round(age, 0) if age >= 0 else None, # 交易时段 = 连续竞价; polling_window 另行返回,避免午休/收盘缓冲误显示为交易中。 "is_trading_hours": self._is_continuous_trading(), "is_polling_window": self._should_poll_for_phase(phase), "market_phase": phase, "final_sync_done": final_done, "final_sync_failed": final_failed, "last_fetch_ms": round(self._fetched_at, 0) if self._fetched_at else None, } def refresh(self) -> dict: """手动触发一次行情拉取。 午休/收盘定版阶段同样走边界确认: 避免盘后手动刷新把竞价前的陈旧收盘价 重新写回当日分区, 覆盖盘后管道按官方日线重建的结果。 """ phase = self._market_phase() is_final = phase in {"morning_final", "close_final"} self._fetch_quotes( final=is_final, final_boundary_ms=self._final_boundary_ms(phase) if is_final else None, ) return self.status() # ================================================================ # 后台轮询 # ================================================================ def _poll_loop(self) -> None: while self._running and self._enabled: try: # 管道/数据修正运行期间临时暂停取数, 防止与管道写同一批 parquet 竞态。 # 线程继续存活 + 分片 sleep, resume() 后即时恢复, 无需重启线程。 if not self._paused: phase = self._market_phase() if self._should_fetch_for_phase(phase): is_final = phase in {"morning_final", "close_final"} ok = self._fetch_quotes( final=is_final, final_boundary_ms=self._final_boundary_ms(phase), ) if is_final: key = self._final_sync_key(phase) label = "午休" if phase == "morning_final" else "收盘" if key and ok and self._last_final_confirmed: self._final_sync_done.add(key) self._final_sync_failed.pop(key, None) logger.info("%s 最终行情同步完成 (快照时间戳已达边界), 进入休盘态", label) elif key and self._past_final_deadline(phase): # 重试窗口结束仍未取得边界后快照: 接受现状停止轮询。 # 实测有实时源收盘后长期返回竞价前旧价 (快照时间戳可信但价格不更新), # 此时盲目落盘只会固化旧价 —— 交由 15:30 盘后管道按官方日线校正。 self._final_sync_done.add(key) self._final_sync_failed[key] = ( "fetch_failed" if not ok else "unconfirmed_snapshot" ) logger.warning( "%s 定版窗口结束仍未取得边界后快照 (%s), 停止轮询; " "当日分区由盘后管道按官方日线值级校正", label, "拉取失败" if not ok else "快照未确认", ) elif key: self._final_sync_failed[key] = ( "fetch_failed" if not ok else "unconfirmed_snapshot" ) if not ok: logger.warning("%s 最终行情同步失败, 将继续重试", label) else: logger.debug("非轮询阶段(%s), 跳过行情轮询", phase) except Exception as e: # noqa: BLE001 logger.warning("行情轮询异常: %s", e) waited = 0.0 while self._running and self._enabled and waited < self._interval: time.sleep(0.5) waited += 0.5 def _fetch_quotes(self, *, final: bool = False, final_boundary_ms: int | None = None) -> bool: """拉取行情。加锁串行化 (后台轮询 vs 手动 refresh)。返回本轮是否成功更新。 final_boundary_ms: final 定版的边界时间戳 (ms)。传入时快照时间戳未达边界 的本轮不落盘 (见 _process_full_market_records)。 """ with self._fetch_lock: before = self._fetched_at if final: logger.info("最终行情同步开始") self._fetch_full_market_quotes(final_boundary_ms=final_boundary_ms) return self._fetched_at > before def _fetch_full_market_quotes(self, final_boundary_ms: int | None = None) -> None: """拉取全市场行情 → 写 daily + 计算 enriched + 更新缓存。""" from app.services import preferences provider_name = preferences.get_realtime_data_provider() if provider_name != "tickflow": from app.data_providers import custom as custom_sources if custom_sources.provider_has_dataset(provider_name, "realtime"): try: t0 = time.perf_counter() now_ts = time.perf_counter() provider = custom_sources.get_provider(provider_name) records = provider.get_realtime() # 指数补充: A 股快照通常不含指数。插件可选实现 # get_realtime_indices(symbols) 用独立端点补拉 (如 fuyao 指数快照); # 未实现的源指数缓存为空, 由日K兜底接管。 replace_index_cache = True fetch_indices = getattr(provider, "get_realtime_indices", None) if callable(fetch_indices): # 偏离值基准指数 (科创50/创业板综指等) 一并拉取, 供盘中 # attach_deviation_columns_today 实时外推; 展示层仍按核心 # 四只过滤, 多拉的指数不进侧栏。 from app.indicators.pipeline import BENCHMARK_INDEX_SYMBOLS wanted = sorted( set(CORE_INDEX_SYMBOLS) | BENCHMARK_INDEX_SYMBOLS | self._collect_monitor_index_symbols() ) try: fetched_indices = fetch_indices(wanted) if fetched_indices is None: replace_index_cache = False else: records = records + fetched_indices except Exception as e: # noqa: BLE001 logger.warning("自定义源指数行情拉取失败: %s", e) replace_index_cache = False except Exception as e: # noqa: BLE001 logger.warning("自定义实时行情拉取失败: %s", e) return self._process_full_market_records( records, t0=t0, now_ts=now_ts, replace_index_cache=replace_index_cache, final_boundary_ms=final_boundary_ms, ) return # 自定义源未配置 realtime → 回退 TickFlow from app.tickflow.client import get_paid_realtime_client tf = get_paid_realtime_client() if tf is None: logger.warning("实时行情拉取失败:未配置付费服务器 API Key") return t0 = time.perf_counter() now_ts = time.perf_counter() try: from app.services import preferences all_index_symbols = set(self._repo.get_index_symbol_set()) if self._repo else set() core_index_symbols = set(CORE_INDEX_SYMBOLS) all_index_symbols.update(core_index_symbols) # 指数监控规则标的并入显式拉取 (quotes.get 按码覆盖) monitor_index_symbols = self._collect_monitor_index_symbols() all_index_symbols.update(monitor_index_symbols) all_etf_symbols = set() if self._repo: etf_inst = self._repo.get_etf_instruments() if not etf_inst.is_empty() and "symbol" in etf_inst.columns: all_etf_symbols = set(etf_inst["symbol"].cast(pl.Utf8).to_list()) universes: list[str] = [] if preferences.get_realtime_pull_stock(): universes.append("CN_Equity_A") if preferences.get_realtime_pull_etf() and all_etf_symbols: universes.append("CN_ETF") resp = [] if universes: _u0 = time.perf_counter() logger.info("拉取全市场行情 (universes=%s, SDK超时=30s×重试3)", universes) resp.extend(tf.quotes.get_by_universes(universes=universes) or []) logger.info("全市场行情拉取完成: %d 条 (%.2fs)", len(resp), time.perf_counter() - _u0) # 指数: 固定核心四只 + 偏离值基准指数 + 监控规则标的, 按码显式拉取 from app.indicators.pipeline import BENCHMARK_INDEX_SYMBOLS _core_syms = sorted( core_index_symbols | BENCHMARK_INDEX_SYMBOLS | monitor_index_symbols ) if _core_syms: _i0 = time.perf_counter() resp.extend(tf.quotes.get(symbols=_core_syms) or []) logger.info("核心指数行情拉取完成: %d 只 (%.2fs)", len(_core_syms), time.perf_counter() - _i0) except Exception as e: # noqa: BLE001 logger.warning("行情拉取失败 (%.2fs): %s", time.perf_counter() - t0, e) return if not resp: logger.warning("行情数据为空") return # ---- 解析 API 响应 (临时变量, 用完丢弃) ---- records = [] for q in resp: ext = q.get("ext") or {} last_price = q.get("last_price") prev_close = q.get("prev_close") change_amount = ext.get("change_amount") change_pct = ext.get("change_pct") if change_amount is None and last_price is not None and prev_close is not None: change_amount = float(last_price) - float(prev_close) if change_pct is None and change_amount is not None and prev_close not in (None, 0): # 与 API ext.change_pct 同为小数制 (0.0366 = 3.66%), # enriched 全项目约定小数 (见 pipeline.py), 此处不可乘 100 change_pct = float(change_amount) / float(prev_close) records.append({ "symbol": q.get("symbol"), "name": q.get("name") or ext.get("name"), "last_price": last_price, "prev_close": prev_close, "open": q.get("open"), "high": q.get("high"), "low": q.get("low"), "volume": q.get("volume"), "amount": q.get("amount"), "change_pct": change_pct, "change_amount": change_amount, "amplitude": ext.get("amplitude"), "turnover_rate": ext.get("turnover_rate"), "timestamp": q.get("timestamp"), "session": q.get("session"), }) self._process_full_market_records( records, t0=t0, now_ts=now_ts, final_boundary_ms=final_boundary_ms ) def _process_full_market_records( self, records: list[dict], *, t0: float, now_ts: float, replace_index_cache: bool = True, final_boundary_ms: int | None = None, ) -> None: """把全市场 records 写盘并增量计算 enriched。 final_boundary_ms (final 定版边界) 传入时, 快照最大时间戳未达边界的本轮 只更新展示缓存, 不写 daily/enriched、不评估监控 —— 防止收盘后数据源仍 返回竞价前旧价时把陈旧收盘价固化到当日分区。 """ from app.services import preferences all_index_symbols = set(self._repo.get_index_symbol_set()) if self._repo else set() core_index_symbols = set(CORE_INDEX_SYMBOLS) all_index_symbols.update(core_index_symbols) all_etf_symbols = set() if self._repo: etf_inst = self._repo.get_etf_instruments() if not etf_inst.is_empty() and "symbol" in etf_inst.columns: all_etf_symbols = set(etf_inst["symbol"].cast(pl.Utf8).to_list()) if not records: logger.warning("行情数据为空") return # ---- final 定版确认: 快照最大时间戳达到边界 (含容差) 才允许落盘 ---- confirmed_final: bool | None = None if final_boundary_ms is not None: ts_vals = [t for t in (r.get("timestamp") for r in records) if t] max_ts = max(ts_vals) if ts_vals else None confirmed_final = bool( max_ts is not None and max_ts >= final_boundary_ms - _FINAL_CONFIRM_SLACK_MS ) self._last_final_confirmed = confirmed_final index_records = [r for r in records if r.get("symbol") in all_index_symbols] etf_records = [r for r in records if r.get("symbol") in all_etf_symbols] stock_records = [ r for r in records if r.get("symbol") not in all_index_symbols and r.get("symbol") not in all_etf_symbols ] fetch_ms = (time.perf_counter() - t0) * 1000 fetched_at = time.time() * 1000 # ---- 更新元信息 ---- with self._lock: self._fetch_time = now_ts self._fetch_ms = fetch_ms self._fetched_at = fetched_at self._symbol_count = len(stock_records) self._etf_symbol_count = len(etf_records) if replace_index_cache: self._index_symbol_count = len(index_records) self._index_quotes_cache = self._build_index_quotes(index_records) else: logger.info("指数本轮获取失败,沿用上轮缓存: %d 只", self._index_symbol_count) _persist_last_fetch(fetched_at) logger.info("行情刷新: %d 只股票, %d 只ETF, %d 只指数, 耗时 %.0fms", len(stock_records), len(etf_records), len(index_records), fetch_ms) if confirmed_final is False: # 边界前的陈旧快照: 展示缓存已更新, 落盘与监控评估留待边界后快照。 # 轮询线程会在定版窗口内持续重试, 窗口结束由 _poll_loop 放弃并告警。 logger.info( "final 快照未达定版边界 (max quote_ts=%s, 边界=%s), 本轮跳过落盘", max_ts, final_boundary_ms, ) self._broadcast_quote_updated() return # 轮询放量状态更新 (volume_delta 规则的差值来源) self._update_volume_delta(stock_records, fetched_at) # ---- 写 kline_daily (不复权原始价格, 只有 OHLCV) ---- daily_df = self._build_daily(stock_records) if not daily_df.is_empty() and self._repo: try: self._repo.flush_live_daily(daily_df) except Exception as e: # noqa: BLE001 logger.warning("日K写盘失败: %s", e) etf_daily_df = self._build_daily(etf_records) if not etf_daily_df.is_empty() and self._repo: try: self._repo.flush_live_daily_asset("etf", etf_daily_df) except Exception as e: # noqa: BLE001 logger.warning("ETF 日K写盘失败: %s", e) # ---- 构建 API 直接值的补充表 (不写 daily, 只用于 enriched 计算) ---- quote_extra = self._build_quote_extra(stock_records) etf_quote_extra = self._build_quote_extra(etf_records) # ---- 增量计算 enriched + 写盘 + 更新缓存 ---- if not daily_df.is_empty() and self._repo: self._flush_live_enriched(daily_df, quote_extra, asset_type="stock") if not etf_daily_df.is_empty() and self._repo: self._flush_live_enriched(etf_daily_df, etf_quote_extra, asset_type="etf") # ---- 指数: 仅有指数监控规则时才写盘 (无规则零成本) ---- # 指数为按码显式拉取 (部分标的) → merge 不截断分区 engine = getattr(self._app_state, "monitor_engine", None) if self._app_state else None if engine and engine.has_asset_rules("index") and self._repo: index_daily_df = self._build_daily(index_records) if not index_daily_df.is_empty(): try: self._repo.merge_live_daily_asset("index", index_daily_df) except Exception as e: # noqa: BLE001 logger.warning("指数日K写盘失败: %s", e) self._flush_live_enriched(index_daily_df, self._build_quote_extra(index_records), asset_type="index", merge=True) # ---- 通知 SSE ---- self._broadcast_quote_updated() # ---- 策略监控 + 告警评估 ---- self._evaluate_monitors(daily_df, quote_extra) # ================================================================ # 工具 # ================================================================ def _collect_monitor_index_symbols(self) -> set[str]: """启用中的指数监控规则标的 (asset_type=index & scope=symbols)。""" engine = getattr(self._app_state, "monitor_engine", None) if self._app_state else None if not engine: return set() out: set[str] = set() for _r in list(engine.rules.values()): if _r.get("enabled", True) and _r.get("asset_type") == "index" and _r.get("scope") == "symbols": out.update(s for s in _r.get("symbols", []) if s) return out @staticmethod @staticmethod def _build_daily(records: list[dict]) -> pl.DataFrame: """将 API records 转为日K格式 DataFrame (OHLCV + quote_ts, 写 kline_daily 用)。""" if not records: return pl.DataFrame() df = pl.DataFrame(records) cols_map = { "symbol": "symbol", "last_price": "close", "open": "open", "high": "high", "low": "low", "volume": "volume", "amount": "amount", "timestamp": "quote_ts", } select_exprs = [] for src, dst in cols_map.items(): if src in df.columns: select_exprs.append(pl.col(src).cast(pl.Int64, strict=False).alias(dst) if dst == "quote_ts" else pl.col(src).alias(dst)) if not select_exprs: return pl.DataFrame() result = df.select(select_exprs).with_columns( pl.lit(cn_today()).cast(pl.Date).alias("date"), ) # 停牌股回归: 实时源对停牌标的返回停牌前最后一份快照 — OHLCV 全为旧日 # 真实值, 仅 timestamp 停在旧日。这类记录不属于当日, 不过滤会把旧日 K 线 # 原样复制成当日假蜡烛 (如 301266.SZ 2026-09-04)。按 quote_ts 的北京 # 日期归属过滤; 时间戳缺失/为空的源无法判断, 维持原行为保留。 if "quote_ts" in result.columns: day_start_ms = int( datetime.combine(cn_today(), dt_time(0, 0), tzinfo=CN_TZ).timestamp() * 1000 ) result = result.filter( pl.col("quote_ts").is_null() | pl.col("quote_ts").is_between(day_start_ms, day_start_ms + 86_400_000, closed="left") ) # 停牌/尚无集合竞价的记录 open/high 均为 0。必须在下方用 close 填充前 # 过滤, 否则零成交行会被伪装成有效日K, 并在 batch 同步后作为实时残留 # 反复触发历史完整性修复。 from app.indicators.pipeline import filter_halt_days result = filter_halt_days(result) # 修复: API 在非交易时段可能返回 open/high/low=0 或 null, # 导致蜡烛从 0 开始。用 close 填充这些异常值。 for col in ("open", "high", "low"): if col in result.columns: result = result.with_columns( pl.when((pl.col(col) == 0) | pl.col(col).is_null()) .then(pl.col("close")) .otherwise(pl.col(col)) .alias(col) ) return result @staticmethod def _build_quote_extra(records: list[dict]) -> pl.DataFrame: """构建 API 直接提供的补充字段 (不写 daily, 只传给 enriched 计算)。 包含: prev_close, change_pct, change_amount, amplitude, turnover_rate。 """ if not records: return pl.DataFrame() df = pl.DataFrame(records) keep = [c for c in [ "symbol", "prev_close", "change_pct", "change_amount", "amplitude", "turnover_rate", ] if c in df.columns] if not keep or "symbol" not in keep: return pl.DataFrame() out = df.select(keep) # 实时 API 的 turnover_rate 入口契约为小数制(0.05 = 5%). # enriched 内部统一存百分数值(5 = 5%), 后续页面/筛选直接展示和比较。 if "turnover_rate" in out.columns: out = out.with_columns((pl.col("turnover_rate").cast(pl.Float64, strict=False) * 100).alias("turnover_rate")) return out @staticmethod def _build_index_quotes(records: list[dict]) -> pl.DataFrame: """构建指数实时行情缓存，不落股票 parquet。 注意: API 返回的 change_pct/amplitude 是小数 (0.0366 = 3.66%), 统一转成百分比输出, 与 _fallback_index_quotes_from_daily 口径一致 (前端指数侧不×100, 直接 toFixed(2)% 展示)。 """ if not records: return pl.DataFrame() df = pl.DataFrame(records) keep = [c for c in [ "symbol", "name", "last_price", "prev_close", "open", "high", "low", "volume", "amount", "change_pct", "change_amount", "amplitude", "timestamp", "session", ] if c in df.columns] if not keep or "symbol" not in keep: return pl.DataFrame() df = df.select(keep) # 自定义源可能不提供 change_pct/change_amount, 按 last_price/prev_close 补算 # (TickFlow 路径在 _fetch_full_market_quotes 已算好, 此处只补缺失的) if "change_pct" not in df.columns and "last_price" in df.columns and "prev_close" in df.columns: # prev_close=0 → inf (非合法 JSON), prev_close=null → null; 用 when 守护 df = df.with_columns( pl.when(pl.col("prev_close") != 0) .then((pl.col("last_price") - pl.col("prev_close")) / pl.col("prev_close")) .otherwise(None) .alias("change_pct") ) if "change_amount" not in df.columns and "last_price" in df.columns and "prev_close" in df.columns: df = df.with_columns( (pl.col("last_price") - pl.col("prev_close")).alias("change_amount") ) # change_pct / amplitude: 小数 → 百分比 (统一指数展示口径) for col in ("change_pct", "amplitude"): if col in df.columns: df = df.with_columns((pl.col(col).cast(pl.Float64) * 100).alias(col)) if "last_price" in df.columns and "close" not in df.columns: df = df.with_columns(pl.col("last_price").alias("close")) return df @staticmethod def _market_phase() -> str: """A股行情轮询阶段(北京时间)。 final 阶段用于午休/收盘定版: 需要至少成功拉取一版边界后的行情, 才算进入休盘。 """ now = cn_now() if now.weekday() >= 5: return "closed" t = now.time() if dt_time(9, 15) <= t < dt_time(9, 30): return "preopen" if dt_time(9, 30) <= t < dt_time(11, 30): return "morning" if dt_time(11, 30) <= t < dt_time(12, 55): return "morning_final" if dt_time(12, 55) <= t < dt_time(13, 0): return "pre_afternoon" if dt_time(13, 0) <= t < dt_time(15, 0): return "afternoon" if t >= dt_time(15, 0): return "close_final" return "closed" @staticmethod def _final_sync_key(phase: str) -> tuple[date, str] | None: if phase == "morning_final": return (cn_today(), "morning") if phase == "close_final": return (cn_today(), "close") return None @classmethod def _final_boundary_ms(cls, phase: str) -> int | None: """final 阶段定版边界的 epoch ms (按北京时间当日换算, 不依赖服务器时区)。""" b = _FINAL_BOUNDARY.get(phase) if b is None: return None return int(datetime.combine(cn_today(), b, tzinfo=CN_TZ).timestamp() * 1000) @classmethod def _past_final_deadline(cls, phase: str) -> bool: """是否已过 final 重试窗口终点 (用于放弃未确认的定版重试)。""" dl = _FINAL_DEADLINE.get(phase) return dl is not None and cn_now().time() >= dl def _holiday_gate(self) -> bool: """交易日探针门控: 确定休市 → False (停止轮询, 含 final 定版)。 探针未知 (None, 未配置 fuyao 且 tickflow 不可用/开盘缓冲窗内) → True, 维持周几近似现状行为。探针是纯读, 不落盘; 休市结论带 TTL 定期复探, 误判自愈。首次判定变化打一条日志, 避免每拍刷屏。 """ from app.services import trading_day holiday = trading_day.is_trading_day() is False if holiday != self._holiday_active: self._holiday_active = holiday if holiday: logger.info("交易日探针判定休市, 行情轮询暂停 (30 分钟复探)") return not holiday def _should_poll_for_phase(self, phase: str) -> bool: """是否处于会主动拉行情的阶段。final 阶段成功后即停止。 节假日 (工作日但休市) 由交易日探针剔除 — 周几门控覆盖不到的部分。 """ if not self._holiday_gate(): return False if phase in {"preopen", "morning", "pre_afternoon", "afternoon"}: return True key = self._final_sync_key(phase) return bool(key and key not in self._final_sync_done) def _should_fetch_for_phase(self, phase: str) -> bool: return self._should_poll_for_phase(phase) def _is_trading_hours(self) -> bool: """行情轮询窗口(兼容旧调用): 包含盘前预热和未完成的午休/收盘定版。""" return self._should_poll_for_phase(self._market_phase()) @staticmethod def _is_continuous_trading() -> bool: """A股连续竞价时段(北京时间): 9:30-11:30 / 13:00-15:00, 仅工作日。 比 _is_trading_hours 严格: 排除 9:15-9:30 集合竞价(指示价, 非成交价)、 午间与 15:00 后收盘缓冲。监控评估只在此窗口进行, 不对竞价/收盘后的陈旧价告警。 (节假日由 _evaluate_monitors 里的「快照日期=当日」新鲜度判据兜底, 无需交易日历。) """ now = cn_now() t = now.time() morning = dt_time(9, 30) <= t <= dt_time(11, 30) afternoon = dt_time(13, 0) <= t <= dt_time(15, 0) return now.weekday() < 5 and (morning or afternoon) @staticmethod def _save_enabled(enabled: bool) -> None: from app.services import preferences preferences.save({"realtime_quotes_enabled": enabled}) # ================================================================ # 策略监控 # ================================================================ def _evaluate_monitors(self, daily_df: pl.DataFrame, quote_extra: pl.DataFrame | None) -> None: """行情更新后评估统一监控规则引擎,并刷新策略结果缓存。""" try: # 仅在「交易日 + 连续竞价时段」评估监控 —— 避开集合竞价指示价、盘前/收盘后 # 缓冲。轮询窗口(_is_trading_hours)更宽是为盘前预热/收盘捕捉, 但告警不应 # 基于这些非连续竞价价格。 if not self._is_continuous_trading(): return # 获取 enriched 数据 (刚算好的) enriched_today, enriched_date = self.get_enriched_today() # 股票快照就绪 = 非空 + 日期为当日。未就绪时仅跳过股票轮, # ETF/指数轮有各自的空表+日期守卫, 不受影响 (纯指数行情/自选场景可独立评估)。 stock_ready = (not enriched_today.is_empty()) and (enriched_date == cn_today()) if not stock_ready: logger.debug("股票快照未就绪(空=%s, 日期=%s), 跳过股票轮", enriched_today.is_empty(), enriched_date) all_alerts: list[dict] = [] rule_events: list[dict] = [] engine = None # 通用监控规则评估 (统一引擎: signal/price/market/strategy) if self._app_state: engine = getattr(self._app_state, "monitor_engine", None) if engine and engine.rule_count > 0: # 预构建 symbol → name 映射 (enriched 已 drop name 列, 引擎触发时回填用)。 # 股票 + ETF + 指数三表合并走 _monitor_name_map -> repo.get_name_map() # 的进程内 memo, 避免每轮监控对 ~7000 行维表 iter_rows 重建。 try: name_map = _monitor_name_map(self._app_state.repo) if name_map: engine.set_name_map(name_map) except Exception as e: # noqa: BLE001 logger.debug("name_map 构建失败 (不影响监控): %s", e) # 股票轮: 快照未就绪时跳过 (ladder 封单也依赖股票快照日期, 一并跳过) if stock_ready: eval_df = enriched_today if engine.has_rule_type("ladder"): eval_df = self._inject_sealed_vol(enriched_today, enriched_date) if engine.has_rule_type("volume_delta"): eval_df = self._inject_volume_delta(eval_df) eval_df = self._inject_intraday_signals(eval_df, engine, "stock") rule_events = engine.evaluate(eval_df, asset_type="stock") if engine.consume_strategy_result_updates(): self.notify_strategy_results_updated() if engine.has_rule_type("sector"): rule_events += engine.evaluate_sectors( enriched_today if stock_ready else pl.DataFrame(), self.get_index_quotes(), ) # 异动边缘规则轮: 快照 (enriched 偏离列 + 实时叠加) 由 # abnormal_moves.build_overview 统一构建, 引擎只做边缘触发判定。 # 30s 限频 —— 快照历史部分 60s 缓存, 无需跟行情轮询同频重算。 if engine.has_rule_type("abnormal") and self._repo is not None: _now_ts = time.time() if _now_ts - self._abnormal_last_eval >= 30.0: self._abnormal_last_eval = _now_ts try: from app.services import abnormal_moves _overview = abnormal_moves.build_overview( self._repo, self, min_closeness=engine.min_abnormal_closeness(), limit=1000, ) rule_events += engine.evaluate_abnormal(_overview.get("rows") or []) except Exception as e: # noqa: BLE001 logger.warning("异动监控规则评估失败 (不影响其他告警): %s", e) # 日期提醒轮: 纯日历、无行情, 已在盘中; 引擎内按天 cooldown 保证每天一次 if engine.has_rule_type("date"): try: rule_events = rule_events + engine.evaluate_date_rules() except Exception as e: # noqa: BLE001 logger.warning("日期提醒评估失败 (不影响其他告警): %s", e) # ETF 规则轮: 股票快照不含 ETF, 用 ETF enriched 快照单独评估。 # 独立 try —— ETF 轮任何异常都不得丢弃本轮已算出的股票告警。 # refresh=False —— 不在轮询线程上触发 ETF 冷缓存的同步重算 (缓存由 ETF 实时 # flush 焐热; 未焐热说明无 ETF 实时数据, 跳过本轮 ETF 评估)。 if engine.has_asset_rules("etf") and self._repo is not None: try: etf_enriched, _ = self._repo.get_enriched_latest_asset("etf", refresh=False) if not etf_enriched.is_empty(): etf_enriched = self._inject_intraday_signals(etf_enriched, engine, "etf") rule_events = rule_events + engine.evaluate( etf_enriched, asset_type="etf", reset_strategy_results=False, ) except Exception as e: # noqa: BLE001 logger.warning("ETF 监控评估失败 (不影响股票告警): %s", e) # 指数规则轮: 复刻 ETF 轮。快照由指数实时 flush 焐热; # refresh=False 冷缓存不同步重算; 显式日期守卫防陈旧 parquet 误告警 # (ETF 轮靠空表隐式跳过, 指数轮更显式, 行为等价)。 if engine.has_asset_rules("index") and self._repo is not None: try: index_enriched, index_date = self._repo.get_enriched_latest_asset("index", refresh=False) if not index_enriched.is_empty() and index_date == cn_today(): index_enriched = self._inject_intraday_signals(index_enriched, engine, "index") rule_events = rule_events + engine.evaluate( index_enriched, asset_type="index", reset_strategy_results=False, ) except Exception as e: # noqa: BLE001 logger.warning("指数监控评估失败 (不影响股票/ETF 告警): %s", e) if rule_events: rule_events = self._format_extension_notifications(rule_events) # 落盘到 alerts.jsonl try: from app.services import alert_store alert_store.append_many( self._app_state.repo.store.data_dir, rule_events, ) except Exception as e: # noqa: BLE001 logger.warning("告警落盘失败: %s", e) # 转为 SSE 推送格式 (兼容旧 alert schema) for ev in rule_events: alert = { "source": ev["source"], "type": ev["type"], "rule_id": ev.get("rule_id"), "strategy_id": ev.get("strategy_id") if ev["source"] == "strategy" else None, "symbol": ev["symbol"], "name": ev["name"], "message": ev["message"], "price": ev["price"], "change_pct": ev["change_pct"], "signals": ev["signals"], "severity": ev.get("severity", "info"), "conditions": ev.get("conditions") or [], "logic": ev.get("logic") or "and", } for key in ( "sector_kind", "sector_key", "sector_name", "sector_source_field", "sector_value", "sector_level", "window_change_pct", "coverage_ratio", "valid_count", "total_count", "up_count", "down_count", "leader", "abnormal_window", "abnormal_value", "abnormal_threshold", "abnormal_closeness", "volume_delta", "volume_delta_span", "volume_delta_amount", ): if key in ev: alert[key] = ev[key] all_alerts.append(alert) # 策略页实时回显: 不写文件 (实时行情每轮更新 enriched, 写文件会被 read_cache # 的 mtime 校验判过期, 反复读不到)。监控引擎本轮已算出的结果存在内存 # (latest_strategy_results), 由 /api/screener/cached 端点直接叠加读取。 # 广播到所有 SSE 订阅者 (背压保护在订阅者队列内做) if all_alerts: # 按 symbol 富化行业/概念 ext 字段, 使 toast + 触发记录统一展示板块标签。 self._enrich_alerts_ext(all_alerts) self._broadcast_alerts(all_alerts) logger.info("监控评估完成: %d 条通知", len(all_alerts)) # 系统通知 (可选通道, 由 preferences 开关控制)。 # cooldown 去重已在 MonitorRuleEngine 做过, 这里只负责转发。 self._maybe_send_system_notifications(all_alerts) # Webhook 推送 (飞书等外部 IM, 由规则 webhook_channels 指定渠道)。 # 紧随系统通知, 同样静默降级不阻断主流程。 if rule_events: self._maybe_send_webhook(rule_events, engine) except Exception as e: # noqa: BLE001 logger.warning("监控评估失败: %s", e) def _format_extension_notifications(self, events: list[dict]) -> list[dict]: """Apply optional copy formatters after evaluation and before every output channel.""" registry = ( getattr(self._app_state, "extension_registry", None) if self._app_state is not None else None ) if registry is None or not registry.has_notification_formatters: return events from app.extensions.contracts import ( BACKEND_EXTENSION_API_VERSION, NotificationFormatContext, ) formatted_events: list[dict] = [] for event in events: formatted = dict(event) context = NotificationFormatContext( api_version=BACKEND_EXTENSION_API_VERSION, ) for registered in registry.notification_formatters(): try: message = registered.implementation.format_message(dict(formatted), context) if not isinstance(message, str): raise TypeError("notification formatter must return str") formatted["message"] = message except Exception as exc: logger.warning( "notification formatter failed %s: %s", registered.implementation_id, exc, ) formatted_events.append(formatted) return formatted_events def _enrich_alerts_ext(self, alerts: list[dict]) -> None: """就地给告警事件按 symbol 追加行业/概念 ext 字段。 读 preferences.get_monitor_ext_fields() 取字段配置, 用 screener._load_ext_value_maps (带 parquet mtime 缓存) 富化。富化失败静默降级 (告警照常推送, 只是没标签)。 每条事件新增 {configId}__{fieldName} 键 (与 watchlist/screener 输出约定一致)。 """ if not alerts or not self._app_state or self._repo is None: return try: from app.services import preferences fields = preferences.get_monitor_ext_fields() # 新结构 {field, maxTags, hiddenIndices}, 后端只需 .field parts = [] for key in ("concept", "industry"): item = fields.get(key) if isinstance(item, dict) and item.get("field"): parts.append(item["field"]) elif isinstance(item, str) and item: parts.append(item) # 兼容旧格式 if not parts: return ext_columns = ",".join(parts) from app.api.screener import _load_ext_value_maps value_maps = _load_ext_value_maps(self._repo, ext_columns) if not value_maps: return for ev in alerts: sym = ev.get("symbol") if not sym: continue for out_col, vmap in value_maps.items(): ev[out_col] = vmap.get(str(sym)) except Exception as e: # noqa: BLE001 logger.debug("告警 ext 富化失败 (不影响推送): %s", e) def _inject_intraday_signals(self, enriched: pl.DataFrame, engine, asset_type: str) -> pl.DataFrame: """每分钟为分时信号规则批量获取一次数据并注入临时布尔列。""" get_symbols = getattr(engine, "intraday_signal_symbols", None) if not callable(get_symbols): return enriched symbols = get_symbols(asset_type) if not symbols: return enriched now = cn_now() bucket = now.strftime("%Y%m%d%H%M") if self._intraday_signal_bucket.get(asset_type) == bucket: return self._intraday_signal_evaluator.inject(enriched, []) self._intraday_signal_bucket[asset_type] = bucket from app.services.kline_sync import ( fetch_intraday_monitor_batch, intraday_monitor_support, ) capset = getattr(self._app_state, "capabilities", None) # 全量分钟健康时股票读本地分区 (服务按间隔持续落盘, 与 API 同一列契约), # 免去每分钟 bucket 一次的全量 API 拉取; ETF 不在服务 universe 内, # 本地读空/异常回落原 API 路径 (含能力与上限检查) minute_df = pl.DataFrame() if asset_type == "stock": svc = getattr(self._app_state, "minute_refresh", None) if self._app_state else None if svc is not None and svc.is_healthy() and self._repo is not None: try: minute_df = self._repo.get_minute_batch(sorted(symbols), cn_today()) except Exception as e: # 本地读异常回落 API logger.warning("分时信号本地读失败, 回退 API 路径: %s", e) minute_df = pl.DataFrame() if minute_df.is_empty(): support = intraday_monitor_support(capset) if not support["available"] or len(symbols) > int(support["max_symbols"]): return self._intraday_signal_evaluator.inject(enriched, []) minute_df = fetch_intraday_monitor_batch(sorted(symbols), capset, now=now) prev_close: dict[str, float] = {} available_cols = set(enriched.columns) for row in enriched.filter(pl.col("symbol").is_in(sorted(symbols))).iter_rows(named=True): symbol = str(row.get("symbol") or "") reference = row.get("prev_close") if "prev_close" in available_cols else None if reference is None and "close" in available_cols and "change_pct" in available_cols: close = row.get("close") change_pct = row.get("change_pct") if close is not None and change_pct is not None and float(change_pct) > -1: reference = float(close) / (1.0 + float(change_pct)) if symbol and reference is not None: prev_close[symbol] = float(reference) signals = self._intraday_signal_evaluator.evaluate( minute_df, symbols=symbols, prev_close=prev_close, asset_type=asset_type, now=now, ) return self._intraday_signal_evaluator.inject(enriched, signals) @staticmethod def _continuous_session_start_ms() -> float: """当前连续竞价时段的起点 (北京时间 9:30 或 13:00) 的 epoch 毫秒。""" now = cn_now() start_time = dt_time(13, 0) if now.time() >= dt_time(13, 0) else dt_time(9, 30) return datetime.combine(now.date(), start_time, tzinfo=now.tzinfo).timestamp() * 1000.0 def _update_volume_delta(self, stock_records: list[dict], fetched_at_ms: float) -> None: """全市场相邻两次快照的股票累计成交量差值 (手), 供 volume_delta 规则。 - prev 每轮都更新 (含非连续竞价时段); 差值只在连续竞价时段内计算 - 开盘保护: prev 早于本时段起点 (9:30/13:00) 时本轮差值无效 -- 避免 9:25 集合竞价撮合量 / 午休缺口被当成"突然放量" - cur < prev (数据源重置/口径跳变) 的个股丢弃差值; 跨交易日清空 """ today = cn_today() if self._prev_volume_date != today: self._prev_stock_volume = None self._prev_volume_fetched_at = None self._prev_volume_date = today self._volume_delta = {} cur: dict[str, tuple[float, float]] = {} for r in stock_records: sym = r.get("symbol") vol = r.get("volume") amt = r.get("amount") if not sym or not isinstance(vol, (int, float)): continue cur[str(sym)] = ( float(vol), float(amt) if isinstance(amt, (int, float)) else 0.0, ) prev = self._prev_stock_volume prev_ts = self._prev_volume_fetched_at if ( prev is not None and prev_ts is not None and self._is_continuous_trading() and prev_ts >= self._continuous_session_start_ms() ): delta = { sym: (v - prev[sym][0], a - prev[sym][1]) for sym, (v, a) in cur.items() if sym in prev and v >= prev[sym][0] and a >= prev[sym][1] and v - prev[sym][0] > 0 } self._volume_delta = delta self._volume_delta_span_s = max((fetched_at_ms - prev_ts) / 1000.0, 0.001) else: self._volume_delta = {} self._prev_stock_volume = cur self._prev_volume_fetched_at = fetched_at_ms def _inject_volume_delta(self, enriched_today: pl.DataFrame) -> pl.DataFrame: """把最近一轮快照差值作为临时列注入 enriched 副本。 _volume_delta (手) / _volume_delta_amount (元) / _volume_delta_span (秒, 快照间隔)。 无有效差值 (首轮/开盘保护/暂停后恢复) 时返回原 df, 规则安全降级不触发。 """ try: delta = self._volume_delta if not delta: return enriched_today span = self._volume_delta_span_s delta_df = pl.DataFrame({ "symbol": list(delta.keys()), "_volume_delta": [v for v, _ in delta.values()], "_volume_delta_amount": [a for _, a in delta.values()], "_volume_delta_span": [span] * len(delta), }) drop_cols = [ c for c in ("_volume_delta", "_volume_delta_amount", "_volume_delta_span") if c in enriched_today.columns ] df = enriched_today.drop(drop_cols) if drop_cols else enriched_today return df.join(delta_df, on="symbol", how="left") except Exception as e: # noqa: BLE001 logger.debug("快照差值注入失败 (volume_delta 规则将不触发): %s", e) return enriched_today def _inject_sealed_vol(self, enriched_today: pl.DataFrame, enriched_date) -> pl.DataFrame: """从 depth_service 取封单量, 作为临时列 _sealed_vol 注入 enriched 副本。 涨停封单(买一量) + 跌停封单(卖一量)合并, 供 ladder 规则评估。 depth 未就绪时返回原 df (不注入, ladder 规则安全降级不触发)。 """ try: depth_svc = getattr(self._app_state, "depth_service", None) if not depth_svc: return enriched_today # enriched_date 可能是 date 或字符串, 统一为 date from datetime import date as date_cls target_date = enriched_date if isinstance(enriched_date, date_cls) else date_cls.fromisoformat(str(enriched_date)) # 取涨停 + 跌停封单, 合并 {symbol: vol} up_map = depth_svc.get_sealed_map(target_date, is_down=False) down_map = depth_svc.get_sealed_map(target_date, is_down=True) sealed: dict[str, int] = {} for m in (up_map, down_map): for sym, info in m.items(): vol = (info or {}).get("vol") if vol and vol > 0: sealed[sym] = vol # 后者覆盖前者 (同 symbol 不可能在涨跌停都封单) if not sealed: return enriched_today # 构造 (symbol, _sealed_vol) DataFrame, join 到 enriched 副本 sealed_df = pl.DataFrame({ "symbol": list(sealed.keys()), "_sealed_vol": list(sealed.values()), }) # 若已有残留列先移除 (避免重复 join 报错) df = enriched_today.drop("_sealed_vol") if "_sealed_vol" in enriched_today.columns else enriched_today return df.join(sealed_df, on="symbol", how="left") except Exception as e: # noqa: BLE001 logger.debug("封单注入失败 (ladder 规则将不触发): %s", e) return enriched_today def _maybe_send_webhook(self, rule_events: list[dict], engine) -> None: """把告警通过 Webhook 推送到外部 IM (由规则 webhook_channels 指定渠道)。 - 飞书 / 企业微信任一已配置即生效 (两个都没配才跳过) - 仅推送 webhook_channels 非空的规则触发的告警, 且只投递被勾选的渠道 - 失败静默, 不阻断主流程 - 去重: 复用 MonitorRuleEngine 的 cooldown, 此处不重复去重 注意: 用 rule_events (含 rule_id) 而非重建后的 all_alerts, 以便反查引擎规则判断是否启用推送。 """ try: from app.services import preferences from app.services import webhook_adapter feishu_url = preferences.get_feishu_webhook_url() feishu_secret = preferences.get_feishu_webhook_secret() wecom_url = preferences.get_wecom_webhook_url() # 两个通道都没配置才跳过 if not feishu_url and not wecom_url: return # 反查规则, 过滤出启用推送的事件 rules = engine.rules if engine is not None else {} enqueued = 0 for ev in rule_events: rule = rules.get(ev.get("rule_id")) # webhook_channels 指定命中的渠道 (['feishu'] / ['wecom'] / ['feishu','wecom'] / []). # 空列表 = 该规则不推送。仅推送「渠道已选 + 对应地址已配置」的组合。 channels = rule.get("webhook_channels") if rule else None if not channels: continue source = ev.get("source", "") source_label = SOURCE_LABELS.get(source, source or "通知") symbol = ev.get("symbol") or "" name = ev.get("name") or "" message = ev.get("message") or "" title = source_label body = f"{symbol} {name} {message}".strip() if symbol else (message or name) # 补上触发时的现价/涨跌幅, 让推送可执行 (止损到底触发在哪个价位) body = _body_with_quote(body, ev) # 提交到独立线程池, 不阻塞行情轮询线程 (webhook 慢/重试不拖累实时行情+告警)。 # 按渠道独立投递: 飞书 / 企业微信谁被勾选且已配置就推谁。 # 应用内 alerts.jsonl 记录与 SSE 已在前面完成, 不依赖 webhook 成败, # 失败由 webhook_adapter 记 WARNING(可见)。 if feishu_url and "feishu" in channels: _WEBHOOK_EXECUTOR.submit(webhook_adapter.send_feishu, feishu_url, title, body, feishu_secret) enqueued += 1 if wecom_url and "wecom" in channels: _WEBHOOK_EXECUTOR.submit(webhook_adapter.send_wecom, wecom_url, title, body) enqueued += 1 if enqueued: logger.info("Webhook 已提交 %d 条 (异步投递, 按渠道独立投递, 失败记 WARNING)", enqueued) except Exception as e: # noqa: BLE001 logger.warning("Webhook 提交异常 (不影响告警主流程): %s", e) def _maybe_send_system_notifications(self, all_alerts: list[dict]) -> None: """把告警转发到操作系统通知中心 (由 preferences 开关控制)。 - 开关关闭: 直接返回 - 开关开启: 逐条发系统通知; 失败静默, 不阻断主流程 - 去重: 复用 MonitorRuleEngine 的 cooldown, 此处不重复去重 - 批量策略事件 (symbol="") 聚合为一条通知, 避免刷屏 """ try: from app.services import preferences from app.services import notify_adapter if not preferences.get_system_notify_enabled(): return for ev in all_alerts: # 通知标题: 用 source 分类 (策略/信号/价格/异动) source = ev.get("source", "") source_label = SOURCE_LABELS.get(source, source or "通知") name = ev.get("name") or "" symbol = ev.get("symbol") or "" message = ev.get("message") or "" # 正文: 优先用现成 message, 拼上 symbol/name 让用户一眼定位 if symbol: body = f"{symbol} {name} {message}".strip() else: body = message or name # 补上触发时的现价/涨跌幅 (日期提醒无行情, 自然为空) body = _body_with_quote(body, ev) title = f"TickFlow · {source_label}" notify_adapter.notify(title, body) except Exception as e: # noqa: BLE001 logger.debug("系统通知发送异常 (不影响告警主流程): %s", e) @staticmethod def _get_strategy_monitor(): """获取 StrategyMonitorService — 不再使用, 改用 _app_state 注入。""" return None # ================================================================ # enriched 增量计算 # ================================================================ def _flush_live_enriched(self, daily_df: pl.DataFrame, quote_extra: pl.DataFrame = None, asset_type: str = "stock", merge: bool = False) -> None: """增量计算今天的 enriched: 用昨天的递推状态 + 今天 OHLCV → 只算今天 5500 行。 quote_extra: API 直接提供的补充字段 (prev_close, change_pct 等), 不写 daily, 直接传给 compute_enriched_today 避免重复计算。 """ try: today = cn_today() t0 = time.perf_counter() # ---- 尝试增量路径 ---- live_agg = self._repo.get_live_agg() if asset_type == "stock" else pl.DataFrame() prev_enriched, prev_date = ( self._repo.get_enriched_latest() if asset_type == "stock" else self._repo.get_enriched_latest_asset(asset_type) ) use_incremental = ( asset_type == "stock" and not live_agg.is_empty() and not prev_enriched.is_empty() and prev_date is not None ) if use_incremental: from app.indicators.pipeline import compute_enriched_today from app.market_time import trading_minutes_elapsed_from_ts, trading_minutes_elapsed instruments = self._repo.get_instruments() # 将 API 直接提供的补充字段 JOIN 到 daily_df today_ohlcv = daily_df if quote_extra is not None and not quote_extra.is_empty(): today_ohlcv = daily_df.join(quote_extra, on="symbol", how="left") # 量比时间折算: 优先用行情 quote_ts (真实成交时间), 缺失则兜底服务端时间 elapsed_minutes: float | None = None if "quote_ts" in daily_df.columns and not daily_df.is_empty(): valid_ts = daily_df["quote_ts"].drop_nulls() if not valid_ts.is_empty(): elapsed_minutes = trading_minutes_elapsed_from_ts(valid_ts.median()) if elapsed_minutes is None: elapsed_minutes = trading_minutes_elapsed() enriched_today = compute_enriched_today( live_agg=live_agg, prev_enriched=prev_enriched, today_ohlcv=today_ohlcv, instruments=instruments, elapsed_minutes=elapsed_minutes, ) if enriched_today.is_empty(): logger.warning("增量计算结果为空, 回退到全量计算") use_incremental = False # ---- 全量回退路径 ---- if not use_incremental: from datetime import timedelta from app.indicators.pipeline import compute_enriched logger.info("enriched 全量计算 (live_agg=%s, 上次日期=%s)", "ok" if not live_agg.is_empty() else "空", prev_date) cutoff = today - timedelta(days=90) table = {"etf": "kline_etf_daily", "index": "kline_index_daily"}.get(asset_type, "kline_daily") daily_glob = str(self._repo.store.data_dir / table / "**" / "*.parquet") ohlcv_cols = ["symbol", "date", "open", "high", "low", "close", "volume", "amount", "quote_ts"] hist_df = ( scan_daily_parquet(daily_glob) .filter(pl.col("date") >= cutoff) .sort(["symbol", "date"]) .collect() ) if hist_df.is_empty(): return hist_cols = [c for c in ohlcv_cols if c in hist_df.columns] hist_df = hist_df.select(hist_cols).filter(pl.col("date") != today) daily_ohlcv = daily_df.select([c for c in ohlcv_cols if c in daily_df.columns]) full_df = pl.concat([hist_df, daily_ohlcv], how="diagonal_relaxed") full_df = full_df.sort(["symbol", "date"]) factor_dir = {"stock": "adj_factor", "etf": "adj_factor_etf"}.get(asset_type) factor_path = self._repo.store.data_dir / factor_dir / "all.parquet" if factor_dir else None factors = pl.DataFrame() if factor_path and factor_path.exists(): try: factors = pl.read_parquet(factor_path) except Exception: pass instruments = self._repo.get_instruments() if asset_type == "stock" else None enriched_full = compute_enriched( full_df, factors=factors, instruments=instruments, historical_shares=( self._repo.get_historical_shares() if asset_type == "stock" else None ), ) # momentum_3d 不在指标全集里, 但 deviate_3d 需要; 多日帧上 shift 补算 enriched_full = enriched_full.sort(["symbol", "date"]).with_columns( (pl.col("close") / pl.col("close").shift(3).over("symbol") - 1).alias("momentum_3d") ) enriched_today = enriched_full.filter(pl.col("date") == today) if enriched_today.is_empty(): return # 异动偏离列: 盘中路径不经过 _refresh_enriched 冷刷新, # 需在此附着 (基准 = 历史帧 + 指数实时外推), 否则盘中异动列表为空 if asset_type == "stock": from app.indicators.pipeline import attach_deviation_columns_today try: index_quotes = self.get_index_quotes() except Exception: index_quotes = None enriched_today = attach_deviation_columns_today( enriched_today, self._repo.store.data_dir, index_quotes ) # ---- 写盘 + 更新缓存 ---- if merge: self._repo.merge_live_enriched_asset(asset_type, enriched_today) else: self._repo.flush_live_enriched_asset(asset_type, enriched_today) elapsed = time.perf_counter() - t0 mode_label = "增量" if use_incremental else "全量" logger.info("enriched %s: %d 只, %s, 耗时 %.0fms", mode_label, len(enriched_today), today, elapsed * 1000) except Exception as e: # noqa: BLE001 logger.warning("enriched 计算失败: %s", e)