858 lines
35 KiB
Python
858 lines
35 KiB
Python
"""策略实时监控 — 订阅行情更新，检查策略买卖信号和提醒条件。
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职责: 接收实时行情 DataFrame → 检查监控中策略的信号/提醒 → 推送告警。
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不知道: 策略加载逻辑、AI、API、配置持久化、回测。
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依赖: 外部调用 on_quote_update() 传入实时数据。
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本模块含两个评估器:
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1. StrategyMonitorService — 旧的策略监控 (type=strategy),第二步迁移到 MonitorRuleEngine
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2. MonitorRuleEngine — 通用规则引擎,覆盖 signal/price/market/strategy 四类,
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支持 scope (symbols/all/sector) + 多条件 AND/OR + cooldown 去重
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"""
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from __future__ import annotations
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import datetime as _dt
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import logging
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import time
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from dataclasses import dataclass, field
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from typing import Any, Callable
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import polars as pl
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from app.strategy.custom_signals import _OP_BUILDERS # type: ignore # 复用运算符构造器
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from app.strategy import config as _strategy_config
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logger = logging.getLogger(__name__)
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# 信号 / 字段中文名映射 — 与前端 lib/signals.ts 对齐, 用于告警 message / 推送文案。
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# signal_* 为内置原子信号, 其余为技术指标/行情字段。
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_SIGNAL_CN: dict[str, str] = {
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# 内置信号
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"signal_ma_golden_5_20": "MA5上穿MA20", "signal_ma_dead_5_20": "MA5下穿MA20",
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"signal_ma_golden_20_60": "MA20上穿MA60", "signal_macd_golden": "MACD金叉",
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"signal_macd_dead": "MACD死叉", "signal_ma20_breakout": "突破MA20",
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"signal_ma20_breakdown": "跌破MA20", "signal_n_day_high": "60日新高",
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"signal_n_day_low": "60日新低", "signal_boll_breakout_upper": "突破布林上轨",
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"signal_boll_breakdown_lower": "跌破布林下轨", "signal_volume_surge": "放量",
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"signal_limit_up": "涨停", "signal_limit_down": "跌停",
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"signal_limit_down_recovery": "跌停翘板", "signal_broken_limit_up": "炸板",
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# 行情字段
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"close": "收盘价", "open": "开盘价", "high": "最高价", "low": "最低价",
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"change_pct": "涨跌幅", "change_amount": "涨跌额", "amplitude": "振幅",
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"turnover_rate": "换手率", "volume": "成交量", "amount": "成交额",
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# 均线
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"ma5": "MA5", "ma10": "MA10", "ma20": "MA20", "ma30": "MA30", "ma60": "MA60",
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"ema5": "EMA5", "ema10": "EMA10", "ema20": "EMA20",
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# MACD / BOLL / KDJ / RSI
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"macd_dif": "MACD-DIF", "macd_dea": "MACD-DEA", "macd_hist": "MACD柱",
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"boll_upper": "布林上轨", "boll_lower": "布林下轨",
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"kdj_k": "KDJ-K", "kdj_d": "KDJ-D", "kdj_j": "KDJ-J",
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"rsi_6": "RSI6", "rsi_14": "RSI14", "rsi_24": "RSI24",
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# 量能 / 动量 / 波动
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"vol_ratio_5d": "5日量比", "vol_ratio_20d": "20日量比",
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"vol_ma5": "5日均量", "vol_ma10": "10日均量",
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"high_60d": "60日最高", "low_60d": "60日最低",
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"momentum_5d": "5日动量", "momentum_20d": "20日动量", "momentum_60d": "60日动量",
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"atr_14": "ATR14", "annual_vol_20d": "20日年化波动",
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"consecutive_limit_ups": "连板数", "consecutive_limit_downs": "跌停连板",
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}
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def _signal_cn_name(name: str) -> str:
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"""返回信号/字段的中文名, 找不到原样返回 (与前端 cnSignal 对齐)。"""
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return _SIGNAL_CN.get(name, name)
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@dataclass
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class StrategyAlert:
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"""策略告警"""
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type: str # "entry" | "exit" | "alert"
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strategy_id: str
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symbol: str
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name: str | None
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message: str
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price: float | None = None
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change_pct: float | None = None
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signals: list[str] = field(default_factory=list)
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class StrategyMonitorService:
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"""策略实时监控服务"""
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def __init__(self, alert_handler: Callable[[StrategyAlert], None] | None = None):
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"""
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Args:
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alert_handler: 告警回调 (如推 SSE)
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"""
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self._alert_handler = alert_handler
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# strategy_id → 监控配置
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self._watching: dict[str, dict] = {}
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def start(self, strategy_id: str, config: dict) -> None:
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"""开始监控一个策略
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config: {
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"entry_signals": ["signal_n_day_high", ...],
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"exit_signals": ["signal_ma20_breakdown", ...],
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"alerts": [{"field": "rsi_14", "op": ">", "value": 80, "message": "..."}],
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}
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"""
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self._watching[strategy_id] = config
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logger.info("strategy monitor started: %s", strategy_id)
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def stop(self, strategy_id: str) -> None:
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self._watching.pop(strategy_id, None)
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logger.info("strategy monitor stopped: %s", strategy_id)
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def stop_all(self) -> None:
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self._watching.clear()
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@property
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def watching(self) -> dict[str, dict]:
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return dict(self._watching)
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def on_quote_update(self, df: pl.DataFrame) -> list[StrategyAlert]:
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"""行情更新后调用。向量化检查所有监控策略。
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Args:
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df: 实时 enriched 数据 (~5500行)
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Returns:
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触发的告警列表
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"""
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if not self._watching or df.is_empty():
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return []
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all_alerts: list[StrategyAlert] = []
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for strategy_id, cfg in self._watching.items():
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# 买入信号
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entry_sigs = cfg.get("entry_signals", [])
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if entry_sigs:
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for sym, name, price, pct, hit_sigs in self._check_signals(df, entry_sigs):
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alert = StrategyAlert(
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type="entry",
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strategy_id=strategy_id,
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symbol=sym,
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name=name,
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message=f"买入信号触发",
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price=price,
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change_pct=pct,
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signals=hit_sigs,
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)
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all_alerts.append(alert)
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self._emit(alert)
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# 卖出信号
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exit_sigs = cfg.get("exit_signals", [])
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if exit_sigs:
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for sym, name, price, pct, hit_sigs in self._check_signals(df, exit_sigs):
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alert = StrategyAlert(
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type="exit",
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strategy_id=strategy_id,
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symbol=sym,
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name=name,
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message=f"卖出信号触发",
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price=price,
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change_pct=pct,
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signals=hit_sigs,
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)
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all_alerts.append(alert)
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self._emit(alert)
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# 提醒条件
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for alert_cfg in cfg.get("alerts", []):
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for sym, name, price, pct in self._check_alert(df, alert_cfg):
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alert = StrategyAlert(
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type="alert",
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strategy_id=strategy_id,
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symbol=sym,
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name=name,
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message=alert_cfg.get("message", "提醒"),
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price=price,
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change_pct=pct,
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)
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all_alerts.append(alert)
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self._emit(alert)
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return all_alerts
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def _emit(self, alert: StrategyAlert) -> None:
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if self._alert_handler:
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try:
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self._alert_handler(alert)
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except Exception as e:
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logger.warning("alert handler failed: %s", e)
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@staticmethod
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def _check_signals(
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df: pl.DataFrame,
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signals: list[str],
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) -> list[tuple[str, str | None, float | None, float | None, list[str]]]:
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"""检查信号列，返回 [(symbol, name, price, change_pct, [hit_signals])]。
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支持内置 signal_ 与自定义 csg_ 前缀。"""
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cols = set(df.columns)
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resolved: list[tuple[str, str]] = [] # (原值, 列名)
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for s in signals:
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col = s if (s.startswith("signal_") or s.startswith("csg_")) else f"signal_{s}"
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if col in cols:
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resolved.append((s, col))
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if not resolved:
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return []
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mask = pl.any_horizontal(pl.col(c).fill_null(False) for _, c in resolved)
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hit_df = df.filter(mask)
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results = []
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for row in hit_df.iter_rows(named=True):
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sym = row.get("symbol", "")
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name = row.get("name")
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price = row.get("close")
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pct = row.get("change_pct")
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hit_sigs = [orig for orig, col in resolved if row.get(col)]
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results.append((sym, name, price, pct, hit_sigs))
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return results
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@staticmethod
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def _check_alert(
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df: pl.DataFrame,
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alert: dict,
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) -> list[tuple[str, str | None, float | None, float | None]]:
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"""检查阈值型提醒条件"""
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field = alert.get("field", "")
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if field not in df.columns:
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return []
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if "op" in alert:
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# 阈值比较
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op = alert["op"]
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value = alert["value"]
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col = pl.col(field)
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ops = {
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">": col > value,
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">=": col >= value,
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"<": col < value,
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"<=": col <= value,
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}
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expr = ops.get(op)
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if expr is None:
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return []
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else:
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# 信号列 (布尔)
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expr = pl.col(field).fill_null(False)
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hit_df = df.filter(expr)
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results = []
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for row in hit_df.iter_rows(named=True):
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results.append((
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row.get("symbol", ""),
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row.get("name"),
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row.get("close"),
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row.get("change_pct"),
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))
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return results
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# ================================================================
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# 通用监控规则引擎 MonitorRuleEngine
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# ================================================================
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_SIGNAL_PREFIXES = ("signal_", "csg_")
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def _is_signal_field(field: str) -> bool:
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return any(field.startswith(p) for p in _SIGNAL_PREFIXES)
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def _build_condition_mask(df: pl.DataFrame, conditions: list[dict], logic: str) -> pl.DataFrame:
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"""根据 conditions + logic 构建过滤后的命中 DataFrame。
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conditions: [{"field","op","value"?}] — op=truth 为布尔信号, 否则阈值比较
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logic: "and" | "or"
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返回命中行 (含 symbol/name/close/change_pct + 各信号列)
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"""
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cols = set(df.columns)
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parts: list[pl.Expr] = []
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for c in conditions:
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field = c["field"]
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if field not in cols:
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return df.head(0) # 字段缺失,无法判定 → 空结果
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op = c["op"]
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if op == "truth":
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parts.append(pl.col(field).fill_null(False))
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elif op in _OP_BUILDERS:
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parts.append(_OP_BUILDERS[op](pl.col(field), c["value"]))
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else:
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return df.head(0)
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if not parts:
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return df.head(0)
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if logic == "or":
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mask = pl.any_horizontal(parts)
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else:
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mask = pl.all_horizontal(parts)
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return df.filter(mask)
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class MonitorRuleEngine:
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"""通用监控规则引擎 — 接收实时行情 DataFrame,评估所有规则,返回 AlertEvent。
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与 StrategyMonitorService 的区别:
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- 规则来自 monitor_rules 存储 (用户可配), 而非写死的 strategy config
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- 支持 scope (symbols/all/sector) 过滤作用域
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- 支持 conditions + logic (AND/OR) 任意组合
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- ★ cooldown 去重: 同一 (rule_id, symbol) 在冷却期内不重复触发
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"""
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def __init__(self, alert_handler: Callable[[dict], None] | None = None):
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self._alert_handler = alert_handler
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self._rules: dict[str, dict] = {} # rule_id → rule
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# (rule_id, symbol) → 上次触发时间戳(秒)。用于 cooldown 去重。
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self._last_fire: dict[tuple[str, str], float] = {}
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self._strategy_engine = None # 延迟注入, type=strategy 规则用它跑选股
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# symbol → 股票名 (enriched DataFrame 已 drop name 列, 触发时从此映射回填)
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self._name_map: dict[str, str] = {}
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# 策略选股池状态: strategy_id → 上期选股符号集合 (用于 diff 变更)
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self._strategy_pools: dict[str, set[str]] = {}
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# 数据目录 (用于加载策略 overrides)
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self._data_dir = None
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# 历史窗口加载器: (target_date, lookback_days) → 多日 enriched DataFrame。
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# 用于声明 filter_history 的策略 (如反包), 实时监控时拼历史窗口 + 今日行情跑选股。
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# 为 None 时, filter_history 策略仍会被跳过 (保持旧行为, 不破坏无历史场景)。
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self._history_loader: Callable[[_dt.date, int], "pl.DataFrame"] | None = None
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# 本轮 evaluate() 产出的策略选股结果: strategy_id → {rows, total, as_of}
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# 供策略页实时回显复用 (/api/screener/cached 端点直接读取此内存结果), 避免重跑
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self._latest_strategy_results: dict[str, dict] = {}
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def set_strategy_engine(self, engine) -> None:
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"""注入 StrategyEngine, type=strategy 规则据此跑选股。"""
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self._strategy_engine = engine
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def set_data_dir(self, data_dir) -> None:
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"""注入数据目录, 用于加载策略的用户覆盖配置。"""
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self._data_dir = data_dir
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def set_history_loader(self, fn) -> None:
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"""注入历史窗口加载器, 用于声明 filter_history 的策略跑实时监控。
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loader 签名: (target_date, lookback_days) → 多日 enriched DataFrame。
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复用 ScreenerService._load_enriched_history (三级缓存, 命中 ~0ms)。
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为 None 时 filter_history 策略退回到跳过逻辑 (不破坏无历史场景)。
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"""
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self._history_loader = fn
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def set_name_map(self, name_map: dict[str, str]) -> None:
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"""注入 symbol → 股票名 映射, 用于在告警事件里回填 name 字段。
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enriched DataFrame 在 pipeline 计算后不含 name 列 (见 indicators/pipeline.py),
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触发时从 instruments 表预构建此映射, 保证 AlertEvent.name 有值。
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"""
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self._name_map = name_map or {}
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# ── 规则管理 ───────────────────────────────────────
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def set_rules(self, rules: list[dict]) -> None:
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"""批量设置规则 (覆盖)。用于启动时 reload。"""
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self._rules = {}
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for r in rules:
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if r.get("enabled") is not False:
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self._rules[r["id"]] = r
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logger.info("MonitorRuleEngine: 装载 %d 条规则", len(self._rules))
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def add_rule(self, rule: dict) -> None:
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if rule.get("enabled") is not False:
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self._rules[rule["id"]] = rule
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else:
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self._rules.pop(rule["id"], None)
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def remove_rule(self, rule_id: str) -> None:
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self._rules.pop(rule_id, None)
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# 清理对应的 cooldown 记录
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self._last_fire = {k: v for k, v in self._last_fire.items() if k[0] != rule_id}
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def clear(self) -> None:
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self._rules.clear()
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self._last_fire.clear()
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@property
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def rules(self) -> dict[str, dict]:
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return dict(self._rules)
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@property
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def rule_count(self) -> int:
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return len(self._rules)
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def latest_strategy_results(self) -> dict[str, dict]:
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"""返回本轮 evaluate() 产出的策略选股结果 (strategy_id → {rows, total, as_of})。
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供策略页实时回显复用: /api/screener/cached 端点直接读取此内存结果,
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避免对被监控的策略重跑第二遍。无 type=strategy 规则时返回空 dict。
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"""
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return self._latest_strategy_results
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def has_rule_type(self, rtype: str) -> bool:
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"""是否存在指定类型的 (已启用) 规则。供 quote_service 判断是否需要注入特殊数据。"""
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if not self._rules:
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return False
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return any(
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r.get("enabled", True) and r.get("type") == rtype
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for r in self._rules.values()
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)
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# ── 评估 ───────────────────────────────────────────
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def evaluate(self, df: pl.DataFrame) -> list[dict]:
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"""行情更新后评估所有规则。
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Args:
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df: 实时 enriched 数据 (~5500行, 含 signal_/csg_/指标列)
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Returns:
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触发的 AlertEvent dict 列表 (含 ts/rule_id/source/type/symbol/...)
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"""
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if not self._rules or df.is_empty():
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return []
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now = time.time()
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events: list[dict] = []
|
||
# 每轮重置: 只保留本次 evaluate 产出的策略结果
|
||
self._latest_strategy_results = {}
|
||
|
||
for rule_id, rule in self._rules.items():
|
||
try:
|
||
events.extend(self._evaluate_rule(df, rule, now))
|
||
except Exception as e:
|
||
logger.warning("规则评估失败 %s: %s", rule_id, e)
|
||
|
||
return events
|
||
|
||
def _evaluate_rule(self, df: pl.DataFrame, rule: dict, now: float) -> list[dict]:
|
||
"""评估单条规则,返回触发的 events。"""
|
||
# 1. 按 scope 过滤作用域
|
||
scoped = self._apply_scope(df, rule)
|
||
if scoped.is_empty():
|
||
return []
|
||
|
||
# 2. 根据 type 构建命中集
|
||
# 元组格式: (event_type, symbol, name, price, pct, signals)
|
||
hit_rows: list[tuple[str, str, Any, Any, Any, list[str]]] = []
|
||
|
||
rtype = rule.get("type", "signal")
|
||
if rtype == "strategy":
|
||
# 策略类型: 跑策略选股 → 对比上期选股池 → 产出 new_entry/dropped 事件
|
||
hit_rows = self._match_strategy(scoped, rule)
|
||
elif rtype == "ladder":
|
||
# 连板梯队封单监控: 独立处理 (需带预警封单值, 走专属 message)
|
||
return self._evaluate_ladder(scoped, rule, now)
|
||
else:
|
||
# signal / price / market: 通用条件匹配
|
||
for sym, name, price, pct, hit_sigs in self._match_conditions(scoped, rule):
|
||
hit_rows.append((rtype, sym, name, price, pct, hit_sigs))
|
||
|
||
if not hit_rows:
|
||
return []
|
||
|
||
# 3. cooldown 去重 + 生成 events
|
||
cooldown = rule.get("cooldown_seconds", 3600)
|
||
severity = rule.get("severity", "info")
|
||
source = rtype
|
||
|
||
events: list[dict] = []
|
||
for ev_type, sym, name, price, pct, hit_sigs in hit_rows:
|
||
# cooldown 键: 批量事件用特殊键, 单只事件用 (rule_id, symbol)
|
||
is_batch = sym == "_batch"
|
||
if is_batch:
|
||
key = (rule["id"], f"_{ev_type}_batch")
|
||
else:
|
||
key = (rule["id"], sym)
|
||
last = self._last_fire.get(key)
|
||
if last is not None and (now - last) < cooldown:
|
||
continue # 冷却期内, 跳过
|
||
self._last_fire[key] = now
|
||
|
||
# 批量事件: name 存放预构建的消息文本
|
||
if is_batch:
|
||
resolved_name = ""
|
||
message = name # name 字段即批量消息
|
||
else:
|
||
resolved_name = name if name else self._name_map.get(sym)
|
||
message = rule.get("message", "") or self._default_message(
|
||
rule, ev_type=ev_type, sym=sym, name=resolved_name,
|
||
pct=pct, price=price,
|
||
conditions=list(rule.get("conditions", [])) if rule.get("type") != "strategy" else None,
|
||
)
|
||
|
||
ev = {
|
||
"ts": int(now * 1000),
|
||
"rule_id": rule["id"],
|
||
"rule_name": rule.get("name", ""),
|
||
"source": source,
|
||
"type": ev_type,
|
||
"symbol": "" if is_batch else sym,
|
||
"name": resolved_name,
|
||
"message": message,
|
||
"price": price,
|
||
"change_pct": pct,
|
||
"signals": hit_sigs,
|
||
"severity": severity,
|
||
# 触发条件快照 (signal/price/market 类型): 用于触发记录展示
|
||
# 「命中了什么条件」。strategy 类型靠策略选股池 diff, 不写条件。
|
||
"conditions": list(rule.get("conditions", [])) if rtype != "strategy" else [],
|
||
"logic": rule.get("logic", "and") if rtype != "strategy" else "and",
|
||
}
|
||
events.append(ev)
|
||
if self._alert_handler:
|
||
try:
|
||
self._alert_handler(ev)
|
||
except Exception as e:
|
||
logger.warning("alert handler failed: %s", e)
|
||
|
||
return events
|
||
|
||
@staticmethod
|
||
def _apply_scope(df: pl.DataFrame, rule: dict) -> pl.DataFrame:
|
||
"""按 scope 过滤 DataFrame。"""
|
||
scope = rule.get("scope", "symbols")
|
||
if scope == "all":
|
||
return df
|
||
if scope == "symbols":
|
||
syms = rule.get("symbols", [])
|
||
if not syms:
|
||
return df.head(0)
|
||
return df.filter(pl.col("symbol").is_in(syms))
|
||
if scope == "sector":
|
||
# sector 过滤: 需 df 含板块列 (后续接入 ext_data JOIN)
|
||
# 当前先返回全量, sector 精确过滤第二步完善
|
||
return df
|
||
return df
|
||
|
||
def _match_strategy(
|
||
self, df: pl.DataFrame, rule: dict,
|
||
) -> list[tuple[str, str, Any, Any, Any, list[str]]]:
|
||
"""策略类型评估: 跑策略选股 → 对比上期选股池 → 产出变更事件。
|
||
|
||
返回 [(event_type, symbol, name, price, pct, signals)]
|
||
event_type: "new_entry" (新入选) | "dropped" (已移出)
|
||
单只变更逐只返回; 同一策略 >5 只合并为一条批量事件 (symbol="_batch")
|
||
"""
|
||
if self._strategy_engine is None:
|
||
return []
|
||
sid = rule.get("strategy_id")
|
||
if not sid:
|
||
return []
|
||
try:
|
||
s = self._strategy_engine.get(sid)
|
||
except Exception:
|
||
return []
|
||
if s is None:
|
||
return []
|
||
|
||
# 运行策略选股: 复用当前 enriched DataFrame 跳过数据加载
|
||
overrides = {}
|
||
if self._data_dir:
|
||
try:
|
||
overrides = _strategy_config.load_override(self._data_dir, sid)
|
||
except Exception:
|
||
pass
|
||
|
||
# 声明 filter_history 的策略 (如反包) 需要多日历史窗口才能判定形态。
|
||
# 旧实现因"实时监控不支持 history loader"直接跳过 → 反包等策略盘中永不触发。
|
||
# 现接入 history_loader, 拼历史窗口 + 今日实时行情, 经 precomputed_history 喂给引擎。
|
||
# loader 为 None (未装配) 时退回跳过, 保持旧行为, 不破坏无历史场景。
|
||
run_kwargs: dict = {
|
||
"as_of": _dt.date.today(),
|
||
"overrides": overrides,
|
||
}
|
||
if s.filter_history_fn:
|
||
if self._history_loader is None:
|
||
logger.debug("策略 %s 需要历史数据但未注入 history_loader, 跳过实时监控", sid)
|
||
return []
|
||
try:
|
||
today = _dt.date.today()
|
||
lookback = max(1, getattr(s, "lookback_days", 30))
|
||
hist_df = self._history_loader(today, lookback)
|
||
if hist_df is None or hist_df.is_empty():
|
||
logger.debug("策略 %s 历史数据为空, 跳过本轮实时监控", sid)
|
||
return []
|
||
# 历史窗口可能与今日已落盘数据重叠: 排掉 hist_df 中 date==today 的行,
|
||
# 今日行情始终以实时 df 为准 (盘中逐轮更新, 最接近收盘真相)。
|
||
# 否则 today 行重复会污染 filter_history 的 .over("symbol") 窗口判定。
|
||
if "date" in hist_df.columns:
|
||
hist_df = hist_df.filter(pl.col("date") != today)
|
||
# 拼接历史窗口 + 今日实时行情 (filter_history 用 .over("symbol") 窗口, 多日天然可用)
|
||
run_kwargs["precomputed_history"] = pl.concat(
|
||
[hist_df, df], how="diagonal_relaxed"
|
||
)
|
||
except Exception as e:
|
||
logger.warning("策略 %s 加载历史窗口失败, 跳过: %s", sid, e)
|
||
return []
|
||
else:
|
||
# 普通策略: 复用当前 enriched DataFrame 跳过数据加载
|
||
run_kwargs["precomputed"] = df
|
||
|
||
try:
|
||
result = self._strategy_engine.run(sid, **run_kwargs)
|
||
except Exception as e:
|
||
logger.warning("策略 %s 选股执行失败: %s", sid, e)
|
||
return []
|
||
|
||
# 记录本轮完整选股结果 (供策略页实时回显: /cached 端点直接读取, 不落盘)。
|
||
# 与下面的 diff 事件无关 — 无论是否产生 new_entry/dropped, 结果都该可用于回显。
|
||
try:
|
||
import math
|
||
self._latest_strategy_results[sid] = {
|
||
"total": result.total,
|
||
"as_of": str(_dt.date.today()),
|
||
"rows": [
|
||
{k: (None if isinstance(v, float) and not math.isfinite(v) else v)
|
||
for k, v in row.items()}
|
||
for row in result.rows
|
||
],
|
||
}
|
||
except Exception: # noqa: BLE001
|
||
pass
|
||
|
||
current_pool: set[str] = {r["symbol"] for r in result.rows}
|
||
prev_pool = self._strategy_pools.get(sid)
|
||
|
||
# 首次运行: 仅记录当前选股池, 不产生事件
|
||
if prev_pool is None:
|
||
self._strategy_pools[sid] = current_pool
|
||
return []
|
||
|
||
new_entries = current_pool - prev_pool
|
||
dropped = prev_pool - current_pool
|
||
|
||
# 无变更
|
||
if not new_entries and not dropped:
|
||
return []
|
||
|
||
# 更新存储
|
||
self._strategy_pools[sid] = current_pool
|
||
|
||
sname = s.meta.get("name", "") or s.meta.get("id", sid)
|
||
|
||
# 构建查找表 (新入选股票可在 result.rows 中找到; 移出股票需从 df 找)
|
||
row_map: dict[str, dict] = {r["symbol"]: r for r in result.rows}
|
||
dropped_map: dict[str, dict] = {}
|
||
if dropped:
|
||
try:
|
||
_dd = df.filter(pl.col("symbol").is_in(list(dropped)))
|
||
for row in _dd.iter_rows(named=True):
|
||
dropped_map[row["symbol"]] = row
|
||
except Exception:
|
||
pass
|
||
|
||
results: list[tuple[str, str, Any, Any, Any, list[str]]] = []
|
||
|
||
# ── 新入选 ──
|
||
new_list = sorted(new_entries)
|
||
if len(new_list) > 5:
|
||
names: list[str] = []
|
||
for sym in new_list:
|
||
row = row_map.get(sym, {})
|
||
name = row.get("name") or self._name_map.get(sym, sym)
|
||
names.append(str(name))
|
||
message = f"策略「{sname}」进入 {len(new_entries)} 只：{'、'.join(names)}"
|
||
results.append(("new_entry", "_batch", message, None, None, []))
|
||
else:
|
||
for sym in new_list:
|
||
row = row_map.get(sym, {})
|
||
name = row.get("name") or self._name_map.get(sym, sym)
|
||
price = row.get("close")
|
||
pct = row.get("change_pct")
|
||
results.append(("new_entry", sym, name, price, pct, []))
|
||
|
||
# ── 已移出 ──
|
||
dropped_list = sorted(dropped)
|
||
if len(dropped_list) > 5:
|
||
names = []
|
||
for sym in dropped_list:
|
||
row = dropped_map.get(sym, {})
|
||
name = row.get("name") or self._name_map.get(sym, sym)
|
||
names.append(str(name))
|
||
message = f"策略「{sname}」移出 {len(dropped)} 只：{'、'.join(names)}"
|
||
results.append(("dropped", "_batch", message, None, None, []))
|
||
else:
|
||
for sym in dropped_list:
|
||
row = dropped_map.get(sym, {})
|
||
name = row.get("name") or self._name_map.get(sym, sym)
|
||
price = row.get("close")
|
||
pct = row.get("change_pct")
|
||
results.append(("dropped", sym, name, price, pct, []))
|
||
|
||
return results
|
||
|
||
@staticmethod
|
||
def _match_conditions(
|
||
df: pl.DataFrame, rule: dict,
|
||
) -> list[tuple[str, Any, Any, Any, list[str]]]:
|
||
"""按 conditions + logic 匹配,返回命中行 [(symbol,name,price,pct,signals)]。"""
|
||
conditions = rule.get("conditions", [])
|
||
logic = rule.get("logic", "and")
|
||
if not conditions:
|
||
return []
|
||
hit_df = _build_condition_mask(df, conditions, logic)
|
||
results = []
|
||
for row in hit_df.iter_rows(named=True):
|
||
sym = row.get("symbol", "")
|
||
name = row.get("name")
|
||
price = row.get("close")
|
||
pct = row.get("change_pct")
|
||
# 收集命中的信号列名 (仅 op=truth 且为真的)
|
||
hit_sigs = [
|
||
c["field"] for c in conditions
|
||
if c.get("op") == "truth" and row.get(c["field"])
|
||
]
|
||
results.append((sym, name, price, pct, hit_sigs))
|
||
return results
|
||
|
||
def _evaluate_ladder(self, scoped: pl.DataFrame, rule: dict, now: float) -> list[dict]:
|
||
"""评估连板梯队封单监控规则。
|
||
|
||
封单量从注入的临时列 _sealed_vol (手) 读取 (由 quote_service 评估前注入)。
|
||
命中条件: 封单比较值 <= threshold (且封单 > 0, 排除无 depth 数据的股票)。
|
||
涨停(direction=up) → 炸板预警; 跌停(direction=down) → 翘板预警。
|
||
"""
|
||
if "_sealed_vol" not in scoped.columns:
|
||
return [] # 无封单数据 (depth 未拉取), 安全降级
|
||
|
||
metric = rule.get("metric", "sealed_vol")
|
||
threshold = rule.get("threshold", 0)
|
||
direction = rule.get("direction", "up")
|
||
cooldown = rule.get("cooldown_seconds", 600)
|
||
severity = rule.get("severity", "warn")
|
||
|
||
# 比较值: sealed_vol 直接用 (手), sealed_amount = 手 × 100股 × close
|
||
if metric == "sealed_amount":
|
||
cmp_expr = pl.col("_sealed_vol") * 100 * pl.col("close")
|
||
unit = "元"
|
||
else:
|
||
cmp_expr = pl.col("_sealed_vol")
|
||
unit = "手"
|
||
|
||
# 命中: 封单 > 0 (有数据) 且 比较值 <= 阈值
|
||
hit = scoped.filter(
|
||
pl.col("_sealed_vol").is_not_null()
|
||
& (pl.col("_sealed_vol") > 0)
|
||
& (cmp_expr <= threshold)
|
||
)
|
||
if hit.is_empty():
|
||
return []
|
||
|
||
warn_label = "炸板预警" if direction == "up" else "翘板预警"
|
||
events: list[dict] = []
|
||
for row in hit.iter_rows(named=True):
|
||
sym = row.get("symbol", "")
|
||
key = (rule["id"], sym)
|
||
last = self._last_fire.get(key)
|
||
if last is not None and (now - last) < cooldown:
|
||
continue
|
||
self._last_fire[key] = now
|
||
|
||
name = row.get("name") or self._name_map.get(sym) or sym
|
||
price = row.get("close")
|
||
pct = row.get("change_pct")
|
||
sealed_vol = row.get("_sealed_vol")
|
||
# 预警封单值 (展示用)
|
||
sealed_value = sealed_vol * 100 * (price or 0) if metric == "sealed_amount" else sealed_vol
|
||
|
||
# message 体现预警封单量 + 阈值
|
||
if metric == "sealed_amount":
|
||
sv_text = f"{sealed_value / 1e4:.0f}万{unit}"
|
||
th_text = f"{threshold / 1e4:.0f}万{unit}"
|
||
else:
|
||
sv_text = f"{sealed_value:,.0f} {unit}"
|
||
th_text = f"{threshold:,.0f} {unit}"
|
||
message = f"{warn_label} · 封单 {sv_text} ≤ {th_text}"
|
||
|
||
events.append({
|
||
"ts": int(now * 1000),
|
||
"rule_id": rule["id"],
|
||
"rule_name": rule.get("name", ""),
|
||
"source": "ladder",
|
||
"type": warn_label,
|
||
"symbol": sym,
|
||
"name": name,
|
||
"message": message,
|
||
"price": price,
|
||
"change_pct": pct,
|
||
"signals": [],
|
||
"severity": severity,
|
||
"conditions": [],
|
||
"logic": "and",
|
||
"sealed_value": sealed_value, # 预警封单量/额 (飞书+记录展示)
|
||
"sealed_metric": metric,
|
||
})
|
||
return events
|
||
|
||
def _default_message(self, rule: dict, ev_type: str = "", sym: str = "",
|
||
name: str = "", pct: Any = None, price: Any = None,
|
||
conditions: list[dict] | None = None) -> str:
|
||
"""生成默认 message。
|
||
|
||
- strategy: 按变更方向生成 (进入/移出 + 涨跌幅)
|
||
- signal/price/market: 条件摘要 + 现价 + 涨跌幅 (避免笼统的「信号触发」)
|
||
"""
|
||
rtype = rule.get("type", "signal")
|
||
if rtype == "strategy":
|
||
# 从 StrategyEngine 取策略名; 失败则退化为 rule_name 里截取的部分
|
||
sname = ""
|
||
sid = rule.get("strategy_id")
|
||
if sid and self._strategy_engine is not None:
|
||
try:
|
||
s = self._strategy_engine.get(sid)
|
||
sname = s.meta.get("name", "") or s.meta.get("id", "")
|
||
except Exception: # noqa: BLE001
|
||
sname = ""
|
||
if not sname:
|
||
rn = rule.get("name", "")
|
||
sname = rn.split(" · ", 1)[1] if " · " in rn else (rn or "策略")
|
||
|
||
if ev_type == "new_entry":
|
||
pct_text = ""
|
||
if pct is not None:
|
||
sign = "+" if pct >= 0 else ""
|
||
pct_text = f" {sign}{pct * 100:.1f}%"
|
||
return f"策略「{sname}」进入 {name}{pct_text}"
|
||
elif ev_type == "dropped":
|
||
pct_text = ""
|
||
if pct is not None:
|
||
sign = "+" if pct >= 0 else ""
|
||
pct_text = f" {sign}{pct * 100:.1f}%"
|
||
return f"策略「{sname}」移出 {name}{pct_text}"
|
||
return f"策略「{sname}」变更"
|
||
|
||
# signal / price / market: 条件摘要 + 现价 + 涨跌幅
|
||
# 条件摘要: 把 conditions (truth/比较) 拼成可读串, 如 "MA20金叉 且 量比>2"
|
||
cond_text = self._format_conditions_text(rule, conditions)
|
||
price_text = f"现价 {price}" if price is not None else ""
|
||
pct_text = ""
|
||
if pct is not None:
|
||
sign = "+" if pct >= 0 else ""
|
||
pct_text = f"{sign}{pct * 100:.1f}%"
|
||
tail = " · ".join(s for s in (price_text, pct_text) if s)
|
||
if cond_text and tail:
|
||
return f"{cond_text} · {tail}"
|
||
return cond_text or tail or "监控触发"
|
||
|
||
@staticmethod
|
||
def _format_conditions_text(rule: dict, conditions: list[dict] | None) -> str:
|
||
"""把 rule.conditions 拼成可读文本 (用于 message / 推送)。
|
||
|
||
op=truth: 直接用信号中文名 (如 "MA20金叉")
|
||
op=比较: 字段中文名 + 操作符 + 值 (如 "涨跌幅≥5")
|
||
logic: and → "且", or → "或"
|
||
"""
|
||
conds = conditions if conditions is not None else list(rule.get("conditions", []))
|
||
if not conds:
|
||
return ""
|
||
logic_word = "且" if rule.get("logic", "and") == "and" else "或"
|
||
parts: list[str] = []
|
||
for c in conds:
|
||
field = c.get("field", "")
|
||
op = c.get("op", "truth")
|
||
value = c.get("value")
|
||
label = _signal_cn_name(field) or field
|
||
if op == "truth":
|
||
parts.append(label)
|
||
else:
|
||
op_map = {"gte": "≥", "lte": "≤", "gt": ">", "lt": "<", "eq": "="}
|
||
parts.append(f"{label}{op_map.get(op, op)}{value}")
|
||
return f" {logic_word} ".join(parts)
|