#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""strategy_alert.py — MoFin 策略失效预警（三振出局，2026-08-13 落地）

核心理念（机器学习策略攻防体系文）：
- 三振出局：黄牌（减半）→ 橙牌（1/4）→ 红牌（清仓淘汰）
- 五类失效预警：胜率持续下降 / 盈亏比恶化 / 波动新高 / 信号质量突变 / 风格漂移
- 原则：不是 IC 掉到负就删，是降权到观察模式（权重设 0 保留，恢复可启用）

数据源：strategy_health（已有）+ strategy_research（回测）+ 实盘持仓表现
"""
import sqlite3
import json
from pathlib import Path
from datetime import datetime, timedelta

DB = "/home/hmo/MoFin/data/mofin.db"
OUT = Path("/home/hmo/MoFin/data/strategy_alerts.json")

# 三振阈值（机器学习策略文）：
# 黄牌：滚动20日风险调整收益连续3日 < -0.5，或单日波动 > 5%
# 橙牌：黄牌后5日未回升零以上 → 权重降至1/4
# 红牌：橙牌后5日持续不佳，或累计亏损 > 10% → 清仓移除
# MoFin 简化版（基于回测/健康度，实盘数据不足时用回测滚动胜率）：
YELLOW_WIN_RATE = 0.40   # 滚动胜率 < 40% → 黄牌（v_weak 近1年 39.3% 已触发）
ORANGE_WIN_RATE = 0.35   # 滚动胜率 < 35% → 橙牌
RED_WIN_RATE = 0.30      # 滚动胜率 < 30% → 红牌（淘汰）
ORANGE_PNL_RATIO = 0.5   # 盈亏比 < 0.5（赚的越来越少亏的越来越多）→ 橙牌

def rolling_stats(version, days=60):
    """从 strategy_research 提取该策略近期交易的滚动胜率/盈亏比"""
    c = sqlite3.connect(DB)
    rows = c.execute(
        "SELECT results_json FROM strategy_research WHERE version=? ORDER BY period_tag DESC LIMIT 1",
        (version,)
    ).fetchall()
    c.close()
    if not rows:
        return None
    try:
        res = json.loads(rows[0][0])
        trades = res.get("trades", [])
        cutoff = (datetime.now() - timedelta(days=days)).strftime("%Y-%m-%d")
        recent = [t for t in trades if t.get("entry_date", "") >= cutoff]
        if len(recent) < 5:
            return None
        wins = [t for t in recent if t.get("profit_pct", 0) > 0]
        losses = [t for t in recent if t.get("profit_pct", 0) <= 0]
        win_rate = len(wins) / len(recent) if recent else 0
        avg_win = sum(t.get("profit_pct", 0) for t in wins) / len(wins) if wins else 0
        avg_loss = abs(sum(t.get("profit_pct", 0) for t in losses) / len(losses)) if losses else 1
        pnl_ratio = avg_win / avg_loss if avg_loss > 0 else 0
        return {
            "n": len(recent), "win_rate": win_rate,
            "avg_win": avg_win, "avg_loss": avg_loss, "pnl_ratio": pnl_ratio,
        }
    except Exception:
        return None

def assess(version, stats):
    """三振评估"""
    if not stats:
        return {"level": "ok", "action": "数据不足，无法评估", "weight": 1.0}
    wr, pr = stats["win_rate"], stats["pnl_ratio"]
    if wr < RED_WIN_RATE:
        return {"level": "red", "action": f"红牌：滚动胜率{wr:.1%}<30%，淘汰（权重0，观察模式）", "weight": 0.0}
    if wr < ORANGE_WIN_RATE or pr < ORANGE_PNL_RATIO:
        return {"level": "orange", "action": f"橙牌：胜率{wr:.1%}或盈亏比{pr:.2f}恶化，权重降1/4", "weight": 0.25}
    if wr < YELLOW_WIN_RATE:
        return {"level": "yellow", "action": f"黄牌：滚动胜率{wr:.1%}<40%，权重减半", "weight": 0.5}
    return {"level": "ok", "action": f"正常：胜率{wr:.1%}，盈亏比{pr:.2f}", "weight": 1.0}

def main():
    alerts = {}
    for version in ["v_weak", "v_oversold"]:
        stats = rolling_stats(version, days=90)
        a = assess(version, stats)
        alerts[version] = {**a, "stats": stats}
        print(f"{version}: {a['level']} | {a['action']} | weight={a['weight']}")
        if stats:
            print(f"  近90日: {stats['n']}笔, 胜率{stats['win_rate']:.1%}, 盈亏比{stats['pnl_ratio']:.2f}")
    alerts["updated_at"] = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
    OUT.write_text(json.dumps(alerts, ensure_ascii=False, indent=1), encoding="utf-8")
    print(f"\nstrategy_alerts.json 写入")

if __name__ == "__main__":
    main()