291 lines
11 KiB
Python
291 lines
11 KiB
Python
#!/usr/bin/env python3
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"""策略到期评估模块：用完整K线数据评估每个区间的职责履行"""
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import os, sys, sqlite3, json
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from datetime import datetime, timedelta
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# ── 消息通道统一路由(broadcast/xmpp by delivery) ──
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try:
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from messenger import install_stdio_hook as _msh
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_msh()
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except Exception:
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pass
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DB = "/home/hmo/MoFin/data/mofin.db"
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def get_expired_strategies(conn):
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"""获取已到期且未评估的策略"""
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now = datetime.now().strftime("%Y-%m-%d")
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return conn.execute("""
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SELECT id, code, version, strategy_source, entry_low, entry_high,
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stop_loss, take_profit, buy_zone_expected_days, take_profit_expected_days,
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created_at, superseded_at
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FROM holding_strategies
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WHERE status IN ('active', 'superseded')
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AND superseded_at IS NOT NULL
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AND id NOT IN (SELECT strategy_id FROM strategy_effectiveness)
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ORDER BY superseded_at
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""").fetchall()
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def get_kline(conn, code, start_date, end_date):
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"""获取K线数据"""
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return conn.execute("""
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SELECT date, open, close, high, low
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FROM stock_daily
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WHERE code=? AND date BETWEEN ? AND ?
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ORDER BY date
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""", (code, start_date, end_date)).fetchall()
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def evaluate_zone(klines, zone_type, zone_low, zone_high, expected_days):
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"""评估单个区间的职责履行"""
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if not klines or zone_low is None or zone_high is None:
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return {"accuracy": "no_data", "detail": "无K线数据或区间未设定"}
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first_date = klines[0][0]
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last_date = klines[-1][0]
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# 统计每日形态
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days_in_zone = 0 # 收盘在区间内
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days_penetrated_above = 0 # 收盘突破上沿
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days_penetrated_below = 0 # 收盘突破下沿
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v_shape_days = 0 # V型（进入后离开）
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first_trigger_day = None # 首次触发日
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prev_close = None
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for date, open_p, close, high, low in klines:
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if close is None:
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continue
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# 收盘价相对于区间
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if zone_low <= close <= zone_high:
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days_in_zone += 1
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if first_trigger_day is None:
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first_trigger_day = date
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elif close > zone_high:
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days_penetrated_above += 1
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elif close < zone_low:
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days_penetrated_below += 1
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# V型检测：前一天在区间内，今天离开
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if prev_close is not None:
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if (zone_low <= prev_close <= zone_high) and (close < zone_low or close > zone_high):
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v_shape_days += 1
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prev_close = close
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total_days = len(klines)
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actual_days_to_trigger = None
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if first_trigger_day:
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fd = datetime.strptime(first_trigger_day, "%Y-%m-%d")
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sd = datetime.strptime(first_date, "%Y-%m-%d")
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actual_days_to_trigger = (fd - sd).days
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# 区间职责评估
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if zone_type == "buy_zone":
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# 买入区职责：触发后是否上涨
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if first_trigger_day:
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trigger_idx = next(i for i, k in enumerate(klines) if k[0] == first_trigger_day)
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after_klines = klines[trigger_idx:]
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if after_klines:
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trigger_close = after_klines[0][2]
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max_after = max(k[2] for k in after_klines if k[2])
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profit_pct = (max_after - trigger_close) / trigger_close * 100 if trigger_close else 0
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if profit_pct > 5:
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accuracy = "effective"
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elif profit_pct > 0:
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accuracy = "partially_effective"
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else:
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accuracy = "ineffective"
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else:
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accuracy = "no_data"
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else:
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accuracy = "not_triggered"
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elif zone_type == "stop_loss":
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# 止损职责：触发后是否继续跌（避免更大损失）
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if first_trigger_day:
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trigger_idx = next(i for i, k in enumerate(klines) if k[0] == first_trigger_day)
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after_klines = klines[trigger_idx:]
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if len(after_klines) >= 2:
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trigger_close = after_klines[0][2]
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# 之后1-2天是否继续跌
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next_2d_low = min(k[4] for k in after_klines[1:3] if k[4])
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if next_2d_low and next_2d_low < trigger_close * 0.97:
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accuracy = "effective" # 止损后继续跌，止损正确
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elif next_2d_low and next_2d_low > trigger_close * 1.03:
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accuracy = "ineffective" # 止损后反弹，被洗盘
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else:
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accuracy = "neutral"
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else:
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accuracy = "no_data"
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else:
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accuracy = "not_triggered"
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elif zone_type == "take_profit":
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# 止盈职责：触发后是否回落（锁定正确）
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if first_trigger_day:
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trigger_idx = next(i for i, k in enumerate(klines) if k[0] == first_trigger_day)
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after_klines = klines[trigger_idx:]
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if len(after_klines) >= 2:
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trigger_close = after_klines[0][2]
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# 之后3天最高价
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max_3d = max(k[3] for k in after_klines[1:4] if k[3])
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if max_3d and max_3d > trigger_close * 1.05:
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accuracy = "ineffective" # 止盈后继续涨，偏保守
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elif max_3d and max_3d < trigger_close:
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accuracy = "effective" # 止盈后回落，锁定正确
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else:
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accuracy = "neutral"
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else:
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accuracy = "no_data"
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else:
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accuracy = "not_triggered"
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# 时间准确性
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if actual_days_to_trigger and expected_days:
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ratio = actual_days_to_trigger / expected_days
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if ratio <= 1:
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time_accuracy = "on_time"
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elif ratio <= 1.5:
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time_accuracy = "slightly_late"
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else:
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time_accuracy = "late"
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else:
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time_accuracy = "no_data"
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return {
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"accuracy": accuracy,
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"time_accuracy": time_accuracy,
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"actual_days": actual_days_to_trigger,
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"expected_days": expected_days,
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"days_in_zone": days_in_zone,
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"v_shape_days": v_shape_days,
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"total_days": total_days,
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}
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def evaluate_strategy(conn, strategy):
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"""评估单个策略"""
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sid, code, ver, source, el, eh, sl, tp, buy_exp, tp_exp, created, superseded = strategy
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# 获取K线数据（从创建到被替代）
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klines = get_kline(conn, code, created[:10], superseded[:10])
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if not klines:
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return None
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buy_eval = evaluate_zone(klines, "buy_zone", el, eh, buy_exp)
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tp_eval = evaluate_zone(klines, "take_profit", tp, tp*1.05 if tp else None, tp_exp) if tp else None
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sl_eval = evaluate_zone(klines, "stop_loss", sl*0.95 if sl else None, sl, None) if sl else None
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# 综合评价
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assessment = []
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if buy_eval["accuracy"] == "ineffective":
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assessment.append("买入区预判有误（触发后下跌）")
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elif buy_eval["accuracy"] == "effective":
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assessment.append("买入区预判准确")
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if sl_eval and sl_eval["accuracy"] == "ineffective":
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assessment.append("止损被洗盘（触发后反弹）")
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elif sl_eval and sl_eval["accuracy"] == "effective":
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assessment.append("止损有效（避免更大损失）")
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if tp_eval and tp_eval["accuracy"] == "ineffective":
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assessment.append("止盈偏保守（触发后继续涨）")
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elif tp_eval and tp_eval["accuracy"] == "effective":
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assessment.append("止盈锁定正确")
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if buy_eval["time_accuracy"] == "late":
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assessment.append("入场时机偏晚")
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elif buy_eval["time_accuracy"] == "on_time":
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assessment.append("入场时机准确")
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# 改进建议
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suggestions = []
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if buy_eval["accuracy"] == "ineffective":
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suggestions.append("买入区需要下调（当前价位偏高）")
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if buy_eval["time_accuracy"] == "late":
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suggestions.append("买入区预期天数需要延长")
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if sl_eval and sl_eval["accuracy"] == "ineffective":
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suggestions.append("止损位设置过于激进，考虑放宽")
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if tp_eval and tp_eval["accuracy"] == "ineffective":
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suggestions.append("止盈位偏保守，可适当提高")
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return {
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"strategy_id": sid,
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"code": code,
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"strategy_source": source,
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"version": ver,
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"period_start": created[:10],
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"period_end": superseded[:10],
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"buy_zone_accuracy": buy_eval["accuracy"],
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"take_profit_accuracy": tp_eval["accuracy"] if tp_eval else "no_zone",
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"stop_loss_accuracy": sl_eval["accuracy"] if sl_eval else "no_zone",
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"time_accuracy": buy_eval["time_accuracy"],
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"overall_assessment": "; ".join(assessment) if assessment else "数据不足",
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"improvement_suggestion": "; ".join(suggestions) if suggestions else "暂无改进建议",
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}
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def main():
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conn = sqlite3.connect(DB, timeout=30)
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expired = get_expired_strategies(conn)
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print(f"待评估策略: {len(expired)} 个")
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ok = 0
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for strategy in expired:
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result = evaluate_strategy(conn, strategy)
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if result:
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conn.execute("""
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INSERT INTO strategy_effectiveness
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(strategy_id, code, strategy_source, version, period_start, period_end,
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buy_zone_accuracy, take_profit_accuracy, stop_loss_accuracy,
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time_accuracy, overall_assessment, improvement_suggestion)
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VALUES (?,?,?,?,?,?,?,?,?,?,?,?)
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""", (result["strategy_id"], result["code"], result["strategy_source"],
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result["version"], result["period_start"], result["period_end"],
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result["buy_zone_accuracy"], result["take_profit_accuracy"],
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result["stop_loss_accuracy"], result["time_accuracy"],
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result["overall_assessment"], result["improvement_suggestion"]))
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ok += 1
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print(f" {result['code']} v{result['version']}: {result['overall_assessment'][:50]}")
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conn.commit()
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conn.close()
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print(f"\n评估完成: {ok} 个策略已评估")
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if __name__ == "__main__":
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main()
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# ── 扩展评估：读取 recommendation_log + execution_log ──
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def evaluate_with_logs(conn, strategy):
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"""用推荐记录+执行记录增强评估"""
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sid = strategy[0]
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code = strategy[1]
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# 读推荐记录
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recs = conn.execute(
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"SELECT recommend_time, action, entry_low, entry_high, stop_loss, take_profit "
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"FROM recommendation_log WHERE strategy_id=? ORDER BY recommend_time", (sid,)).fetchall()
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# 读执行记录
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execs = conn.execute(
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"SELECT execute_time, action, shares, price FROM execution_log "
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"WHERE code=? ORDER BY execute_time", (code,)).fetchall()
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# 匹配：推荐后是否有对应执行
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rec_followed = 0
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rec_not_followed = 0
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for rec in recs:
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rec_time, rec_action = rec[0], rec[1]
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found = False
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for exe in execs:
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if exe[0] >= rec_time and exe[1] == rec_action:
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found = True
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rec_followed += 1
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break
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if not found:
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rec_not_followed += 1
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compliance_rate = rec_followed / (rec_followed + rec_not_followed) * 100 if (rec_followed + rec_not_followed) > 0 else 0
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return {
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"recommendations": len(recs),
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"executions_matched": rec_followed,
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"executions_missed": rec_not_followed,
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"compliance_rate": round(compliance_rate, 1),
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}
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