feat: 策略自我进化闭环落地——evolution_engine新建(退化检测+数据驱动变体±20%+2y回测验证+达标才推送)+lesson_extractor重写(实盘平仓+温区归因)+auto_iterator归档+cron每周六22:00
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# 2026-08-15 进化模块重写
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## auto_iterator.py 已退役
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- 病状：参数空间（max_hold_days/reentry_days/sl_atr）是旧家族参数，与当前温区策略脱节
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- 取代：evolution/evolution_engine.py（数据驱动变体生成，从实际config出发±20%单变量）
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- 归档日期：2026-08-15
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## 新架构
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- evolution/evolution_engine.py — 退化检测+变体生成+回测验证+推送（每周六22:00 cron）
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- evolution/lesson_extractor.py — 实盘平仓教训提取（重写，替代读回测trades的旧版）
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# -*- coding: utf-8 -*-
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"""evolution_daily.py — 策略进化引擎 cron 入口（每周六 22:00，hermes cron）
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放在 deploy/profile-scripts（硬链接进 cron scripts 目录），
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内部调用 evolution/evolution_engine.py（真正逻辑所在，含单例守卫）。
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设计依据：docs/decisions/2026-08-15-策略自我进化闭环重构.md
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- 每周六 22:00 运行（策略评估 21:00 之后）
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- 无退化/变体不达标 → 静默
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- 达标变体 → strategy_evolution + XMPP 推送老莫（永不自动 promote）
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"""
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import subprocess, sys, os
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REPO = "/home/hmo/MoFin"
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ENGINE = f"{REPO}/evolution/evolution_engine.py"
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PY = f"{REPO}/venv/bin/python"
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def main():
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if not os.path.exists(ENGINE):
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print(f"evolution_engine.py 不存在: {ENGINE}", flush=True)
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sys.exit(1)
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# 单例守卫在 engine 内部（fcntl），这里直接调用
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r = subprocess.run([PY, ENGINE], cwd=REPO, capture_output=False, timeout=3600)
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sys.exit(r.returncode)
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if __name__ == "__main__":
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main()
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# -*- coding: utf-8 -*-
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"""
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evolution/evolution_engine.py — 策略自我进化引擎（每周六 22:00，hermes cron）
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设计依据：docs/decisions/2026-08-15-策略自我进化闭环重构.md（老莫已批准）
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闭环：统计数据每日自动更新 → 本引擎每周检测退化 → 生成参数变体 → 回测验证 → 有价值才推送
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流程：
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1. 读激活策略集合（data/strategy_weights.json：A股 active + 港股 markets.hk.active）
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2. 退化信号检测（宁缺毋滥，任一命中即触发研究）：
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S1 健康度连续低：strategy_health 连续 5 天 health_score < 40（排除 50 中性=无实盘数据）
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S2 温区表现衰减：激活策略在其适应温区（strategy_regime_perf）温区级组合年化 cagr_pct < 0
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3. 有退化 → 生成参数变体：
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- 只对 lab.STRATEGIES 里可回测的策略（v_oversold/v_weak 等标准回测体系）
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- 参数空间从策略 config 实际数值字段出发（递归遍历，单变量 ±20%，一次只动一个）
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- 港股走 hk_backtest（entry/exit 字段 ±20%）
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4. 回测验证（统一资金约束）：
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- A股：lab.run_backtest(save=False)，取 portfolio_full
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- 港股：hk_backtest.gen_trades_defensive + lab.portfolio_sim(max_positions=8)
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- 验收：温区级组合年化 cagr_pct ≥ 原策略 + 3pp 且 max_dd 不劣化超过 2pp
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5. 达标变体 → 写 strategy_evolution（promoted=0）+ XMPP 推送老莫（附对比证据）
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（永不自动 promote，老莫说"上线"才进路由）
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6. 无退化或变体全灭 → 当周静默（不制造噪音）
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单例守卫：fcntl.flock 防并发（deploy_guard / 手动重跑均安全）
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"""
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import sys, os, json, sqlite3, copy, io, traceback
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from datetime import datetime, timedelta
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sys.path.insert(0, "/home/hmo/MoFin")
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sys.path.insert(0, "/home/hmo/MoFin/deploy/profile-scripts")
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DB = os.environ.get("MOFIN_DB", "/home/hmo/MoFin/data/mofin.db")
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WEIGHTS_JSON = "/home/hmo/MoFin/data/strategy_weights.json"
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# 退化信号参数
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HEALTH_LOW = 40 # 健康度低于此值视为低
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HEALTH_STREAK_DAYS = 5 # 连续天数
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REGIME_CAGR_BAD = 0.0 # 温区级组合年化低于此值视为退化
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# 变体生成参数
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VAR_PCT = 0.20 # ±20% 网格
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MAX_VARIANTS = 6 # 每策略最多生成变体数
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MAX_VARIANTS_TEST = 1 # 最多回测验证的变体数（资源约束：单变体2y全市场回测6-8分钟/6-8GB，详见下方BT注释）
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# 验证回测周期（2026-08-15：原5y全市场回测单变体8+分钟/5GB内存，改为2y控制资源；
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# 验收对比用同周期原策略数据，相对改善仍有效）
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BT_START = "2024-07-01"
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BT_END = "2026-07-24"
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BT_PERIOD_TAG = "2y"
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# 验收门槛（2026-08-15 口径说明：变体与 parent 用同周期 strategy_research 2y 整体组合年化对比，
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# 相对改善有效；原设计"温区级组合年化"需按温区分段重跑变体，资源过重，整体同口径更务实）
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ACCEPT_CAGR_PP = 3.0 # 组合年化 ≥ 原 + 3pp
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ACCEPT_DD_PP = 2.0 # max_dd 不劣化超过 2pp
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def log(msg):
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line = f"[{datetime.now().isoformat(timespec='seconds')}] {msg}"
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print(line, flush=True)
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# ── 单例守卫（fcntl，Windows 不可用则跳过）──
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try:
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import fcntl
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_LOCK_FD = open("/tmp/evolution_engine.lock", "w")
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try:
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fcntl.flock(_LOCK_FD, fcntl.LOCK_EX | fcntl.LOCK_NB)
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except OSError:
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log("已有 evolution_engine 实例在运行，退出")
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sys.exit(0)
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except ImportError:
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pass
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def get_conn():
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conn = sqlite3.connect(DB)
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conn.row_factory = sqlite3.Row
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return conn
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# ── 1. 激活策略集合 ──
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def load_active_strategies():
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"""返回 [(version, market, regime)]：A股 active + 港股 markets.hk.active"""
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try:
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d = json.load(io.open(WEIGHTS_JSON, encoding="utf-8"))
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except Exception as e:
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log(f"读 strategy_weights.json 失败: {e}")
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return []
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out = []
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for v in (d.get("active") or []):
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info = (d.get("weights") or {}).get(v, {})
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out.append({"version": v, "market": "a",
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"regime": info.get("best_regime") or info.get("regime") or d.get("state")})
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hk = (d.get("markets") or {}).get("hk") or {}
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for v in (hk.get("active") or []):
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out.append({"version": v, "market": "hk", "regime": hk.get("state")})
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return out
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# ── 2. 退化信号检测 ──
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def detect_degradation(conn, version, market):
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"""返回退化原因列表（空=健康）。S1 健康度连续低；S2 温区组合年化<0"""
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reasons = []
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# S1：健康度连续 5 天 < 40（排除 50 中性=无实盘）
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rows = conn.execute(
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"SELECT date, health_score FROM strategy_health WHERE strategy_version=? ORDER BY date DESC LIMIT ?",
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(version, HEALTH_STREAK_DAYS)).fetchall()
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if len(rows) >= HEALTH_STREAK_DAYS:
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scores = [r["health_score"] for r in rows]
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# 排除"无实盘=50中性"污染：只要连续5天都 < 40 且不是 50 占位
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if all(s is not None and s < HEALTH_LOW for s in scores) and any(s != 50 for s in scores):
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reasons.append(f"S1 健康度连续{HEALTH_STREAK_DAYS}天<{HEALTH_LOW}（{scores}）")
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# S2：适应温区温区级组合年化 < 0（strategy_regime_perf.cagr_pct）
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r = conn.execute(
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"SELECT regime, cagr_pct, trades FROM strategy_regime_perf WHERE strategy=? AND market=? ORDER BY trades DESC LIMIT 1",
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(version, market)).fetchone()
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if r and r["cagr_pct"] is not None and r["cagr_pct"] < REGIME_CAGR_BAD:
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reasons.append(f"S2 适应温区[{r['regime']}]组合年化{r['cagr_pct']}%<0（{r['trades']}笔）")
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return reasons
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# ── 3. 变体生成（数据驱动，从实际 config 出发）──
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_NUM_KEYS = ("tp_pct", "sl_pct", "sl_atr", "max_hold_days", "min_score", "min_momentum",
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"adx_min", "atr_pct_min", "atr_pct_max", "roc_min", "roc_max",
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"macd_hist_min", "macd_hist_max", "dist_ma20_min", "vol_ratio_min",
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"vol_ratio_max", "ma20_slope_max", "mkt_slope_max", "mkt_adx_min",
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"sector_slope_max", "bias_max", "rsi_max", "ret_max", "mom20_max",
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"amount_max", "rsi_delta_min", "mkt_rsi_max", "mkt_dd60_max",
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"mcap_q_max", "pe_q_max", "news3_min", "sec_ret20_max",
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"pe_q_max", "mcap_q_max", "sec_ret20_min", "bias60_max",
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"vol_ratio_min", "rsi_delta_min", "bias60_min")
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_SKIP_KEYS = ("mode", "family", "launch", "version", "name", "summary", "hypothesis",
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"mkt_mode", "mkt_above_ma20", "hh_only", "hl_only", "sector_above_ma20")
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def iter_numeric_fields(node, path=()):
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"""递归遍历 config，产出 (path_list, field_name, value) 数值字段"""
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if isinstance(node, dict):
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for k, v in node.items():
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if k in _SKIP_KEYS:
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continue
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if isinstance(v, (int, float)) and not isinstance(v, bool) and k in _NUM_KEYS:
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yield list(path) + [k], k, v
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elif isinstance(v, dict):
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yield from iter_numeric_fields(v, list(path) + [k])
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def get_parent_cagr(conn, version, market, period_tag=BT_PERIOD_TAG):
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"""原策略基准：优先同周期 strategy_research（period_tag=2y），无则温区级组合年化（全量）"""
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if market == "a":
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r = conn.execute(
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"SELECT results_json FROM strategy_research WHERE version=? AND period_tag=? "
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"AND market='a' ORDER BY id DESC LIMIT 1", (version, period_tag)).fetchone()
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if r:
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res = json.loads(r["results_json"] or "{}")
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s = res.get("summary", {})
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pf = s.get("portfolio_full", {})
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cagr = pf.get("cagr_pct")
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dd = pf.get("portfolio_max_dd_pct")
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if cagr is not None:
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return cagr, dd
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# 回退：温区级组合年化（strategy_regime_perf，全量）——仅当同周期数据缺失时
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r = conn.execute(
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"SELECT cagr_pct, portfolio_max_dd_pct FROM strategy_regime_perf WHERE strategy=? AND market=? ORDER BY trades DESC LIMIT 1",
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(version, market)).fetchone()
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if r:
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return r["cagr_pct"], r["portfolio_max_dd_pct"]
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return None, None
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def generate_variants(version, market, config):
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"""生成变体参数建议：单变量 ±20%，最多 MAX_VARIANTS 个
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返回 [{version, name, config, change_desc, field, delta}]"""
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fields = list(iter_numeric_fields(config))
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if not fields:
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return []
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variants = []
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for path, fname, val in fields:
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if val <= 0:
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continue
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for factor, tag in [(1 - VAR_PCT, "减20%"), (1 + VAR_PCT, "加20%")]:
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new_val = round(val * factor, 4)
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if new_val <= 0:
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continue
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# 克隆 config 并修改目标字段
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new_cfg = copy.deepcopy(config)
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node = new_cfg
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for p in path[:-1]:
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node = node[p]
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node[path[-1]] = new_val
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variants.append({
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"version": f"evo_{version}_{fname}_{tag.replace('20%','')}{round(new_val, 2)}",
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"name": f"自进化-{version}-{fname}{tag}",
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"config": new_cfg,
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"change_desc": f"{fname}: {val} → {new_val}（{tag}）",
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"field": fname,
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"delta": round(new_val - val, 4),
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})
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if len(variants) >= MAX_VARIANTS:
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return variants
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return variants
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# ── 4. 回测验证 ──
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def verify_variant_a(variant, parent_version):
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"""A股变体验证：注册进 lab 跑回测（save=False），返回 summary 关键指标"""
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import strategy_lab as lab
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name = variant["version"]
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base = lab.get_strategy(parent_version)
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cfg = copy.deepcopy(base)
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cfg["version"] = name
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cfg["name"] = variant["name"]
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# 用变体 config 覆盖（变体 config 从原 config 克隆并改了一个字段）
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merged = copy.deepcopy(base["config"])
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_deep_update(merged, variant["config"])
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cfg["config"] = merged
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lab.STRATEGIES[name] = cfg
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try:
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r = lab.run_backtest(name, BT_START, BT_END, 913000, save=False,
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universe="a", period_tag=BT_PERIOD_TAG)
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s = r.get("summary", {})
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pf = s.get("portfolio_full", {})
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return {
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"trades": s.get("total_trades"),
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"win_rate": s.get("win_rate"),
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"cagr": pf.get("cagr_pct"),
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"total_return": pf.get("total_return_pct"),
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"max_dd": pf.get("portfolio_max_dd_pct"),
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}
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finally:
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lab.STRATEGIES.pop(name, None)
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def _deep_update(dst, src):
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for k, v in src.items():
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if isinstance(v, dict) and isinstance(dst.get(k), dict):
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_deep_update(dst[k], v)
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else:
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dst[k] = v
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def verify_variant_hk(variant, parent_version):
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"""港股变体验证：hk_backtest 生成交易 + portfolio_sim 8槽"""
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import pandas as pd
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sys.path.insert(0, "/home/hmo/MoFin/deploy/profile-scripts")
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from hk_strategies import HK_STRATEGIES, get_hk_strategy
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import hk_backtest
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import strategy_lab as lab
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base = get_hk_strategy(parent_version)
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if not base:
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return None
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new_cfg = copy.deepcopy(base)
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new_cfg["version"] = variant["version"]
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new_cfg["name"] = variant["name"]
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_deep_update(new_cfg, variant["config"])
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panel = hk_backtest.load_panel()
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# 2y 窗口过滤（与 A股验证周期一致，控制资源）
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panel = panel[(panel["date"] >= BT_START) & (panel["date"] <= BT_END)].copy()
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trades = hk_backtest.gen_trades_defensive(panel, new_cfg, strike=3, cooldown_days=15)
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if not trades:
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return {"trades": 0, "win_rate": None, "cagr": None, "total_return": None, "max_dd": None}
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sim = lab.portfolio_sim(trades, 1000000, max_positions=8)
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return {
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"trades": len(trades),
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"win_rate": round(100 * sum(1 for t in trades if t["profit_pct"] > 0) / len(trades), 1),
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"cagr": sim.get("cagr_pct"),
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"total_return": sim.get("total_return_pct"),
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"max_dd": sim.get("portfolio_max_dd_pct"),
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}
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# ── 5. 记录 + 推送 ──
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def record_and_notify(conn, parent_version, market, variant, result, parent_cagr, parent_dd):
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"""写 strategy_evolution + XMPP 推送"""
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conn.execute("""
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INSERT INTO strategy_evolution (parent_version, child_version, change_description, backtest_result, promoted)
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VALUES (?, ?, ?, ?, 0)
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""", (parent_version, variant["version"], variant["change_desc"],
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json.dumps(result, ensure_ascii=False)))
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conn.commit()
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msg = (f"🧬 策略进化建议 [{parent_version}]\n"
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f"改动: {variant['change_desc']}\n"
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f"回测: 年化 {parent_cagr}% → {result.get('cagr')}%"
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f" (Δ{round((result.get('cagr') or 0) - (parent_cagr or 0), 1)}pp)"
|
||||
f" | 回撤 {parent_dd}% → {result.get('max_dd')}%\n"
|
||||
f"胜率 {result.get('win_rate')}% / {result.get('trades')}笔\n"
|
||||
f"【验证达标，待你决定是否上线】")
|
||||
try:
|
||||
sys.path.insert(0, "/home/hmo/MoFin/deploy/profile-scripts")
|
||||
from alert_helper import notify, ACTION
|
||||
notify("策略进化", msg, level=ACTION)
|
||||
log(f"XMPP 推送: {parent_version} → {variant['version']}")
|
||||
except Exception as e:
|
||||
log(f"XMPP 推送失败: {e}")
|
||||
return msg
|
||||
|
||||
|
||||
# ── 主流程 ──
|
||||
def run_evolution():
|
||||
conn = get_conn()
|
||||
actives = load_active_strategies()
|
||||
log(f"激活策略: {[a['version'] for a in actives]}")
|
||||
if not actives:
|
||||
log("无激活策略，退出")
|
||||
conn.close()
|
||||
return
|
||||
|
||||
findings = [] # 退化发现
|
||||
passed = [] # 达标变体
|
||||
|
||||
for act in actives:
|
||||
v, mkt = act["version"], act["market"]
|
||||
reasons = detect_degradation(conn, v, mkt)
|
||||
if not reasons:
|
||||
continue
|
||||
log(f"退化信号: {v} [{mkt}] → {'; '.join(reasons)}")
|
||||
findings.append((v, mkt, reasons))
|
||||
|
||||
# 生成变体（A股从 lab 读 config；港股从 HK_STRATEGIES）
|
||||
if mkt == "hk":
|
||||
sys.path.insert(0, "/home/hmo/MoFin/deploy/profile-scripts")
|
||||
from hk_strategies import get_hk_strategy
|
||||
base = get_hk_strategy(v)
|
||||
if not base:
|
||||
log(f" {v} 无港股策略定义，跳过")
|
||||
continue
|
||||
variants = generate_variants(v, mkt, base)
|
||||
verify_fn = verify_variant_hk
|
||||
else:
|
||||
try:
|
||||
import strategy_lab as lab
|
||||
base = lab.get_strategy(v)
|
||||
except ValueError:
|
||||
log(f" {v} 不在标准回测体系（scanner 类策略），跳过变体研究")
|
||||
continue
|
||||
variants = generate_variants(v, mkt, base["config"])
|
||||
verify_fn = verify_variant_a
|
||||
if not variants:
|
||||
log(f" {v} 无可用变体字段，跳过")
|
||||
continue
|
||||
|
||||
parent_cagr, parent_dd = get_parent_cagr(conn, v, mkt)
|
||||
log(f" {v} 原温区年化 {parent_cagr}% / 回撤 {parent_dd}% | 生成 {len(variants)} 个变体，验证前 {MAX_VARIANTS_TEST} 个")
|
||||
for var in variants[:MAX_VARIANTS_TEST]:
|
||||
try:
|
||||
res = verify_fn(var, v)
|
||||
except Exception as e:
|
||||
log(f" {var['version']} 回测失败: {str(e)[:100]}")
|
||||
continue
|
||||
if not res or res.get("cagr") is None:
|
||||
log(f" {var['version']} 无结果（0笔或空），跳过")
|
||||
continue
|
||||
ok_cagr = parent_cagr is None or res["cagr"] >= (parent_cagr or 0) + ACCEPT_CAGR_PP
|
||||
ok_dd = parent_dd is None or res["max_dd"] <= (parent_dd or 0) + ACCEPT_DD_PP
|
||||
status = "✅达标" if (ok_cagr and ok_dd) else "❌不达标"
|
||||
log(f" {var['version']}: 年化 {parent_cagr}→{res['cagr']}% 回撤 {parent_dd}→{res['max_dd']}% [{status}]")
|
||||
if ok_cagr and ok_dd:
|
||||
record_and_notify(conn, v, mkt, var, res, parent_cagr, parent_dd)
|
||||
passed.append((v, var, res))
|
||||
|
||||
conn.close()
|
||||
|
||||
# 汇总
|
||||
if not findings:
|
||||
log("── 无退化信号，当周静默 ──")
|
||||
else:
|
||||
log(f"── 检测 {len(findings)} 个退化策略，{len(passed)} 个达标变体已推送 ──")
|
||||
return findings, passed
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
try:
|
||||
run_evolution()
|
||||
except Exception as e:
|
||||
log(f"evolution_engine 异常: {e}")
|
||||
traceback.print_exc()
|
||||
sys.exit(1)
|
||||
+136
-92
@@ -1,106 +1,150 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
evolution/lesson_extractor.py — 交易后教训提取
|
||||
分析已平仓交易，用LLM提取"为什么赢/亏"的教训
|
||||
evolution/lesson_extractor.py — 实盘平仓教训提取（2026-08-15 重写）
|
||||
|
||||
旧版病状（见 docs/decisions/2026-08-15-策略自我进化闭环重构.md）：
|
||||
- 硬编码 version='v_next4'（已证伪策略）
|
||||
- 名为"已平仓交易教训"，实际读的是回测 trades 而非实盘平仓
|
||||
- 用 LLM 逐笔分析回测 trades（既贵又假——回测交易没有"教训"可挖）
|
||||
|
||||
重写方向（设计文档批准）：
|
||||
1. 数据源改实盘：strategy_tracking 已平仓记录（status=hit_tp/hit_sl/expired/manual_close）
|
||||
2. 结合当日温区（market_regime）归因
|
||||
3. 规则化提取（非 LLM）：命中止盈=盈利规律，止损/超时=亏损教训
|
||||
4. 每周一次，跟随 evolution_engine 同跑（周六 22:00）
|
||||
|
||||
幂等：按 trade_id 去重（同笔不重复写）；已写过的 lesson_text 跳过。
|
||||
"""
|
||||
import sys, os, json, sqlite3
|
||||
import sys, os, sqlite3
|
||||
from datetime import datetime, timedelta
|
||||
|
||||
sys.path.insert(0, '/home/hmo/MoFin')
|
||||
sys.path.insert(0, '/home/hmo/MoFin/deploy/profile-scripts')
|
||||
sys.path.insert(0, "/home/hmo/MoFin")
|
||||
|
||||
DB = os.environ.get('MOFIN_DB', '/home/hmo/MoFin/data/mofin.db')
|
||||
DB = os.environ.get("MOFIN_DB", "/home/hmo/MoFin/data/mofin.db")
|
||||
LOOKBACK_DAYS = 30 # 提取近30天已平仓
|
||||
|
||||
# 状态 → 教训类型映射
|
||||
STATUS_LESSON = {
|
||||
"hit_tp": ("win_pattern", "止盈有效"),
|
||||
"hit_sl": ("loss_pattern", "止损生效"),
|
||||
"expired": ("loss_pattern", "持有到期未达目标"),
|
||||
"manual_close": ("loss_pattern", "人工平仓"),
|
||||
}
|
||||
|
||||
# 平仓原因 → 细化教训
|
||||
REASON_TEXT = {
|
||||
"止盈触发": "触达止盈位落袋",
|
||||
"止损触发": "跌破止损位离场",
|
||||
"反弹减仓触发": "反弹遇阻减仓",
|
||||
"超时退出": "持有超时退出",
|
||||
}
|
||||
|
||||
|
||||
def get_closed_trades(conn, days=30):
|
||||
"""取近N天已平仓的交易（从strategy_research的trades里取最近的，或从holding_strategies推断）"""
|
||||
# 从最近的回测结果取交易（作为样本分析）
|
||||
r = conn.execute("""
|
||||
SELECT results_json FROM strategy_research
|
||||
WHERE version='v_next4' AND period_tag='5y' ORDER BY id DESC LIMIT 1
|
||||
""").fetchone()
|
||||
if not r:
|
||||
def get_conn():
|
||||
conn = sqlite3.connect(DB)
|
||||
conn.row_factory = sqlite3.Row
|
||||
return conn
|
||||
|
||||
|
||||
def get_regime_for(conn, date_str, market="a"):
|
||||
"""取指定日期最近的市场温区"""
|
||||
r = conn.execute(
|
||||
"SELECT regime FROM market_regime WHERE market=? AND date<=? ORDER BY date DESC LIMIT 1",
|
||||
(market, date_str)).fetchone()
|
||||
return r["regime"] if r else None
|
||||
|
||||
|
||||
def extract_lessons(days=LOOKBACK_DAYS, verbose=True):
|
||||
"""提取近 N 天实盘已平仓交易的教训"""
|
||||
conn = get_conn()
|
||||
since = (datetime.now() - timedelta(days=days)).strftime("%Y-%m-%d")
|
||||
|
||||
rows = conn.execute("""
|
||||
SELECT id, code, name, status, closed_at, close_reason, theoretical_pnl,
|
||||
actual_pnl, actual_exit_reason
|
||||
FROM strategy_tracking
|
||||
WHERE status != 'active' AND closed_at >= ?
|
||||
ORDER BY closed_at DESC
|
||||
""", (since,)).fetchall()
|
||||
if not rows:
|
||||
if verbose:
|
||||
print(f"近{days}天无已平仓记录，跳过", flush=True)
|
||||
conn.close()
|
||||
return []
|
||||
res = json.loads(r[0])
|
||||
trades = res.get('trades', [])
|
||||
# 按日期排序，取最近的
|
||||
recent = sorted(trades, key=lambda x: x.get('entry_date', ''), reverse=True)[:10]
|
||||
return recent
|
||||
|
||||
|
||||
def analyze_trade(trade):
|
||||
"""分析单笔交易的成败原因（规则化，非LLM）"""
|
||||
profit = trade.get('profit_pct', 0)
|
||||
hold_days = trade.get('hold_days', 0)
|
||||
factors = trade.get('factors', {})
|
||||
|
||||
# 统计 + 提取
|
||||
stats = {"hit_tp": 0, "hit_sl": 0, "expired": 0, "manual_close": 0}
|
||||
lessons = []
|
||||
|
||||
# 盈利交易的共性
|
||||
if profit > 15:
|
||||
if factors.get('mkt_adx', 0) > 25:
|
||||
lessons.append(('win_pattern', f"大盘趋势强(ADX={factors['mkt_adx']:.0f})时盈利{profit:.1f}%", 0.8))
|
||||
if factors.get('sector_above_ma20'):
|
||||
lessons.append(('win_pattern', f"板块在MA20上方时盈利{profit:.1f}%", 0.7))
|
||||
if trade.get('dna'):
|
||||
lessons.append(('win_pattern', f"动量基因(DNA)票盈利{profit:.1f}%", 0.9))
|
||||
|
||||
# 亏损交易的共性
|
||||
if profit < -5:
|
||||
if factors.get('mkt_slope', 0) < -0.5:
|
||||
lessons.append(('loss_pattern', f"大盘斜率负({factors['mkt_slope']:.2f})时亏损{profit:.1f}%", 0.7))
|
||||
if not factors.get('sector_above_ma20'):
|
||||
lessons.append(('loss_pattern', f"板块在MA20下方时亏损{profit:.1f}%", 0.6))
|
||||
if hold_days < 5:
|
||||
lessons.append(('loss_pattern', f"持仓{hold_days}天短于5天时亏损{profit:.1f}%", 0.5))
|
||||
|
||||
# 长持盈利
|
||||
if profit > 10 and hold_days > 30:
|
||||
lessons.append(('win_pattern', f"长持{hold_days}天盈利{profit:.1f}%", 0.85))
|
||||
|
||||
written = 0
|
||||
for r in rows:
|
||||
status = r["status"]
|
||||
stats[status] = stats.get(status, 0) + 1
|
||||
# 只对止盈/止损提取（expired/manual_close 噪音大，跳过教训提取但统计）
|
||||
if status not in ("hit_tp", "hit_sl"):
|
||||
continue
|
||||
pnl = r["actual_pnl"] if r["actual_pnl"] is not None else r["theoretical_pnl"]
|
||||
if pnl is None:
|
||||
continue
|
||||
# 幂等：同 trade_id 已写过则跳过
|
||||
exist = conn.execute(
|
||||
"SELECT 1 FROM strategy_lessons WHERE trade_id=? AND lesson_type=?",
|
||||
(r["id"], "win_pattern" if status == "hit_tp" else "loss_pattern")).fetchone()
|
||||
if exist:
|
||||
continue
|
||||
|
||||
regime = get_regime_for(conn, (r["closed_at"] or "")[:10])
|
||||
reason_txt = REASON_TEXT.get(r["close_reason"], r["close_reason"] or "平仓")
|
||||
if status == "hit_tp":
|
||||
ltype = "win_pattern"
|
||||
conf = 0.6 if pnl >= 5 else 0.4
|
||||
text = (f"实盘止盈：{r['name']}({r['code']}) {reason_txt}，"
|
||||
f"收益{pnl:+.1f}%" + (f"（{regime}温区）" if regime else ""))
|
||||
else:
|
||||
ltype = "loss_pattern"
|
||||
conf = 0.6 if pnl <= -5 else 0.4
|
||||
text = (f"实盘止损：{r['name']}({r['code']}) {reason_txt}，"
|
||||
f"亏损{pnl:+.1f}%" + (f"（{regime}温区）" if regime else ""))
|
||||
lessons.append({
|
||||
"trade_id": r["id"], "lesson_type": ltype, "lesson_text": text,
|
||||
"confidence": conf, "profit_pct": pnl,
|
||||
})
|
||||
|
||||
# 写库
|
||||
for l in lessons:
|
||||
conn.execute("""
|
||||
INSERT INTO strategy_lessons (strategy_version, trade_id, lesson_type, lesson_text, confidence, applied)
|
||||
VALUES ('live_trades', ?, ?, ?, ?, 0)
|
||||
""", (l["trade_id"], l["lesson_type"], l["lesson_text"], l["confidence"]))
|
||||
written += 1
|
||||
conn.commit()
|
||||
|
||||
# 温区级汇总教训（全部已平仓按温区归因）
|
||||
if stats["hit_tp"] + stats["hit_sl"] > 0:
|
||||
tp_pnl = sum((r["actual_pnl"] if r["actual_pnl"] is not None else r["theoretical_pnl"] or 0)
|
||||
for r in rows if r["status"] == "hit_tp")
|
||||
sl_pnl = sum((r["actual_pnl"] if r["actual_pnl"] is not None else r["theoretical_pnl"] or 0)
|
||||
for r in rows if r["status"] == "hit_sl")
|
||||
summary = (f"近{days}天实盘复盘：止盈{stats['hit_tp']}笔(均{round(tp_pnl/max(stats['hit_tp'],1),1)}%)"
|
||||
f" / 止损{stats['hit_sl']}笔(均{round(sl_pnl/max(stats['hit_sl'],1),1)}%)")
|
||||
# 汇总教训写一条（幂等：按文本）
|
||||
exist_sum = conn.execute(
|
||||
"SELECT 1 FROM strategy_lessons WHERE lesson_text=? AND lesson_type='summary'",
|
||||
(summary,)).fetchone()
|
||||
if not exist_sum:
|
||||
conn.execute("""
|
||||
INSERT INTO strategy_lessons (strategy_version, trade_id, lesson_type, lesson_text, confidence, applied)
|
||||
VALUES ('live_trades', NULL, 'summary', ?, 0.8, 0)
|
||||
""", (summary,))
|
||||
written += 1
|
||||
conn.commit()
|
||||
|
||||
conn.close()
|
||||
if verbose:
|
||||
print(f"近{days}天已平仓: {stats}，新增教训 {written} 条", flush=True)
|
||||
for l in lessons[:5]:
|
||||
print(f" [{l['lesson_type']}] {l['lesson_text']} ({l['confidence']})", flush=True)
|
||||
return lessons
|
||||
|
||||
|
||||
def extract_lessons(days=30):
|
||||
"""提取近N天交易的教训"""
|
||||
conn = sqlite3.connect(DB)
|
||||
conn.row_factory = sqlite3.Row
|
||||
|
||||
trades = get_closed_trades(conn, days)
|
||||
if not trades:
|
||||
print("无交易数据", flush=True)
|
||||
conn.close()
|
||||
return []
|
||||
|
||||
all_lessons = []
|
||||
for t in trades:
|
||||
lessons = analyze_trade(t)
|
||||
for lesson_type, text, confidence in lessons:
|
||||
all_lessons.append({
|
||||
'strategy_version': 'v_next4',
|
||||
'trade_id': t.get('id', 0),
|
||||
'lesson_type': lesson_type,
|
||||
'lesson_text': text,
|
||||
'confidence': confidence,
|
||||
'profit_pct': t.get('profit_pct', 0),
|
||||
'entry_date': t.get('entry_date', ''),
|
||||
})
|
||||
|
||||
# 写入 strategy_lessons 表
|
||||
for l in all_lessons:
|
||||
conn.execute("""
|
||||
INSERT INTO strategy_lessons (strategy_version, trade_id, lesson_type, lesson_text, confidence, applied)
|
||||
VALUES (?, ?, ?, ?, ?, 0)
|
||||
""", (l['strategy_version'], l['trade_id'], l['lesson_type'], l['lesson_text'], l['confidence']))
|
||||
conn.commit()
|
||||
|
||||
# 打印
|
||||
print(f"分析 {len(trades)} 笔交易, 提取 {len(all_lessons)} 条教训", flush=True)
|
||||
for l in all_lessons[:5]:
|
||||
print(f" [{l['lesson_type']}] {l['lesson_text']} (置信度{l['confidence']})", flush=True)
|
||||
|
||||
conn.close()
|
||||
return all_lessons
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
if __name__ == "__main__":
|
||||
extract_lessons()
|
||||
|
||||
Reference in New Issue
Block a user