# -*- coding: utf-8 -*- """s2_panic_v2 正式生成器（2026-08-16 由果及因升级版） 洞审计结论： 原 s2_panic 信号无 score → portfolio_sim 按 code 顺序随机成交（洗牌漂移16pp） s2 业绩 = 大恐慌日 beta（2025-04-09 全市场 +19.85%），无选股 alpha 由果及因（72941 恐慌日信号，tp30/sl12/60日 评估）： alpha 入场组合：mkt_rsi<25 + mcap_q<0.4 + rsi>=35 + sec_ret20>=-10 → 胜率60.8% vs 基线30.7% 每日 top-N 截断：信号/成交比可控（top8: 141信号/86成交/比1.6） score 精细排序无效（诚实：top5 11.79% vs 随机 13.33%）——截断是解法，不假装排序有效 参数：top_n=8（每日最多8信号，10仓位槽内可控），出场 tp30/sl12/max60 """ import sys, json sys.path.insert(0, "/home/hmo/MoFin") import pandas as pd import numpy as np TOP_N = 8 def alpha_score(mcap_q, rsi, sec_ret20, news3): """由果及因 alpha 评分（保留字段供 portfolio_sim 机制，诚实：精细排序无效）""" sc = 0 if mcap_q is not None and not np.isnan(mcap_q): sc += 40 if mcap_q < 0.2 else 32 if mcap_q < 0.4 else 24 if mcap_q < 0.6 else 16 if mcap_q < 0.8 else 8 if rsi is not None and not np.isnan(rsi): sc += 30 if rsi >= 45 else 22 if rsi >= 35 else 12 if rsi >= 25 else 6 if sec_ret20 is not None and not np.isnan(sec_ret20): sc += 20 if sec_ret20 >= 0 else 16 if sec_ret20 >= -10 else 8 if sec_ret20 >= -20 else 3 if news3 is not None and not np.isnan(news3): sc += 10 if news3 >= 2 else 7 if news3 >= 1 else 2 return sc def gen_trades(start, end, top_n=TOP_N): """生成 s2_panic_v2 在 [start,end] 窗口的信号 trades""" panel = pd.read_pickle("/tmp/panel_12d.pkl") panel = panel.sort_values(["code", "date"]).reset_index(drop=True) g = panel.groupby("code", group_keys=False) def fwd_max(s, w): return s[::-1].rolling(w, min_periods=1).max()[::-1] def fwd_min(s, w): return s[::-1].rolling(w, min_periods=1).min()[::-1] panel["fwd_max60"] = g["close"].transform(lambda x: fwd_max(x, 60)) panel["fwd_min60"] = g["close"].transform(lambda x: fwd_min(x, 60)) panic = panel[(panel["mkt_rsi"] < 25) & (panel["date"] >= start) & (panel["date"] <= end)].copy() sig = panic[(panic["mcap_q"] < 0.4) & (panic["rsi"] >= 35) & (panic["sec_ret20"] >= -10)].copy() sig["score"] = sig.apply(lambda r: alpha_score(r["mcap_q"], r["rsi"], r["sec_ret20"], r["news3"]), axis=1) # 每日 top-N（score 降序，同日择优——虽精细排序无效，但高分不劣于随机，且机制一致） sig = sig.sort_values(["date", "score"], ascending=[True, False]).groupby("date").head(top_n) trades = [] for _, s in sig.iterrows(): ep = s["close"] if ep <= 0: continue fmax, fmin = s["fwd_max60"], s["fwd_min60"] hit_tp = fmax >= ep * 1.30 hit_sl = fmin <= ep * 0.88 if hit_tp: pnl, reason = 30.0, "target" elif hit_sl: pnl, reason = -12.0, "stop" else: pnl, reason = (fmax / ep - 1) * 100 if not pd.isna(fmax) else 0, "time" trades.append({ "code": s["code"], "name": str(s["code"]), "entry_date": s["date"], "entry_price": round(ep, 2), "exit_price": round(ep * (1 + pnl / 100), 2), "profit_pct": round(pnl, 2), "exit_reason": reason, "hold_days": 60, "score": int(s["score"]), "boost": 1.0, }) return trades def build_results(trades): """trades → results_json（summary + portfolio_sim）""" import copy, random from strategy_lab import portfolio_sim n = len(trades) wins = [t for t in trades if t["profit_pct"] > 0] losses = [t for t in trades if t["profit_pct"] <= 0] wr = len(wins) / n * 100 if n else 0 avg = sum(t["profit_pct"] for t in trades) / n if n else 0 avg_w = sum(t["profit_pct"] for t in wins) / len(wins) if wins else 0 avg_l = abs(sum(t["profit_pct"] for t in losses) / len(losses)) if losses else 1 pf = avg_w / avg_l if avg_l else 0 sim = portfolio_sim(trades, 1000000, max_positions=10) # 洗牌稳健性（5次） rets = [] for seed in range(5): t2 = copy.deepcopy(trades) rng = random.Random(seed) rng.shuffle(t2) rets.append(portfolio_sim(t2, 1000000, max_positions=10).get("total_return_pct")) return { "summary": { "total_trades": n, "win_rate": round(wr, 1), "avg_profit_pct": round(avg, 2), "avg_win_pct": round(avg_w, 2), "avg_loss_pct": round(-avg_l, 2), "avg_hold_days": round(sum(t["hold_days"] for t in trades) / n, 1) if n else 0, "sharpe_ratio": round(sim.get("sharpe_ratio", 0) or 0, 2), "profit_factor": round(pf, 2), }, "portfolio": { "capital_final": sim.get("capital_final"), "total_return_pct": sim.get("total_return_pct"), "cagr_pct": sim.get("cagr_pct"), "portfolio_max_dd_pct": sim.get("portfolio_max_dd_pct"), "positions_taken": sim.get("positions_taken"), "positions_skipped": sim.get("positions_skipped"), }, "robustness": {"shuffle_total_return": rets, "spread_pp": round(max(rets) - min(rets), 1)}, "trades": trades, } if __name__ == "__main__": import sqlite3 from datetime import datetime DB = "/home/hmo/MoFin/data/mofin.db" VERSION = "s2_panic_v2" # 各周期窗口 windows = { "1y": ("2025-07-01", "2026-07-01"), "2y": ("2024-07-01", "2026-07-01"), "5y": ("2021-07-01", "2026-07-01"), "10y": ("2016-01-01", "2026-07-01"), } conn = sqlite3.connect(DB, timeout=30) now = datetime.now().strftime("%Y-%m-%d %H:%M:%S") for pt, (s, e) in windows.items(): trades = gen_trades(s, e) results = build_results(trades) # upsert strategy_research exist = conn.execute("SELECT id FROM strategy_research WHERE version=? AND period_tag=?", (VERSION, pt)).fetchone() if exist: conn.execute("UPDATE strategy_research SET results_json=?, updated_at=? WHERE version=? AND period_tag=?", (json.dumps(results, ensure_ascii=False), now, VERSION, pt)) else: conn.execute("""INSERT INTO strategy_research (version, name, summary, hypothesis, parent, config_json, results_json, period, created_at, market, period_tag, deprecated) VALUES (?,?,?,?,?,?,?,?,?,?,?,?)""", (VERSION, "S2恐慌买alpha升级", "恐慌日+小市值+强势+行业抗跌,每日top8截断", "由果及因: alpha组合胜率60.8% vs 基线30.7%; 每日top-N截断填'多信号少成交'洞", "S2家族", json.dumps({"top_n": TOP_N, "entry": {"mkt_rsi_max": 25, "mcap_q_max": 0.4, "rsi_min": 35, "sec_ret20_min": -10}, "exit": {"tp": 30, "sl": 12, "max_hold": 20}}, ensure_ascii=False), json.dumps(results, ensure_ascii=False), None, now, "a", pt, None)) s = results["summary"] p = results["portfolio"] print(f"[{pt}] 信号{s['total_trades']} 成交{p.get('positions_taken')} 比{s['total_trades']/max(p.get('positions_taken',1),1):.1f} " f"胜率{s['win_rate']}% 年化{p.get('cagr_pct')}% 回撤{p.get('portfolio_max_dd_pct')}% 洗牌差{results['robustness']['spread_pp']}pp") conn.commit() conn.close() print(f"\n{len(windows)} 个周期已写入 strategy_research (version={VERSION})")