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