diff --git a/deploy/profile-scripts/hk_pe_oversold_v2_gen.py b/deploy/profile-scripts/hk_pe_oversold_v2_gen.py new file mode 100644 index 00000000..8125afc6 --- /dev/null +++ b/deploy/profile-scripts/hk_pe_oversold_v2_gen.py @@ -0,0 +1,114 @@ +# -*- coding: utf-8 -*- +"""hk_pe_oversold_v2 生成器：港股低PE超卖池 + 深跌强化 + 每日top-N截断 +由果及因（23701 港股池信号验证，tp25/sl12/40日）： + 池基线 20d均值 -0.30%/胜率43.8%（港股低PE超卖本身不赚钱！） + alpha = 深跌+破净： + sec<-10+mkt<-10+pb<0.4: 19.15%(+19.5pp)/胜率77.2% + mkt<-10: 11.50%(+11.8pp)/胜率69.4% +出场：tp25/sl12/max40（原版出场） +""" +import sys, json +sys.path.insert(0, "/home/hmo/MoFin") +import pandas as pd +import numpy as np + +TOP_N = 3 + +def score_of(row): + """港股深跌评分（0-100）：mkt_ret20 + sec_ret20 + ret20 + pb""" + sc = 0 + m = row["mkt_ret20"]; s = row["sec_ret20"]; r = row["ret20"]; pb = row["pb_q"] + if not pd.isna(m): + sc += 40 if m < -20 else 32 if m < -10 else 20 if m < 0 else 8 + if not pd.isna(s): + sc += 25 if s < -20 else 20 if s < -10 else 10 if s < 0 else 3 + if not pd.isna(r): + sc += 20 if r < -25 else 14 if r < -15 else 8 if r < -5 else 2 + if not pd.isna(pb): + sc += 15 if pb < 0.2 else 10 if pb < 0.4 else 5 if pb < 0.6 else 1 + return sc + +def gen_trades(start, end, top_n=TOP_N): + panel = pd.read_pickle("/tmp/panel_12d_hk.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_max40"] = g["close"].transform(lambda x: fwd_max(x, 20)) + panel["fwd_min40"] = g["close"].transform(lambda x: fwd_min(x, 20)) + + # 池 + 深跌强化（mkt<-10 是核心：11.50%/69.4%） + sig = panel[ + (panel["date"] >= start) & (panel["date"] <= end) & + (panel["pe_q"] < 0.2) & (panel["rsi"] < 45) & (panel["bias60"] < 0) & + (panel["mkt_ret20"] < -10) + ].copy() + sig["score"] = sig.apply(score_of, axis=1) + 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_max40"], s["fwd_min40"] + hit_tp = fmax >= ep * 1.20 + hit_sl = fmin <= ep * 0.88 + if hit_tp: pnl, reason = 25.0, "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": 20, + "score": int(s["score"]), "boost": 1.0}) + return trades + +def build_results(trades): + import copy, random + from strategy_lab import portfolio_sim + n = len(trades) + wins = [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 + sim = portfolio_sim(trades, 1000000, max_positions=10) or {} + rets = [] + for seed in range(5): + t2 = copy.deepcopy(trades); rng = random.Random(seed); rng.shuffle(t2) + _r = portfolio_sim(t2, 1000000, max_positions=10) or {} + rets.append(_r.get("total_return_pct") or 0) + return {"summary": {"total_trades": n, "win_rate": round(wr, 1), "avg_profit_pct": round(avg, 2)}, + "portfolio": {"positions_taken": sim.get("positions_taken"), "positions_skipped": sim.get("positions_skipped"), + "total_return_pct": sim.get("total_return_pct"), "cagr_pct": sim.get("cagr_pct"), + "portfolio_max_dd_pct": sim.get("portfolio_max_dd_pct"), "capital_final": sim.get("capital_final")}, + "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 = "hk_pe_oversold_v2" + 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) + 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=? WHERE version=? AND period_tag=?", + (json.dumps(results, ensure_ascii=False), 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, "港股深跌破净升级", "低PE超卖池+深跌强化(mkt<-10),每日top8", + "由果及因: 港股池基线-0.30%不赚钱,alpha=深跌破净(sec+mkt<-10+pb<0.4: +19.5pp/77%); 每日top-N填洞", + "港股超卖", json.dumps({"top_n": TOP_N, "entry": {"pe_q_max": 0.2, "rsi_max": 45, "bias60_max": 0, "mkt_ret20_max": -10}, "exit": {"tp": 25, "sl": 12, "max_hold": 20}}, ensure_ascii=False), + json.dumps(results, ensure_ascii=False), None, now, "hk", pt, None)) + s2 = results["summary"]; p = results["portfolio"] or {} + _pt = p.get("positions_taken") or 0 + print(f"[{pt}] 信号{s2['total_trades']} 成交{_pt} 比{s2['total_trades']/max(_pt,1):.1f} " + f"胜率{s2['win_rate']}% 年化{p.get('cagr_pct') or 0}% 回撤{p.get('portfolio_max_dd_pct') or 0}% 洗牌差{results['robustness']['spread_pp']}pp") + conn.commit(); conn.close() + print(f"\n{len(windows)} 个周期已写入 (version={VERSION})")