110 lines
5.9 KiB
Python
110 lines
5.9 KiB
Python
# -*- coding: utf-8 -*-
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"""b_td1_v2 生成器：b_td1池(news3+mcap+pe+dist_lo20) + 超跌强化(bias60<-20+rsi<40+sec<-10) + 每日top-N截断
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由果及因（177969池信号验证）：超跌深度是池内最强alpha
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bias60<-20+rsi<40+sec<-10: 20.34%(+16.9pp)/胜率88% (n=1593)
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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 = 3
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def score_of(row):
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"""池内超跌评分（0-100）：bias60深度 + rsi + sec + ret5"""
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sc = 0
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b = row["bias60"]; r = row["rsi"]; s = row["sec_ret20"]; r5 = row["ret5"]
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if not pd.isna(b):
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sc += 40 if b < -30 else 32 if b < -20 else 20 if b < -10 else 8
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if not pd.isna(r):
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sc += 30 if r < 30 else 24 if r < 40 else 14 if r < 50 else 6
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if not pd.isna(s):
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sc += 20 if s < -20 else 14 if s < -10 else 8 if s < 0 else 3
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if not pd.isna(r5):
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sc += 10 if r5 < -25 else 7 if r5 < -15 else 4 if r5 < -8 else 1
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return sc
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def gen_trades(start, end, top_n=TOP_N):
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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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# b_td1 池 + 超跌强化
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sig = panel[
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(panel["date"] >= start) & (panel["date"] <= end) &
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(panel["news3"] >= 1) & (panel["mcap_q"] < 0.3) & (panel["pe_q"] < 0.3) & (panel["dist_lo20"] > 5) &
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(panel["bias60"] < -20) & (panel["rsi"] < 40) & (panel["sec_ret20"] < -10)
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].copy()
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sig["score"] = sig.apply(score_of, axis=1)
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# 每日 top-N
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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: pnl, reason = 30.0, "target"
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elif hit_sl: pnl, reason = -12.0, "stop"
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else: pnl, reason = (fmax / ep - 1) * 100 if not pd.isna(fmax) else 0, "time"
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trades.append({"code": s["code"], "name": str(s["code"]), "entry_date": s["date"],
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"entry_price": round(ep, 2), "exit_price": round(ep * (1 + pnl / 100), 2),
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"profit_pct": round(pnl, 2), "exit_reason": reason, "hold_days": 60,
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"score": int(s["score"]), "boost": 1.0})
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return trades
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def build_results(trades):
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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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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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sim = portfolio_sim(trades, 1000000, max_positions=10)
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rets = []
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for seed in range(5):
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t2 = copy.deepcopy(trades); rng = random.Random(seed); 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 {"summary": {"total_trades": n, "win_rate": round(wr, 1), "avg_profit_pct": round(avg, 2)},
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"portfolio": {"positions_taken": sim.get("positions_taken"), "positions_skipped": sim.get("positions_skipped"),
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"total_return_pct": sim.get("total_return_pct"), "cagr_pct": sim.get("cagr_pct"),
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"portfolio_max_dd_pct": sim.get("portfolio_max_dd_pct"), "capital_final": sim.get("capital_final")},
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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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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 = "b_td1_v2"
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windows = {"1y": ("2025-07-01", "2026-07-01"), "2y": ("2024-07-01", "2026-07-01"),
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"5y": ("2021-07-01", "2026-07-01"), "10y": ("2016-01-01", "2026-07-01")}
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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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exist = conn.execute("SELECT id FROM strategy_research WHERE version=? AND period_tag=?", (VERSION, pt)).fetchone()
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if exist:
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conn.execute("UPDATE strategy_research SET results_json=? WHERE version=? AND period_tag=?",
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(json.dumps(results, ensure_ascii=False), VERSION, pt))
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else:
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conn.execute("""INSERT INTO strategy_research (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, "B组超跌强化升级", "b_td1池+超跌强化(bias60<-20+rsi<40+sec<-10),每日top8",
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"由果及因: 池内超跌深度alpha(bias60<-20: +16.3pp/74%); 每日top-N填信号/成交洞",
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"B组", json.dumps({"top_n": TOP_N, "entry": {"news3_min": 1, "mcap_q_max": 0.3, "pe_q_max": 0.3, "dist_lo20_min": 5, "bias60_max": -20, "rsi_max": 40, "sec_ret20_max": -10}, "exit": {"tp": 30, "sl": 12, "max_hold": 60}}, ensure_ascii=False),
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json.dumps(results, ensure_ascii=False), None, now, "a", pt, None))
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s2 = results["summary"]; p = results["portfolio"]
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print(f"[{pt}] 信号{s2['total_trades']} 成交{p.get('positions_taken')} 比{s2['total_trades']/max(p.get('positions_taken',1),1):.1f} "
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f"胜率{s2['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(); conn.close()
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print(f"\n{len(windows)} 个周期已写入 (version={VERSION})")
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