118 lines
4.7 KiB
Python
118 lines
4.7 KiB
Python
# -*- coding: utf-8 -*-
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"""evolution/b_group_miner.py — B组挖掘 v3（务实版）
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聚焦缺口温区（A股震荡市/港股下跌市），用【短期反弹】作果（fwd_ret10>=8% 或 fwd_ret20>=12%），
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在超跌+企稳因子池上扫描组合。短期反弹样本充足，能稳定产出候选。
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"""
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import json
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import sqlite3
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import numpy as np
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import pandas as pd
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from datetime import datetime
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DATA_DIR = "/home/hmo/MoFin/data"
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OUT_JSON = f"{DATA_DIR}/b_group_candidates.json"
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def load_regime_map(market="a"):
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conn = sqlite3.connect("/home/hmo/MoFin/data/mofin.db", timeout=10)
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rows = conn.execute("SELECT date, regime FROM market_regime WHERE market=?", (market,)).fetchall()
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conn.close()
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return {d: r for d, r in rows}
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def load_panel(market):
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path = "/tmp/panel_12d_hk.pkl" if market == "hk" else "/tmp/panel_12d.pkl"
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p = pd.read_pickle(path)
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p = p.sort_values(["code", "date"]).reset_index(drop=True)
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p["fwd_ret10"] = p.groupby("code")["close"].transform(lambda x: x.shift(-10) / x - 1) * 100
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p["fwd_ret20"] = p.groupby("code")["close"].transform(lambda x: x.shift(-20) / x - 1) * 100
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return p
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def scan(market, regime, panel, target="ret10", min_n=200):
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"""扫描超跌+企稳因子组合的短期反弹概率"""
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rm = load_regime_map(market)
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p = panel.copy()
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p["_regime"] = p["date"].map(rm)
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col = "fwd_ret10" if target == "ret10" else "fwd_ret20"
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sub = p[p["_regime"] == regime].dropna(subset=[col])
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if len(sub) < min_n:
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return []
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th = 8 if target == "ret10" else 12
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sub["is_ok"] = (sub[col] >= th).astype(int)
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br = sub["is_ok"].mean() * 100
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print(f"[{market}/{regime}] 样本{len(sub)} 基线短期反弹率({th}%/{target}){br:.1f}%")
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# 因子：超跌 + 企稳 + 小盘低估值（温区通用的候选）
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factor_defs = {
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"bias60": ("<", -5), "bias60_deep": ("<", -15), "rsi": ("<", 40),
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"rsi_shallow": ("<", 55), "dist_lo20": (">", 3), "vol_ratio": (">", 1.0),
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"ret5": (">", -3), "mcap_q": ("<", 0.3), "pe_q": ("<", 0.3),
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"sec_ret20": ("<", 0), "mkt_rsi": ("<", 50), "mkt_ret20": ("<", -3),
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}
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single = []
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for feat, (op, val) in factor_defs.items():
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if feat not in sub.columns:
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continue
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cond = sub[feat] < val if op == "<" else sub[feat] > val
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m = sub[cond]
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if len(m) < 100:
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continue
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rate = m["is_ok"].mean() * 100
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if rate > br + 2:
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single.append((feat, round(rate, 1), len(m), round(rate - br, 1)))
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single.sort(key=lambda x: -x[3])
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print(" 单条件:", single[:5])
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results = []
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strong = [s[0] for s in single[:6]]
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for i in range(len(strong)):
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for j in range(i+1, len(strong)):
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f1, f2 = strong[i], strong[j]
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cond = pd.Series(True, index=sub.index)
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for feat, (op, val) in [(f1, factor_defs[f1]), (f2, factor_defs[f2])]:
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cond &= (sub[feat] < val) if op == "<" else (sub[feat] > val)
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m = sub[cond]
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if len(m) >= 100:
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rate = m["is_ok"].mean() * 100
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results.append(({f1: factor_defs[f1], f2: factor_defs[f2]},
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len(m), round(rate, 1), round(m[col].mean(), 1), round(rate - br, 1)))
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results.sort(key=lambda x: -x[4])
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return results[:5]
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def to_entry(cond_dict):
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entry = {}
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for feat, (op, val) in cond_dict.items():
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key = feat + ("_min" if op == ">" else "_max")
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entry[key] = float(val)
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return entry
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def mine(market="a", regimes=None, target="ret10"):
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regimes = regimes or ["trend_up", "choppy", "trend_down"]
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panel = load_panel(market)
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out = {"market": market, "mined_at": datetime.now().strftime("%Y-%m-%d %H:%M:%S"), "candidates": []}
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for rg in regimes:
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combos = scan(market, rg, panel, target=target)
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for cond, n, rate, avg, extra in combos[:3]:
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cand = {
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"regime": rg, "market": market, "group": "B", "status": "candidate",
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"entry": to_entry(cond),
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"trades_est": n, "ok_rate": rate, "avg_ret": avg, "excess_pp": extra,
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"hypothesis": f"[{rg}] 由果及因: {cond} → 短期反弹率{rate}%(基线+{extra}pp)",
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}
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out["candidates"].append(cand)
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print(f" [{rg}] {cond} n={n} 反弹率{rate}% 超额+{extra}pp")
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return out
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if __name__ == "__main__":
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import sys
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market = sys.argv[1] if len(sys.argv) > 1 else "hk"
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target = sys.argv[2] if len(sys.argv) > 2 else "ret10"
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res = mine(market, target=target)
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with open(OUT_JSON, "w", encoding="utf-8") as f:
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json.dump(res, f, ensure_ascii=False, indent=1)
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print(f"写入 {OUT_JSON}: {len(res['candidates'])} 个候选")
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