From 48765e615b9eaac933fde2db2ffd6239135d4578 Mon Sep 17 00:00:00 2001 From: xxm Date: Sun, 16 Aug 2026 13:04:22 +0800 Subject: [PATCH] =?UTF-8?q?feat:=20B=E7=BB=84=E7=AD=96=E7=95=A5=E6=8C=96?= =?UTF-8?q?=E6=8E=98=E7=AE=A1=E7=BA=BF(b=5Fgroup=5Fminer)=E2=80=94?= =?UTF-8?q?=E2=80=94=E7=94=B1=E6=9E=9C=E5=8F=8A=E5=9B=A0=E6=8C=89=E6=B8=A9?= =?UTF-8?q?=E5=8C=BA=E6=89=AB=E6=8F=8F=E5=9B=A0=E5=AD=90=E7=BB=84=E5=90=88?= =?UTF-8?q?,=E4=BA=A7=E5=87=BAB=E7=BB=84=E5=80=99=E9=80=89=E4=BE=9BAB?= =?UTF-8?q?=E5=AF=B9=E7=85=A7?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- evolution/b_group_miner.py | 117 +++++++++++++++++++++++++++++++++++++ 1 file changed, 117 insertions(+) create mode 100644 evolution/b_group_miner.py diff --git a/evolution/b_group_miner.py b/evolution/b_group_miner.py new file mode 100644 index 00000000..57423179 --- /dev/null +++ b/evolution/b_group_miner.py @@ -0,0 +1,117 @@ +# -*- coding: utf-8 -*- +"""evolution/b_group_miner.py — B组挖掘 v3（务实版） +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%）， +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。 +""" +import json +import sqlite3 +import numpy as np +import pandas as pd +from datetime import datetime + +DATA_DIR = "/home/hmo/MoFin/data" +OUT_JSON = f"{DATA_DIR}/b_group_candidates.json" + + +def load_regime_map(market="a"): + conn = sqlite3.connect("/home/hmo/MoFin/data/mofin.db", timeout=10) + rows = conn.execute("SELECT date, regime FROM market_regime WHERE market=?", (market,)).fetchall() + conn.close() + return {d: r for d, r in rows} + + +def load_panel(market): + path = "/tmp/panel_12d_hk.pkl" if market == "hk" else "/tmp/panel_12d.pkl" + p = pd.read_pickle(path) + p = p.sort_values(["code", "date"]).reset_index(drop=True) + p["fwd_ret10"] = p.groupby("code")["close"].transform(lambda x: x.shift(-10) / x - 1) * 100 + p["fwd_ret20"] = p.groupby("code")["close"].transform(lambda x: x.shift(-20) / x - 1) * 100 + return p + + +def scan(market, regime, panel, target="ret10", min_n=200): + """扫描超跌+企稳因子组合的短期反弹概率""" + rm = load_regime_map(market) + p = panel.copy() + p["_regime"] = p["date"].map(rm) + col = "fwd_ret10" if target == "ret10" else "fwd_ret20" + sub = p[p["_regime"] == regime].dropna(subset=[col]) + if len(sub) < min_n: + return [] + th = 8 if target == "ret10" else 12 + sub["is_ok"] = (sub[col] >= th).astype(int) + br = sub["is_ok"].mean() * 100 + print(f"[{market}/{regime}] 样本{len(sub)} 基线短期反弹率({th}%/{target}){br:.1f}%") + + # 因子：超跌 + 企稳 + 小盘低估值（温区通用的候选） + factor_defs = { + "bias60": ("<", -5), "bias60_deep": ("<", -15), "rsi": ("<", 40), + "rsi_shallow": ("<", 55), "dist_lo20": (">", 3), "vol_ratio": (">", 1.0), + "ret5": (">", -3), "mcap_q": ("<", 0.3), "pe_q": ("<", 0.3), + "sec_ret20": ("<", 0), "mkt_rsi": ("<", 50), "mkt_ret20": ("<", -3), + } + single = [] + for feat, (op, val) in factor_defs.items(): + if feat not in sub.columns: + continue + cond = sub[feat] < val if op == "<" else sub[feat] > val + m = sub[cond] + if len(m) < 100: + continue + rate = m["is_ok"].mean() * 100 + if rate > br + 2: + single.append((feat, round(rate, 1), len(m), round(rate - br, 1))) + single.sort(key=lambda x: -x[3]) + print(" 单条件:", single[:5]) + + results = [] + strong = [s[0] for s in single[:6]] + for i in range(len(strong)): + for j in range(i+1, len(strong)): + f1, f2 = strong[i], strong[j] + cond = pd.Series(True, index=sub.index) + for feat, (op, val) in [(f1, factor_defs[f1]), (f2, factor_defs[f2])]: + cond &= (sub[feat] < val) if op == "<" else (sub[feat] > val) + m = sub[cond] + if len(m) >= 100: + rate = m["is_ok"].mean() * 100 + results.append(({f1: factor_defs[f1], f2: factor_defs[f2]}, + len(m), round(rate, 1), round(m[col].mean(), 1), round(rate - br, 1))) + results.sort(key=lambda x: -x[4]) + return results[:5] + + +def to_entry(cond_dict): + entry = {} + for feat, (op, val) in cond_dict.items(): + key = feat + ("_min" if op == ">" else "_max") + entry[key] = float(val) + return entry + + +def mine(market="a", regimes=None, target="ret10"): + regimes = regimes or ["trend_up", "choppy", "trend_down"] + panel = load_panel(market) + out = {"market": market, "mined_at": datetime.now().strftime("%Y-%m-%d %H:%M:%S"), "candidates": []} + for rg in regimes: + combos = scan(market, rg, panel, target=target) + for cond, n, rate, avg, extra in combos[:3]: + cand = { + "regime": rg, "market": market, "group": "B", "status": "candidate", + "entry": to_entry(cond), + "trades_est": n, "ok_rate": rate, "avg_ret": avg, "excess_pp": extra, + "hypothesis": f"[{rg}] 由果及因: {cond} → 短期反弹率{rate}%(基线+{extra}pp)", + } + out["candidates"].append(cand) + print(f" [{rg}] {cond} n={n} 反弹率{rate}% 超额+{extra}pp") + return out + + +if __name__ == "__main__": + import sys + market = sys.argv[1] if len(sys.argv) > 1 else "hk" + target = sys.argv[2] if len(sys.argv) > 2 else "ret10" + res = mine(market, target=target) + with open(OUT_JSON, "w", encoding="utf-8") as f: + json.dump(res, f, ensure_ascii=False, indent=1) + print(f"写入 {OUT_JSON}: {len(res['candidates'])} 个候选")