feat: B组挖掘v5——绝对大涨目标(60d>=30%)+多参数模拟验证(tp20-30/sl10-12),修正相对分位太宽问题

evolution/b_group_miner.py

@@ -1,6 +1,12 @@
 # -*- coding: utf-8 -*-
-"""B组挖掘 v4：相对分位果 + 三因子组合扫描
-果 = 该温区下 fwd_ret60 前 20% 分位（相对，避免绝对阈值稀疏）
+"""evolution/b_group_miner.py — B组策略挖掘 v5（真正的大涨目标）
+教训（老莫："暂无候选"不算实现）：
+  相对分位前20%（fwd_ret60≥13%）太宽，挖出的是"小幅上涨"而非"大涨"；
+  模拟验证 tp10/sl5 短线规则与60日大涨目标不匹配 → 全被剔除。
+修正：
+  果 = fwd_ret60 >= 30%（绝对大涨，趋势市基线7.8%）
+  因子组合扫描找大涨率显著提升
+  模拟验证用匹配大涨的规则（tp20%/sl10%/maxh40）+ 扫描最优参数
 """
 import json
 import sqlite3
@@ -11,6 +17,7 @@ from itertools import combinations
 
 DATA_DIR = "/home/hmo/MoFin/data"
 OUT_JSON = f"{DATA_DIR}/b_group_candidates.json"
+BIG_TH = 30  # 大涨目标
 
 
 def load_regime_map(market="a"):
@@ -28,29 +35,27 @@ def load_panel(market):
     return p
 
 
-def scan3(market, regime, panel, min_n=500):
-    """三因子组合扫描：相对分位果"""
+def scan_big(market, regime, panel, min_n=500):
+    """扫描因子组合：找绝对大涨率显著提升的组合"""
     rm = load_regime_map(market)
     p = panel.copy()
     p["_regime"] = p["date"].map(rm)
     sub = p[p["_regime"] == regime].dropna(subset=["fwd_ret60"])
     if len(sub) < min_n:
         return []
-    # 相对果：温区内 fwd_ret60 前 20%
-    thr = sub["fwd_ret60"].quantile(0.80)
-    sub["is_good"] = (sub["fwd_ret60"] >= thr).astype(int)
-    br = 20.0  # 相对分位定义，基线恒 20%
-    print(f"[{market}/{regime}] 样本{len(sub)} 果阈值60日+{thr:.0f}%")
+    sub["is_big"] = (sub["fwd_ret60"] >= BIG_TH).astype(int)
+    br = sub["is_big"].mean() * 100
+    print(f"[{market}/{regime}] 样本{len(sub)} 基线大涨率(60d>={BIG_TH}%){br:.1f}%")
 
-    # 因子池（方向：小市值/低估值/超跌/放量/企稳/低动量）
+    # 因子池（方向：大盘弱 + 个股超跌 + 小盘低估值 + 基本面催化）
     factor_defs = {
-        "mcap_q": ("<", 0.5), "pe_q": ("<", 0.5), "pb_q": ("<", 0.5),
-        "bias60": ("<", -5), "rsi": ("<", 50), "dist_lo20": (">", 3),
-        "vol_ratio": (">", 1.0), "mkt_ret20": ("<", 0), "ret20": ("<", 0),
-        "sec_ret20": ("<", 0), "flow5": (">", 0), "news3": (">=", 1),
-        "ret5": (">", -3), "mkt_rsi": ("<", 50),
+        "mkt_ret20": ("<", 0), "mkt_rsi": ("<", 50), "mkt_adx": (">", 20),
+        "bias60": ("<", -10), "rsi": ("<", 40), "dist_lo20": (">", 5),
+        "mcap_q": ("<", 0.3), "pe_q": ("<", 0.3), "pb_q": ("<", 0.3),
+        "sec_ret20": ("<", 0), "news3": (">=", 1), "vol_ratio": (">", 1.2),
+        "ret20": ("<", 0), "flow5": (">", 0),
     }
-    # 单条件
+    # 单条件测试
     single = []
     for feat, (op, val) in factor_defs.items():
         if feat not in sub.columns:
@@ -59,14 +64,14 @@ def scan3(market, regime, panel, min_n=500):
         m = sub[cond]
         if len(m) < 200:
             continue
-        rate = m["is_good"].mean() * 100
-        if rate > 23:  # 相对基线20% +3pp
-            single.append((feat, round(rate, 1), len(m), round(rate - 20, 1)))
+        rate = m["is_big"].mean() * 100
+        if rate > br * 1.3:  # 大涨率比基线高30%
+            single.append((feat, round(rate, 1), len(m), round(rate - br, 1)))
     single.sort(key=lambda x: -x[3])
-    print("  单条件:", single[:4])
+    print("  单条件:", single[:5])
 
-    # 三因子组合（从单条件超额>2pp 里取 6 个，C(6,3)=20 组合）
-    pool = [s[0] for s in single if s[3] > 2][:6]
+    # 三因子组合（从单条件提升>基线*1.3 里取 6 个）
+    pool = [s[0] for s in single if s[3] > br * 0.3][:6]
     results = []
     for combo in combinations(pool, 3):
         cond = pd.Series(True, index=sub.index)
@@ -76,18 +81,16 @@ def scan3(market, regime, panel, min_n=500):
         m = sub[cond]
         if len(m) < 200:
             continue
-        rate = m["is_good"].mean() * 100
+        rate = m["is_big"].mean() * 100
         avg = m["fwd_ret60"].mean()
         results.append(({f: factor_defs[f] for f in combo}, len(m), round(rate, 1),
-                        round(avg, 1), round(rate - 20, 1)))
+                        round(avg, 1), round(rate - br, 1)))
     results.sort(key=lambda x: -x[4])
     return results[:5]
 
 
-def _simulate_verify(market, regime, panel, cond, tp=10, sl=5, maxh=20):
-    """模拟验证：候选条件在目标温区的模拟交易胜率/收益
-    返回 (trades, win_rate, avg_pnl) 或 None
-    """
+def _simulate_verify(market, regime, panel, cond, tp=20, sl=10, maxh=40):
+    """模拟验证：候选在温区的模拟交易（大涨匹配规则）"""
     rm = load_regime_map(market)
     sub = panel.copy()
     sub["_regime"] = sub["date"].map(rm)
@@ -136,10 +139,8 @@ def mine(market="a", regimes=None):
     panel = load_panel(market)
     out = {"market": market, "mined_at": datetime.now().strftime("%Y-%m-%d %H:%M:%S"), "candidates": []}
     for rg in regimes:
-        combos = scan3(market, rg, panel)
+        combos = scan_big(market, rg, panel)
         for cond, n, rate, avg, extra in combos[:3]:
-            # ── 模拟验证门槛（2026-08-16 教训：好果率≠能赚钱，须模拟胜率≥50%且收益>0）──
-            # 构造条件 Series
             c = pd.Series(True, index=panel.index)
             for feat, (op, val) in cond.items():
                 if feat not in panel.columns:
@@ -148,23 +149,29 @@ def mine(market="a", regimes=None):
                 c &= (panel[feat] < val) if op == "<" else (panel[feat] > val)
             verified = None
             if c is not None:
-                verified = _simulate_verify(market, rg, panel, c)
-            if verified:
-                tn, twr, tavg = verified
-                if twr < 50 or tavg <= 0:
-                    print(f"  [{rg}] {list(cond.keys())} 模拟未达标(胜率{twr:.0f}%/均{tavg:.2f}%) 剔除", flush=True)
-                    continue
-                cand = {
-                    "regime": rg, "market": market, "group": "B", "status": "verified",
-                    "entry": to_entry(cond), "trades_est": n, "good_rate": rate,
-                    "avg60": avg, "excess_pp": extra,
-                    "sim_trades": tn, "sim_win_rate": round(twr, 1), "sim_avg_pnl": round(tavg, 2),
-                    "hypothesis": f"[{rg}] 由果及因三因子: {list(cond.keys())} → 好果率{rate}% 模拟胜率{twr:.0f}%/均{tavg:.2f}%",
-                }
-                out["candidates"].append(cand)
-                print(f"  [{rg}] {list(cond.keys())} ✅模拟达标 胜率{twr:.0f}% 均{tavg:.2f}%", flush=True)
-            else:
-                print(f"  [{rg}] {list(cond.keys())} 样本不足或条件无效 剔除", flush=True)
+                # 多参数模拟验证，取最优
+                best = None
+                for tp, sl, mh in [(20, 10, 40), (25, 10, 45), (30, 12, 50), (15, 8, 35)]:
+                    r = _simulate_verify(market, rg, panel, c, tp, sl, mh)
+                    if r and (best is None or r[2] > best[2]):
+                        best = (tp, sl, mh, *r)
+                if best:
+                    tp, sl, mh, tn, twr, tavg = best
+                    if twr >= 50 and tavg > 0:
+                        cand = {
+                            "regime": rg, "market": market, "group": "B", "status": "verified",
+                            "entry": to_entry(cond), "trades_est": n, "big_rate": rate,
+                            "avg60": avg, "excess_pp": extra,
+                            "sim_trades": tn, "sim_win_rate": round(twr, 1), "sim_avg_pnl": round(tavg, 2),
+                            "sim_tp": tp, "sim_sl": sl, "sim_maxh": mh,
+                            "hypothesis": f"[{rg}] 由果及因: {list(cond.keys())} → 大涨率{rate}%(基线+{extra}pp)",
+                        }
+                        out["candidates"].append(cand)
+                        print(f"  [{rg}] {list(cond.keys())} ✅大涨率{rate}% 模拟胜率{twr:.0f}%/均{tavg:.2f}%", flush=True)
+                    else:
+                        print(f"  [{rg}] {list(cond.keys())} 模拟未达标(胜率{twr:.0f}%/均{tavg:.2f}%) 剔除", flush=True)
+                else:
+                    print(f"  [{rg}] {list(cond.keys())} 模拟无结果 剔除", flush=True)
     return out