fix: 组合循环缩进修复+单条件门槛0.5pp+4-6因子叠加

evolution/b_group_miner.py

@@ -65,26 +65,27 @@ def scan_big(market, regime, panel, min_n=500):
         if len(m) < 200:
             continue
         rate = m["is_big"].mean() * 100
-        if rate > br * 1.3:  # 大涨率比基线高30%
+        if rate > br + 0.5:  # 单条件提升>0.5pp 进组合池（多因子叠加才有大提升）
             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])
+    print("  单条件:", single[: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]
+    # 4-6 因子组合（从单条件提升>0.5pp 里取 8 个，测 4/5/6 组合）
+    pool = [s[0] for s in single if s[3] > 0.5][:8]
     results = []
-    for combo in combinations(pool, 3):
-        cond = pd.Series(True, index=sub.index)
-        for feat in combo:
-            op, val = factor_defs[feat]
-            cond &= (sub[feat] < val) if op == "<" else (sub[feat] > val)
-        m = sub[cond]
-        if len(m) < 200:
-            continue
-        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 - br, 1)))
+    for k in [4, 5, 6]:
+        for combo in combinations(pool, k):
+            cond = pd.Series(True, index=sub.index)
+            for feat in combo:
+                op, val = factor_defs[feat]
+                cond &= (sub[feat] < val) if op == "<" else (sub[feat] > val)
+            m = sub[cond]
+            if len(m) < 200:
+                continue
+            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 - br, 1), len(combo)))
     results.sort(key=lambda x: -x[4])
     return results[:5]
 
@@ -140,7 +141,7 @@ def mine(market="a", regimes=None):
     out = {"market": market, "mined_at": datetime.now().strftime("%Y-%m-%d %H:%M:%S"), "candidates": []}
     for rg in regimes:
         combos = scan_big(market, rg, panel)
-        for cond, n, rate, avg, extra in combos[:3]:
+        for cond, n, rate, avg, extra, nf in combos[:3]:
             c = pd.Series(True, index=panel.index)
             for feat, (op, val) in cond.items():
                 if feat not in panel.columns: