0.95

model_core/config.py

@@ -37,7 +37,7 @@ class ModelConfig:
     #   2. 放大采样预算：BATCH_SIZE 128→256，TRAIN_STEPS 3000→8000
     #   3. 更大精英池（60）保留更多历史最优
     BATCH_SIZE      = 192   # 每步采样公式数（原 128，1.5x 提升覆盖率）
-    TRAIN_STEPS     = 5000  # 每组训练步数（原 3000）
+    TRAIN_STEPS     = 9000  # 每组训练步数（55次重启需要更多步数）
     MAX_FORMULA_LEN = 8     # 公式长度上限：保持 8（10 会导致 CPU 训练慢 3 倍）
 
     # ── 特征维度（由 vocab.py 自动派生，无需手动修改）──────────────────
@@ -86,9 +86,10 @@ class ModelConfig:
     ELITE_REWARD_SCALE: float = 1.2
 
     # ── 坍塌重启（大空间加强版）─────────────────────────────────────────
-    # MAX_RESTARTS 8→25、RESTART_NOISE 0.05→0.1：时间不敏感，多给机会+更强扰动。
+    # MAX_RESTARTS 8→25→55、RESTART_NOISE 0.05→0.1→0.25：时间不敏感，多给机会+更强扰动。
     # 配合 engine.py：超过 MAX_RESTARTS 后不再 Early Stop，改为强扰动继续训练。
-    MAX_RESTARTS:   int   = 25
+    # 2026-07-09: US100 训练 24/25 重启仍有突破，扩到 55 次。
+    MAX_RESTARTS:   int   = 55
     RESTART_NOISE:  float = 0.25
 
     # ── 自适应噪声：Best 停滞时自动增大扰动 ─────────────────────────────

strategies/best_US100.cash.json

@@ -2,14 +2,14 @@
   "vocab_version": "v9217a2c0d91a",
   "symbol": "US100.cash",
   "formula": [
-    2,
-    96,
-    121,
-    71,
-    78,
-    113,
-    119,
-    89
+    57,
+    89,
+    75,
+    89,
+    39,
+    116,
+    117,
+    123
   ],
-  "best_score": 1.945467233657837
+  "best_score": 1.377672553062439
 }

strategies/best_US30.cash.json

@@ -2,14 +2,14 @@
   "vocab_version": "v9217a2c0d91a",
   "symbol": "US30.cash",
   "formula": [
-    12,
-    100,
-    50,
-    86,
-    120,
-    13,
-    86,
-    98
+    20,
+    76,
+    24,
+    104,
+    93,
+    114,
+    77,
+    102
   ],
-  "best_score": 1.1718814373016357
+  "best_score": 0.880893886089325
 }

strategies/us100_v2.json

@@ -0,0 +1,90 @@
+{
+  "name": "us100_v2",
+  "version": "2026-07-09",
+  "vocab_version": "v9217a2c0d91a",
+  "status": "verified",
+  "symbol": "US100.cash (Nasdaq-100)",
+  "mode": "single + OOS_gate + extended(55 restarts)",
+  "formula_tokens": [
+    57,
+    89,
+    75,
+    89,
+    39,
+    116,
+    117,
+    123
+  ],
+  "formula_readable": "ROLL_SKEW_20 -> TS_MAX_10 -> DELAY1 -> TS_MAX_10 -> YANG_ZHANG_VOL -> MAX -> POWER -> CLIP",
+  "formula_operators": [
+    "ROLL_SKEW_20",
+    "TS_MAX_10",
+    "DELAY1",
+    "TS_MAX_10",
+    "YANG_ZHANG_VOL",
+    "MAX",
+    "POWER",
+    "CLIP"
+  ],
+  "train_best_score": 1.377672553062439,
+  "train_step": 3740,
+  "train_restarts": 23,
+  "train_total_duration": "~9h (5000+9000 steps, 30 restarts)",
+  "backtest": {
+    "annual_return": "+34.5%",
+    "sharpe": 1.86,
+    "ic": "+0.020",
+    "win_rate": "53.1%",
+    "turnover": "188 trades/year",
+    "position_bias": "59% long / 40% short",
+    "data": "5.3 years, 32,973 H1 bars",
+    "note": "OOS门控+扩展重启版。vs v1：Sharpe从2.18降到1.86但5/5年全盈+4/4 WF。vs step2320(早期)：Sharpe 1.27→1.86(+46%)。核心突破在于把DELAY1从第一位换到TS_MAX_10之后",
+    "walk_forward": {
+      "fold1": {
+        "oos": "+0.50",
+        "sharpe": 2.13
+      },
+      "fold2": {
+        "oos": "+0.15",
+        "sharpe": 1.09
+      },
+      "fold3": {
+        "oos": "+0.37",
+        "sharpe": 1.86
+      },
+      "fold4": {
+        "oos": "+0.23",
+        "sharpe": 1.69
+      },
+      "total_oos": "+1.25",
+      "verdict": "PASS — ALL 4 folds profitable (4/4)"
+    },
+    "yearly": {
+      "year1": {
+        "pnl": "+0.37",
+        "sharpe": 1.4
+      },
+      "year2": {
+        "pnl": "+0.34",
+        "sharpe": 1.56
+      },
+      "year3": {
+        "pnl": "+0.08",
+        "sharpe": 0.61
+      },
+      "year4": {
+        "pnl": "+0.38",
+        "sharpe": 2.79
+      },
+      "year5": {
+        "pnl": "+0.21",
+        "sharpe": 1.08
+      }
+    }
+  },
+  "logic": {
+    "summary": "偏斜脉冲+Yang-Zhang波动+双层峰值筛选",
+    "detail": "ROLL_SKEW_20找价格分布偏斜→TS_MAX_10先取峰值→DELAY1延迟确认→二次TS_MAX_10强化→YANG_ZHANG_VOL(含隔夜跳空)波动归一→MAX取大→POWER放大→CLIP截断",
+    "key_insight": "TS_MAX_10放在DELAY1之前是关键：先捞到最强信号再等确认，比先等再找更有效。YANG_ZHANG_VOL比ATR更适合纳指因其能捕捉盘后跳空"
+  }
+}