docs: update README with ML_THRESHOLD configuration and add new training log entry

- Added `ML_THRESHOLD` parameter to README, specifying its role in ML filter predictions.
- Included a new entry in the training log with detailed metrics from a recent model training session, enhancing performance tracking and documentation.

README.md

@@ -269,6 +269,7 @@ pytest tests/ -v
 | `MARGIN_MIN_RATIO` | `0.20` | 최소 증거금 비율 (잔고 대비 20%) |
 | `MARGIN_DECAY_RATE` | `0.0006` | 잔고 증가 시 증거금 비율 감소 속도 |
 | `NO_ML_FILTER` | — | `true`/`1`/`yes` 설정 시 ML 필터 완전 비활성화 — 모델 로드 없이 모든 신호 허용 |
+| `ML_THRESHOLD` | `0.55` | ML 필터 예측 확률 임계값 — 이 값 이상이어야 진입 허용 (기본값 0.55) |
 
 ---
 

models/training_log.json

@@ -426,5 +426,30 @@
       "reg_lambda": 0.082872
     },
     "weight_scale": 1.431662
+  },
+  {
+    "date": "2026-03-03T21:21:48.047541",
+    "backend": "lgbm",
+    "auc": 0.9494,
+    "best_threshold": 0.4622,
+    "best_precision": 0.556,
+    "best_recall": 0.25,
+    "samples": 3222,
+    "features": 24,
+    "time_weight_decay": 2.0,
+    "model_path": "models/lgbm_filter.pkl",
+    "tuned_params_path": null,
+    "lgbm_params": {
+      "n_estimators": 221,
+      "learning_rate": 0.031072,
+      "max_depth": 5,
+      "num_leaves": 20,
+      "min_child_samples": 39,
+      "subsample": 0.83244,
+      "colsample_bytree": 0.526349,
+      "reg_alpha": 0.062177,
+      "reg_lambda": 0.082872
+    },
+    "weight_scale": 1.431662
   }
 ]