Use actual plan edge in OFI diagnostics

training/tests/test_training_contract.py

@@ -18,7 +18,7 @@ from trader_training.dynamic_exit_search import search_dynamic_exit_plans
 from trader_training.entry_feature_screen import _bucket_edges, _screen_edge_column
 from trader_training.io_utils import read_json, write_json
 from trader_training.labels import ENTRY_LABEL_METHOD, _path_stats_for_group, build_entry_labels
-from trader_training.ofi_feature_experiment import l1_snapshot_diff_ofi_quote
+from trader_training.ofi_feature_experiment import _load_entry_dataset, l1_snapshot_diff_ofi_quote
 from trader_training.promote import promote_artifact_bundle
 from trader_training.replay import build_splits
 from trader_training.schemas import FEATURE_ORDER, LATEST_STRESS_SPLIT, MODEL_OUTPUTS, OUTPUT_MAPPING, TRAINING_SPLITS, VALIDATION_LOCKED_SPLIT
@@ -117,6 +117,38 @@ class TrainingContractTest(unittest.TestCase):
             self.assertEqual(1, result["candidate_count"])
             self.assertTrue((run_root / "dynamic-exit-search" / "dynamic_exit_search_report.md").is_file())
 
+    def test_ofi_entry_dataset_uses_actual_plan_edge(self) -> None:
+        with tempfile.TemporaryDirectory() as tmp:
+            baseline_root = Path(tmp)
+            dataset_path = baseline_root / "dataset" / "entry_train.parquet"
+            dataset_path.parent.mkdir(parents=True)
+            pd.DataFrame(
+                {
+                    "sample_id": ["s1"],
+                    "long_entry_target": [1],
+                    "short_entry_target": [0],
+                    "long_actual_plan_net_edge_bps": [4.0],
+                    "short_actual_plan_net_edge_bps": [-7.0],
+                }
+            ).to_parquet(dataset_path, index=False)
+            feature = pd.DataFrame(
+                {
+                    "sample_id": ["s1"],
+                    "symbol": ["BTC-USDT-PERP"],
+                    "event_time": pd.to_datetime(["2026-01-01T00:00:00Z"]),
+                    "open_time_ms": [0],
+                    "split_id": ["fit_inner"],
+                    "walk_forward_fold": [0],
+                    "data_quality_flag": ["OK"],
+                }
+            )
+
+            dataset = _load_entry_dataset(baseline_root, feature)
+
+            self.assertIn("long_actual_plan_net_edge_bps", dataset.columns)
+            self.assertNotIn("long_expected_net_edge_bps", dataset.columns)
+            self.assertEqual(4.0, float(dataset.loc[0, "long_actual_plan_net_edge_bps"]))
+
     def test_split_builder_uses_locked_validation_contract(self) -> None:
         with tempfile.TemporaryDirectory() as tmp:
             data_root = Path(tmp)

training/trader_training/ofi_feature_experiment.py

@@ -337,6 +337,23 @@ def _load_direction_dataset(baseline_root: Path, feature: pd.DataFrame) -> pd.Da
 
 
 def _load_entry_dataset(baseline_root: Path, feature: pd.DataFrame) -> pd.DataFrame:
+    dataset_path = baseline_root / "dataset" / "entry_train.parquet"
+    if dataset_path.is_file():
+        labels = read_parquet(dataset_path)
+        required = {
+            "sample_id",
+            "long_entry_target",
+            "short_entry_target",
+            "long_actual_plan_net_edge_bps",
+            "short_actual_plan_net_edge_bps",
+        }
+        missing = sorted(required.difference(labels.columns))
+        if missing:
+            raise ValueError(f"entry_train dataset missing columns: {missing}")
+        dataset = feature.merge(labels[list(required)], on="sample_id", how="inner")
+        logging.info("trader.training.ofi_entry_dataset_loaded source=entry_train rowCount=%s", len(dataset))
+        return dataset
+
     labels = read_parquet(baseline_root / "label" / "entry_labels.parquet")
     required = {"sample_id", "side", "entry_target", "expected_net_edge_bps"}
     missing = sorted(required.difference(labels.columns))
@@ -350,7 +367,7 @@ def _load_entry_dataset(baseline_root: Path, feature: pd.DataFrame) -> pd.DataFr
     )
     pivot = long.merge(short, on="sample_id", how="inner")
     dataset = feature.merge(pivot, on="sample_id", how="inner")
-    logging.info("trader.training.ofi_entry_dataset_loaded rowCount=%s", len(dataset))
+    logging.info("trader.training.ofi_entry_dataset_loaded source=entry_labels_legacy rowCount=%s", len(dataset))
     return dataset
 
 
@@ -407,8 +424,8 @@ def _train_entry(frame: pd.DataFrame, feature_columns: list[str]) -> tuple[dict[
     specs = [
         ("long_entry_prob", "binary", "long_entry_target"),
         ("short_entry_prob", "binary", "short_entry_target"),
-        ("long_expected_net_edge_bps", "regression", "long_expected_net_edge_bps"),
-        ("short_expected_net_edge_bps", "regression", "short_expected_net_edge_bps"),
+        ("long_actual_plan_net_edge_bps", "regression", "long_actual_plan_net_edge_bps"),
+        ("short_actual_plan_net_edge_bps", "regression", "short_actual_plan_net_edge_bps"),
     ]
     results: dict[str, Any] = {"feature_count": len(feature_columns), "feature_hash": sha256_json(feature_columns)}
     split_predictions: dict[str, pd.DataFrame] = {
@@ -785,8 +802,8 @@ def _model_compare_report(args: Any, baseline_root: Path, results: dict[str, Any
         f"| Direction | neutral_auc | {baseline_direction.get('neutral_auc')} |",
         f"| Entry | long_auc | {baseline_entry['long_entry_prob'].get('auc')} |",
         f"| Entry | short_auc | {baseline_entry['short_entry_prob'].get('auc')} |",
-        f"| Entry | long_edge_mae_ratio | {baseline_entry['long_expected_net_edge_bps'].get('mae_vs_constant_ratio')} |",
-        f"| Entry | short_edge_mae_ratio | {baseline_entry['short_expected_net_edge_bps'].get('mae_vs_constant_ratio')} |",
+        f"| Entry | long_exported_edge_mae_ratio | {baseline_entry['long_expected_net_edge_bps'].get('mae_vs_constant_ratio')} |",
+        f"| Entry | short_exported_edge_mae_ratio | {baseline_entry['short_expected_net_edge_bps'].get('mae_vs_constant_ratio')} |",
         "",
         "## Diagnostic Direction Result",
         "",
@@ -817,7 +834,7 @@ def _model_compare_report(args: Any, baseline_root: Path, results: dict[str, Any
                 lines.append(
                     f"| {head} | {feature_set_name} | {split_id} | {metric.get('auc')} | {metric.get('brier_vs_constant_ratio')} | {metric.get('top10_hit_rate')} |"
                 )
-        for head in ("long_expected_net_edge_bps", "short_expected_net_edge_bps"):
+        for head in ("long_actual_plan_net_edge_bps", "short_actual_plan_net_edge_bps"):
             for split_id in EVAL_SPLITS:
                 metric = entry.get(head, {}).get(split_id, {})
                 lines.append(f"| {head} | {feature_set_name} | {split_id} | {metric.get('mae_vs_constant_ratio')} |  |  |")
@@ -827,6 +844,7 @@ def _model_compare_report(args: Any, baseline_root: Path, results: dict[str, Any
             "## Verdict Rule",
             "",
             "只有 `market_plus_ofi` 在 validation_locked 和 latest_stress 上同时好过 `market_only`，才进入正式特征链路。",
+            "Entry 的收益回归诊断使用 `actual_plan_net_edge_bps`，也就是真实按价格计划出场后的净收益。",
             "",
         ]
     )