Add Entry low-drawdown diagnostics

training/tests/test_training_contract.py

@@ -17,6 +17,7 @@ from trader_training.onnx_export import LinearHead, export_heads
 from trader_training.dynamic_exit_search import search_dynamic_exit_plans
 from trader_training.entry_condition_pair_screen import screen_entry_condition_pairs
 from trader_training.entry_feature_screen import _bucket_edges, _screen_edge_column
+from trader_training.entry_mae_label_diagnostic import diagnose_entry_mae_labels
 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 _load_entry_dataset, l1_snapshot_diff_ofi_quote
@@ -116,6 +117,52 @@ class TrainingContractTest(unittest.TestCase):
             self.assertEqual("LONG", best["side"])
             self.assertGreater(float(best["min_eval_edge_bps"]), 0.0)
 
+    def test_entry_mae_label_diagnostic_finds_low_drawdown_target(self) -> None:
+        with tempfile.TemporaryDirectory() as tmp:
+            data_root = Path(tmp)
+            run_root = data_root / "trader-v4" / "runs" / "unit-mae-diagnostic"
+            dataset_path = run_root / "dataset" / "entry_train.parquet"
+            dataset_path.parent.mkdir(parents=True)
+
+            frames = []
+            row_count = 800
+            base_feature_values = np.linspace(0.0, 0.999, row_count)
+            for split_id in TRAINING_SPLITS:
+                frame = pd.DataFrame({feature: 0.0 for feature in FEATURE_ORDER}, index=np.arange(row_count))
+                frame["split_id"] = split_id
+                frame["ret_1m_bps"] = base_feature_values
+                good_mask = frame["ret_1m_bps"] > 0.85
+                frame["long_entry_target"] = good_mask.astype(int)
+                frame["short_entry_target"] = 0
+                frame["long_actual_plan_net_edge_bps"] = np.where(good_mask, 9.0, -6.0)
+                frame["short_actual_plan_net_edge_bps"] = -6.0
+                frame["long_max_achievable_net_edge_bps"] = np.where(good_mask, 18.0, 2.0)
+                frame["short_max_achievable_net_edge_bps"] = 2.0
+                frame["long_mae_bps"] = np.where(good_mask, 2.0, 15.0)
+                frame["short_mae_bps"] = 15.0
+                frames.append(frame)
+            pd.concat(frames, ignore_index=True).to_parquet(dataset_path, index=False)
+
+            diagnose_entry_mae_labels(
+                Namespace(
+                    data_root=data_root,
+                    run_id="unit-mae-diagnostic",
+                    max_mae_bps=(4.0,),
+                    min_opportunity_bps=(12.0,),
+                    model_families=("linear",),
+                    top_fraction=0.10,
+                    max_train_rows=0,
+                )
+            )
+
+            result = read_json(run_root / "diagnostics" / "entry_mae_label_diagnostic_result.json")
+            candidates = pd.read_csv(run_root / "diagnostics" / "entry_mae_label_diagnostic_candidates.csv")
+
+            self.assertGreater(result["positive_top_edge_candidate_count"], 0)
+            best = candidates.iloc[0]
+            self.assertEqual("LONG", best["side"])
+            self.assertTrue(bool(best["stable_top_edge_positive"]))
+
     def test_dynamic_exit_search_writes_plan_diagnostics(self) -> None:
         with tempfile.TemporaryDirectory() as tmp:
             data_root = Path(tmp)