Add Entry condition pair diagnostics

training/tests/test_training_contract.py

@@ -15,6 +15,7 @@ if str(TRAINING_ROOT) not in sys.path:
 
 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.io_utils import read_json, write_json
 from trader_training.labels import ENTRY_LABEL_METHOD, _path_stats_for_group, build_entry_labels
@@ -70,6 +71,51 @@ class TrainingContractTest(unittest.TestCase):
         self.assertEqual(-np.inf, edges[0])
         self.assertEqual(np.inf, edges[-1])
 
+    def test_entry_condition_pair_screen_finds_stable_two_feature_filter(self) -> None:
+        with tempfile.TemporaryDirectory() as tmp:
+            data_root = Path(tmp)
+            run_root = data_root / "trader-v4" / "runs" / "unit-condition-pair"
+            dataset_path = run_root / "dataset" / "entry_train.parquet"
+            dataset_path.parent.mkdir(parents=True)
+
+            frames = []
+            row_count = 1200
+            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
+                frame["ret_5m_bps"] = base_feature_values
+                good_mask = (frame["ret_1m_bps"] > 0.9) & (frame["ret_5m_bps"] > 0.9)
+                frame["long_entry_target"] = good_mask.astype(int)
+                frame["short_entry_target"] = 0
+                frame["long_actual_plan_net_edge_bps"] = np.where(good_mask, 8.0, -6.0)
+                frame["short_actual_plan_net_edge_bps"] = -6.0
+                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)
+
+            screen_entry_condition_pairs(
+                Namespace(
+                    data_root=data_root,
+                    run_id="unit-condition-pair",
+                    min_seed_rows=50,
+                    min_pair_rows=50,
+                    max_seed_conditions_per_side=8,
+                    max_buckets_per_feature=2,
+                )
+            )
+
+            result = read_json(run_root / "diagnostics" / "entry_condition_pair_screen_result.json")
+            candidates = pd.read_csv(run_root / "diagnostics" / "entry_condition_pair_candidates.csv")
+
+            self.assertGreater(result["stable_candidate_count"], 0)
+            self.assertTrue(candidates["usable_candidate"].any())
+            best = candidates.iloc[0]
+            self.assertEqual("LONG", best["side"])
+            self.assertGreater(float(best["min_eval_edge_bps"]), 0.0)
+
     def test_dynamic_exit_search_writes_plan_diagnostics(self) -> None:
         with tempfile.TemporaryDirectory() as tmp:
             data_root = Path(tmp)