Improve nonlinear PM diagnostics

2026-06-28 09:59:36 +08:00
parent 6be4bb976a
commit 0fe3bd864e
4 changed files with 282 additions and 46 deletions
@@ -23,7 +23,7 @@ from trader_training.entry_mae_label_diagnostic import diagnose_entry_mae_labels
 from trader_training.good_trade_structure import _side_frame, _top_fraction_metrics
 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.nonlinear_pm_probe import _expanded_threshold_candidates
+from trader_training.nonlinear_pm_probe import _entry_side_fit_frame, _exit_metrics, _expanded_threshold_candidates
 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
@@ -58,6 +58,57 @@ class TrainingContractTest(unittest.TestCase):
            },
            candidates,
        )
+        self.assertIn(
+            {
+                "long_open_prob": 1.01,
+                "short_open_prob": 0.2,
+                "min_entry_prob": 0.05,
+                "max_market_risk_prob": 0.45,
+                "min_expected_edge_bps": -5.0,
+                "min_direction_margin": 0.0,
+            },
+            candidates,
+        )
+
+    def test_nonlinear_entry_tree_probe_can_use_side_opportunity_rows(self) -> None:
+        direction = pd.DataFrame(
+            {
+                "sample_id": ["s1", "s2", "s3", "s4"],
+                "long_target": [1, 0, 0, 0],
+                "short_target": [0, 1, 0, 0],
+            }
+        )
+        entry = pd.DataFrame(
+            {
+                "sample_id": ["s1", "s2", "s3", "s4"],
+                "split_id": ["fit_inner", "fit_inner", "fit_inner", "fit_inner"],
+                "long_max_achievable_net_edge_bps": [45.0, 10.0, 65.0, 39.0],
+                "short_max_achievable_net_edge_bps": [8.0, 41.0, 15.0, 70.0],
+            }
+        )
+
+        long_frame = _entry_side_fit_frame(direction, entry, "LONG", "side_opportunity", 40.0)
+        short_frame = _entry_side_fit_frame(direction, entry, "SHORT", "side_opportunity", 40.0)
+
+        self.assertEqual(["s1", "s3"], long_frame["sample_id"].tolist())
+        self.assertEqual(["s2", "s4"], short_frame["sample_id"].tolist())
+
+    def test_nonlinear_pm_probe_exit_metrics_describe_trade_outcomes(self) -> None:
+        trades = pd.DataFrame(
+            {
+                "target_hit": [1, 0, 0],
+                "stop_hit": [0, 1, 0],
+                "time_to_exit_ms": [300_000, 600_000, 2_700_000],
+            }
+        )
+
+        metrics = _exit_metrics(trades)
+
+        self.assertAlmostEqual(1 / 3, metrics["target_hit_rate"])
+        self.assertAlmostEqual(1 / 3, metrics["stop_hit_rate"])
+        self.assertAlmostEqual(1 / 3, metrics["timeout_exit_rate"])
+        self.assertAlmostEqual(20.0, metrics["avg_time_to_exit_min"])
+        self.assertAlmostEqual(10.0, metrics["p50_time_to_exit_min"])

    def test_entry_feature_screen_prefers_actual_plan_edge(self) -> None:
        dataset = pd.DataFrame(