Add honest Torch validation gate

tests/test_strategy.py

@@ -284,6 +284,40 @@ def test_torch_forecast_blocks_without_valid_torch_model(make_settings, tmp_path
     assert signal.diagnostics["checks"]["torch_model_ok"] is False
 
 
+def test_torch_forecast_blocks_failed_quality_gate(make_settings, tmp_path) -> None:
+    settings = make_settings(
+        tmp_path,
+        strategy_mode="torch_forecast",
+        time_series_min_edge_percent=0.10,
+        time_series_min_probability_up=0.57,
+        max_position_usdt=25,
+        stop_loss_percent=0.04,
+    )
+    strategy = SpotStrategy(settings)
+    ticker = Ticker("BTCUSDT", 105, 104.99, 105.01, 10_000_000, 1000, 1.0)
+
+    signal = strategy.entry_signal(
+        "BTCUSDT",
+        [],
+        ticker,
+        open_positions_for_symbol=0,
+        forecast={
+            "usable": True,
+            "model": "torch_gru",
+            "expected_return_percent": 0.36,
+            "probability_up": 0.66,
+            "skill": 0.22,
+            "block_entry": False,
+            "quality_gate_passed": False,
+            "quality_gate": {"status": "fail"},
+        },
+        account={"equity": 100.0},
+    )
+
+    assert signal.action == "HOLD"
+    assert signal.diagnostics["checks"]["quality_gate_ok"] is False
+
+
 def test_torch_forecast_probe_buys_on_positive_high_probability(make_settings, tmp_path) -> None:
     settings = make_settings(
         tmp_path,

tests/test_time_series.py

@@ -282,6 +282,28 @@ def test_time_series_forecaster_reads_torch_gru_artifact(make_settings, tmp_path
     assert forecast.probability_up > 0.5
 
 
+def test_time_series_forecaster_attaches_quality_gate(make_settings, tmp_path) -> None:
+    artifact_path = tmp_path / "lstm_forecaster.json"
+    _write_torch_gru_artifact(artifact_path, head_bias=0.2)
+    (tmp_path / "torch_threshold_calibration.json").write_text(
+        json.dumps({"validation": {"status": "fail", "passed": False, "checks": []}}),
+        encoding="utf-8",
+    )
+    settings = make_settings(
+        tmp_path,
+        time_series_min_candles=80,
+        time_series_lstm_enabled=True,
+        time_series_lstm_model_path=artifact_path,
+    )
+    returns = [0.00015 if index % 4 else -0.00005 for index in range(140)]
+
+    forecast = TimeSeriesForecaster(settings).forecast(_candles_from_returns(returns), symbol="BTCUSDT")
+
+    assert forecast.usable is True
+    assert forecast.quality_gate_passed is False
+    assert forecast.quality_gate["status"] == "fail"
+
+
 def test_time_series_forecaster_reads_multifeature_direct_horizon_artifact(make_settings, tmp_path) -> None:
     artifact_path = tmp_path / "lstm_forecaster.json"
     _write_multifeature_torch_gru_artifact(artifact_path, head_bias=0.2)

tests/test_torch_candidate_guard.py

@@ -0,0 +1,44 @@
+from __future__ import annotations
+
+from tools.accept_torch_candidate import _decision
+
+
+def _report(*, validation_passed: bool = True, trades: int = 30, total: float = 10.0) -> dict:
+    return {
+        "recommended": {"score": 0.5},
+        "full_replay": {
+            "trades": trades,
+            "avg_net_percent": 0.4,
+            "total_net_percent": total,
+            "profit_factor": 2.0,
+            "max_drawdown_percent": 1.0,
+        },
+        "walk_forward": {"summary": {"trades": trades, "avg_net_percent": 0.3}},
+        "validation": {"passed": validation_passed, "status": "pass" if validation_passed else "fail"},
+    }
+
+
+def test_guard_rejects_candidate_without_honest_validation() -> None:
+    decision = _decision(
+        _report(),
+        _report(validation_passed=False),
+        min_trades=8,
+        min_profit_factor=1.05,
+        min_avg_net_percent=0.0,
+        max_score_regression=0.05,
+    )
+
+    assert decision == {"accepted": False, "reason": "candidate_failed_honest_validation"}
+
+
+def test_guard_accepts_candidate_that_passes_honest_validation() -> None:
+    decision = _decision(
+        _report(total=9.0),
+        _report(total=12.0),
+        min_trades=8,
+        min_profit_factor=1.05,
+        min_avg_net_percent=0.0,
+        max_score_regression=0.05,
+    )
+
+    assert decision == {"accepted": True, "reason": "candidate_passed_guard"}