Prediction and model training work

tests/test_prediction.py

@@ -1,8 +1,72 @@
 from pathlib import Path
 
+import numpy as np
+import pandas as pd
+
 from trading_bot.models.prediction import FEATURE_COLUMNS, predict_latest_probability
 
 
+class FakeModel:
+    def predict_proba(self, model_input):
+        return np.array([[0.1, 0.9]])
+
+
+def test_predict_latest_probability_refreshes_recent_market_data(
+    monkeypatch,
+) -> None:
+    refresh_calls: dict[str, object] = {}
+
+    def fake_refresh_recent_data(
+        output_dir: Path,
+        symbols: dict[str, str] | None = None,
+        api_key: str | None = None,
+        secret_key: str | None = None,
+    ) -> None:
+        refresh_calls["symbols"] = list((symbols or {}).keys())
+        refresh_calls["output_dir"] = output_dir
+        refresh_calls["api_key"] = api_key
+        refresh_calls["secret_key"] = secret_key
+
+    monkeypatch.setattr(
+        "trading_bot.models.prediction.refresh_recent_market_data",
+        fake_refresh_recent_data,
+    )
+    monkeypatch.setattr(
+        "trading_bot.models.prediction.load_model",
+        lambda model_path: FakeModel(),
+    )
+    monkeypatch.setattr(
+        "trading_bot.models.prediction.load_feature_metadata",
+        lambda metadata_path: {"feature_cols": FEATURE_COLUMNS, "p_base": 0.5},
+    )
+    monkeypatch.setattr(
+        "trading_bot.models.prediction.get_latest_inference_features",
+        lambda raw_data_dir, symbols=None, max_age_days=1: (
+            pd.Timestamp("2026-08-03"),
+            pd.DataFrame(
+                [[0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08]],
+                columns=FEATURE_COLUMNS,
+                index=[pd.Timestamp("2026-08-03")],
+            ),
+        ),
+    )
+
+    result = predict_latest_probability(
+        model_path=Path("notebooks/models/spy_xgb_v1.json"),
+        metadata_path=Path("notebooks/models/spy_xgb_v1_meta.json"),
+        raw_data_dir=Path("data/alpaca/daily"),
+        api_key="test-key",
+        secret_key="test-secret",
+        fetch_recent_data=True,
+    )
+
+    assert refresh_calls["output_dir"] == Path("data/alpaca/daily")
+    assert refresh_calls["symbols"] == ["SPY", "VIXY", "TLT", "USO"]
+    assert refresh_calls["api_key"] == "test-key"
+    assert refresh_calls["secret_key"] == "test-secret"
+    assert result.probability == 0.9
+
+
 def test_predict_latest_probability_returns_probability_between_zero_and_one() -> None:
     result = predict_latest_probability(
         model_path=Path("notebooks/models/spy_xgb_v1.json"),