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"),
        metadata_path=Path("notebooks/models/spy_xgb_v1_meta.json"),
        raw_data_dir=Path("data/alpaca/daily"),
    )

    assert set(result.feature_columns).issubset(FEATURE_COLUMNS)
    assert 0.0 <= result.probability <= 1.0
    assert result.probability > 0.0