import numpy as np
import pandas as pd
import pytest
from src.dataset_builder import generate_dataset_vectorized, _calc_labels_vectorized


@pytest.fixture
def signal_df():
    """시그널이 발생하는 데이터."""
    rng = np.random.default_rng(7)
    n = 800
    trend = np.linspace(1.5, 3.0, n)
    noise = np.cumsum(rng.normal(0, 0.04, n))
    close = np.clip(trend + noise, 0.01, None)
    high = close * (1 + rng.uniform(0, 0.015, n))
    low = close * (1 - rng.uniform(0, 0.015, n))
    volume = rng.uniform(1e6, 3e6, n)
    volume[::30] *= 3.0
    return pd.DataFrame({
        "open": close, "high": high, "low": low,
        "close": close, "volume": volume,
    })


def test_sltp_params_are_passed_through(signal_df):
    """SL/TP 배수가 generate_dataset_vectorized에 전달되어야 한다."""
    # 파라미터가 수용되는지(TypeError 없이) 확인하는 것이 핵심
    r1 = generate_dataset_vectorized(
        signal_df, atr_sl_mult=1.5, atr_tp_mult=2.0,
        adx_threshold=0, volume_multiplier=1.5,
    )
    r2 = generate_dataset_vectorized(
        signal_df, atr_sl_mult=2.0, atr_tp_mult=2.0,
        adx_threshold=0, volume_multiplier=1.5,
    )
    # 두 결과 모두 DataFrame이어야 한다
    assert isinstance(r1, pd.DataFrame)
    assert isinstance(r2, pd.DataFrame)
    # 신호가 충분히 많을 경우, 다른 SL 배수는 레이블 분포에 영향을 줄 수 있다
    if len(r1) > 10 and len(r2) > 10:
        assert not (r1["label"].values == r2["label"].values).all() or len(r1) != len(r2), \
            "SL 배수가 다르면 레이블이 달라져야 한다"


def test_default_sltp_backward_compatible(signal_df):
    """SL/TP 파라미터 미지정 시 기존 기본값(1.5, 2.0)으로 동작해야 한다."""
    r_default = generate_dataset_vectorized(
        signal_df, adx_threshold=0, volume_multiplier=1.5,
    )
    r_explicit = generate_dataset_vectorized(
        signal_df, atr_sl_mult=1.5, atr_tp_mult=2.0,
        adx_threshold=0, volume_multiplier=1.5,
    )
    if len(r_default) > 0:
        assert len(r_default) == len(r_explicit)
        assert (r_default["label"].values == r_explicit["label"].values).all()


def test_equity_curve_includes_unrealized_pnl():
    """에퀴티 커브에 미실현 PnL이 반영되어야 한다."""
    from src.backtester import Backtester, BacktestConfig, Position
    import pandas as pd

    cfg = BacktestConfig(symbols=["TEST"], initial_balance=1000.0)
    bt = Backtester.__new__(Backtester)
    bt.cfg = cfg
    bt.balance = 1000.0
    bt._peak_equity = 1000.0
    bt.equity_curve = []
    bt.positions = {"TEST": Position(
        symbol="TEST", side="LONG", entry_price=100.0,
        quantity=10.0, sl=95.0, tp=110.0,
        entry_time=pd.Timestamp("2026-01-01"), entry_fee=0.4,
    )}

    bt._record_equity(pd.Timestamp("2026-01-01 00:15:00"), current_prices={"TEST": 105.0})

    last = bt.equity_curve[-1]
    assert last["equity"] == 1050.0, f"Expected 1050.0 (1000+50), got {last['equity']}"


def test_mlx_no_double_normalization():
    """MLXFilter.fit()에 normalize=False를 전달하면 내부 정규화를 건너뛰어야 한다."""
    pytest.importorskip("mlx.core")
    import numpy as np
    import pandas as pd
    from src.mlx_filter import MLXFilter
    from src.ml_features import FEATURE_COLS

    n_features = len(FEATURE_COLS)
    rng = np.random.default_rng(42)
    X = pd.DataFrame(
        rng.standard_normal((100, n_features)).astype(np.float32),
        columns=FEATURE_COLS,
    )
    y = pd.Series(rng.integers(0, 2, 100).astype(np.float32))

    model = MLXFilter(input_dim=n_features, hidden_dim=16, epochs=1, batch_size=32)
    model.fit(X, y, normalize=False)

    assert np.allclose(model._mean, 0.0), "normalize=False시 mean은 0이어야 한다"
    assert np.allclose(model._std, 1.0), "normalize=False시 std는 1이어야 한다"


def test_ml_filter_from_model():
    """MLFilter.from_model()로 LightGBM 모델을 주입할 수 있어야 한다."""
    from src.ml_filter import MLFilter
    from unittest.mock import MagicMock

    mock_model = MagicMock()
    mock_model.predict_proba.return_value = [[0.3, 0.7]]

    mf = MLFilter.from_model(mock_model, threshold=0.55)
    assert mf.is_model_loaded()
    assert mf.active_backend == "LightGBM"