import numpy as np

from trademind.features import (
    FEATURE_NAMES,
    N_FEATURES,
    build_feature_matrix,
    compute_features,
    required_bars,
)

from .conftest import make_candles


def test_matrix_has_expected_shape(candles, rules):
    matrix = build_feature_matrix(candles, rules)
    assert matrix is not None
    assert matrix.values.shape == (len(candles), N_FEATURES)
    assert matrix.first_valid == required_bars(rules) - 1


def test_all_feature_values_are_finite_and_bounded(candles, rules):
    matrix = build_feature_matrix(candles, rules)
    valid = matrix.values[matrix.first_valid :]
    assert np.isfinite(valid).all()
    assert np.abs(valid).max() <= 8.0


def test_too_short_history_returns_none(rules):
    short = make_candles(n=50)
    assert build_feature_matrix(short, rules) is None
    assert compute_features(short, rules) is None


def test_snapshot_exposes_raw_indicators(candles, rules):
    snapshot = compute_features(candles, rules)
    assert snapshot is not None
    assert snapshot.price == float(candles.close[-1])
    assert snapshot.atr > 0
    assert 0.0 <= snapshot.rsi <= 100.0
    assert len(snapshot.values) == len(FEATURE_NAMES)
    assert set(snapshot.as_dict()) == set(FEATURE_NAMES)


def test_snapshot_before_warmup_is_none(candles, rules):
    matrix = build_feature_matrix(candles, rules)
    assert matrix.snapshot(matrix.first_valid - 1) is None
    assert matrix.snapshot(matrix.first_valid) is not None


def test_uptrend_produces_positive_trend_distance(rules):
    up = make_candles(n=500, trend=0.001, noise=0.0005, seed=11)
    snapshot = compute_features(up, rules)
    features = snapshot.as_dict()
    assert features["trend_dist"] > 0
    assert features["ema_spread"] > 0


def test_downtrend_produces_negative_trend_distance(rules):
    down = make_candles(n=500, trend=-0.001, noise=0.0005, seed=12)
    features = compute_features(down, rules).as_dict()
    assert features["trend_dist"] < 0
    assert features["ema_spread"] < 0


def test_features_are_scale_invariant(rules):
    """Ein zehnfach höherer Kurs darf die normierten Merkmale kaum verändern."""
    cheap = make_candles(n=400, start_price=100.0, seed=5)
    expensive = make_candles(n=400, start_price=1_000.0, seed=5)
    a = compute_features(cheap, rules).values
    b = compute_features(expensive, rules).values
    assert np.allclose(a, b, atol=1e-8)


def test_time_features_are_on_the_unit_circle(candles, rules):
    snapshot = compute_features(candles, rules).as_dict()
    radius = snapshot["time_sin"] ** 2 + snapshot["time_cos"] ** 2
    assert radius == 1.0 or abs(radius - 1.0) < 1e-9


def test_matrix_rows_match_pointwise_snapshots(candles, rules):
    matrix = build_feature_matrix(candles, rules)
    index = matrix.first_valid + 25
    assert np.allclose(matrix.snapshot(index).values, matrix.values[index])