import numpy as np
import pytest

from trademind.indicators import atr, bollinger, donchian_position, ema, macd, roc, rsi, sma, true_range


def test_ema_of_constant_series_is_constant():
    values = np.full(50, 42.0)
    assert np.allclose(ema(values, 10), 42.0)


def test_ema_reacts_faster_than_sma():
    values = np.concatenate([np.full(30, 100.0), np.full(30, 110.0)])
    fast = ema(values, 10)[35]
    slow = sma(values, 10)[35]
    assert fast > slow  # EMA hat den Sprung stärker eingepreist


def test_sma_matches_manual_mean():
    values = np.arange(1.0, 11.0)
    result = sma(values, 3)
    assert np.isnan(result[:2]).all()
    assert result[2] == pytest.approx(2.0)
    assert result[-1] == pytest.approx(9.0)


def test_rsi_bounds_and_extremes():
    rising = np.arange(1.0, 60.0)
    values = rsi(rising, 14)
    finite = values[np.isfinite(values)]
    assert finite.min() >= 0.0 and finite.max() <= 100.0
    assert finite[-1] == pytest.approx(100.0)  # nur Gewinne

    falling = rising[::-1].copy()
    assert rsi(falling, 14)[-1] == pytest.approx(0.0)


def test_rsi_of_flat_series_is_neutral():
    values = rsi(np.full(60, 25.0), 14)
    assert values[-1] == pytest.approx(50.0)


def test_true_range_covers_gaps():
    high = np.array([10.0, 20.0])
    low = np.array([9.0, 19.0])
    close = np.array([9.5, 19.5])
    tr = true_range(high, low, close)
    assert tr[0] == pytest.approx(1.0)
    assert tr[1] == pytest.approx(10.5)  # Lücke gegenüber dem Vortagesschluss


def test_atr_is_positive_and_warm():
    n = 100
    rng = np.random.default_rng(3)
    close = 100 + np.cumsum(rng.normal(0, 1, n))
    high = close + 1.0
    low = close - 1.0
    values = atr(high, low, close, 14)
    assert np.isnan(values[:13]).all()
    assert (values[13:] > 0).all()


def test_macd_histogram_is_difference():
    values = 100 + np.cumsum(np.random.default_rng(1).normal(0, 1, 200))
    line, signal, hist = macd(values)
    assert np.allclose(hist, line - signal)


def test_bollinger_bands_are_ordered():
    values = 100 + np.cumsum(np.random.default_rng(2).normal(0, 1, 200))
    lower, mid, upper = bollinger(values, 20, 2.0)
    valid = ~np.isnan(mid)
    assert (lower[valid] <= mid[valid]).all()
    assert (mid[valid] <= upper[valid]).all()


def test_roc_is_relative_change():
    values = np.array([100.0] * 10 + [110.0])
    assert roc(values, 10)[-1] == pytest.approx(0.10)


def test_donchian_position_hits_extremes():
    close = np.array([float(i) for i in range(1, 41)])
    high = close + 0.0
    low = close - 0.0
    pos = donchian_position(high, low, close, 20)
    assert pos[-1] == pytest.approx(1.0)  # Schluss auf dem Hoch der Range


def test_indicators_reject_wrong_dimensions():
    with pytest.raises(ValueError):
        ema(np.zeros((5, 2)), 3)