"""Tests for alpha signal computation."""
import pandas as pd

from signals.reversal import ReversalSignal


def _make_df(closes):
    return pd.DataFrame({"close": closes})


def test_reversal_name():
    assert ReversalSignal(lookback=5).name == "reversal_5d"
    assert ReversalSignal(lookback=10).name == "reversal_10d"


def test_reversal_is_negative_trailing_return():
    # Monotonically rising prices -> negative (bearish) reversal signal.
    df = _make_df([10.0, 11.0, 12.0, 13.0, 14.0, 15.0])
    sig = ReversalSignal(lookback=5).compute(df)
    # First 5 values are NaN (insufficient lookback).
    assert sig.iloc[:5].isna().all()
    # 15/10 - 1 = 0.5 return -> signal = -0.5
    assert abs(sig.iloc[5] - (-0.5)) < 1e-9


def test_reversal_oversold_is_positive():
    # Falling prices -> positive (bullish) reversal signal.
    df = _make_df([20.0, 18.0, 16.0, 14.0, 12.0, 10.0])
    sig = ReversalSignal(lookback=5).compute(df)
    assert sig.iloc[5] > 0
    # 10/20 - 1 = -0.5 -> signal = +0.5
    assert abs(sig.iloc[5] - 0.5) < 1e-9


def test_reversal_output_length_matches_input():
    df = _make_df([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0])
    sig = ReversalSignal(lookback=3).compute(df)
    assert len(sig) == len(df)