"""Tests for cross-sectional IC evaluation."""
import numpy as np
import pandas as pd

from eval.metrics import evaluate_cross_sectional


def test_cross_sectional_keys_present():
    dates = pd.date_range("2024-01-01", periods=10)
    cols = ["a", "b", "c"]
    rng = np.random.default_rng(0)
    signals = pd.DataFrame(rng.standard_normal((10, 3)), index=dates, columns=cols)
    returns = pd.DataFrame(rng.standard_normal((10, 3)), index=dates, columns=cols)
    res = evaluate_cross_sectional(signals, returns)
    for key in (
        "ic_mean", "ic_std", "ir", "rank_ic_mean", "rank_ic_std",
        "rank_ir", "hit_rate", "n_periods",
    ):
        assert key in res


def test_perfect_signal_has_positive_rank_ic():
    # When the signal equals next-period returns, rank IC should be ~1 each day.
    dates = pd.date_range("2024-01-01", periods=8)
    cols = ["a", "b", "c"]
    rng = np.random.default_rng(42)
    returns = pd.DataFrame(rng.standard_normal((8, 3)), index=dates, columns=cols)
    signals = returns.copy()  # perfect foresight
    res = evaluate_cross_sectional(signals, returns)
    assert res["rank_ic_mean"] > 0.99
    assert res["hit_rate"] == 1.0
    assert res["n_periods"] == 8


def test_inverted_signal_has_negative_rank_ic():
    dates = pd.date_range("2024-01-01", periods=6)
    cols = ["a", "b", "c"]
    rng = np.random.default_rng(7)
    returns = pd.DataFrame(rng.standard_normal((6, 3)), index=dates, columns=cols)
    signals = -returns  # perfectly wrong
    res = evaluate_cross_sectional(signals, returns)
    assert res["rank_ic_mean"] < -0.99


def test_single_stock_falls_back_to_rolling():
    dates = pd.date_range("2024-01-01", periods=40)
    rng = np.random.default_rng(1)
    signals = pd.DataFrame({"a": rng.standard_normal(40)}, index=dates)
    returns = pd.DataFrame({"a": rng.standard_normal(40)}, index=dates)
    res = evaluate_cross_sectional(signals, returns)
    # Rolling fallback still yields the standard metric keys.
    assert "rank_ic_mean" in res
    assert res["n_periods"] > 0