from __future__ import annotations

import math

import pytest

from app.services import fundamentals_research as research


def test_favorable_percentiles_are_tie_aware():
    ranks = research.favorable_percentiles(
        {"a": 3, "b": 3, "c": 3, "d": 3, "e": 3},
        higher_is_better=True,
    )
    assert set(ranks.values()) == {50.0}


def test_lower_is_better_flips_the_rank():
    ranks = research.favorable_percentiles(
        {"a": 1, "b": 2, "c": 3, "d": 4, "e": 5},
        higher_is_better=False,
    )
    assert ranks["a"] == 100.0
    assert ranks["e"] == 0.0


def test_invalid_and_thin_cross_sections_stay_null():
    ranks = research.favorable_percentiles(
        {"a": 1, "b": 2, "c": math.nan, "d": None, "e": 5},
        higher_is_better=True,
    )
    assert all(value is None for value in ranks.values())


def test_composites_use_equal_subgroup_weighting():
    features = {
        str(index): {
            "operating_margin": index,
            "fcf_margin": index,
            "net_debt_to_ebitda": 6 - index,
            "share_count_change_yoy": 6 - index,
            "revenue_growth_yoy": index,
            "eps_growth_yoy": index,
        }
        for index in range(1, 6)
    }
    scores = research.cross_section_scores(features)
    assert scores["5"]["quality"] == 100.0
    assert scores["5"]["growth"] == 100.0
    assert scores["5"]["balanced"] == 100.0
    assert scores["3"]["balanced"] == 50.0


def test_overlay_uses_neutral_missing_score_and_validates_weight():
    assert research.overlay_rank(90, None, 0.2) == 82.0
    assert research.overlay_rank(90, 100, 0.2) == 92.0
    with pytest.raises(ValueError, match="weight"):
        research.overlay_rank(90, 50, 1.1)