from app.analysis.confluence import cluster_levels
from app.analysis.levels import Level, LevelKind, Side
from app.bars.models import Timeframe


def level(id_: str, price: float, weight: float, tf=Timeframe.H1):
    return Level(id_, LevelKind.MA, tf, Side.RESISTANCE, weight, 1, id_, 100, price, 0, None, 0, 100, 100, False, False)


def test_single_linkage_cluster_has_known_score_and_effective_side():
    clusters = cluster_levels(
        [level("a", 99.8, 2), level("b", 100.1, 4), level("c", 105, 1)],
        current_t=200,
        current_price=99,
        atr15=1,
    )

    assert len(clusters) == 1
    assert clusters[0].low == 99.8
    assert clusters[0].high == 100.1
    assert clusters[0].score == 6
    assert clusters[0].side is Side.RESISTANCE


def test_lone_daily_level_is_emitted():
    clusters = cluster_levels([level("daily", 98, 12, Timeframe.D1)], 200, 100, 1)
    assert len(clusters) == 1
    assert clusters[0].side is Side.SUPPORT


def test_levels_on_opposite_sides_of_price_do_not_cluster():
    clusters = cluster_levels([level("below", 99.9, 2), level("above", 100.1, 2)], 200, 100, 1)
    assert clusters == []


def test_level_ended_before_current_time_is_excluded():
    ended = level("ended", 98, 12, Timeframe.D1)
    ended.cutoff_t = 150
    assert cluster_levels([ended], 200, 100, 1) == []


def test_cluster_members_omit_point_history():
    # Clusters are re-sent on every closed 1m bar. A moving average's point
    # history is hundreds of entries, so embedding whole levels here shipped the
    # entire levels payload once a minute.
    heavy = level("ma", 98, 12, Timeframe.D1)
    heavy.points = [(t, 1.0) for t in range(600)]

    payload = cluster_levels([heavy], 200, 100, 1)[0].to_dict()

    assert payload["members"] == [
        {
            "id": "ma",
            "kind": "ma",
            "tf": "1d",
            "side": "resistance",
            "weight": 12,
            "label": "ma",
        }
    ]
    assert "points" not in payload["members"][0]