from __future__ import annotations

import pandas as pd

from web_core.constants import TREND_BEAR, TREND_BULL, TREND_NEUTRAL
from web_core.strategy import classify_bars, detect_trends


def make_df(rows: list[dict[str, float]]) -> pd.DataFrame:
    idx = pd.date_range("2025-01-01", periods=len(rows), freq="D")
    return pd.DataFrame(rows, index=idx)


def test_classify_bars_real_bull_real_bear_and_fake() -> None:
    df = make_df(
        [
            {"Open": 100, "High": 110, "Low": 95, "Close": 105, "Volume": 1000},
            {"Open": 106, "High": 111, "Low": 102, "Close": 112, "Volume": 1000},
            {"Open": 111, "High": 112, "Low": 100, "Close": 99, "Volume": 1000},
            {"Open": 100, "High": 103, "Low": 97, "Close": 101, "Volume": 1000},
        ]
    )

    out = classify_bars(
        df,
        use_body_range=False,
        volume_filter_enabled=False,
        volume_sma_window=20,
        volume_multiplier=1.0,
    )

    assert list(out["classification"]) == ["unclassified", "real_bull", "real_bear", "fake"]


def test_classify_bars_with_body_range_ignores_wicks() -> None:
    df = make_df(
        [
            {"Open": 100, "High": 130, "Low": 90, "Close": 105, "Volume": 1000},
            {"Open": 106, "High": 109, "Low": 104, "Close": 108, "Volume": 1000},
        ]
    )

    wick_based = classify_bars(
        df,
        use_body_range=False,
        volume_filter_enabled=False,
        volume_sma_window=20,
        volume_multiplier=1.0,
    )
    body_based = classify_bars(
        df,
        use_body_range=True,
        volume_filter_enabled=False,
        volume_sma_window=20,
        volume_multiplier=1.0,
    )

    assert wick_based.iloc[1]["classification"] == "fake"
    assert body_based.iloc[1]["classification"] == "real_bull"


def test_detect_trends_fake_bars_do_not_break_real_bar_sequence() -> None:
    df = make_df(
        [
            {"Open": 100, "High": 110, "Low": 95, "Close": 105, "Volume": 1000},
            {"Open": 106, "High": 112, "Low": 104, "Close": 113, "Volume": 1000},
            {"Open": 113, "High": 115, "Low": 108, "Close": 112, "Volume": 1000},
            {"Open": 112, "High": 120, "Low": 111, "Close": 121, "Volume": 1000},
        ]
    )

    classified = classify_bars(
        df,
        use_body_range=False,
        volume_filter_enabled=False,
        volume_sma_window=20,
        volume_multiplier=1.0,
    )
    trended, events = detect_trends(classified)

    assert list(classified["classification"]) == ["unclassified", "real_bull", "fake", "real_bull"]
    assert trended.iloc[-1]["trend_state"] == TREND_BULL
    assert any(e.event == "Bullish trend started" for e in events)


def test_detect_trends_reversal_requires_two_opposite_real_bars() -> None:
    df = make_df(
        [
            {"Open": 100, "High": 110, "Low": 95, "Close": 105, "Volume": 1000},
            {"Open": 106, "High": 112, "Low": 104, "Close": 113, "Volume": 1000},
            {"Open": 113, "High": 120, "Low": 111, "Close": 121, "Volume": 1000},
            {"Open": 120, "High": 121, "Low": 109, "Close": 108, "Volume": 1000},
            {"Open": 108, "High": 109, "Low": 95, "Close": 94, "Volume": 1000},
        ]
    )

    classified = classify_bars(
        df,
        use_body_range=False,
        volume_filter_enabled=False,
        volume_sma_window=20,
        volume_multiplier=1.0,
    )
    trended, events = detect_trends(classified)

    assert trended.iloc[2]["trend_state"] == TREND_BULL
    assert trended.iloc[3]["trend_state"] == TREND_BULL
    assert trended.iloc[4]["trend_state"] == TREND_BEAR
    assert any("Bearish reversal confirmed" in e.event for e in events)


def test_detect_trends_remains_neutral_without_two_consecutive_real_bars() -> None:
    df = make_df(
        [
            {"Open": 100, "High": 110, "Low": 95, "Close": 105, "Volume": 1000},
            {"Open": 105, "High": 108, "Low": 102, "Close": 106, "Volume": 1000},
            {"Open": 106, "High": 116, "Low": 100, "Close": 117, "Volume": 1000},
            {"Open": 117, "High": 118, "Low": 111, "Close": 115, "Volume": 1000},
            {"Open": 115, "High": 116, "Low": 104, "Close": 103, "Volume": 1000},
        ]
    )

    classified = classify_bars(
        df,
        use_body_range=False,
        volume_filter_enabled=False,
        volume_sma_window=20,
        volume_multiplier=1.0,
    )
    trended, events = detect_trends(classified)

    assert trended.iloc[-1]["trend_state"] == TREND_NEUTRAL
    assert len(events) == 0