"""다이버전스 유형 복합 기법."""

from __future__ import annotations

import pandas as pd

from deepcoin.techniques.base import BaseTechnique, TechniqueParams, TechniqueSignal
from deepcoin.techniques.composite_base import (
    cluster_events,
    collect_weighted_events,
    score_clusters_to_signals,
)
from deepcoin.techniques.macd_cross import MacdCrossTechnique
from deepcoin.techniques.macd_divergence import MacdDivergenceTechnique
from deepcoin.techniques.obv_divergence import ObvDivergenceTechnique
from deepcoin.techniques.rsi_divergence import RsiDivergenceTechnique
from deepcoin.techniques.rsi_swing import RsiSwingTechnique

_SUB = [
    RsiDivergenceTechnique(),
    MacdDivergenceTechnique(),
    ObvDivergenceTechnique(),
    RsiSwingTechnique(),
    MacdCrossTechnique(),
]

_WEIGHTS: dict[str, tuple[float, float]] = {
    "rsi_divergence": (2.5, 2.5),
    "macd_divergence": (2.5, 2.5),
    "obv_divergence": (2.0, 2.0),
    "rsi_swing": (1.2, 1.2),
    "macd_cross": (1.0, 1.0),
}


class CompositeDivergenceTechnique(BaseTechnique):
    """다이버전스 Bd/Sd 유형 전담 복합 기법."""

    technique_id = "composite_divergence"
    technique_name = "다이버전스 복합"
    category = "composite"
    causal = True
    description = "RSI/MACD/OBV 다이버전스 가중 투표 (Bd/Sd)"

    def default_extra_params(self) -> dict:
        return {"min_score": 2.0, "merge_bars": 5, "trend_ema_span": 60}

    def generate_signals(self, df: pd.DataFrame, params: TechniqueParams) -> list[TechniqueSignal]:
        min_score = float(params.extra.get("min_score", 2.0))
        merge_bars = int(params.extra.get("merge_bars", 5))
        trend_span = int(params.extra.get("trend_ema_span", 60))
        events = collect_weighted_events(_SUB, _WEIGHTS, df, params)
        clusters = cluster_events(events, merge_bars=merge_bars)
        return score_clusters_to_signals(
            df, clusters, min_score=min_score, trend_span=trend_span, use_trend_filter=False,
        )