"""RSI 다이버전스 기법."""

from __future__ import annotations

import pandas as pd

from deepcoin.techniques.base import BaseTechnique, TechniqueParams, TechniqueSignal
from deepcoin.techniques.helpers import (
    dedupe_signals,
    detect_bearish_divergence,
    detect_bullish_divergence,
    find_confirmed_pivots,
    make_signal,
)
from deepcoin.techniques.indicators import rsi


class RsiDivergenceTechnique(BaseTechnique):
    """RSI 상승·하락 다이버전스 매수·매도."""

    technique_id = "rsi_divergence"
    technique_name = "RSI 다이버전스"
    category = "divergence"
    causal = True
    description = "RSI 상승(Bd)·하락(Sd) 다이버전스"

    def default_extra_params(self) -> dict:
        return {"period": 14, "order": 12, "min_bars_between": 15, "max_bars_between": 400}

    def generate_signals(self, df: pd.DataFrame, params: TechniqueParams) -> list[TechniqueSignal]:
        period = int(params.extra.get("period", 14))
        order = int(params.extra.get("order", 12))
        min_bars = int(params.extra.get("min_bars_between", 15))
        max_bars = int(params.extra.get("max_bars_between", 400))

        close = df["close"].astype(float)
        low = df["low"].astype(float)
        high = df["high"].astype(float)
        rsi_vals = rsi(close, period=period)

        low_pivots = find_confirmed_pivots(low, order, "low")
        high_pivots = find_confirmed_pivots(high, order, "high")
        signals: list[TechniqueSignal] = []

        for pivot_idx, _ in detect_bullish_divergence(
            low_pivots, rsi_vals, min_bars_between=min_bars, max_bars_between=max_bars,
        ):
            confirm_idx = pivot_idx + order
            if confirm_idx >= len(df):
                continue
            signals.append(
                make_signal(
                    df, confirm_idx, float(close.iloc[confirm_idx]), "buy",
                    "rsi_bull_divergence", pivot_bar_index=pivot_idx, confidence=0.78,
                )
            )

        for pivot_idx, _ in detect_bearish_divergence(
            high_pivots, rsi_vals, min_bars_between=min_bars, max_bars_between=max_bars,
        ):
            confirm_idx = pivot_idx + order
            if confirm_idx >= len(df):
                continue
            signals.append(
                make_signal(
                    df, confirm_idx, float(close.iloc[confirm_idx]), "sell",
                    "rsi_bear_divergence", pivot_bar_index=pivot_idx, confidence=0.78,
                )
            )

        return dedupe_signals(signals, min_bars=min_bars)