# pragma pylint: disable=missing-docstring, invalid-name, too-few-public-methods
"""
IchimokuHyperVol — IchimokuHyper + VOLATILITY TARGETING (sizing dynamique).

Même logique d'entrée/sortie qu'IchimokuHyper. Seule différence : la TAILLE de
position s'ajuste à l'inverse de la volatilité récente (ATR%) :
  vol haute  → position réduite → pertes bornées (notamment dans le chop volatil)
  vol normale→ pleine taille     → gains préservés en tendance

Contrôle de risque DYNAMIQUE et non-prédictif (pas de filtre de régime overfit).
Référence de vol = médiane glissante de l'ATR% (trailing → pas de fuite du futur).
"""
from __future__ import annotations

import numpy as np
import talib.abstract as ta
from pandas import DataFrame

from freqtrade.strategy import (
    IStrategy,
    IntParameter,
    DecimalParameter,
    BooleanParameter,
)


class IchimokuHyperVol(IStrategy):
    INTERFACE_VERSION = 3
    timeframe = "1h"
    can_short = True

    minimal_roi = {"0": 0.08, "240": 0.04, "720": 0.02, "1440": 0}
    stoploss = -0.08
    trailing_stop = True
    trailing_stop_positive = 0.02
    trailing_stop_positive_offset = 0.03
    trailing_only_offset_is_reached = True

    startup_candle_count: int = 520   # médiane ATR% sur 500 + Ichimoku
    process_only_new_candles = True
    use_exit_signal = True

    buy_adx_min = IntParameter(15, 40, default=25, space="buy", optimize=True)
    buy_cloud_min_pct = DecimalParameter(0.0, 2.0, default=0.3, decimals=2, space="buy", optimize=True)
    require_tk_cross = BooleanParameter(default=False, space="buy", optimize=True)

    # Bornes du facteur de sizing (réduit jusqu'à 0.4x, augmente jusqu'à 1.4x)
    VOL_FACTOR_MIN = 0.4
    VOL_FACTOR_MAX = 1.4

    def leverage(self, pair, current_time, current_rate, proposed_leverage,
                 max_leverage, entry_tag, side, **kwargs) -> float:
        return 1.0

    def custom_stake_amount(self, pair, current_time, current_rate, proposed_stake,
                            min_stake, max_stake, leverage, entry_tag, side, **kwargs):
        df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        if df is None or len(df) == 0:
            return proposed_stake
        factor = df["vol_factor"].iat[-1]
        if factor is None or not np.isfinite(factor):
            return proposed_stake
        stake = proposed_stake * float(factor)
        if min_stake is not None:
            stake = max(stake, min_stake)
        return min(stake, max_stake)

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        high, low, close = dataframe["high"], dataframe["low"], dataframe["close"]
        tenkan = (high.rolling(9).max() + low.rolling(9).min()) / 2
        kijun = (high.rolling(26).max() + low.rolling(26).min()) / 2
        dataframe["tenkan"] = tenkan
        dataframe["kijun"] = kijun
        dataframe["senkou_a"] = ((tenkan + kijun) / 2).shift(26)
        dataframe["senkou_b"] = ((high.rolling(52).max() + low.rolling(52).min()) / 2).shift(26)
        dataframe["cloud_top"] = dataframe[["senkou_a", "senkou_b"]].max(axis=1)
        dataframe["cloud_bot"] = dataframe[["senkou_a", "senkou_b"]].min(axis=1)
        dataframe["cloud_width_pct"] = (dataframe["cloud_top"] - dataframe["cloud_bot"]) / close * 100
        dataframe["close_prev26"] = close.shift(26)
        dataframe["adx"] = ta.ADX(dataframe, timeperiod=14)

        # --- Volatility targeting ---
        atr_pct = ta.ATR(dataframe, timeperiod=14) / close * 100
        atr_ref = atr_pct.rolling(500).median()             # vol "normale" (trailing)
        factor = (atr_ref / atr_pct).clip(self.VOL_FACTOR_MIN, self.VOL_FACTOR_MAX)
        dataframe["vol_factor"] = factor.fillna(1.0)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        adx_min = self.buy_adx_min.value
        cloud_min = self.buy_cloud_min_pct.value
        long_cond = (
            (dataframe["close"] > dataframe["cloud_top"])
            & (dataframe["tenkan"] > dataframe["kijun"])
            & (dataframe["close"] > dataframe["close_prev26"])
            & (dataframe["adx"] > adx_min)
            & (dataframe["cloud_width_pct"] > cloud_min)
            & (dataframe["volume"] > 0)
        )
        short_cond = (
            (dataframe["close"] < dataframe["cloud_bot"])
            & (dataframe["tenkan"] < dataframe["kijun"])
            & (dataframe["close"] < dataframe["close_prev26"])
            & (dataframe["adx"] > adx_min)
            & (dataframe["cloud_width_pct"] > cloud_min)
            & (dataframe["volume"] > 0)
        )
        if self.require_tk_cross.value:
            long_cond &= dataframe["tenkan"].shift(1) <= dataframe["kijun"].shift(1)
            short_cond &= dataframe["tenkan"].shift(1) >= dataframe["kijun"].shift(1)
        dataframe.loc[long_cond, "enter_long"] = 1
        dataframe.loc[short_cond, "enter_short"] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (((dataframe["tenkan"] < dataframe["kijun"]) | (dataframe["close"] < dataframe["cloud_bot"]))
             & (dataframe["volume"] > 0)),
            "exit_long",
        ] = 1
        dataframe.loc[
            (((dataframe["tenkan"] > dataframe["kijun"]) | (dataframe["close"] > dataframe["cloud_top"]))
             & (dataframe["volume"] > 0)),
            "exit_short",
        ] = 1
        return dataframe