# pragma pylint: disable=missing-docstring, invalid-name, too-few-public-methods
"""
IchimokuHyper3 — espace de paramètres ÉLARGI (périodes Ichimoku optimisables).

Round 3 de la boucle d'optimisation : on donne plus de liberté à l'optimiseur
(Tenkan/Kijun/Senkou B + lookback momentum + filtres) pour pousser le gain.
⚠️ Plus de paramètres = plus de capacité d'overfitting (le gain in-sample monte,
mais l'OOS sera à surveiller).
"""
from __future__ import annotations

import talib.abstract as ta
from pandas import DataFrame

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


class IchimokuHyper3(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 = 260
    process_only_new_candles = True
    use_exit_signal = True

    # Périodes Ichimoku optimisables
    p_tenkan = IntParameter(5, 20, default=9, space="buy", optimize=True)
    p_kijun = IntParameter(15, 45, default=26, space="buy", optimize=True)
    p_senkou = IntParameter(40, 90, default=52, space="buy", optimize=True)
    p_shift = IntParameter(15, 40, default=26, space="buy", optimize=True)
    p_mom = IntParameter(10, 40, default=26, space="buy", optimize=True)
    # Filtres
    buy_adx_min = IntParameter(10, 40, default=20, 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)
    use_macro = BooleanParameter(default=False, space="buy", optimize=True)

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

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        high, low, close = dataframe["high"], dataframe["low"], dataframe["close"]
        t, k, s, sh = (
            self.p_tenkan.value, self.p_kijun.value,
            self.p_senkou.value, self.p_shift.value,
        )
        tenkan = (high.rolling(t).max() + low.rolling(t).min()) / 2
        kijun = (high.rolling(k).max() + low.rolling(k).min()) / 2
        dataframe["tenkan"] = tenkan
        dataframe["kijun"] = kijun
        dataframe["senkou_a"] = ((tenkan + kijun) / 2).shift(sh)
        dataframe["senkou_b"] = ((high.rolling(s).max() + low.rolling(s).min()) / 2).shift(sh)
        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_prev"] = close.shift(self.p_mom.value)
        dataframe["adx"] = ta.ADX(dataframe, timeperiod=14)
        dataframe["ema200"] = ta.EMA(dataframe, timeperiod=200)
        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_prev"])
            & (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_prev"])
            & (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)
        if self.use_macro.value:
            long_cond &= dataframe["close"] > dataframe["ema200"]
            short_cond &= dataframe["close"] < dataframe["ema200"]
        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