# pragma pylint: disable=missing-docstring, invalid-name, too-few-public-methods
"""
IchimokuLS — Ichimoku long/short avec FILTRE DE TENDANCE MACRO (EMA200).

Reprend les paramètres optimisés d'IchimokuHyper (figés), et ajoute :
  - LONG  uniquement si close > EMA200 (tendance de fond haussière)
  - SHORT uniquement si close < EMA200 (tendance de fond baissière)

But : réparer le côté long, qui perdait en entrant à contre-tendance macro.
On compare A/B contre IchimokuHyper (mêmes params, sans le filtre).
"""
from __future__ import annotations

import talib.abstract as ta
from pandas import DataFrame

from freqtrade.strategy import IStrategy


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

    # --- Paramètres figés (issus de l'hyperopt d'IchimokuHyper) ---
    minimal_roi = {"0": 0.488, "213": 0.136, "639": 0.05, "2021": 0}
    stoploss = -0.232
    trailing_stop = True
    trailing_stop_positive = 0.341
    trailing_stop_positive_offset = 0.441
    trailing_only_offset_is_reached = False

    buy_adx_min = 36
    buy_cloud_min_pct = 0.56

    startup_candle_count: int = 220  # EMA200 + marge
    process_only_new_candles = True
    use_exit_signal = 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"]
        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)
        dataframe["ema200"] = ta.EMA(dataframe, timeperiod=200)  # filtre macro
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (dataframe["close"] > dataframe["cloud_top"])
                & (dataframe["tenkan"] > dataframe["kijun"])
                & (dataframe["close"] > dataframe["close_prev26"])
                & (dataframe["adx"] > self.buy_adx_min)
                & (dataframe["cloud_width_pct"] > self.buy_cloud_min_pct)
                & (dataframe["close"] > dataframe["ema200"])   # ← filtre macro LONG
                & (dataframe["volume"] > 0)
            ),
            "enter_long",
        ] = 1
        dataframe.loc[
            (
                (dataframe["close"] < dataframe["cloud_bot"])
                & (dataframe["tenkan"] < dataframe["kijun"])
                & (dataframe["close"] < dataframe["close_prev26"])
                & (dataframe["adx"] > self.buy_adx_min)
                & (dataframe["cloud_width_pct"] > self.buy_cloud_min_pct)
                & (dataframe["close"] < dataframe["ema200"])   # ← filtre macro SHORT
                & (dataframe["volume"] > 0)
            ),
            "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