# pragma pylint: disable=missing-docstring, invalid-name, too-few-public-methods
"""
LongShortStrategy — stratégie symétrique (futures) qui gagne dans les deux sens.

- LONG quand la tendance est haussière (EMA fast>slow, prix>EMA50, momentum +).
- SHORT quand la tendance est baissière (miroir exact).
But : ne plus subir les marchés baissiers — profiter de la baisse comme de la hausse.
Levier 1x par défaut (on isole l'edge directionnel ; le levier viendra après si robuste).
"""
from __future__ import annotations

import talib.abstract as ta
from pandas import DataFrame

from freqtrade.strategy import IStrategy


class LongShortStrategy(IStrategy):
    INTERFACE_VERSION = 3
    timeframe = "1h"
    can_short = True  # futures requis

    minimal_roi = {"0": 0.05, "120": 0.03, "360": 0.01, "720": 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 = 60
    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  # 1x — edge directionnel pur, sans amplification

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe["ema_fast"] = ta.EMA(dataframe, timeperiod=9)
        dataframe["ema_slow"] = ta.EMA(dataframe, timeperiod=21)
        dataframe["ema_trend"] = ta.EMA(dataframe, timeperiod=50)
        dataframe["adx"] = ta.ADX(dataframe, timeperiod=14)
        dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # LONG : croisement haussier + tendance MT haussière + tendance forte
        dataframe.loc[
            (
                (dataframe["ema_fast"] > dataframe["ema_slow"])
                & (dataframe["ema_fast"].shift(1) <= dataframe["ema_slow"].shift(1))
                & (dataframe["close"] > dataframe["ema_trend"])
                & (dataframe["adx"] > 20)
                & (dataframe["rsi"] > 45)
                & (dataframe["rsi"] < 75)
                & (dataframe["volume"] > 0)
            ),
            "enter_long",
        ] = 1
        # SHORT : miroir exact
        dataframe.loc[
            (
                (dataframe["ema_fast"] < dataframe["ema_slow"])
                & (dataframe["ema_fast"].shift(1) >= dataframe["ema_slow"].shift(1))
                & (dataframe["close"] < dataframe["ema_trend"])
                & (dataframe["adx"] > 20)
                & (dataframe["rsi"] < 55)
                & (dataframe["rsi"] > 25)
                & (dataframe["volume"] > 0)
            ),
            "enter_short",
        ] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Sortie LONG : la tendance courte se retourne à la baisse
        dataframe.loc[
            (
                (dataframe["ema_fast"] < dataframe["ema_slow"])
                & (dataframe["ema_fast"].shift(1) >= dataframe["ema_slow"].shift(1))
                & (dataframe["volume"] > 0)
            ),
            "exit_long",
        ] = 1
        # Sortie SHORT : la tendance courte se retourne à la hausse
        dataframe.loc[
            (
                (dataframe["ema_fast"] > dataframe["ema_slow"])
                & (dataframe["ema_fast"].shift(1) <= dataframe["ema_slow"].shift(1))
                & (dataframe["volume"] > 0)
            ),
            "exit_short",
        ] = 1
        return dataframe