MidasBot/freqtrade/user_data/strategies/IchimokuHyper2.py

# pragma pylint: disable=missing-docstring, invalid-name, too-few-public-methods
"""
IchimokuHyper2 — Ichimoku long/short hyperoptable, AVEC contraintes de risque.

Améliorations vs IchimokuHyper :
  - Filtre macro EMA200 optimisable (use_macro).
  - STOP-LOSS CONTRAINT à une plage réaliste [-10% .. -2%] (le -23% précédent était le
    vrai point faible : trop large pour envisager le levier).
  - ROI plafonné à des valeurs réalistes.
Optimisation orientée robustesse (Sharpe) + train/test strict pour éviter l'overfit.
"""
from __future__ import annotations

import talib.abstract as ta
from pandas import DataFrame

from freqtrade.strategy import (
    IStrategy,
    IntParameter,
    DecimalParameter,
    BooleanParameter,
)
from freqtrade.optimize.space import Categorical, Dimension, Integer, SKDecimal


class IchimokuHyper2(IStrategy):
    INTERFACE_VERSION = 3
    timeframe = "1h"
    can_short = True

    # Défauts (surchargés par hyperopt)
    minimal_roi = {"0": 0.08, "240": 0.04, "720": 0.02, "1440": 0}
    stoploss = -0.06
    trailing_stop = True
    trailing_stop_positive = 0.02
    trailing_stop_positive_offset = 0.03
    trailing_only_offset_is_reached = True

    startup_candle_count: int = 220
    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)
    use_macro = BooleanParameter(default=True, space="buy", optimize=True)

    # --- Espaces hyperopt CONTRAINTS (le coeur de cette optimisation) ---
    class HyperOpt:
        @staticmethod
        def stoploss_space() -> list[Dimension]:
            # Stop réaliste : entre -2% et -10% (fini le -23%).
            return [SKDecimal(-0.10, -0.02, decimals=3, name="stoploss")]

        @staticmethod
        def roi_space() -> list[Dimension]:
            return [
                Integer(0, 120, name="roi_t1"),
                Integer(0, 60, name="roi_t2"),
                Integer(0, 30, name="roi_t3"),
                SKDecimal(0.02, 0.12, decimals=3, name="roi_p1"),
                SKDecimal(0.01, 0.06, decimals=3, name="roi_p2"),
                SKDecimal(0.005, 0.03, decimals=3, name="roi_p3"),
            ]

        @staticmethod
        def generate_roi_table(params: dict) -> dict[int, float]:
            roi = {}
            roi[0] = params["roi_p1"] + params["roi_p2"] + params["roi_p3"]
            roi[params["roi_t3"]] = params["roi_p1"] + params["roi_p2"]
            roi[params["roi_t3"] + params["roi_t2"]] = params["roi_p1"]
            roi[params["roi_t3"] + params["roi_t2"] + params["roi_t1"]] = 0
            return roi

        @staticmethod
        def trailing_space() -> list[Dimension]:
            return [
                Categorical([True], name="trailing_stop"),
                SKDecimal(0.01, 0.06, decimals=3, name="trailing_stop_positive"),
                SKDecimal(0.005, 0.05, decimals=3, name="trailing_stop_positive_offset_p1"),
                Categorical([True, False], name="trailing_only_offset_is_reached"),
            ]

    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)
        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)
        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