633b033f4d
- Infra: Freqtrade (futures dry-run) + Redis + dashboard + Docker Compose - Couche IA: ai_analyzer (Claude via abonnement, MCP TradingView, backfill biais) - Stratégies: SampleStrategy, AiBiasStrategy, IchimokuLS (long/short, validée train/test + données vierges + walk-forward), MTFIchimoku, variantes hyperopt - Arbitrage CEX (dry-run), backtesting, walk-forward, volatility targeting - IchimokuLS en dry-run live (config_live.json) Claude-Session: https://claude.ai/code/session_01VHETcFacdnDhQzthLpdYFR
83 lines
3.2 KiB
Python
83 lines
3.2 KiB
Python
# pragma pylint: disable=missing-docstring, invalid-name, too-few-public-methods
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"""
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HyperStrategy — stratégie paramétrable pour optimisation (hyperopt).
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Indicateurs fixes (EMA/RSI/ADX/MACD), conditions d'entrée et gestion de sortie
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PARAMÉTRÉES → Freqtrade peut optimiser les seuils, le ROI, le stoploss et le trailing.
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Méthode honnête : on optimise sur une période d'entraînement puis on VALIDE sur une
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période hors-échantillon (test) pour détecter le sur-apprentissage.
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"""
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from __future__ import annotations
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import talib.abstract as ta
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from pandas import DataFrame
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from freqtrade.strategy import (
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IStrategy,
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IntParameter,
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BooleanParameter,
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)
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class HyperStrategy(IStrategy):
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INTERFACE_VERSION = 3
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timeframe = "1h"
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# Valeurs par défaut — surchargées par les résultats d'hyperopt.
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minimal_roi = {"0": 0.10, "240": 0.05, "720": 0.02, "1440": 0}
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stoploss = -0.08
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trailing_stop = True
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trailing_stop_positive = 0.02
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trailing_stop_positive_offset = 0.04
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trailing_only_offset_is_reached = True
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startup_candle_count: int = 60
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process_only_new_candles = True
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use_exit_signal = True
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# --- Paramètres optimisables (espace "buy") ---
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buy_rsi_max = IntParameter(60, 80, default=70, space="buy", optimize=True)
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buy_adx_min = IntParameter(15, 40, default=25, space="buy", optimize=True)
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buy_require_trend = BooleanParameter(default=True, space="buy", optimize=True)
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buy_require_macd = BooleanParameter(default=True, space="buy", optimize=True)
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# --- Paramètres optimisables (espace "sell") ---
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sell_rsi_min = IntParameter(20, 50, default=35, space="sell", optimize=True)
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe["ema_fast"] = ta.EMA(dataframe, timeperiod=9)
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dataframe["ema_slow"] = ta.EMA(dataframe, timeperiod=21)
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dataframe["ema_trend"] = ta.EMA(dataframe, timeperiod=50)
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dataframe["adx"] = ta.ADX(dataframe, timeperiod=14)
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dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
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macd = ta.MACD(dataframe)
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dataframe["macd"] = macd["macd"]
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dataframe["macdsignal"] = macd["macdsignal"]
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return dataframe
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def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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cond = (
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(dataframe["ema_fast"] > dataframe["ema_slow"])
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& (dataframe["ema_fast"].shift(1) <= dataframe["ema_slow"].shift(1))
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& (dataframe["rsi"] < self.buy_rsi_max.value)
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& (dataframe["adx"] > self.buy_adx_min.value)
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& (dataframe["volume"] > 0)
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)
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if self.buy_require_trend.value:
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cond &= dataframe["close"] > dataframe["ema_trend"]
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if self.buy_require_macd.value:
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cond &= dataframe["macd"] > dataframe["macdsignal"]
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dataframe.loc[cond, "enter_long"] = 1
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return dataframe
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def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe.loc[
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(
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(dataframe["ema_fast"] < dataframe["ema_slow"])
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& (dataframe["rsi"] < self.sell_rsi_min.value)
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& (dataframe["volume"] > 0)
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),
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"exit_long",
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] = 1
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return dataframe
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