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
69 lines
2.7 KiB
Python
69 lines
2.7 KiB
Python
# pragma pylint: disable=missing-docstring, invalid-name, too-few-public-methods
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"""
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TrendMomentumStrategy — tentative d'amélioration du rendement (vs SampleStrategy).
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Idée : n'entrer que sur des tendances confirmées et fortes (filtre EMA50 + ADX),
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laisser courir les gains (ROI plus haut + trailing), couper vite les perdants.
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Objectif : meilleur rendement ajusté du risque — PAS de promesse de +10 %/semaine.
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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 IStrategy
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class TrendMomentumStrategy(IStrategy):
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INTERFACE_VERSION = 3
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timeframe = "1h"
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# Laisser courir : on vise des gains plus gros, on attend plus longtemps.
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minimal_roi = {"0": 0.12, "240": 0.06, "720": 0.03, "1440": 0}
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stoploss = -0.06 # on coupe vite les perdants
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trailing_stop = True
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trailing_stop_positive = 0.025
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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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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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dataframe.loc[
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(
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(dataframe["ema_fast"] > dataframe["ema_slow"]) # tendance courte haussière
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& (dataframe["close"] > dataframe["ema_trend"]) # au-dessus tendance MT
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& (dataframe["adx"] > 25) # tendance forte
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& (dataframe["macd"] > dataframe["macdsignal"]) # momentum haussier
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& (dataframe["rsi"] > 50)
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& (dataframe["rsi"] < 75) # pas en surachat extrême
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& (dataframe["volume"] > 0)
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),
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"enter_long",
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] = 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["macd"] < dataframe["macdsignal"]) # momentum se retourne
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& (dataframe["close"] < dataframe["ema_slow"])
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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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