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
109 lines
4.2 KiB
Python
109 lines
4.2 KiB
Python
# pragma pylint: disable=missing-docstring, invalid-name, too-few-public-methods
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"""
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IchimokuLS — Ichimoku long/short avec FILTRE DE TENDANCE MACRO (EMA200).
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Reprend les paramètres optimisés d'IchimokuHyper (figés), et ajoute :
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- LONG uniquement si close > EMA200 (tendance de fond haussière)
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- SHORT uniquement si close < EMA200 (tendance de fond baissière)
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But : réparer le côté long, qui perdait en entrant à contre-tendance macro.
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On compare A/B contre IchimokuHyper (mêmes params, sans le filtre).
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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 IchimokuLS(IStrategy):
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INTERFACE_VERSION = 3
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timeframe = "1h"
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can_short = True
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# --- Paramètres figés (issus de l'hyperopt d'IchimokuHyper) ---
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minimal_roi = {"0": 0.488, "213": 0.136, "639": 0.05, "2021": 0}
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stoploss = -0.232
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trailing_stop = True
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trailing_stop_positive = 0.341
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trailing_stop_positive_offset = 0.441
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trailing_only_offset_is_reached = False
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buy_adx_min = 36
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buy_cloud_min_pct = 0.56
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startup_candle_count: int = 220 # EMA200 + marge
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process_only_new_candles = True
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use_exit_signal = True
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def leverage(self, pair, current_time, current_rate, proposed_leverage,
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max_leverage, entry_tag, side, **kwargs) -> float:
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return 1.0
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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high, low, close = dataframe["high"], dataframe["low"], dataframe["close"]
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tenkan = (high.rolling(9).max() + low.rolling(9).min()) / 2
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kijun = (high.rolling(26).max() + low.rolling(26).min()) / 2
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dataframe["tenkan"] = tenkan
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dataframe["kijun"] = kijun
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dataframe["senkou_a"] = ((tenkan + kijun) / 2).shift(26)
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dataframe["senkou_b"] = (
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(high.rolling(52).max() + low.rolling(52).min()) / 2
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).shift(26)
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dataframe["cloud_top"] = dataframe[["senkou_a", "senkou_b"]].max(axis=1)
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dataframe["cloud_bot"] = dataframe[["senkou_a", "senkou_b"]].min(axis=1)
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dataframe["cloud_width_pct"] = (
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(dataframe["cloud_top"] - dataframe["cloud_bot"]) / close * 100
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)
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dataframe["close_prev26"] = close.shift(26)
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dataframe["adx"] = ta.ADX(dataframe, timeperiod=14)
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dataframe["ema200"] = ta.EMA(dataframe, timeperiod=200) # filtre macro
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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["close"] > dataframe["cloud_top"])
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& (dataframe["tenkan"] > dataframe["kijun"])
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& (dataframe["close"] > dataframe["close_prev26"])
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& (dataframe["adx"] > self.buy_adx_min)
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& (dataframe["cloud_width_pct"] > self.buy_cloud_min_pct)
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& (dataframe["close"] > dataframe["ema200"]) # ← filtre macro LONG
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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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dataframe.loc[
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(
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(dataframe["close"] < dataframe["cloud_bot"])
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& (dataframe["tenkan"] < dataframe["kijun"])
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& (dataframe["close"] < dataframe["close_prev26"])
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& (dataframe["adx"] > self.buy_adx_min)
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& (dataframe["cloud_width_pct"] > self.buy_cloud_min_pct)
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& (dataframe["close"] < dataframe["ema200"]) # ← filtre macro SHORT
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& (dataframe["volume"] > 0)
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),
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"enter_short",
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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["tenkan"] < dataframe["kijun"])
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| (dataframe["close"] < dataframe["cloud_bot"]))
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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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dataframe.loc[
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(
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((dataframe["tenkan"] > dataframe["kijun"])
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| (dataframe["close"] > dataframe["cloud_top"]))
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& (dataframe["volume"] > 0)
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),
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"exit_short",
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] = 1
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return dataframe
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