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
118 lines
5.0 KiB
Python
118 lines
5.0 KiB
Python
# pragma pylint: disable=missing-docstring, invalid-name, too-few-public-methods
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"""
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IchimokuHyper3 — espace de paramètres ÉLARGI (périodes Ichimoku optimisables).
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Round 3 de la boucle d'optimisation : on donne plus de liberté à l'optimiseur
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(Tenkan/Kijun/Senkou B + lookback momentum + filtres) pour pousser le gain.
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⚠️ Plus de paramètres = plus de capacité d'overfitting (le gain in-sample monte,
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mais l'OOS sera à surveiller).
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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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DecimalParameter,
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BooleanParameter,
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)
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class IchimokuHyper3(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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minimal_roi = {"0": 0.08, "240": 0.04, "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 = 0.03
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trailing_only_offset_is_reached = True
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startup_candle_count: int = 260
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process_only_new_candles = True
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use_exit_signal = True
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# Périodes Ichimoku optimisables
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p_tenkan = IntParameter(5, 20, default=9, space="buy", optimize=True)
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p_kijun = IntParameter(15, 45, default=26, space="buy", optimize=True)
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p_senkou = IntParameter(40, 90, default=52, space="buy", optimize=True)
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p_shift = IntParameter(15, 40, default=26, space="buy", optimize=True)
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p_mom = IntParameter(10, 40, default=26, space="buy", optimize=True)
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# Filtres
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buy_adx_min = IntParameter(10, 40, default=20, space="buy", optimize=True)
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buy_cloud_min_pct = DecimalParameter(0.0, 2.0, default=0.3, decimals=2, space="buy", optimize=True)
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require_tk_cross = BooleanParameter(default=False, space="buy", optimize=True)
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use_macro = BooleanParameter(default=False, space="buy", optimize=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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t, k, s, sh = (
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self.p_tenkan.value, self.p_kijun.value,
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self.p_senkou.value, self.p_shift.value,
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)
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tenkan = (high.rolling(t).max() + low.rolling(t).min()) / 2
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kijun = (high.rolling(k).max() + low.rolling(k).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(sh)
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dataframe["senkou_b"] = ((high.rolling(s).max() + low.rolling(s).min()) / 2).shift(sh)
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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_prev"] = close.shift(self.p_mom.value)
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dataframe["adx"] = ta.ADX(dataframe, timeperiod=14)
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dataframe["ema200"] = ta.EMA(dataframe, timeperiod=200)
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return dataframe
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def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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adx_min = self.buy_adx_min.value
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cloud_min = self.buy_cloud_min_pct.value
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long_cond = (
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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_prev"])
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& (dataframe["adx"] > adx_min)
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& (dataframe["cloud_width_pct"] > cloud_min)
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& (dataframe["volume"] > 0)
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)
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short_cond = (
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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_prev"])
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& (dataframe["adx"] > adx_min)
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& (dataframe["cloud_width_pct"] > cloud_min)
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& (dataframe["volume"] > 0)
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)
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if self.require_tk_cross.value:
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long_cond &= dataframe["tenkan"].shift(1) <= dataframe["kijun"].shift(1)
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short_cond &= dataframe["tenkan"].shift(1) >= dataframe["kijun"].shift(1)
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if self.use_macro.value:
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long_cond &= dataframe["close"] > dataframe["ema200"]
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short_cond &= dataframe["close"] < dataframe["ema200"]
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dataframe.loc[long_cond, "enter_long"] = 1
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dataframe.loc[short_cond, "enter_short"] = 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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(((dataframe["tenkan"] < dataframe["kijun"]) | (dataframe["close"] < dataframe["cloud_bot"]))
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& (dataframe["volume"] > 0)),
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"exit_long",
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] = 1
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dataframe.loc[
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(((dataframe["tenkan"] > dataframe["kijun"]) | (dataframe["close"] > dataframe["cloud_top"]))
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& (dataframe["volume"] > 0)),
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"exit_short",
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] = 1
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return dataframe
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