MidasBot: bot trading crypto IA + stratégies Ichimoku validées
- 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
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jerem
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freqtrade/user_data/strategies/IchimokuHyperVol.py
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126
freqtrade/user_data/strategies/IchimokuHyperVol.py
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# pragma pylint: disable=missing-docstring, invalid-name, too-few-public-methods
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"""
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IchimokuHyperVol — IchimokuHyper + VOLATILITY TARGETING (sizing dynamique).
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Même logique d'entrée/sortie qu'IchimokuHyper. Seule différence : la TAILLE de
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position s'ajuste à l'inverse de la volatilité récente (ATR%) :
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vol haute → position réduite → pertes bornées (notamment dans le chop volatil)
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vol normale→ pleine taille → gains préservés en tendance
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Contrôle de risque DYNAMIQUE et non-prédictif (pas de filtre de régime overfit).
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Référence de vol = médiane glissante de l'ATR% (trailing → pas de fuite du futur).
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"""
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from __future__ import annotations
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import numpy as np
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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 IchimokuHyperVol(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 = 520 # médiane ATR% sur 500 + Ichimoku
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process_only_new_candles = True
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use_exit_signal = True
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buy_adx_min = IntParameter(15, 40, default=25, 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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# Bornes du facteur de sizing (réduit jusqu'à 0.4x, augmente jusqu'à 1.4x)
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VOL_FACTOR_MIN = 0.4
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VOL_FACTOR_MAX = 1.4
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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 custom_stake_amount(self, pair, current_time, current_rate, proposed_stake,
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min_stake, max_stake, leverage, entry_tag, side, **kwargs):
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df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
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if df is None or len(df) == 0:
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return proposed_stake
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factor = df["vol_factor"].iat[-1]
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if factor is None or not np.isfinite(factor):
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return proposed_stake
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stake = proposed_stake * float(factor)
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if min_stake is not None:
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stake = max(stake, min_stake)
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return min(stake, max_stake)
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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"] = ((high.rolling(52).max() + low.rolling(52).min()) / 2).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"] = (dataframe["cloud_top"] - dataframe["cloud_bot"]) / close * 100
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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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# --- Volatility targeting ---
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atr_pct = ta.ATR(dataframe, timeperiod=14) / close * 100
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atr_ref = atr_pct.rolling(500).median() # vol "normale" (trailing)
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factor = (atr_ref / atr_pct).clip(self.VOL_FACTOR_MIN, self.VOL_FACTOR_MAX)
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dataframe["vol_factor"] = factor.fillna(1.0)
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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_prev26"])
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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_prev26"])
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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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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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