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/IchimokuHyper2.py
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freqtrade/user_data/strategies/IchimokuHyper2.py
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# pragma pylint: disable=missing-docstring, invalid-name, too-few-public-methods
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"""
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IchimokuHyper2 — Ichimoku long/short hyperoptable, AVEC contraintes de risque.
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Améliorations vs IchimokuHyper :
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- Filtre macro EMA200 optimisable (use_macro).
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- STOP-LOSS CONTRAINT à une plage réaliste [-10% .. -2%] (le -23% précédent était le
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vrai point faible : trop large pour envisager le levier).
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- ROI plafonné à des valeurs réalistes.
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Optimisation orientée robustesse (Sharpe) + train/test strict pour éviter l'overfit.
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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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from freqtrade.optimize.space import Categorical, Dimension, Integer, SKDecimal
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class IchimokuHyper2(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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# Défauts (surchargés par hyperopt)
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minimal_roi = {"0": 0.08, "240": 0.04, "720": 0.02, "1440": 0}
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stoploss = -0.06
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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 = 220
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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(
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0.0, 2.0, default=0.3, decimals=2, space="buy", optimize=True
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)
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require_tk_cross = BooleanParameter(default=False, space="buy", optimize=True)
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use_macro = BooleanParameter(default=True, space="buy", optimize=True)
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# --- Espaces hyperopt CONTRAINTS (le coeur de cette optimisation) ---
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class HyperOpt:
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@staticmethod
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def stoploss_space() -> list[Dimension]:
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# Stop réaliste : entre -2% et -10% (fini le -23%).
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return [SKDecimal(-0.10, -0.02, decimals=3, name="stoploss")]
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@staticmethod
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def roi_space() -> list[Dimension]:
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return [
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Integer(0, 120, name="roi_t1"),
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Integer(0, 60, name="roi_t2"),
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Integer(0, 30, name="roi_t3"),
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SKDecimal(0.02, 0.12, decimals=3, name="roi_p1"),
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SKDecimal(0.01, 0.06, decimals=3, name="roi_p2"),
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SKDecimal(0.005, 0.03, decimals=3, name="roi_p3"),
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]
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@staticmethod
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def generate_roi_table(params: dict) -> dict[int, float]:
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roi = {}
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roi[0] = params["roi_p1"] + params["roi_p2"] + params["roi_p3"]
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roi[params["roi_t3"]] = params["roi_p1"] + params["roi_p2"]
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roi[params["roi_t3"] + params["roi_t2"]] = params["roi_p1"]
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roi[params["roi_t3"] + params["roi_t2"] + params["roi_t1"]] = 0
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return roi
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@staticmethod
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def trailing_space() -> list[Dimension]:
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return [
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Categorical([True], name="trailing_stop"),
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SKDecimal(0.01, 0.06, decimals=3, name="trailing_stop_positive"),
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SKDecimal(0.005, 0.05, decimals=3, name="trailing_stop_positive_offset_p1"),
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Categorical([True, False], name="trailing_only_offset_is_reached"),
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]
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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)
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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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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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(
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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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