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/MTFIchimoku.py
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freqtrade/user_data/strategies/MTFIchimoku.py
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# pragma pylint: disable=missing-docstring, invalid-name, too-few-public-methods
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"""
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MTFIchimoku — Ichimoku MULTI-TIMEFRAME (technique S/R inter-unités).
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Idée (méthode de l'utilisateur) : trader sur une unité basse (15m) en utilisant
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les Tenkan/Kijun de l'unité SUPÉRIEURE (1h) comme supports/résistances.
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Règles :
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- Tendance 1h donnée par tenkan_1h vs kijun_1h.
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- LONG : en tendance 1h haussière, le prix 15m RECLAIME le support (croise au-dessus
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de la Kijun 1h) → rebond sur support.
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- SHORT : en tendance 1h baissière, le prix 15m CASSE le support (croise sous la
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Kijun 1h) → rejet sur résistance.
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- Sortie : perte du niveau (close repasse de l'autre côté de la Kijun 1h) + ROI/stop.
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La Kijun 1h sert de S/R principal (niveau lent/fort), la Tenkan 1h de filtre de tendance.
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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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informative,
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IntParameter,
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DecimalParameter,
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)
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class MTFIchimoku(IStrategy):
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INTERFACE_VERSION = 3
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timeframe = "15m" # unité de trading
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can_short = True
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# Paramètres optimisables
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buy_adx_min = IntParameter(15, 40, default=25, space="buy", optimize=True)
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buy_pullback_pct = DecimalParameter(0.004, 0.03, default=0.012, decimals=3, space="buy", optimize=True)
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buy_rsi_max = IntParameter(50, 72, default=60, space="buy", optimize=True)
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sell_rsi_min = IntParameter(28, 50, default=40, space="sell", optimize=True)
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minimal_roi = {"0": 0.02, "60": 0.012, "180": 0.006, "360": 0}
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stoploss = -0.025
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trailing_stop = True
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trailing_stop_positive = 0.008
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trailing_stop_positive_offset = 0.014
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trailing_only_offset_is_reached = True
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startup_candle_count: int = 240
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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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@informative("1h")
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def populate_indicators_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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# Tenkan / Kijun sur l'unité supérieure (1h) → deviendront tenkan_1h / kijun_1h.
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high, low = dataframe["high"], dataframe["low"]
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dataframe["tenkan"] = (high.rolling(9).max() + low.rolling(9).min()) / 2
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dataframe["kijun"] = (high.rolling(26).max() + low.rolling(26).min()) / 2
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return dataframe
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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# tenkan_1h / kijun_1h sont injectés automatiquement (forward-fill sur le 15m).
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dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
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dataframe["adx"] = ta.ADX(dataframe, timeperiod=14)
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# Plus haut/bas récents (pour exiger un VRAI pullback vers le niveau)
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dataframe["hh8"] = dataframe["high"].rolling(8).max()
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dataframe["ll8"] = dataframe["low"].rolling(8).min()
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return dataframe
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def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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kijun = dataframe["kijun_1h"]
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tenkan = dataframe["tenkan_1h"]
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close, op, low, high = (
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dataframe["close"], dataframe["open"], dataframe["low"], dataframe["high"]
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)
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rsi = dataframe["rsi"]
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adx = dataframe["adx"]
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adx_min = self.buy_adx_min.value
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pb = self.buy_pullback_pct.value
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uptrend_1h = (tenkan > kijun) & (close > kijun) # biais haussier 1h
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downtrend_1h = (tenkan < kijun) & (close < kijun) # biais baissier 1h
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# LONG : en tendance forte, VRAI pullback vers le support Kijun 1h puis rebond + momentum.
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dataframe.loc[
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(
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uptrend_1h
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& (adx > adx_min) # tendance forte
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& (dataframe["hh8"] > kijun * (1 + pb)) # le prix venait nettement au-dessus
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& (low <= kijun * 1.001) # mèche teste le support
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& (close > kijun) # clôture au-dessus (support tient)
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& (close > op) # bougie de rebond
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& (rsi > rsi.shift(1)) # momentum qui se retourne à la hausse
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& (rsi < self.buy_rsi_max.value) # pas déjà suracheté
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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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# SHORT : miroir exact (rejet sur résistance en tendance baissière forte).
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dataframe.loc[
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(
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downtrend_1h
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& (adx > adx_min)
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& (dataframe["ll8"] < kijun * (1 - pb))
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& (high >= kijun * 0.999)
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& (close < kijun)
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& (close < op)
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& (rsi < rsi.shift(1))
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& (rsi > self.sell_rsi_min.value)
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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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kijun = dataframe["kijun_1h"]
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tenkan = dataframe["tenkan_1h"]
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close = dataframe["close"]
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# Sortie LONG : cassure NETTE du support (clôture sous la Kijun ET sous la Tenkan 1h)
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dataframe.loc[
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((close < kijun) & (close < tenkan) & (dataframe["volume"] > 0)),
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"exit_long",
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] = 1
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# Sortie SHORT : reprise NETTE au-dessus de la résistance
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dataframe.loc[
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((close > kijun) & (close > tenkan) & (dataframe["volume"] > 0)),
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"exit_short",
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] = 1
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return dataframe
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