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

freqtrade/user_data/strategies/IchimokuHyper3.py

@@ -0,0 +1,117 @@
+# pragma pylint: disable=missing-docstring, invalid-name, too-few-public-methods
+"""
+IchimokuHyper3 — espace de paramètres ÉLARGI (périodes Ichimoku optimisables).
+
+Round 3 de la boucle d'optimisation : on donne plus de liberté à l'optimiseur
+(Tenkan/Kijun/Senkou B + lookback momentum + filtres) pour pousser le gain.
+⚠️ Plus de paramètres = plus de capacité d'overfitting (le gain in-sample monte,
+mais l'OOS sera à surveiller).
+"""
+from __future__ import annotations
+
+import talib.abstract as ta
+from pandas import DataFrame
+
+from freqtrade.strategy import (
+    IStrategy,
+    IntParameter,
+    DecimalParameter,
+    BooleanParameter,
+)
+
+
+class IchimokuHyper3(IStrategy):
+    INTERFACE_VERSION = 3
+    timeframe = "1h"
+    can_short = True
+
+    minimal_roi = {"0": 0.08, "240": 0.04, "720": 0.02, "1440": 0}
+    stoploss = -0.08
+    trailing_stop = True
+    trailing_stop_positive = 0.02
+    trailing_stop_positive_offset = 0.03
+    trailing_only_offset_is_reached = True
+
+    startup_candle_count: int = 260
+    process_only_new_candles = True
+    use_exit_signal = True
+
+    # Périodes Ichimoku optimisables
+    p_tenkan = IntParameter(5, 20, default=9, space="buy", optimize=True)
+    p_kijun = IntParameter(15, 45, default=26, space="buy", optimize=True)
+    p_senkou = IntParameter(40, 90, default=52, space="buy", optimize=True)
+    p_shift = IntParameter(15, 40, default=26, space="buy", optimize=True)
+    p_mom = IntParameter(10, 40, default=26, space="buy", optimize=True)
+    # Filtres
+    buy_adx_min = IntParameter(10, 40, default=20, space="buy", optimize=True)
+    buy_cloud_min_pct = DecimalParameter(0.0, 2.0, default=0.3, decimals=2, space="buy", optimize=True)
+    require_tk_cross = BooleanParameter(default=False, space="buy", optimize=True)
+    use_macro = BooleanParameter(default=False, space="buy", optimize=True)
+
+    def leverage(self, pair, current_time, current_rate, proposed_leverage,
+                 max_leverage, entry_tag, side, **kwargs) -> float:
+        return 1.0
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        high, low, close = dataframe["high"], dataframe["low"], dataframe["close"]
+        t, k, s, sh = (
+            self.p_tenkan.value, self.p_kijun.value,
+            self.p_senkou.value, self.p_shift.value,
+        )
+        tenkan = (high.rolling(t).max() + low.rolling(t).min()) / 2
+        kijun = (high.rolling(k).max() + low.rolling(k).min()) / 2
+        dataframe["tenkan"] = tenkan
+        dataframe["kijun"] = kijun
+        dataframe["senkou_a"] = ((tenkan + kijun) / 2).shift(sh)
+        dataframe["senkou_b"] = ((high.rolling(s).max() + low.rolling(s).min()) / 2).shift(sh)
+        dataframe["cloud_top"] = dataframe[["senkou_a", "senkou_b"]].max(axis=1)
+        dataframe["cloud_bot"] = dataframe[["senkou_a", "senkou_b"]].min(axis=1)
+        dataframe["cloud_width_pct"] = (
+            (dataframe["cloud_top"] - dataframe["cloud_bot"]) / close * 100
+        )
+        dataframe["close_prev"] = close.shift(self.p_mom.value)
+        dataframe["adx"] = ta.ADX(dataframe, timeperiod=14)
+        dataframe["ema200"] = ta.EMA(dataframe, timeperiod=200)
+        return dataframe
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        adx_min = self.buy_adx_min.value
+        cloud_min = self.buy_cloud_min_pct.value
+        long_cond = (
+            (dataframe["close"] > dataframe["cloud_top"])
+            & (dataframe["tenkan"] > dataframe["kijun"])
+            & (dataframe["close"] > dataframe["close_prev"])
+            & (dataframe["adx"] > adx_min)
+            & (dataframe["cloud_width_pct"] > cloud_min)
+            & (dataframe["volume"] > 0)
+        )
+        short_cond = (
+            (dataframe["close"] < dataframe["cloud_bot"])
+            & (dataframe["tenkan"] < dataframe["kijun"])
+            & (dataframe["close"] < dataframe["close_prev"])
+            & (dataframe["adx"] > adx_min)
+            & (dataframe["cloud_width_pct"] > cloud_min)
+            & (dataframe["volume"] > 0)
+        )
+        if self.require_tk_cross.value:
+            long_cond &= dataframe["tenkan"].shift(1) <= dataframe["kijun"].shift(1)
+            short_cond &= dataframe["tenkan"].shift(1) >= dataframe["kijun"].shift(1)
+        if self.use_macro.value:
+            long_cond &= dataframe["close"] > dataframe["ema200"]
+            short_cond &= dataframe["close"] < dataframe["ema200"]
+        dataframe.loc[long_cond, "enter_long"] = 1
+        dataframe.loc[short_cond, "enter_short"] = 1
+        return dataframe
+
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe.loc[
+            (((dataframe["tenkan"] < dataframe["kijun"]) | (dataframe["close"] < dataframe["cloud_bot"]))
+             & (dataframe["volume"] > 0)),
+            "exit_long",
+        ] = 1
+        dataframe.loc[
+            (((dataframe["tenkan"] > dataframe["kijun"]) | (dataframe["close"] > dataframe["cloud_top"]))
+             & (dataframe["volume"] > 0)),
+            "exit_short",
+        ] = 1
+        return dataframe