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/IchimokuLS.py

@@ -0,0 +1,108 @@
+# pragma pylint: disable=missing-docstring, invalid-name, too-few-public-methods
+"""
+IchimokuLS — Ichimoku long/short avec FILTRE DE TENDANCE MACRO (EMA200).
+
+Reprend les paramètres optimisés d'IchimokuHyper (figés), et ajoute :
+  - LONG  uniquement si close > EMA200 (tendance de fond haussière)
+  - SHORT uniquement si close < EMA200 (tendance de fond baissière)
+
+But : réparer le côté long, qui perdait en entrant à contre-tendance macro.
+On compare A/B contre IchimokuHyper (mêmes params, sans le filtre).
+"""
+from __future__ import annotations
+
+import talib.abstract as ta
+from pandas import DataFrame
+
+from freqtrade.strategy import IStrategy
+
+
+class IchimokuLS(IStrategy):
+    INTERFACE_VERSION = 3
+    timeframe = "1h"
+    can_short = True
+
+    # --- Paramètres figés (issus de l'hyperopt d'IchimokuHyper) ---
+    minimal_roi = {"0": 0.488, "213": 0.136, "639": 0.05, "2021": 0}
+    stoploss = -0.232
+    trailing_stop = True
+    trailing_stop_positive = 0.341
+    trailing_stop_positive_offset = 0.441
+    trailing_only_offset_is_reached = False
+
+    buy_adx_min = 36
+    buy_cloud_min_pct = 0.56
+
+    startup_candle_count: int = 220  # EMA200 + marge
+    process_only_new_candles = True
+    use_exit_signal = 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"]
+        tenkan = (high.rolling(9).max() + low.rolling(9).min()) / 2
+        kijun = (high.rolling(26).max() + low.rolling(26).min()) / 2
+        dataframe["tenkan"] = tenkan
+        dataframe["kijun"] = kijun
+        dataframe["senkou_a"] = ((tenkan + kijun) / 2).shift(26)
+        dataframe["senkou_b"] = (
+            (high.rolling(52).max() + low.rolling(52).min()) / 2
+        ).shift(26)
+        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_prev26"] = close.shift(26)
+        dataframe["adx"] = ta.ADX(dataframe, timeperiod=14)
+        dataframe["ema200"] = ta.EMA(dataframe, timeperiod=200)  # filtre macro
+        return dataframe
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe.loc[
+            (
+                (dataframe["close"] > dataframe["cloud_top"])
+                & (dataframe["tenkan"] > dataframe["kijun"])
+                & (dataframe["close"] > dataframe["close_prev26"])
+                & (dataframe["adx"] > self.buy_adx_min)
+                & (dataframe["cloud_width_pct"] > self.buy_cloud_min_pct)
+                & (dataframe["close"] > dataframe["ema200"])   # ← filtre macro LONG
+                & (dataframe["volume"] > 0)
+            ),
+            "enter_long",
+        ] = 1
+        dataframe.loc[
+            (
+                (dataframe["close"] < dataframe["cloud_bot"])
+                & (dataframe["tenkan"] < dataframe["kijun"])
+                & (dataframe["close"] < dataframe["close_prev26"])
+                & (dataframe["adx"] > self.buy_adx_min)
+                & (dataframe["cloud_width_pct"] > self.buy_cloud_min_pct)
+                & (dataframe["close"] < dataframe["ema200"])   # ← filtre macro SHORT
+                & (dataframe["volume"] > 0)
+            ),
+            "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