Update app.py

app.py

@@ -157,6 +157,17 @@ async def prepare_all_features(symbol, timeframe='1h'):
         df['VOL_RATIO'] = df['vol'] / df['vol'].rolling(20).mean()
         df['vol_lag1'] = df['vol'].shift(1)
         df["RSI_Macro"] = df["RSI"]
+
+        # --- 🐋 SMART MONEY CONCEPTS (LIQUIDITY SWEEPS) ---
+        # On repère les anciens plus bas / plus hauts majeurs
+        prev_low = df["low"].rolling(24).min().shift(1)
+        prev_high = df["high"].rolling(24).max().shift(1)
+        
+        # Piège Vendeur (Sweep Low) : La mèche casse le support, mais le prix remonte clôturer au-dessus
+        df["Sweep_Low"] = ((df["low"] < prev_low) & (df["close"] > prev_low)).astype(int)
+        
+        # Piège Acheteur (Sweep High) : La mèche casse la résistance, mais le prix retombe clôturer en dessous
+        df["Sweep_High"] = ((df["high"] > prev_high) & (df["close"] < prev_high)).astype(int)
         
         return df.dropna().copy()
         
@@ -212,6 +223,20 @@ async def predict_signal(symbol, timeframe="1h"):
         
         final_p = (time_prob * wt) + (ml_prob * wm) + (lstm_prob * wl) + (p_sent * ws)
 
+        # --- 🐋 BOOST SMART MONEY (CHASSE AUX BALEINES) ---
+        sweep_low = int(last_row["Sweep_Low"].iloc[0])
+        sweep_high = int(last_row["Sweep_High"].iloc[0])
+        smc_status = "AUCUN"
+
+        if sweep_low == 1:
+            # Les baleines ont piégé les vendeurs, le marché va exploser à la hausse
+            final_p = min(0.95, final_p + 0.20) 
+            smc_status = "LONG SWEEP DETECTED 🐋"
+        elif sweep_high == 1:
+            # Les baleines ont piégé les acheteurs, le marché va s'effondrer
+            final_p = max(0.05, final_p - 0.20)
+            smc_status = "SHORT SWEEP DETECTED 🐋"
+
         # 4. CALCUL DU SCORE & RISK (INSTITUTIONAL GRADE)
         strength = abs(final_p - 0.5) * 2
         conf_val = max(0, min(1, 1 - np.std([time_prob, ml_prob, lstm_prob, p_sent])))
@@ -266,7 +291,8 @@ async def predict_signal(symbol, timeframe="1h"):
             "final_score": round(final_p, 4), 
             "score": int(composite_score),
             "risk_percent": round(risk_pct, 2),
-            "estimated_profit": round(gain_estime_usd, 2), 
+            "estimated_profit": round(gain_estime_usd, 2),
+            "smart_money": smc_status,
             "price": prix, 
             "tp": round(tp, 6), 
             "sl": round(sl, 6),