Update pipeline.py

pipeline.py

@@ -13,6 +13,7 @@ from rl_agent import rl_signal
 
 from backtest_engine import backtest_strategy
 from ml_model import predict_probability
+from monte_carlo import monte_carlo_probability
 
 from market_overview import compute_sector_strength
 
@@ -75,7 +76,6 @@ def run_pipeline():
 
         confidence = max(0, min(100, round((total_score / 15) * 100, 2)))
 
-        # ATR calculation
         atr = ta.volatility.AverageTrueRange(
             high=high,
             low=low,
@@ -100,11 +100,14 @@ def run_pipeline():
 
         ml_probability = predict_probability(df)
 
+        mc_probability = monte_carlo_probability(df, target, stop)
+
         ranking_score = (
-            confidence * 0.35 +
-            winrate * 0.25 +
-            risk_reward * 15 +
-            ml_probability * 0.25
+            confidence * 0.30 +
+            winrate * 0.20 +
+            risk_reward * 12 +
+            ml_probability * 0.20 +
+            mc_probability * 0.18
         )
 
         results.append({
@@ -116,6 +119,7 @@ def run_pipeline():
             "Risk Reward": risk_reward,
             "Confidence %": confidence,
             "ML Probability %": ml_probability,
+            "Monte Carlo %": mc_probability,
             "Backtest Win Rate": winrate,
             "Market Regime": regime,
             "Signal Strength": total_score,