try to fix live RT updates on ANNOTATE

ANNOTATE/core/live_pivot_trainer.py

@@ -16,6 +16,8 @@ import time
 from typing import Dict, List, Optional, Tuple
 from datetime import datetime, timezone
 from collections import deque
+import numpy as np
+import pandas as pd
 
 logger = logging.getLogger(__name__)
 
@@ -146,20 +148,50 @@ class LivePivotTrainer:
             if williams is None:
                 return
             
-            pivots = williams.calculate_pivots(candles)
+            # Prepare data for Williams Market Structure
+            # Convert DataFrame to numpy array format
+            df = candles.copy()
+            ohlcv_array = df[['open', 'high', 'low', 'close', 'volume']].copy()
             
-            if not pivots or 'L2' not in pivots:
+            # Handle timestamp conversion based on index type
+            if isinstance(df.index, pd.DatetimeIndex):
+                # Convert ns to ms
+                timestamps = df.index.astype(np.int64) // 10**6
+            else:
+                # Assume it's already timestamp or handle accordingly
+                timestamps = df.index
+                
+            ohlcv_array.insert(0, 'timestamp', timestamps)
+            ohlcv_array = ohlcv_array.to_numpy()
+            
+            # Calculate pivots
+            pivot_levels = williams.calculate_recursive_pivot_points(ohlcv_array)
+            
+            if not pivot_levels or 2 not in pivot_levels:
                 return
             
-            l2_pivots = pivots['L2']
+            # Get Level 2 pivots
+            l2_trend_level = pivot_levels[2]
+            l2_pivots_objs = l2_trend_level.pivot_points
             
+            if not l2_pivots_objs:
+                return
+
             # Check for new L2 pivots (not in history)
             new_pivots = []
-            for pivot in l2_pivots:
-                pivot_id = f"{symbol}_{timeframe}_{pivot['timestamp']}_{pivot['type']}"
+            for p in l2_pivots_objs:
+                # Convert pivot object to dict for compatibility
+                pivot_dict = {
+                    'timestamp': p.timestamp,  # Keep as datetime object for compatibility
+                    'price': p.price,
+                    'type': p.pivot_type,
+                    'strength': p.strength
+                }
+                
+                pivot_id = f"{symbol}_{timeframe}_{pivot_dict['timestamp']}_{pivot_dict['type']}"
                 
                 if pivot_id not in self.trained_pivots:
-                    new_pivots.append(pivot)
+                    new_pivots.append(pivot_dict)
                     self.trained_pivots.append(pivot_id)
             
             if new_pivots: