stored

ANNOTATE/core/annotation_manager.py

@@ -7,10 +7,11 @@ Handles storage, retrieval, and test case generation from manual trade annotatio
 import json
 import uuid
 from pathlib import Path
-from datetime import datetime
+from datetime import datetime, timedelta
 from typing import List, Dict, Optional, Any
 from dataclasses import dataclass, asdict
 import logging
+import pytz
 
 logger = logging.getLogger(__name__)
 
@@ -205,49 +206,20 @@ class AnnotationManager:
             }
         }
         
-        # Populate market state with ±5 minutes of data for negative examples
+        # Populate market state with ±5 minutes of data for training
         if data_provider:
             try:
                 entry_time = datetime.fromisoformat(annotation.entry['timestamp'].replace('Z', '+00:00'))
                 exit_time = datetime.fromisoformat(annotation.exit['timestamp'].replace('Z', '+00:00'))
                 
-                # Calculate time window: ±5 minutes around entry
-                time_window_before = timedelta(minutes=5)
-                time_window_after = timedelta(minutes=5)
+                logger.info(f"Fetching market state for {annotation.symbol} at {entry_time} (±5min around entry)")
                 
-                start_time = entry_time - time_window_before
-                end_time = entry_time + time_window_after
-                
-                logger.info(f"Fetching market data from {start_time} to {end_time} (±5min around entry)")
-                
-                # Fetch OHLCV data for all timeframes
-                timeframes = ['1s', '1m', '1h', '1d']
-                market_state = {}
-                
-                for tf in timeframes:
-                    # Get data for the time window
-                    df = data_provider.get_historical_data(
-                        symbol=annotation.symbol,
-                        timeframe=tf,
-                        limit=1000  # Get enough data to cover ±5 minutes
-                    )
-                    
-                    if df is not None and not df.empty:
-                        # Filter to time window
-                        df_window = df[(df.index >= start_time) & (df.index <= end_time)]
-                        
-                        if not df_window.empty:
-                            # Convert to list format
-                            market_state[f'ohlcv_{tf}'] = {
-                                'timestamps': df_window.index.strftime('%Y-%m-%d %H:%M:%S').tolist(),
-                                'open': df_window['open'].tolist(),
-                                'high': df_window['high'].tolist(),
-                                'low': df_window['low'].tolist(),
-                                'close': df_window['close'].tolist(),
-                                'volume': df_window['volume'].tolist()
-                            }
-                            
-                            logger.info(f"  {tf}: {len(df_window)} candles in ±5min window")
+                # Use the new data provider method to get market state at the entry time
+                market_state = data_provider.get_market_state_at_time(
+                    symbol=annotation.symbol,
+                    timestamp=entry_time,
+                    context_window_minutes=5
+                )
                 
                 # Add training labels for each timestamp
                 # This helps model learn WHERE to signal and WHERE NOT to signal
@@ -330,6 +302,9 @@ class AnnotationManager:
             for ts_str in timestamps:
                 try:
                     ts = datetime.strptime(ts_str, '%Y-%m-%d %H:%M:%S')
+                    # Make timezone-aware to match entry_time
+                    if ts.tzinfo is None:
+                        ts = pytz.UTC.localize(ts)
                     
                     # Determine label based on position relative to entry/exit
                     if abs((ts - entry_time).total_seconds()) < 60:  # Within 1 minute of entry