pivot points

config.yaml

@@ -1,5 +1,11 @@
 # Enhanced Multi-Modal Trading System Configuration
 
+# System Settings
+system:
+  timezone: "Europe/Sofia"     # Configurable timezone for all timestamps
+  log_level: "INFO"            # DEBUG, INFO, WARNING, ERROR
+  session_timeout: 3600        # Session timeout in seconds
+
 # Trading Symbols (extendable/configurable)
 symbols:
   - "ETH/USDC"  # MEXC supports ETHUSDC for API trading
@@ -161,7 +167,7 @@ mexc_trading:
   # Risk management
   max_daily_loss_usd: 5.0        # Stop trading if daily loss exceeds $5
   max_concurrent_positions: 3    # Only 1 position at a time for testing
-  max_trades_per_hour: 60         # Maximum 60 trades per hour
+  max_trades_per_hour: 600         # Maximum 60 trades per hour
   min_trade_interval_seconds: 30  # Minimum between trades
   
   # Order configuration

debug_dashboard_issue.py

@@ -0,0 +1,142 @@
+#!/usr/bin/env python3
+"""
+Debug Dashboard Data Flow
+
+Check if the dashboard is receiving data and updating properly.
+"""
+
+import sys
+import logging
+import time
+import requests
+import json
+from pathlib import Path
+
+# Add project root to path
+project_root = Path(__file__).parent
+sys.path.insert(0, str(project_root))
+
+from core.config import get_config, setup_logging
+from core.data_provider import DataProvider
+
+# Setup logging
+setup_logging()
+logger = logging.getLogger(__name__)
+
+def test_data_provider():
+    """Test if data provider is working"""
+    logger.info("=== TESTING DATA PROVIDER ===")
+    
+    try:
+        # Test data provider
+        data_provider = DataProvider()
+        
+        # Test current price
+        logger.info("Testing current price retrieval...")
+        current_price = data_provider.get_current_price('ETH/USDT')
+        logger.info(f"Current ETH/USDT price: ${current_price}")
+        
+        # Test historical data
+        logger.info("Testing historical data retrieval...")
+        df = data_provider.get_historical_data('ETH/USDT', '1m', limit=5, refresh=True)
+        if df is not None and not df.empty:
+            logger.info(f"Historical data: {len(df)} rows")
+            logger.info(f"Latest price: ${df['close'].iloc[-1]:.2f}")
+            logger.info(f"Latest timestamp: {df.index[-1]}")
+        else:
+            logger.error("No historical data available!")
+        
+        return True
+        
+    except Exception as e:
+        logger.error(f"Data provider test failed: {e}")
+        return False
+
+def test_dashboard_api():
+    """Test if dashboard API is responding"""
+    logger.info("=== TESTING DASHBOARD API ===")
+    
+    try:
+        # Test main dashboard page
+        response = requests.get("http://127.0.0.1:8050", timeout=5)
+        logger.info(f"Dashboard main page status: {response.status_code}")
+        
+        if response.status_code == 200:
+            logger.info("Dashboard is responding")
+            
+            # Check if there are any JavaScript errors in the page
+            content = response.text
+            if 'error' in content.lower():
+                logger.warning("Possible errors found in dashboard HTML")
+            
+            return True
+        else:
+            logger.error(f"Dashboard returned status {response.status_code}")
+            return False
+            
+    except Exception as e:
+        logger.error(f"Dashboard API test failed: {e}")
+        return False
+
+def test_dashboard_callbacks():
+    """Test dashboard callback updates"""
+    logger.info("=== TESTING DASHBOARD CALLBACKS ===")
+    
+    try:
+        # Test the callback endpoint (this would need to be exposed)
+        # For now, just check if the dashboard is serving content
+        
+        # Wait a bit and check again
+        time.sleep(2)
+        
+        response = requests.get("http://127.0.0.1:8050", timeout=5)
+        if response.status_code == 200:
+            logger.info("Dashboard callbacks appear to be working")
+            return True
+        else:
+            logger.error("Dashboard callbacks may be stuck")
+            return False
+            
+    except Exception as e:
+        logger.error(f"Dashboard callback test failed: {e}")
+        return False
+
+def main():
+    """Run all diagnostic tests"""
+    logger.info("DASHBOARD DIAGNOSTIC TOOL")
+    logger.info("=" * 50)
+    
+    results = {
+        'data_provider': test_data_provider(),
+        'dashboard_api': test_dashboard_api(),
+        'dashboard_callbacks': test_dashboard_callbacks()
+    }
+    
+    logger.info("=" * 50)
+    logger.info("DIAGNOSTIC RESULTS:")
+    
+    for test_name, result in results.items():
+        status = "PASS" if result else "FAIL"
+        logger.info(f"  {test_name}: {status}")
+    
+    if all(results.values()):
+        logger.info("All tests passed - issue may be browser-side")
+        logger.info("Try refreshing the dashboard at http://127.0.0.1:8050")
+    else:
+        logger.error("Issues detected - check logs above")
+        logger.info("Recommendations:")
+        
+        if not results['data_provider']:
+            logger.info("  - Check internet connection")
+            logger.info("  - Verify Binance API is accessible")
+            
+        if not results['dashboard_api']:
+            logger.info("  - Restart the dashboard")
+            logger.info("  - Check if port 8050 is blocked")
+            
+        if not results['dashboard_callbacks']:
+            logger.info("  - Dashboard may be frozen")
+            logger.info("  - Consider restarting")
+
+if __name__ == "__main__":
+    main() 

main_clean.py

@@ -286,7 +286,7 @@ def run_web_dashboard():
         data_provider = DataProvider()
         
         # Verify we have real data connection
-        logger.info("🔍 Verifying REAL data connection...")
+        logger.info("[DATA] Verifying REAL data connection...")
         test_data = data_provider.get_historical_data('ETH/USDT', '1m', limit=10, refresh=True)
         if test_data is None or test_data.empty:
             logger.warning("⚠️ No fresh data available - trying cached data...")

web/dashboard.py

@@ -10,49 +10,57 @@ This module provides a modern, responsive web dashboard for the trading system:
 """
 
 import asyncio
-import json
-import logging
-import time
-from datetime import datetime, timedelta, timezone
-from threading import Thread
-from typing import Dict, List, Optional, Any, Tuple
-from collections import deque
-
-# Optional WebSocket support
-try:
-    import websocket
-    import threading
-    WEBSOCKET_AVAILABLE = True
-except ImportError:
-    WEBSOCKET_AVAILABLE = False
-    logger = logging.getLogger(__name__)
-    logger.warning("websocket-client not available. Install with: pip install websocket-client")
-
 import dash
-from dash import dcc, html, Input, Output, State, callback_context
+from dash import dcc, html, Input, Output
 import plotly.graph_objects as go
-import plotly.express as px
 from plotly.subplots import make_subplots
+import plotly.express as px
 import pandas as pd
 import numpy as np
+from datetime import datetime, timedelta, timezone
+import pytz
+import logging
+import json
+import time
+import threading
+from threading import Thread, Lock
+from collections import deque
+import warnings
+from typing import Dict, List, Optional, Any, Union, Tuple
+import websocket
 
+# Setup logger immediately after logging import
+logger = logging.getLogger(__name__)
+
+# WebSocket availability check
+try:
+    import websocket
+    WEBSOCKET_AVAILABLE = True
+    logger.info("WebSocket client available")
+except ImportError:
+    WEBSOCKET_AVAILABLE = False
+    logger.warning("websocket-client not available. Real-time data will use API fallback.")
+
+# Import trading system components
 from core.config import get_config
 from core.data_provider import DataProvider
 from core.orchestrator import TradingOrchestrator, TradingDecision
 from core.trading_executor import TradingExecutor
+from core.trading_action import TradingAction
+from models import get_model_registry
 
-# Enhanced RL Training Integration
+
+# Import enhanced RL components if available
 try:
+    from core.enhanced_orchestrator import EnhancedTradingOrchestrator
+    from core.universal_data_adapter import UniversalDataAdapter
     from core.unified_data_stream import UnifiedDataStream, TrainingDataPacket, UIDataPacket
-    from core.enhanced_orchestrator import EnhancedTradingOrchestrator, MarketState, TradingAction
-    from training.enhanced_rl_trainer import EnhancedRLTrainer
     ENHANCED_RL_AVAILABLE = True
-    logger = logging.getLogger(__name__)
     logger.info("Enhanced RL training components available")
 except ImportError as e:
+    logger.warning(f"Enhanced RL components not available: {e}")
     ENHANCED_RL_AVAILABLE = False
-    logger = logging.getLogger(__name__)
-    logger.warning(f"Enhanced RL training not available: {e}")
+    
     # Fallback classes
     class UnifiedDataStream:
         def __init__(self, *args, **kwargs): pass
@@ -68,36 +76,6 @@ except ImportError as e:
     class UIDataPacket:
         def __init__(self, *args, **kwargs): pass
 
-# Try to import model registry, fallback if not available
-try:
-    from models import get_model_registry
-except ImportError:
-    logger.warning("Models module not available, creating fallback registry")
-    
-    class FallbackModelRegistry:
-        def __init__(self):
-            self.total_memory_limit_mb = 8192  # 8GB
-            self.models = {}
-        
-        def get_memory_stats(self):
-            return {
-                'utilization_percent': 0,
-                'total_used_mb': 0,
-                'total_limit_mb': self.total_memory_limit_mb,
-                'models': {}
-            }
-        
-        def get_models_by_type(self, model_type: str):
-            """Get models by type - fallback implementation returns empty dict"""
-            return {}
-        
-        def register_model(self, model, weight=1.0):
-            return True
-    
-    def get_model_registry():
-        return FallbackModelRegistry()
-
-logger = logging.getLogger(__name__)
 
 class AdaptiveThresholdLearner:
     """Learn optimal confidence thresholds based on real trade outcomes"""
@@ -190,11 +168,17 @@ class AdaptiveThresholdLearner:
             return {'error': str(e)}
 
 class TradingDashboard:
-    """Modern trading dashboard with real-time updates and enhanced RL training integration"""
+    """Enhanced Trading Dashboard with Williams pivot points and unified timezone handling"""
     
     def __init__(self, data_provider: DataProvider = None, orchestrator: TradingOrchestrator = None, trading_executor: TradingExecutor = None):
         """Initialize the dashboard with unified data stream and enhanced RL training"""
         self.config = get_config()
+        
+        # Initialize timezone from config
+        timezone_name = self.config.get('system', {}).get('timezone', 'Europe/Sofia')
+        self.timezone = pytz.timezone(timezone_name)
+        logger.info(f"Dashboard timezone set to: {timezone_name}")
+        
         self.data_provider = data_provider or DataProvider()
         
         # Enhanced orchestrator support
@@ -314,16 +298,60 @@ class TradingDashboard:
         logger.info(f"Enhanced RL enabled: {self.enhanced_rl_training_enabled}")
         logger.info(f"Stream consumer ID: {self.stream_consumer_id}")
     
+    def _to_local_timezone(self, dt: datetime) -> datetime:
+        """Convert datetime to configured local timezone"""
+        try:
+            if dt is None:
+                return None
+            
+            # If datetime is naive, assume it's UTC
+            if dt.tzinfo is None:
+                dt = pytz.UTC.localize(dt)
+            
+            # Convert to local timezone
+            return dt.astimezone(self.timezone)
+        except Exception as e:
+            logger.warning(f"Error converting timezone: {e}")
+            return dt
+    
+    def _now_local(self) -> datetime:
+        """Get current time in configured local timezone"""
+        return datetime.now(self.timezone)
+    
+    def _ensure_timezone_consistency(self, df: pd.DataFrame) -> pd.DataFrame:
+        """Ensure DataFrame index is in consistent timezone"""
+        try:
+            if hasattr(df.index, 'tz'):
+                if df.index.tz is None:
+                    # Assume UTC if no timezone
+                    df.index = df.index.tz_localize('UTC')
+                
+                # Convert to local timezone
+                df.index = df.index.tz_convert(self.timezone)
+            
+            return df
+        except Exception as e:
+            logger.warning(f"Error ensuring timezone consistency: {e}")
+            return df
+    
     def _initialize_streaming(self):
         """Initialize unified data streaming and WebSocket fallback"""
         try:
+            # Start WebSocket first (non-blocking)
+            self._start_websocket_stream()
+            logger.info("WebSocket streaming initialized")
+            
             if ENHANCED_RL_AVAILABLE:
-                # Start unified data stream
+                # Start unified data stream in background
+                def start_unified_stream():
+                    try:
                         asyncio.run(self.unified_stream.start_streaming())
                         logger.info("Unified data stream started")
+                    except Exception as e:
+                        logger.error(f"Error starting unified stream: {e}")
                 
-            # Start WebSocket as backup/additional data source
-            self._start_websocket_stream()
+                unified_thread = Thread(target=start_unified_stream, daemon=True)
+                unified_thread.start()
             
             # Start background data collection
             self._start_enhanced_training_data_collection()
@@ -332,7 +360,7 @@ class TradingDashboard:
             
         except Exception as e:
             logger.error(f"Error initializing streaming: {e}")
-            # Fallback to WebSocket only
+            # Ensure WebSocket is started as fallback
             self._start_websocket_stream()
     
     def _start_enhanced_training_data_collection(self):
@@ -855,20 +883,32 @@ class TradingDashboard:
                             current_threshold = self.adaptive_learner.get_current_threshold()
                             should_execute = signal['confidence'] >= current_threshold
                             
-                            if should_execute:
+                            # Check position limits before execution
+                            can_execute = self._can_execute_new_position(signal['action'])
+                            
+                            if should_execute and can_execute:
                                 signal['signal_type'] = 'EXECUTED'
                                 signal['threshold_used'] = current_threshold  # Track threshold for learning
                                 signal['reason'] = f"ADAPTIVE EXECUTE (≥{current_threshold:.2%}): {signal['reason']}"
                                 logger.debug(f"[EXECUTE] {signal['action']} signal @ ${signal['price']:.2f} (confidence: {signal['confidence']:.1%} ≥ {current_threshold:.1%})")
                                 self._process_trading_decision(signal)
+                            elif should_execute and not can_execute:
+                                # Signal meets confidence but we're at position limit
+                                signal['signal_type'] = 'NOT_EXECUTED_POSITION_LIMIT'
+                                signal['threshold_used'] = current_threshold
+                                signal['reason'] = f"BLOCKED BY POSITION LIMIT (≥{current_threshold:.2%}): {signal['reason']} [Positions: {self._count_open_positions()}/{self.config.get('trading', {}).get('max_concurrent_positions', 3)}]"
+                                logger.info(f"[BLOCKED] {signal['action']} signal @ ${signal['price']:.2f} - Position limit reached ({self._count_open_positions()}/{self.config.get('trading', {}).get('max_concurrent_positions', 3)})")
+                                
+                                # Still add to training queue for RL learning
+                                self._queue_signal_for_training(signal, current_price, symbol)
                             else:
-                                signal['signal_type'] = 'IGNORED'
-                                signal['reason'] = f"ADAPTIVE IGNORE (<{current_threshold:.2%}): {signal['reason']}"
-                                logger.debug(f"[IGNORE] {signal['action']} signal @ ${signal['price']:.2f} (confidence: {signal['confidence']:.1%} < {current_threshold:.1%})")
-                                # Add to recent decisions for display but don't execute trade
-                                self.recent_decisions.append(signal)
-                                if len(self.recent_decisions) > 500:  # Keep last 500 decisions
-                                    self.recent_decisions = self.recent_decisions[-500:]
+                                signal['signal_type'] = 'NOT_EXECUTED_LOW_CONFIDENCE'
+                                signal['threshold_used'] = current_threshold
+                                signal['reason'] = f"LOW CONFIDENCE (<{current_threshold:.2%}): {signal['reason']}"
+                                logger.debug(f"[SKIP] {signal['action']} signal @ ${signal['price']:.2f} (confidence: {signal['confidence']:.1%} < {current_threshold:.1%})")
+                                
+                                # Still add to training queue for RL learning
+                                self._queue_signal_for_training(signal, current_price, symbol)
                     else:
                         # Fallback: Add a simple monitoring update
                         if n_intervals % 10 == 0 and current_price:  # Every 10 seconds
@@ -1100,7 +1140,7 @@ class TradingDashboard:
         return fig
     
     def _create_price_chart(self, symbol: str) -> go.Figure:
-        """Create enhanced 1-second price chart with volume from WebSocket stream"""
+        """Create enhanced 1-second price chart with volume and Williams pivot points from WebSocket stream"""
         try:
             # Get 1-second bars from WebSocket stream
             df = self.get_one_second_bars(count=300)  # Last 5 minutes of 1s bars
@@ -1111,6 +1151,8 @@ class TradingDashboard:
                 try:
                     df = self.data_provider.get_historical_data(symbol, '1m', limit=50, refresh=True)
                     if df is not None and not df.empty:
+                        # Ensure timezone consistency for fallback data
+                        df = self._ensure_timezone_consistency(df)
                         # Add volume column if missing
                         if 'volume' not in df.columns:
                             df['volume'] = 100  # Default volume for demo
@@ -1127,15 +1169,17 @@ class TradingDashboard:
                         f"Chart Error: {str(e)}"
                     )
             else:
+                # Ensure timezone consistency for WebSocket data
+                df = self._ensure_timezone_consistency(df)
                 actual_timeframe = '1s'
-                logger.debug(f"[CHART] Using {len(df)} 1s bars from WebSocket stream")
+                logger.debug(f"[CHART] Using {len(df)} 1s bars from WebSocket stream in {self.timezone}")
             
             # Create subplot with secondary y-axis for volume
             fig = make_subplots(
                 rows=2, cols=1,
                 shared_xaxes=True,
                 vertical_spacing=0.1,
-                subplot_titles=(f'{symbol} Price ({actual_timeframe.upper()})', 'Volume'),
+                subplot_titles=(f'{symbol} Price ({actual_timeframe.upper()}) with Williams Pivot Points', 'Volume'),
                 row_heights=[0.7, 0.3]
             )
             
@@ -1152,6 +1196,14 @@ class TradingDashboard:
                 row=1, col=1
             )
             
+            # Add Williams Market Structure pivot points
+            try:
+                pivot_points = self._get_williams_pivot_points_for_chart(df)
+                if pivot_points:
+                    self._add_williams_pivot_points_to_chart(fig, pivot_points, row=1)
+            except Exception as e:
+                logger.debug(f"Error adding Williams pivot points to chart: {e}")
+            
             # Add moving averages if we have enough data
             if len(df) >= 20:
                 # 20-period SMA
@@ -1240,12 +1292,12 @@ class TradingDashboard:
                 
                 # Add BUY markers with different styles for executed vs ignored
                 executed_buys = [d[0] for d in buy_decisions if d[1] == 'EXECUTED']
-                ignored_buys = [d[0] for d in buy_decisions if d[1] == 'IGNORED']
+                ignored_buys = [d[0] for d in buy_decisions if d[1] in ['NOT_EXECUTED_POSITION_LIMIT', 'NOT_EXECUTED_LOW_CONFIDENCE']]
                 
                 if executed_buys:
                     fig.add_trace(
                         go.Scatter(
-                            x=[d['timestamp'] for d in executed_buys],
+                            x=[self._to_local_timezone(d['timestamp']) for d in executed_buys],
                             y=[d['price'] for d in executed_buys],
                             mode='markers',
                             marker=dict(
@@ -1256,7 +1308,8 @@ class TradingDashboard:
                             ),
                             name="BUY (Executed)",
                             showlegend=True,
-                            hovertemplate="<b>BUY EXECUTED</b><br>Price: $%{y:.2f}<br>Time: %{x}<br><extra></extra>"
+                            hovertemplate="<b>BUY EXECUTED</b><br>Price: $%{y:.2f}<br>Time: %{x}<br>Confidence: %{customdata:.1%}<extra></extra>",
+                            customdata=[d.get('confidence', 0) for d in executed_buys]
                         ),
                         row=1, col=1
                     )
@@ -1264,7 +1317,7 @@ class TradingDashboard:
                 if ignored_buys:
                     fig.add_trace(
                         go.Scatter(
-                            x=[d['timestamp'] for d in ignored_buys],
+                            x=[self._to_local_timezone(d['timestamp']) for d in ignored_buys],
                             y=[d['price'] for d in ignored_buys],
                             mode='markers',
                             marker=dict(
@@ -1273,21 +1326,22 @@ class TradingDashboard:
                                 symbol='triangle-up-open',
                                 line=dict(color='#00ff88', width=2)
                             ),
-                            name="BUY (Ignored)",
+                            name="BUY (Blocked)",
                             showlegend=True,
-                            hovertemplate="<b>BUY IGNORED</b><br>Price: $%{y:.2f}<br>Time: %{x}<br><extra></extra>"
+                            hovertemplate="<b>BUY BLOCKED</b><br>Price: $%{y:.2f}<br>Time: %{x}<br>Confidence: %{customdata:.1%}<extra></extra>",
+                            customdata=[d.get('confidence', 0) for d in ignored_buys]
                         ),
                         row=1, col=1
                     )
                 
                 # Add SELL markers with different styles for executed vs ignored
                 executed_sells = [d[0] for d in sell_decisions if d[1] == 'EXECUTED']
-                ignored_sells = [d[0] for d in sell_decisions if d[1] == 'IGNORED']
+                ignored_sells = [d[0] for d in sell_decisions if d[1] in ['NOT_EXECUTED_POSITION_LIMIT', 'NOT_EXECUTED_LOW_CONFIDENCE']]
                 
                 if executed_sells:
                     fig.add_trace(
                         go.Scatter(
-                            x=[d['timestamp'] for d in executed_sells],
+                            x=[self._to_local_timezone(d['timestamp']) for d in executed_sells],
                             y=[d['price'] for d in executed_sells],
                             mode='markers',
                             marker=dict(
@@ -1298,7 +1352,8 @@ class TradingDashboard:
                             ),
                             name="SELL (Executed)",
                             showlegend=True,
-                            hovertemplate="<b>SELL EXECUTED</b><br>Price: $%{y:.2f}<br>Time: %{x}<br><extra></extra>"
+                            hovertemplate="<b>SELL EXECUTED</b><br>Price: $%{y:.2f}<br>Time: %{x}<br>Confidence: %{customdata:.1%}<extra></extra>",
+                            customdata=[d.get('confidence', 0) for d in executed_sells]
                         ),
                         row=1, col=1
                     )
@@ -1306,7 +1361,7 @@ class TradingDashboard:
                 if ignored_sells:
                     fig.add_trace(
                         go.Scatter(
-                            x=[d['timestamp'] for d in ignored_sells],
+                            x=[self._to_local_timezone(d['timestamp']) for d in ignored_sells],
                             y=[d['price'] for d in ignored_sells],
                             mode='markers',
                             marker=dict(
@@ -1315,9 +1370,10 @@ class TradingDashboard:
                                 symbol='triangle-down-open',
                                 line=dict(color='#ff6b6b', width=2)
                             ),
-                            name="SELL (Ignored)",
+                            name="SELL (Blocked)",
                             showlegend=True,
-                            hovertemplate="<b>SELL IGNORED</b><br>Price: $%{y:.2f}<br>Time: %{x}<br><extra></extra>"
+                            hovertemplate="<b>SELL BLOCKED</b><br>Price: $%{y:.2f}<br>Time: %{x}<br>Confidence: %{customdata:.1%}<extra></extra>",
+                            customdata=[d.get('confidence', 0) for d in ignored_sells]
                         ),
                         row=1, col=1
                     )
@@ -2801,14 +2857,15 @@ class TradingDashboard:
             self.is_streaming = False
 
     def _process_tick_data(self, tick_data: Dict):
-        """Process incoming tick data and update 1-second bars"""
+        """Process incoming tick data and update 1-second bars with consistent timezone"""
         try:
             # Extract price and volume from Binance ticker data
             price = float(tick_data.get('c', 0))  # Current price
             volume = float(tick_data.get('v', 0))  # 24h volume
-            timestamp = datetime.now(timezone.utc)
+            # Use configured timezone instead of UTC for consistency
+            timestamp = self._now_local()
             
-            # Add to tick cache
+            # Add to tick cache with consistent timezone
             tick = {
                 'timestamp': timestamp,
                 'price': price,
@@ -2821,7 +2878,7 @@ class TradingDashboard:
             
             self.tick_cache.append(tick)
             
-            # Update current second bar
+            # Update current second bar using local timezone
             current_second = timestamp.replace(microsecond=0)
             
             if self.current_second_data['timestamp'] != current_second:
@@ -2849,6 +2906,8 @@ class TradingDashboard:
             # Update current price for dashboard
             self.current_prices[tick_data.get('s', 'ETHUSDT')] = price
             
+            logger.debug(f"[TICK] Processed tick at {timestamp.strftime('%H:%M:%S')} {self.timezone.zone}: ${price:.2f}")
+            
         except Exception as e:
             logger.warning(f"Error processing tick data: {e}")
 
@@ -2889,7 +2948,7 @@ class TradingDashboard:
             return []
 
     def get_one_second_bars(self, count: int = 300) -> pd.DataFrame:
-        """Get recent 1-second bars as DataFrame"""
+        """Get recent 1-second bars as DataFrame with consistent timezone"""
         try:
             if len(self.one_second_bars) == 0:
                 return pd.DataFrame()
@@ -2903,6 +2962,11 @@ class TradingDashboard:
                 df.set_index('timestamp', inplace=True)
                 df.sort_index(inplace=True)
                 
+                # Ensure timezone consistency
+                df = self._ensure_timezone_consistency(df)
+                
+                logger.debug(f"[BARS] Retrieved {len(df)} 1s bars in {self.timezone.zone}")
+            
             return df
             
         except Exception as e:
@@ -4274,16 +4338,16 @@ class TradingDashboard:
                 logger.warning(f"Error calculating Williams pivot points: {e}")
                 state_features.extend([0.0] * 250)  # Default features
             
-            # Add multi-timeframe OHLCV features (300 features)
+            # Add multi-timeframe OHLCV features (200 features: ETH 1s/1m/1d + BTC 1s)
             try:
                 multi_tf_features = self._get_multi_timeframe_features(training_episode.get('symbol', 'ETH/USDT'))
                 if multi_tf_features:
                     state_features.extend(multi_tf_features)
                 else:
-                    state_features.extend([0.0] * 300)  # Default if calculation fails
+                    state_features.extend([0.0] * 200)  # Default if calculation fails
             except Exception as e:
                 logger.warning(f"Error calculating multi-timeframe features: {e}")
-                state_features.extend([0.0] * 300)  # Default features
+                state_features.extend([0.0] * 200)  # Default features
             
             # Add trade-specific context
             entry_price = training_episode['entry_price']
@@ -4458,7 +4522,7 @@ class TradingDashboard:
                 return None
                 
             ohlcv_array = np.array([
-                [df.index[i].timestamp() if hasattr(df.index[i], 'timestamp') else time.time(),
+                [self._to_local_timezone(df.index[i]).timestamp() if hasattr(df.index[i], 'timestamp') else time.time(),
                  df['open'].iloc[i], df['high'].iloc[i], df['low'].iloc[i], 
                  df['close'].iloc[i], df['volume'].iloc[i]]
                 for i in range(len(df))
@@ -4479,16 +4543,40 @@ class TradingDashboard:
             return None
     
     def _get_multi_timeframe_features(self, symbol: str) -> Optional[List[float]]:
-        """Get multi-timeframe OHLCV features for comprehensive market context"""
+        """Get multi-timeframe OHLCV features for ETH and BTC only (focused timeframes)"""
         try:
             features = []
-            timeframes = ['1m', '5m', '15m', '1h', '4h', '1d']  # 6 timeframes
+            
+            # Focus only on key timeframes for ETH and BTC
+            if symbol.startswith('ETH'):
+                timeframes = ['1s', '1m', '1d']  # ETH: 3 key timeframes
+                target_symbol = 'ETH/USDT'
+            elif symbol.startswith('BTC'):
+                timeframes = ['1s']  # BTC: only 1s for reference
+                target_symbol = 'BTC/USDT'
+            else:
+                # Default to ETH if unknown symbol
+                timeframes = ['1s', '1m', '1d']
+                target_symbol = 'ETH/USDT'
             
             for timeframe in timeframes:
                 try:
                     # Get data for this timeframe
+                    if timeframe == '1s':
+                        # For 1s data, use our tick aggregation
+                        df = self.get_one_second_bars(count=60)  # Last 60 seconds
+                        if df.empty:
+                            # Fallback to 1m data
                             df = self.data_provider.get_historical_data(
-                        symbol=symbol,
+                                symbol=target_symbol,
+                                timeframe='1m',
+                                limit=50,
+                                refresh=True
+                            )
+                    else:
+                        # Get historical data for other timeframes
+                        df = self.data_provider.get_historical_data(
+                            symbol=target_symbol,
                             timeframe=timeframe,
                             limit=50,  # Last 50 bars
                             refresh=True
@@ -4503,14 +4591,40 @@ class TradingDashboard:
                         features.extend([0.0] * 50)  # 50 features per timeframe
                         
                 except Exception as e:
-                    logger.debug(f"Error getting {timeframe} data: {e}")
+                    logger.debug(f"Error getting {timeframe} data for {target_symbol}: {e}")
                     features.extend([0.0] * 50)  # 50 features per timeframe
             
-            # Total: 6 timeframes * 50 features = 300 features
-            return features[:300]
+            # Pad to ensure consistent feature count
+            # ETH: 3 timeframes * 50 = 150 features
+            # BTC: 1 timeframe * 50 = 50 features  
+            # Total expected: 200 features (150 ETH + 50 BTC)
+            
+            # Add BTC 1s data if we're processing ETH (for correlation analysis)
+            if symbol.startswith('ETH'):
+                try:
+                    btc_1s_df = self.get_one_second_bars(count=60, symbol='BTC/USDT')
+                    if btc_1s_df.empty:
+                        btc_1s_df = self.data_provider.get_historical_data(
+                            symbol='BTC/USDT',
+                            timeframe='1m',
+                            limit=50,
+                            refresh=True
+                        )
+                    
+                    if btc_1s_df is not None and not btc_1s_df.empty and len(btc_1s_df) >= 10:
+                        btc_features = self._extract_timeframe_features(btc_1s_df, '1s_btc')
+                        features.extend(btc_features)
+                    else:
+                        features.extend([0.0] * 50)  # BTC features
+                except Exception as e:
+                    logger.debug(f"Error getting BTC correlation data: {e}")
+                    features.extend([0.0] * 50)  # BTC features
+            
+            # Total: ETH(150) + BTC(50) = 200 features (reduced from 300)
+            return features[:200]
             
         except Exception as e:
-            logger.warning(f"Error calculating multi-timeframe features: {e}")
+            logger.warning(f"Error calculating focused multi-timeframe features: {e}")
             return None
     
     def _extract_timeframe_features(self, df: pd.DataFrame, timeframe: str) -> List[float]:
@@ -4647,6 +4761,276 @@ class TradingDashboard:
             logger.warning(f"Error extracting features for {timeframe}: {e}")
             return [0.0] * 50
 
+    def _get_williams_pivot_points_for_chart(self, df: pd.DataFrame) -> Optional[Dict]:
+        """Calculate Williams pivot points specifically for chart visualization with consistent timezone"""
+        try:
+            # Import Williams Market Structure
+            try:
+                from training.williams_market_structure import WilliamsMarketStructure
+            except ImportError:
+                logger.warning("Williams Market Structure not available for chart")
+                return None
+            
+            # Need at least 50 bars for meaningful pivot calculation
+            if len(df) < 50:
+                logger.debug(f"[WILLIAMS] Insufficient data for pivot calculation: {len(df)} bars")
+                return None
+            
+            # Ensure timezone consistency for the chart data
+            df = self._ensure_timezone_consistency(df)
+                
+            # Convert DataFrame to numpy array for Williams calculation with proper timezone handling
+            ohlcv_array = []
+            for i in range(len(df)):
+                timestamp = df.index[i]
+                
+                # Convert timestamp to local timezone and then to Unix timestamp
+                if hasattr(timestamp, 'timestamp'):
+                    local_time = self._to_local_timezone(timestamp)
+                    unix_timestamp = local_time.timestamp()
+                else:
+                    unix_timestamp = time.time()
+                
+                ohlcv_array.append([
+                    unix_timestamp,
+                    df['open'].iloc[i], 
+                    df['high'].iloc[i], 
+                    df['low'].iloc[i], 
+                    df['close'].iloc[i], 
+                    df['volume'].iloc[i]
+                ])
+            
+            ohlcv_array = np.array(ohlcv_array)
+            
+            # Calculate Williams pivot points
+            williams = WilliamsMarketStructure()
+            structure_levels = williams.calculate_recursive_pivot_points(ohlcv_array)
+            
+            # Extract pivot points for chart display
+            chart_pivots = {}
+            level_colors = ['#FF6B6B', '#4ECDC4', '#45B7D1', '#96CEB4', '#FFEAA7']  # Different colors per level
+            level_sizes = [8, 7, 6, 5, 4]  # Different sizes for each level
+            
+            total_pivots = 0
+            for level in range(5):
+                level_key = f'level_{level}'
+                if level_key in structure_levels:
+                    level_data = structure_levels[level_key]
+                    swing_points = level_data.swing_points
+                    
+                    if swing_points:
+                        # Log swing point details for validation
+                        highs = [s for s in swing_points if s.swing_type.name == 'SWING_HIGH']
+                        lows = [s for s in swing_points if s.swing_type.name == 'SWING_LOW']
+                        logger.debug(f"[WILLIAMS] Level {level}: {len(highs)} highs, {len(lows)} lows, total: {len(swing_points)}")
+                        
+                        # Convert swing points to chart format
+                        chart_pivots[f'level_{level}'] = {
+                            'swing_points': swing_points,
+                            'color': level_colors[level],
+                            'name': f'L{level + 1} Pivots',  # Shorter name
+                            'size': level_sizes[level],  # Different sizes for validation
+                            'opacity': max(0.9 - (level * 0.15), 0.4)  # High opacity for validation
+                        }
+                        total_pivots += len(swing_points)
+            
+            logger.info(f"[WILLIAMS] Calculated {total_pivots} total pivot points across {len(chart_pivots)} levels")
+            return chart_pivots
+            
+        except Exception as e:
+            logger.warning(f"Error calculating Williams pivot points for chart: {e}")
+            return None
+    
+    def _add_williams_pivot_points_to_chart(self, fig, pivot_points: Dict, row: int = 1):
+        """Add Williams pivot points as small triangles to the chart with proper timezone conversion"""
+        try:
+            for level_key, level_data in pivot_points.items():
+                swing_points = level_data['swing_points']
+                if not swing_points:
+                    continue
+                
+                # Validate swing alternation (shouldn't have consecutive highs or lows)
+                self._validate_swing_alternation(swing_points, level_key)
+                
+                # Separate swing highs and lows
+                swing_highs_x = []
+                swing_highs_y = []
+                swing_lows_x = []
+                swing_lows_y = []
+                
+                for swing in swing_points:
+                    # Ensure proper timezone conversion for swing point timestamps
+                    if hasattr(swing, 'timestamp'):
+                        timestamp = swing.timestamp
+                        
+                        # Convert swing timestamp to local timezone
+                        if isinstance(timestamp, datetime):
+                            # Williams Market Structure creates naive datetimes that are actually in local time
+                            # but without timezone info, so we need to localize them to our configured timezone
+                            if timestamp.tzinfo is None:
+                                # Williams creates timestamps in local time (Europe/Sofia), so localize directly
+                                local_timestamp = self.timezone.localize(timestamp)
+                            else:
+                                # If it has timezone info, convert to local timezone
+                                local_timestamp = timestamp.astimezone(self.timezone)
+                        else:
+                            # Fallback if timestamp is not a datetime
+                            local_timestamp = self._now_local()
+                    else:
+                        local_timestamp = self._now_local()
+                    
+                    price = swing.price
+                    
+                    if swing.swing_type.name == 'SWING_HIGH':
+                        swing_highs_x.append(local_timestamp)
+                        swing_highs_y.append(price)
+                    elif swing.swing_type.name == 'SWING_LOW':
+                        swing_lows_x.append(local_timestamp)
+                        swing_lows_y.append(price)
+                
+                # Add swing highs (triangle-up above the price)
+                if swing_highs_x:
+                    fig.add_trace(
+                        go.Scatter(
+                            x=swing_highs_x,
+                            y=swing_highs_y,
+                            mode='markers',
+                            name=f"{level_data['name']} (Highs)",
+                            marker=dict(
+                                color=level_data['color'],
+                                size=max(level_data['size'] - 2, 4),  # Smaller triangles
+                                symbol='triangle-up',  # Triangle pointing up for highs
+                                line=dict(color='white', width=1),
+                                opacity=level_data['opacity']
+                            ),
+                            hovertemplate=f'<b>Swing High</b><br>Price: $%{{y:.2f}}<br>%{{x}}<br>{level_data["name"]}<br>Strength: {swing_points[0].strength if swing_points else "N/A"}<extra></extra>',
+                            showlegend=True
+                        ),
+                        row=row, col=1
+                    )
+                
+                # Add swing lows (triangle-down below the price)
+                if swing_lows_x:
+                    fig.add_trace(
+                        go.Scatter(
+                            x=swing_lows_x,
+                            y=swing_lows_y,
+                            mode='markers',
+                            name=f"{level_data['name']} (Lows)",
+                            marker=dict(
+                                color=level_data['color'],
+                                size=max(level_data['size'] - 2, 4),  # Smaller triangles
+                                symbol='triangle-down',  # Triangle pointing down for lows
+                                line=dict(color='white', width=1),
+                                opacity=level_data['opacity']
+                            ),
+                            hovertemplate=f'<b>Swing Low</b><br>Price: $%{{y:.2f}}<br>%{{x}}<br>{level_data["name"]}<br>Strength: {swing_points[0].strength if swing_points else "N/A"}<extra></extra>',
+                            showlegend=True,
+                            legendgroup=level_data['name']  # Group with highs in legend
+                        ),
+                        row=row, col=1
+                    )
+            
+            logger.debug(f"[CHART] Added Williams pivot points as triangles with proper timezone conversion")
+            
+        except Exception as e:
+            logger.warning(f"Error adding Williams pivot points to chart: {e}")
+    
+    def _validate_swing_alternation(self, swing_points: List, level_key: str):
+        """Validate that swing points alternate correctly between highs and lows"""
+        try:
+            if len(swing_points) < 2:
+                return
+            
+            # Sort by index to check chronological order
+            sorted_swings = sorted(swing_points, key=lambda x: x.index)
+            
+            consecutive_issues = 0
+            last_type = None
+            
+            for i, swing in enumerate(sorted_swings):
+                current_type = swing.swing_type.name
+                
+                if last_type and last_type == current_type:
+                    consecutive_issues += 1
+                    logger.debug(f"[WILLIAMS_VALIDATION] {level_key}: Consecutive {current_type} at index {swing.index}")
+                
+                last_type = current_type
+            
+            if consecutive_issues > 0:
+                logger.warning(f"[WILLIAMS_VALIDATION] {level_key}: Found {consecutive_issues} consecutive swing issues")
+            else:
+                logger.debug(f"[WILLIAMS_VALIDATION] {level_key}: Swing alternation is correct ({len(sorted_swings)} swings)")
+                
+        except Exception as e:
+            logger.warning(f"Error validating swing alternation: {e}")
+
+    def _can_execute_new_position(self, action):
+        """Check if a new position can be executed based on current position limits"""
+        try:
+            # Get max concurrent positions from config
+            max_positions = self.config.get('trading', {}).get('max_concurrent_positions', 3)
+            current_open_positions = self._count_open_positions()
+            
+            # Check if we can open a new position
+            if current_open_positions >= max_positions:
+                logger.debug(f"[POSITION_LIMIT] Cannot execute {action} - at max positions ({current_open_positions}/{max_positions})")
+                return False
+            
+            # Additional check: if we have a current position, only allow closing trades
+            if self.current_position:
+                current_side = self.current_position['side']
+                if current_side == 'LONG' and action == 'BUY':
+                    return False  # Already long, can't buy more
+                elif current_side == 'SHORT' and action == 'SELL':
+                    return False  # Already short, can't sell more
+            
+            return True
+            
+        except Exception as e:
+            logger.error(f"Error checking position limits: {e}")
+            return False
+    
+    def _count_open_positions(self):
+        """Count current open positions"""
+        try:
+            # Simple count: 1 if we have a current position, 0 otherwise
+            return 1 if self.current_position else 0
+        except Exception as e:
+            logger.error(f"Error counting open positions: {e}")
+            return 0
+    
+    def _queue_signal_for_training(self, signal, current_price, symbol):
+        """Add a signal to training queue for RL learning (even if not executed)"""
+        try:
+            # Add to recent decisions for display
+            signal['timestamp'] = datetime.now()
+            self.recent_decisions.append(signal.copy())
+            if len(self.recent_decisions) > 500:
+                self.recent_decisions = self.recent_decisions[-500:]
+            
+            # Create synthetic trade for RL training
+            training_trade = {
+                'trade_id': f"training_{len(self.closed_trades) + 1}",
+                'symbol': symbol,
+                'side': 'LONG' if signal['action'] == 'BUY' else 'SHORT',
+                'entry_price': current_price,
+                'exit_price': current_price,  # Immediate close for training
+                'size': 0.01,  # Small size for training
+                'net_pnl': 0.0,  # Neutral outcome for blocked signals
+                'fees': 0.001,
+                'duration': timedelta(seconds=1),
+                'timestamp': datetime.now(),
+                'mexc_executed': False
+            }
+            
+            # Trigger RL training with this synthetic trade
+            self._trigger_rl_training_on_closed_trade(training_trade)
+            
+            logger.debug(f"[TRAINING] Queued {signal['action']} signal for RL learning")
+            
+        except Exception as e:
+            logger.warning(f"Error queuing signal for training: {e}")
 
 def create_dashboard(data_provider: DataProvider = None, orchestrator: TradingOrchestrator = None, trading_executor: TradingExecutor = None) -> TradingDashboard:
     """Factory function to create a trading dashboard"""