gogo2/SCALPING_DASHBOARD_WEBSOCKET_TICK_STREAMING_SUMMARY.md

Scalping Dashboard WebSocket Tick Streaming Implementation

Major Improvements Implemented

1. WebSocket Real-Time Tick Streaming for Main Chart

Problem: Main 1s chart was not loading due to candlestick chart issues and lack of real-time data Solution: Implemented direct WebSocket tick streaming with zero latency

Key Features:

• Direct WebSocket Feed: Main chart now uses live tick data from Binance WebSocket
• Tick Buffer: Maintains 200 most recent ticks for immediate chart updates
• Zero Latency: No API calls or caching - direct from WebSocket stream
• Volume Integration: Real-time volume data included in tick stream

Implementation Details:

# Real-time tick buffer for main chart
self.live_tick_buffer = {
    'ETH/USDT': [],
    'BTC/USDT': []
}

# WebSocket tick processing with volume
tick_entry = {
    'timestamp': timestamp,
    'price': price,
    'volume': volume,
    'open': price,  # For tick data, OHLC are same as current price
    'high': price,
    'low': price,
    'close': price
}

2. Fixed Candlestick Chart Issues

Problem: Candlestick charts failing due to unsupported hovertemplate property Solution: Removed incompatible properties and optimized chart creation

Changes Made:

• Removed hovertemplate from go.Candlestick() traces
• Fixed volume bar chart properties
• Maintained proper OHLC data structure
• Added proper error handling for chart creation

3. Enhanced Dynamic Throttling System

Problem: Dashboard was over-throttling and preventing updates Solution: Optimized throttling parameters and logic

Improvements:

• More Lenient Thresholds: Fast < 1.0s, Slow > 3.0s, Critical > 8.0s
• Reduced Max Throttle Level: From 5 to 3 levels
• Faster Recovery: Reduced consecutive updates needed from 5 to 3
• Conservative Start: Begin with 2-second intervals for stability

Performance Optimization:

# Optimized throttling parameters
self.update_frequency = 2000      # Start conservative (2s)
self.min_frequency = 5000         # Max throttling (5s)
self.max_frequency = 1000         # Min throttling (1s)
self.throttle_level = 0           # Max level 3 (reduced from 5)

4. Dual Chart System

Main Chart: WebSocket tick streaming (zero latency)

• Real-time tick data from WebSocket
• Line chart for high-frequency data visualization
• Live price markers and trading signals
• Volume overlay on secondary axis

Small Charts: Traditional candlestick charts

• ETH/USDT: 1m, 1h, 1d timeframes
• BTC/USDT: 1s reference chart
• Proper OHLC candlestick visualization
• Live price indicators

5. WebSocket Integration Enhancements

Enhanced Data Processing:

• Volume data extraction from WebSocket
• Timestamp synchronization
• Buffer size management (200 ticks max)
• Error handling and reconnection logic

Real-Time Features:

• Live price updates every tick
• Tick count display
• WebSocket connection status
• Automatic buffer maintenance

Technical Implementation

WebSocket Tick Processing

def _websocket_price_stream(self, symbol: str):
    # Enhanced to capture volume and create tick entries
    tick_data = json.loads(message)
    price = float(tick_data.get('c', 0))
    volume = float(tick_data.get('v', 0))
    timestamp = datetime.now()
    
    # Add to tick buffer for real-time chart
    tick_entry = {
        'timestamp': timestamp,
        'price': price,
        'volume': volume,
        'open': price,
        'high': price,
        'low': price,
        'close': price
    }
    
    self.live_tick_buffer[formatted_symbol].append(tick_entry)

Main Tick Chart Creation

def _create_main_tick_chart(self, symbol: str):
    # Convert tick buffer to DataFrame
    df = pd.DataFrame(tick_buffer)
    
    # Line chart for high-frequency tick data
    fig.add_trace(go.Scatter(
        x=df['timestamp'],
        y=df['price'],
        mode='lines',
        name=f"{symbol} Live Ticks",
        line=dict(color='#00ff88', width=2)
    ))
    
    # Volume bars on secondary axis
    fig.add_trace(go.Bar(
        x=df['timestamp'],
        y=df['volume'],
        name="Tick Volume",
        yaxis='y2',
        opacity=0.3
    ))

Performance Benefits

1. Zero Latency Main Chart

• Direct WebSocket: No API delays
• Tick-Level Updates: Sub-second price movements
• Buffer Management: Efficient memory usage
• Real-Time Volume: Live trading activity

2. Optimized Update Frequency

• Adaptive Throttling: Responds to system performance
• Conservative Start: Stable initial operation
• Fast Recovery: Quick optimization when performance improves
• Intelligent Skipping: Maintains responsiveness under load

3. Robust Error Handling

• Chart Fallbacks: Graceful degradation on errors
• WebSocket Reconnection: Automatic recovery
• Data Validation: Prevents crashes from bad data
• Performance Monitoring: Continuous optimization

User Experience Improvements

1. Immediate Visual Feedback

• Live Tick Stream: Real-time price movements
• Trading Signals: Buy/sell markers on charts
• Volume Activity: Live trading volume display
• Connection Status: WebSocket connectivity indicators

2. Professional Trading Interface

• Candlestick Charts: Proper OHLC visualization for small charts
• Tick Stream: High-frequency data for main chart
• Multiple Timeframes: 1s, 1m, 1h, 1d views
• Volume Integration: Trading activity visualization

3. Stable Performance

• Dynamic Throttling: Prevents system overload
• Error Recovery: Graceful handling of issues
• Memory Management: Efficient tick buffer handling
• Connection Resilience: Automatic WebSocket reconnection

Testing Results

✅ Fixed Issues

1. Main Chart Loading: Now displays WebSocket tick stream
2. Candlestick Charts: Proper OHLC visualization in small charts
3. Volume Display: Real-time volume data shown correctly
4. Update Frequency: Optimized throttling prevents over-throttling
5. Chart Responsiveness: Immediate updates from WebSocket feed

✅ Performance Metrics

1. Dashboard Startup: HTTP 200 response confirmed
2. WebSocket Connections: Active connections established
3. Tick Buffer: 200-tick buffer maintained efficiently
4. Chart Updates: Real-time updates without lag
5. Error Handling: Graceful fallbacks implemented

Usage Instructions

Launch Dashboard

python run_scalping_dashboard.py --port 8051 --leverage 500

Access Dashboard

• URL: http://127.0.0.1:8051
• Main Chart: ETH/USDT WebSocket tick stream
• Small Charts: Traditional candlestick charts
• Real-Time Data: Live price and volume updates

Monitor Performance

• Throttle Level: Displayed in logs
• Update Frequency: Adaptive based on performance
• Tick Count: Shown in main chart title
• WebSocket Status: Connection indicators in interface

Conclusion

The scalping dashboard now features:

• Zero-latency main chart with WebSocket tick streaming
• Proper candlestick charts for traditional timeframes
• Real-time volume data integration
• Optimized performance with intelligent throttling
• Professional trading interface with live signals

The implementation provides immediate visual feedback for scalping operations while maintaining system stability and performance optimization.