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SCALPING_DASHBOARD_WEBSOCKET_TICK_STREAMING_SUMMARY.md
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224
SCALPING_DASHBOARD_WEBSOCKET_TICK_STREAMING_SUMMARY.md
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@ -0,0 +1,224 @@
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# Scalping Dashboard WebSocket Tick Streaming Implementation
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## Major Improvements Implemented
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### 1. WebSocket Real-Time Tick Streaming for Main Chart
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**Problem**: Main 1s chart was not loading due to candlestick chart issues and lack of real-time data
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**Solution**: Implemented direct WebSocket tick streaming with zero latency
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#### Key Features:
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- **Direct WebSocket Feed**: Main chart now uses live tick data from Binance WebSocket
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- **Tick Buffer**: Maintains 200 most recent ticks for immediate chart updates
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- **Zero Latency**: No API calls or caching - direct from WebSocket stream
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- **Volume Integration**: Real-time volume data included in tick stream
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#### Implementation Details:
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```python
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# Real-time tick buffer for main chart
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self.live_tick_buffer = {
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'ETH/USDT': [],
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'BTC/USDT': []
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}
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# WebSocket tick processing with volume
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tick_entry = {
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'timestamp': timestamp,
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'price': price,
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'volume': volume,
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'open': price, # For tick data, OHLC are same as current price
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'high': price,
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'low': price,
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'close': price
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}
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```
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### 2. Fixed Candlestick Chart Issues
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**Problem**: Candlestick charts failing due to unsupported `hovertemplate` property
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**Solution**: Removed incompatible properties and optimized chart creation
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#### Changes Made:
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- Removed `hovertemplate` from `go.Candlestick()` traces
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- Fixed volume bar chart properties
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- Maintained proper OHLC data structure
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- Added proper error handling for chart creation
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### 3. Enhanced Dynamic Throttling System
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**Problem**: Dashboard was over-throttling and preventing updates
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**Solution**: Optimized throttling parameters and logic
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#### Improvements:
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- **More Lenient Thresholds**: Fast < 1.0s, Slow > 3.0s, Critical > 8.0s
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- **Reduced Max Throttle Level**: From 5 to 3 levels
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- **Faster Recovery**: Reduced consecutive updates needed from 5 to 3
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- **Conservative Start**: Begin with 2-second intervals for stability
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#### Performance Optimization:
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```python
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# Optimized throttling parameters
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self.update_frequency = 2000 # Start conservative (2s)
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self.min_frequency = 5000 # Max throttling (5s)
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self.max_frequency = 1000 # Min throttling (1s)
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self.throttle_level = 0 # Max level 3 (reduced from 5)
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```
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### 4. Dual Chart System
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**Main Chart**: WebSocket tick streaming (zero latency)
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- Real-time tick data from WebSocket
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- Line chart for high-frequency data visualization
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- Live price markers and trading signals
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- Volume overlay on secondary axis
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**Small Charts**: Traditional candlestick charts
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- ETH/USDT: 1m, 1h, 1d timeframes
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- BTC/USDT: 1s reference chart
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- Proper OHLC candlestick visualization
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- Live price indicators
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### 5. WebSocket Integration Enhancements
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**Enhanced Data Processing**:
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- Volume data extraction from WebSocket
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- Timestamp synchronization
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- Buffer size management (200 ticks max)
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- Error handling and reconnection logic
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**Real-Time Features**:
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- Live price updates every tick
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- Tick count display
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- WebSocket connection status
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- Automatic buffer maintenance
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## Technical Implementation
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### WebSocket Tick Processing
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```python
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def _websocket_price_stream(self, symbol: str):
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# Enhanced to capture volume and create tick entries
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tick_data = json.loads(message)
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price = float(tick_data.get('c', 0))
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volume = float(tick_data.get('v', 0))
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timestamp = datetime.now()
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# Add to tick buffer for real-time chart
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tick_entry = {
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'timestamp': timestamp,
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'price': price,
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'volume': volume,
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'open': price,
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'high': price,
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'low': price,
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'close': price
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}
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self.live_tick_buffer[formatted_symbol].append(tick_entry)
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```
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### Main Tick Chart Creation
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```python
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def _create_main_tick_chart(self, symbol: str):
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# Convert tick buffer to DataFrame
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df = pd.DataFrame(tick_buffer)
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# Line chart for high-frequency tick data
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fig.add_trace(go.Scatter(
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x=df['timestamp'],
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y=df['price'],
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mode='lines',
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name=f"{symbol} Live Ticks",
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line=dict(color='#00ff88', width=2)
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))
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# Volume bars on secondary axis
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fig.add_trace(go.Bar(
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x=df['timestamp'],
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y=df['volume'],
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name="Tick Volume",
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yaxis='y2',
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opacity=0.3
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))
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```
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## Performance Benefits
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### 1. Zero Latency Main Chart
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- **Direct WebSocket**: No API delays
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- **Tick-Level Updates**: Sub-second price movements
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- **Buffer Management**: Efficient memory usage
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- **Real-Time Volume**: Live trading activity
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### 2. Optimized Update Frequency
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- **Adaptive Throttling**: Responds to system performance
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- **Conservative Start**: Stable initial operation
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- **Fast Recovery**: Quick optimization when performance improves
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- **Intelligent Skipping**: Maintains responsiveness under load
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### 3. Robust Error Handling
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- **Chart Fallbacks**: Graceful degradation on errors
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- **WebSocket Reconnection**: Automatic recovery
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- **Data Validation**: Prevents crashes from bad data
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- **Performance Monitoring**: Continuous optimization
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## User Experience Improvements
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### 1. Immediate Visual Feedback
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- **Live Tick Stream**: Real-time price movements
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- **Trading Signals**: Buy/sell markers on charts
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- **Volume Activity**: Live trading volume display
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- **Connection Status**: WebSocket connectivity indicators
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### 2. Professional Trading Interface
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- **Candlestick Charts**: Proper OHLC visualization for small charts
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- **Tick Stream**: High-frequency data for main chart
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- **Multiple Timeframes**: 1s, 1m, 1h, 1d views
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- **Volume Integration**: Trading activity visualization
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### 3. Stable Performance
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- **Dynamic Throttling**: Prevents system overload
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- **Error Recovery**: Graceful handling of issues
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- **Memory Management**: Efficient tick buffer handling
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- **Connection Resilience**: Automatic WebSocket reconnection
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## Testing Results
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### ✅ Fixed Issues
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1. **Main Chart Loading**: Now displays WebSocket tick stream
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2. **Candlestick Charts**: Proper OHLC visualization in small charts
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3. **Volume Display**: Real-time volume data shown correctly
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4. **Update Frequency**: Optimized throttling prevents over-throttling
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5. **Chart Responsiveness**: Immediate updates from WebSocket feed
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### ✅ Performance Metrics
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1. **Dashboard Startup**: HTTP 200 response confirmed
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2. **WebSocket Connections**: Active connections established
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3. **Tick Buffer**: 200-tick buffer maintained efficiently
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4. **Chart Updates**: Real-time updates without lag
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5. **Error Handling**: Graceful fallbacks implemented
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## Usage Instructions
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### Launch Dashboard
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```bash
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python run_scalping_dashboard.py --port 8051 --leverage 500
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```
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### Access Dashboard
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- **URL**: http://127.0.0.1:8051
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- **Main Chart**: ETH/USDT WebSocket tick stream
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- **Small Charts**: Traditional candlestick charts
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- **Real-Time Data**: Live price and volume updates
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### Monitor Performance
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- **Throttle Level**: Displayed in logs
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- **Update Frequency**: Adaptive based on performance
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- **Tick Count**: Shown in main chart title
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- **WebSocket Status**: Connection indicators in interface
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## Conclusion
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The scalping dashboard now features:
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- **Zero-latency main chart** with WebSocket tick streaming
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- **Proper candlestick charts** for traditional timeframes
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- **Real-time volume data** integration
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- **Optimized performance** with intelligent throttling
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- **Professional trading interface** with live signals
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The implementation provides immediate visual feedback for scalping operations while maintaining system stability and performance optimization.
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@ -7,6 +7,7 @@ This module consolidates all data functionality including:
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- Multi-timeframe candle generation
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- Caching and data management
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- Technical indicators calculation
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- Centralized data distribution to multiple subscribers (AI models, dashboard, etc.)
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"""
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import asyncio
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@ -14,23 +15,51 @@ import json
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import logging
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import os
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import time
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import uuid
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import websockets
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import requests
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import pandas as pd
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import numpy as np
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from datetime import datetime, timedelta
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from pathlib import Path
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from typing import Dict, List, Optional, Tuple, Any
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from typing import Dict, List, Optional, Tuple, Any, Callable
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from dataclasses import dataclass, field
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import ta
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from threading import Thread, Lock
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from collections import deque
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from dataclasses import dataclass, field
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import uuid
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from .config import get_config
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logger = logging.getLogger(__name__)
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@dataclass
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class MarketTick:
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"""Standardized market tick data structure"""
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symbol: str
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timestamp: datetime
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price: float
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volume: float
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quantity: float
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side: str # 'buy' or 'sell'
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trade_id: str
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is_buyer_maker: bool
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raw_data: Dict[str, Any] = field(default_factory=dict)
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@dataclass
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class DataSubscriber:
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"""Data subscriber information"""
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subscriber_id: str
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callback: Callable[[MarketTick], None]
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symbols: List[str]
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active: bool = True
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last_update: datetime = field(default_factory=datetime.now)
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tick_count: int = 0
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subscriber_name: str = "unknown"
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class DataProvider:
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"""Unified data provider for historical and real-time market data"""
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"""Unified data provider for historical and real-time market data with centralized distribution"""
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def __init__(self, symbols: List[str] = None, timeframes: List[str] = None):
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"""Initialize the data provider"""
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@ -48,6 +77,30 @@ class DataProvider:
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self.is_streaming = False
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self.data_lock = Lock()
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# Subscriber management for centralized data distribution
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self.subscribers: Dict[str, DataSubscriber] = {}
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self.subscriber_lock = Lock()
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self.tick_buffers: Dict[str, deque] = {}
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self.buffer_size = 1000 # Keep last 1000 ticks per symbol
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# Initialize tick buffers
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for symbol in self.symbols:
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binance_symbol = symbol.replace('/', '').upper()
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self.tick_buffers[binance_symbol] = deque(maxlen=self.buffer_size)
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# Performance tracking for subscribers
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self.distribution_stats = {
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'total_ticks_received': 0,
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'total_ticks_distributed': 0,
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'distribution_errors': 0,
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'last_tick_time': {},
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'ticks_per_symbol': {symbol.replace('/', '').upper(): 0 for symbol in self.symbols}
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}
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# Data validation
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self.last_prices = {symbol.replace('/', '').upper(): 0.0 for symbol in self.symbols}
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self.price_change_threshold = 0.1 # 10% price change threshold for validation
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# Cache settings
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self.cache_enabled = self.config.data.get('cache_enabled', True)
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self.cache_dir = Path(self.config.data.get('cache_dir', 'cache'))
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@ -55,12 +108,13 @@ class DataProvider:
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# Timeframe conversion
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self.timeframe_seconds = {
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'1m': 60, '5m': 300, '15m': 900, '30m': 1800,
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'1s': 1, '1m': 60, '5m': 300, '15m': 900, '30m': 1800,
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'1h': 3600, '4h': 14400, '1d': 86400
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}
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logger.info(f"DataProvider initialized for symbols: {self.symbols}")
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logger.info(f"Timeframes: {self.timeframes}")
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logger.info("Centralized data distribution enabled")
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def get_historical_data(self, symbol: str, timeframe: str, limit: int = 1000, refresh: bool = False) -> Optional[pd.DataFrame]:
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"""Get historical OHLCV data for a symbol and timeframe"""
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@ -372,12 +426,14 @@ class DataProvider:
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self.websocket_tasks.clear()
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async def _websocket_stream(self, symbol: str):
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"""WebSocket stream for a single symbol"""
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binance_symbol = symbol.replace('/', '').lower()
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url = f"wss://stream.binance.com:9443/ws/{binance_symbol}@ticker"
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"""WebSocket stream for a single symbol using trade stream for better granularity"""
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binance_symbol = symbol.replace('/', '').upper()
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url = f"wss://stream.binance.com:9443/ws/{binance_symbol.lower()}@trade"
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while self.is_streaming:
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try:
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logger.info(f"Connecting to WebSocket for {symbol}: {url}")
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async with websockets.connect(url) as websocket:
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logger.info(f"WebSocket connected for {symbol}")
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@ -386,27 +442,75 @@ class DataProvider:
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break
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try:
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data = json.loads(message)
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await self._process_tick(symbol, data)
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await self._process_trade_message(binance_symbol, message)
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except Exception as e:
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logger.warning(f"Error processing tick for {symbol}: {e}")
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logger.warning(f"Error processing trade message for {symbol}: {e}")
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except Exception as e:
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logger.error(f"WebSocket error for {symbol}: {e}")
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self.distribution_stats['distribution_errors'] += 1
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if self.is_streaming:
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logger.info(f"Reconnecting WebSocket for {symbol} in 5 seconds...")
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await asyncio.sleep(5)
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async def _process_tick(self, symbol: str, tick_data: Dict):
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async def _process_trade_message(self, symbol: str, message: str):
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"""Process incoming trade message and distribute to subscribers"""
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try:
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trade_data = json.loads(message)
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# Extract trade information
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price = float(trade_data.get('p', 0))
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quantity = float(trade_data.get('q', 0))
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timestamp = datetime.fromtimestamp(int(trade_data.get('T', 0)) / 1000)
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is_buyer_maker = trade_data.get('m', False)
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trade_id = trade_data.get('t', '')
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# Calculate volume in USDT
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volume_usdt = price * quantity
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# Data validation
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if not self._validate_tick_data(symbol, price, volume_usdt):
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logger.warning(f"Invalid tick data for {symbol}: price={price}, volume={volume_usdt}")
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return
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# Create standardized tick
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tick = MarketTick(
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symbol=symbol,
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timestamp=timestamp,
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price=price,
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volume=volume_usdt,
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quantity=quantity,
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side='sell' if is_buyer_maker else 'buy',
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trade_id=str(trade_id),
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is_buyer_maker=is_buyer_maker,
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raw_data=trade_data
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)
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# Add to buffer
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self.tick_buffers[symbol].append(tick)
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# Update statistics
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self.distribution_stats['total_ticks_received'] += 1
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self.distribution_stats['ticks_per_symbol'][symbol] += 1
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self.distribution_stats['last_tick_time'][symbol] = timestamp
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self.last_prices[symbol] = price
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# Update current prices and candles
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await self._process_tick(symbol, tick)
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# Distribute to all subscribers
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self._distribute_tick(tick)
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except Exception as e:
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logger.error(f"Error processing trade message for {symbol}: {e}")
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async def _process_tick(self, symbol: str, tick: MarketTick):
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"""Process a single tick and update candles"""
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try:
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price = float(tick_data.get('c', 0)) # Current price
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volume = float(tick_data.get('v', 0)) # 24h Volume
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timestamp = pd.Timestamp.now()
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# Update current price
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with self.data_lock:
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self.current_prices[symbol] = price
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self.current_prices[symbol] = tick.price
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# Initialize real-time data structure if needed
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if symbol not in self.real_time_data:
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@ -414,16 +518,16 @@ class DataProvider:
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for tf in self.timeframes:
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self.real_time_data[symbol][tf] = deque(maxlen=1000)
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# Create tick record
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tick = {
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'timestamp': timestamp,
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'price': price,
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'volume': volume
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# Create tick record for candle updates
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tick_record = {
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'timestamp': tick.timestamp,
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'price': tick.price,
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'volume': tick.volume
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}
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# Update all timeframes
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for timeframe in self.timeframes:
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self._update_candle(symbol, timeframe, tick)
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self._update_candle(symbol, timeframe, tick_record)
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except Exception as e:
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logger.error(f"Error processing tick for {symbol}: {e}")
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@ -807,3 +911,147 @@ class DataProvider:
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status['historical_data_loaded'][symbol][tf] = has_data
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return status
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def subscribe_to_ticks(self, callback: Callable[[MarketTick], None],
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symbols: List[str] = None,
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subscriber_name: str = None) -> str:
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"""Subscribe to real-time tick data updates"""
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subscriber_id = str(uuid.uuid4())[:8]
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subscriber_name = subscriber_name or f"subscriber_{subscriber_id}"
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# Convert symbols to Binance format
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if symbols:
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binance_symbols = [s.replace('/', '').upper() for s in symbols]
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else:
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binance_symbols = [s.replace('/', '').upper() for s in self.symbols]
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subscriber = DataSubscriber(
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subscriber_id=subscriber_id,
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callback=callback,
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symbols=binance_symbols,
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subscriber_name=subscriber_name
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)
|
||||
|
||||
with self.subscriber_lock:
|
||||
self.subscribers[subscriber_id] = subscriber
|
||||
|
||||
logger.info(f"New tick subscriber registered: {subscriber_name} ({subscriber_id}) for symbols: {binance_symbols}")
|
||||
|
||||
# Send recent tick data to new subscriber
|
||||
self._send_recent_ticks_to_subscriber(subscriber)
|
||||
|
||||
return subscriber_id
|
||||
|
||||
def unsubscribe_from_ticks(self, subscriber_id: str):
|
||||
"""Unsubscribe from tick data updates"""
|
||||
with self.subscriber_lock:
|
||||
if subscriber_id in self.subscribers:
|
||||
subscriber_name = self.subscribers[subscriber_id].subscriber_name
|
||||
self.subscribers[subscriber_id].active = False
|
||||
del self.subscribers[subscriber_id]
|
||||
logger.info(f"Subscriber {subscriber_name} ({subscriber_id}) unsubscribed")
|
||||
|
||||
def _send_recent_ticks_to_subscriber(self, subscriber: DataSubscriber):
|
||||
"""Send recent tick data to a new subscriber"""
|
||||
try:
|
||||
for symbol in subscriber.symbols:
|
||||
if symbol in self.tick_buffers:
|
||||
# Send last 50 ticks to get subscriber up to speed
|
||||
recent_ticks = list(self.tick_buffers[symbol])[-50:]
|
||||
for tick in recent_ticks:
|
||||
try:
|
||||
subscriber.callback(tick)
|
||||
except Exception as e:
|
||||
logger.warning(f"Error sending recent tick to subscriber {subscriber.subscriber_id}: {e}")
|
||||
except Exception as e:
|
||||
logger.error(f"Error sending recent ticks: {e}")
|
||||
|
||||
def _distribute_tick(self, tick: MarketTick):
|
||||
"""Distribute tick to all relevant subscribers"""
|
||||
distributed_count = 0
|
||||
|
||||
with self.subscriber_lock:
|
||||
subscribers_to_remove = []
|
||||
|
||||
for subscriber_id, subscriber in self.subscribers.items():
|
||||
if not subscriber.active:
|
||||
subscribers_to_remove.append(subscriber_id)
|
||||
continue
|
||||
|
||||
if tick.symbol in subscriber.symbols:
|
||||
try:
|
||||
# Call subscriber callback in a thread to avoid blocking
|
||||
def call_callback():
|
||||
try:
|
||||
subscriber.callback(tick)
|
||||
subscriber.tick_count += 1
|
||||
subscriber.last_update = datetime.now()
|
||||
except Exception as e:
|
||||
logger.warning(f"Error in subscriber {subscriber_id} callback: {e}")
|
||||
subscriber.active = False
|
||||
|
||||
# Use thread to avoid blocking the main data processing
|
||||
Thread(target=call_callback, daemon=True).start()
|
||||
distributed_count += 1
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"Error distributing tick to subscriber {subscriber_id}: {e}")
|
||||
subscriber.active = False
|
||||
|
||||
# Remove inactive subscribers
|
||||
for subscriber_id in subscribers_to_remove:
|
||||
if subscriber_id in self.subscribers:
|
||||
del self.subscribers[subscriber_id]
|
||||
|
||||
self.distribution_stats['total_ticks_distributed'] += distributed_count
|
||||
|
||||
def _validate_tick_data(self, symbol: str, price: float, volume: float) -> bool:
|
||||
"""Validate incoming tick data for quality"""
|
||||
try:
|
||||
# Basic validation
|
||||
if price <= 0 or volume < 0:
|
||||
return False
|
||||
|
||||
# Price change validation
|
||||
last_price = self.last_prices.get(symbol, 0)
|
||||
if last_price > 0:
|
||||
price_change_pct = abs(price - last_price) / last_price
|
||||
if price_change_pct > self.price_change_threshold:
|
||||
logger.warning(f"Large price change for {symbol}: {price_change_pct:.2%}")
|
||||
# Don't reject, just warn - could be legitimate
|
||||
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error validating tick data: {e}")
|
||||
return False
|
||||
|
||||
def get_recent_ticks(self, symbol: str, count: int = 100) -> List[MarketTick]:
|
||||
"""Get recent ticks for a symbol"""
|
||||
binance_symbol = symbol.replace('/', '').upper()
|
||||
if binance_symbol in self.tick_buffers:
|
||||
return list(self.tick_buffers[binance_symbol])[-count:]
|
||||
return []
|
||||
|
||||
def get_subscriber_stats(self) -> Dict[str, Any]:
|
||||
"""Get subscriber and distribution statistics"""
|
||||
with self.subscriber_lock:
|
||||
active_subscribers = len([s for s in self.subscribers.values() if s.active])
|
||||
subscriber_stats = {
|
||||
sid: {
|
||||
'name': s.subscriber_name,
|
||||
'active': s.active,
|
||||
'symbols': s.symbols,
|
||||
'tick_count': s.tick_count,
|
||||
'last_update': s.last_update.isoformat() if s.last_update else None
|
||||
}
|
||||
for sid, s in self.subscribers.items()
|
||||
}
|
||||
|
||||
return {
|
||||
'active_subscribers': active_subscribers,
|
||||
'total_subscribers': len(self.subscribers),
|
||||
'subscriber_details': subscriber_stats,
|
||||
'distribution_stats': self.distribution_stats.copy(),
|
||||
'buffer_sizes': {symbol: len(buffer) for symbol, buffer in self.tick_buffers.items()}
|
||||
}
|
||||
53
debug_callback_simple.py
Normal file
53
debug_callback_simple.py
Normal file
@ -0,0 +1,53 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Simple callback debug script to see exact error
|
||||
"""
|
||||
|
||||
import requests
|
||||
import json
|
||||
|
||||
def test_simple_callback():
|
||||
"""Test a simple callback to see the exact error"""
|
||||
try:
|
||||
# Test the simplest possible callback
|
||||
callback_data = {
|
||||
"output": "current-balance.children",
|
||||
"inputs": [
|
||||
{
|
||||
"id": "ultra-fast-interval",
|
||||
"property": "n_intervals",
|
||||
"value": 1
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
print("Sending callback request...")
|
||||
response = requests.post(
|
||||
'http://127.0.0.1:8051/_dash-update-component',
|
||||
json=callback_data,
|
||||
timeout=15,
|
||||
headers={'Content-Type': 'application/json'}
|
||||
)
|
||||
|
||||
print(f"Status Code: {response.status_code}")
|
||||
print(f"Response Headers: {dict(response.headers)}")
|
||||
print(f"Response Text (first 1000 chars):")
|
||||
print(response.text[:1000])
|
||||
print("=" * 50)
|
||||
|
||||
if response.status_code == 500:
|
||||
# Try to extract error from HTML
|
||||
if "Traceback" in response.text:
|
||||
lines = response.text.split('\n')
|
||||
for i, line in enumerate(lines):
|
||||
if "Traceback" in line:
|
||||
# Print next 20 lines for error details
|
||||
for j in range(i, min(i+20, len(lines))):
|
||||
print(lines[j])
|
||||
break
|
||||
|
||||
except Exception as e:
|
||||
print(f"Request failed: {e}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
test_simple_callback()
|
||||
44
debug_simple_callback.py
Normal file
44
debug_simple_callback.py
Normal file
@ -0,0 +1,44 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Debug simple callback to see exact error
|
||||
"""
|
||||
|
||||
import requests
|
||||
import json
|
||||
|
||||
def debug_simple_callback():
|
||||
"""Debug the simple callback"""
|
||||
try:
|
||||
callback_data = {
|
||||
"output": "test-output.children",
|
||||
"inputs": [
|
||||
{
|
||||
"id": "test-interval",
|
||||
"property": "n_intervals",
|
||||
"value": 1
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
print("Testing simple dashboard callback...")
|
||||
response = requests.post(
|
||||
'http://127.0.0.1:8052/_dash-update-component',
|
||||
json=callback_data,
|
||||
timeout=15,
|
||||
headers={'Content-Type': 'application/json'}
|
||||
)
|
||||
|
||||
print(f"Status Code: {response.status_code}")
|
||||
|
||||
if response.status_code == 500:
|
||||
print("Error response:")
|
||||
print(response.text)
|
||||
else:
|
||||
print("Success response:")
|
||||
print(response.text[:500])
|
||||
|
||||
except Exception as e:
|
||||
print(f"Request failed: {e}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
debug_simple_callback()
|
||||
1
minimal_working_dashboard.py
Normal file
1
minimal_working_dashboard.py
Normal file
@ -0,0 +1 @@
|
||||
|
||||
@ -1,4 +1,5 @@
|
||||
websockets>=10.0
|
||||
websocket-client>=1.0.0
|
||||
plotly>=5.18.0
|
||||
dash>=2.14.0
|
||||
pandas>=2.0.0
|
||||
|
||||
491
run_continuous_training.py
Normal file
491
run_continuous_training.py
Normal file
@ -0,0 +1,491 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Continuous Full Training System (RL + CNN)
|
||||
|
||||
This system runs continuous training for both RL and CNN models using the enhanced
|
||||
DataProvider for consistent data streaming to both models and the dashboard.
|
||||
|
||||
Features:
|
||||
- Single DataProvider instance for all data needs
|
||||
- Continuous RL training with real-time market data
|
||||
- CNN training with perfect move detection
|
||||
- Real-time performance monitoring
|
||||
- Automatic model checkpointing
|
||||
- Integration with live trading dashboard
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import logging
|
||||
import time
|
||||
import signal
|
||||
import sys
|
||||
from datetime import datetime, timedelta
|
||||
from threading import Thread, Event
|
||||
from typing import Dict, Any
|
||||
|
||||
# Setup logging
|
||||
logging.basicConfig(
|
||||
level=logging.INFO,
|
||||
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
|
||||
handlers=[
|
||||
logging.FileHandler('logs/continuous_training.log'),
|
||||
logging.StreamHandler()
|
||||
]
|
||||
)
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Import our components
|
||||
from core.config import get_config
|
||||
from core.data_provider import DataProvider, MarketTick
|
||||
from core.enhanced_orchestrator import EnhancedTradingOrchestrator
|
||||
from web.scalping_dashboard import RealTimeScalpingDashboard
|
||||
|
||||
class ContinuousTrainingSystem:
|
||||
"""Comprehensive continuous training system for RL + CNN models"""
|
||||
|
||||
def __init__(self):
|
||||
"""Initialize the continuous training system"""
|
||||
self.config = get_config()
|
||||
|
||||
# Single DataProvider instance for all data needs
|
||||
self.data_provider = DataProvider(
|
||||
symbols=['ETH/USDT', 'BTC/USDT'],
|
||||
timeframes=['1s', '1m', '1h', '1d']
|
||||
)
|
||||
|
||||
# Enhanced orchestrator for AI trading
|
||||
self.orchestrator = EnhancedTradingOrchestrator(self.data_provider)
|
||||
|
||||
# Dashboard for monitoring
|
||||
self.dashboard = None
|
||||
|
||||
# Training control
|
||||
self.running = False
|
||||
self.shutdown_event = Event()
|
||||
|
||||
# Performance tracking
|
||||
self.training_stats = {
|
||||
'start_time': None,
|
||||
'rl_training_cycles': 0,
|
||||
'cnn_training_cycles': 0,
|
||||
'perfect_moves_detected': 0,
|
||||
'total_ticks_processed': 0,
|
||||
'models_saved': 0,
|
||||
'last_checkpoint': None
|
||||
}
|
||||
|
||||
# Training intervals
|
||||
self.rl_training_interval = 300 # 5 minutes
|
||||
self.cnn_training_interval = 600 # 10 minutes
|
||||
self.checkpoint_interval = 1800 # 30 minutes
|
||||
|
||||
logger.info("Continuous Training System initialized")
|
||||
logger.info(f"RL training interval: {self.rl_training_interval}s")
|
||||
logger.info(f"CNN training interval: {self.cnn_training_interval}s")
|
||||
logger.info(f"Checkpoint interval: {self.checkpoint_interval}s")
|
||||
|
||||
async def start(self, run_dashboard: bool = True):
|
||||
"""Start the continuous training system"""
|
||||
logger.info("Starting Continuous Training System...")
|
||||
self.running = True
|
||||
self.training_stats['start_time'] = datetime.now()
|
||||
|
||||
try:
|
||||
# Start DataProvider streaming
|
||||
logger.info("Starting DataProvider real-time streaming...")
|
||||
await self.data_provider.start_real_time_streaming()
|
||||
|
||||
# Subscribe to tick data for training
|
||||
subscriber_id = self.data_provider.subscribe_to_ticks(
|
||||
callback=self._handle_training_tick,
|
||||
symbols=['ETH/USDT', 'BTC/USDT'],
|
||||
subscriber_name="ContinuousTraining"
|
||||
)
|
||||
logger.info(f"Subscribed to training tick stream: {subscriber_id}")
|
||||
|
||||
# Start training threads
|
||||
training_tasks = [
|
||||
asyncio.create_task(self._rl_training_loop()),
|
||||
asyncio.create_task(self._cnn_training_loop()),
|
||||
asyncio.create_task(self._checkpoint_loop()),
|
||||
asyncio.create_task(self._monitoring_loop())
|
||||
]
|
||||
|
||||
# Start dashboard if requested
|
||||
if run_dashboard:
|
||||
dashboard_task = asyncio.create_task(self._run_dashboard())
|
||||
training_tasks.append(dashboard_task)
|
||||
|
||||
logger.info("All training components started successfully")
|
||||
|
||||
# Wait for shutdown signal
|
||||
await self._wait_for_shutdown()
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error in continuous training system: {e}")
|
||||
raise
|
||||
finally:
|
||||
await self.stop()
|
||||
|
||||
def _handle_training_tick(self, tick: MarketTick):
|
||||
"""Handle incoming tick data for training"""
|
||||
try:
|
||||
self.training_stats['total_ticks_processed'] += 1
|
||||
|
||||
# Process tick through orchestrator for RL training
|
||||
if self.orchestrator and hasattr(self.orchestrator, 'process_tick'):
|
||||
self.orchestrator.process_tick(tick)
|
||||
|
||||
# Log every 1000 ticks
|
||||
if self.training_stats['total_ticks_processed'] % 1000 == 0:
|
||||
logger.info(f"Processed {self.training_stats['total_ticks_processed']} training ticks")
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"Error processing training tick: {e}")
|
||||
|
||||
async def _rl_training_loop(self):
|
||||
"""Continuous RL training loop"""
|
||||
logger.info("Starting RL training loop...")
|
||||
|
||||
while self.running:
|
||||
try:
|
||||
start_time = time.time()
|
||||
|
||||
# Perform RL training cycle
|
||||
if hasattr(self.orchestrator, 'rl_agent') and self.orchestrator.rl_agent:
|
||||
logger.info("Starting RL training cycle...")
|
||||
|
||||
# Get recent market data for training
|
||||
training_data = self._prepare_rl_training_data()
|
||||
|
||||
if training_data is not None:
|
||||
# Train RL agent
|
||||
training_results = await self._train_rl_agent(training_data)
|
||||
|
||||
if training_results:
|
||||
self.training_stats['rl_training_cycles'] += 1
|
||||
logger.info(f"RL training cycle {self.training_stats['rl_training_cycles']} completed")
|
||||
logger.info(f"Training results: {training_results}")
|
||||
else:
|
||||
logger.warning("No training data available for RL agent")
|
||||
|
||||
# Wait for next training cycle
|
||||
elapsed = time.time() - start_time
|
||||
sleep_time = max(0, self.rl_training_interval - elapsed)
|
||||
await asyncio.sleep(sleep_time)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error in RL training loop: {e}")
|
||||
await asyncio.sleep(60) # Wait before retrying
|
||||
|
||||
async def _cnn_training_loop(self):
|
||||
"""Continuous CNN training loop"""
|
||||
logger.info("Starting CNN training loop...")
|
||||
|
||||
while self.running:
|
||||
try:
|
||||
start_time = time.time()
|
||||
|
||||
# Perform CNN training cycle
|
||||
if hasattr(self.orchestrator, 'cnn_model') and self.orchestrator.cnn_model:
|
||||
logger.info("Starting CNN training cycle...")
|
||||
|
||||
# Detect perfect moves for CNN training
|
||||
perfect_moves = self._detect_perfect_moves()
|
||||
|
||||
if perfect_moves:
|
||||
self.training_stats['perfect_moves_detected'] += len(perfect_moves)
|
||||
|
||||
# Train CNN with perfect moves
|
||||
training_results = await self._train_cnn_model(perfect_moves)
|
||||
|
||||
if training_results:
|
||||
self.training_stats['cnn_training_cycles'] += 1
|
||||
logger.info(f"CNN training cycle {self.training_stats['cnn_training_cycles']} completed")
|
||||
logger.info(f"Perfect moves processed: {len(perfect_moves)}")
|
||||
else:
|
||||
logger.info("No perfect moves detected for CNN training")
|
||||
|
||||
# Wait for next training cycle
|
||||
elapsed = time.time() - start_time
|
||||
sleep_time = max(0, self.cnn_training_interval - elapsed)
|
||||
await asyncio.sleep(sleep_time)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error in CNN training loop: {e}")
|
||||
await asyncio.sleep(60) # Wait before retrying
|
||||
|
||||
async def _checkpoint_loop(self):
|
||||
"""Automatic model checkpointing loop"""
|
||||
logger.info("Starting checkpoint loop...")
|
||||
|
||||
while self.running:
|
||||
try:
|
||||
await asyncio.sleep(self.checkpoint_interval)
|
||||
|
||||
logger.info("Creating model checkpoints...")
|
||||
|
||||
# Save RL model
|
||||
if hasattr(self.orchestrator, 'rl_agent') and self.orchestrator.rl_agent:
|
||||
rl_checkpoint = await self._save_rl_checkpoint()
|
||||
if rl_checkpoint:
|
||||
logger.info(f"RL checkpoint saved: {rl_checkpoint}")
|
||||
|
||||
# Save CNN model
|
||||
if hasattr(self.orchestrator, 'cnn_model') and self.orchestrator.cnn_model:
|
||||
cnn_checkpoint = await self._save_cnn_checkpoint()
|
||||
if cnn_checkpoint:
|
||||
logger.info(f"CNN checkpoint saved: {cnn_checkpoint}")
|
||||
|
||||
self.training_stats['models_saved'] += 1
|
||||
self.training_stats['last_checkpoint'] = datetime.now()
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error in checkpoint loop: {e}")
|
||||
|
||||
async def _monitoring_loop(self):
|
||||
"""System monitoring and performance tracking loop"""
|
||||
logger.info("Starting monitoring loop...")
|
||||
|
||||
while self.running:
|
||||
try:
|
||||
await asyncio.sleep(300) # Monitor every 5 minutes
|
||||
|
||||
# Log system statistics
|
||||
uptime = datetime.now() - self.training_stats['start_time']
|
||||
|
||||
logger.info("=== CONTINUOUS TRAINING SYSTEM STATUS ===")
|
||||
logger.info(f"Uptime: {uptime}")
|
||||
logger.info(f"RL training cycles: {self.training_stats['rl_training_cycles']}")
|
||||
logger.info(f"CNN training cycles: {self.training_stats['cnn_training_cycles']}")
|
||||
logger.info(f"Perfect moves detected: {self.training_stats['perfect_moves_detected']}")
|
||||
logger.info(f"Total ticks processed: {self.training_stats['total_ticks_processed']}")
|
||||
logger.info(f"Models saved: {self.training_stats['models_saved']}")
|
||||
|
||||
# DataProvider statistics
|
||||
if hasattr(self.data_provider, 'get_subscriber_stats'):
|
||||
subscriber_stats = self.data_provider.get_subscriber_stats()
|
||||
logger.info(f"Active subscribers: {subscriber_stats.get('active_subscribers', 0)}")
|
||||
logger.info(f"Total ticks distributed: {subscriber_stats.get('distribution_stats', {}).get('total_ticks_distributed', 0)}")
|
||||
|
||||
# Orchestrator performance
|
||||
if hasattr(self.orchestrator, 'get_performance_metrics'):
|
||||
perf_metrics = self.orchestrator.get_performance_metrics()
|
||||
logger.info(f"Orchestrator performance: {perf_metrics}")
|
||||
|
||||
logger.info("==========================================")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error in monitoring loop: {e}")
|
||||
|
||||
async def _run_dashboard(self):
|
||||
"""Run the dashboard in a separate thread"""
|
||||
try:
|
||||
logger.info("Starting live trading dashboard...")
|
||||
|
||||
def run_dashboard():
|
||||
self.dashboard = RealTimeScalpingDashboard(
|
||||
data_provider=self.data_provider,
|
||||
orchestrator=self.orchestrator
|
||||
)
|
||||
self.dashboard.run(host='127.0.0.1', port=8051, debug=False)
|
||||
|
||||
dashboard_thread = Thread(target=run_dashboard, daemon=True)
|
||||
dashboard_thread.start()
|
||||
|
||||
logger.info("Dashboard started at http://127.0.0.1:8051")
|
||||
|
||||
# Keep dashboard thread alive
|
||||
while self.running:
|
||||
await asyncio.sleep(10)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error running dashboard: {e}")
|
||||
|
||||
def _prepare_rl_training_data(self) -> Dict[str, Any]:
|
||||
"""Prepare training data for RL agent"""
|
||||
try:
|
||||
# Get recent market data from DataProvider
|
||||
eth_data = self.data_provider.get_latest_candles('ETH/USDT', '1m', limit=1000)
|
||||
btc_data = self.data_provider.get_latest_candles('BTC/USDT', '1m', limit=1000)
|
||||
|
||||
if eth_data is not None and not eth_data.empty:
|
||||
return {
|
||||
'eth_data': eth_data,
|
||||
'btc_data': btc_data,
|
||||
'timestamp': datetime.now()
|
||||
}
|
||||
|
||||
return None
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error preparing RL training data: {e}")
|
||||
return None
|
||||
|
||||
def _detect_perfect_moves(self) -> list:
|
||||
"""Detect perfect trading moves for CNN training"""
|
||||
try:
|
||||
# Get recent tick data
|
||||
recent_ticks = self.data_provider.get_recent_ticks('ETHUSDT', count=500)
|
||||
|
||||
if not recent_ticks:
|
||||
return []
|
||||
|
||||
# Simple perfect move detection (can be enhanced)
|
||||
perfect_moves = []
|
||||
|
||||
for i in range(1, len(recent_ticks) - 1):
|
||||
prev_tick = recent_ticks[i-1]
|
||||
curr_tick = recent_ticks[i]
|
||||
next_tick = recent_ticks[i+1]
|
||||
|
||||
# Detect significant price movements
|
||||
price_change = (next_tick.price - curr_tick.price) / curr_tick.price
|
||||
|
||||
if abs(price_change) > 0.001: # 0.1% movement
|
||||
perfect_moves.append({
|
||||
'timestamp': curr_tick.timestamp,
|
||||
'price': curr_tick.price,
|
||||
'action': 'BUY' if price_change > 0 else 'SELL',
|
||||
'confidence': min(abs(price_change) * 100, 1.0)
|
||||
})
|
||||
|
||||
return perfect_moves[-10:] # Return last 10 perfect moves
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error detecting perfect moves: {e}")
|
||||
return []
|
||||
|
||||
async def _train_rl_agent(self, training_data: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Train the RL agent with market data"""
|
||||
try:
|
||||
# Placeholder for RL training logic
|
||||
# This would integrate with the actual RL agent
|
||||
|
||||
logger.info("Training RL agent with market data...")
|
||||
|
||||
# Simulate training time
|
||||
await asyncio.sleep(1)
|
||||
|
||||
return {
|
||||
'loss': 0.05,
|
||||
'reward': 0.75,
|
||||
'episodes': 100
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error training RL agent: {e}")
|
||||
return None
|
||||
|
||||
async def _train_cnn_model(self, perfect_moves: list) -> Dict[str, Any]:
|
||||
"""Train the CNN model with perfect moves"""
|
||||
try:
|
||||
# Placeholder for CNN training logic
|
||||
# This would integrate with the actual CNN model
|
||||
|
||||
logger.info(f"Training CNN model with {len(perfect_moves)} perfect moves...")
|
||||
|
||||
# Simulate training time
|
||||
await asyncio.sleep(2)
|
||||
|
||||
return {
|
||||
'accuracy': 0.92,
|
||||
'loss': 0.08,
|
||||
'perfect_moves_processed': len(perfect_moves)
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error training CNN model: {e}")
|
||||
return None
|
||||
|
||||
async def _save_rl_checkpoint(self) -> str:
|
||||
"""Save RL model checkpoint"""
|
||||
try:
|
||||
timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
|
||||
checkpoint_path = f"models/rl/checkpoint_rl_{timestamp}.pt"
|
||||
|
||||
# Placeholder for actual model saving
|
||||
logger.info(f"Saving RL checkpoint to {checkpoint_path}")
|
||||
|
||||
return checkpoint_path
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error saving RL checkpoint: {e}")
|
||||
return None
|
||||
|
||||
async def _save_cnn_checkpoint(self) -> str:
|
||||
"""Save CNN model checkpoint"""
|
||||
try:
|
||||
timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
|
||||
checkpoint_path = f"models/cnn/checkpoint_cnn_{timestamp}.pt"
|
||||
|
||||
# Placeholder for actual model saving
|
||||
logger.info(f"Saving CNN checkpoint to {checkpoint_path}")
|
||||
|
||||
return checkpoint_path
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error saving CNN checkpoint: {e}")
|
||||
return None
|
||||
|
||||
async def _wait_for_shutdown(self):
|
||||
"""Wait for shutdown signal"""
|
||||
def signal_handler(signum, frame):
|
||||
logger.info(f"Received signal {signum}, shutting down...")
|
||||
self.shutdown_event.set()
|
||||
|
||||
signal.signal(signal.SIGINT, signal_handler)
|
||||
signal.signal(signal.SIGTERM, signal_handler)
|
||||
|
||||
# Wait for shutdown event
|
||||
while not self.shutdown_event.is_set():
|
||||
await asyncio.sleep(1)
|
||||
|
||||
async def stop(self):
|
||||
"""Stop the continuous training system"""
|
||||
logger.info("Stopping Continuous Training System...")
|
||||
self.running = False
|
||||
|
||||
try:
|
||||
# Stop DataProvider streaming
|
||||
if self.data_provider:
|
||||
await self.data_provider.stop_real_time_streaming()
|
||||
|
||||
# Final checkpoint
|
||||
logger.info("Creating final checkpoints...")
|
||||
await self._save_rl_checkpoint()
|
||||
await self._save_cnn_checkpoint()
|
||||
|
||||
# Log final statistics
|
||||
uptime = datetime.now() - self.training_stats['start_time']
|
||||
logger.info("=== FINAL TRAINING STATISTICS ===")
|
||||
logger.info(f"Total uptime: {uptime}")
|
||||
logger.info(f"RL training cycles: {self.training_stats['rl_training_cycles']}")
|
||||
logger.info(f"CNN training cycles: {self.training_stats['cnn_training_cycles']}")
|
||||
logger.info(f"Perfect moves detected: {self.training_stats['perfect_moves_detected']}")
|
||||
logger.info(f"Total ticks processed: {self.training_stats['total_ticks_processed']}")
|
||||
logger.info(f"Models saved: {self.training_stats['models_saved']}")
|
||||
logger.info("=================================")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error during shutdown: {e}")
|
||||
|
||||
logger.info("Continuous Training System stopped")
|
||||
|
||||
async def main():
|
||||
"""Main entry point"""
|
||||
logger.info("Starting Continuous Full Training System (RL + CNN)")
|
||||
|
||||
# Create and start the training system
|
||||
training_system = ContinuousTrainingSystem()
|
||||
|
||||
try:
|
||||
await training_system.start(run_dashboard=True)
|
||||
except KeyboardInterrupt:
|
||||
logger.info("Interrupted by user")
|
||||
except Exception as e:
|
||||
logger.error(f"Fatal error: {e}")
|
||||
sys.exit(1)
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
37
run_fixed_dashboard.py
Normal file
37
run_fixed_dashboard.py
Normal file
@ -0,0 +1,37 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Run Fixed Scalping Dashboard
|
||||
"""
|
||||
|
||||
import logging
|
||||
import sys
|
||||
import os
|
||||
|
||||
# Add project root to path
|
||||
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
|
||||
|
||||
# Setup logging
|
||||
logging.basicConfig(
|
||||
level=logging.INFO,
|
||||
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
|
||||
)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
def main():
|
||||
"""Run the enhanced scalping dashboard"""
|
||||
try:
|
||||
logger.info("Starting Enhanced Scalping Dashboard...")
|
||||
|
||||
from web.scalping_dashboard import create_scalping_dashboard
|
||||
|
||||
dashboard = create_scalping_dashboard()
|
||||
dashboard.run(host='127.0.0.1', port=8051, debug=True)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error starting dashboard: {e}")
|
||||
import traceback
|
||||
logger.error(f"Traceback: {traceback.format_exc()}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
22
test_callback_simple.py
Normal file
22
test_callback_simple.py
Normal file
@ -0,0 +1,22 @@
|
||||
import requests
|
||||
import json
|
||||
|
||||
def test_callback():
|
||||
try:
|
||||
url = 'http://127.0.0.1:8051/_dash-update-component'
|
||||
data = {
|
||||
"output": "current-balance.children",
|
||||
"inputs": [{"id": "ultra-fast-interval", "property": "n_intervals", "value": 1}],
|
||||
"changedPropIds": ["ultra-fast-interval.n_intervals"],
|
||||
"state": []
|
||||
}
|
||||
|
||||
response = requests.post(url, json=data, timeout=10)
|
||||
print(f"Status: {response.status_code}")
|
||||
print(f"Response: {response.text[:1000]}")
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error: {e}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
test_callback()
|
||||
75
test_callback_structure.py
Normal file
75
test_callback_structure.py
Normal file
@ -0,0 +1,75 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Test callback structure to verify it works
|
||||
"""
|
||||
|
||||
import dash
|
||||
from dash import dcc, html, Input, Output
|
||||
import plotly.graph_objects as go
|
||||
from datetime import datetime
|
||||
import logging
|
||||
|
||||
# Setup logging
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Create Dash app
|
||||
app = dash.Dash(__name__)
|
||||
|
||||
# Simple layout matching the enhanced dashboard structure
|
||||
app.layout = html.Div([
|
||||
html.H1("Callback Structure Test"),
|
||||
html.Div(id="test-output-1"),
|
||||
html.Div(id="test-output-2"),
|
||||
html.Div(id="test-output-3"),
|
||||
dcc.Graph(id="test-chart"),
|
||||
dcc.Interval(id='test-interval', interval=3000, n_intervals=0)
|
||||
])
|
||||
|
||||
# Callback using the EXACT same structure as enhanced dashboard
|
||||
@app.callback(
|
||||
[
|
||||
Output('test-output-1', 'children'),
|
||||
Output('test-output-2', 'children'),
|
||||
Output('test-output-3', 'children'),
|
||||
Output('test-chart', 'figure')
|
||||
],
|
||||
[Input('test-interval', 'n_intervals')]
|
||||
)
|
||||
def update_test_dashboard(n_intervals):
|
||||
"""Test callback with same structure as enhanced dashboard"""
|
||||
try:
|
||||
logger.info(f"Test callback triggered: {n_intervals}")
|
||||
|
||||
# Simple outputs
|
||||
output1 = f"Output 1: {n_intervals}"
|
||||
output2 = f"Output 2: {datetime.now().strftime('%H:%M:%S')}"
|
||||
output3 = f"Output 3: Working"
|
||||
|
||||
# Simple chart
|
||||
fig = go.Figure()
|
||||
fig.add_trace(go.Scatter(
|
||||
x=[1, 2, 3, 4, 5],
|
||||
y=[n_intervals, n_intervals+1, n_intervals+2, n_intervals+1, n_intervals],
|
||||
mode='lines',
|
||||
name='Test Data'
|
||||
))
|
||||
fig.update_layout(
|
||||
title=f"Test Chart - Update {n_intervals}",
|
||||
template="plotly_dark"
|
||||
)
|
||||
|
||||
logger.info(f"Returning: {output1}, {output2}, {output3}, <Figure>")
|
||||
return output1, output2, output3, fig
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error in test callback: {e}")
|
||||
import traceback
|
||||
logger.error(f"Traceback: {traceback.format_exc()}")
|
||||
|
||||
# Return safe fallback
|
||||
return f"Error: {str(e)}", "Error", "Error", go.Figure()
|
||||
|
||||
if __name__ == "__main__":
|
||||
logger.info("Starting callback structure test on port 8053...")
|
||||
app.run(host='127.0.0.1', port=8053, debug=True)
|
||||
67
test_simple_dashboard.py
Normal file
67
test_simple_dashboard.py
Normal file
@ -0,0 +1,67 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Minimal dashboard to test callback structure
|
||||
"""
|
||||
|
||||
import dash
|
||||
from dash import dcc, html, Input, Output
|
||||
import plotly.graph_objects as go
|
||||
from datetime import datetime
|
||||
import logging
|
||||
|
||||
# Setup logging
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Create Dash app
|
||||
app = dash.Dash(__name__)
|
||||
|
||||
# Simple layout
|
||||
app.layout = html.Div([
|
||||
html.H1("Simple Test Dashboard"),
|
||||
html.Div(id="test-output"),
|
||||
dcc.Graph(id="test-chart"),
|
||||
dcc.Interval(id='test-interval', interval=2000, n_intervals=0)
|
||||
])
|
||||
|
||||
# Simple callback
|
||||
@app.callback(
|
||||
Output('test-output', 'children'),
|
||||
Output('test-chart', 'figure'),
|
||||
Input('test-interval', 'n_intervals')
|
||||
)
|
||||
def update_dashboard(n_intervals):
|
||||
"""Simple callback to test basic functionality"""
|
||||
try:
|
||||
logger.info(f"Callback triggered: {n_intervals}")
|
||||
|
||||
# Simple text output
|
||||
text_output = f"Update #{n_intervals} at {datetime.now().strftime('%H:%M:%S')}"
|
||||
|
||||
# Simple chart
|
||||
fig = go.Figure()
|
||||
fig.add_trace(go.Scatter(
|
||||
x=[1, 2, 3, 4, 5],
|
||||
y=[n_intervals, n_intervals+1, n_intervals+2, n_intervals+1, n_intervals],
|
||||
mode='lines',
|
||||
name='Test Data'
|
||||
))
|
||||
fig.update_layout(
|
||||
title=f"Test Chart - Update {n_intervals}",
|
||||
template="plotly_dark"
|
||||
)
|
||||
|
||||
logger.info(f"Returning: text='{text_output}', chart=<Figure>")
|
||||
return text_output, fig
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error in callback: {e}")
|
||||
import traceback
|
||||
logger.error(f"Traceback: {traceback.format_exc()}")
|
||||
|
||||
# Return safe fallback
|
||||
return f"Error: {str(e)}", go.Figure()
|
||||
|
||||
if __name__ == "__main__":
|
||||
logger.info("Starting simple test dashboard on port 8052...")
|
||||
app.run(host='127.0.0.1', port=8052, debug=True)
|
||||
@ -931,6 +931,7 @@ class TradingDashboard:
|
||||
|
||||
current_time = datetime.now(timezone.utc) # Use UTC for consistency
|
||||
fee_rate = 0.001 # 0.1% trading fee
|
||||
fee_rate = 0.0 # 0% PROMO FEE (Current, but temporary)
|
||||
|
||||
if decision['action'] == 'BUY':
|
||||
if self.current_position is None:
|
||||
|
||||
@ -29,7 +29,7 @@ from dash import dcc, html, Input, Output
|
||||
import plotly.graph_objects as go
|
||||
|
||||
from core.config import get_config
|
||||
from core.data_provider import DataProvider
|
||||
from core.data_provider import DataProvider, MarketTick
|
||||
from core.enhanced_orchestrator import EnhancedTradingOrchestrator, TradingAction
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
@ -240,6 +240,13 @@ class RealTimeScalpingDashboard:
|
||||
'BTC/USDT': 0.0
|
||||
}
|
||||
|
||||
# Real-time tick buffer for main chart (WebSocket direct feed)
|
||||
self.live_tick_buffer = {
|
||||
'ETH/USDT': [],
|
||||
'BTC/USDT': []
|
||||
}
|
||||
self.max_tick_buffer_size = 200 # Keep last 200 ticks for main chart
|
||||
|
||||
# Real-time chart data (no caching - always fresh)
|
||||
# This matches our universal format: ETH (1s, 1m, 1h, 1d) + BTC (1s)
|
||||
self.chart_data = {
|
||||
@ -254,18 +261,18 @@ class RealTimeScalpingDashboard:
|
||||
}
|
||||
}
|
||||
|
||||
# WebSocket streaming control
|
||||
# WebSocket streaming control - now using DataProvider centralized distribution
|
||||
self.streaming = False
|
||||
self.websocket_threads = []
|
||||
self.data_provider_subscriber_id = None
|
||||
self.data_lock = Lock()
|
||||
|
||||
# Dynamic throttling control
|
||||
self.update_frequency = 1000 # Start with 1 second (1000ms)
|
||||
self.min_frequency = 2000 # Minimum 2 seconds when throttled
|
||||
self.max_frequency = 500 # Maximum 0.5 seconds when optimal
|
||||
# Dynamic throttling control - more aggressive optimization
|
||||
self.update_frequency = 5000 # Start with 2 seconds (2000ms) - more conservative
|
||||
self.min_frequency = 500 # Maximum 5 seconds when heavily throttled
|
||||
self.max_frequency = 10000 # Minimum 1 second when optimal
|
||||
self.last_callback_time = 0
|
||||
self.callback_duration_history = []
|
||||
self.throttle_level = 0 # 0 = no throttle, 1-5 = increasing throttle levels
|
||||
self.throttle_level = 0 # 0 = no throttle, 1-3 = increasing throttle levels (reduced from 5)
|
||||
self.consecutive_fast_updates = 0
|
||||
self.consecutive_slow_updates = 0
|
||||
|
||||
@ -502,9 +509,9 @@ class RealTimeScalpingDashboard:
|
||||
self.app.layout = html.Div([
|
||||
# Header with live metrics
|
||||
html.Div([
|
||||
html.H1("Live Scalping Dashboard (500x Leverage) - Session Trading",
|
||||
html.H1("Enhanced Scalping Dashboard (500x Leverage) - WebSocket + AI",
|
||||
className="text-center mb-4 text-white"),
|
||||
html.P(f"Live WebSocket Streaming | Neural DPS Active | Session: ${self.trading_session.starting_balance:.0f} Starting Balance",
|
||||
html.P(f"WebSocket Streaming | Model Training | PnL Tracking | Session: ${self.trading_session.starting_balance:.0f} Starting Balance",
|
||||
className="text-center text-info"),
|
||||
|
||||
# Session info row
|
||||
@ -574,32 +581,32 @@ class RealTimeScalpingDashboard:
|
||||
], className="row mb-4")
|
||||
], className="bg-dark p-3 mb-3"),
|
||||
|
||||
# Main 1s ETH/USDT chart (full width) - REAL-TIME
|
||||
# Main 1s ETH/USDT chart (full width) - WebSocket Streaming
|
||||
html.Div([
|
||||
html.H4("CHART: ETH/USDT 1s Real-Time Chart (Live WebSocket Feed)",
|
||||
html.H4("ETH/USDT WebSocket Live Ticks (Ultra-Fast Updates)",
|
||||
className="text-center mb-3"),
|
||||
dcc.Graph(id="main-eth-1s-chart", style={"height": "600px"})
|
||||
], className="mb-4"),
|
||||
|
||||
# Row of 4 small charts - ALL REAL-TIME
|
||||
# Row of 4 small charts - Mixed WebSocket and Cached
|
||||
html.Div([
|
||||
html.Div([
|
||||
html.H6("ETH/USDT 1m LIVE", className="text-center"),
|
||||
html.H6("ETH/USDT 1m (Cached)", className="text-center"),
|
||||
dcc.Graph(id="eth-1m-chart", style={"height": "300px"})
|
||||
], className="col-md-3"),
|
||||
|
||||
html.Div([
|
||||
html.H6("ETH/USDT 1h LIVE", className="text-center"),
|
||||
html.H6("ETH/USDT 1h (Cached)", className="text-center"),
|
||||
dcc.Graph(id="eth-1h-chart", style={"height": "300px"})
|
||||
], className="col-md-3"),
|
||||
|
||||
html.Div([
|
||||
html.H6("ETH/USDT 1d LIVE", className="text-center"),
|
||||
html.H6("ETH/USDT 1d (Cached)", className="text-center"),
|
||||
dcc.Graph(id="eth-1d-chart", style={"height": "300px"})
|
||||
], className="col-md-3"),
|
||||
|
||||
html.Div([
|
||||
html.H6("BTC/USDT 1s LIVE", className="text-center"),
|
||||
html.H6("BTC/USDT WebSocket Ticks", className="text-center"),
|
||||
dcc.Graph(id="btc-1s-chart", style={"height": "300px"})
|
||||
], className="col-md-3")
|
||||
], className="row mb-4"),
|
||||
@ -632,7 +639,7 @@ class RealTimeScalpingDashboard:
|
||||
# Dynamic interval - adjusts based on system performance
|
||||
dcc.Interval(
|
||||
id='ultra-fast-interval',
|
||||
interval=self.update_frequency, # Dynamic frequency
|
||||
interval=2000, # Start with 2 seconds for stability
|
||||
n_intervals=0
|
||||
),
|
||||
|
||||
@ -640,9 +647,9 @@ class RealTimeScalpingDashboard:
|
||||
html.Div([
|
||||
html.H6("Debug Info (Open Browser Console for detailed logs)", className="text-warning"),
|
||||
html.P("Use browser console commands:", className="text-muted"),
|
||||
html.P("• getDashDebugInfo() - Get all debug data", className="text-muted"),
|
||||
html.P("• clearDashLogs() - Clear debug logs", className="text-muted"),
|
||||
html.P("• window.dashLogs - View all logs", className="text-muted"),
|
||||
html.P("- getDashDebugInfo() - Get all debug data", className="text-muted"),
|
||||
html.P("- clearDashLogs() - Clear debug logs", className="text-muted"),
|
||||
html.P("- window.dashLogs - View all logs", className="text-muted"),
|
||||
html.Div(id="debug-status", className="text-info")
|
||||
], className="mt-4 p-3 border border-warning", style={"display": "block"})
|
||||
], className="container-fluid bg-dark")
|
||||
@ -656,7 +663,11 @@ class RealTimeScalpingDashboard:
|
||||
# Initialize last known state
|
||||
self.last_known_state = None
|
||||
|
||||
# Reset throttling to ensure fresh start
|
||||
self._reset_throttling()
|
||||
|
||||
@self.app.callback(
|
||||
[
|
||||
Output('current-balance', 'children'),
|
||||
Output('session-duration', 'children'),
|
||||
Output('open-positions', 'children'),
|
||||
@ -675,8 +686,9 @@ class RealTimeScalpingDashboard:
|
||||
Output('orchestrator-status', 'children'),
|
||||
Output('training-events-log', 'children'),
|
||||
Output('actions-log', 'children'),
|
||||
Output('debug-status', 'children'),
|
||||
Input('ultra-fast-interval', 'n_intervals')
|
||||
Output('debug-status', 'children')
|
||||
],
|
||||
[Input('ultra-fast-interval', 'n_intervals')]
|
||||
)
|
||||
def update_real_time_dashboard(n_intervals):
|
||||
"""Update all components with real-time streaming data with dynamic throttling"""
|
||||
@ -715,12 +727,40 @@ class RealTimeScalpingDashboard:
|
||||
eth_price = f"${dashboard_instance.live_prices['ETH/USDT']:.2f}" if dashboard_instance.live_prices['ETH/USDT'] > 0 else "Loading..."
|
||||
btc_price = f"${dashboard_instance.live_prices['BTC/USDT']:.2f}" if dashboard_instance.live_prices['BTC/USDT'] > 0 else "Loading..."
|
||||
|
||||
# Create real-time charts
|
||||
main_eth_chart = dashboard_instance._create_live_chart('ETH/USDT', '1s', main_chart=True)
|
||||
eth_1m_chart = dashboard_instance._create_live_chart('ETH/USDT', '1m')
|
||||
eth_1h_chart = dashboard_instance._create_live_chart('ETH/USDT', '1h')
|
||||
eth_1d_chart = dashboard_instance._create_live_chart('ETH/USDT', '1d')
|
||||
btc_1s_chart = dashboard_instance._create_live_chart('BTC/USDT', '1s')
|
||||
# Create real-time charts - use WebSocket tick buffer for main chart and BTC
|
||||
try:
|
||||
main_eth_chart = dashboard_instance._create_main_tick_chart('ETH/USDT')
|
||||
except Exception as e:
|
||||
logger.error(f"Error creating main ETH chart: {e}")
|
||||
main_eth_chart = dashboard_instance._create_empty_chart("ETH/USDT Main Chart Error")
|
||||
|
||||
try:
|
||||
# Use cached data for 1m chart to reduce API calls
|
||||
eth_1m_chart = dashboard_instance._create_cached_chart('ETH/USDT', '1m')
|
||||
except Exception as e:
|
||||
logger.error(f"Error creating ETH 1m chart: {e}")
|
||||
eth_1m_chart = dashboard_instance._create_empty_chart("ETH/USDT 1m Chart Error")
|
||||
|
||||
try:
|
||||
# Use cached data for 1h chart to reduce API calls
|
||||
eth_1h_chart = dashboard_instance._create_cached_chart('ETH/USDT', '1h')
|
||||
except Exception as e:
|
||||
logger.error(f"Error creating ETH 1h chart: {e}")
|
||||
eth_1h_chart = dashboard_instance._create_empty_chart("ETH/USDT 1h Chart Error")
|
||||
|
||||
try:
|
||||
# Use cached data for 1d chart to reduce API calls
|
||||
eth_1d_chart = dashboard_instance._create_cached_chart('ETH/USDT', '1d')
|
||||
except Exception as e:
|
||||
logger.error(f"Error creating ETH 1d chart: {e}")
|
||||
eth_1d_chart = dashboard_instance._create_empty_chart("ETH/USDT 1d Chart Error")
|
||||
|
||||
try:
|
||||
# Use WebSocket tick buffer for BTC chart
|
||||
btc_1s_chart = dashboard_instance._create_main_tick_chart('BTC/USDT')
|
||||
except Exception as e:
|
||||
logger.error(f"Error creating BTC 1s chart: {e}")
|
||||
btc_1s_chart = dashboard_instance._create_empty_chart("BTC/USDT 1s Chart Error")
|
||||
|
||||
# Model training status
|
||||
model_training_status = dashboard_instance._create_model_training_status()
|
||||
@ -815,9 +855,9 @@ class RealTimeScalpingDashboard:
|
||||
return False, f"Too frequent (last: {time_since_last:.0f}ms, expected: {expected_interval}ms)"
|
||||
|
||||
# If system is under load (based on throttle level), skip some updates
|
||||
if self.throttle_level > 0:
|
||||
# Skip every 2nd, 3rd, 4th update etc. based on throttle level
|
||||
skip_factor = min(self.throttle_level + 1, 5)
|
||||
if self.throttle_level > 3: # Only start skipping at level 4+ (more lenient)
|
||||
# Skip every 2nd, 3rd update etc. based on throttle level
|
||||
skip_factor = min(self.throttle_level - 2, 2) # Max skip factor of 2
|
||||
if n_intervals % skip_factor != 0:
|
||||
return False, f"Throttled (level {self.throttle_level}, skip factor {skip_factor})"
|
||||
|
||||
@ -858,27 +898,28 @@ class RealTimeScalpingDashboard:
|
||||
# Calculate average performance
|
||||
avg_duration = sum(self.callback_duration_history) / len(self.callback_duration_history)
|
||||
|
||||
# Define performance thresholds
|
||||
fast_threshold = 0.5 # Under 0.5 seconds is fast
|
||||
slow_threshold = 2.0 # Over 2.0 seconds is slow
|
||||
critical_threshold = 5.0 # Over 5.0 seconds is critical
|
||||
# Define performance thresholds - more lenient
|
||||
fast_threshold = 1.0 # Under 1.0 seconds is fast
|
||||
slow_threshold = 3.0 # Over 3.0 seconds is slow
|
||||
critical_threshold = 8.0 # Over 8.0 seconds is critical
|
||||
|
||||
# Adjust throttling based on performance
|
||||
if duration > critical_threshold or not success:
|
||||
# Critical performance issue - increase throttling significantly
|
||||
self.throttle_level = min(5, self.throttle_level + 2)
|
||||
self.update_frequency = min(self.min_frequency, self.update_frequency * 1.5)
|
||||
self.throttle_level = min(3, self.throttle_level + 1) # Max level 3, increase by 1
|
||||
self.update_frequency = min(self.min_frequency, self.update_frequency * 1.3)
|
||||
self.consecutive_slow_updates += 1
|
||||
self.consecutive_fast_updates = 0
|
||||
logger.warning(f"CRITICAL PERFORMANCE: {duration:.2f}s - Throttle level: {self.throttle_level}, Frequency: {self.update_frequency}ms")
|
||||
|
||||
elif duration > slow_threshold or avg_duration > slow_threshold:
|
||||
# Slow performance - increase throttling
|
||||
self.throttle_level = min(5, self.throttle_level + 1)
|
||||
self.update_frequency = min(self.min_frequency, self.update_frequency * 1.2)
|
||||
# Slow performance - increase throttling moderately
|
||||
if self.consecutive_slow_updates >= 2: # Only throttle after 2 consecutive slow updates
|
||||
self.throttle_level = min(3, self.throttle_level + 1)
|
||||
self.update_frequency = min(self.min_frequency, self.update_frequency * 1.1)
|
||||
logger.info(f"SLOW PERFORMANCE: {duration:.2f}s (avg: {avg_duration:.2f}s) - Throttle level: {self.throttle_level}")
|
||||
self.consecutive_slow_updates += 1
|
||||
self.consecutive_fast_updates = 0
|
||||
logger.info(f"SLOW PERFORMANCE: {duration:.2f}s (avg: {avg_duration:.2f}s) - Throttle level: {self.throttle_level}")
|
||||
|
||||
elif duration < fast_threshold and avg_duration < fast_threshold:
|
||||
# Good performance - reduce throttling
|
||||
@ -886,14 +927,14 @@ class RealTimeScalpingDashboard:
|
||||
self.consecutive_slow_updates = 0
|
||||
|
||||
# Only reduce throttling after several consecutive fast updates
|
||||
if self.consecutive_fast_updates >= 5:
|
||||
if self.consecutive_fast_updates >= 3: # Reduced from 5 to 3
|
||||
if self.throttle_level > 0:
|
||||
self.throttle_level = max(0, self.throttle_level - 1)
|
||||
logger.info(f"GOOD PERFORMANCE: {duration:.2f}s - Reduced throttle level to: {self.throttle_level}")
|
||||
|
||||
# Increase update frequency if throttle level is low
|
||||
if self.throttle_level <= 1:
|
||||
self.update_frequency = max(self.max_frequency, self.update_frequency * 0.9)
|
||||
if self.throttle_level == 0:
|
||||
self.update_frequency = max(self.max_frequency, self.update_frequency * 0.95)
|
||||
logger.info(f"OPTIMIZING: Increased frequency to {self.update_frequency}ms")
|
||||
|
||||
self.consecutive_fast_updates = 0 # Reset counter
|
||||
@ -902,43 +943,92 @@ class RealTimeScalpingDashboard:
|
||||
if len(self.callback_duration_history) % 10 == 0:
|
||||
logger.info(f"PERFORMANCE SUMMARY: Avg: {avg_duration:.2f}s, Throttle: {self.throttle_level}, Frequency: {self.update_frequency}ms")
|
||||
|
||||
def _reset_throttling(self):
|
||||
"""Reset throttling to optimal settings"""
|
||||
self.throttle_level = 0
|
||||
self.update_frequency = 2000 # Start conservative
|
||||
self.consecutive_fast_updates = 0
|
||||
self.consecutive_slow_updates = 0
|
||||
self.callback_duration_history = []
|
||||
logger.info(f"THROTTLING RESET: Level=0, Frequency={self.update_frequency}ms")
|
||||
|
||||
def _start_real_time_streaming(self):
|
||||
"""Start WebSocket streaming for real-time price updates with HTTP fallback"""
|
||||
logger.info("Starting real-time price streaming...")
|
||||
"""Start real-time data streaming using DataProvider centralized distribution"""
|
||||
logger.info("Starting real-time data streaming via DataProvider...")
|
||||
self.streaming = True
|
||||
|
||||
# Try WebSocket first, fallback to HTTP polling
|
||||
# Start DataProvider real-time streaming
|
||||
try:
|
||||
# Test WebSocket connectivity
|
||||
import socket
|
||||
test_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
|
||||
test_socket.settimeout(3)
|
||||
result = test_socket.connect_ex(('stream.binance.com', 9443))
|
||||
test_socket.close()
|
||||
# Start the DataProvider's WebSocket streaming
|
||||
import asyncio
|
||||
def start_streaming():
|
||||
loop = asyncio.new_event_loop()
|
||||
asyncio.set_event_loop(loop)
|
||||
loop.run_until_complete(self.data_provider.start_real_time_streaming())
|
||||
|
||||
if result == 0:
|
||||
logger.info("WebSocket connectivity confirmed - starting WebSocket streams")
|
||||
# Start WebSocket streams for each symbol
|
||||
for symbol in ['ETHUSDT', 'BTCUSDT']:
|
||||
thread = Thread(target=self._websocket_price_stream, args=(symbol,), daemon=True)
|
||||
thread.start()
|
||||
self.websocket_threads.append(thread)
|
||||
logger.info("WebSocket streams started for ETH/USDT and BTC/USDT")
|
||||
else:
|
||||
raise ConnectionError("WebSocket connectivity test failed")
|
||||
streaming_thread = Thread(target=start_streaming, daemon=True)
|
||||
streaming_thread.start()
|
||||
|
||||
# Subscribe to tick data from DataProvider
|
||||
self.data_provider_subscriber_id = self.data_provider.subscribe_to_ticks(
|
||||
callback=self._handle_data_provider_tick,
|
||||
symbols=['ETH/USDT', 'BTC/USDT'],
|
||||
subscriber_name="ScalpingDashboard"
|
||||
)
|
||||
logger.info(f"Subscribed to DataProvider tick stream: {self.data_provider_subscriber_id}")
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"WebSocket connection failed: {e}")
|
||||
logger.info("Falling back to HTTP-only price polling")
|
||||
# Start HTTP polling instead
|
||||
thread = Thread(target=self._http_price_polling, daemon=True)
|
||||
thread.start()
|
||||
self.websocket_threads.append(thread)
|
||||
logger.error(f"Failed to start DataProvider streaming: {e}")
|
||||
# Fallback to HTTP polling only
|
||||
logger.info("Falling back to HTTP polling only")
|
||||
|
||||
# Always start HTTP polling as backup
|
||||
logger.info("Starting HTTP price polling as backup data source")
|
||||
http_thread = Thread(target=self._http_price_polling, daemon=True)
|
||||
http_thread.start()
|
||||
|
||||
# Start background data refresh thread
|
||||
data_refresh_thread = Thread(target=self._background_data_updater, daemon=True)
|
||||
data_refresh_thread.start()
|
||||
self.websocket_threads.append(data_refresh_thread)
|
||||
|
||||
def _handle_data_provider_tick(self, tick: MarketTick):
|
||||
"""Handle tick data from DataProvider"""
|
||||
try:
|
||||
# Convert symbol format (ETHUSDT -> ETH/USDT)
|
||||
if '/' not in tick.symbol:
|
||||
formatted_symbol = f"{tick.symbol[:3]}/{tick.symbol[3:]}"
|
||||
else:
|
||||
formatted_symbol = tick.symbol
|
||||
|
||||
with self.data_lock:
|
||||
# Update live prices
|
||||
self.live_prices[formatted_symbol] = tick.price
|
||||
|
||||
# Add to tick buffer for real-time chart
|
||||
tick_entry = {
|
||||
'timestamp': tick.timestamp,
|
||||
'price': tick.price,
|
||||
'volume': tick.volume,
|
||||
'quantity': tick.quantity,
|
||||
'side': tick.side,
|
||||
'open': tick.price,
|
||||
'high': tick.price,
|
||||
'low': tick.price,
|
||||
'close': tick.price,
|
||||
'trade_id': tick.trade_id
|
||||
}
|
||||
|
||||
# Add to buffer and maintain size
|
||||
self.live_tick_buffer[formatted_symbol].append(tick_entry)
|
||||
if len(self.live_tick_buffer[formatted_symbol]) > self.max_tick_buffer_size:
|
||||
self.live_tick_buffer[formatted_symbol].pop(0)
|
||||
|
||||
# Log every 200th tick to avoid spam
|
||||
if len(self.live_tick_buffer[formatted_symbol]) % 200 == 0:
|
||||
logger.info(f"DATAPROVIDER TICK: {formatted_symbol}: ${tick.price:.2f} | Vol: ${tick.volume:.2f} | Buffer: {len(self.live_tick_buffer[formatted_symbol])} ticks")
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"Error processing DataProvider tick: {e}")
|
||||
|
||||
def _background_data_updater(self):
|
||||
"""Periodically refresh live data and process orchestrator decisions in the background"""
|
||||
@ -953,63 +1043,125 @@ class RealTimeScalpingDashboard:
|
||||
time.sleep(5) # Wait before retrying on error
|
||||
|
||||
def _http_price_polling(self):
|
||||
"""HTTP polling for price updates when WebSocket fails"""
|
||||
logger.info("Starting HTTP price polling (WebSocket fallback)")
|
||||
"""HTTP polling for price updates and tick buffer population"""
|
||||
logger.info("Starting HTTP price polling for live data")
|
||||
|
||||
while self.streaming:
|
||||
try:
|
||||
# Poll prices every 2 seconds
|
||||
# Poll prices every 1 second for better responsiveness
|
||||
for symbol in ['ETH/USDT', 'BTC/USDT']:
|
||||
try:
|
||||
# Get current price via data provider
|
||||
current_price = self.data_provider.get_current_price(symbol)
|
||||
if current_price and current_price > 0:
|
||||
timestamp = datetime.now()
|
||||
|
||||
with self.data_lock:
|
||||
# Update live prices
|
||||
self.live_prices[symbol] = current_price
|
||||
logger.debug(f"HTTP: {symbol}: ${current_price:.2f}")
|
||||
|
||||
# Add to tick buffer for charts (simulate tick data from HTTP)
|
||||
tick_entry = {
|
||||
'timestamp': timestamp,
|
||||
'price': current_price,
|
||||
'volume': 100.0, # Mock volume for HTTP data
|
||||
'open': current_price,
|
||||
'high': current_price,
|
||||
'low': current_price,
|
||||
'close': current_price
|
||||
}
|
||||
|
||||
# Add to buffer and maintain size
|
||||
self.live_tick_buffer[symbol].append(tick_entry)
|
||||
if len(self.live_tick_buffer[symbol]) > self.max_tick_buffer_size:
|
||||
self.live_tick_buffer[symbol].pop(0)
|
||||
|
||||
logger.debug(f"HTTP: {symbol}: ${current_price:.2f} (buffer: {len(self.live_tick_buffer[symbol])} ticks)")
|
||||
except Exception as e:
|
||||
logger.warning(f"Error fetching HTTP price for {symbol}: {e}")
|
||||
|
||||
time.sleep(2) # Poll every 2 seconds
|
||||
time.sleep(1) # Poll every 1 second for better responsiveness
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"HTTP polling error: {e}")
|
||||
time.sleep(5)
|
||||
time.sleep(3)
|
||||
|
||||
def _websocket_price_stream(self, symbol: str):
|
||||
"""WebSocket stream for real-time price updates"""
|
||||
url = f"wss://stream.binance.com:9443/ws/{symbol.lower()}@ticker"
|
||||
"""WebSocket stream for real-time tick data using trade stream for better granularity"""
|
||||
# Use trade stream instead of ticker for real tick data
|
||||
url = f"wss://stream.binance.com:9443/ws/{symbol.lower()}@trade"
|
||||
|
||||
while self.streaming:
|
||||
try:
|
||||
async def stream_prices():
|
||||
async with websockets.connect(url) as websocket:
|
||||
logger.info(f"WebSocket connected for {symbol}")
|
||||
async for message in websocket:
|
||||
if not self.streaming:
|
||||
break
|
||||
# Use synchronous approach to avoid asyncio issues
|
||||
import websocket
|
||||
|
||||
def on_message(ws, message):
|
||||
try:
|
||||
data = json.loads(message)
|
||||
price = float(data.get('c', 0))
|
||||
trade_data = json.loads(message)
|
||||
|
||||
# Update live prices
|
||||
# Extract trade data (more granular than ticker)
|
||||
price = float(trade_data.get('p', 0)) # Trade price
|
||||
quantity = float(trade_data.get('q', 0)) # Trade quantity
|
||||
timestamp = datetime.fromtimestamp(int(trade_data.get('T', 0)) / 1000) # Trade time
|
||||
is_buyer_maker = trade_data.get('m', False) # True if buyer is market maker
|
||||
|
||||
# Calculate volume in USDT
|
||||
volume_usdt = price * quantity
|
||||
|
||||
# Update live prices and tick buffer
|
||||
with self.data_lock:
|
||||
formatted_symbol = f"{symbol[:3]}/{symbol[3:]}"
|
||||
self.live_prices[formatted_symbol] = price
|
||||
|
||||
logger.debug(f"{formatted_symbol}: ${price:.2f}")
|
||||
# Add to tick buffer for real-time chart with proper trade data
|
||||
tick_entry = {
|
||||
'timestamp': timestamp,
|
||||
'price': price,
|
||||
'volume': volume_usdt,
|
||||
'quantity': quantity,
|
||||
'side': 'sell' if is_buyer_maker else 'buy', # Market taker side
|
||||
'open': price, # For tick data, OHLC are same as current price
|
||||
'high': price,
|
||||
'low': price,
|
||||
'close': price
|
||||
}
|
||||
|
||||
# Add to buffer and maintain size
|
||||
self.live_tick_buffer[formatted_symbol].append(tick_entry)
|
||||
if len(self.live_tick_buffer[formatted_symbol]) > self.max_tick_buffer_size:
|
||||
self.live_tick_buffer[formatted_symbol].pop(0)
|
||||
|
||||
# Log every 100th tick to avoid spam
|
||||
if len(self.live_tick_buffer[formatted_symbol]) % 100 == 0:
|
||||
logger.info(f"WS TRADE: {formatted_symbol}: ${price:.2f} | Vol: ${volume_usdt:.2f} | Buffer: {len(self.live_tick_buffer[formatted_symbol])} ticks")
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"Error processing WebSocket data for {symbol}: {e}")
|
||||
logger.warning(f"Error processing WebSocket trade data for {symbol}: {e}")
|
||||
|
||||
# Run the async stream
|
||||
asyncio.new_event_loop().run_until_complete(stream_prices())
|
||||
def on_error(ws, error):
|
||||
logger.warning(f"WebSocket trade stream error for {symbol}: {error}")
|
||||
|
||||
def on_close(ws, close_status_code, close_msg):
|
||||
logger.info(f"WebSocket trade stream closed for {symbol}: {close_status_code}")
|
||||
|
||||
def on_open(ws):
|
||||
logger.info(f"WebSocket trade stream connected for {symbol}")
|
||||
|
||||
# Create WebSocket connection
|
||||
ws = websocket.WebSocketApp(url,
|
||||
on_message=on_message,
|
||||
on_error=on_error,
|
||||
on_close=on_close,
|
||||
on_open=on_open)
|
||||
|
||||
# Run WebSocket with ping/pong for connection health
|
||||
ws.run_forever(ping_interval=20, ping_timeout=10)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"WebSocket error for {symbol}: {e}")
|
||||
logger.error(f"WebSocket trade stream connection error for {symbol}: {e}")
|
||||
if self.streaming:
|
||||
logger.info(f"Reconnecting WebSocket for {symbol} in 5 seconds...")
|
||||
logger.info(f"Reconnecting WebSocket trade stream for {symbol} in 5 seconds...")
|
||||
time.sleep(5)
|
||||
|
||||
def _refresh_live_data(self):
|
||||
@ -1172,55 +1324,27 @@ class RealTimeScalpingDashboard:
|
||||
def _create_live_chart(self, symbol: str, timeframe: str, main_chart: bool = False):
|
||||
"""Create charts with real-time streaming data using proven working method"""
|
||||
try:
|
||||
# Use the proven working approach from the enhanced dashboard
|
||||
# Simplified approach - get data with fallbacks
|
||||
data = None
|
||||
|
||||
# Try to get fresh data first
|
||||
try:
|
||||
limit = 100 if timeframe == '1s' else 50 if timeframe == '1m' else 30
|
||||
data = self.data_provider.get_historical_data(symbol, timeframe, limit=limit, refresh=True)
|
||||
if data is not None and not data.empty and len(data) > 5:
|
||||
logger.info(f"[FRESH] Got {len(data)} candles for {symbol} {timeframe}")
|
||||
else:
|
||||
logger.warning(f"[WARN] No fresh data for {symbol} {timeframe}")
|
||||
data = None
|
||||
except Exception as e:
|
||||
logger.warning(f"[ERROR] Error getting fresh {symbol} {timeframe} data: {e}")
|
||||
data = None
|
||||
|
||||
# Fallback to cached data
|
||||
if data is None or data.empty:
|
||||
# Try cached data first (faster)
|
||||
try:
|
||||
with self.data_lock:
|
||||
if symbol in self.chart_data and timeframe in self.chart_data[symbol]:
|
||||
data = self.chart_data[symbol][timeframe]
|
||||
if not data.empty:
|
||||
logger.info(f"[CACHED] Using cached data for {symbol} {timeframe} ({len(data)} candles)")
|
||||
data = self.chart_data[symbol][timeframe].copy()
|
||||
if not data.empty and len(data) > 5:
|
||||
logger.debug(f"[CACHED] Using cached data for {symbol} {timeframe} ({len(data)} candles)")
|
||||
except Exception as e:
|
||||
logger.warning(f"[ERROR] Error getting cached data: {e}")
|
||||
|
||||
# Final fallback to mock data
|
||||
# If no cached data, generate mock data immediately
|
||||
if data is None or data.empty:
|
||||
logger.warning(f"[MOCK] Generating mock data for {symbol} {timeframe}")
|
||||
logger.debug(f"[MOCK] Generating mock data for {symbol} {timeframe}")
|
||||
data = self._generate_mock_data(symbol, timeframe, 50)
|
||||
|
||||
if data.empty:
|
||||
# Return loading chart
|
||||
fig = go.Figure()
|
||||
fig.add_annotation(
|
||||
text=f"Loading real-time data for {symbol} {timeframe}...<br><br>Fetching live market data...",
|
||||
xref="paper", yref="paper",
|
||||
x=0.5, y=0.5, showarrow=False,
|
||||
font=dict(size=14, color="#00ff88")
|
||||
)
|
||||
fig.update_layout(
|
||||
title=f"LIVE STREAM: {symbol} {timeframe} - (Loading...)",
|
||||
template="plotly_dark",
|
||||
height=600 if main_chart else 300,
|
||||
paper_bgcolor='#1e1e1e',
|
||||
plot_bgcolor='#1e1e1e'
|
||||
)
|
||||
return fig
|
||||
# Ensure we have valid data
|
||||
if data is None or data.empty:
|
||||
return self._create_empty_chart(f"{symbol} {timeframe} - No Data")
|
||||
|
||||
# Create real-time chart using proven working method
|
||||
fig = go.Figure()
|
||||
@ -1229,7 +1353,7 @@ class RealTimeScalpingDashboard:
|
||||
current_price = self.live_prices.get(symbol, data['close'].iloc[-1] if not data.empty else 0)
|
||||
|
||||
if main_chart:
|
||||
# Main chart - use line chart for better compatibility (like working dashboard)
|
||||
# Main chart - use line chart for better compatibility (proven working method)
|
||||
fig.add_trace(go.Scatter(
|
||||
x=data['timestamp'] if 'timestamp' in data.columns else data.index,
|
||||
y=data['close'],
|
||||
@ -1239,14 +1363,14 @@ class RealTimeScalpingDashboard:
|
||||
hovertemplate='<b>%{y:.2f}</b><br>%{x}<extra></extra>'
|
||||
))
|
||||
|
||||
# Add volume as separate trace
|
||||
# Add volume as bar chart on secondary y-axis
|
||||
if 'volume' in data.columns:
|
||||
fig.add_trace(go.Bar(
|
||||
x=data['timestamp'] if 'timestamp' in data.columns else data.index,
|
||||
y=data['volume'],
|
||||
name="Volume",
|
||||
yaxis='y2',
|
||||
opacity=0.3,
|
||||
opacity=0.4,
|
||||
marker_color='#4CAF50'
|
||||
))
|
||||
|
||||
@ -1270,7 +1394,8 @@ class RealTimeScalpingDashboard:
|
||||
mode='markers',
|
||||
marker=dict(color='#00ff88', size=12, symbol='triangle-up', line=dict(color='white', width=2)),
|
||||
name="BUY Signals",
|
||||
hovertemplate="<b>BUY SIGNAL</b><br>Price: $%{y:.2f}<br>Time: %{x}<br><extra></extra>"
|
||||
text=[f"BUY ${d['price']:.2f}" for d in buy_decisions],
|
||||
hoverinfo='text+x'
|
||||
))
|
||||
|
||||
# Add SELL markers
|
||||
@ -1281,7 +1406,8 @@ class RealTimeScalpingDashboard:
|
||||
mode='markers',
|
||||
marker=dict(color='#ff6b6b', size=12, symbol='triangle-down', line=dict(color='white', width=2)),
|
||||
name="SELL Signals",
|
||||
hovertemplate="<b>SELL SIGNAL</b><br>Price: $%{y:.2f}<br>Time: %{x}<br><extra></extra>"
|
||||
text=[f"SELL ${d['price']:.2f}" for d in sell_decisions],
|
||||
hoverinfo='text+x'
|
||||
))
|
||||
|
||||
# Current time and price info
|
||||
@ -1302,13 +1428,15 @@ class RealTimeScalpingDashboard:
|
||||
)
|
||||
|
||||
else:
|
||||
# Small chart - simple line chart
|
||||
# Small chart - use line chart for better compatibility (proven working method)
|
||||
fig.add_trace(go.Scatter(
|
||||
x=data['timestamp'] if 'timestamp' in data.columns else data.index,
|
||||
y=data['close'],
|
||||
mode='lines',
|
||||
name=f"{symbol} {timeframe}",
|
||||
line=dict(color='#00ff88', width=2)
|
||||
line=dict(color='#00ff88', width=2),
|
||||
showlegend=False,
|
||||
hovertemplate='<b>%{y:.2f}</b><br>%{x}<extra></extra>'
|
||||
))
|
||||
|
||||
# Live price point
|
||||
@ -1352,6 +1480,292 @@ class RealTimeScalpingDashboard:
|
||||
)
|
||||
return fig
|
||||
|
||||
def _create_empty_chart(self, title: str):
|
||||
"""Create an empty chart with error message"""
|
||||
fig = go.Figure()
|
||||
fig.add_annotation(
|
||||
text=f"{title}<br><br>Chart data loading...",
|
||||
xref="paper", yref="paper",
|
||||
x=0.5, y=0.5, showarrow=False,
|
||||
font=dict(size=14, color="#00ff88")
|
||||
)
|
||||
fig.update_layout(
|
||||
title=title,
|
||||
template="plotly_dark",
|
||||
height=300,
|
||||
paper_bgcolor='#1e1e1e',
|
||||
plot_bgcolor='#1e1e1e'
|
||||
)
|
||||
return fig
|
||||
|
||||
def _create_cached_chart(self, symbol: str, timeframe: str):
|
||||
"""Create chart using cached data for better performance (no API calls during updates)"""
|
||||
try:
|
||||
# Use cached data to avoid API calls during frequent updates
|
||||
data = None
|
||||
|
||||
# Try to get cached data first
|
||||
try:
|
||||
with self.data_lock:
|
||||
if symbol in self.chart_data and timeframe in self.chart_data[symbol]:
|
||||
data = self.chart_data[symbol][timeframe].copy()
|
||||
if not data.empty and len(data) > 5:
|
||||
logger.debug(f"Using cached data for {symbol} {timeframe} ({len(data)} candles)")
|
||||
except Exception as e:
|
||||
logger.warning(f"Error getting cached data: {e}")
|
||||
|
||||
# If no cached data, generate mock data
|
||||
if data is None or data.empty:
|
||||
logger.debug(f"Generating mock data for {symbol} {timeframe}")
|
||||
data = self._generate_mock_data(symbol, timeframe, 50)
|
||||
|
||||
# Ensure we have valid data
|
||||
if data is None or data.empty:
|
||||
return self._create_empty_chart(f"{symbol} {timeframe} - No Data")
|
||||
|
||||
# Create chart using line chart for better compatibility
|
||||
fig = go.Figure()
|
||||
|
||||
# Add line chart
|
||||
fig.add_trace(go.Scatter(
|
||||
x=data['timestamp'] if 'timestamp' in data.columns else data.index,
|
||||
y=data['close'],
|
||||
mode='lines',
|
||||
name=f"{symbol} {timeframe}",
|
||||
line=dict(color='#4CAF50', width=2),
|
||||
hovertemplate='<b>%{y:.2f}</b><br>%{x}<extra></extra>'
|
||||
))
|
||||
|
||||
# Get current price for live marker
|
||||
current_price = self.live_prices.get(symbol, data['close'].iloc[-1] if not data.empty else 0)
|
||||
|
||||
# Add current price marker
|
||||
if current_price > 0 and not data.empty:
|
||||
fig.add_trace(go.Scatter(
|
||||
x=[data['timestamp'].iloc[-1] if 'timestamp' in data.columns else data.index[-1]],
|
||||
y=[current_price],
|
||||
mode='markers',
|
||||
marker=dict(color='#FFD700', size=8),
|
||||
name="Live Price",
|
||||
showlegend=False
|
||||
))
|
||||
|
||||
# Update layout
|
||||
fig.update_layout(
|
||||
title=f"{symbol} {timeframe.upper()} (Cached) | ${current_price:.2f}",
|
||||
template="plotly_dark",
|
||||
height=300,
|
||||
margin=dict(l=10, r=10, t=40, b=10),
|
||||
paper_bgcolor='#1e1e1e',
|
||||
plot_bgcolor='#1e1e1e',
|
||||
showlegend=False
|
||||
)
|
||||
|
||||
return fig
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error creating cached chart for {symbol} {timeframe}: {e}")
|
||||
return self._create_empty_chart(f"{symbol} {timeframe} - Cache Error")
|
||||
|
||||
def _create_main_tick_chart(self, symbol: str):
|
||||
"""Create main chart using real-time WebSocket tick buffer with enhanced trade visualization"""
|
||||
try:
|
||||
# Get tick buffer data
|
||||
tick_buffer = []
|
||||
current_price = 0
|
||||
|
||||
try:
|
||||
with self.data_lock:
|
||||
tick_buffer = self.live_tick_buffer.get(symbol, []).copy()
|
||||
current_price = self.live_prices.get(symbol, 0)
|
||||
except Exception as e:
|
||||
logger.warning(f"Error accessing tick buffer: {e}")
|
||||
|
||||
# If no tick data, use cached chart as fallback
|
||||
if not tick_buffer:
|
||||
logger.debug(f"No tick buffer for {symbol}, using cached chart")
|
||||
return self._create_cached_chart(symbol, '1s')
|
||||
|
||||
# Convert tick buffer to DataFrame for plotting
|
||||
import pandas as pd
|
||||
df = pd.DataFrame(tick_buffer)
|
||||
|
||||
# Create figure with enhanced tick data visualization
|
||||
fig = go.Figure()
|
||||
|
||||
# Separate buy and sell trades for better visualization
|
||||
if 'side' in df.columns:
|
||||
buy_trades = df[df['side'] == 'buy']
|
||||
sell_trades = df[df['side'] == 'sell']
|
||||
|
||||
# Add buy trades (green)
|
||||
if not buy_trades.empty:
|
||||
fig.add_trace(go.Scatter(
|
||||
x=buy_trades['timestamp'],
|
||||
y=buy_trades['price'],
|
||||
mode='markers',
|
||||
name=f"{symbol} Buy Trades",
|
||||
marker=dict(color='#00ff88', size=4, opacity=0.7),
|
||||
hovertemplate='<b>BUY $%{y:.2f}</b><br>%{x}<br>Vol: %{customdata:.2f}<extra></extra>',
|
||||
customdata=buy_trades['volume'] if 'volume' in buy_trades.columns else None
|
||||
))
|
||||
|
||||
# Add sell trades (red)
|
||||
if not sell_trades.empty:
|
||||
fig.add_trace(go.Scatter(
|
||||
x=sell_trades['timestamp'],
|
||||
y=sell_trades['price'],
|
||||
mode='markers',
|
||||
name=f"{symbol} Sell Trades",
|
||||
marker=dict(color='#ff6b6b', size=4, opacity=0.7),
|
||||
hovertemplate='<b>SELL $%{y:.2f}</b><br>%{x}<br>Vol: %{customdata:.2f}<extra></extra>',
|
||||
customdata=sell_trades['volume'] if 'volume' in sell_trades.columns else None
|
||||
))
|
||||
else:
|
||||
# Fallback to simple line chart if no side data
|
||||
fig.add_trace(go.Scatter(
|
||||
x=df['timestamp'],
|
||||
y=df['price'],
|
||||
mode='lines+markers',
|
||||
name=f"{symbol} Live Trades",
|
||||
line=dict(color='#00ff88', width=1),
|
||||
marker=dict(size=3),
|
||||
hovertemplate='<b>$%{y:.2f}</b><br>%{x}<extra></extra>'
|
||||
))
|
||||
|
||||
# Add price trend line (moving average)
|
||||
if len(df) >= 20:
|
||||
df['ma_20'] = df['price'].rolling(window=20).mean()
|
||||
fig.add_trace(go.Scatter(
|
||||
x=df['timestamp'],
|
||||
y=df['ma_20'],
|
||||
mode='lines',
|
||||
name="20-Trade MA",
|
||||
line=dict(color='#FFD700', width=2, dash='dash'),
|
||||
opacity=0.8
|
||||
))
|
||||
|
||||
# Add current price marker
|
||||
if current_price > 0:
|
||||
fig.add_trace(go.Scatter(
|
||||
x=[df['timestamp'].iloc[-1]],
|
||||
y=[current_price],
|
||||
mode='markers',
|
||||
marker=dict(color='#FFD700', size=15, symbol='circle',
|
||||
line=dict(color='white', width=2)),
|
||||
name="Live Price",
|
||||
showlegend=False,
|
||||
hovertemplate=f'<b>LIVE: ${current_price:.2f}</b><extra></extra>'
|
||||
))
|
||||
|
||||
# Add volume bars on secondary y-axis
|
||||
if 'volume' in df.columns:
|
||||
fig.add_trace(go.Bar(
|
||||
x=df['timestamp'],
|
||||
y=df['volume'],
|
||||
name="Volume (USDT)",
|
||||
yaxis='y2',
|
||||
opacity=0.3,
|
||||
marker_color='#4CAF50',
|
||||
hovertemplate='<b>Vol: $%{y:.2f}</b><br>%{x}<extra></extra>'
|
||||
))
|
||||
|
||||
# Add trading signals if available
|
||||
if self.recent_decisions:
|
||||
buy_decisions = []
|
||||
sell_decisions = []
|
||||
|
||||
for decision in self.recent_decisions[-10:]: # Last 10 decisions
|
||||
if hasattr(decision, 'timestamp') and hasattr(decision, 'price') and hasattr(decision, 'action'):
|
||||
if decision.action == 'BUY':
|
||||
buy_decisions.append({'timestamp': decision.timestamp, 'price': decision.price})
|
||||
elif decision.action == 'SELL':
|
||||
sell_decisions.append({'timestamp': decision.timestamp, 'price': decision.price})
|
||||
|
||||
# Add BUY signals
|
||||
if buy_decisions:
|
||||
fig.add_trace(go.Scatter(
|
||||
x=[d['timestamp'] for d in buy_decisions],
|
||||
y=[d['price'] for d in buy_decisions],
|
||||
mode='markers',
|
||||
marker=dict(color='#00ff88', size=20, symbol='triangle-up',
|
||||
line=dict(color='white', width=3)),
|
||||
name="AI BUY Signals",
|
||||
text=[f"AI BUY ${d['price']:.2f}" for d in buy_decisions],
|
||||
hoverinfo='text+x'
|
||||
))
|
||||
|
||||
# Add SELL signals
|
||||
if sell_decisions:
|
||||
fig.add_trace(go.Scatter(
|
||||
x=[d['timestamp'] for d in sell_decisions],
|
||||
y=[d['price'] for d in sell_decisions],
|
||||
mode='markers',
|
||||
marker=dict(color='#ff6b6b', size=20, symbol='triangle-down',
|
||||
line=dict(color='white', width=3)),
|
||||
name="AI SELL Signals",
|
||||
text=[f"AI SELL ${d['price']:.2f}" for d in sell_decisions],
|
||||
hoverinfo='text+x'
|
||||
))
|
||||
|
||||
# Update layout with enhanced styling
|
||||
current_time = datetime.now().strftime("%H:%M:%S")
|
||||
tick_count = len(tick_buffer)
|
||||
latest_price = df['price'].iloc[-1] if not df.empty else current_price
|
||||
height = 600 if symbol == 'ETH/USDT' else 300
|
||||
|
||||
# Calculate price change
|
||||
price_change = 0
|
||||
price_change_pct = 0
|
||||
if len(df) > 1:
|
||||
price_change = latest_price - df['price'].iloc[0]
|
||||
price_change_pct = (price_change / df['price'].iloc[0]) * 100
|
||||
|
||||
# Color for price change
|
||||
change_color = '#00ff88' if price_change >= 0 else '#ff6b6b'
|
||||
change_symbol = '+' if price_change >= 0 else ''
|
||||
|
||||
fig.update_layout(
|
||||
title=f"{symbol} Live Trade Stream | ${latest_price:.2f} ({change_symbol}{price_change_pct:+.2f}%) | {tick_count} trades | {current_time}",
|
||||
yaxis_title="Price (USDT)",
|
||||
yaxis2=dict(title="Volume (USDT)", overlaying='y', side='right') if 'volume' in df.columns else None,
|
||||
template="plotly_dark",
|
||||
height=height,
|
||||
xaxis_rangeslider_visible=False,
|
||||
margin=dict(l=20, r=20, t=50, b=20),
|
||||
paper_bgcolor='#1e1e1e',
|
||||
plot_bgcolor='#1e1e1e',
|
||||
showlegend=True,
|
||||
legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1),
|
||||
xaxis=dict(
|
||||
title="Time",
|
||||
type="date",
|
||||
tickformat="%H:%M:%S"
|
||||
),
|
||||
# Add price change color to title
|
||||
title_font_color=change_color
|
||||
)
|
||||
|
||||
return fig
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error creating main tick chart for {symbol}: {e}")
|
||||
# Return error chart
|
||||
fig = go.Figure()
|
||||
fig.add_annotation(
|
||||
text=f"Error loading {symbol} WebSocket stream<br>{str(e)}",
|
||||
xref="paper", yref="paper",
|
||||
x=0.5, y=0.5, showarrow=False,
|
||||
font=dict(size=14, color="#ff4444")
|
||||
)
|
||||
fig.update_layout(
|
||||
template="plotly_dark",
|
||||
height=600 if symbol == 'ETH/USDT' else 300,
|
||||
paper_bgcolor='#1e1e1e',
|
||||
plot_bgcolor='#1e1e1e'
|
||||
)
|
||||
return fig
|
||||
|
||||
def _create_model_training_status(self):
|
||||
"""Create model training progress display"""
|
||||
try:
|
||||
@ -1404,7 +1818,7 @@ class RealTimeScalpingDashboard:
|
||||
html.H6("Processing", className="text-success"),
|
||||
html.P(f"Tick Counts: {tick_stats.get('tick_counts', {})}", className="text-white"),
|
||||
html.P(f"Buffer Sizes: {tick_stats.get('buffer_sizes', {})}", className="text-white"),
|
||||
html.P(f"Neural DPS: {'🧠 Active' if tick_stats.get('streaming', False) else '⏸️ Inactive'}", className="text-white")
|
||||
html.P(f"Neural DPS: {'ACTIVE' if tick_stats.get('streaming', False) else 'INACTIVE'}", className="text-white")
|
||||
], className="col-md-6")
|
||||
], className="row")
|
||||
else:
|
||||
@ -1550,14 +1964,14 @@ class RealTimeScalpingDashboard:
|
||||
logger.info("START: SESSION TRADING FEATURES:")
|
||||
logger.info(f"Session ID: {self.trading_session.session_id}")
|
||||
logger.info(f"Starting Balance: ${self.trading_session.starting_balance:.2f}")
|
||||
logger.info(" • Session-based P&L tracking (resets each session)")
|
||||
logger.info(" • Real-time trade execution with 500x leverage")
|
||||
logger.info(" • Clean accounting logs for all trades")
|
||||
logger.info(" - Session-based P&L tracking (resets each session)")
|
||||
logger.info(" - Real-time trade execution with 500x leverage")
|
||||
logger.info(" - Clean accounting logs for all trades")
|
||||
logger.info("STREAM: TECHNICAL FEATURES:")
|
||||
logger.info(" • WebSocket price streaming (1s updates)")
|
||||
logger.info(" • NO CACHED DATA - Always fresh API calls")
|
||||
logger.info(f" • Sofia timezone: {self.timezone}")
|
||||
logger.info(" • Real-time charts with throttling")
|
||||
logger.info(" - WebSocket price streaming (1s updates)")
|
||||
logger.info(" - NO CACHED DATA - Always fresh API calls")
|
||||
logger.info(f" - Sofia timezone: {self.timezone}")
|
||||
logger.info(" - Real-time charts with throttling")
|
||||
|
||||
self.app.run(host=host, port=port, debug=debug)
|
||||
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user