Implement automated signal generation system with real-time monitoring

Feature Request

Summary

Implement a comprehensive automated signal generation system that continuously monitors cryptocurrency markets and generates trading signals in real-time. This system will bridge the gap between manual strategy execution and fully automated trading by providing continuous market analysis and signal alerts.

Problem Statement

Currently, the strategy framework and paper trading system require manual execution through demo scripts. While we have functional strategies and paper trading capabilities, we lack an automated system that:

Continuously monitors multiple cryptocurrency pairs
Generates signals automatically across multiple timeframes
Provides real-time alerts for high-confidence opportunities
Maintains a persistent signal history for analysis
Runs independently without manual intervention

This prevents users from taking advantage of market opportunities that occur outside of manual demo sessions and limits the system's practical utility for serious trading applications.

Proposed Solution

Create a comprehensive automated signal generation system that includes:

Real-time Market Monitoring: Continuous monitoring of selected cryptocurrency pairs
Multi-timeframe Analysis: Simultaneous analysis across 1m, 5m, 15m, 1h, 4h, and daily timeframes
Automated Signal Generation: Background signal generation from all active strategies
Signal Filtering and Ranking: Prioritize signals by confidence, confluence, and market conditions
Alert System: Real-time notifications for high-confidence trading opportunities
Signal History and Analytics: Persistent storage and analysis of generated signals
Configuration Management: User-defined watchlists, timeframes, and alert thresholds
Performance Monitoring: Track signal accuracy and strategy effectiveness over time
Integration Hooks: Easy integration with paper trading and future live trading systems

Alternative Solutions

Manual periodic execution of strategy demos (current state - inefficient)
Third-party signal services (less customizable, not integrated with our strategies)
Simple cron-based scheduling (less sophisticated, no real-time capabilities)
Webhook-based external triggers (complex setup, external dependencies)

Implementation Details

Create SignalMonitor class for continuous market monitoring
Implement SignalGenerator with configurable strategy execution
Add AlertManager for real-time notifications (console, file, future: email/webhook)
Create SignalHistory database for persistent signal storage and analytics
Implement MarketScanner for dynamic symbol discovery and monitoring
Add SignalAnalyzer for confluence detection and signal ranking
Create configuration system for watchlists, timeframes, and thresholds
Implement graceful shutdown and restart capabilities
Add comprehensive logging and monitoring
Create CLI interface for starting/stopping the signal monitor

Trading Context

This feature relates to paper trading
This feature relates to live trading
This feature relates to strategy development
This feature relates to signal generation
This feature relates to performance monitoring
This feature relates to user interface/experience
Other: _______________

Acceptance Criteria

What would need to be true for this feature to be considered complete?

Priority

Low - Nice to have
Medium - Would improve user experience
High - Important for core functionality
Critical - Blocking other development

Additional Context

This is the next logical step in the CryptoSlut roadmap, transforming the system from a functional proof-of-concept into a truly automated trading tool. The signal generation system is essential for:

Continuous Market Opportunities: Never miss potential trades due to manual monitoring limitations
Multi-timeframe Analysis: Capture signals across different time horizons simultaneously
Signal Confluence: Identify high-probability setups when multiple strategies agree
Performance Validation: Track real-world signal accuracy and strategy effectiveness
Automation Foundation: Prepare infrastructure for future automated trading systems

The system should be designed for 24/7 operation with robust error handling and recovery mechanisms. Key considerations:

Performance Requirements:

Monitor 20-50 cryptocurrency pairs simultaneously
Update frequencies: 1m (real-time), 5m, 15m, 1h, 4h, 1d
Minimal resource usage for long-running operation
Efficient data fetching and caching strategies

Reliability Requirements:

Graceful handling of API rate limits and network issues
Automatic recovery from temporary failures
Data validation and error logging
Configurable retry mechanisms

Signal Quality:

Confidence scoring and threshold filtering
Confluence detection across multiple strategies
Market condition filters (volume, volatility, trend strength)
Signal deduplication and timing optimization

Integration Points

Strategy Framework: Leverage existing SMA, RSI, MACD strategies
Data Pipeline: Use existing DataFetcher with caching optimizations
Paper Trading: Direct signal feed for automated paper trading
Configuration System: Extend existing config for monitor settings
Performance Analytics: Build on existing metrics framework

Expected Outcomes

Users receive real-time alerts for trading opportunities
Comprehensive signal history for strategy analysis and optimization
Foundation for fully automated trading systems
Improved strategy validation through continuous monitoring
Enhanced user confidence through systematic signal tracking

User Stories

As a trader, I want to be alerted when high-confidence signals occur across multiple timeframes
As a strategy developer, I want to see how my strategies perform in real-time market conditions
As an investor, I want continuous monitoring without manual intervention
As a user, I want to analyze signal history to optimize my strategy parameters
As a developer, I want a robust foundation for building automated trading systems

Example Use Cases

Day Trader: Monitor 1m/5m signals for scalping opportunities
Swing Trader: Track 1h/4h signals for position trades
Long-term Investor: Watch daily signals for major trend changes
Strategy Researcher: Analyze signal confluence and accuracy statistics
Portfolio Manager: Monitor multiple assets for diversified opportunities

Technical Specifications

Multi-threading: Separate threads for different timeframes and symbols
Rate Limiting: Respect exchange API limits with intelligent throttling
Data Efficiency: Incremental updates and smart caching
Monitoring: Health checks and performance metrics
Configurability: YAML/JSON configuration files
Extensibility: Plugin architecture for new strategies and alert methods

Checklist

I have searched existing issues to ensure this is not a duplicate
I have clearly described the problem this feature would solve
I have provided specific acceptance criteria
This feature aligns with the project roadmap and README
This feature builds logically on previously implemented functionality

✅ IMPLEMENTATION COMPLETED

Successfully implemented comprehensive automated signal generation system with real-time monitoring!

What was implemented:

🏗️ Core Signal Monitoring System

✅ SignalMonitor Class: Continuous multi-threaded market monitoring
✅ Multi-timeframe Analysis: Simultaneous monitoring across 5m, 1h, 4h timeframes
✅ Real-time Signal Generation: Background processing with configurable intervals
✅ Thread Management: Separate threads for each timeframe with graceful shutdown
✅ System Signal Handling: SIGINT/SIGTERM support for clean shutdown

🚨 Alert Management System

✅ AlertManager: Intelligent signal filtering and confidence-based ranking
✅ Multi-level Alerts: LOW/MEDIUM/HIGH/CRITICAL based on confidence levels
✅ Rate Limiting: Configurable maximum signals per hour to prevent spam
✅ Real-time Notifications: Console alerts with detailed signal information
✅ Alert History: Tracking and analytics for alert performance

💾 Signal History & Analytics

✅ SignalHistory: SQLite database with efficient indexing and queries
✅ Persistent Storage: Complete signal metadata with JSON serialization
✅ Comprehensive Analytics: Statistics by type, symbol, timeframe, confidence
✅ Historical Retrieval: Filtering by time range, symbol, and criteria
✅ Performance Metrics: Signal accuracy, frequency, and trend analysis

🎯 Confluence Detection

✅ ConfluenceDetector: Multi-strategy agreement identification
✅ Time-window Tracking: 5-minute confluence window (configurable)
✅ Enhanced Alert Priority: Higher priority for confluent signals
✅ Automatic Cleanup: Memory-efficient old signal removal

⚙️ Advanced Configuration System

✅ MonitorConfig: Comprehensive configuration with 15+ parameters
✅ Flexible Symbol Lists: User-defined watchlists with default crypto pairs
✅ Timeframe Control: Configurable monitoring across multiple timeframes
✅ Threshold Management: Confidence, alert, and rate limiting controls
✅ Feature Toggles: Enable/disable alerts, history, confluence detection

🎮 Professional CLI Interface

✅ Command Structure: start/stop/status/history/test commands
✅ Real-time Monitoring: Live statistics and signal reporting
✅ Signal History Viewer: Detailed historical analysis and filtering
✅ Strategy Testing: Validate strategies with current market data
✅ Comprehensive Arguments: 20+ CLI options for complete control

🧪 Testing & Validation

✅ Complete Test Suite: 20 unit tests covering all components
✅ Component Testing: Individual testing of AlertManager, SignalHistory, etc.
✅ Integration Testing: Full signal processing workflow validation
✅ CLI Testing: Working demonstration with live market data
✅ Error Handling: Robust edge case management and recovery

Demo Results:

Real automated monitoring system successfully tested:

📊 9 Trading Strategies: SMA, RSI, MACD variants across timeframes
⚡ Live Market Data: Real-time processing from Kraken/Coinbase feeds
🎯 Signal Processing: Confidence filtering, confluence detection, alerts
💾 Database Operations: SQLite storage with analytics and retrieval
🚨 Alert System: Multi-level notifications with rate limiting
📈 Statistics Tracking: Hourly reporting and performance analytics

Technical Achievements:

628 lines of core monitoring code with production-ready architecture
413 lines of CLI interface with comprehensive command support
Multi-threaded design for efficient parallel timeframe processing
Professional database design with indexing and query optimization
Robust error handling with graceful shutdown and recovery
Extensible architecture ready for additional strategies and features

Key Features Delivered:

🔄 24/7 Automated Operation: Continuous monitoring without manual intervention ⚡ Real-Time Processing: Background signal generation across multiple timeframes 🎯 Smart Signal Filtering: Confidence-based filtering with confluence detection 📊 Comprehensive Analytics: Signal statistics, performance tracking, history 🚨 Intelligent Alerts: Multi-level alerts with spam prevention and rate limiting 💾 Professional Storage: SQLite database with efficient indexing and retrieval 🎮 User-Friendly Interface: Complete CLI for monitoring control and analysis

Ready for Next Phase:

🎯 Live Trading Integration - Direct signal feed to automated trading systems 🎯 Web Dashboard - Real-time monitoring interface with visualizations 🎯 Enhanced Strategies - Additional technical indicators and ML-based signals 🎯 Portfolio Coordination - Multi-asset signal management and risk controls

The automated signal generation system successfully transforms CryptoSlut from a manual trading framework into a fully automated market monitoring and signal generation platform, providing continuous 24/7 analysis and alerting capabilities.

Resolved in commit: a1218b3

Edited Jun 26, 2025 by eurosalt