206 lines
9.2 KiB
Markdown
206 lines
9.2 KiB
Markdown
# TradingAgents Personal Fork Roadmap
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## Overview
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This roadmap outlines the technical development path for the personal fork of TradingAgents, focusing on building a robust data infrastructure with PostgreSQL + TimescaleDB + pgvectorscale, implementing RAG-powered agents, and establishing automated data collection pipelines with Dagster.
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## Current Status: Phase 1 - News Domain (95% Complete)
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The foundation has been established with core domain architecture, comprehensive testing framework, and the news domain nearly complete.
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### Completed Infrastructure
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- **Domain Architecture**: Clean separation of news, marketdata, and socialmedia domains
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- **Testing Framework**: Pragmatic TDD with 85%+ coverage, pytest-vcr for HTTP mocking
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- **Repository Pattern**: Efficient data caching and management system
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- **News Domain**: Article scraping, sentiment analysis, and storage (95% complete)
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- **Basic Agent System**: Multi-agent trading analysis framework with LangGraph
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## Development Phases
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### Phase 1: News Domain Completion (Current - 95% Complete)
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**Timeline**: 2-3 weeks
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**Status**: 🔄 In Progress
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#### Remaining Work
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- **News Processing Pipeline**: Complete article content processing and deduplication
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- **Sentiment Analysis Optimization**: Fine-tune sentiment scoring algorithms
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- **News Repository**: Finalize PostgreSQL integration for news storage
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- **Testing Coverage**: Achieve 85%+ test coverage for news domain
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- **Performance Optimization**: Optimize news retrieval and search performance
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#### Success Criteria
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- ✅ All news APIs integrated and tested
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- ✅ Sentiment analysis producing consistent scores
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- ✅ News data properly stored in PostgreSQL
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- ✅ Comprehensive test suite covering edge cases
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- ✅ News domain ready for RAG integration
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### Phase 2: Market Data Domain + PostgreSQL Migration (Next Priority)
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**Timeline**: 4-6 weeks
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**Status**: 📋 Planned
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#### Core Objectives
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- **TimescaleDB Integration**: Implement hypertables for efficient time-series storage
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- **Market Data Collection**: Complete price, volume, and technical indicator collection
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- **PostgreSQL Migration**: Move all data persistence from file-based to PostgreSQL
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- **Technical Analysis**: Implement MACD, RSI, and other technical indicators
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- **Database Schema**: Design optimized schema for market data with proper indexing
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#### Key Deliverables
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- Market data repository with TimescaleDB optimization
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- Real-time and historical price data collection
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- Technical analysis calculation engine
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- Migration scripts for moving existing data
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- Performance benchmarks for time-series queries
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#### Success Criteria
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- ✅ Market data efficiently stored in TimescaleDB hypertables
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- ✅ Sub-100ms queries for common market data retrievals
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- ✅ All technical indicators calculating accurately
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- ✅ Complete migration from file-based storage
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- ✅ Market data domain ready for agent integration
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### Phase 3: Social Media Domain (Following Phase 2)
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**Timeline**: 3-4 weeks
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**Status**: 📋 Planned
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#### Core Objectives
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- **Reddit Integration**: Implement Reddit API for financial subreddits
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- **Twitter/X Integration**: Add social sentiment from Twitter feeds
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- **Social Sentiment Analysis**: Aggregate sentiment scoring across platforms
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- **Cross-Domain Relations**: Link social sentiment to market data and news
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- **pgvectorscale Preparation**: Prepare social data for vector search
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#### Key Deliverables
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- Reddit and Twitter data collection clients
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- Social sentiment aggregation algorithms
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- Social media data repository with PostgreSQL storage
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- Cross-domain correlation analysis tools
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- Foundation for RAG implementation
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#### Success Criteria
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- ✅ Social media data collected from multiple sources
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- ✅ Sentiment scores integrated with market events
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- ✅ Cross-domain relationships established in database
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- ✅ Social media domain ready for RAG enhancement
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- ✅ Three-domain architecture complete
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### Phase 4: Dagster Data Collection Orchestration
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**Timeline**: 3-4 weeks
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**Status**: 📋 Planned
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#### Core Objectives
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- **Pipeline Architecture**: Design daily/twice-daily data collection workflows
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- **Data Quality Monitoring**: Implement validation and gap detection
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- **Automated Backfill**: Handle missing data and API failures gracefully
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- **Performance Monitoring**: Track pipeline health and data freshness
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- **Alerting System**: Notify on pipeline failures or data quality issues
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#### Key Deliverables
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- Dagster asset definitions for all data domains
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- Automated data quality checks and validation
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- Gap detection and backfill capabilities
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- Monitoring dashboard for pipeline health
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- Comprehensive logging and error handling
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#### Success Criteria
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- ✅ Fully automated data collection running daily
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- ✅ Data quality monitoring with automated alerts
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- ✅ Zero-downtime pipeline updates and maintenance
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- ✅ Historical data gaps automatically detected and filled
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- ✅ Pipeline performance metrics tracked and optimized
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### Phase 5: RAG Implementation + OpenRouter Migration
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**Timeline**: 4-5 weeks
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**Status**: 📋 Planned
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#### Core Objectives
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- **pgvectorscale Integration**: Implement vector storage for historical patterns
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- **RAG Agent Enhancement**: Agents use similarity search for context
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- **OpenRouter Migration**: Complete migration to unified LLM provider
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- **Historical Context**: Agents reference past decisions and market conditions
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- **Pattern Recognition**: Semantic similarity for comparable market scenarios
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#### Key Deliverables
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- pgvectorscale extension configured and optimized
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- Vector embeddings for all historical data
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- RAG-enhanced agent decision making
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- OpenRouter integration replacing all LLM providers
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- Similarity search for historical pattern matching
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#### Success Criteria
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- ✅ All agents using RAG for contextual decisions
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- ✅ Vector search performing sub-50ms similarity queries
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- ✅ OpenRouter as sole LLM provider across all agents
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- ✅ Agents demonstrating improved decision accuracy
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- ✅ Historical pattern matching enhancing trading analysis
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## Technical Milestones
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### Database Architecture
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- **Month 1**: Complete PostgreSQL foundation with news domain
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- **Month 2**: TimescaleDB hypertables optimized for market data
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- **Month 3**: pgvectorscale configured for RAG implementation
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- **Month 4**: Full database optimization and performance tuning
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### Agent Capabilities
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- **Month 1**: Basic multi-agent framework operational
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- **Month 2**: Agents using PostgreSQL for all data access
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- **Month 3**: Cross-domain agent collaboration established
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- **Month 4**: RAG-powered agents with historical context
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### Data Pipeline Maturity
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- **Month 1**: Manual data collection with basic automation
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- **Month 2**: Automated collection for market data
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- **Month 3**: Full three-domain automated collection
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- **Month 4**: Production-grade pipeline with monitoring and alerting
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## Success Metrics
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### Technical Excellence
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- **Test Coverage**: Maintain 85%+ across all domains
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- **Query Performance**: < 100ms for common database operations
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- **Pipeline Reliability**: 99%+ uptime for data collection
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- **Data Quality**: < 0.1% missing data points across all domains
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### Feature Completeness
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- **Domain Coverage**: 100% implementation across news, marketdata, socialmedia
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- **Agent Capabilities**: RAG-enhanced decision making operational
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- **Data Infrastructure**: Complete PostgreSQL + TimescaleDB + pgvectorscale stack
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- **Automation**: Fully automated data collection and processing
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### Development Velocity
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- **Code Quality**: Consistent formatting, type checking, and documentation
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- **Testing Strategy**: Comprehensive test suite with domain-specific approaches
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- **Architecture Consistency**: Clean domain separation and layered architecture
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- **Performance Optimization**: Regular profiling and optimization cycles
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## Risk Management
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### Technical Risks
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- **Database Performance**: Mitigate with proper indexing and query optimization
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- **API Rate Limits**: Implement intelligent backoff and caching strategies
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- **Data Quality**: Establish comprehensive validation and monitoring
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- **Vector Search Performance**: Optimize pgvectorscale configuration and queries
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### Development Risks
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- **Scope Creep**: Maintain focus on sequential domain completion
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- **Technical Debt**: Regular refactoring and code quality maintenance
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- **Testing Coverage**: Continuous integration with coverage enforcement
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- **Documentation**: Maintain comprehensive documentation throughout development
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## Long-Term Vision (6+ Months)
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### Advanced Capabilities
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- **Strategy Backtesting**: Historical strategy validation with complete data
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- **Real-Time Analysis**: Live market analysis with sub-second agent responses
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- **Advanced RAG**: Multi-modal RAG with charts, documents, and audio data
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- **Performance Analytics**: Comprehensive analysis of agent decision accuracy
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### Research Applications
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- **Academic Research**: Platform for publishing trading AI research
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- **Strategy Development**: Complete environment for developing proprietary strategies
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- **Data Science**: Advanced analytics and machine learning on financial data
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- **Educational Use**: Comprehensive learning platform for financial AI
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This roadmap prioritizes building a solid data foundation before enhancing agent capabilities, ensuring each phase delivers measurable value while maintaining high code quality and comprehensive testing. |