feat(news): enhance NewsArticle entity with sentiment fields and add Dagster orchestration specs
- Add sentiment_confidence and sentiment_label fields to NewsArticle - Update database configuration for PostgreSQL + TimescaleDB + pgvectorscale - Add comprehensive Dagster orchestration specifications - Update project dependencies and tooling configuration - Enhance test coverage for news repository functionality - Align news domain specs with project roadmap and architecture
This commit is contained in:
Martin C. Richards
parent
c20771bf20
commit
c5ced8cd66
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@ -7,3 +7,4 @@ eval_results/
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eval_data/
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*.egg-info/
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.env
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.coverage
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10
.mise.toml
10
.mise.toml
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@ -37,6 +37,11 @@ description = "Run tests with pytest (with database)"
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depends = ["docker"]
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run = "uv run pytest"
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[tasks.coverage]
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description = "Run tests with coverage report"
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depends = ["docker"]
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run = "uv run pytest --cov=tradingagents --cov-report=html --cov-report=term-missing"
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[tasks.lint]
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description = "Run ruff linting"
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run = "ruff check ."
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@ -57,6 +62,11 @@ run = "ruff check --fix ."
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description = "Run format, lint, and typecheck"
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run = ["ruff format .", "ruff check .", "uv run pyrefly check ."]
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[tasks.quality]
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description = "Run complete quality check (format, lint, typecheck, test, coverage)"
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depends = ["docker"]
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run = ["ruff format .", "ruff check .", "uv run pyrefly check .", "uv run pytest --cov=tradingagents --cov-report=term-missing"]
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[tasks.clean]
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description = "Clean up cache and build artifacts"
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run = [
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@ -1,6 +1,6 @@
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services:
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timescaledb:
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build: ./db
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build: ./docker/db
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container_name: tradingagents_timescaledb
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environment:
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POSTGRES_USER: postgres
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@ -16,12 +16,27 @@ RUN echo "deb https://packagecloud.io/timescale/timescaledb/debian/ $(lsb_releas
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# Install TimescaleDB for PostgreSQL 16
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RUN apt-get install -y timescaledb-2-postgresql-17
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# Install pgxman
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# Install pgvector manually for PostgreSQL 17
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RUN apt-get update && apt-get install -y \
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build-essential \
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git \
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postgresql-server-dev-17 \
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&& rm -rf /var/lib/apt/lists/*
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# Clone and build pgvector
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RUN cd /tmp && \
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git clone --branch v0.7.4 https://github.com/pgvector/pgvector.git && \
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cd pgvector && \
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make && \
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make install && \
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cd / && \
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rm -rf /tmp/pgvector
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# Install pgxman for pgvectorscale
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RUN curl -sfL https://install.pgx.sh | sh -
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# Install pgvector and pgvectorscale using pgxman
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RUN pgxman install pgvector || echo "pgvector install failed" \
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&& pgxman install pgvectorscale || echo "pgvectorscale install failed"
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# Install pgvectorscale using pgxman
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RUN pgxman install pgvectorscale || echo "pgvectorscale install failed"
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# Configure PostgreSQL for TimescaleDB (instead of using timescaledb-tune)
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RUN echo "shared_preload_libraries = 'timescaledb'" >> /usr/share/postgresql/postgresql.conf.sample \
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@ -4,156 +4,183 @@
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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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**Last Updated**: 2025-11-11
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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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### Key Roadmap Changes
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- **Pragmatic Dagster Integration**: Dagster jobs built incrementally per domain (not separate phase)
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- **Accurate Timeline**: 10-14 weeks total (vs original 16-22 weeks) based on actual progress
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- **Incremental Automation**: Each domain gets automated collection as it completes
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- **Earlier Production Readiness**: Automated data collection starts Week 1 (not Month 4)
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### Development Velocity
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- **Observed Completion Rate**: News clients 85-90% complete with 600+ lines of quality tests
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- **AI-Assisted Multiplier**: 3-4x faster development with spec-driven workflow
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- **Target Task Velocity**: 15-20 tasks/week with AI assistance
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- **Test Coverage**: Maintained 85%+ with pytest-vcr pattern
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## Current Status: Phase 1 - News Domain + Dagster Integration (85% Complete)
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The foundation has been established with core domain architecture, comprehensive testing framework, and the news domain clients 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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- **News Clients**: Google News RSS + Article Scraper with comprehensive tests (600+ lines)
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- **Database Stack**: PostgreSQL + TimescaleDB + pgvectorscale ready
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- **Basic Agent System**: Multi-agent trading analysis framework with LangGraph
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### Current Priorities (Next 5-7 Days)
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1. **Complete News Domain Foundation** - Repository, Service, Entity layers
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2. **LLM Integration** - OpenRouter sentiment analysis + vector embeddings
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3. **Basic Dagster Job** - Automated daily news collection
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4. **Spec Documentation** - Create status.md and tasks.md for progress tracking
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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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### Phase 1: News Domain + Basic Dagster (Current - 85% Complete)
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**Timeline**: 5-7 days remaining
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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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#### Remaining Work (5-7 days)
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- **News Repository Layer**: PostgreSQL async operations with TimescaleDB (1-2 days)
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- **News Service Layer**: Business logic with LLM integration (1-2 days)
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- **NewsArticle Entity**: Domain models with sentiment and embeddings (1 day)
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- **OpenRouter Integration**: Sentiment analysis via LLM (1-2 days)
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- **Vector Embeddings**: OpenAI embeddings via OpenRouter for semantic search (1 day)
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- **Basic Dagster Job**: Daily news collection automation (1-2 days)
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- **Integration Testing**: End-to-end workflow validation (1 day)
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#### Key Deliverables
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- News domain following Router → Service → Repository → Entity → Database pattern
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- OpenRouter LLM sentiment analysis operational
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- pgvectorscale vector embeddings for semantic search
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- Automated Dagster job for daily news collection
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- 85%+ test coverage maintained
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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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- ✅ Complete layered architecture implemented
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- ✅ LLM sentiment scores with confidence ratings
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- ✅ Vector embeddings enabling semantic search
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- ✅ Dagster job running daily news collection
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- ✅ Query performance < 2 seconds
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- ✅ News domain ready for agent 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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### Phase 2: Market Data Domain + Dagster Integration (Next Priority)
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**Timeline**: 4-5 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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- **TimescaleDB Hypertables**: Efficient time-series storage for price/volume data
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- **Market Data Collection**: FinnHub/yfinance integration with retry logic
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- **PostgreSQL Migration**: Move from file-based to database storage
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- **Technical Indicators**: MACD, RSI, Bollinger Bands calculations
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- **Dagster Market Data Job**: Twice-daily price data collection automation
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- **Performance Optimization**: Sub-100ms queries 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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- MarketDataRepository with TimescaleDB optimization
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- MarketDataService with technical analysis calculations
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- MarketData entities (Price, OHLCV, TechnicalIndicators)
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- Dagster job for automated twice-daily collection
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- pytest-vcr tests for API clients
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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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- ✅ TimescaleDB hypertables storing historical price data
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- ✅ Sub-100ms queries for price lookups and indicators
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- ✅ Technical indicators calculating accurately
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- ✅ Dagster job running twice daily (market open/close)
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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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### Phase 3: Social Media Domain + Dagster Integration
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**Timeline**: 2-3 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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- **Reddit Integration**: PRAW library for financial subreddits (r/wallstreetbets, r/stocks)
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- **Twitter/X Alternative**: Evaluate Reddit-only approach or alternative sources
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- **Social Sentiment Analysis**: OpenRouter LLM sentiment across posts
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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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- **Dagster Social Media Job**: Daily social sentiment collection
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- **Vector Embeddings**: Semantic search across social discussions
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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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- RedditClient with pytest-vcr tests
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- SocialMediaRepository with PostgreSQL + pgvectorscale
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- SocialMediaService with sentiment aggregation
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- Dagster job for daily Reddit data collection
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- Cross-domain correlation queries (social ↔ news ↔ price)
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- Vector embeddings for semantic post search
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#### Success Criteria
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- ✅ Social media data collected from multiple sources
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- ✅ Reddit data collected daily from financial subreddits
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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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- ✅ Cross-domain relationships queryable in database
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- ✅ Dagster job running daily social collection
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- ✅ Vector embeddings enabling semantic social search
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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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#### Blockers to Resolve
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- **Reddit API Access**: Obtain REDDIT_CLIENT_ID and REDDIT_CLIENT_SECRET
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- **Twitter/X Alternative**: Evaluate API costs or alternative data sources
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### Phase 4: RAG Enhancement + Advanced 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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- **RAG Agent Enhancement**: All agents use vector similarity search for context
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- **Historical Pattern Matching**: Semantic search for comparable market scenarios
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- **Cross-Domain RAG**: Agents query across news, price, and social data
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- **Advanced Dagster Features**: Data quality monitoring, gap detection, backfill
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- **Performance Optimization**: Vector query tuning, database optimization
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- **Monitoring & Alerting**: Pipeline health tracking and failure notifications
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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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- RAG-enhanced agents with similarity-based context retrieval
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- Cross-domain vector search (find similar market conditions)
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- Dagster data quality checks and validation
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- Automated backfill for missing historical data
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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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- Performance benchmarks for vector queries (< 50ms target)
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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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- ✅ Vector similarity search < 50ms across all domains
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- ✅ Cross-domain queries enabling holistic analysis
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- ✅ Dagster monitoring with automated alerts
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- ✅ Data quality metrics tracked and reported
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- ✅ Historical gaps detected and auto-filled
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- ✅ Production-ready data infrastructure complete
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## Technical Milestones
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### Revised Timeline: 10-14 weeks (vs original 16-22 weeks)
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**Phase Breakdown:**
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- Phase 1 (News + Dagster): 5-7 days
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- Phase 2 (Market Data + Dagster): 4-5 weeks
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- Phase 3 (Social Media + Dagster): 2-3 weeks
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- Phase 4 (RAG + Advanced Orchestration): 3-4 weeks
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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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- **Week 1**: PostgreSQL + TimescaleDB + pgvectorscale operational (News domain)
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- **Week 6**: TimescaleDB hypertables optimized for market data time-series
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- **Week 9**: Three-domain database architecture complete with vector embeddings
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- **Week 12**: Full RAG implementation with cross-domain similarity search
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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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- **Week 1**: News Analysts accessing news with LLM sentiment
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- **Week 6**: Technical Analysts using market data with indicators
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- **Week 9**: Sentiment Analysts using social media data
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- **Week 12**: All agents RAG-enhanced 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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### Data Pipeline Maturity (Incremental Dagster)
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- **Week 1**: Daily news collection automated via Dagster
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- **Week 6**: Twice-daily market data collection automated
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- **Week 9**: Daily social media collection automated
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- **Week 12**: Production-grade orchestration with monitoring, backfill, and alerting
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## Success Metrics
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File diff suppressed because it is too large
Load Diff
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@ -10,12 +10,12 @@ Complete final 5% of news domain: add scheduled execution, LLM sentiment analysi
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### 1. Scheduled Execution
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- Daily job at 6 AM UTC for all configured tickers
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- APScheduler integration (no Dagster dependency)
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- Graceful error handling with comprehensive logging
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- Dagster orchestration with partitioned schedules
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- Graceful error handling with Dagster sensors and alerting
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### 2. LLM Sentiment Analysis
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### 2. LLM Sentiment Analysis
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- OpenRouter integration using `quick_think_llm` (claude-3.5-haiku)
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- Structured output: `{"sentiment": "positive|negative|neutral", "confidence": 0.0-1.0}`
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- Structured output: `{"sentiment": "positive|negative|neutral", "confidence": 0.0-1.0, "label": "positive|negative|neutral"}`
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- Best-effort processing - failures don't stop pipeline
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### 3. Vector Embeddings
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@ -27,54 +27,75 @@ Complete final 5% of news domain: add scheduled execution, LLM sentiment analysi
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### Architecture Pattern
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```
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ScheduledNewsJob → NewsService → NewsRepository → NewsArticle → PostgreSQL+pgvectorscale
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Dagster Job → Dagster Op → NewsService → NewsRepository → NewsArticle → PostgreSQL+pgvectorscale
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```
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### Database Changes
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```sql
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ALTER TABLE news_articles
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ADD COLUMN sentiment_score JSONB,
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ADD COLUMN title_embedding vector(1536),
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ADD COLUMN content_embedding vector(1536);
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ALTER TABLE news_articles
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ADD COLUMN sentiment_confidence FLOAT,
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ADD COLUMN sentiment_label VARCHAR(20);
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-- Vector columns already exist from 95% complete infrastructure
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-- title_embedding vector(1536)
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-- content_embedding vector(1536)
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```
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### Key Integration Points
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- **Existing NewsService**: Enhance `update_news_for_symbol` method
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- **LLM Integration**: OpenRouter unified provider for sentiment
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- **Vector Generation**: text-embedding-3-small model (1536 dims)
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- **Job Scheduling**: APScheduler with cron trigger
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- **Existing NewsService**: Enhance `update_company_news` method
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- **LLM Integration**: OpenRouter unified provider for sentiment and embeddings
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- **Vector Generation**: OpenAI text-embedding-ada-002 via OpenRouter (1536 dims)
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- **Job Scheduling**: Dagster jobs with daily partitioned schedules
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## Implementation Phases
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1. **Scheduled Execution** (2-3h): APScheduler + config management
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2. **LLM Sentiment** (3-4h): OpenRouter integration + structured prompts
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3. **Vector Embeddings** (2-3h): Embedding generation + database schema
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4. **Testing & Monitoring** (2h): Coverage + performance validation
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1. **Entity Layer** (2-3h): Enhance NewsArticle dataclass + migration
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2. **Repository Layer** (2-3h): RAG vector similarity search methods
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3. **LLM Integration** (4-5h): OpenRouter sentiment + embeddings clients
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4. **Service Enhancement** (2-3h): Integrate LLM clients into NewsService
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5. **Dagster Orchestration** (3-4h): Jobs, ops, and schedules
|
||||
6. **Testing & Monitoring** (2-3h): Coverage + performance validation
|
||||
|
||||
**Total: 9-12 hours**
|
||||
**Total: 15-20 hours**
|
||||
|
||||
## Success Criteria
|
||||
- ✅ Daily automated news collection without manual intervention
|
||||
- ✅ Daily automated news collection via Dagster without manual intervention
|
||||
- ✅ News retrieval with sentiment scores < 2 seconds response time
|
||||
- ✅ Vector embeddings enable semantic search for News Analysts
|
||||
- ✅ >95% article processing success rate despite paywall/blocking
|
||||
- ✅ Maintain >85% test coverage including new components
|
||||
- ✅ Dagster UI provides monitoring and alerting for job failures
|
||||
|
||||
## Dependencies
|
||||
- **APIs**: OpenRouter (sentiment), OpenAI (embeddings)
|
||||
- **APIs**: OpenRouter (sentiment + embeddings via unified provider)
|
||||
- **Infrastructure**: PostgreSQL + TimescaleDB + pgvectorscale
|
||||
- **New Package**: `apscheduler` for job scheduling
|
||||
- **Existing**: 95% complete news domain components
|
||||
- **Orchestration**: Dagster for job scheduling and monitoring
|
||||
- **Existing**: 95% complete news domain components (clients, repository, service)
|
||||
|
||||
## Configuration
|
||||
```yaml
|
||||
# Dagster workspace.yaml
|
||||
schedules:
|
||||
news_collection_daily:
|
||||
cron_schedule: "0 6 * * *" # Daily at 6 AM UTC
|
||||
execution_timezone: "UTC"
|
||||
|
||||
# Dagster run config
|
||||
ops:
|
||||
collect_news:
|
||||
config:
|
||||
symbols: ["AAPL", "GOOGL", "MSFT", "TSLA"]
|
||||
lookback_days: 1
|
||||
```
|
||||
|
||||
```bash
|
||||
OPENROUTER_API_KEY="sk-or-..."
|
||||
OPENAI_API_KEY="sk-..."
|
||||
NEWS_SCHEDULE_HOUR=6
|
||||
NEWS_TICKERS="AAPL,GOOGL,MSFT,TSLA"
|
||||
# Environment variables
|
||||
OPENROUTER_API_KEY="sk-or-..." # Unified LLM provider
|
||||
DATABASE_URL="postgresql+asyncpg://..."
|
||||
```
|
||||
|
||||
## Risk Mitigation
|
||||
- **API Rate Limits**: Exponential backoff + batch processing
|
||||
- **Paywall Blocking**: Metadata-only storage with warnings
|
||||
- **Job Failures**: Monitoring + alerting for operational visibility
|
||||
- **Performance**: Vector indexes + query optimization for <2s target
|
||||
- **Paywall Blocking**: Metadata-only storage with warnings
|
||||
- **Job Failures**: Dagster sensors + alerting for operational visibility
|
||||
- **Performance**: Vector indexes + query optimization for <2s target
|
||||
- **LLM Failures**: Keyword-based fallback for sentiment, zero-vector fallback for embeddings
|
||||
|
|
|
|||
|
|
@ -99,10 +99,10 @@ Complete the final 5% of the news domain by adding scheduled execution, LLM sent
|
|||
|
||||
### Architecture Alignment
|
||||
|
||||
Follows established **Router → Service → Repository → Entity → Database** pattern:
|
||||
Follows established **Router → Service → Repository → Entity → Database** pattern with Dagster orchestration:
|
||||
|
||||
```
|
||||
ScheduledNewsJob → NewsService → NewsRepository → NewsArticle → PostgreSQL+pgvectorscale
|
||||
Dagster Schedule → Dagster Job → NewsService → NewsRepository → NewsArticle → PostgreSQL+pgvectorscale
|
||||
```
|
||||
|
||||
### Database Schema Integration
|
||||
|
|
@ -156,29 +156,70 @@ content_embedding = await embedding_client.create_embedding(
|
|||
|
||||
### Scheduled Execution Framework
|
||||
|
||||
Use APScheduler for job orchestration (Dagster not in current dependencies):
|
||||
Use Dagster for job orchestration (existing dependency in project):
|
||||
|
||||
```python
|
||||
from apscheduler.schedulers.asyncio import AsyncIOScheduler
|
||||
|
||||
scheduler = AsyncIOScheduler()
|
||||
scheduler.add_job(
|
||||
run_news_collection,
|
||||
'cron',
|
||||
hour=6, # 6 AM UTC
|
||||
minute=0,
|
||||
timezone=timezone.utc,
|
||||
id='daily_news_collection'
|
||||
from dagster import (
|
||||
job,
|
||||
schedule,
|
||||
ScheduleDefinition,
|
||||
op,
|
||||
In,
|
||||
Out,
|
||||
AssetMaterialization
|
||||
)
|
||||
from dagster._core.scheduler import ScheduleExecutionContext
|
||||
|
||||
@op
|
||||
def fetch_news_for_tickers(context, tickers: list[str]) -> list[dict]:
|
||||
"""Fetch news articles for configured tickers"""
|
||||
pass
|
||||
|
||||
@op
|
||||
def process_articles_with_sentiment(context, articles: list[dict]) -> list[dict]:
|
||||
"""Process articles with LLM sentiment analysis and embeddings"""
|
||||
pass
|
||||
|
||||
@op
|
||||
def store_articles(context, processed_articles: list[dict]) -> None:
|
||||
"""Store articles with sentiment and embeddings in database"""
|
||||
pass
|
||||
|
||||
@job
|
||||
def daily_news_collection_job():
|
||||
"""Daily news collection pipeline"""
|
||||
tickers = ["AAPL", "GOOGL", "MSFT", "TSLA"] # From config
|
||||
articles = fetch_news_for_tickers(tickers)
|
||||
processed = process_articles_with_sentiment(articles)
|
||||
store_articles(processed)
|
||||
|
||||
@schedule(
|
||||
cron_schedule="0 6 * * *", # Daily at 6 AM UTC
|
||||
job=daily_news_collection_job,
|
||||
execution_timezone="UTC"
|
||||
)
|
||||
def daily_news_collection_schedule(context: ScheduleExecutionContext):
|
||||
"""Schedule for daily news collection"""
|
||||
run_config = {
|
||||
"ops": {
|
||||
"fetch_news_for_tickers": {
|
||||
"inputs": {
|
||||
"tickers": ["AAPL", "GOOGL", "MSFT", "TSLA"]
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
return run_config
|
||||
```
|
||||
|
||||
## Implementation Approach
|
||||
|
||||
### Phase 1: Scheduled Execution (2-3 hours)
|
||||
1. Configure APScheduler for daily news collection
|
||||
2. Create job configuration management for ticker lists
|
||||
3. Implement job monitoring and status tracking
|
||||
4. Add manual execution capability for testing
|
||||
### Phase 1: Dagster Scheduling Integration (2-3 hours)
|
||||
1. Create Dagster ops for news collection pipeline
|
||||
2. Configure daily schedule with cron expression
|
||||
3. Set up job configuration management for ticker lists
|
||||
4. Add manual job execution capability for testing
|
||||
5. Implement job monitoring and asset tracking
|
||||
|
||||
### Phase 2: LLM Sentiment Integration (3-4 hours)
|
||||
1. Integrate OpenRouter LLM for sentiment analysis
|
||||
|
|
@ -218,11 +259,12 @@ scheduler.add_job(
|
|||
- `NewsRepository` with async PostgreSQL operations
|
||||
- `NewsArticle` domain model with validation
|
||||
- Comprehensive test coverage with pytest-vcr
|
||||
- Dagster framework for data orchestration (existing dependency)
|
||||
|
||||
### New Dependencies
|
||||
- `apscheduler` for job scheduling
|
||||
- Enhanced vector embedding capabilities
|
||||
- LLM client integration for sentiment analysis
|
||||
- Dagster scheduling integration (existing dependency)
|
||||
|
||||
## Configuration Management
|
||||
|
||||
|
|
|
|||
|
|
@ -1,336 +1,310 @@
|
|||
# News Domain Completion - Progress Status
|
||||
|
||||
## Overview
|
||||
|
||||
**Feature**: News Domain Final 5% Completion
|
||||
**Status**: Ready for Implementation
|
||||
**Total Estimated Time**: 12-16 hours with AI assistance
|
||||
**Target Timeline**: 3-4 days
|
||||
**Current Progress**: 95% complete (infrastructure ready)
|
||||
|
||||
---
|
||||
|
||||
## Progress Summary
|
||||
|
||||
### Overall Completion: 0% (95% + 0% of final 5%)
|
||||
|
||||
| Phase | Status | Progress | Duration | Completion |
|
||||
|-------|--------|----------|----------|------------|
|
||||
| Phase 1: Foundation | ⏳ Not Started | 0/3 tasks | 0h/4-7h | ⬜⬜⬜⬜⬜⬜⬜ |
|
||||
| Phase 2: Data Access | ⏳ Not Started | 0/1 tasks | 0h/2-3h | ⬜⬜⬜ |
|
||||
| Phase 3: LLM Integration | ⏳ Not Started | 0/3 tasks | 0h/5-8h | ⬜⬜⬜⬜⬜⬜⬜⬜ |
|
||||
| Phase 4: Scheduling | ⏳ Not Started | 0/2 tasks | 0h/4-6h | ⬜⬜⬜⬜⬜⬜ |
|
||||
| Phase 5: Validation | ⏳ Not Started | 0/2 tasks | 0h/3-5h | ⬜⬜⬜⬜⬜ |
|
||||
|
||||
**Legend**: ✅ Complete | 🟡 In Progress | ⏳ Not Started | ❌ Blocked
|
||||
|
||||
---
|
||||
|
||||
## Task Status Tracking
|
||||
|
||||
### Phase 1: Foundation (0% Complete)
|
||||
|
||||
#### ⏳ T001: Database Migration - NewsJobConfig Table
|
||||
- **Status**: Not Started
|
||||
- **Priority**: Critical
|
||||
- **Estimated**: 1-2 hours
|
||||
- **Dependencies**: None
|
||||
- **Progress**: 0%
|
||||
- **Acceptance Criteria**: 0/4 completed
|
||||
- [ ] `news_job_configs` table created with UUID primary key
|
||||
- [ ] JSONB fields for symbols and categories with validation
|
||||
- [ ] Proper indexes for enabled/frequency queries
|
||||
- [ ] Migration script tests with rollback capability
|
||||
- **Blocking Issues**: None
|
||||
- **Next Actions**: Create Alembic migration script
|
||||
|
||||
#### ⏳ T002: Enhance NewsArticle Entity - Sentiment and Embeddings
|
||||
- **Status**: Not Started
|
||||
- **Priority**: Critical
|
||||
- **Estimated**: 2-3 hours
|
||||
- **Dependencies**: T001
|
||||
- **Progress**: 0%
|
||||
- **Acceptance Criteria**: 0/5 completed
|
||||
- [ ] Add sentiment_score, sentiment_confidence, sentiment_label fields
|
||||
- [ ] Add title_embedding and content_embedding vector fields
|
||||
- [ ] Enhanced validate() method with sentiment range checks
|
||||
- [ ] Updated transformations for vector handling
|
||||
- [ ] Embedding dimension validation (1536)
|
||||
- **Blocking Issues**: None
|
||||
- **Next Actions**: Extend NewsArticle dataclass
|
||||
|
||||
#### ⏳ T003: Create NewsJobConfig Entity
|
||||
- **Status**: Not Started
|
||||
- **Priority**: Critical
|
||||
- **Estimated**: 1-2 hours
|
||||
- **Dependencies**: T001
|
||||
- **Progress**: 0%
|
||||
- **Acceptance Criteria**: 0/5 completed
|
||||
- [ ] NewsJobConfig dataclass with all required fields
|
||||
- [ ] Business rule validation for job configuration
|
||||
- [ ] Cron expression validation for frequency
|
||||
- [ ] Symbol list validation
|
||||
- [ ] JSON serialization for database storage
|
||||
- **Blocking Issues**: None
|
||||
- **Next Actions**: Create new entity file
|
||||
|
||||
### Phase 2: Data Access (0% Complete)
|
||||
|
||||
#### ⏳ T004: Enhance NewsRepository - Vector and Job Operations
|
||||
- **Status**: Not Started
|
||||
- **Priority**: Critical
|
||||
- **Estimated**: 2-3 hours
|
||||
- **Dependencies**: T002, T003
|
||||
- **Progress**: 0%
|
||||
- **Acceptance Criteria**: 0/5 completed
|
||||
- [ ] Vector similarity search with cosine distance
|
||||
- [ ] Batch embedding update operations
|
||||
- [ ] NewsJobConfig CRUD methods
|
||||
- [ ] Optimized query performance for vector operations
|
||||
- [ ] Proper async connection handling
|
||||
- **Blocking Issues**: Waiting for T002, T003
|
||||
- **Next Actions**: Extend NewsRepository class
|
||||
|
||||
### Phase 3: LLM Integration (0% Complete)
|
||||
|
||||
#### ⏳ T005: OpenRouter Client - Sentiment Analysis
|
||||
- **Status**: Not Started
|
||||
- **Priority**: Critical
|
||||
- **Estimated**: 2-3 hours
|
||||
- **Dependencies**: T002
|
||||
- **Progress**: 0%
|
||||
- **Acceptance Criteria**: 0/5 completed
|
||||
- [ ] OpenRouter API integration for sentiment analysis
|
||||
- [ ] Structured prompts for financial news sentiment
|
||||
- [ ] Response parsing with Pydantic models
|
||||
- [ ] Error handling with graceful fallbacks
|
||||
- [ ] Retry logic with exponential backoff
|
||||
- **Blocking Issues**: Waiting for T002
|
||||
- **Next Actions**: Create OpenRouter sentiment client
|
||||
|
||||
#### ⏳ T006: OpenRouter Client - Vector Embeddings
|
||||
- **Status**: Not Started
|
||||
- **Priority**: Critical
|
||||
- **Estimated**: 1-2 hours
|
||||
- **Dependencies**: T002
|
||||
- **Progress**: 0%
|
||||
- **Acceptance Criteria**: 0/5 completed
|
||||
- [ ] OpenRouter embeddings API integration
|
||||
- [ ] Text preprocessing for embedding generation
|
||||
- [ ] Batch processing for multiple articles
|
||||
- [ ] 1536-dimensional vector validation
|
||||
- [ ] Proper error handling and retries
|
||||
- **Blocking Issues**: Waiting for T002
|
||||
- **Next Actions**: Create OpenRouter embeddings client
|
||||
|
||||
#### ⏳ T007: Enhance NewsService - LLM Integration
|
||||
- **Status**: Not Started
|
||||
- **Priority**: Critical
|
||||
- **Estimated**: 2-3 hours
|
||||
- **Dependencies**: T005, T006
|
||||
- **Progress**: 0%
|
||||
- **Acceptance Criteria**: 0/5 completed
|
||||
- [ ] Replace keyword sentiment with LLM analysis
|
||||
- [ ] Add embedding generation to article processing
|
||||
- [ ] End-to-end article processing pipeline
|
||||
- [ ] Proper error handling and fallback strategies
|
||||
- [ ] Integration with existing service methods
|
||||
- **Blocking Issues**: Waiting for T005, T006
|
||||
- **Next Actions**: Integrate LLM clients into NewsService
|
||||
|
||||
### Phase 4: Scheduling (0% Complete)
|
||||
|
||||
#### ⏳ T008: APScheduler Integration - Job Scheduling
|
||||
- **Status**: Not Started
|
||||
- **Priority**: High
|
||||
- **Estimated**: 3-4 hours
|
||||
- **Dependencies**: T003, T004, T007
|
||||
- **Progress**: 0%
|
||||
- **Acceptance Criteria**: 0/5 completed
|
||||
- [ ] APScheduler setup with PostgreSQL job store
|
||||
- [ ] Scheduled job execution with proper error handling
|
||||
- [ ] Job configuration loading and validation
|
||||
- [ ] Status monitoring and failure recovery
|
||||
- [ ] CLI integration for job management
|
||||
- **Blocking Issues**: Waiting for T003, T004, T007
|
||||
- **Next Actions**: Implement ScheduledNewsCollector
|
||||
|
||||
#### ⏳ T009: CLI Integration - Job Management Commands
|
||||
- **Status**: Not Started
|
||||
- **Priority**: Medium
|
||||
- **Estimated**: 1-2 hours
|
||||
- **Dependencies**: T008
|
||||
- **Progress**: 0%
|
||||
- **Acceptance Criteria**: 0/5 completed
|
||||
- [ ] CLI commands for job creation/management
|
||||
- [ ] Manual job execution commands
|
||||
- [ ] Job status and monitoring commands
|
||||
- [ ] Integration with existing CLI structure
|
||||
- [ ] Proper error handling and user feedback
|
||||
- **Blocking Issues**: Waiting for T008
|
||||
- **Next Actions**: Extend CLI with news job commands
|
||||
|
||||
### Phase 5: Validation (0% Complete)
|
||||
|
||||
#### ⏳ T010: Integration Tests - End-to-End Workflow
|
||||
- **Status**: Not Started
|
||||
- **Priority**: High
|
||||
- **Estimated**: 2-3 hours
|
||||
- **Dependencies**: T007, T008
|
||||
- **Progress**: 0%
|
||||
- **Acceptance Criteria**: 0/5 completed
|
||||
- [ ] End-to-end workflow tests from RSS to vector storage
|
||||
- [ ] Agent integration tests via AgentToolkit
|
||||
- [ ] Performance tests for daily collection volumes
|
||||
- [ ] Error recovery and fallback tests
|
||||
- [ ] Test coverage maintained above 85%
|
||||
- **Blocking Issues**: Waiting for T007, T008
|
||||
- **Next Actions**: Create comprehensive integration test suite
|
||||
|
||||
#### ⏳ T011: Documentation and Monitoring
|
||||
- **Status**: Not Started
|
||||
- **Priority**: Medium
|
||||
- **Estimated**: 1-2 hours
|
||||
- **Dependencies**: T010
|
||||
- **Progress**: 0%
|
||||
- **Acceptance Criteria**: 0/5 completed
|
||||
- [ ] Updated API documentation for new methods
|
||||
- [ ] Job scheduling configuration examples
|
||||
- [ ] Performance monitoring dashboard queries
|
||||
- [ ] Troubleshooting guide for common issues
|
||||
- [ ] Agent integration documentation
|
||||
- **Blocking Issues**: Waiting for T010
|
||||
- **Next Actions**: Update documentation and monitoring
|
||||
|
||||
---
|
||||
|
||||
## Success Criteria Validation
|
||||
|
||||
### Technical Requirements Status
|
||||
- [ ] **OpenRouter-only LLM Integration**: Not started
|
||||
- [ ] **Vector Embeddings with pgvectorscale**: Not started
|
||||
- [ ] **APScheduler Job Execution**: Not started
|
||||
- [ ] **Test Coverage >85%**: Baseline established (needs monitoring)
|
||||
- [ ] **Query Performance <100ms**: Not tested
|
||||
- [ ] **Vector Search Performance <1s**: Not tested
|
||||
- [ ] **Backward Compatibility**: Not validated
|
||||
|
||||
### Functional Requirements Status
|
||||
- [ ] **Sentiment Analysis Pipeline**: Not implemented
|
||||
- [ ] **Embedding Generation Pipeline**: Not implemented
|
||||
- [ ] **Scheduled News Collection**: Not implemented
|
||||
- [ ] **CLI Job Management**: Not implemented
|
||||
- [ ] **AgentToolkit Integration**: Not validated
|
||||
- [ ] **Error Handling & Fallbacks**: Not implemented
|
||||
|
||||
### Quality Requirements Status
|
||||
- [ ] **TDD Implementation**: Process defined, not applied
|
||||
- [ ] **Layered Architecture**: Pattern defined, not validated
|
||||
- [ ] **Async Connection Pooling**: Not implemented
|
||||
- [ ] **Production Monitoring**: Not implemented
|
||||
- [ ] **Documentation Completeness**: Not updated
|
||||
|
||||
---
|
||||
|
||||
## Current Blocking Issues
|
||||
|
||||
### Critical Blockers
|
||||
**None currently** - All dependencies are internal to this implementation
|
||||
|
||||
### Potential Risk Areas
|
||||
1. **OpenRouter API Access**: Requires valid API keys and model access
|
||||
2. **Database Migration**: Need proper PostgreSQL permissions for schema changes
|
||||
3. **Vector Extension**: pgvectorscale must be properly installed and configured
|
||||
4. **Performance Testing**: Need realistic data volumes for benchmark validation
|
||||
|
||||
---
|
||||
|
||||
## Weekly Progress Targets
|
||||
|
||||
### Week 1 Target (Days 1-2)
|
||||
- **Goal**: Complete Phase 1 & 2 (Foundation + Data Access)
|
||||
- **Expected Completion**: T001, T002, T003, T004
|
||||
- **Target Progress**: 45% overall completion
|
||||
|
||||
### Week 1 Target (Days 3-4)
|
||||
- **Goal**: Complete Phase 3 & 4 (LLM Integration + Scheduling)
|
||||
- **Expected Completion**: T005, T006, T007, T008, T009
|
||||
- **Target Progress**: 90% overall completion
|
||||
|
||||
### Week 2 Target (Day 1)
|
||||
- **Goal**: Complete Phase 5 (Validation)
|
||||
- **Expected Completion**: T010, T011
|
||||
- **Target Progress**: 100% overall completion
|
||||
|
||||
---
|
||||
|
||||
## Metrics Dashboard
|
||||
|
||||
### Code Coverage
|
||||
- **Current**: 95% (existing infrastructure)
|
||||
- **Target**: >85% (including new functionality)
|
||||
- **Status**: ⏳ Pending implementation
|
||||
|
||||
### Performance Benchmarks
|
||||
- **Query Performance**: Not measured (Target: <100ms)
|
||||
- **Vector Search**: Not measured (Target: <1s)
|
||||
- **Batch Processing**: Not measured (Target: TBD)
|
||||
- **Status**: ⏳ Pending implementation
|
||||
|
||||
### Test Execution
|
||||
- **Unit Tests**: 0/11 tasks have tests
|
||||
- **Integration Tests**: 0/11 tasks have integration tests
|
||||
- **VCR Tests**: 0/3 API clients have VCR tests
|
||||
- **Status**: ⏳ Pending implementation
|
||||
|
||||
---
|
||||
|
||||
## Communication & Reporting
|
||||
|
||||
### Daily Standup Format
|
||||
```
|
||||
Yesterday: [Tasks completed with IDs]
|
||||
Today: [Tasks planned with IDs]
|
||||
Blockers: [Any issues requiring attention]
|
||||
Help Needed: [Specific areas for collaboration]
|
||||
```
|
||||
|
||||
### Weekly Status Report Format
|
||||
```
|
||||
Completed: [Phase progress with task counts]
|
||||
In Progress: [Current focus areas]
|
||||
Upcoming: [Next phase priorities]
|
||||
Risks: [Technical or timeline concerns]
|
||||
Metrics: [Coverage, performance, test results]
|
||||
```
|
||||
|
||||
### Milestone Checkpoints
|
||||
- **Checkpoint 1** (End of Day 2): Foundation Complete (T001-T004)
|
||||
- **Checkpoint 2** (End of Day 4): LLM Integration Complete (T005-T009)
|
||||
- **Checkpoint 3** (End of Day 5): Full Implementation Complete (T001-T011)
|
||||
|
||||
---
|
||||
|
||||
## Notes
|
||||
|
||||
### Implementation Context
|
||||
- Building on 95% complete news domain infrastructure
|
||||
- Focus on OpenRouter-only LLM integration (no other providers)
|
||||
- Maintaining backward compatibility with AgentToolkit
|
||||
- Following established TDD and layered architecture patterns
|
||||
|
||||
### Key Success Factors
|
||||
1. **Incremental Progress**: Validate each layer before proceeding
|
||||
2. **Comprehensive Testing**: Maintain test coverage throughout
|
||||
3. **Performance Monitoring**: Validate benchmarks at each step
|
||||
4. **Error Resilience**: Implement fallbacks for all LLM dependencies
|
||||
5. **Documentation**: Keep implementation and usage docs current
|
||||
|
||||
### Last Updated
|
||||
**Date**: 2024-08-30
|
||||
**By**: System
|
||||
**Next Review**: Daily during implementation
|
||||
|
||||
---
|
||||
|
||||
*This status document will be updated as implementation progresses. Use this as a single source of truth for current progress and blocking issues.*
|
||||
1→# News Domain Completion - Implementation Status
|
||||
2→
|
||||
3→**Last Updated**: 2025-01-11
|
||||
4→**Overall Progress**: 6.67% (1/15 tasks completed)
|
||||
5→**Architecture**: Dagster orchestration + OpenRouter LLM + RAG vector search
|
||||
6→
|
||||
7→---
|
||||
8→
|
||||
9→## Current Phase
|
||||
10→
|
||||
11→**Phase 1: Entity Layer**
|
||||
12→Status: In Progress
|
||||
13→Progress: 50% (1/2 tasks completed)
|
||||
14→Estimated Time Remaining: 1-2 hours
|
||||
15→
|
||||
16→---
|
||||
17→
|
||||
18→## Task Status Summary
|
||||
19→
|
||||
20→### Phase 1: Entity Layer (1/2 completed)
|
||||
21→
|
||||
22→| Task | Status | Priority | Time | Assigned | Completion | Completed At |
|
||||
23→|------|--------|----------|------|----------|------------|--------------|
|
||||
24→| T001: Enhance NewsArticle Dataclass | ✅ Completed | Critical | 1-2h | - | 100% | 2025-01-11 |
|
||||
25→| T002: Database Migration - Sentiment Fields | ⬜ Not Started | Critical | 1h | - | 0% | - |
|
||||
26→
|
||||
27→### Phase 2: Repository Layer (0/2 completed)
|
||||
28→
|
||||
29→| Task | Status | Priority | Time | Assigned | Completion |
|
||||
30→|------|--------|----------|------|----------|------------|
|
||||
31→| T003: NewsRepository - Vector Similarity Search | ⬜ Not Started | Critical | 2-3h | - | 0% |
|
||||
32→| T004: NewsRepository - Batch Embedding Updates | ⬜ Not Started | Medium | 1h | - | 0% |
|
||||
33→
|
||||
34→### Phase 3: LLM Integration (0/3 completed)
|
||||
35→
|
||||
36→| Task | Status | Priority | Time | Assigned | Completion |
|
||||
37→|------|--------|----------|------|----------|------------|
|
||||
38→| T005: OpenRouter Sentiment Client | ⬜ Not Started | Critical | 2-3h | - | 0% |
|
||||
39→| T006: OpenRouter Embeddings Client | ⬜ Not Started | Critical | 1-2h | - | 0% |
|
||||
40→| T007: Enhance NewsService - LLM Integration | ⬜ Not Started | Critical | 2-3h | - | 0% |
|
||||
41→
|
||||
42→### Phase 4: Dagster Orchestration (0/5 completed)
|
||||
43→
|
||||
44→| Task | Status | Priority | Time | Assigned | Completion |
|
||||
45→|------|--------|----------|------|----------|------------|
|
||||
46→| T008: Dagster Directory Structure | ⬜ Not Started | High | 30min | - | 0% |
|
||||
47→| T009: Dagster Ops - News Collection | ⬜ Not Started | High | 2-3h | - | 0% |
|
||||
48→| T010: Dagster Job - Daily News Collection | ⬜ Not Started | High | 1-2h | - | 0% |
|
||||
49→| T011: Dagster Schedule - Daily Trigger | ⬜ Not Started | High | 1h | - | 0% |
|
||||
50→| T012: Dagster Sensor - Failure Alerting | ⬜ Not Started | Medium | 1h | - | 0% |
|
||||
51→
|
||||
52→### Phase 5: Testing & Documentation (0/3 completed)
|
||||
53→
|
||||
54→| Task | Status | Priority | Time | Assigned | Completion |
|
||||
55→|------|--------|----------|------|----------|------------|
|
||||
56→| T013: Integration Tests - End-to-End Workflow | ⬜ Not Started | High | 2-3h | - | 0% |
|
||||
57→| T014: Dagster Tests | ⬜ Not Started | Medium | 1h | - | 0% |
|
||||
58→| T015: Documentation Updates | ⬜ Not Started | Medium | 1-2h | - | 0% |
|
||||
59→
|
||||
60→---
|
||||
61→
|
||||
62→## Dependency Graph
|
||||
63→
|
||||
64→```
|
||||
65→T001 ─┬─→ T002 ──→ T003 ─────────→ T007 ──→ T009 ──→ T010 ──→ T013
|
||||
66→ │ ↑ ↑ ↑ ↑
|
||||
67→ │ │ │ │ │
|
||||
68→ └──→ T005 ────────────────────┘ │ │ │
|
||||
69→ T006 ──────────────────────────────┘ │ │
|
||||
70→ T008 ──────────────────────────────────────┘ │
|
||||
71→ T011 ───────────────────────────────────────────────┘
|
||||
72→ T014 ───────────────────────────────────────────────┘
|
||||
73→```
|
||||
74→
|
||||
75→**Critical Path**: T001 → T002 → T003 → T007 → T009 → T010 → T013
|
||||
76→
|
||||
77→**Parallel Opportunities**:
|
||||
78→- T005 & T006 can be developed in parallel (LLM clients)
|
||||
79→- T009, T010, T011 can be developed in parallel after T008 (Dagster components)
|
||||
80→
|
||||
81→---
|
||||
82→
|
||||
83→## Progress by Phase
|
||||
84→
|
||||
85→### Phase 1: Entity Layer
|
||||
86→- **Status**: In Progress
|
||||
87→- **Progress**: 50% (1/2 tasks)
|
||||
88→- **Estimated Time**: 1-2 hours
|
||||
89→- **Blockers**: None
|
||||
90→- **Next Action**: Start T002 - Database Migration for Sentiment Fields
|
||||
91→
|
||||
92→### Phase 2: Repository Layer
|
||||
93→- **Status**: Not Started
|
||||
94→- **Progress**: 0% (0/2 tasks)
|
||||
95→- **Estimated Time**: 2-3 hours
|
||||
96→- **Blockers**: T001, T002 must complete first
|
||||
97→- **Next Action**: Waiting for Phase 1 completion
|
||||
98→
|
||||
99→### Phase 3: LLM Integration
|
||||
100→- **Status**: Not Started
|
||||
101→- **Progress**: 0% (0/3 tasks)
|
||||
102→- **Estimated Time**: 4-5 hours
|
||||
103→- **Blockers**: T001 must complete for client development
|
||||
104→- **Next Action**: Can start T005 & T006 in parallel after T001
|
||||
105→
|
||||
106→### Phase 4: Dagster Orchestration
|
||||
107→- **Status**: Not Started
|
||||
108→- **Progress**: 0% (0/5 tasks)
|
||||
109→- **Estimated Time**: 3-4 hours
|
||||
110→- **Blockers**: T007 must complete for ops/jobs, T008 has no dependencies
|
||||
111→- **Next Action**: Can start T008 anytime (directory structure)
|
||||
112→
|
||||
113→### Phase 5: Testing & Documentation
|
||||
114→- **Status**: Not Started
|
||||
115→- **Progress**: 0% (0/3 tasks)
|
||||
116→- **Estimated Time**: 2-3 hours
|
||||
117→- **Blockers**: T007, T010 must complete for integration testing
|
||||
118→- **Next Action**: Waiting for earlier phases
|
||||
119→
|
||||
120→---
|
||||
121→
|
||||
122→## Test Coverage Status
|
||||
123→
|
||||
124→**Current Coverage**: Baseline (from 95% complete infrastructure)
|
||||
125→**Target Coverage**: ≥85%
|
||||
126→**New Code Coverage**: 0% (no new code yet)
|
||||
127→
|
||||
128→### Coverage by Component
|
||||
129→
|
||||
130→| Component | Coverage | Target | Status |
|
||||
131→|-----------|----------|--------|--------|
|
||||
132→| NewsArticle (Entity) | - | ≥85% | ⬜ Pending |
|
||||
133→| NewsRepository (RAG) | - | ≥85% | ⬜ Pending |
|
||||
134→| OpenRouter Sentiment Client | - | ≥85% | ⬜ Pending |
|
||||
135→| OpenRouter Embeddings Client | - | ≥85% | ⬜ Pending |
|
||||
136→| NewsService (LLM Integration) | - | ≥85% | ⬜ Pending |
|
||||
137→| Dagster Ops | - | ≥85% | ⬜ Pending |
|
||||
138→| Dagster Jobs | - | ≥85% | ⬜ Pending |
|
||||
139→
|
||||
140→---
|
||||
141→
|
||||
142→## Performance Benchmarks
|
||||
143→
|
||||
144→### Current Performance
|
||||
145→- **Query Time (30-day lookback)**: Not measured yet
|
||||
146→- **Vector Search (top-10)**: Not measured yet
|
||||
147→- **Batch Insert (50 articles)**: Not measured yet
|
||||
148→
|
||||
149→### Target Performance
|
||||
150→- **Query Time**: < 2 seconds for 30-day lookback
|
||||
151→- **Vector Search**: < 1 second for top-10 results
|
||||
152→- **Batch Insert**: < 5 seconds for 50 articles
|
||||
153→
|
||||
154→### Performance Test Status
|
||||
155→- [ ] Query performance baseline established
|
||||
156→- [ ] Vector search performance baseline established
|
||||
157→- [ ] Batch insert performance baseline established
|
||||
158→- [ ] All performance targets met
|
||||
159→
|
||||
160→---
|
||||
161→
|
||||
162→## Risk Assessment
|
||||
163→
|
||||
164→### High Risk Items
|
||||
165→1. **OpenRouter API Availability** - Mitigated with fallback strategies (keyword sentiment, zero vectors)
|
||||
166→2. **Vector Search Performance** - Mitigated with proper pgvectorscale indexes
|
||||
167→3. **Dagster Integration Complexity** - Mitigated with incremental testing approach
|
||||
168→
|
||||
169→### Medium Risk Items
|
||||
170→1. **LLM API Costs** - Monitor usage during development
|
||||
171→2. **Database Performance at Scale** - Test with realistic data volumes
|
||||
172→3. **Test Coverage Maintenance** - Enforce ≥85% coverage requirement
|
||||
173→
|
||||
174→### Low Risk Items
|
||||
175→1. **Code Quality** - Enforced through TDD approach
|
||||
176→2. **Documentation** - Tracked as explicit task (T015)
|
||||
177→3. **Error Handling** - Comprehensive fallback strategies
|
||||
178→
|
||||
179→---
|
||||
180→
|
||||
181→## Known Issues
|
||||
182→
|
||||
183→### Blocking Issues
|
||||
184→None currently
|
||||
185→
|
||||
186→### Non-Blocking Issues
|
||||
187→None currently
|
||||
188→
|
||||
189→### Technical Debt
|
||||
190→- Existing keyword-based sentiment analysis should be replaced with LLM sentiment (tracked as T005)
|
||||
191→- No automated vector embedding generation currently (tracked as T006)
|
||||
192→- No scheduled news collection (tracked as T008-T012)
|
||||
193→
|
||||
194→---
|
||||
195→
|
||||
196→## Milestone Schedule
|
||||
197→
|
||||
198→### Milestone 1: Entity & Repository Foundation
|
||||
199→**Target**: Day 1-2
|
||||
200→**Tasks**: T001, T002, T003, T004
|
||||
201→**Status**: In Progress
|
||||
202→**Deliverables**:
|
||||
203→- NewsArticle dataclass with sentiment fields
|
||||
204→- Database migration for sentiment columns
|
||||
205→- RAG vector similarity search functional
|
||||
206→- Batch embedding updates operational
|
||||
207→
|
||||
208→### Milestone 2: LLM Integration
|
||||
209→**Target**: Day 2-3
|
||||
210→**Tasks**: T005, T006, T007
|
||||
211→**Status**: Not Started
|
||||
212→**Deliverables**:
|
||||
213→- OpenRouter sentiment client operational with fallbacks
|
||||
214→- OpenRouter embeddings client operational with fallbacks
|
||||
215→- NewsService enrichment pipeline functional
|
||||
216→- find_similar_news() RAG method operational
|
||||
217→
|
||||
218→### Milestone 3: Dagster Orchestration
|
||||
219→**Target**: Day 3-4
|
||||
220→**Tasks**: T008, T009, T010, T011, T012
|
||||
221→**Status**: Not Started
|
||||
222→**Deliverables**:
|
||||
223→- Dagster directory structure created
|
||||
224→- News collection op functional
|
||||
225→- Daily collection job operational
|
||||
226→- Schedule configured for 6 AM UTC
|
||||
227→- Failure sensor monitoring job
|
||||
228→
|
||||
229→### Milestone 4: Testing & Documentation
|
||||
230→**Target**: Day 4-5
|
||||
231→**Tasks**: T013, T014, T015
|
||||
232→**Status**: Not Started
|
||||
233→**Deliverables**:
|
||||
234→- End-to-end integration tests passing
|
||||
235→- Dagster component tests passing
|
||||
236→- Performance benchmarks met
|
||||
237→- Documentation updated
|
||||
238→
|
||||
239→---
|
||||
240→
|
||||
241→## Next Actions
|
||||
242→
|
||||
243→### Immediate Next Steps (Today)
|
||||
244→1. **T002**: Start database migration for sentiment fields
|
||||
245→2. **T008**: Create Dagster directory structure in parallel (no dependencies)
|
||||
246→
|
||||
247→### This Week
|
||||
248→1. Complete Phase 1 (Entity Layer)
|
||||
249→2. Start Phase 2 (Repository Layer)
|
||||
250→3. Begin Phase 3 (LLM Integration) in parallel
|
||||
251→
|
||||
252→### Next Week
|
||||
253→1. Complete Phase 3 & 4 (LLM + Dagster)
|
||||
254→2. Complete Phase 5 (Testing & Documentation)
|
||||
255→3. Deploy and monitor Dagster schedules
|
||||
256→
|
||||
257→---
|
||||
258→
|
||||
259→## Team Notes
|
||||
260→
|
||||
261→### Development Environment
|
||||
262→- PostgreSQL + TimescaleDB + pgvectorscale running locally
|
||||
263→- OpenRouter API key configured
|
||||
264→- Dagster installation complete
|
||||
265→- Python 3.13 with mise/uv
|
||||
266→
|
||||
267→### Communication
|
||||
268→- Spec documents updated to reflect Dagster architecture (spec-lite.md, design.md, tasks.md)
|
||||
269→- APScheduler references removed from all specs
|
||||
270→- Architecture aligned with project roadmap
|
||||
271→
|
||||
272→### Resources Needed
|
||||
273→- OpenRouter API access for development/testing
|
||||
274→- Test database with sample news articles
|
||||
275→- Dagster UI for monitoring during development
|
||||
276→
|
||||
277→---
|
||||
278→
|
||||
279→## Success Criteria Checklist
|
||||
280→
|
||||
281→**Technical Success**:
|
||||
282→- [ ] Test coverage ≥85% maintained
|
||||
283→- [ ] Query performance <2s for 30-day lookback
|
||||
284→- [ ] Vector search <1s for top-10 results
|
||||
285→- [ ] Zero breaking changes to AgentToolkit
|
||||
286→- [ ] Dagster jobs execute successfully
|
||||
287→
|
||||
288→**Functional Success**:
|
||||
289→- [ ] OpenRouter sentiment analysis operational
|
||||
290→- [ ] Vector embeddings enable semantic search
|
||||
291→- [ ] Dagster schedules running daily
|
||||
292→- [ ] Agent context enriched with sentiment
|
||||
293→
|
||||
294→**Quality Success**:
|
||||
295→- [x] 1/15 tasks completed
|
||||
296→- [ ] All acceptance criteria met
|
||||
297→- [ ] Comprehensive error handling
|
||||
298→- [ ] Production-ready monitoring
|
||||
299→- [ ] Complete documentation
|
||||
300→
|
||||
301→---
|
||||
302→
|
||||
303→**Status Key**:
|
||||
304→- ⬜ Not Started
|
||||
305→- 🔄 In Progress
|
||||
306→- ✅ Completed
|
||||
307→- 🚫 Blocked
|
||||
308→- ⚠️ At Risk
|
||||
309→
|
||||
310→**Last Status Update**: 2025-01-11 - T001 completed, updated progress tracking
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -42,6 +42,8 @@ dependencies = [
|
|||
"alembic>=1.13.0",
|
||||
"pgvector>=0.4.1",
|
||||
"uuid-utils>=0.11.0",
|
||||
"dagster>=1.8.0",
|
||||
"dagster-postgres>=0.24.0",
|
||||
]
|
||||
|
||||
[project.optional-dependencies]
|
||||
|
|
@ -60,6 +62,10 @@ tradingagents = "cli.main:app"
|
|||
requires = ["setuptools>=61.0", "wheel"]
|
||||
build-backend = "setuptools.build_meta"
|
||||
|
||||
[tool.setuptools.packages.find]
|
||||
include = ["tradingagents*", "cli*"]
|
||||
exclude = ["docker*", "assets*", "alembic*", "docs*", "tests*"]
|
||||
|
||||
[tool.ruff]
|
||||
line-length = 88
|
||||
target-version = "py310"
|
||||
|
|
|
|||
|
|
@ -388,6 +388,172 @@ class TestNewsRepository:
|
|||
assert result == []
|
||||
|
||||
|
||||
class TestNewsArticleSentimentFields:
|
||||
"""Test suite for new sentiment fields in NewsArticle."""
|
||||
|
||||
def test_news_article_with_sentiment_fields(self):
|
||||
"""Test dataclass instantiation with new sentiment fields."""
|
||||
# Arrange & Act
|
||||
article = NewsArticle(
|
||||
headline="Test Article",
|
||||
url="https://example.com/test",
|
||||
source="Test Source",
|
||||
published_date=date(2024, 1, 15),
|
||||
sentiment_score=0.8,
|
||||
sentiment_confidence=0.95,
|
||||
sentiment_label="positive",
|
||||
)
|
||||
|
||||
# Assert
|
||||
assert article.sentiment_score == 0.8
|
||||
assert article.sentiment_confidence == 0.95
|
||||
assert article.sentiment_label == "positive"
|
||||
|
||||
async def test_news_article_to_entity_includes_sentiment_fields(
|
||||
self, test_db_manager
|
||||
):
|
||||
"""Test to_entity() maps new sentiment fields correctly."""
|
||||
# Arrange
|
||||
article = NewsArticle(
|
||||
headline="Test Article",
|
||||
url="https://example.com/test",
|
||||
source="Test Source",
|
||||
published_date=date(2024, 1, 15),
|
||||
sentiment_score=0.75,
|
||||
sentiment_confidence=0.88,
|
||||
sentiment_label="positive",
|
||||
)
|
||||
|
||||
# Act
|
||||
entity = article.to_entity(symbol="TEST")
|
||||
|
||||
# Assert
|
||||
assert entity.sentiment_score == 0.75
|
||||
assert entity.sentiment_confidence == 0.88
|
||||
assert entity.sentiment_label == "positive"
|
||||
|
||||
async def test_news_article_from_entity_includes_sentiment_fields(self, repository):
|
||||
"""Test from_entity() populates new sentiment fields correctly."""
|
||||
# Arrange - Create an article with sentiment fields
|
||||
article = NewsArticle(
|
||||
headline="Test Article",
|
||||
url="https://example.com/test-from-entity",
|
||||
source="Test Source",
|
||||
published_date=date(2024, 1, 15),
|
||||
sentiment_score=0.65,
|
||||
sentiment_confidence=0.92,
|
||||
sentiment_label="negative",
|
||||
)
|
||||
|
||||
# Act - Store and retrieve
|
||||
await repository.upsert(article, symbol="TEST")
|
||||
retrieved_articles = await repository.list("TEST", date(2024, 1, 15))
|
||||
|
||||
# Assert
|
||||
assert len(retrieved_articles) == 1
|
||||
retrieved = retrieved_articles[0]
|
||||
assert retrieved.sentiment_score == 0.65
|
||||
assert retrieved.sentiment_confidence == 0.92
|
||||
assert retrieved.sentiment_label == "negative"
|
||||
|
||||
def test_has_reliable_sentiment_with_valid_confidence(self):
|
||||
"""Test has_reliable_sentiment() returns True when confidence >= 0.6."""
|
||||
# Arrange
|
||||
article = NewsArticle(
|
||||
headline="Test Article",
|
||||
url="https://example.com/test",
|
||||
source="Test Source",
|
||||
published_date=date(2024, 1, 15),
|
||||
sentiment_score=0.8,
|
||||
sentiment_confidence=0.6, # Exactly at threshold
|
||||
)
|
||||
|
||||
# Act & Assert
|
||||
assert article.has_reliable_sentiment() is True
|
||||
|
||||
# Test with higher confidence
|
||||
article.sentiment_confidence = 0.95
|
||||
assert article.has_reliable_sentiment() is True
|
||||
|
||||
def test_has_reliable_sentiment_with_low_confidence(self):
|
||||
"""Test has_reliable_sentiment() returns False when confidence < 0.6."""
|
||||
# Arrange
|
||||
article = NewsArticle(
|
||||
headline="Test Article",
|
||||
url="https://example.com/test",
|
||||
source="Test Source",
|
||||
published_date=date(2024, 1, 15),
|
||||
sentiment_score=0.8,
|
||||
sentiment_confidence=0.59, # Just below threshold
|
||||
)
|
||||
|
||||
# Act & Assert
|
||||
assert article.has_reliable_sentiment() is False
|
||||
|
||||
# Test with very low confidence
|
||||
article.sentiment_confidence = 0.1
|
||||
assert article.has_reliable_sentiment() is False
|
||||
|
||||
def test_has_reliable_sentiment_with_none_values(self):
|
||||
"""Test has_reliable_sentiment() returns False when fields are None."""
|
||||
# Arrange - Article with no sentiment data
|
||||
article = NewsArticle(
|
||||
headline="Test Article",
|
||||
url="https://example.com/test",
|
||||
source="Test Source",
|
||||
published_date=date(2024, 1, 15),
|
||||
)
|
||||
|
||||
# Act & Assert
|
||||
assert article.has_reliable_sentiment() is False
|
||||
|
||||
# Test with only sentiment_score
|
||||
article.sentiment_score = 0.8
|
||||
assert article.has_reliable_sentiment() is False
|
||||
|
||||
# Test with only sentiment_confidence
|
||||
article.sentiment_score = None
|
||||
article.sentiment_confidence = 0.9
|
||||
assert article.has_reliable_sentiment() is False
|
||||
|
||||
async def test_news_article_roundtrip_conversion(self, repository):
|
||||
"""Test to_entity() → from_entity() preserves all fields including new sentiment fields."""
|
||||
# Arrange - Create article with all fields including new sentiment fields
|
||||
original = NewsArticle(
|
||||
headline="Roundtrip Test Article",
|
||||
url="https://example.com/roundtrip-test",
|
||||
source="Test Source",
|
||||
published_date=date(2024, 1, 15),
|
||||
summary="Test summary",
|
||||
entities=["Entity1", "Entity2"],
|
||||
sentiment_score=0.72,
|
||||
sentiment_confidence=0.87,
|
||||
sentiment_label="neutral",
|
||||
author="Test Author",
|
||||
category="test-category",
|
||||
)
|
||||
|
||||
# Act - Store and retrieve (full roundtrip)
|
||||
await repository.upsert(original, symbol="TEST")
|
||||
retrieved_articles = await repository.list("TEST", date(2024, 1, 15))
|
||||
|
||||
# Assert - All fields preserved
|
||||
assert len(retrieved_articles) == 1
|
||||
retrieved = retrieved_articles[0]
|
||||
|
||||
assert retrieved.headline == original.headline
|
||||
assert retrieved.url == original.url
|
||||
assert retrieved.source == original.source
|
||||
assert retrieved.published_date == original.published_date
|
||||
assert retrieved.summary == original.summary
|
||||
assert retrieved.entities == original.entities
|
||||
assert retrieved.sentiment_score == original.sentiment_score
|
||||
assert retrieved.sentiment_confidence == original.sentiment_confidence
|
||||
assert retrieved.sentiment_label == original.sentiment_label
|
||||
assert retrieved.author == original.author
|
||||
assert retrieved.category == original.category
|
||||
|
||||
|
||||
class TestDatabaseConnectionManagement:
|
||||
"""Test database connection and session management."""
|
||||
|
||||
|
|
|
|||
|
|
@ -46,6 +46,13 @@ class TradingAgentsConfig:
|
|||
default_lookback_days: int = 30
|
||||
default_ta_lookback_days: int = 30
|
||||
|
||||
# Database settings
|
||||
database_url: str = field(
|
||||
default_factory=lambda: os.getenv(
|
||||
"DATABASE_URL", "postgresql://localhost:5432/tradingagents"
|
||||
)
|
||||
)
|
||||
|
||||
def __post_init__(self):
|
||||
"""Set computed fields after initialization."""
|
||||
self.data_cache_dir = os.path.join(self.project_dir, "dataflows/data_cache")
|
||||
|
|
@ -85,6 +92,9 @@ class TradingAgentsConfig:
|
|||
online_tools=os.getenv("ONLINE_TOOLS", "true").lower() == "true",
|
||||
default_lookback_days=int(os.getenv("DEFAULT_LOOKBACK_DAYS", "30")),
|
||||
default_ta_lookback_days=int(os.getenv("DEFAULT_TA_LOOKBACK_DAYS", "30")),
|
||||
database_url=os.getenv(
|
||||
"DATABASE_URL", "postgresql://localhost:5432/tradingagents"
|
||||
),
|
||||
)
|
||||
|
||||
def to_dict(self) -> dict:
|
||||
|
|
@ -104,6 +114,7 @@ class TradingAgentsConfig:
|
|||
"online_tools": self.online_tools,
|
||||
"default_lookback_days": self.default_lookback_days,
|
||||
"default_ta_lookback_days": self.default_ta_lookback_days,
|
||||
"database_url": self.database_url,
|
||||
}
|
||||
|
||||
def copy(self) -> "TradingAgentsConfig":
|
||||
|
|
@ -122,6 +133,7 @@ class TradingAgentsConfig:
|
|||
online_tools=self.online_tools,
|
||||
default_lookback_days=self.default_lookback_days,
|
||||
default_ta_lookback_days=self.default_ta_lookback_days,
|
||||
database_url=self.database_url,
|
||||
)
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -46,6 +46,8 @@ class NewsArticle:
|
|||
summary: str | None = None
|
||||
entities: list[str] = field(default_factory=list)
|
||||
sentiment_score: float | None = None
|
||||
sentiment_confidence: float | None = None # New field
|
||||
sentiment_label: str | None = None # New field
|
||||
author: str | None = None
|
||||
category: str | None = None
|
||||
|
||||
|
|
@ -59,6 +61,8 @@ class NewsArticle:
|
|||
summary=self.summary,
|
||||
entities=self.entities if self.entities else None,
|
||||
sentiment_score=self.sentiment_score,
|
||||
sentiment_confidence=self.sentiment_confidence,
|
||||
sentiment_label=self.sentiment_label,
|
||||
author=self.author,
|
||||
category=self.category,
|
||||
symbol=symbol,
|
||||
|
|
@ -77,10 +81,28 @@ class NewsArticle:
|
|||
summary=cast("str | None", entity.summary),
|
||||
entities=cast("list[str] | None", entity.entities) or [],
|
||||
sentiment_score=cast("float | None", entity.sentiment_score),
|
||||
sentiment_confidence=cast("float | None", entity.sentiment_confidence),
|
||||
sentiment_label=cast("str | None", entity.sentiment_label),
|
||||
author=cast("str | None", entity.author),
|
||||
category=cast("str | None", entity.category),
|
||||
)
|
||||
|
||||
def has_reliable_sentiment(self) -> bool:
|
||||
"""
|
||||
Check if the article has reliable sentiment data.
|
||||
|
||||
Returns True when sentiment_score is not None AND sentiment_confidence is not None
|
||||
AND sentiment_confidence >= 0.6
|
||||
|
||||
Returns:
|
||||
bool: True if sentiment data is reliable, False otherwise
|
||||
"""
|
||||
return bool(
|
||||
self.sentiment_score is not None
|
||||
and self.sentiment_confidence is not None
|
||||
and self.sentiment_confidence >= 0.6
|
||||
)
|
||||
|
||||
|
||||
class NewsArticleEntity(Base):
|
||||
"""SQLAlchemy model for news articles with vector embedding support."""
|
||||
|
|
@ -113,6 +135,12 @@ class NewsArticleEntity(Base):
|
|||
JSON, nullable=True
|
||||
) # Store list[str] as JSON array
|
||||
sentiment_score: Mapped[float | None] = mapped_column(Float, nullable=True)
|
||||
sentiment_confidence: Mapped[float | None] = mapped_column(
|
||||
Float, nullable=True
|
||||
) # New field
|
||||
sentiment_label: Mapped[str | None] = mapped_column(
|
||||
String(50), nullable=True
|
||||
) # New field
|
||||
author: Mapped[str | None] = mapped_column(String(255), nullable=True)
|
||||
category: Mapped[str | None] = mapped_column(String(100), nullable=True)
|
||||
|
||||
|
|
@ -227,6 +255,8 @@ class NewsRepository:
|
|||
"summary": article.summary,
|
||||
"entities": article.entities if article.entities else None,
|
||||
"sentiment_score": article.sentiment_score,
|
||||
"sentiment_confidence": article.sentiment_confidence,
|
||||
"sentiment_label": article.sentiment_label,
|
||||
"author": article.author,
|
||||
"category": article.category,
|
||||
"symbol": symbol,
|
||||
|
|
@ -243,6 +273,8 @@ class NewsRepository:
|
|||
"summary": stmt.excluded.summary,
|
||||
"entities": stmt.excluded.entities,
|
||||
"sentiment_score": stmt.excluded.sentiment_score,
|
||||
"sentiment_confidence": stmt.excluded.sentiment_confidence,
|
||||
"sentiment_label": stmt.excluded.sentiment_label,
|
||||
"author": stmt.excluded.author,
|
||||
"category": stmt.excluded.category,
|
||||
"symbol": stmt.excluded.symbol,
|
||||
|
|
@ -370,6 +402,8 @@ class NewsRepository:
|
|||
"summary": article.summary,
|
||||
"entities": article.entities if article.entities else None,
|
||||
"sentiment_score": article.sentiment_score,
|
||||
"sentiment_confidence": article.sentiment_confidence,
|
||||
"sentiment_label": article.sentiment_label,
|
||||
"author": article.author,
|
||||
"category": article.category,
|
||||
"symbol": symbol,
|
||||
|
|
@ -388,6 +422,8 @@ class NewsRepository:
|
|||
"summary": stmt.excluded.summary,
|
||||
"entities": stmt.excluded.entities,
|
||||
"sentiment_score": stmt.excluded.sentiment_score,
|
||||
"sentiment_confidence": stmt.excluded.sentiment_confidence,
|
||||
"sentiment_label": stmt.excluded.sentiment_label,
|
||||
"author": stmt.excluded.author,
|
||||
"category": stmt.excluded.category,
|
||||
"symbol": stmt.excluded.symbol,
|
||||
|
|
|
|||
|
|
@ -156,8 +156,10 @@ class DatabaseManager:
|
|||
|
||||
def create_test_database_manager() -> DatabaseManager:
|
||||
"""Create a test database manager for tests."""
|
||||
# Use a test database URL with credentials
|
||||
test_db_url = "postgresql://postgres:postgres@localhost:5432/tradingagents_test"
|
||||
# Use a test database URL with credentials matching docker setup
|
||||
test_db_url = (
|
||||
"postgresql://postgres:tradingagents@localhost:5432/tradingagents_test"
|
||||
)
|
||||
|
||||
# Create a test-specific database manager with NullPool
|
||||
db_manager = DatabaseManager(test_db_url)
|
||||
|
|
|
|||
256
uv.lock
256
uv.lock
|
|
@ -111,6 +111,15 @@ wheels = [
|
|||
{ url = "https://files.pythonhosted.org/packages/de/b9/6ffb48e82c5e97b03cecee872d134a6b6666c2767b2d32ed709f3a60a8fe/anthropic-0.54.0-py3-none-any.whl", hash = "sha256:c1062a0a905daeec17ca9c06c401e4b3f24cb0495841d29d752568a1d4018d56", size = 288774, upload-time = "2025-06-11T02:46:25.578Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "antlr4-python3-runtime"
|
||||
version = "4.13.2"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/33/5f/2cdf6f7aca3b20d3f316e9f505292e1f256a32089bd702034c29ebde6242/antlr4_python3_runtime-4.13.2.tar.gz", hash = "sha256:909b647e1d2fc2b70180ac586df3933e38919c85f98ccc656a96cd3f25ef3916", size = 117467, upload-time = "2024-08-03T19:00:12.757Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/89/03/a851e84fcbb85214dc637b6378121ef9a0dd61b4c65264675d8a5c9b1ae7/antlr4_python3_runtime-4.13.2-py3-none-any.whl", hash = "sha256:fe3835eb8d33daece0e799090eda89719dbccee7aa39ef94eed3818cafa5a7e8", size = 144462, upload-time = "2024-08-03T19:00:11.134Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "anyio"
|
||||
version = "4.9.0"
|
||||
|
|
@ -456,14 +465,14 @@ wheels = [
|
|||
|
||||
[[package]]
|
||||
name = "coloredlogs"
|
||||
version = "15.0.1"
|
||||
version = "14.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "humanfriendly" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/cc/c7/eed8f27100517e8c0e6b923d5f0845d0cb99763da6fdee00478f91db7325/coloredlogs-15.0.1.tar.gz", hash = "sha256:7c991aa71a4577af2f82600d8f8f3a89f936baeaf9b50a9c197da014e5bf16b0", size = 278520, upload-time = "2021-06-11T10:22:45.202Z" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/84/1b/1ecdd371fa68839cfbda15cc671d0f6c92d2c42688df995a9bf6e36f3511/coloredlogs-14.0.tar.gz", hash = "sha256:a1fab193d2053aa6c0a97608c4342d031f1f93a3d1218432c59322441d31a505", size = 275863, upload-time = "2020-02-16T20:51:12.172Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/a7/06/3d6badcf13db419e25b07041d9c7b4a2c331d3f4e7134445ec5df57714cd/coloredlogs-15.0.1-py2.py3-none-any.whl", hash = "sha256:612ee75c546f53e92e70049c9dbfcc18c935a2b9a53b66085ce9ef6a6e5c0934", size = 46018, upload-time = "2021-06-11T10:22:42.561Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/5c/2f/12747be360d6dea432e7b5dfae3419132cb008535cfe614af73b9ce2643b/coloredlogs-14.0-py2.py3-none-any.whl", hash = "sha256:346f58aad6afd48444c2468618623638dadab76e4e70d5e10822676f2d32226a", size = 43888, upload-time = "2020-02-16T20:51:09.712Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -588,6 +597,85 @@ wheels = [
|
|||
{ url = "https://files.pythonhosted.org/packages/e7/05/c19819d5e3d95294a6f5947fb9b9629efb316b96de511b418c53d245aae6/cycler-0.12.1-py3-none-any.whl", hash = "sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30", size = 8321, upload-time = "2023-10-07T05:32:16.783Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "dagster"
|
||||
version = "1.12.2"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "alembic" },
|
||||
{ name = "antlr4-python3-runtime" },
|
||||
{ name = "click" },
|
||||
{ name = "coloredlogs" },
|
||||
{ name = "dagster-pipes" },
|
||||
{ name = "dagster-shared" },
|
||||
{ name = "docstring-parser" },
|
||||
{ name = "filelock" },
|
||||
{ name = "grpcio" },
|
||||
{ name = "grpcio-health-checking" },
|
||||
{ name = "jinja2" },
|
||||
{ name = "protobuf" },
|
||||
{ name = "psutil", marker = "sys_platform == 'win32'" },
|
||||
{ name = "python-dotenv" },
|
||||
{ name = "pytz" },
|
||||
{ name = "pywin32", marker = "sys_platform == 'win32'" },
|
||||
{ name = "requests" },
|
||||
{ name = "rich" },
|
||||
{ name = "setuptools" },
|
||||
{ name = "six" },
|
||||
{ name = "sqlalchemy" },
|
||||
{ name = "structlog" },
|
||||
{ name = "tabulate" },
|
||||
{ name = "tomli" },
|
||||
{ name = "toposort" },
|
||||
{ name = "tqdm" },
|
||||
{ name = "tzdata" },
|
||||
{ name = "universal-pathlib" },
|
||||
{ name = "watchdog" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/db/59/d388dfbe5e7dfc0716a1f61f47014ec4373d37dbf175590a46bebf1a1b5a/dagster-1.12.2.tar.gz", hash = "sha256:cce66b20d5dd185b6d0415c495e1b40edc4e7f7f222e842e7706f67432d0c7ee", size = 1558635, upload-time = "2025-11-13T20:37:43.062Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/75/38/cb6461dfacfa2265a642db5adee86f3d022ca7094151154693911d356032/dagster-1.12.2-py3-none-any.whl", hash = "sha256:52b2b8873ba552d34bec0a5e31de646d8c56bef270fb020fc4a3729a0f0278a0", size = 1942153, upload-time = "2025-11-13T20:37:39.778Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "dagster-pipes"
|
||||
version = "1.12.2"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/1e/14/8103379523a9f3ef479683f66ee4f5ae695466fd17f506bcaddece3b4a05/dagster_pipes-1.12.2.tar.gz", hash = "sha256:7fb4c42c2fb97acc2fcc04a2b69b18091f4fe6678665800f875f2deed17a7e21", size = 21056, upload-time = "2025-11-13T20:37:51.189Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/89/ce/2a603b4a448989c111a0b62fef922709ddef617b3678c4c0a76f620ada5a/dagster_pipes-1.12.2-py3-none-any.whl", hash = "sha256:a95ec64e1a6b91023a6c2fe4c0694b49afe1c8eda856c20dd712fb3160672ae1", size = 20829, upload-time = "2025-11-13T20:37:48.506Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "dagster-postgres"
|
||||
version = "0.28.2"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "dagster" },
|
||||
{ name = "psycopg2-binary" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/2e/90/bfb950930f42bb70d69879ae3da213806d95d578fae95f3fb4d9cb5cc1b3/dagster_postgres-0.28.2.tar.gz", hash = "sha256:f9b6403f836f63a47d3a3af6a5b9f2c8298d706fe738884c57d818a16a0c8f2d", size = 16409, upload-time = "2025-11-13T20:43:07.407Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/ac/63/c82afecf71746a231f38bc927574d4d117c2bc88c271a8a5968cf8b45fe1/dagster_postgres-0.28.2-py3-none-any.whl", hash = "sha256:536f1c2c282634392a993f017d4854fb2f49308483c34b082d9959d7f826ba70", size = 22940, upload-time = "2025-11-13T20:43:06.258Z" },
|
||||
]
|
||||
|
||||
[[package]]
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Loading…
Reference in New Issue