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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eval_data/
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*.egg-info/
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*.egg-info/
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.env
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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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depends = ["docker"]
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run = "uv run pytest"
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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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[tasks.lint]
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description = "Run ruff linting"
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description = "Run ruff linting"
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run = "ruff check ."
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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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description = "Run format, lint, and typecheck"
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run = ["ruff format .", "ruff check .", "uv run pyrefly check ."]
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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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[tasks.clean]
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description = "Clean up cache and build artifacts"
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description = "Clean up cache and build artifacts"
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run = [
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run = [
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@ -1,6 +1,6 @@
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services:
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services:
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timescaledb:
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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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container_name: tradingagents_timescaledb
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environment:
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environment:
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POSTGRES_USER: postgres
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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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# Install TimescaleDB for PostgreSQL 16
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RUN apt-get install -y timescaledb-2-postgresql-17
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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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RUN curl -sfL https://install.pgx.sh | sh -
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# Install pgvector and pgvectorscale using pgxman
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# Install pgvectorscale using pgxman
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RUN pgxman install pgvector || echo "pgvector install failed" \
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RUN pgxman install pgvectorscale || echo "pgvectorscale install failed"
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&& pgxman install pgvectorscale || echo "pgvectorscale install failed"
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# Configure PostgreSQL for TimescaleDB (instead of using timescaledb-tune)
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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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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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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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### Completed Infrastructure
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- **Domain Architecture**: Clean separation of news, marketdata, and socialmedia domains
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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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- **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 Clients**: Google News RSS + Article Scraper with comprehensive tests (600+ lines)
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- **News Domain**: Article scraping, sentiment analysis, and storage (95% complete)
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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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- **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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## Development Phases
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### Phase 1: News Domain Completion (Current - 95% Complete)
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### Phase 1: News Domain + Basic Dagster (Current - 85% Complete)
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**Timeline**: 2-3 weeks
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**Timeline**: 5-7 days remaining
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**Status**: 🔄 In Progress
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**Status**: 🔄 In Progress
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#### Remaining Work
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#### Remaining Work (5-7 days)
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- **News Processing Pipeline**: Complete article content processing and deduplication
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- **News Repository Layer**: PostgreSQL async operations with TimescaleDB (1-2 days)
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- **Sentiment Analysis Optimization**: Fine-tune sentiment scoring algorithms
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- **News Service Layer**: Business logic with LLM integration (1-2 days)
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- **News Repository**: Finalize PostgreSQL integration for news storage
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- **NewsArticle Entity**: Domain models with sentiment and embeddings (1 day)
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- **Testing Coverage**: Achieve 85%+ test coverage for news domain
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- **OpenRouter Integration**: Sentiment analysis via LLM (1-2 days)
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- **Performance Optimization**: Optimize news retrieval and search performance
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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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#### Success Criteria
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- ✅ All news APIs integrated and tested
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- ✅ Complete layered architecture implemented
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- ✅ Sentiment analysis producing consistent scores
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- ✅ LLM sentiment scores with confidence ratings
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- ✅ News data properly stored in PostgreSQL
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- ✅ Vector embeddings enabling semantic search
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- ✅ Comprehensive test suite covering edge cases
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- ✅ Dagster job running daily news collection
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- ✅ News domain ready for RAG integration
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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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### Phase 2: Market Data Domain + Dagster Integration (Next Priority)
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**Timeline**: 4-6 weeks
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**Timeline**: 4-5 weeks
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**Status**: 📋 Planned
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**Status**: 📋 Planned
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#### Core Objectives
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#### Core Objectives
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- **TimescaleDB Integration**: Implement hypertables for efficient time-series storage
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- **TimescaleDB Hypertables**: Efficient time-series storage for price/volume data
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- **Market Data Collection**: Complete price, volume, and technical indicator collection
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- **Market Data Collection**: FinnHub/yfinance integration with retry logic
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- **PostgreSQL Migration**: Move all data persistence from file-based to PostgreSQL
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- **PostgreSQL Migration**: Move from file-based to database storage
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- **Technical Analysis**: Implement MACD, RSI, and other technical indicators
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- **Technical Indicators**: MACD, RSI, Bollinger Bands calculations
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- **Database Schema**: Design optimized schema for market data with proper indexing
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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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#### Key Deliverables
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- Market data repository with TimescaleDB optimization
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- MarketDataRepository with TimescaleDB optimization
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- Real-time and historical price data collection
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- MarketDataService with technical analysis calculations
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- Technical analysis calculation engine
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- MarketData entities (Price, OHLCV, TechnicalIndicators)
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- Migration scripts for moving existing data
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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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- Performance benchmarks for time-series queries
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#### Success Criteria
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#### Success Criteria
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- ✅ Market data efficiently stored in TimescaleDB hypertables
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- ✅ TimescaleDB hypertables storing historical price data
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- ✅ Sub-100ms queries for common market data retrievals
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- ✅ Sub-100ms queries for price lookups and indicators
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- ✅ All technical indicators calculating accurately
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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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- ✅ Complete migration from file-based storage
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- ✅ Market data domain ready for agent integration
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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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### Phase 3: Social Media Domain + Dagster Integration
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**Timeline**: 3-4 weeks
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**Timeline**: 2-3 weeks
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**Status**: 📋 Planned
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**Status**: 📋 Planned
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#### Core Objectives
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#### Core Objectives
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- **Reddit Integration**: Implement Reddit API for financial subreddits
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- **Reddit Integration**: PRAW library for financial subreddits (r/wallstreetbets, r/stocks)
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- **Twitter/X Integration**: Add social sentiment from Twitter feeds
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- **Twitter/X Alternative**: Evaluate Reddit-only approach or alternative sources
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- **Social Sentiment Analysis**: Aggregate sentiment scoring across platforms
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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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- **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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#### Key Deliverables
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- Reddit and Twitter data collection clients
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- RedditClient with pytest-vcr tests
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- Social sentiment aggregation algorithms
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- SocialMediaRepository with PostgreSQL + pgvectorscale
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- Social media data repository with PostgreSQL storage
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- SocialMediaService with sentiment aggregation
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- Cross-domain correlation analysis tools
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- Dagster job for daily Reddit data collection
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- Foundation for RAG implementation
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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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#### 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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- ✅ Sentiment scores integrated with market events
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- ✅ Cross-domain relationships established in database
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- ✅ Cross-domain relationships queryable in database
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- ✅ Social media domain ready for RAG enhancement
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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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- ✅ Three-domain architecture complete
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### Phase 4: Dagster Data Collection Orchestration
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#### Blockers to Resolve
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**Timeline**: 3-4 weeks
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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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**Status**: 📋 Planned
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#### Core Objectives
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#### Core Objectives
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- **Pipeline Architecture**: Design daily/twice-daily data collection workflows
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- **RAG Agent Enhancement**: All agents use vector similarity search for context
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- **Data Quality Monitoring**: Implement validation and gap detection
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- **Historical Pattern Matching**: Semantic search for comparable market scenarios
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- **Automated Backfill**: Handle missing data and API failures gracefully
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- **Cross-Domain RAG**: Agents query across news, price, and social data
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- **Performance Monitoring**: Track pipeline health and data freshness
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- **Advanced Dagster Features**: Data quality monitoring, gap detection, backfill
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- **Alerting System**: Notify on pipeline failures or data quality issues
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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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#### Key Deliverables
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- Dagster asset definitions for all data domains
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- RAG-enhanced agents with similarity-based context retrieval
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- Automated data quality checks and validation
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- Cross-domain vector search (find similar market conditions)
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- Gap detection and backfill capabilities
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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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- Monitoring dashboard for pipeline health
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- Comprehensive logging and error handling
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- Performance benchmarks for vector queries (< 50ms target)
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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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#### Success Criteria
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- ✅ All agents using RAG for contextual decisions
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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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- ✅ Vector similarity search < 50ms across all domains
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- ✅ OpenRouter as sole LLM provider across all agents
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- ✅ Cross-domain queries enabling holistic analysis
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- ✅ Agents demonstrating improved decision accuracy
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- ✅ Dagster monitoring with automated alerts
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- ✅ Historical pattern matching enhancing trading analysis
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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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## 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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### Database Architecture
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- **Month 1**: Complete PostgreSQL foundation with news domain
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- **Week 1**: PostgreSQL + TimescaleDB + pgvectorscale operational (News domain)
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- **Month 2**: TimescaleDB hypertables optimized for market data
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- **Week 6**: TimescaleDB hypertables optimized for market data time-series
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- **Month 3**: pgvectorscale configured for RAG implementation
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- **Week 9**: Three-domain database architecture complete with vector embeddings
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- **Month 4**: Full database optimization and performance tuning
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- **Week 12**: Full RAG implementation with cross-domain similarity search
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### Agent Capabilities
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### Agent Capabilities
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- **Month 1**: Basic multi-agent framework operational
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- **Week 1**: News Analysts accessing news with LLM sentiment
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- **Month 2**: Agents using PostgreSQL for all data access
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- **Week 6**: Technical Analysts using market data with indicators
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- **Month 3**: Cross-domain agent collaboration established
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- **Week 9**: Sentiment Analysts using social media data
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- **Month 4**: RAG-powered agents with historical context
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- **Week 12**: All agents RAG-enhanced with historical context
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### Data Pipeline Maturity
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### Data Pipeline Maturity (Incremental Dagster)
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- **Month 1**: Manual data collection with basic automation
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- **Week 1**: Daily news collection automated via Dagster
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- **Month 2**: Automated collection for market data
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- **Week 6**: Twice-daily market data collection automated
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- **Month 3**: Full three-domain automated collection
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- **Week 9**: Daily social media collection automated
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- **Month 4**: Production-grade pipeline with monitoring and alerting
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- **Week 12**: Production-grade orchestration with monitoring, backfill, and alerting
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## Success Metrics
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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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### 1. Scheduled Execution
|
||||||
- Daily job at 6 AM UTC for all configured tickers
|
- Daily job at 6 AM UTC for all configured tickers
|
||||||
- APScheduler integration (no Dagster dependency)
|
- Dagster orchestration with partitioned schedules
|
||||||
- Graceful error handling with comprehensive logging
|
- Graceful error handling with Dagster sensors and alerting
|
||||||
|
|
||||||
### 2. LLM Sentiment Analysis
|
### 2. LLM Sentiment Analysis
|
||||||
- OpenRouter integration using `quick_think_llm` (claude-3.5-haiku)
|
- OpenRouter integration using `quick_think_llm` (claude-3.5-haiku)
|
||||||
- Structured output: `{"sentiment": "positive|negative|neutral", "confidence": 0.0-1.0}`
|
- Structured output: `{"sentiment": "positive|negative|neutral", "confidence": 0.0-1.0, "label": "positive|negative|neutral"}`
|
||||||
- Best-effort processing - failures don't stop pipeline
|
- Best-effort processing - failures don't stop pipeline
|
||||||
|
|
||||||
### 3. Vector Embeddings
|
### 3. Vector Embeddings
|
||||||
|
|
@ -27,54 +27,75 @@ Complete final 5% of news domain: add scheduled execution, LLM sentiment analysi
|
||||||
|
|
||||||
### Architecture Pattern
|
### Architecture Pattern
|
||||||
```
|
```
|
||||||
ScheduledNewsJob → NewsService → NewsRepository → NewsArticle → PostgreSQL+pgvectorscale
|
Dagster Job → Dagster Op → NewsService → NewsRepository → NewsArticle → PostgreSQL+pgvectorscale
|
||||||
```
|
```
|
||||||
|
|
||||||
### Database Changes
|
### Database Changes
|
||||||
```sql
|
```sql
|
||||||
ALTER TABLE news_articles
|
ALTER TABLE news_articles
|
||||||
ADD COLUMN sentiment_score JSONB,
|
ADD COLUMN sentiment_confidence FLOAT,
|
||||||
ADD COLUMN title_embedding vector(1536),
|
ADD COLUMN sentiment_label VARCHAR(20);
|
||||||
ADD COLUMN content_embedding vector(1536);
|
|
||||||
|
-- Vector columns already exist from 95% complete infrastructure
|
||||||
|
-- title_embedding vector(1536)
|
||||||
|
-- content_embedding vector(1536)
|
||||||
```
|
```
|
||||||
|
|
||||||
### Key Integration Points
|
### Key Integration Points
|
||||||
- **Existing NewsService**: Enhance `update_news_for_symbol` method
|
- **Existing NewsService**: Enhance `update_company_news` method
|
||||||
- **LLM Integration**: OpenRouter unified provider for sentiment
|
- **LLM Integration**: OpenRouter unified provider for sentiment and embeddings
|
||||||
- **Vector Generation**: text-embedding-3-small model (1536 dims)
|
- **Vector Generation**: OpenAI text-embedding-ada-002 via OpenRouter (1536 dims)
|
||||||
- **Job Scheduling**: APScheduler with cron trigger
|
- **Job Scheduling**: Dagster jobs with daily partitioned schedules
|
||||||
|
|
||||||
## Implementation Phases
|
## Implementation Phases
|
||||||
1. **Scheduled Execution** (2-3h): APScheduler + config management
|
1. **Entity Layer** (2-3h): Enhance NewsArticle dataclass + migration
|
||||||
2. **LLM Sentiment** (3-4h): OpenRouter integration + structured prompts
|
2. **Repository Layer** (2-3h): RAG vector similarity search methods
|
||||||
3. **Vector Embeddings** (2-3h): Embedding generation + database schema
|
3. **LLM Integration** (4-5h): OpenRouter sentiment + embeddings clients
|
||||||
4. **Testing & Monitoring** (2h): Coverage + performance validation
|
4. **Service Enhancement** (2-3h): Integrate LLM clients into NewsService
|
||||||
|
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
|
## 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
|
- ✅ News retrieval with sentiment scores < 2 seconds response time
|
||||||
- ✅ Vector embeddings enable semantic search for News Analysts
|
- ✅ Vector embeddings enable semantic search for News Analysts
|
||||||
- ✅ >95% article processing success rate despite paywall/blocking
|
- ✅ >95% article processing success rate despite paywall/blocking
|
||||||
- ✅ Maintain >85% test coverage including new components
|
- ✅ Maintain >85% test coverage including new components
|
||||||
|
- ✅ Dagster UI provides monitoring and alerting for job failures
|
||||||
|
|
||||||
## Dependencies
|
## Dependencies
|
||||||
- **APIs**: OpenRouter (sentiment), OpenAI (embeddings)
|
- **APIs**: OpenRouter (sentiment + embeddings via unified provider)
|
||||||
- **Infrastructure**: PostgreSQL + TimescaleDB + pgvectorscale
|
- **Infrastructure**: PostgreSQL + TimescaleDB + pgvectorscale
|
||||||
- **New Package**: `apscheduler` for job scheduling
|
- **Orchestration**: Dagster for job scheduling and monitoring
|
||||||
- **Existing**: 95% complete news domain components
|
- **Existing**: 95% complete news domain components (clients, repository, service)
|
||||||
|
|
||||||
## Configuration
|
## 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
|
```bash
|
||||||
OPENROUTER_API_KEY="sk-or-..."
|
# Environment variables
|
||||||
OPENAI_API_KEY="sk-..."
|
OPENROUTER_API_KEY="sk-or-..." # Unified LLM provider
|
||||||
NEWS_SCHEDULE_HOUR=6
|
DATABASE_URL="postgresql+asyncpg://..."
|
||||||
NEWS_TICKERS="AAPL,GOOGL,MSFT,TSLA"
|
|
||||||
```
|
```
|
||||||
|
|
||||||
## Risk Mitigation
|
## Risk Mitigation
|
||||||
- **API Rate Limits**: Exponential backoff + batch processing
|
- **API Rate Limits**: Exponential backoff + batch processing
|
||||||
- **Paywall Blocking**: Metadata-only storage with warnings
|
- **Paywall Blocking**: Metadata-only storage with warnings
|
||||||
- **Job Failures**: Monitoring + alerting for operational visibility
|
- **Job Failures**: Dagster sensors + alerting for operational visibility
|
||||||
- **Performance**: Vector indexes + query optimization for <2s target
|
- **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
|
### 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
|
### Database Schema Integration
|
||||||
|
|
@ -156,29 +156,70 @@ content_embedding = await embedding_client.create_embedding(
|
||||||
|
|
||||||
### Scheduled Execution Framework
|
### Scheduled Execution Framework
|
||||||
|
|
||||||
Use APScheduler for job orchestration (Dagster not in current dependencies):
|
Use Dagster for job orchestration (existing dependency in project):
|
||||||
|
|
||||||
```python
|
```python
|
||||||
from apscheduler.schedulers.asyncio import AsyncIOScheduler
|
from dagster import (
|
||||||
|
job,
|
||||||
scheduler = AsyncIOScheduler()
|
schedule,
|
||||||
scheduler.add_job(
|
ScheduleDefinition,
|
||||||
run_news_collection,
|
op,
|
||||||
'cron',
|
In,
|
||||||
hour=6, # 6 AM UTC
|
Out,
|
||||||
minute=0,
|
AssetMaterialization
|
||||||
timezone=timezone.utc,
|
|
||||||
id='daily_news_collection'
|
|
||||||
)
|
)
|
||||||
|
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
|
## Implementation Approach
|
||||||
|
|
||||||
### Phase 1: Scheduled Execution (2-3 hours)
|
### Phase 1: Dagster Scheduling Integration (2-3 hours)
|
||||||
1. Configure APScheduler for daily news collection
|
1. Create Dagster ops for news collection pipeline
|
||||||
2. Create job configuration management for ticker lists
|
2. Configure daily schedule with cron expression
|
||||||
3. Implement job monitoring and status tracking
|
3. Set up job configuration management for ticker lists
|
||||||
4. Add manual execution capability for testing
|
4. Add manual job execution capability for testing
|
||||||
|
5. Implement job monitoring and asset tracking
|
||||||
|
|
||||||
### Phase 2: LLM Sentiment Integration (3-4 hours)
|
### Phase 2: LLM Sentiment Integration (3-4 hours)
|
||||||
1. Integrate OpenRouter LLM for sentiment analysis
|
1. Integrate OpenRouter LLM for sentiment analysis
|
||||||
|
|
@ -218,11 +259,12 @@ scheduler.add_job(
|
||||||
- `NewsRepository` with async PostgreSQL operations
|
- `NewsRepository` with async PostgreSQL operations
|
||||||
- `NewsArticle` domain model with validation
|
- `NewsArticle` domain model with validation
|
||||||
- Comprehensive test coverage with pytest-vcr
|
- Comprehensive test coverage with pytest-vcr
|
||||||
|
- Dagster framework for data orchestration (existing dependency)
|
||||||
|
|
||||||
### New Dependencies
|
### New Dependencies
|
||||||
- `apscheduler` for job scheduling
|
|
||||||
- Enhanced vector embedding capabilities
|
- Enhanced vector embedding capabilities
|
||||||
- LLM client integration for sentiment analysis
|
- LLM client integration for sentiment analysis
|
||||||
|
- Dagster scheduling integration (existing dependency)
|
||||||
|
|
||||||
## Configuration Management
|
## Configuration Management
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -1,336 +1,310 @@
|
||||||
# News Domain Completion - Progress Status
|
1→# News Domain Completion - Implementation Status
|
||||||
|
2→
|
||||||
## Overview
|
3→**Last Updated**: 2025-01-11
|
||||||
|
4→**Overall Progress**: 6.67% (1/15 tasks completed)
|
||||||
**Feature**: News Domain Final 5% Completion
|
5→**Architecture**: Dagster orchestration + OpenRouter LLM + RAG vector search
|
||||||
**Status**: Ready for Implementation
|
6→
|
||||||
**Total Estimated Time**: 12-16 hours with AI assistance
|
7→---
|
||||||
**Target Timeline**: 3-4 days
|
8→
|
||||||
**Current Progress**: 95% complete (infrastructure ready)
|
9→## Current Phase
|
||||||
|
10→
|
||||||
---
|
11→**Phase 1: Entity Layer**
|
||||||
|
12→Status: In Progress
|
||||||
## Progress Summary
|
13→Progress: 50% (1/2 tasks completed)
|
||||||
|
14→Estimated Time Remaining: 1-2 hours
|
||||||
### Overall Completion: 0% (95% + 0% of final 5%)
|
15→
|
||||||
|
16→---
|
||||||
| Phase | Status | Progress | Duration | Completion |
|
17→
|
||||||
|-------|--------|----------|----------|------------|
|
18→## Task Status Summary
|
||||||
| Phase 1: Foundation | ⏳ Not Started | 0/3 tasks | 0h/4-7h | ⬜⬜⬜⬜⬜⬜⬜ |
|
19→
|
||||||
| Phase 2: Data Access | ⏳ Not Started | 0/1 tasks | 0h/2-3h | ⬜⬜⬜ |
|
20→### Phase 1: Entity Layer (1/2 completed)
|
||||||
| Phase 3: LLM Integration | ⏳ Not Started | 0/3 tasks | 0h/5-8h | ⬜⬜⬜⬜⬜⬜⬜⬜ |
|
21→
|
||||||
| Phase 4: Scheduling | ⏳ Not Started | 0/2 tasks | 0h/4-6h | ⬜⬜⬜⬜⬜⬜ |
|
22→| Task | Status | Priority | Time | Assigned | Completion | Completed At |
|
||||||
| Phase 5: Validation | ⏳ Not Started | 0/2 tasks | 0h/3-5h | ⬜⬜⬜⬜⬜ |
|
23→|------|--------|----------|------|----------|------------|--------------|
|
||||||
|
24→| T001: Enhance NewsArticle Dataclass | ✅ Completed | Critical | 1-2h | - | 100% | 2025-01-11 |
|
||||||
**Legend**: ✅ Complete | 🟡 In Progress | ⏳ Not Started | ❌ Blocked
|
25→| T002: Database Migration - Sentiment Fields | ⬜ Not Started | Critical | 1h | - | 0% | - |
|
||||||
|
26→
|
||||||
---
|
27→### Phase 2: Repository Layer (0/2 completed)
|
||||||
|
28→
|
||||||
## Task Status Tracking
|
29→| Task | Status | Priority | Time | Assigned | Completion |
|
||||||
|
30→|------|--------|----------|------|----------|------------|
|
||||||
### Phase 1: Foundation (0% Complete)
|
31→| T003: NewsRepository - Vector Similarity Search | ⬜ Not Started | Critical | 2-3h | - | 0% |
|
||||||
|
32→| T004: NewsRepository - Batch Embedding Updates | ⬜ Not Started | Medium | 1h | - | 0% |
|
||||||
#### ⏳ T001: Database Migration - NewsJobConfig Table
|
33→
|
||||||
- **Status**: Not Started
|
34→### Phase 3: LLM Integration (0/3 completed)
|
||||||
- **Priority**: Critical
|
35→
|
||||||
- **Estimated**: 1-2 hours
|
36→| Task | Status | Priority | Time | Assigned | Completion |
|
||||||
- **Dependencies**: None
|
37→|------|--------|----------|------|----------|------------|
|
||||||
- **Progress**: 0%
|
38→| T005: OpenRouter Sentiment Client | ⬜ Not Started | Critical | 2-3h | - | 0% |
|
||||||
- **Acceptance Criteria**: 0/4 completed
|
39→| T006: OpenRouter Embeddings Client | ⬜ Not Started | Critical | 1-2h | - | 0% |
|
||||||
- [ ] `news_job_configs` table created with UUID primary key
|
40→| T007: Enhance NewsService - LLM Integration | ⬜ Not Started | Critical | 2-3h | - | 0% |
|
||||||
- [ ] JSONB fields for symbols and categories with validation
|
41→
|
||||||
- [ ] Proper indexes for enabled/frequency queries
|
42→### Phase 4: Dagster Orchestration (0/5 completed)
|
||||||
- [ ] Migration script tests with rollback capability
|
43→
|
||||||
- **Blocking Issues**: None
|
44→| Task | Status | Priority | Time | Assigned | Completion |
|
||||||
- **Next Actions**: Create Alembic migration script
|
45→|------|--------|----------|------|----------|------------|
|
||||||
|
46→| T008: Dagster Directory Structure | ⬜ Not Started | High | 30min | - | 0% |
|
||||||
#### ⏳ T002: Enhance NewsArticle Entity - Sentiment and Embeddings
|
47→| T009: Dagster Ops - News Collection | ⬜ Not Started | High | 2-3h | - | 0% |
|
||||||
- **Status**: Not Started
|
48→| T010: Dagster Job - Daily News Collection | ⬜ Not Started | High | 1-2h | - | 0% |
|
||||||
- **Priority**: Critical
|
49→| T011: Dagster Schedule - Daily Trigger | ⬜ Not Started | High | 1h | - | 0% |
|
||||||
- **Estimated**: 2-3 hours
|
50→| T012: Dagster Sensor - Failure Alerting | ⬜ Not Started | Medium | 1h | - | 0% |
|
||||||
- **Dependencies**: T001
|
51→
|
||||||
- **Progress**: 0%
|
52→### Phase 5: Testing & Documentation (0/3 completed)
|
||||||
- **Acceptance Criteria**: 0/5 completed
|
53→
|
||||||
- [ ] Add sentiment_score, sentiment_confidence, sentiment_label fields
|
54→| Task | Status | Priority | Time | Assigned | Completion |
|
||||||
- [ ] Add title_embedding and content_embedding vector fields
|
55→|------|--------|----------|------|----------|------------|
|
||||||
- [ ] Enhanced validate() method with sentiment range checks
|
56→| T013: Integration Tests - End-to-End Workflow | ⬜ Not Started | High | 2-3h | - | 0% |
|
||||||
- [ ] Updated transformations for vector handling
|
57→| T014: Dagster Tests | ⬜ Not Started | Medium | 1h | - | 0% |
|
||||||
- [ ] Embedding dimension validation (1536)
|
58→| T015: Documentation Updates | ⬜ Not Started | Medium | 1-2h | - | 0% |
|
||||||
- **Blocking Issues**: None
|
59→
|
||||||
- **Next Actions**: Extend NewsArticle dataclass
|
60→---
|
||||||
|
61→
|
||||||
#### ⏳ T003: Create NewsJobConfig Entity
|
62→## Dependency Graph
|
||||||
- **Status**: Not Started
|
63→
|
||||||
- **Priority**: Critical
|
64→```
|
||||||
- **Estimated**: 1-2 hours
|
65→T001 ─┬─→ T002 ──→ T003 ─────────→ T007 ──→ T009 ──→ T010 ──→ T013
|
||||||
- **Dependencies**: T001
|
66→ │ ↑ ↑ ↑ ↑
|
||||||
- **Progress**: 0%
|
67→ │ │ │ │ │
|
||||||
- **Acceptance Criteria**: 0/5 completed
|
68→ └──→ T005 ────────────────────┘ │ │ │
|
||||||
- [ ] NewsJobConfig dataclass with all required fields
|
69→ T006 ──────────────────────────────┘ │ │
|
||||||
- [ ] Business rule validation for job configuration
|
70→ T008 ──────────────────────────────────────┘ │
|
||||||
- [ ] Cron expression validation for frequency
|
71→ T011 ───────────────────────────────────────────────┘
|
||||||
- [ ] Symbol list validation
|
72→ T014 ───────────────────────────────────────────────┘
|
||||||
- [ ] JSON serialization for database storage
|
73→```
|
||||||
- **Blocking Issues**: None
|
74→
|
||||||
- **Next Actions**: Create new entity file
|
75→**Critical Path**: T001 → T002 → T003 → T007 → T009 → T010 → T013
|
||||||
|
76→
|
||||||
### Phase 2: Data Access (0% Complete)
|
77→**Parallel Opportunities**:
|
||||||
|
78→- T005 & T006 can be developed in parallel (LLM clients)
|
||||||
#### ⏳ T004: Enhance NewsRepository - Vector and Job Operations
|
79→- T009, T010, T011 can be developed in parallel after T008 (Dagster components)
|
||||||
- **Status**: Not Started
|
80→
|
||||||
- **Priority**: Critical
|
81→---
|
||||||
- **Estimated**: 2-3 hours
|
82→
|
||||||
- **Dependencies**: T002, T003
|
83→## Progress by Phase
|
||||||
- **Progress**: 0%
|
84→
|
||||||
- **Acceptance Criteria**: 0/5 completed
|
85→### Phase 1: Entity Layer
|
||||||
- [ ] Vector similarity search with cosine distance
|
86→- **Status**: In Progress
|
||||||
- [ ] Batch embedding update operations
|
87→- **Progress**: 50% (1/2 tasks)
|
||||||
- [ ] NewsJobConfig CRUD methods
|
88→- **Estimated Time**: 1-2 hours
|
||||||
- [ ] Optimized query performance for vector operations
|
89→- **Blockers**: None
|
||||||
- [ ] Proper async connection handling
|
90→- **Next Action**: Start T002 - Database Migration for Sentiment Fields
|
||||||
- **Blocking Issues**: Waiting for T002, T003
|
91→
|
||||||
- **Next Actions**: Extend NewsRepository class
|
92→### Phase 2: Repository Layer
|
||||||
|
93→- **Status**: Not Started
|
||||||
### Phase 3: LLM Integration (0% Complete)
|
94→- **Progress**: 0% (0/2 tasks)
|
||||||
|
95→- **Estimated Time**: 2-3 hours
|
||||||
#### ⏳ T005: OpenRouter Client - Sentiment Analysis
|
96→- **Blockers**: T001, T002 must complete first
|
||||||
- **Status**: Not Started
|
97→- **Next Action**: Waiting for Phase 1 completion
|
||||||
- **Priority**: Critical
|
98→
|
||||||
- **Estimated**: 2-3 hours
|
99→### Phase 3: LLM Integration
|
||||||
- **Dependencies**: T002
|
100→- **Status**: Not Started
|
||||||
- **Progress**: 0%
|
101→- **Progress**: 0% (0/3 tasks)
|
||||||
- **Acceptance Criteria**: 0/5 completed
|
102→- **Estimated Time**: 4-5 hours
|
||||||
- [ ] OpenRouter API integration for sentiment analysis
|
103→- **Blockers**: T001 must complete for client development
|
||||||
- [ ] Structured prompts for financial news sentiment
|
104→- **Next Action**: Can start T005 & T006 in parallel after T001
|
||||||
- [ ] Response parsing with Pydantic models
|
105→
|
||||||
- [ ] Error handling with graceful fallbacks
|
106→### Phase 4: Dagster Orchestration
|
||||||
- [ ] Retry logic with exponential backoff
|
107→- **Status**: Not Started
|
||||||
- **Blocking Issues**: Waiting for T002
|
108→- **Progress**: 0% (0/5 tasks)
|
||||||
- **Next Actions**: Create OpenRouter sentiment client
|
109→- **Estimated Time**: 3-4 hours
|
||||||
|
110→- **Blockers**: T007 must complete for ops/jobs, T008 has no dependencies
|
||||||
#### ⏳ T006: OpenRouter Client - Vector Embeddings
|
111→- **Next Action**: Can start T008 anytime (directory structure)
|
||||||
- **Status**: Not Started
|
112→
|
||||||
- **Priority**: Critical
|
113→### Phase 5: Testing & Documentation
|
||||||
- **Estimated**: 1-2 hours
|
114→- **Status**: Not Started
|
||||||
- **Dependencies**: T002
|
115→- **Progress**: 0% (0/3 tasks)
|
||||||
- **Progress**: 0%
|
116→- **Estimated Time**: 2-3 hours
|
||||||
- **Acceptance Criteria**: 0/5 completed
|
117→- **Blockers**: T007, T010 must complete for integration testing
|
||||||
- [ ] OpenRouter embeddings API integration
|
118→- **Next Action**: Waiting for earlier phases
|
||||||
- [ ] Text preprocessing for embedding generation
|
119→
|
||||||
- [ ] Batch processing for multiple articles
|
120→---
|
||||||
- [ ] 1536-dimensional vector validation
|
121→
|
||||||
- [ ] Proper error handling and retries
|
122→## Test Coverage Status
|
||||||
- **Blocking Issues**: Waiting for T002
|
123→
|
||||||
- **Next Actions**: Create OpenRouter embeddings client
|
124→**Current Coverage**: Baseline (from 95% complete infrastructure)
|
||||||
|
125→**Target Coverage**: ≥85%
|
||||||
#### ⏳ T007: Enhance NewsService - LLM Integration
|
126→**New Code Coverage**: 0% (no new code yet)
|
||||||
- **Status**: Not Started
|
127→
|
||||||
- **Priority**: Critical
|
128→### Coverage by Component
|
||||||
- **Estimated**: 2-3 hours
|
129→
|
||||||
- **Dependencies**: T005, T006
|
130→| Component | Coverage | Target | Status |
|
||||||
- **Progress**: 0%
|
131→|-----------|----------|--------|--------|
|
||||||
- **Acceptance Criteria**: 0/5 completed
|
132→| NewsArticle (Entity) | - | ≥85% | ⬜ Pending |
|
||||||
- [ ] Replace keyword sentiment with LLM analysis
|
133→| NewsRepository (RAG) | - | ≥85% | ⬜ Pending |
|
||||||
- [ ] Add embedding generation to article processing
|
134→| OpenRouter Sentiment Client | - | ≥85% | ⬜ Pending |
|
||||||
- [ ] End-to-end article processing pipeline
|
135→| OpenRouter Embeddings Client | - | ≥85% | ⬜ Pending |
|
||||||
- [ ] Proper error handling and fallback strategies
|
136→| NewsService (LLM Integration) | - | ≥85% | ⬜ Pending |
|
||||||
- [ ] Integration with existing service methods
|
137→| Dagster Ops | - | ≥85% | ⬜ Pending |
|
||||||
- **Blocking Issues**: Waiting for T005, T006
|
138→| Dagster Jobs | - | ≥85% | ⬜ Pending |
|
||||||
- **Next Actions**: Integrate LLM clients into NewsService
|
139→
|
||||||
|
140→---
|
||||||
### Phase 4: Scheduling (0% Complete)
|
141→
|
||||||
|
142→## Performance Benchmarks
|
||||||
#### ⏳ T008: APScheduler Integration - Job Scheduling
|
143→
|
||||||
- **Status**: Not Started
|
144→### Current Performance
|
||||||
- **Priority**: High
|
145→- **Query Time (30-day lookback)**: Not measured yet
|
||||||
- **Estimated**: 3-4 hours
|
146→- **Vector Search (top-10)**: Not measured yet
|
||||||
- **Dependencies**: T003, T004, T007
|
147→- **Batch Insert (50 articles)**: Not measured yet
|
||||||
- **Progress**: 0%
|
148→
|
||||||
- **Acceptance Criteria**: 0/5 completed
|
149→### Target Performance
|
||||||
- [ ] APScheduler setup with PostgreSQL job store
|
150→- **Query Time**: < 2 seconds for 30-day lookback
|
||||||
- [ ] Scheduled job execution with proper error handling
|
151→- **Vector Search**: < 1 second for top-10 results
|
||||||
- [ ] Job configuration loading and validation
|
152→- **Batch Insert**: < 5 seconds for 50 articles
|
||||||
- [ ] Status monitoring and failure recovery
|
153→
|
||||||
- [ ] CLI integration for job management
|
154→### Performance Test Status
|
||||||
- **Blocking Issues**: Waiting for T003, T004, T007
|
155→- [ ] Query performance baseline established
|
||||||
- **Next Actions**: Implement ScheduledNewsCollector
|
156→- [ ] Vector search performance baseline established
|
||||||
|
157→- [ ] Batch insert performance baseline established
|
||||||
#### ⏳ T009: CLI Integration - Job Management Commands
|
158→- [ ] All performance targets met
|
||||||
- **Status**: Not Started
|
159→
|
||||||
- **Priority**: Medium
|
160→---
|
||||||
- **Estimated**: 1-2 hours
|
161→
|
||||||
- **Dependencies**: T008
|
162→## Risk Assessment
|
||||||
- **Progress**: 0%
|
163→
|
||||||
- **Acceptance Criteria**: 0/5 completed
|
164→### High Risk Items
|
||||||
- [ ] CLI commands for job creation/management
|
165→1. **OpenRouter API Availability** - Mitigated with fallback strategies (keyword sentiment, zero vectors)
|
||||||
- [ ] Manual job execution commands
|
166→2. **Vector Search Performance** - Mitigated with proper pgvectorscale indexes
|
||||||
- [ ] Job status and monitoring commands
|
167→3. **Dagster Integration Complexity** - Mitigated with incremental testing approach
|
||||||
- [ ] Integration with existing CLI structure
|
168→
|
||||||
- [ ] Proper error handling and user feedback
|
169→### Medium Risk Items
|
||||||
- **Blocking Issues**: Waiting for T008
|
170→1. **LLM API Costs** - Monitor usage during development
|
||||||
- **Next Actions**: Extend CLI with news job commands
|
171→2. **Database Performance at Scale** - Test with realistic data volumes
|
||||||
|
172→3. **Test Coverage Maintenance** - Enforce ≥85% coverage requirement
|
||||||
### Phase 5: Validation (0% Complete)
|
173→
|
||||||
|
174→### Low Risk Items
|
||||||
#### ⏳ T010: Integration Tests - End-to-End Workflow
|
175→1. **Code Quality** - Enforced through TDD approach
|
||||||
- **Status**: Not Started
|
176→2. **Documentation** - Tracked as explicit task (T015)
|
||||||
- **Priority**: High
|
177→3. **Error Handling** - Comprehensive fallback strategies
|
||||||
- **Estimated**: 2-3 hours
|
178→
|
||||||
- **Dependencies**: T007, T008
|
179→---
|
||||||
- **Progress**: 0%
|
180→
|
||||||
- **Acceptance Criteria**: 0/5 completed
|
181→## Known Issues
|
||||||
- [ ] End-to-end workflow tests from RSS to vector storage
|
182→
|
||||||
- [ ] Agent integration tests via AgentToolkit
|
183→### Blocking Issues
|
||||||
- [ ] Performance tests for daily collection volumes
|
184→None currently
|
||||||
- [ ] Error recovery and fallback tests
|
185→
|
||||||
- [ ] Test coverage maintained above 85%
|
186→### Non-Blocking Issues
|
||||||
- **Blocking Issues**: Waiting for T007, T008
|
187→None currently
|
||||||
- **Next Actions**: Create comprehensive integration test suite
|
188→
|
||||||
|
189→### Technical Debt
|
||||||
#### ⏳ T011: Documentation and Monitoring
|
190→- Existing keyword-based sentiment analysis should be replaced with LLM sentiment (tracked as T005)
|
||||||
- **Status**: Not Started
|
191→- No automated vector embedding generation currently (tracked as T006)
|
||||||
- **Priority**: Medium
|
192→- No scheduled news collection (tracked as T008-T012)
|
||||||
- **Estimated**: 1-2 hours
|
193→
|
||||||
- **Dependencies**: T010
|
194→---
|
||||||
- **Progress**: 0%
|
195→
|
||||||
- **Acceptance Criteria**: 0/5 completed
|
196→## Milestone Schedule
|
||||||
- [ ] Updated API documentation for new methods
|
197→
|
||||||
- [ ] Job scheduling configuration examples
|
198→### Milestone 1: Entity & Repository Foundation
|
||||||
- [ ] Performance monitoring dashboard queries
|
199→**Target**: Day 1-2
|
||||||
- [ ] Troubleshooting guide for common issues
|
200→**Tasks**: T001, T002, T003, T004
|
||||||
- [ ] Agent integration documentation
|
201→**Status**: In Progress
|
||||||
- **Blocking Issues**: Waiting for T010
|
202→**Deliverables**:
|
||||||
- **Next Actions**: Update documentation and monitoring
|
203→- NewsArticle dataclass with sentiment fields
|
||||||
|
204→- Database migration for sentiment columns
|
||||||
---
|
205→- RAG vector similarity search functional
|
||||||
|
206→- Batch embedding updates operational
|
||||||
## Success Criteria Validation
|
207→
|
||||||
|
208→### Milestone 2: LLM Integration
|
||||||
### Technical Requirements Status
|
209→**Target**: Day 2-3
|
||||||
- [ ] **OpenRouter-only LLM Integration**: Not started
|
210→**Tasks**: T005, T006, T007
|
||||||
- [ ] **Vector Embeddings with pgvectorscale**: Not started
|
211→**Status**: Not Started
|
||||||
- [ ] **APScheduler Job Execution**: Not started
|
212→**Deliverables**:
|
||||||
- [ ] **Test Coverage >85%**: Baseline established (needs monitoring)
|
213→- OpenRouter sentiment client operational with fallbacks
|
||||||
- [ ] **Query Performance <100ms**: Not tested
|
214→- OpenRouter embeddings client operational with fallbacks
|
||||||
- [ ] **Vector Search Performance <1s**: Not tested
|
215→- NewsService enrichment pipeline functional
|
||||||
- [ ] **Backward Compatibility**: Not validated
|
216→- find_similar_news() RAG method operational
|
||||||
|
217→
|
||||||
### Functional Requirements Status
|
218→### Milestone 3: Dagster Orchestration
|
||||||
- [ ] **Sentiment Analysis Pipeline**: Not implemented
|
219→**Target**: Day 3-4
|
||||||
- [ ] **Embedding Generation Pipeline**: Not implemented
|
220→**Tasks**: T008, T009, T010, T011, T012
|
||||||
- [ ] **Scheduled News Collection**: Not implemented
|
221→**Status**: Not Started
|
||||||
- [ ] **CLI Job Management**: Not implemented
|
222→**Deliverables**:
|
||||||
- [ ] **AgentToolkit Integration**: Not validated
|
223→- Dagster directory structure created
|
||||||
- [ ] **Error Handling & Fallbacks**: Not implemented
|
224→- News collection op functional
|
||||||
|
225→- Daily collection job operational
|
||||||
### Quality Requirements Status
|
226→- Schedule configured for 6 AM UTC
|
||||||
- [ ] **TDD Implementation**: Process defined, not applied
|
227→- Failure sensor monitoring job
|
||||||
- [ ] **Layered Architecture**: Pattern defined, not validated
|
228→
|
||||||
- [ ] **Async Connection Pooling**: Not implemented
|
229→### Milestone 4: Testing & Documentation
|
||||||
- [ ] **Production Monitoring**: Not implemented
|
230→**Target**: Day 4-5
|
||||||
- [ ] **Documentation Completeness**: Not updated
|
231→**Tasks**: T013, T014, T015
|
||||||
|
232→**Status**: Not Started
|
||||||
---
|
233→**Deliverables**:
|
||||||
|
234→- End-to-end integration tests passing
|
||||||
## Current Blocking Issues
|
235→- Dagster component tests passing
|
||||||
|
236→- Performance benchmarks met
|
||||||
### Critical Blockers
|
237→- Documentation updated
|
||||||
**None currently** - All dependencies are internal to this implementation
|
238→
|
||||||
|
239→---
|
||||||
### Potential Risk Areas
|
240→
|
||||||
1. **OpenRouter API Access**: Requires valid API keys and model access
|
241→## Next Actions
|
||||||
2. **Database Migration**: Need proper PostgreSQL permissions for schema changes
|
242→
|
||||||
3. **Vector Extension**: pgvectorscale must be properly installed and configured
|
243→### Immediate Next Steps (Today)
|
||||||
4. **Performance Testing**: Need realistic data volumes for benchmark validation
|
244→1. **T002**: Start database migration for sentiment fields
|
||||||
|
245→2. **T008**: Create Dagster directory structure in parallel (no dependencies)
|
||||||
---
|
246→
|
||||||
|
247→### This Week
|
||||||
## Weekly Progress Targets
|
248→1. Complete Phase 1 (Entity Layer)
|
||||||
|
249→2. Start Phase 2 (Repository Layer)
|
||||||
### Week 1 Target (Days 1-2)
|
250→3. Begin Phase 3 (LLM Integration) in parallel
|
||||||
- **Goal**: Complete Phase 1 & 2 (Foundation + Data Access)
|
251→
|
||||||
- **Expected Completion**: T001, T002, T003, T004
|
252→### Next Week
|
||||||
- **Target Progress**: 45% overall completion
|
253→1. Complete Phase 3 & 4 (LLM + Dagster)
|
||||||
|
254→2. Complete Phase 5 (Testing & Documentation)
|
||||||
### Week 1 Target (Days 3-4)
|
255→3. Deploy and monitor Dagster schedules
|
||||||
- **Goal**: Complete Phase 3 & 4 (LLM Integration + Scheduling)
|
256→
|
||||||
- **Expected Completion**: T005, T006, T007, T008, T009
|
257→---
|
||||||
- **Target Progress**: 90% overall completion
|
258→
|
||||||
|
259→## Team Notes
|
||||||
### Week 2 Target (Day 1)
|
260→
|
||||||
- **Goal**: Complete Phase 5 (Validation)
|
261→### Development Environment
|
||||||
- **Expected Completion**: T010, T011
|
262→- PostgreSQL + TimescaleDB + pgvectorscale running locally
|
||||||
- **Target Progress**: 100% overall completion
|
263→- OpenRouter API key configured
|
||||||
|
264→- Dagster installation complete
|
||||||
---
|
265→- Python 3.13 with mise/uv
|
||||||
|
266→
|
||||||
## Metrics Dashboard
|
267→### Communication
|
||||||
|
268→- Spec documents updated to reflect Dagster architecture (spec-lite.md, design.md, tasks.md)
|
||||||
### Code Coverage
|
269→- APScheduler references removed from all specs
|
||||||
- **Current**: 95% (existing infrastructure)
|
270→- Architecture aligned with project roadmap
|
||||||
- **Target**: >85% (including new functionality)
|
271→
|
||||||
- **Status**: ⏳ Pending implementation
|
272→### Resources Needed
|
||||||
|
273→- OpenRouter API access for development/testing
|
||||||
### Performance Benchmarks
|
274→- Test database with sample news articles
|
||||||
- **Query Performance**: Not measured (Target: <100ms)
|
275→- Dagster UI for monitoring during development
|
||||||
- **Vector Search**: Not measured (Target: <1s)
|
276→
|
||||||
- **Batch Processing**: Not measured (Target: TBD)
|
277→---
|
||||||
- **Status**: ⏳ Pending implementation
|
278→
|
||||||
|
279→## Success Criteria Checklist
|
||||||
### Test Execution
|
280→
|
||||||
- **Unit Tests**: 0/11 tasks have tests
|
281→**Technical Success**:
|
||||||
- **Integration Tests**: 0/11 tasks have integration tests
|
282→- [ ] Test coverage ≥85% maintained
|
||||||
- **VCR Tests**: 0/3 API clients have VCR tests
|
283→- [ ] Query performance <2s for 30-day lookback
|
||||||
- **Status**: ⏳ Pending implementation
|
284→- [ ] Vector search <1s for top-10 results
|
||||||
|
285→- [ ] Zero breaking changes to AgentToolkit
|
||||||
---
|
286→- [ ] Dagster jobs execute successfully
|
||||||
|
287→
|
||||||
## Communication & Reporting
|
288→**Functional Success**:
|
||||||
|
289→- [ ] OpenRouter sentiment analysis operational
|
||||||
### Daily Standup Format
|
290→- [ ] Vector embeddings enable semantic search
|
||||||
```
|
291→- [ ] Dagster schedules running daily
|
||||||
Yesterday: [Tasks completed with IDs]
|
292→- [ ] Agent context enriched with sentiment
|
||||||
Today: [Tasks planned with IDs]
|
293→
|
||||||
Blockers: [Any issues requiring attention]
|
294→**Quality Success**:
|
||||||
Help Needed: [Specific areas for collaboration]
|
295→- [x] 1/15 tasks completed
|
||||||
```
|
296→- [ ] All acceptance criteria met
|
||||||
|
297→- [ ] Comprehensive error handling
|
||||||
### Weekly Status Report Format
|
298→- [ ] Production-ready monitoring
|
||||||
```
|
299→- [ ] Complete documentation
|
||||||
Completed: [Phase progress with task counts]
|
300→
|
||||||
In Progress: [Current focus areas]
|
301→---
|
||||||
Upcoming: [Next phase priorities]
|
302→
|
||||||
Risks: [Technical or timeline concerns]
|
303→**Status Key**:
|
||||||
Metrics: [Coverage, performance, test results]
|
304→- ⬜ Not Started
|
||||||
```
|
305→- 🔄 In Progress
|
||||||
|
306→- ✅ Completed
|
||||||
### Milestone Checkpoints
|
307→- 🚫 Blocked
|
||||||
- **Checkpoint 1** (End of Day 2): Foundation Complete (T001-T004)
|
308→- ⚠️ At Risk
|
||||||
- **Checkpoint 2** (End of Day 4): LLM Integration Complete (T005-T009)
|
309→
|
||||||
- **Checkpoint 3** (End of Day 5): Full Implementation Complete (T001-T011)
|
310→**Last Status Update**: 2025-01-11 - T001 completed, updated progress tracking
|
||||||
|
|
||||||
---
|
|
||||||
|
|
||||||
## 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.*
|
|
||||||
File diff suppressed because it is too large
Load Diff
|
|
@ -42,6 +42,8 @@ dependencies = [
|
||||||
"alembic>=1.13.0",
|
"alembic>=1.13.0",
|
||||||
"pgvector>=0.4.1",
|
"pgvector>=0.4.1",
|
||||||
"uuid-utils>=0.11.0",
|
"uuid-utils>=0.11.0",
|
||||||
|
"dagster>=1.8.0",
|
||||||
|
"dagster-postgres>=0.24.0",
|
||||||
]
|
]
|
||||||
|
|
||||||
[project.optional-dependencies]
|
[project.optional-dependencies]
|
||||||
|
|
@ -60,6 +62,10 @@ tradingagents = "cli.main:app"
|
||||||
requires = ["setuptools>=61.0", "wheel"]
|
requires = ["setuptools>=61.0", "wheel"]
|
||||||
build-backend = "setuptools.build_meta"
|
build-backend = "setuptools.build_meta"
|
||||||
|
|
||||||
|
[tool.setuptools.packages.find]
|
||||||
|
include = ["tradingagents*", "cli*"]
|
||||||
|
exclude = ["docker*", "assets*", "alembic*", "docs*", "tests*"]
|
||||||
|
|
||||||
[tool.ruff]
|
[tool.ruff]
|
||||||
line-length = 88
|
line-length = 88
|
||||||
target-version = "py310"
|
target-version = "py310"
|
||||||
|
|
|
||||||
|
|
@ -388,6 +388,172 @@ class TestNewsRepository:
|
||||||
assert result == []
|
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:
|
class TestDatabaseConnectionManagement:
|
||||||
"""Test database connection and session management."""
|
"""Test database connection and session management."""
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -46,6 +46,13 @@ class TradingAgentsConfig:
|
||||||
default_lookback_days: int = 30
|
default_lookback_days: int = 30
|
||||||
default_ta_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):
|
def __post_init__(self):
|
||||||
"""Set computed fields after initialization."""
|
"""Set computed fields after initialization."""
|
||||||
self.data_cache_dir = os.path.join(self.project_dir, "dataflows/data_cache")
|
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",
|
online_tools=os.getenv("ONLINE_TOOLS", "true").lower() == "true",
|
||||||
default_lookback_days=int(os.getenv("DEFAULT_LOOKBACK_DAYS", "30")),
|
default_lookback_days=int(os.getenv("DEFAULT_LOOKBACK_DAYS", "30")),
|
||||||
default_ta_lookback_days=int(os.getenv("DEFAULT_TA_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:
|
def to_dict(self) -> dict:
|
||||||
|
|
@ -104,6 +114,7 @@ class TradingAgentsConfig:
|
||||||
"online_tools": self.online_tools,
|
"online_tools": self.online_tools,
|
||||||
"default_lookback_days": self.default_lookback_days,
|
"default_lookback_days": self.default_lookback_days,
|
||||||
"default_ta_lookback_days": self.default_ta_lookback_days,
|
"default_ta_lookback_days": self.default_ta_lookback_days,
|
||||||
|
"database_url": self.database_url,
|
||||||
}
|
}
|
||||||
|
|
||||||
def copy(self) -> "TradingAgentsConfig":
|
def copy(self) -> "TradingAgentsConfig":
|
||||||
|
|
@ -122,6 +133,7 @@ class TradingAgentsConfig:
|
||||||
online_tools=self.online_tools,
|
online_tools=self.online_tools,
|
||||||
default_lookback_days=self.default_lookback_days,
|
default_lookback_days=self.default_lookback_days,
|
||||||
default_ta_lookback_days=self.default_ta_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
|
summary: str | None = None
|
||||||
entities: list[str] = field(default_factory=list)
|
entities: list[str] = field(default_factory=list)
|
||||||
sentiment_score: float | None = None
|
sentiment_score: float | None = None
|
||||||
|
sentiment_confidence: float | None = None # New field
|
||||||
|
sentiment_label: str | None = None # New field
|
||||||
author: str | None = None
|
author: str | None = None
|
||||||
category: str | None = None
|
category: str | None = None
|
||||||
|
|
||||||
|
|
@ -59,6 +61,8 @@ class NewsArticle:
|
||||||
summary=self.summary,
|
summary=self.summary,
|
||||||
entities=self.entities if self.entities else None,
|
entities=self.entities if self.entities else None,
|
||||||
sentiment_score=self.sentiment_score,
|
sentiment_score=self.sentiment_score,
|
||||||
|
sentiment_confidence=self.sentiment_confidence,
|
||||||
|
sentiment_label=self.sentiment_label,
|
||||||
author=self.author,
|
author=self.author,
|
||||||
category=self.category,
|
category=self.category,
|
||||||
symbol=symbol,
|
symbol=symbol,
|
||||||
|
|
@ -77,10 +81,28 @@ class NewsArticle:
|
||||||
summary=cast("str | None", entity.summary),
|
summary=cast("str | None", entity.summary),
|
||||||
entities=cast("list[str] | None", entity.entities) or [],
|
entities=cast("list[str] | None", entity.entities) or [],
|
||||||
sentiment_score=cast("float | None", entity.sentiment_score),
|
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),
|
author=cast("str | None", entity.author),
|
||||||
category=cast("str | None", entity.category),
|
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):
|
class NewsArticleEntity(Base):
|
||||||
"""SQLAlchemy model for news articles with vector embedding support."""
|
"""SQLAlchemy model for news articles with vector embedding support."""
|
||||||
|
|
@ -113,6 +135,12 @@ class NewsArticleEntity(Base):
|
||||||
JSON, nullable=True
|
JSON, nullable=True
|
||||||
) # Store list[str] as JSON array
|
) # Store list[str] as JSON array
|
||||||
sentiment_score: Mapped[float | None] = mapped_column(Float, nullable=True)
|
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)
|
author: Mapped[str | None] = mapped_column(String(255), nullable=True)
|
||||||
category: Mapped[str | None] = mapped_column(String(100), nullable=True)
|
category: Mapped[str | None] = mapped_column(String(100), nullable=True)
|
||||||
|
|
||||||
|
|
@ -227,6 +255,8 @@ class NewsRepository:
|
||||||
"summary": article.summary,
|
"summary": article.summary,
|
||||||
"entities": article.entities if article.entities else None,
|
"entities": article.entities if article.entities else None,
|
||||||
"sentiment_score": article.sentiment_score,
|
"sentiment_score": article.sentiment_score,
|
||||||
|
"sentiment_confidence": article.sentiment_confidence,
|
||||||
|
"sentiment_label": article.sentiment_label,
|
||||||
"author": article.author,
|
"author": article.author,
|
||||||
"category": article.category,
|
"category": article.category,
|
||||||
"symbol": symbol,
|
"symbol": symbol,
|
||||||
|
|
@ -243,6 +273,8 @@ class NewsRepository:
|
||||||
"summary": stmt.excluded.summary,
|
"summary": stmt.excluded.summary,
|
||||||
"entities": stmt.excluded.entities,
|
"entities": stmt.excluded.entities,
|
||||||
"sentiment_score": stmt.excluded.sentiment_score,
|
"sentiment_score": stmt.excluded.sentiment_score,
|
||||||
|
"sentiment_confidence": stmt.excluded.sentiment_confidence,
|
||||||
|
"sentiment_label": stmt.excluded.sentiment_label,
|
||||||
"author": stmt.excluded.author,
|
"author": stmt.excluded.author,
|
||||||
"category": stmt.excluded.category,
|
"category": stmt.excluded.category,
|
||||||
"symbol": stmt.excluded.symbol,
|
"symbol": stmt.excluded.symbol,
|
||||||
|
|
@ -370,6 +402,8 @@ class NewsRepository:
|
||||||
"summary": article.summary,
|
"summary": article.summary,
|
||||||
"entities": article.entities if article.entities else None,
|
"entities": article.entities if article.entities else None,
|
||||||
"sentiment_score": article.sentiment_score,
|
"sentiment_score": article.sentiment_score,
|
||||||
|
"sentiment_confidence": article.sentiment_confidence,
|
||||||
|
"sentiment_label": article.sentiment_label,
|
||||||
"author": article.author,
|
"author": article.author,
|
||||||
"category": article.category,
|
"category": article.category,
|
||||||
"symbol": symbol,
|
"symbol": symbol,
|
||||||
|
|
@ -388,6 +422,8 @@ class NewsRepository:
|
||||||
"summary": stmt.excluded.summary,
|
"summary": stmt.excluded.summary,
|
||||||
"entities": stmt.excluded.entities,
|
"entities": stmt.excluded.entities,
|
||||||
"sentiment_score": stmt.excluded.sentiment_score,
|
"sentiment_score": stmt.excluded.sentiment_score,
|
||||||
|
"sentiment_confidence": stmt.excluded.sentiment_confidence,
|
||||||
|
"sentiment_label": stmt.excluded.sentiment_label,
|
||||||
"author": stmt.excluded.author,
|
"author": stmt.excluded.author,
|
||||||
"category": stmt.excluded.category,
|
"category": stmt.excluded.category,
|
||||||
"symbol": stmt.excluded.symbol,
|
"symbol": stmt.excluded.symbol,
|
||||||
|
|
|
||||||
|
|
@ -156,8 +156,10 @@ class DatabaseManager:
|
||||||
|
|
||||||
def create_test_database_manager() -> DatabaseManager:
|
def create_test_database_manager() -> DatabaseManager:
|
||||||
"""Create a test database manager for tests."""
|
"""Create a test database manager for tests."""
|
||||||
# Use a test database URL with credentials
|
# Use a test database URL with credentials matching docker setup
|
||||||
test_db_url = "postgresql://postgres:postgres@localhost:5432/tradingagents_test"
|
test_db_url = (
|
||||||
|
"postgresql://postgres:tradingagents@localhost:5432/tradingagents_test"
|
||||||
|
)
|
||||||
|
|
||||||
# Create a test-specific database manager with NullPool
|
# Create a test-specific database manager with NullPool
|
||||||
db_manager = DatabaseManager(test_db_url)
|
db_manager = DatabaseManager(test_db_url)
|
||||||
|
|
|
||||||
256
uv.lock
256
uv.lock
|
|
@ -111,6 +111,15 @@ wheels = [
|
||||||
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sdist = { url = "https://files.pythonhosted.org/packages/ef/52/9ba0f43b686e7f3ddfeaa78ac3af750292662284b3661e91ad5494f21dbc/structlog-25.5.0.tar.gz", hash = "sha256:098522a3bebed9153d4570c6d0288abf80a031dfdb2048d59a49e9dc2190fc98", size = 1460830, upload-time = "2025-10-27T08:28:23.028Z" }
|
||||||
|
wheels = [
|
||||||
|
{ url = "https://files.pythonhosted.org/packages/a8/45/a132b9074aa18e799b891b91ad72133c98d8042c70f6240e4c5f9dabee2f/structlog-25.5.0-py3-none-any.whl", hash = "sha256:a8453e9b9e636ec59bd9e79bbd4a72f025981b3ba0f5837aebf48f02f37a7f9f", size = 72510, upload-time = "2025-10-27T08:28:21.535Z" },
|
||||||
|
]
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "sympy"
|
name = "sympy"
|
||||||
version = "1.14.0"
|
version = "1.14.0"
|
||||||
|
|
@ -3521,6 +3706,15 @@ dependencies = [
|
||||||
]
|
]
|
||||||
sdist = { url = "https://files.pythonhosted.org/packages/ba/97/a49816dd468a18ee080cf3a04640772a9f6321790d4049cece2490c4b7ad/ta_lib-0.6.4.tar.gz", hash = "sha256:08f55bc5771a6d1ceb1a2b713aad7b05f04eb0061e980c9113571c532d32e9cb", size = 381774, upload-time = "2025-06-08T15:28:15.452Z" }
|
sdist = { url = "https://files.pythonhosted.org/packages/ba/97/a49816dd468a18ee080cf3a04640772a9f6321790d4049cece2490c4b7ad/ta_lib-0.6.4.tar.gz", hash = "sha256:08f55bc5771a6d1ceb1a2b713aad7b05f04eb0061e980c9113571c532d32e9cb", size = 381774, upload-time = "2025-06-08T15:28:15.452Z" }
|
||||||
|
|
||||||
|
[[package]]
|
||||||
|
name = "tabulate"
|
||||||
|
version = "0.9.0"
|
||||||
|
source = { registry = "https://pypi.org/simple" }
|
||||||
|
sdist = { url = "https://files.pythonhosted.org/packages/ec/fe/802052aecb21e3797b8f7902564ab6ea0d60ff8ca23952079064155d1ae1/tabulate-0.9.0.tar.gz", hash = "sha256:0095b12bf5966de529c0feb1fa08671671b3368eec77d7ef7ab114be2c068b3c", size = 81090, upload-time = "2022-10-06T17:21:48.54Z" }
|
||||||
|
wheels = [
|
||||||
|
{ url = "https://files.pythonhosted.org/packages/40/44/4a5f08c96eb108af5cb50b41f76142f0afa346dfa99d5296fe7202a11854/tabulate-0.9.0-py3-none-any.whl", hash = "sha256:024ca478df22e9340661486f85298cff5f6dcdba14f3813e8830015b9ed1948f", size = 35252, upload-time = "2022-10-06T17:21:44.262Z" },
|
||||||
|
]
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "tenacity"
|
name = "tenacity"
|
||||||
version = "9.1.2"
|
version = "9.1.2"
|
||||||
|
|
@ -3607,6 +3801,24 @@ wheels = [
|
||||||
{ url = "https://files.pythonhosted.org/packages/6e/c2/61d3e0f47e2b74ef40a68b9e6ad5984f6241a942f7cd3bbfbdbd03861ea9/tomli-2.2.1-py3-none-any.whl", hash = "sha256:cb55c73c5f4408779d0cf3eef9f762b9c9f147a77de7b258bef0a5628adc85cc", size = 14257, upload-time = "2024-11-27T22:38:35.385Z" },
|
{ url = "https://files.pythonhosted.org/packages/6e/c2/61d3e0f47e2b74ef40a68b9e6ad5984f6241a942f7cd3bbfbdbd03861ea9/tomli-2.2.1-py3-none-any.whl", hash = "sha256:cb55c73c5f4408779d0cf3eef9f762b9c9f147a77de7b258bef0a5628adc85cc", size = 14257, upload-time = "2024-11-27T22:38:35.385Z" },
|
||||||
]
|
]
|
||||||
|
|
||||||
|
[[package]]
|
||||||
|
name = "tomlkit"
|
||||||
|
version = "0.13.3"
|
||||||
|
source = { registry = "https://pypi.org/simple" }
|
||||||
|
sdist = { url = "https://files.pythonhosted.org/packages/cc/18/0bbf3884e9eaa38819ebe46a7bd25dcd56b67434402b66a58c4b8e552575/tomlkit-0.13.3.tar.gz", hash = "sha256:430cf247ee57df2b94ee3fbe588e71d362a941ebb545dec29b53961d61add2a1", size = 185207, upload-time = "2025-06-05T07:13:44.947Z" }
|
||||||
|
wheels = [
|
||||||
|
{ url = "https://files.pythonhosted.org/packages/bd/75/8539d011f6be8e29f339c42e633aae3cb73bffa95dd0f9adec09b9c58e85/tomlkit-0.13.3-py3-none-any.whl", hash = "sha256:c89c649d79ee40629a9fda55f8ace8c6a1b42deb912b2a8fd8d942ddadb606b0", size = 38901, upload-time = "2025-06-05T07:13:43.546Z" },
|
||||||
|
]
|
||||||
|
|
||||||
|
[[package]]
|
||||||
|
name = "toposort"
|
||||||
|
version = "1.10"
|
||||||
|
source = { registry = "https://pypi.org/simple" }
|
||||||
|
sdist = { url = "https://files.pythonhosted.org/packages/69/19/8e955d90985ecbd3b9adb2a759753a6840da2dff3c569d412b2c9217678b/toposort-1.10.tar.gz", hash = "sha256:bfbb479c53d0a696ea7402601f4e693c97b0367837c8898bc6471adfca37a6bd", size = 11132, upload-time = "2023-02-27T13:59:51.834Z" }
|
||||||
|
wheels = [
|
||||||
|
{ url = "https://files.pythonhosted.org/packages/f6/17/57b444fd314d5e1593350b9a31d000e7411ba8e17ce12dc7ad54ca76b810/toposort-1.10-py3-none-any.whl", hash = "sha256:cbdbc0d0bee4d2695ab2ceec97fe0679e9c10eab4b2a87a9372b929e70563a87", size = 8500, upload-time = "2023-02-25T20:07:06.538Z" },
|
||||||
|
]
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "tqdm"
|
name = "tqdm"
|
||||||
version = "4.67.1"
|
version = "4.67.1"
|
||||||
|
|
@ -3684,6 +3896,8 @@ dependencies = [
|
||||||
{ name = "backtrader" },
|
{ name = "backtrader" },
|
||||||
{ name = "chainlit" },
|
{ name = "chainlit" },
|
||||||
{ name = "chromadb" },
|
{ name = "chromadb" },
|
||||||
|
{ name = "dagster" },
|
||||||
|
{ name = "dagster-postgres" },
|
||||||
{ name = "eodhd" },
|
{ name = "eodhd" },
|
||||||
{ name = "feedparser" },
|
{ name = "feedparser" },
|
||||||
{ name = "finnhub-python" },
|
{ name = "finnhub-python" },
|
||||||
|
|
@ -3744,6 +3958,8 @@ requires-dist = [
|
||||||
{ name = "backtrader", specifier = ">=1.9.78.123" },
|
{ name = "backtrader", specifier = ">=1.9.78.123" },
|
||||||
{ name = "chainlit", specifier = ">=2.5.5" },
|
{ name = "chainlit", specifier = ">=2.5.5" },
|
||||||
{ name = "chromadb", specifier = ">=1.0.12" },
|
{ name = "chromadb", specifier = ">=1.0.12" },
|
||||||
|
{ name = "dagster", specifier = ">=1.8.0" },
|
||||||
|
{ name = "dagster-postgres", specifier = ">=0.24.0" },
|
||||||
{ name = "eodhd", specifier = ">=1.0.32" },
|
{ name = "eodhd", specifier = ">=1.0.32" },
|
||||||
{ name = "feedparser", specifier = ">=6.0.11" },
|
{ name = "feedparser", specifier = ">=6.0.11" },
|
||||||
{ name = "finnhub-python", specifier = ">=2.4.23" },
|
{ name = "finnhub-python", specifier = ">=2.4.23" },
|
||||||
|
|
@ -3870,6 +4086,19 @@ wheels = [
|
||||||
{ url = "https://files.pythonhosted.org/packages/5c/23/c7abc0ca0a1526a0774eca151daeb8de62ec457e77262b66b359c3c7679e/tzdata-2025.2-py2.py3-none-any.whl", hash = "sha256:1a403fada01ff9221ca8044d701868fa132215d84beb92242d9acd2147f667a8", size = 347839, upload-time = "2025-03-23T13:54:41.845Z" },
|
{ url = "https://files.pythonhosted.org/packages/5c/23/c7abc0ca0a1526a0774eca151daeb8de62ec457e77262b66b359c3c7679e/tzdata-2025.2-py2.py3-none-any.whl", hash = "sha256:1a403fada01ff9221ca8044d701868fa132215d84beb92242d9acd2147f667a8", size = 347839, upload-time = "2025-03-23T13:54:41.845Z" },
|
||||||
]
|
]
|
||||||
|
|
||||||
|
[[package]]
|
||||||
|
name = "universal-pathlib"
|
||||||
|
version = "0.3.6"
|
||||||
|
source = { registry = "https://pypi.org/simple" }
|
||||||
|
dependencies = [
|
||||||
|
{ name = "fsspec" },
|
||||||
|
{ name = "pathlib-abc" },
|
||||||
|
]
|
||||||
|
sdist = { url = "https://files.pythonhosted.org/packages/76/db/6874223d251a2e146dae57a27ca8cb1f71e7e135aa51ad394173ffe18fc0/universal_pathlib-0.3.6.tar.gz", hash = "sha256:d8640454ff08305fc639f7980e8bad4a7d38e82f6389ff993fb0e7b2a4969de9", size = 249113, upload-time = "2025-11-13T17:05:29.882Z" }
|
||||||
|
wheels = [
|
||||||
|
{ url = "https://files.pythonhosted.org/packages/47/5d/fc1f5478eb486a59549e0dbea5827633bbba01139b549968d4936154b756/universal_pathlib-0.3.6-py3-none-any.whl", hash = "sha256:ff10a86e5340ad986b6f04847bb64ba397dff7467450234ffa2ab5ff135641d8", size = 78715, upload-time = "2025-11-13T17:05:28.101Z" },
|
||||||
|
]
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "update-checker"
|
name = "update-checker"
|
||||||
version = "0.18.0"
|
version = "0.18.0"
|
||||||
|
|
@ -4009,6 +4238,27 @@ wheels = [
|
||||||
{ url = "https://files.pythonhosted.org/packages/58/dd/56f0d8af71e475ed194d702f8b4cf9cea812c95e82ad823d239023c6558c/w3lib-2.3.1-py3-none-any.whl", hash = "sha256:9ccd2ae10c8c41c7279cd8ad4fe65f834be894fe7bfdd7304b991fd69325847b", size = 21751, upload-time = "2025-01-27T14:22:09.421Z" },
|
{ url = "https://files.pythonhosted.org/packages/58/dd/56f0d8af71e475ed194d702f8b4cf9cea812c95e82ad823d239023c6558c/w3lib-2.3.1-py3-none-any.whl", hash = "sha256:9ccd2ae10c8c41c7279cd8ad4fe65f834be894fe7bfdd7304b991fd69325847b", size = 21751, upload-time = "2025-01-27T14:22:09.421Z" },
|
||||||
]
|
]
|
||||||
|
|
||||||
|
[[package]]
|
||||||
|
name = "watchdog"
|
||||||
|
version = "6.0.0"
|
||||||
|
source = { registry = "https://pypi.org/simple" }
|
||||||
|
sdist = { url = "https://files.pythonhosted.org/packages/db/7d/7f3d619e951c88ed75c6037b246ddcf2d322812ee8ea189be89511721d54/watchdog-6.0.0.tar.gz", hash = "sha256:9ddf7c82fda3ae8e24decda1338ede66e1c99883db93711d8fb941eaa2d8c282", size = 131220, upload-time = "2024-11-01T14:07:13.037Z" }
|
||||||
|
wheels = [
|
||||||
|
{ url = "https://files.pythonhosted.org/packages/68/98/b0345cabdce2041a01293ba483333582891a3bd5769b08eceb0d406056ef/watchdog-6.0.0-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:490ab2ef84f11129844c23fb14ecf30ef3d8a6abafd3754a6f75ca1e6654136c", size = 96480, upload-time = "2024-11-01T14:06:42.952Z" },
|
||||||
|
{ url = "https://files.pythonhosted.org/packages/85/83/cdf13902c626b28eedef7ec4f10745c52aad8a8fe7eb04ed7b1f111ca20e/watchdog-6.0.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:76aae96b00ae814b181bb25b1b98076d5fc84e8a53cd8885a318b42b6d3a5134", size = 88451, upload-time = "2024-11-01T14:06:45.084Z" },
|
||||||
|
{ url = "https://files.pythonhosted.org/packages/fe/c4/225c87bae08c8b9ec99030cd48ae9c4eca050a59bf5c2255853e18c87b50/watchdog-6.0.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:a175f755fc2279e0b7312c0035d52e27211a5bc39719dd529625b1930917345b", size = 89057, upload-time = "2024-11-01T14:06:47.324Z" },
|
||||||
|
{ url = "https://files.pythonhosted.org/packages/a9/c7/ca4bf3e518cb57a686b2feb4f55a1892fd9a3dd13f470fca14e00f80ea36/watchdog-6.0.0-py3-none-manylinux2014_aarch64.whl", hash = "sha256:7607498efa04a3542ae3e05e64da8202e58159aa1fa4acddf7678d34a35d4f13", size = 79079, upload-time = "2024-11-01T14:06:59.472Z" },
|
||||||
|
{ url = "https://files.pythonhosted.org/packages/5c/51/d46dc9332f9a647593c947b4b88e2381c8dfc0942d15b8edc0310fa4abb1/watchdog-6.0.0-py3-none-manylinux2014_armv7l.whl", hash = "sha256:9041567ee8953024c83343288ccc458fd0a2d811d6a0fd68c4c22609e3490379", size = 79078, upload-time = "2024-11-01T14:07:01.431Z" },
|
||||||
|
{ url = "https://files.pythonhosted.org/packages/d4/57/04edbf5e169cd318d5f07b4766fee38e825d64b6913ca157ca32d1a42267/watchdog-6.0.0-py3-none-manylinux2014_i686.whl", hash = "sha256:82dc3e3143c7e38ec49d61af98d6558288c415eac98486a5c581726e0737c00e", size = 79076, upload-time = "2024-11-01T14:07:02.568Z" },
|
||||||
|
{ url = "https://files.pythonhosted.org/packages/ab/cc/da8422b300e13cb187d2203f20b9253e91058aaf7db65b74142013478e66/watchdog-6.0.0-py3-none-manylinux2014_ppc64.whl", hash = "sha256:212ac9b8bf1161dc91bd09c048048a95ca3a4c4f5e5d4a7d1b1a7d5752a7f96f", size = 79077, upload-time = "2024-11-01T14:07:03.893Z" },
|
||||||
|
{ url = "https://files.pythonhosted.org/packages/2c/3b/b8964e04ae1a025c44ba8e4291f86e97fac443bca31de8bd98d3263d2fcf/watchdog-6.0.0-py3-none-manylinux2014_ppc64le.whl", hash = "sha256:e3df4cbb9a450c6d49318f6d14f4bbc80d763fa587ba46ec86f99f9e6876bb26", size = 79078, upload-time = "2024-11-01T14:07:05.189Z" },
|
||||||
|
{ url = "https://files.pythonhosted.org/packages/62/ae/a696eb424bedff7407801c257d4b1afda455fe40821a2be430e173660e81/watchdog-6.0.0-py3-none-manylinux2014_s390x.whl", hash = "sha256:2cce7cfc2008eb51feb6aab51251fd79b85d9894e98ba847408f662b3395ca3c", size = 79077, upload-time = "2024-11-01T14:07:06.376Z" },
|
||||||
|
{ url = "https://files.pythonhosted.org/packages/b5/e8/dbf020b4d98251a9860752a094d09a65e1b436ad181faf929983f697048f/watchdog-6.0.0-py3-none-manylinux2014_x86_64.whl", hash = "sha256:20ffe5b202af80ab4266dcd3e91aae72bf2da48c0d33bdb15c66658e685e94e2", size = 79078, upload-time = "2024-11-01T14:07:07.547Z" },
|
||||||
|
{ url = "https://files.pythonhosted.org/packages/07/f6/d0e5b343768e8bcb4cda79f0f2f55051bf26177ecd5651f84c07567461cf/watchdog-6.0.0-py3-none-win32.whl", hash = "sha256:07df1fdd701c5d4c8e55ef6cf55b8f0120fe1aef7ef39a1c6fc6bc2e606d517a", size = 79065, upload-time = "2024-11-01T14:07:09.525Z" },
|
||||||
|
{ url = "https://files.pythonhosted.org/packages/db/d9/c495884c6e548fce18a8f40568ff120bc3a4b7b99813081c8ac0c936fa64/watchdog-6.0.0-py3-none-win_amd64.whl", hash = "sha256:cbafb470cf848d93b5d013e2ecb245d4aa1c8fd0504e863ccefa32445359d680", size = 79070, upload-time = "2024-11-01T14:07:10.686Z" },
|
||||||
|
{ url = "https://files.pythonhosted.org/packages/33/e8/e40370e6d74ddba47f002a32919d91310d6074130fe4e17dabcafc15cbf1/watchdog-6.0.0-py3-none-win_ia64.whl", hash = "sha256:a1914259fa9e1454315171103c6a30961236f508b9b623eae470268bbcc6a22f", size = 79067, upload-time = "2024-11-01T14:07:11.845Z" },
|
||||||
|
]
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "watchfiles"
|
name = "watchfiles"
|
||||||
version = "0.20.0"
|
version = "0.20.0"
|
||||||
|
|
|
||||||
Loading…
Reference in New Issue