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Removes dual-mode deployment complexity, focusing on REST API service only. Changes: - Removed batch mode from docker-compose.yml (now single ai-trader service) - Deleted scripts/test_batch_mode.sh validation script - Renamed entrypoint-api.sh to entrypoint.sh (now default) - Simplified Dockerfile (single entrypoint, removed CMD) - Updated validation scripts to use 'ai-trader' service name - Updated documentation (README.md, TESTING_GUIDE.md, CHANGELOG.md) Benefits: - Eliminates port conflicts between batch and API services - Simpler configuration and deployment - API-first architecture aligned with Windmill integration - Reduced maintenance complexity Breaking Changes: - Batch mode no longer available - All simulations must use REST API endpoints
9.0 KiB
9.0 KiB
Changelog
All notable changes to the AI-Trader project will be documented in this file.
The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.
Unreleased
Changed
- Simplified Deployment - Removed batch mode, now API-only
- Single docker-compose service (ai-trader) instead of dual mode
- Removed scripts/test_batch_mode.sh
- Streamlined entrypoint (entrypoint.sh now runs API server)
- Simplified docker-compose.yml configuration
Removed
- Batch Mode - Eliminated one-time batch simulation mode
- All simulations now run through REST API
- Removes complexity of dual-mode deployment
- Focus on API-first architecture for Windmill integration
0.3.0 - 2025-10-31
Added - API Service Transformation
- REST API Service - Complete FastAPI implementation for external orchestration
POST /simulate/trigger- Trigger simulation jobs with config, date range, and modelsGET /simulate/status/{job_id}- Query job progress and execution detailsGET /results- Retrieve simulation results with filtering (job_id, date, model)GET /health- Service health check with database connectivity verification
- SQLite Database - Complete persistence layer replacing JSONL files
- Jobs table - Job metadata and lifecycle tracking
- Job details table - Per model-day execution status
- Positions table - Trading position records with P&L
- Holdings table - Portfolio holdings breakdown
- Reasoning logs table - AI decision reasoning history
- Tool usage table - MCP tool usage statistics
- Backend Components
- JobManager - Job lifecycle management with concurrent job prevention
- RuntimeConfigManager - Isolated runtime configs for thread-safe execution
- ModelDayExecutor - Single model-day execution engine
- SimulationWorker - Job orchestration with date-sequential, model-parallel execution
- Comprehensive Test Suite
- 102 unit and integration tests (85% coverage)
- 19 database tests (98% coverage)
- 23 job manager tests (98% coverage)
- 10 model executor tests (84% coverage)
- 20 API endpoint tests (81% coverage)
- 20 Pydantic model tests (100% coverage)
- 10 runtime manager tests (89% coverage)
- Docker Dual-Mode Deployment
- API server mode - Persistent REST API service with health checks
- Batch mode - One-time simulation execution (backwards compatible)
- Separate entrypoints for each mode
- Health check configuration (30s interval, 3 retries)
- Volume persistence for SQLite database and logs
- Validation & Testing Tools
scripts/validate_docker_build.sh- Docker build and startup validationscripts/test_api_endpoints.sh- Complete API endpoint testing suitescripts/test_batch_mode.sh- Batch mode execution validation- TESTING_GUIDE.md - Comprehensive testing procedures and troubleshooting
- Documentation
- DOCKER_API.md - API deployment guide with examples
- TESTING_GUIDE.md - Validation procedures and troubleshooting
- API endpoint documentation with request/response examples
- Windmill integration patterns and examples
Changed
- Architecture - Transformed from batch-only to API service with database persistence
- Data Storage - Migrated from JSONL files to SQLite relational database
- Deployment - Added dual-mode Docker deployment (API server + batch)
- Configuration - Added API_PORT environment variable (default: 8080)
- Requirements - Added fastapi>=0.120.0, uvicorn[standard]>=0.27.0, pydantic>=2.0.0
- Docker Compose - Split into two services (ai-trader-api and ai-trader-batch)
- Dockerfile - Added port 8080 exposure for API server
- .env.example - Added API server configuration
Technical Implementation
- Test-Driven Development - All components written with tests first
- Mock-based Testing - Avoid heavy dependencies in unit tests
- Pydantic V2 - Type-safe request/response validation
- Foreign Key Constraints - Database referential integrity with cascade deletes
- Thread-safe Execution - Isolated runtime configs per model-day
- Background Job Execution - ThreadPoolExecutor for parallel model execution
- Automatic Status Transitions - Job status updates based on model-day completion
Performance & Quality
- Code Coverage - 85% overall (84.63% measured)
- Database layer: 98%
- Job manager: 98%
- Pydantic models: 100%
- Runtime manager: 89%
- Model executor: 84%
- FastAPI app: 81%
- Test Execution - 102 tests in ~2.5 seconds
- Zero Test Failures - All tests passing (threading tests excluded)
Integration Ready
- Windmill.dev - HTTP-based integration with polling support
- External Orchestration - RESTful API for workflow automation
- Monitoring - Health checks and status tracking
- Persistence - SQLite database survives container restarts
Backwards Compatibility
- Batch Mode - Original batch functionality preserved via Docker profile
- Configuration - Existing config files still work
- Data Migration - No automatic migration (fresh start recommended)
0.2.0 - 2025-10-31
Added
- Complete Docker deployment support with containerization
- Docker Compose orchestration for easy local deployment
- Multi-stage Dockerfile with Python 3.10-slim base image
- Automated CI/CD pipeline via GitHub Actions for release builds
- Automatic draft release creation with version tagging
- Docker images published to GitHub Container Registry (ghcr.io)
- Comprehensive Docker documentation (docs/DOCKER.md)
- Release process documentation (docs/RELEASING.md)
- Data cache reuse design documentation (docs/DESIGN_DATA_CACHE_REUSE.md)
- CLAUDE.md repository guidance for development
- Docker deployment section in main README
- Environment variable configuration via docker-compose
- Sequential startup script (entrypoint.sh) for data fetch, MCP services, and trading agent
- Volume mounts for data and logs persistence
- Pre-built image support from ghcr.io/xe138/ai-trader
- Configurable volume path for persistent data
- Configurable web interface host port
- Automated merged.jsonl creation during price fetching
- API key registration URLs in .env.example
Changed
- Updated .env.example with Docker-specific configuration, API key URLs, and paths
- Updated .gitignore to exclude git worktrees directory
- Removed deprecated version tag from docker-compose.yml
- Updated repository URLs to Xe138/AI-Trader fork
- Docker Compose now uses pre-built image by default
- Simplified Docker config file selection with convention over configuration
- Fixed internal ports with configurable host ports
- Separated data scripts from volume mount directory
- Reduced log flooding during data fetch
- OPENAI_API_BASE can now be left empty in configuration
Fixed
- Docker Compose configuration now follows modern best practices (version-less)
- Prevent restart loop on missing API keys with proper validation
- Docker tag generation now converts repository owner to lowercase
- Validate GITHUB_REF is a tag in docker-release workflow
- Correct Dockerfile FROM AS casing
- Module import errors for MCP services resolved with PYTHONPATH
- Prevent price data overwrite on container restart
- Merge script now writes to current directory for volume compatibility
0.1.0 - Initial Release
Added
- AI trading competition platform for NASDAQ 100 stocks
- Support for multiple AI models (GPT, Claude, Qwen, DeepSeek, Gemini)
- MCP (Model Context Protocol) toolchain integration
- Mathematical calculation tools
- Market intelligence search via Jina AI
- Trading execution tools
- Price query tools
- Historical replay architecture with anti-look-ahead controls
- Alpha Vantage API integration for price data
- Autonomous AI decision-making with zero human intervention
- Real-time performance analytics and leaderboard
- Position tracking and trading logs
- Web-based performance dashboard
- Complete NASDAQ 100 stock universe support
- Initial capital: $10,000 per AI model
- Configurable date range for backtesting
- Multi-model concurrent trading support
- Automatic data fetching and merging
- Comprehensive README with quick start guide
Technical Details
- Python 3.10+ support
- LangChain framework integration
- FastMCP for MCP service implementation
- JSONL format for position and log storage
- Weekday-only trading simulation
- Configurable agent parameters (max_steps, max_retries, initial_cash)
Release Notes Template
For future releases, use this template:
## [X.Y.Z] - YYYY-MM-DD
### Added
- New features
### Changed
- Changes to existing functionality
### Deprecated
- Soon-to-be removed features
### Removed
- Removed features
### Fixed
- Bug fixes
### Security
- Security improvements