# Changelog All notable changes to the AI-Trader project will be documented in this file. The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). ## [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 models - `GET /simulate/status/{job_id}` - Query job progress and execution details - `GET /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 validation - `scripts/test_api_endpoints.sh` - Complete API endpoint testing suite - `scripts/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: ```markdown ## [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 ``` --- [Unreleased]: https://github.com/Xe138/AI-Trader/compare/v0.3.0...HEAD [0.3.0]: https://github.com/Xe138/AI-Trader/compare/v0.2.0...v0.3.0 [0.2.0]: https://github.com/Xe138/AI-Trader/compare/v0.1.0...v0.2.0 [0.1.0]: https://github.com/Xe138/AI-Trader/releases/tag/v0.1.0