AI-Trader/CHANGELOG.md

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

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 Deployment - Persistent REST API service
  • API-only deployment (batch mode removed for simplicity)
  • Single docker-compose service (ai-trader)
  • Health check configuration (30s interval, 3 retries)
  • Volume persistence for SQLite database and logs
  • Configurable API_PORT for flexible deployment
  • System dependencies (curl, procps) for health checks and debugging
• Validation & Testing Tools
  • scripts/validate_docker_build.sh - Docker build and startup validation with port awareness
  • scripts/test_api_endpoints.sh - Complete API endpoint testing suite with port awareness
  • TESTING_GUIDE.md - Comprehensive testing procedures and troubleshooting (including port conflicts)
• 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-first service with database persistence
• Data Storage - Migrated from JSONL files to SQLite relational database
• Deployment - Simplified to single API-only Docker service
• Configuration - Simplified environment variable configuration
  • Added configurable API_PORT for host port mapping (default: 8080, customizable for port conflicts)
  • Removed RUNTIME_ENV_PATH (API dynamically manages runtime configs via RuntimeConfigManager)
  • Removed MCP service port configuration (MATH_HTTP_PORT, SEARCH_HTTP_PORT, TRADE_HTTP_PORT, GETPRICE_HTTP_PORT)
  • MCP services use fixed internal ports (8000-8003) and are no longer exposed to host
  • Container always uses port 8080 internally for API (hardcoded in entrypoint.sh)
  • Only API port (8080) and web dashboard (8888) are exposed to host
  • Reduces configuration complexity and attack surface
• Requirements - Added fastapi>=0.120.0, uvicorn[standard]>=0.27.0, pydantic>=2.0.0
• Docker Compose - Single service (ai-trader) instead of dual-mode
• Dockerfile - Added system dependencies (curl, procps) and port 8080 exposure
• .env.example - Simplified configuration with only essential variables
• Entrypoint - Unified entrypoint.sh with proper signal handling (exec uvicorn)

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

Breaking Changes

• Batch Mode Removed - All simulations now run through REST API
  • Simplifies deployment and eliminates dual-mode complexity
  • Focus on API-first architecture for external orchestration
  • Migration: Use POST /simulate/trigger endpoint instead of batch execution

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)

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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

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