Add comprehensive suite of testing scripts for different workflows:
- test.sh: Interactive menu for all testing operations
- quick_test.sh: Fast unit test feedback (~10-30s)
- run_tests.sh: Main test runner with full configuration options
- coverage_report.sh: Coverage analysis with HTML/JSON/terminal reports
- ci_test.sh: CI/CD optimized testing with JUnit/coverage XML output
Features:
- Colored terminal output with clear error messages
- Consistent option flags across all scripts
- Support for test markers (unit, integration, e2e, slow, etc.)
- Parallel execution support
- Coverage thresholds (default: 85%)
- Virtual environment and dependency checks
Documentation:
- Update CLAUDE.md with testing section and examples
- Expand docs/developer/testing.md with comprehensive guide
- Add scripts/README.md with quick reference
All scripts are tested and executable. This standardizes the testing
process for local development, CI/CD, and pull request workflows.
Complete rebrand of project to reflect REST API service architecture:
- Updated all documentation (README, guides, API reference)
- Updated Docker configuration (compose, Dockerfile, images)
- Updated all repository URLs to Xe138/AI-Trader-Server
- Updated all Docker images to ghcr.io/xe138/ai-trader-server
- Added fork acknowledgment crediting HKUDS/AI-Trader
- Updated GitHub Actions workflows and shell scripts
All 4 phases completed with validation checkpoints.
Co-Authored-By: Claude <noreply@anthropic.com>
Update project overview and Docker commands to reflect AI-Trader-Server
naming:
- Change project description to emphasize REST API service
- Update Docker image references from ghcr.io/hkuds/ai-trader to
ghcr.io/xe138/ai-trader-server
- Update container names from ai-trader to ai-trader-server
- Update GitHub Actions URL to Xe138/AI-Trader-Server repository
Part of Phase 3: Developer & Deployment Documentation
Add comprehensive development mode documentation to README.md, API_REFERENCE.md, and CLAUDE.md:
README.md:
- New "Development Mode" section after Configuration
- Quick start guide with environment variables
- Explanation of DEV vs PROD mode behavior
- Mock AI behavior and stock rotation details
- Environment variables reference
- Use cases and limitations
API_REFERENCE.md:
- New "Deployment Mode" section after health check
- Response format with deployment_mode fields
- DEV mode behavior explanation
- Health check example with deployment fields
- Use cases for testing and CI/CD
CLAUDE.md:
- New "Development Mode" subsection in Important Implementation Details
- Deployment modes overview
- DEV mode characteristics
- Implementation details with file references
- Testing commands and mock behavior notes
All sections explain:
- DEPLOYMENT_MODE environment variable (PROD/DEV)
- PRESERVE_DEV_DATA flag for dev data persistence
- Mock AI provider with deterministic stock rotation
- Separate dev database and data paths
- Use cases for development and testing
Reorganize documentation into user-focused, developer-focused, and deployment-focused sections.
**New structure:**
- Root: README.md (streamlined), QUICK_START.md, API_REFERENCE.md
- docs/user-guide/: configuration, API usage, integrations, troubleshooting
- docs/developer/: contributing, development setup, testing, architecture
- docs/deployment/: Docker deployment, production checklist, monitoring
- docs/reference/: environment variables, MCP tools, data formats
**Changes:**
- Streamline README.md from 831 to 469 lines
- Create QUICK_START.md for 5-minute onboarding
- Create API_REFERENCE.md as single source of truth for API
- Remove 9 outdated specification docs (v0.2.0 API design)
- Remove DOCKER_API.md (content consolidated into new structure)
- Remove docs/plans/ directory with old design documents
- Update CLAUDE.md with documentation structure guide
- Remove orchestration-specific references
**Benefits:**
- Clear entry points for different audiences
- No content duplication
- Better discoverability through logical hierarchy
- All content reflects current v0.3.0 API
MCP services are completely internal to the container and accessed
only via localhost. They should not be configurable or exposed.
Changes:
- Remove MATH_HTTP_PORT, SEARCH_HTTP_PORT, TRADE_HTTP_PORT,
GETPRICE_HTTP_PORT from docker-compose.yml environment
- Remove MCP service port mappings from docker-compose.yml
- Remove MCP port configuration from .env.example
- Update README.md to remove MCP port configuration
- Update CLAUDE.md to clarify MCP services use fixed internal ports
- Update CHANGELOG.md with these simplifications
Technical details:
- MCP services hardcode to ports 8000-8003 via os.getenv() defaults
- Services only accessed via localhost URLs within container:
- http://localhost:8000/mcp (math)
- http://localhost:8001/mcp (search)
- http://localhost:8002/mcp (trade)
- http://localhost:8003/mcp (price)
- No external access needed or desired for these services
- Only API (8080) and web dashboard (8888) should be exposed
Benefits:
- Simpler configuration (4 fewer environment variables)
- Reduced attack surface (4 fewer exposed ports)
- Clearer architecture (internal vs external services)
- Prevents accidental misconfiguration of internal services
Simplifies deployment configuration by removing the RUNTIME_ENV_PATH
environment variable, which is no longer needed for API mode.
Changes:
- Remove RUNTIME_ENV_PATH from docker-compose.yml
- Remove RUNTIME_ENV_PATH from .env.example
- Update CLAUDE.md to reflect API-managed runtime configs
- Update README.md to remove RUNTIME_ENV_PATH from config examples
- Update CHANGELOG.md with this simplification
Technical details:
- API mode dynamically creates isolated runtime config files via
RuntimeConfigManager (data/runtime_env_{job_id}_{model}_{date}.json)
- tools/general_tools.py already handles missing RUNTIME_ENV_PATH
gracefully, returning empty dict and warning on writes
- No functional impact - all tests pass without this variable set
- Reduces configuration complexity for new deployments
Breaking change: None - variable was vestigial from batch mode era
Provides comprehensive guidance for working with the AI-Trader codebase including:
- Development commands for setup, data preparation, and running simulations
- Architecture overview of agent system, MCP toolchain, and data flow
- Configuration system with multi-layered priority
- Data formats for positions and price data
- Implementation details and common troubleshooting steps
