Add validation at startup to check required environment variables:
- OPENAI_API_KEY
- ALPHAADVANTAGE_API_KEY
- JINA_API_KEY
If any are missing, display clear error message with setup
instructions and exit immediately (no restart loop).
Change restart policy from 'unless-stopped' to 'on-failure:3'
to limit restart attempts and prevent endless loops on
configuration errors.
Move get_daily_price.py and merge_jsonl.py to /app/scripts
to prevent volume mount from overlaying the scripts.
Scripts run from /app/data context to output correctly.
Fixes container startup error where scripts were not found
after ./data volume mount replaced container's /app/data.
Add WEB_HTTP_PORT environment variable to configure
the host port for the web interface (port 8888).
Container still uses 8888 internally, but host port
can be customized via .env file.
Internal container ports are now fixed at 8000-8003.
Host ports are configurable via .env variables.
This prevents port conflicts inside the container while
allowing users to map to different host ports if needed.
Pull ghcr.io/xe138/ai-trader:latest by default instead of building.
Add commented build option for local development.
Makes deployment faster for end users.
Replace hardcoded release notes with generic template.
User can complete the release notes before publishing.
Includes TODO markers for summary and changelog content.
Complete guide for creating releases:
- Tag creation and push process
- GitHub Actions automation workflow
- Verification and testing steps
- Troubleshooting common issues
Triggers on release tags (v*) and manual dispatch
Builds and pushes to GitHub Container Registry
Tags with both version and latest
Uses build caching for faster builds
Mounts data and logs volumes for persistence
Injects environment variables from .env file
Exposes all MCP service ports and web dashboard
Auto-restart on failure
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
