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Author SHA1 Message Date
5da02b4ba0 docs: update CHANGELOG.md for v0.2.0 release
Update changelog with comprehensive release notes including:
- All features added during alpha testing phase
- Configuration improvements and new documentation
- Bug fixes and stability improvements
- Corrected release date to 2025-10-31

Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
2025-10-31 00:28:13 -04:00
11509ba8c7 fix: merge script now writes to current directory for volume compatibility
Changed merge_jsonl.py to use os.getcwd() instead of os.path.dirname(__file__)
to ensure merged.jsonl is written to the working directory where data files exist,
not to the script's installation directory.

Root cause:
- Dockerfile copies scripts to /app/scripts/ for volume compatibility
- entrypoint.sh runs: cd /app/data && python /app/scripts/merge_jsonl.py
- Old logic used script directory (/app/scripts/), ignoring working directory
- This caused merged.jsonl to be created in /app/scripts/ instead of /app/data/
- Since /app/data/ is volume-mounted, merged file was not visible to host

Solution:
- Scripts now respect current working directory (Unix philosophy)
- Works correctly with volume mounts and script relocation
- Tested in both local and Docker directory structure scenarios

Fixes the issue where merged.jsonl was missing from mounted data volume.
2025-10-31 00:03:39 -04:00
1785f9b06f feat: automate merged.jsonl creation during price fetching
Streamline the data preparation workflow by having get_daily_price.py
automatically invoke merge_jsonl.py after fetching all stock prices.

Changes:
- Modified get_daily_price.py to call merge_jsonl.py automatically
- Updated entrypoint.sh to remove redundant merge_jsonl.py call
- Updated main.sh to remove redundant merge_jsonl.py call
- Fixed import order for linting compliance

Benefits:
- Single command now handles both fetching and merging
- Ensures merged.jsonl is always created after price updates
- Simplifies Docker container startup process
- Prevents missing merged.jsonl errors in production
2025-10-30 23:48:12 -04:00
55206549c7 feat: add configurable volume path for persistent data
Add VOLUME_PATH environment variable to customize storage location for
all persistent data (data/, logs/, configs/ subdirectories).

Changes:
- .env.example: Add VOLUME_PATH variable with documentation
- docker-compose.yml: Use ${VOLUME_PATH:-.} for all volume mounts
- docs/DOCKER.md: Document custom volume location feature

Benefits:
- Store data outside project directory (e.g., on separate disk)
- Easier backup/restore (single directory)
- Clean separation of code and data
- Support for absolute or relative paths

Usage:
  # In .env file
  VOLUME_PATH=/home/user/trading-data

  # Results in mounts:
  /home/user/trading-data/data:/app/data
  /home/user/trading-data/logs:/app/logs
  /home/user/trading-data/configs:/app/configs

Defaults to current directory (.) if not set for backward compatibility.
2025-10-30 23:40:21 -04:00
9e05ce0891 fix: prevent price data overwrite on container restart
Preserve existing merged.jsonl to avoid data loss and API rate limits.
Only fetch new data if merged.jsonl is missing or empty.

Problem:
- Entrypoint always fetched fresh data from Alpha Vantage on every start
- Overwrote existing mounted data directory
- Caused API rate limit issues and data inconsistencies
- Lost historical data needed for backtesting specific date ranges

Solution:
- Check if merged.jsonl exists and has content before fetching
- Display stock count when using existing data
- Provide manual refresh instructions for when updates are needed

Benefits:
- Faster container startup (no API calls if data exists)
- Avoids Alpha Vantage rate limits (5 calls/min, 500/day)
- Preserves user's existing historical datasets
- Enables reliable backtesting with consistent data

To refresh data: rm data/merged.jsonl && docker-compose restart
2025-10-30 23:27:58 -04:00
2f2c1d6ea2 feat: simplify Docker config file selection with convention over configuration
Implement automatic detection of custom_config.json for simpler Docker usage.
No environment variables or command-line arguments needed for most users.

Changes:
- entrypoint.sh: Add smart config detection (custom_config.json > CLI arg > default_config.json)
- docker-compose.yml: Add configs volume mount for editing without rebuilds
- docs/DOCKER.md: Update documentation with simplified workflow
- .gitignore: Add custom_config.json to prevent accidental commits

Usage:
  cp configs/default_config.json configs/custom_config.json
  nano configs/custom_config.json
  docker-compose up  # Automatically uses custom_config.json

Simplifies user experience by following convention over configuration principle.
2025-10-30 22:34:22 -04:00
e9c571402a fix: set PYTHONPATH for MCP services to resolve import errors
Root cause: Python adds script directory (not working directory)
to sys.path. Services in /app/agent_tools/ couldn't import from
/app/tools/ because sys.path[0] was /app/agent_tools.

Solution: Set PYTHONPATH=/app when starting services so /app is
always in the Python import path.

Fixes: ModuleNotFoundError: No module named 'tools' in price.log
2025-10-30 21:47:58 -04:00
79d14444ed docs: add data cache reuse design document
Captures design for staleness-based data refresh to avoid
re-fetching all 103 NASDAQ tickers on every container restart.

Key features:
- Check all daily_prices_*.json files for staleness
- Configurable MAX_DATA_AGE_DAYS threshold (default: 7)
- Bash wrapper logic in entrypoint.sh
- Handles edge cases (partial data, missing files, rate limits)
2025-10-30 21:46:05 -04:00
12ecb1e6b6 docs: clarify OPENAI_API_BASE can be left empty
Add comment explaining OPENAI_API_BASE can be left empty
to use the default OpenAI endpoint, or set to a custom
proxy URL if needed.

Sets default value to empty instead of placeholder URL.
2025-10-30 21:34:16 -04:00
203b60b252 Revert "fix: improve MCP service startup reliability"
This reverts commit d70362b9d4.
2025-10-30 21:33:12 -04:00
d70362b9d4 fix: improve MCP service startup reliability
- Clarify OPENAI_API_BASE can be left empty for default endpoint
- Increase initial wait time from 3s to 5s
- Add health checking for all MCP services (ports 8000-8003)
- Retry up to 10 times with 1s intervals
- Install netcat for port checking
- Display warnings if services aren't ready

Helps diagnose and prevent MCP client initialization errors.
2025-10-30 21:32:42 -04:00
5ec7977b47 fix: resolve module import error for MCP services
Run MCP service manager from /app instead of /app/agent_tools
to ensure services can import from tools module.

Changes:
- entrypoint.sh: Run start_mcp_services.py from /app directory
- start_mcp_services.py: Use absolute paths for service scripts
- start_mcp_services.py: Set working directory to /app for services

Fixes ModuleNotFoundError: No module named 'tools' in price.log
2025-10-30 21:22:42 -04:00
8142f38ab9 docs: add API key registration URLs to .env.example
Add inline comments with URLs for API key registration:
- OPENAI_API_KEY: https://platform.openai.com/api-keys
- ALPHAADVANTAGE_API_KEY: https://www.alphavantage.co/support/#api-key
- JINA_API_KEY: https://jina.ai/

Makes it easier for users to find where to get their API keys.
2025-10-30 21:17:21 -04:00
595a659fe7 fix: reduce log flooding during data fetch
Replace verbose JSON logging with concise status messages:
- Success: '✓ Fetched SYMBOL'
- Error: '⚠️  SYMBOL: API rate limit or error - [message]'

Prevents logs from being flooded with full JSON responses
for 100+ stock symbols during startup.
2025-10-30 21:15:59 -04:00
a4bc4fd0de fix: update repository URLs to Xe138/AI-Trader fork
Update all GitHub URLs in README from HKUDS/AI-Trader to
Xe138/AI-Trader including:
- Clone URLs (2 locations)
- Support & Community links (Discussions, Issues)
- Badge URLs (stars, forks)
2025-10-30 21:06:28 -04:00
4666e09385 fix: prevent restart loop on missing API keys
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.
2025-10-30 21:00:12 -04:00
0c58baed42 fix: separate data scripts from volume mount directory
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.
2025-10-30 20:53:44 -04:00
2d1c356199 feat: add configurable web interface host port
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.
2025-10-30 20:51:21 -04:00
ffa158224d fix: use fixed internal ports with configurable host ports
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.
2025-10-30 20:50:37 -04:00
6825a60b20 feat: use pre-built image by default in docker-compose
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.
2025-10-30 20:47:11 -04:00
9e308ee243 fix: use generic template for draft releases
Replace hardcoded release notes with generic template.
User can complete the release notes before publishing.
Includes TODO markers for summary and changelog content.
2025-10-30 20:40:30 -04:00
90ea10c7ef feat: add automatic draft release creation to workflow
Automatically create GitHub draft release with comprehensive
release notes when Docker build succeeds. Includes:
- Quick start instructions for Docker Compose and pre-built images
- Full changelog for v0.2.0
- Documentation links
- Automatic pre-release detection for alpha/beta/rc tags
2025-10-30 20:38:59 -04:00
77ce1b2b11 fix: correct Dockerfile FROM AS casing
Use uppercase AS keyword to match FROM keyword casing,
following Docker best practices and eliminating build warning.
2025-10-30 20:36:40 -04:00
2585045be1 fix: convert repository owner to lowercase for Docker tags
Docker tags require lowercase repository names. Convert
github.repository_owner to lowercase before using in tags.
2025-10-30 20:34:43 -04:00
9d5f449b1c fix: validate GITHUB_REF is a tag in docker-release workflow
Add validation to ensure workflow only processes tag pushes.
Prevents invalid Docker tags when workflow runs on non-tag refs.
2025-10-30 20:31:55 -04:00
e4b7e197d3 Update repository URLs for fork
Change HKUDS/AI-Trader to Xe138/AI-Trader
Update all documentation links and references
2025-10-30 20:26:18 -04:00
928f5fb53f Update CHANGELOG for v0.2.0 release
Set version to 0.2.0 for Docker deployment feature
Release date: 2025-10-30
Update comparison links for proper version tracking
2025-10-30 20:24:46 -04:00
46582d38bb Add CHANGELOG.md
Document all changes including Docker deployment feature
Follow Keep a Changelog format with semantic versioning
Include template for future releases
2025-10-30 20:22:10 -04:00
147fab11d5 Remove deprecated version tag from docker-compose.yml
Docker Compose no longer requires version specification
Maintains compatibility with modern docker-compose versions
2025-10-30 20:20:49 -04:00
85fc925b4e Add release process documentation
Complete guide for creating releases:
- Tag creation and push process
- GitHub Actions automation workflow
- Verification and testing steps
- Troubleshooting common issues
2025-10-30 19:26:56 -04:00
156d0cab21 Add Docker build test results
Local build verification completed successfully
Image builds without errors
Configuration parses correctly
2025-10-30 19:19:00 -04:00
4e7c4ccf99 Update CLAUDE.md with Docker commands
Add Docker build and run commands
Include release process for Docker images
Document GitHub Actions automation
2025-10-30 19:05:23 -04:00
9dfc8f1ea6 Add GitHub Actions workflow for Docker builds
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
2025-10-30 19:01:09 -04:00
6af1808c2f Add Docker deployment section to README
Include quick start with Docker Compose
Add pre-built image usage instructions
Link to detailed Docker documentation
2025-10-30 18:44:04 -04:00
733b6a4b1c Add Docker deployment documentation
Comprehensive guide including:
- Quick start instructions
- Configuration options
- Usage examples and volume persistence
- Troubleshooting common issues
- Pre-built image usage
2025-10-30 18:41:27 -04:00
592b105b19 Update .env.example with Docker configuration
Add Docker-specific paths and documentation
Include all required API keys and MCP ports
Show default values for optional settings
2025-10-30 18:39:01 -04:00
4af00d3709 Add docker-compose configuration
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
2025-10-30 18:36:25 -04:00
e9baa818a4 Add entrypoint script for container startup
Sequential execution: data fetch → MCP services → trading agent
Handles graceful shutdown of background services
Supports custom config file as argument
2025-10-30 18:33:40 -04:00
f25bd87c26 Add Dockerfile for containerization
Multi-stage build with Python 3.10-slim base
Exposes MCP service ports and web dashboard
Uses entrypoint.sh for sequential startup
2025-10-30 18:30:38 -04:00
794028e910 Add .dockerignore for Docker builds
Excludes git history, Python cache, secrets, and runtime data
2025-10-30 18:28:15 -04:00
efc2d754d6 Add Docker deployment implementation plan
Comprehensive 12-task plan including:
- Dockerfile and docker-compose.yml creation
- entrypoint.sh sequential startup script
- GitHub Actions CI/CD workflow
- Complete documentation (DOCKER.md, RELEASING.md)
- Testing and validation steps
2025-10-30 18:26:47 -04:00
5225950703 Add .worktrees/ to .gitignore
Prevents worktree contents from being tracked in repository
2025-10-30 18:23:14 -04:00
6794a65ef6 Add Docker deployment design document
Comprehensive design for Docker containerization including:
- Single monolithic container approach for dev/testing
- docker-compose.yml with environment variable injection
- Sequential startup via entrypoint.sh script
- GitHub Actions workflow for automated image builds on release tags
- Documentation strategy and implementation checklist
2025-10-30 18:22:03 -04:00
c15e25f826 Add CLAUDE.md for repository guidance
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
2025-10-30 18:03:24 -04:00
20 changed files with 3384 additions and 31 deletions

60
.dockerignore Normal file
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@@ -0,0 +1,60 @@
# Version control
.git/
.gitignore
# Python
__pycache__/
*.py[cod]
*$py.class
*.so
.Python
venv/
env/
ENV/
.venv/
# IDE
.vscode/
.idea/
*.swp
*.swo
# Environment and secrets
.env
.env.*
!.env.example
# Data files (fetched at runtime)
data/*.json
data/agent_data/
data/merged.jsonl
data/merged_daily.jsonl
data/merged_hour.jsonl
# Logs
logs/
*.log
# Runtime state
runtime_env.json
.runtime_env.json
# Documentation (not needed in image)
docs/
!README.md
# CI/CD
.github/
# Git worktrees
.worktrees/
# Test files
test.py
delete.py
refresh_data.sh
# Build artifacts
build/
dist/
*.egg-info/

View File

@@ -1,13 +1,38 @@
OPENAI_API_BASE=""
OPENAI_API_KEY=""
ALPHAADVANTAGE_API_KEY =""
JINA_API_KEY=""
# =============================================================================
# AI-Trader Environment Configuration
# =============================================================================
# Copy this file to .env and fill in your actual values
# Docker Compose automatically reads .env from project root
# AI Model API Configuration
# OPENAI_API_BASE: Leave empty to use default OpenAI endpoint, or set to custom proxy URL
OPENAI_API_BASE=
OPENAI_API_KEY=your_openai_key_here # https://platform.openai.com/api-keys
# Data Source Configuration
ALPHAADVANTAGE_API_KEY=your_alphavantage_key_here # https://www.alphavantage.co/support/#api-key
JINA_API_KEY=your_jina_key_here # https://jina.ai/
# System Configuration (Docker default paths)
RUNTIME_ENV_PATH=/app/data/runtime_env.json
# MCP Service Host Ports (exposed on host machine)
# Container always uses 8000-8003 internally
# Change these if you need different ports on your host
MATH_HTTP_PORT=8000
SEARCH_HTTP_PORT=8001
TRADE_HTTP_PORT=8002
GETPRICE_HTTP_PORT=8003
# Web Interface Host Port (exposed on host machine)
# Container always uses 8888 internally
WEB_HTTP_PORT=8888
# Agent Configuration
AGENT_MAX_STEP=30
RUNTIME_ENV_PATH = ""
# Data Volume Configuration
# Base directory for all persistent data (will contain data/, logs/, configs/ subdirectories)
# Use relative paths (./volumes) or absolute paths (/home/user/ai-trader-volumes)
# Defaults to current directory (.) if not set
VOLUME_PATH=.

139
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@@ -0,0 +1,139 @@
name: Build and Push Docker Image
on:
push:
tags:
- 'v*' # Triggers on v1.0.0, v2.1.3, etc.
workflow_dispatch: # Manual trigger option
jobs:
build-and-push:
runs-on: ubuntu-latest
permissions:
contents: write
packages: write
steps:
- name: Checkout code
uses: actions/checkout@v4
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v3
- name: Login to GitHub Container Registry
uses: docker/login-action@v3
with:
registry: ghcr.io
username: ${{ github.actor }}
password: ${{ secrets.GITHUB_TOKEN }}
- name: Extract version from tag
id: meta
run: |
# Ensure we're building from a tag
if [[ "$GITHUB_REF" != refs/tags/* ]]; then
echo "Error: This workflow should only run on tag pushes"
echo "GITHUB_REF: $GITHUB_REF"
exit 1
fi
VERSION=${GITHUB_REF#refs/tags/v}
echo "version=$VERSION" >> $GITHUB_OUTPUT
echo "Building version: $VERSION"
# Docker tags must be lowercase
REPO_OWNER_LOWER=$(echo "${{ github.repository_owner }}" | tr '[:upper:]' '[:lower:]')
echo "repo_owner_lower=$REPO_OWNER_LOWER" >> $GITHUB_OUTPUT
echo "Repository owner (lowercase): $REPO_OWNER_LOWER"
- name: Build and push Docker image
uses: docker/build-push-action@v5
with:
context: .
push: true
tags: |
ghcr.io/${{ steps.meta.outputs.repo_owner_lower }}/ai-trader:${{ steps.meta.outputs.version }}
ghcr.io/${{ steps.meta.outputs.repo_owner_lower }}/ai-trader:latest
cache-from: type=gha
cache-to: type=gha,mode=max
- name: Image published
run: |
echo "✅ Docker image published successfully!"
echo "📦 Pull with: docker pull ghcr.io/${{ steps.meta.outputs.repo_owner_lower }}/ai-trader:${{ steps.meta.outputs.version }}"
echo "📦 Or latest: docker pull ghcr.io/${{ steps.meta.outputs.repo_owner_lower }}/ai-trader:latest"
- name: Generate release notes
id: release_notes
run: |
VERSION="${{ steps.meta.outputs.version }}"
REPO_OWNER_LOWER="${{ steps.meta.outputs.repo_owner_lower }}"
# Check if this is an alpha/beta/rc release
if [[ "$VERSION" == *"-alpha"* ]] || [[ "$VERSION" == *"-beta"* ]] || [[ "$VERSION" == *"-rc"* ]]; then
PRERELEASE="true"
RELEASE_TYPE="Pre-release"
else
PRERELEASE="false"
RELEASE_TYPE="Release"
fi
echo "prerelease=$PRERELEASE" >> $GITHUB_OUTPUT
# Generate generic release notes template
cat > /tmp/release_notes.md << 'EOF'
## Release Notes
<!-- TODO: Add release summary and highlights here -->
### Quick Start
**Using Docker Compose:**
```bash
git clone https://github.com/Xe138/AI-Trader.git
cd AI-Trader
cp .env.example .env
# Edit .env with your API keys
docker-compose up
```
**Using pre-built image:**
```bash
docker pull ghcr.io/REPO_OWNER/ai-trader:VERSION
docker run --env-file .env \
-v $(pwd)/data:/app/data \
-v $(pwd)/logs:/app/logs \
ghcr.io/REPO_OWNER/ai-trader:VERSION
```
### Documentation
- [Docker Deployment Guide](docs/DOCKER.md)
- [Release Process](docs/RELEASING.md)
- [Full Changelog](CHANGELOG.md)
### Changes
<!-- TODO: Add changes from CHANGELOG.md here -->
See [CHANGELOG.md](CHANGELOG.md) for detailed changes.
---
**Container Registry:** `ghcr.io/REPO_OWNER/ai-trader:VERSION`
**Docker Image:** `ghcr.io/REPO_OWNER/ai-trader:latest`
EOF
# Replace placeholders
sed -i "s/REPO_OWNER/$REPO_OWNER_LOWER/g" /tmp/release_notes.md
sed -i "s/VERSION/$VERSION/g" /tmp/release_notes.md
cat /tmp/release_notes.md
- name: Create GitHub Release
uses: softprops/action-gh-release@v1
with:
tag_name: v${{ steps.meta.outputs.version }}
name: v${{ steps.meta.outputs.version }}
body_path: /tmp/release_notes.md
draft: true
prerelease: ${{ steps.release_notes.outputs.prerelease }}
token: ${{ secrets.GITHUB_TOKEN }}

4
.gitignore vendored
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@@ -57,6 +57,7 @@ delete.py
refresh_data.sh
# Config files (optional - uncomment if needed)
configs/custom_config.json
configs/day_config.json
configs/hour_config.json
configs/test_config.json
@@ -81,3 +82,6 @@ htmlcov/
.dmypy.json
dmypy.json
# Git worktrees
.worktrees/

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CHANGELOG.md Normal file
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@@ -0,0 +1,118 @@
# 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]
## [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/v0.2.0...HEAD
[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

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@@ -0,0 +1,329 @@
# CLAUDE.md
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
## Project Overview
AI-Trader is an autonomous AI trading competition platform where multiple AI models compete in NASDAQ 100 trading with zero human intervention. Each AI starts with $10,000 and uses standardized MCP (Model Context Protocol) tools to make fully autonomous trading decisions.
**Key Innovation:** Historical replay architecture with anti-look-ahead controls ensures AI agents can only access data from the current simulation date and earlier.
## Development Commands
### Environment Setup
```bash
# Install dependencies
pip install -r requirements.txt
# Configure environment variables
cp .env.example .env
# Edit .env and set:
# - OPENAI_API_BASE, OPENAI_API_KEY
# - ALPHAADVANTAGE_API_KEY, JINA_API_KEY
# - RUNTIME_ENV_PATH (recommended: absolute path to runtime_env.json)
# - MCP service ports (default: 8000-8003)
# - AGENT_MAX_STEP (default: 30)
```
### Data Preparation
```bash
# Download/update NASDAQ 100 stock data
cd data
python get_daily_price.py # Fetch daily prices from Alpha Vantage
python merge_jsonl.py # Merge into unified format (merged.jsonl)
cd ..
```
### Starting Services
```bash
# Start all MCP services (Math, Search, Trade, LocalPrices)
cd agent_tools
python start_mcp_services.py
cd ..
# Services run on ports defined in .env:
# - MATH_HTTP_PORT (default: 8000)
# - SEARCH_HTTP_PORT (default: 8001)
# - TRADE_HTTP_PORT (default: 8002)
# - GETPRICE_HTTP_PORT (default: 8003)
```
### Docker Deployment
```bash
# Build Docker image
docker-compose build
# Run with Docker Compose
docker-compose up
# Run in background
docker-compose up -d
# Run with custom config
docker-compose run ai-trader configs/my_config.json
# View logs
docker-compose logs -f
# Stop and remove containers
docker-compose down
# Pull pre-built image
docker pull ghcr.io/hkuds/ai-trader:latest
# Test local Docker build
docker build -t ai-trader-test .
docker run --env-file .env -v $(pwd)/data:/app/data ai-trader-test
```
### Releasing Docker Images
```bash
# Create and push release tag
git tag v1.0.0
git push origin v1.0.0
# GitHub Actions automatically:
# 1. Builds Docker image
# 2. Tags with version and latest
# 3. Pushes to ghcr.io/hkuds/ai-trader
# Verify build in Actions tab
# https://github.com/HKUDS/AI-Trader/actions
```
### Running Trading Simulations
```bash
# Run with default config
python main.py
# Run with custom config
python main.py configs/my_config.json
# Environment variables can override config dates:
INIT_DATE=2025-01-01 END_DATE=2025-01-31 python main.py
```
### Complete Workflow
```bash
# All-in-one startup script (data + services + trading + web)
bash main.sh
```
## Architecture
### Core Components
**1. Agent System** (`agent/base_agent/base_agent.py`)
- `BaseAgent`: Base class for all trading agents
- Manages MCP tool connections, AI model initialization, trading execution loops
- Handles position management and logging
- Supports retry logic with exponential backoff (`max_retries`, `base_delay`)
**2. Main Entry Point** (`main.py`)
- Dynamic agent class loading via `AGENT_REGISTRY`
- Multi-model concurrent trading support
- Date range validation and weekday filtering
- Configuration management (JSON + environment variables)
**3. MCP Toolchain** (`agent_tools/`)
- `tool_math.py`: Mathematical calculations (port 8000)
- `tool_jina_search.py`: Market intelligence search (port 8001)
- `tool_trade.py`: Buy/sell execution (port 8002)
- `tool_get_price_local.py`: Price queries (port 8003)
- `start_mcp_services.py`: Service orchestration with health checks
**4. Data Management** (`data/`)
- `daily_prices_*.json`: Individual stock OHLCV data
- `merged.jsonl`: Unified price data format
- `agent_data/[signature]/position/position.jsonl`: Position records
- `agent_data/[signature]/log/[date]/log.jsonl`: Trading logs
**5. Utilities** (`tools/`)
- `general_tools.py`: Config management, message extraction
- `price_tools.py`: Price queries, position updates
- `result_tools.py`: Performance calculations
### Data Flow
1. **Initialization**: Agent loads config, connects to MCP services, initializes AI model
2. **Trading Loop**: For each date:
- Get system prompt with current positions, yesterday's prices, today's buy prices
- AI agent analyzes market, calls search/math/price tools
- Makes buy/sell decisions via trade tool
- Logs all decisions and updates position.jsonl
3. **Position Tracking**: Each trade appends to `position.jsonl` with date, action, and updated holdings
### Configuration System
**Multi-layered config priority:**
1. Environment variables (highest)
2. Model-specific config (`openai_base_url`, `openai_api_key` in model config)
3. JSON config file
4. Default values (lowest)
**Runtime configuration** (`runtime_env.json` at `RUNTIME_ENV_PATH`):
- Dynamic state: `TODAY_DATE`, `SIGNATURE`, `IF_TRADE`
- Written by `write_config_value()`, read by `get_config_value()`
### Agent System
**BaseAgent Key Methods:**
- `initialize()`: Connect to MCP services, create AI model
- `run_trading_session(date)`: Execute single day's trading with retry logic
- `run_date_range(init_date, end_date)`: Process all weekdays in range
- `get_trading_dates()`: Resume from last date in position.jsonl
- `register_agent()`: Create initial position file with $10,000 cash
**Adding Custom Agents:**
1. Create new class inheriting from `BaseAgent`
2. Add to `AGENT_REGISTRY` in `main.py`:
```python
"CustomAgent": {
"module": "agent.custom.custom_agent",
"class": "CustomAgent"
}
```
3. Set `"agent_type": "CustomAgent"` in config JSON
### System Prompt Construction
**Dynamic prompt generation** (`prompts/agent_prompt.py`):
- `get_agent_system_prompt()` builds prompt with:
- Current date
- Yesterday's closing positions
- Yesterday's closing prices
- Today's buy prices
- Yesterday's profit/loss
- AI agent must output `<FINISH_SIGNAL>` to end trading session
### Anti-Look-Ahead Controls
**Data access restrictions:**
- Price data: Only returns data for `date <= TODAY_DATE`
- Search results: News filtered by publication date
- All tools enforce temporal boundaries via `TODAY_DATE` from `runtime_env.json`
## Configuration File Format
```json
{
"agent_type": "BaseAgent",
"date_range": {
"init_date": "2025-01-01",
"end_date": "2025-01-31"
},
"models": [
{
"name": "model-display-name",
"basemodel": "provider/model-id",
"signature": "unique-identifier",
"enabled": true,
"openai_base_url": "optional-override",
"openai_api_key": "optional-override"
}
],
"agent_config": {
"max_steps": 30, // Max reasoning iterations per day
"max_retries": 3, // Retry attempts on failure
"base_delay": 1.0, // Base retry delay (seconds)
"initial_cash": 10000.0
},
"log_config": {
"log_path": "./data/agent_data"
}
}
```
## Data Formats
**Position Record** (`position.jsonl`):
```json
{
"date": "2025-01-20",
"id": 1,
"this_action": {
"action": "buy",
"symbol": "AAPL",
"amount": 10
},
"positions": {
"AAPL": 10,
"MSFT": 0,
"CASH": 9737.6
}
}
```
**Price Data** (`merged.jsonl`):
```json
{
"Meta Data": {
"2. Symbol": "AAPL",
"3. Last Refreshed": "2025-01-20"
},
"Time Series (Daily)": {
"2025-01-20": {
"1. buy price": "255.8850",
"2. high": "264.3750",
"3. low": "255.6300",
"4. sell price": "262.2400",
"5. volume": "90483029"
}
}
}
```
## Important Implementation Details
**Trading Day Logic:**
- Only weekdays (Monday-Friday) are processed
- `get_trading_dates()` automatically resumes from last date in `position.jsonl`
- Skips days already processed (idempotent)
**Error Handling:**
- All async operations use `_ainvoke_with_retry()` with exponential backoff
- MCP service failures raise detailed error messages with troubleshooting hints
- Missing API keys halt startup with clear error messages
**Tool Message Extraction:**
- `extract_conversation(response, "final")`: Get AI's final answer
- `extract_tool_messages(response)`: Get all tool results
- Handles both dict and object-based message formats
**Logging:**
- Each trading day creates `log/[date]/log.jsonl`
- Logs include timestamps, signature, and all message exchanges
- Position updates append to single `position/position.jsonl`
## Testing Changes
When modifying agent behavior or adding tools:
1. Create test config with short date range (2-3 days)
2. Set `max_steps` low (e.g., 10) to iterate faster
3. Check logs in `data/agent_data/[signature]/log/[date]/`
4. Verify position updates in `position/position.jsonl`
5. Use `main.sh` only for full end-to-end testing
## Common Issues
**MCP Services Not Running:**
- Error: "Failed to initialize MCP client"
- Fix: `cd agent_tools && python start_mcp_services.py`
- Verify ports not already in use: `lsof -i :8000-8003`
**Missing Price Data:**
- Ensure `data/merged.jsonl` exists
- Run `cd data && python get_daily_price.py && python merge_jsonl.py`
- Check Alpha Vantage API key is valid
**Runtime Config Issues:**
- Set `RUNTIME_ENV_PATH` to absolute path in `.env`
- Ensure directory is writable
- File gets created automatically on first run
**Agent Doesn't Stop Trading:**
- Agent must output `<FINISH_SIGNAL>` within `max_steps`
- Increase `max_steps` if agent needs more reasoning time
- Check `log.jsonl` for errors preventing completion

38
Dockerfile Normal file
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@@ -0,0 +1,38 @@
# Base stage - dependency installation
FROM python:3.10-slim AS base
WORKDIR /app
# Install dependencies
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
# Application stage
FROM base
WORKDIR /app
# Copy application code
COPY . .
# Copy data scripts to separate directory (volume mount won't overlay these)
RUN mkdir -p /app/scripts && \
cp data/get_daily_price.py /app/scripts/ && \
cp data/get_interdaily_price.py /app/scripts/ && \
cp data/merge_jsonl.py /app/scripts/
# Create necessary directories
RUN mkdir -p data logs data/agent_data
# Make entrypoint executable
RUN chmod +x entrypoint.sh
# Expose MCP service ports and web dashboard
EXPOSE 8000 8001 8002 8003 8888
# Set Python to run unbuffered for real-time logs
ENV PYTHONUNBUFFERED=1
# Use entrypoint script
ENTRYPOINT ["./entrypoint.sh"]
CMD ["configs/default_config.json"]

View File

@@ -218,7 +218,7 @@ AI-Trader Bench/
```bash
# 1. Clone project
git clone https://github.com/HKUDS/AI-Trader.git
git clone https://github.com/Xe138/AI-Trader.git
cd AI-Trader
# 2. Install dependencies
@@ -323,6 +323,52 @@ python3 -m http.server 8000
# Visit http://localhost:8000
```
## 🐳 Docker Deployment
### Using Docker Compose (Recommended)
The easiest way to run AI-Trader is with Docker Compose:
```bash
# 1. Clone and setup
git clone https://github.com/Xe138/AI-Trader.git
cd AI-Trader
# 2. Configure environment
cp .env.example .env
# Edit .env with your API keys:
# - OPENAI_API_KEY
# - ALPHAADVANTAGE_API_KEY
# - JINA_API_KEY
# 3. Run with Docker Compose
docker-compose up
```
The container automatically:
- Fetches latest NASDAQ 100 price data
- Starts all MCP services
- Runs AI trading agents
### Using Pre-built Images
Pull and run pre-built images from GitHub Container Registry:
```bash
# Pull latest version
docker pull ghcr.io/hkuds/ai-trader:latest
# Run container
docker run --env-file .env \
-v $(pwd)/data:/app/data \
-v $(pwd)/logs:/app/logs \
ghcr.io/hkuds/ai-trader:latest
```
**📖 See [docs/DOCKER.md](docs/DOCKER.md) for detailed Docker usage, troubleshooting, and advanced configuration.**
---
## 📈 Performance Analysis
### 🏆 Competition Rules
@@ -544,8 +590,8 @@ We welcome contributions of all kinds! Especially AI trading strategies and agen
## 📞 Support & Community
- **💬 Discussions**: [GitHub Discussions](https://github.com/HKUDS/AI-Trader/discussions)
- **🐛 Issues**: [GitHub Issues](https://github.com/HKUDS/AI-Trader/issues)
- **💬 Discussions**: [GitHub Discussions](https://github.com/Xe138/AI-Trader/discussions)
- **🐛 Issues**: [GitHub Issues](https://github.com/Xe138/AI-Trader/issues)
## 📄 License
@@ -569,8 +615,8 @@ The materials provided by the AI-Trader project are for research purposes only a
**🌟 If this project helps you, please give us a Star!**
[![GitHub stars](https://img.shields.io/github/stars/HKUDS/AI-Trader?style=social)](https://github.com/HKUDS/AI-Trader)
[![GitHub forks](https://img.shields.io/github/forks/HKUDS/AI-Trader?style=social)](https://github.com/HKUDS/AI-Trader)
[![GitHub stars](https://img.shields.io/github/stars/Xe138/AI-Trader?style=social)](https://github.com/Xe138/AI-Trader)
[![GitHub forks](https://img.shields.io/github/forks/Xe138/AI-Trader?style=social)](https://github.com/Xe138/AI-Trader)
**🤖 Experience AI's full potential in financial markets through complete autonomous decision-making!**
**🛠️ Pure tool-driven execution with zero human intervention—a genuine AI trading arena!** 🚀

View File

@@ -27,32 +27,33 @@ class MCPServiceManager:
'price': int(os.getenv('GETPRICE_HTTP_PORT', '8003'))
}
# Service configurations
# Service configurations (paths relative to /app)
self.agent_tools_dir = Path(__file__).parent.resolve()
self.service_configs = {
'math': {
'script': 'tool_math.py',
'script': self.agent_tools_dir / 'tool_math.py',
'name': 'Math',
'port': self.ports['math']
},
'search': {
'script': 'tool_jina_search.py',
'script': self.agent_tools_dir / 'tool_jina_search.py',
'name': 'Search',
'port': self.ports['search']
},
'trade': {
'script': 'tool_trade.py',
'script': self.agent_tools_dir / 'tool_trade.py',
'name': 'TradeTools',
'port': self.ports['trade']
},
'price': {
'script': 'tool_get_price_local.py',
'script': self.agent_tools_dir / 'tool_get_price_local.py',
'name': 'LocalPrices',
'port': self.ports['price']
}
}
# Create logs directory
self.log_dir = Path('../logs')
self.log_dir = Path('logs')
self.log_dir.mkdir(exist_ok=True)
# Set signal handlers
@@ -70,20 +71,26 @@ class MCPServiceManager:
script_path = config['script']
service_name = config['name']
port = config['port']
if not Path(script_path).exists():
if not script_path.exists():
print(f"❌ Script file not found: {script_path}")
return False
try:
# Start service process
log_file = self.log_dir / f"{service_id}.log"
# Set PYTHONPATH to /app so services can import from tools module
env = os.environ.copy()
env['PYTHONPATH'] = str(Path.cwd())
with open(log_file, 'w') as f:
process = subprocess.Popen(
[sys.executable, script_path],
[sys.executable, str(script_path)],
stdout=f,
stderr=subprocess.STDOUT,
cwd=os.getcwd()
cwd=Path.cwd(), # Use current working directory (/app)
env=env # Pass environment with PYTHONPATH
)
self.services[service_id] = {

View File

@@ -1,8 +1,12 @@
import requests
import os
from dotenv import load_dotenv
load_dotenv()
import json
import os
import subprocess
import sys
import requests
from dotenv import load_dotenv
load_dotenv()
all_nasdaq_100_symbols = [
@@ -26,10 +30,10 @@ def get_daily_price(SYMBOL: str):
url = f'https://www.alphavantage.co/query?function={FUNCTION}&symbol={SYMBOL}&outputsize={OUTPUTSIZE}&apikey={APIKEY}'
r = requests.get(url)
data = r.json()
print(data)
if data.get('Note') is not None or data.get('Information') is not None:
print(f"Error")
print(f"⚠️ {SYMBOL}: API rate limit or error - {data.get('Note') or data.get('Information')}")
return
print(f"✓ Fetched {SYMBOL}")
with open(f'./daily_prices_{SYMBOL}.json', 'w', encoding='utf-8') as f:
json.dump(data, f, ensure_ascii=False, indent=4)
if SYMBOL == "QQQ":
@@ -42,4 +46,15 @@ if __name__ == "__main__":
for symbol in all_nasdaq_100_symbols:
get_daily_price(symbol)
get_daily_price("QQQ")
get_daily_price("QQQ")
# Automatically run merge after fetching
print("\n📦 Merging price data...")
try:
script_dir = os.path.dirname(os.path.abspath(__file__))
merge_script = os.path.join(script_dir, "merge_jsonl.py")
subprocess.run([sys.executable, merge_script], check=True)
print("✅ Price data merged successfully")
except Exception as e:
print(f"⚠️ Failed to merge data: {e}")
print(" Please run 'python merge_jsonl.py' manually")

View File

@@ -18,7 +18,8 @@ all_nasdaq_100_symbols = [
]
# 合并所有以 daily_price 开头的 json逐文件一行写入 merged.jsonl
current_dir = os.path.dirname(__file__)
# Use current working directory instead of script directory for volume compatibility
current_dir = os.getcwd()
pattern = os.path.join(current_dir, 'daily_price*.json')
files = sorted(glob.glob(pattern))

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docker-compose.yml Normal file
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@@ -0,0 +1,38 @@
services:
ai-trader:
image: ghcr.io/xe138/ai-trader:latest
# Uncomment to build locally instead of pulling:
# build: .
container_name: ai-trader-app
volumes:
- ${VOLUME_PATH:-.}/data:/app/data
- ${VOLUME_PATH:-.}/logs:/app/logs
- ${VOLUME_PATH:-.}/configs:/app/configs
environment:
# AI Model API Configuration
- OPENAI_API_BASE=${OPENAI_API_BASE}
- OPENAI_API_KEY=${OPENAI_API_KEY}
# Data Source Configuration
- ALPHAADVANTAGE_API_KEY=${ALPHAADVANTAGE_API_KEY}
- JINA_API_KEY=${JINA_API_KEY}
# System Configuration
- RUNTIME_ENV_PATH=/app/data/runtime_env.json
# MCP Service Ports (fixed internally)
- MATH_HTTP_PORT=8000
- SEARCH_HTTP_PORT=8001
- TRADE_HTTP_PORT=8002
- GETPRICE_HTTP_PORT=8003
# Agent Configuration
- AGENT_MAX_STEP=${AGENT_MAX_STEP:-30}
ports:
# Format: "HOST:CONTAINER" - container ports are fixed, host ports configurable via .env
- "${MATH_HTTP_PORT:-8000}:8000"
- "${SEARCH_HTTP_PORT:-8001}:8001"
- "${TRADE_HTTP_PORT:-8002}:8002"
- "${GETPRICE_HTTP_PORT:-8003}:8003"
- "${WEB_HTTP_PORT:-8888}:8888"
restart: on-failure:3 # Restart max 3 times on failure, prevents endless loops

329
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@@ -0,0 +1,329 @@
# Docker Deployment Guide
## Quick Start
### Prerequisites
- Docker Engine 20.10+
- Docker Compose 2.0+
- API keys for OpenAI, Alpha Vantage, and Jina AI
### First-Time Setup
1. **Clone repository:**
```bash
git clone https://github.com/Xe138/AI-Trader.git
cd AI-Trader
```
2. **Configure environment:**
```bash
cp .env.example .env
# Edit .env and add your API keys
```
3. **Run with Docker Compose:**
```bash
docker-compose up
```
That's it! The container will:
- Fetch latest price data from Alpha Vantage
- Start all MCP services
- Run the trading agent with default configuration
## Configuration
### Environment Variables
Edit `.env` file with your credentials:
```bash
# Required
OPENAI_API_KEY=sk-...
ALPHAADVANTAGE_API_KEY=...
JINA_API_KEY=...
# Optional (defaults shown)
MATH_HTTP_PORT=8000
SEARCH_HTTP_PORT=8001
TRADE_HTTP_PORT=8002
GETPRICE_HTTP_PORT=8003
AGENT_MAX_STEP=30
```
### Custom Trading Configuration
**Simple Method (Recommended):**
Create a `configs/custom_config.json` file - it will be automatically used:
```bash
# Copy default config as starting point
cp configs/default_config.json configs/custom_config.json
# Edit your custom config
nano configs/custom_config.json
# Run normally - custom_config.json is automatically detected!
docker-compose up
```
**Priority order:**
1. `configs/custom_config.json` (if exists) - **Highest priority**
2. Command-line argument: `docker-compose run ai-trader configs/other.json`
3. `configs/default_config.json` (fallback)
**Advanced: Use a different config file name:**
```bash
docker-compose run ai-trader configs/my_special_config.json
```
## Usage Examples
### Run in foreground with logs
```bash
docker-compose up
```
### Run in background (detached)
```bash
docker-compose up -d
docker-compose logs -f # Follow logs
```
### Run with custom config
```bash
docker-compose run ai-trader configs/custom_config.json
```
### Stop containers
```bash
docker-compose down
```
### Rebuild after code changes
```bash
docker-compose build
docker-compose up
```
## Data Persistence
### Volume Mounts
Docker Compose mounts three volumes for persistent data. By default, these are stored in the project directory:
- `./data:/app/data` - Price data and trading records
- `./logs:/app/logs` - MCP service logs
- `./configs:/app/configs` - Configuration files (allows editing configs without rebuilding)
### Custom Volume Location
You can change where data is stored by setting `VOLUME_PATH` in your `.env` file:
```bash
# Store data in a different location
VOLUME_PATH=/home/user/trading-data
# Or use a relative path
VOLUME_PATH=./volumes
```
This will store data in:
- `/home/user/trading-data/data/`
- `/home/user/trading-data/logs/`
- `/home/user/trading-data/configs/`
**Note:** The directory structure is automatically created. You'll need to copy your existing configs:
```bash
# After changing VOLUME_PATH
mkdir -p /home/user/trading-data/configs
cp configs/custom_config.json /home/user/trading-data/configs/
```
### Reset Data
To reset all trading data:
```bash
docker-compose down
rm -rf ${VOLUME_PATH:-.}/data/agent_data/* ${VOLUME_PATH:-.}/logs/*
docker-compose up
```
### Backup Trading Data
```bash
# Backup
tar -czf ai-trader-backup-$(date +%Y%m%d).tar.gz data/agent_data/
# Restore
tar -xzf ai-trader-backup-YYYYMMDD.tar.gz
```
## Using Pre-built Images
### Pull from GitHub Container Registry
```bash
docker pull ghcr.io/hkuds/ai-trader:latest
```
### Run without Docker Compose
```bash
docker run --env-file .env \
-v $(pwd)/data:/app/data \
-v $(pwd)/logs:/app/logs \
-p 8000-8003:8000-8003 \
ghcr.io/hkuds/ai-trader:latest
```
### Specific version
```bash
docker pull ghcr.io/hkuds/ai-trader:v1.0.0
```
## Troubleshooting
### MCP Services Not Starting
**Symptom:** Container exits immediately or errors about ports
**Solutions:**
- Check ports 8000-8003 not already in use: `lsof -i :8000-8003`
- View container logs: `docker-compose logs`
- Check MCP service logs: `cat logs/math.log`
### Missing API Keys
**Symptom:** Errors about missing environment variables
**Solutions:**
- Verify `.env` file exists: `ls -la .env`
- Check required variables set: `grep OPENAI_API_KEY .env`
- Ensure `.env` in same directory as docker-compose.yml
### Data Fetch Failures
**Symptom:** Container exits during data preparation step
**Solutions:**
- Verify Alpha Vantage API key valid
- Check API rate limits (5 requests/minute for free tier)
- View logs: `docker-compose logs | grep "Fetching and merging"`
### Permission Issues
**Symptom:** Cannot write to data or logs directories
**Solutions:**
- Ensure directories writable: `chmod -R 755 data logs`
- Check volume mount permissions
- May need to create directories first: `mkdir -p data logs`
### Container Keeps Restarting
**Symptom:** Container restarts repeatedly
**Solutions:**
- View logs to identify error: `docker-compose logs --tail=50`
- Disable auto-restart: Comment out `restart: unless-stopped` in docker-compose.yml
- Check if main.py exits with error
## Advanced Usage
### Override Entrypoint
Run bash inside container for debugging:
```bash
docker-compose run --entrypoint /bin/bash ai-trader
```
### Build Multi-platform Images
For ARM64 (Apple Silicon) and AMD64:
```bash
docker buildx build --platform linux/amd64,linux/arm64 -t ai-trader .
```
### View Container Resource Usage
```bash
docker stats ai-trader-app
```
### Access MCP Services Directly
Services exposed on host:
- Math: http://localhost:8000
- Search: http://localhost:8001
- Trade: http://localhost:8002
- Price: http://localhost:8003
## Development Workflow
### Local Code Changes
1. Edit code in project root
2. Rebuild image: `docker-compose build`
3. Run updated container: `docker-compose up`
### Test Different Configurations
**Method 1: Use the standard custom_config.json**
```bash
# Create and edit your config
cp configs/default_config.json configs/custom_config.json
nano configs/custom_config.json
# Run - automatically uses custom_config.json
docker-compose up
```
**Method 2: Test multiple configs with different names**
```bash
# Create multiple test configs
cp configs/default_config.json configs/conservative.json
cp configs/default_config.json configs/aggressive.json
# Edit each config...
# Test conservative strategy
docker-compose run ai-trader configs/conservative.json
# Test aggressive strategy
docker-compose run ai-trader configs/aggressive.json
```
**Method 3: Temporarily switch configs**
```bash
# Temporarily rename your custom config
mv configs/custom_config.json configs/custom_config.json.backup
cp configs/test_strategy.json configs/custom_config.json
# Run with test strategy
docker-compose up
# Restore original
mv configs/custom_config.json.backup configs/custom_config.json
```
## Production Deployment
For production use, consider:
1. **Use specific version tags** instead of `latest`
2. **External secrets management** (AWS Secrets Manager, etc.)
3. **Health checks** in docker-compose.yml
4. **Resource limits** (CPU/memory)
5. **Log aggregation** (ELK stack, CloudWatch)
6. **Orchestration** (Kubernetes, Docker Swarm)
See design document in `docs/plans/2025-10-30-docker-deployment-design.md` for architecture details.

149
docs/RELEASING.md Normal file
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# Release Process
## Creating a New Release
### 1. Prepare Release
1. Ensure `main` branch is stable and tests pass
2. Update version numbers if needed
3. Update CHANGELOG.md with release notes
### 2. Create Release Tag
```bash
# Ensure on main branch
git checkout main
git pull origin main
# Create annotated tag
git tag -a v1.0.0 -m "Release v1.0.0: Docker deployment support"
# Push tag to trigger CI/CD
git push origin v1.0.0
```
### 3. GitHub Actions Automation
Tag push automatically triggers `.github/workflows/docker-release.yml`:
1. ✅ Checks out code
2. ✅ Sets up Docker Buildx
3. ✅ Logs into GitHub Container Registry
4. ✅ Extracts version from tag
5. ✅ Builds Docker image with caching
6. ✅ Pushes to `ghcr.io/hkuds/ai-trader:VERSION`
7. ✅ Pushes to `ghcr.io/hkuds/ai-trader:latest`
### 4. Verify Build
1. Check GitHub Actions: https://github.com/Xe138/AI-Trader/actions
2. Verify workflow completed successfully (green checkmark)
3. Check packages: https://github.com/Xe138/AI-Trader/pkgs/container/ai-trader
### 5. Test Release
```bash
# Pull released image
docker pull ghcr.io/hkuds/ai-trader:v1.0.0
# Test run
docker run --env-file .env \
-v $(pwd)/data:/app/data \
ghcr.io/hkuds/ai-trader:v1.0.0
```
### 6. Create GitHub Release (Optional)
1. Go to https://github.com/Xe138/AI-Trader/releases/new
2. Select tag: `v1.0.0`
3. Release title: `v1.0.0 - Docker Deployment Support`
4. Add release notes:
```markdown
## 🐳 Docker Deployment
This release adds full Docker support for easy deployment.
### Pull and Run
```bash
docker pull ghcr.io/hkuds/ai-trader:v1.0.0
docker run --env-file .env -v $(pwd)/data:/app/data ghcr.io/hkuds/ai-trader:v1.0.0
```
Or use Docker Compose:
```bash
docker-compose up
```
See [docs/DOCKER.md](docs/DOCKER.md) for details.
### What's New
- Docker containerization with single-container architecture
- docker-compose.yml for easy orchestration
- Automated CI/CD builds on release tags
- Pre-built images on GitHub Container Registry
```
5. Publish release
## Version Numbering
Use Semantic Versioning (SEMVER):
- `v1.0.0` - Major release (breaking changes)
- `v1.1.0` - Minor release (new features, backward compatible)
- `v1.1.1` - Patch release (bug fixes)
## Troubleshooting Releases
### Build Fails in GitHub Actions
1. Check Actions logs for error details
2. Test local build: `docker build .`
3. Fix issues and delete/recreate tag:
```bash
# Delete tag
git tag -d v1.0.0
git push origin :refs/tags/v1.0.0
# Recreate after fixes
git tag v1.0.0
git push origin v1.0.0
```
### Image Not Appearing in Registry
1. Check Actions permissions (Settings → Actions → General)
2. Verify `packages: write` permission in workflow
3. Ensure `GITHUB_TOKEN` has registry access
### Wrong Version Tagged
Delete and recreate:
```bash
git tag -d v1.0.0
git push origin :refs/tags/v1.0.0
git tag v1.0.1
git push origin v1.0.1
```
## Manual Build and Push
If automated build fails, manual push:
```bash
# Build locally
docker build -t ghcr.io/hkuds/ai-trader:v1.0.0 .
# Login to GHCR
echo $GITHUB_TOKEN | docker login ghcr.io -u USERNAME --password-stdin
# Push
docker push ghcr.io/hkuds/ai-trader:v1.0.0
docker tag ghcr.io/hkuds/ai-trader:v1.0.0 ghcr.io/hkuds/ai-trader:latest
docker push ghcr.io/hkuds/ai-trader:latest
```

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# Data Cache Reuse Design
**Date:** 2025-10-30
**Status:** Approved
## Problem Statement
Docker containers currently fetch all 103 NASDAQ 100 tickers from Alpha Vantage on every startup, even when price data is volume-mounted and already cached in `./data`. This causes:
- Slow startup times (103 API calls)
- Unnecessary API quota consumption
- Rate limit risks during frequent development iterations
## Solution Overview
Implement staleness-based data refresh with configurable age threshold. Container checks all `daily_prices_*.json` files and only refetches if any file is missing or older than `MAX_DATA_AGE_DAYS`.
## Design Decisions
### Architecture Choice
**Selected:** Check all `daily_prices_*.json` files individually
**Rationale:** Ensures data integrity by detecting partial/missing files, not just stale merged data
### Implementation Location
**Selected:** Bash wrapper logic in `entrypoint.sh`
**Rationale:** Keeps data fetching scripts unchanged, adds orchestration at container startup layer
### Staleness Threshold
**Selected:** Configurable via `MAX_DATA_AGE_DAYS` environment variable (default: 7 days)
**Rationale:** Balances freshness with API usage; flexible for different use cases (development vs production)
## Technical Design
### Components
#### 1. Staleness Check Function
Location: `entrypoint.sh` (after environment validation, before data fetch)
```bash
should_refresh_data() {
MAX_AGE=${MAX_DATA_AGE_DAYS:-7}
# Check if at least one price file exists
if ! ls /app/data/daily_prices_*.json >/dev/null 2>&1; then
echo "📭 No price data found"
return 0 # Need refresh
fi
# Find any files older than MAX_AGE days
STALE_COUNT=$(find /app/data -name "daily_prices_*.json" -mtime +$MAX_AGE | wc -l)
TOTAL_COUNT=$(ls /app/data/daily_prices_*.json 2>/dev/null | wc -l)
if [ $STALE_COUNT -gt 0 ]; then
echo "📅 Found $STALE_COUNT stale files (>$MAX_AGE days old)"
return 0 # Need refresh
fi
echo "✅ All $TOTAL_COUNT price files are fresh (<$MAX_AGE days old)"
return 1 # Skip refresh
}
```
**Logic:**
- Uses `find -mtime +N` to detect files modified more than N days ago
- Returns shell exit codes: 0 (refresh needed), 1 (skip refresh)
- Logs informative messages for debugging
#### 2. Conditional Data Fetch
Location: `entrypoint.sh` lines 40-46 (replace existing unconditional fetch)
```bash
# Step 1: Data preparation (conditional)
echo "📊 Checking price data freshness..."
if should_refresh_data; then
echo "🔄 Fetching and merging price data..."
cd /app/data
python /app/scripts/get_daily_price.py
python /app/scripts/merge_jsonl.py
cd /app
else
echo "⏭️ Skipping data fetch (using cached data)"
fi
```
#### 3. Environment Configuration
**docker-compose.yml:**
```yaml
environment:
- MAX_DATA_AGE_DAYS=${MAX_DATA_AGE_DAYS:-7}
```
**.env.example:**
```bash
# Data Refresh Configuration
MAX_DATA_AGE_DAYS=7 # Refresh price data older than N days (0=always refresh)
```
### Data Flow
1. **Container Startup** → entrypoint.sh begins execution
2. **Environment Validation** → Check required API keys (existing logic)
3. **Staleness Check**`should_refresh_data()` scans `/app/data/daily_prices_*.json`
- No files found → Return 0 (refresh)
- Any file older than `MAX_DATA_AGE_DAYS` → Return 0 (refresh)
- All files fresh → Return 1 (skip)
4. **Conditional Fetch** → Run get_daily_price.py only if refresh needed
5. **Merge Data** → Always run merge_jsonl.py (handles missing merged.jsonl)
6. **MCP Services** → Start services (existing logic)
7. **Trading Agent** → Begin trading (existing logic)
### Edge Cases
| Scenario | Behavior |
|----------|----------|
| **First run (no data)** | Detects no files → triggers full fetch |
| **Restart within 7 days** | All files fresh → skips fetch (fast startup) |
| **Restart after 7 days** | Files stale → refreshes all data |
| **Partial data (some files missing)** | Missing files treated as infinitely old → triggers refresh |
| **Corrupt merged.jsonl but fresh price files** | Skips fetch, re-runs merge to rebuild merged.jsonl |
| **MAX_DATA_AGE_DAYS=0** | Always refresh (useful for testing/production) |
| **MAX_DATA_AGE_DAYS unset** | Defaults to 7 days |
| **Alpha Vantage rate limit** | get_daily_price.py handles with warning (existing behavior) |
## Configuration Options
| Variable | Default | Purpose |
|----------|---------|---------|
| `MAX_DATA_AGE_DAYS` | 7 | Days before price data considered stale |
**Special Values:**
- `0` → Always refresh (force fresh data)
- `999` → Never refresh (use cached data indefinitely)
## User Experience
### Scenario 1: Fresh Container
```
🚀 Starting AI-Trader...
🔍 Validating environment variables...
✅ Environment variables validated
📊 Checking price data freshness...
📭 No price data found
🔄 Fetching and merging price data...
✓ Fetched NVDA
✓ Fetched MSFT
...
```
### Scenario 2: Restart Within 7 Days
```
🚀 Starting AI-Trader...
🔍 Validating environment variables...
✅ Environment variables validated
📊 Checking price data freshness...
✅ All 103 price files are fresh (<7 days old)
⏭️ Skipping data fetch (using cached data)
🔧 Starting MCP services...
```
### Scenario 3: Restart After 7 Days
```
🚀 Starting AI-Trader...
🔍 Validating environment variables...
✅ Environment variables validated
📊 Checking price data freshness...
📅 Found 103 stale files (>7 days old)
🔄 Fetching and merging price data...
✓ Fetched NVDA
✓ Fetched MSFT
...
```
## Testing Plan
1. **Test fresh container:** Delete `./data/daily_prices_*.json`, start container → should fetch all
2. **Test cached data:** Restart immediately → should skip fetch
3. **Test staleness:** `touch -d "8 days ago" ./data/daily_prices_AAPL.json`, restart → should refresh
4. **Test partial data:** Delete 10 random price files → should refresh all
5. **Test MAX_DATA_AGE_DAYS=0:** Restart with env var set → should always fetch
6. **Test MAX_DATA_AGE_DAYS=30:** Restart with 8-day-old data → should skip
## Documentation Updates
Files requiring updates:
- `entrypoint.sh` → Add function and conditional logic
- `docker-compose.yml` → Add MAX_DATA_AGE_DAYS environment variable
- `.env.example` → Document MAX_DATA_AGE_DAYS with default value
- `CLAUDE.md` → Update "Docker Deployment" section with new env var
- `docs/DOCKER.md` (if exists) → Explain data caching behavior
## Benefits
- **Development:** Instant container restarts during iteration
- **API Quota:** ~103 fewer API calls per restart
- **Reliability:** No rate limit risks during frequent testing
- **Flexibility:** Configurable threshold for different use cases
- **Consistency:** Checks all files to ensure complete data

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# Docker Deployment and CI/CD Design
**Date:** 2025-10-30
**Status:** Approved
**Target:** Development/local testing environment
## Overview
Package AI-Trader as a Docker container with docker-compose orchestration and automated image builds via GitHub Actions on release tags. Focus on simplicity and ease of use for researchers and developers.
## Requirements
- **Primary Use Case:** Development and local testing
- **Deployment Target:** Single monolithic container (all MCP services + trading agent)
- **Secrets Management:** Environment variables (no mounted .env file)
- **Data Strategy:** Fetch price data on container startup
- **Container Registry:** GitHub Container Registry (ghcr.io)
- **Trigger:** Build images automatically on release tag push (`v*` pattern)
## Architecture
### Components
1. **Dockerfile** - Builds Python 3.10 image with all dependencies
2. **docker-compose.yml** - Orchestrates container with volume mounts and environment config
3. **entrypoint.sh** - Sequential startup script (data fetch → MCP services → trading agent)
4. **GitHub Actions Workflow** - Automated image build and push on release tags
5. **.dockerignore** - Excludes unnecessary files from image
6. **Documentation** - Docker usage guide and examples
### Execution Flow
```
Container Start
entrypoint.sh
1. Fetch/merge price data (get_daily_price.py → merge_jsonl.py)
2. Start MCP services in background (start_mcp_services.py)
3. Wait 3 seconds for service stabilization
4. Run trading agent (main.py with config)
Container Exit → Cleanup MCP services
```
## Detailed Design
### 1. Dockerfile
**Multi-stage build:**
```dockerfile
# Base stage
FROM python:3.10-slim as base
WORKDIR /app
# Install dependencies
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
# Application stage
FROM base
WORKDIR /app
# Copy application code
COPY . .
# Create necessary directories
RUN mkdir -p data logs data/agent_data
# Make entrypoint executable
RUN chmod +x entrypoint.sh
# Expose MCP service ports
EXPOSE 8000 8001 8002 8003
# Set Python to run unbuffered
ENV PYTHONUNBUFFERED=1
# Use entrypoint script
ENTRYPOINT ["./entrypoint.sh"]
CMD ["configs/default_config.json"]
```
**Key Features:**
- `python:3.10-slim` base for smaller image size
- Multi-stage for dependency caching
- Non-root user NOT included (dev/testing focus, can add later)
- Unbuffered Python output for real-time logs
- Default config path with override support
### 2. docker-compose.yml
```yaml
version: '3.8'
services:
ai-trader:
build: .
container_name: ai-trader-app
volumes:
- ./data:/app/data
- ./logs:/app/logs
environment:
- OPENAI_API_BASE=${OPENAI_API_BASE}
- OPENAI_API_KEY=${OPENAI_API_KEY}
- ALPHAADVANTAGE_API_KEY=${ALPHAADVANTAGE_API_KEY}
- JINA_API_KEY=${JINA_API_KEY}
- RUNTIME_ENV_PATH=/app/data/runtime_env.json
- MATH_HTTP_PORT=${MATH_HTTP_PORT:-8000}
- SEARCH_HTTP_PORT=${SEARCH_HTTP_PORT:-8001}
- TRADE_HTTP_PORT=${TRADE_HTTP_PORT:-8002}
- GETPRICE_HTTP_PORT=${GETPRICE_HTTP_PORT:-8003}
- AGENT_MAX_STEP=${AGENT_MAX_STEP:-30}
ports:
- "8000:8000"
- "8001:8001"
- "8002:8002"
- "8003:8003"
- "8888:8888" # Optional: web dashboard
restart: unless-stopped
```
**Key Features:**
- Volume mounts for data/logs persistence
- Environment variables interpolated from `.env` file (Docker Compose reads automatically)
- No `.env` file mounted into container (cleaner separation)
- Default port values with override support
- Restart policy for recovery
### 3. entrypoint.sh
```bash
#!/bin/bash
set -e # Exit on any error
echo "🚀 Starting AI-Trader..."
# Step 1: Data preparation
echo "📊 Fetching and merging price data..."
cd /app/data
python get_daily_price.py
python merge_jsonl.py
cd /app
# Step 2: Start MCP services in background
echo "🔧 Starting MCP services..."
cd /app/agent_tools
python start_mcp_services.py &
MCP_PID=$!
cd /app
# Step 3: Wait for services to initialize
echo "⏳ Waiting for MCP services to start..."
sleep 3
# Step 4: Run trading agent with config file
echo "🤖 Starting trading agent..."
CONFIG_FILE="${1:-configs/default_config.json}"
python main.py "$CONFIG_FILE"
# Cleanup on exit
trap "echo '🛑 Stopping MCP services...'; kill $MCP_PID 2>/dev/null" EXIT
```
**Key Features:**
- Sequential execution with clear logging
- MCP services run in background with PID capture
- Trap ensures cleanup on container exit
- Config file path as argument (defaults to `configs/default_config.json`)
- Fail-fast with `set -e`
### 4. GitHub Actions Workflow
**File:** `.github/workflows/docker-release.yml`
```yaml
name: Build and Push Docker Image
on:
push:
tags:
- 'v*' # Triggers on v1.0.0, v2.1.3, etc.
workflow_dispatch: # Manual trigger option
jobs:
build-and-push:
runs-on: ubuntu-latest
permissions:
contents: read
packages: write
steps:
- name: Checkout code
uses: actions/checkout@v4
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v3
- name: Login to GitHub Container Registry
uses: docker/login-action@v3
with:
registry: ghcr.io
username: ${{ github.actor }}
password: ${{ secrets.GITHUB_TOKEN }}
- name: Extract version from tag
id: meta
run: |
VERSION=${GITHUB_REF#refs/tags/v}
echo "version=$VERSION" >> $GITHUB_OUTPUT
- name: Build and push Docker image
uses: docker/build-push-action@v5
with:
context: .
push: true
tags: |
ghcr.io/${{ github.repository_owner }}/ai-trader:${{ steps.meta.outputs.version }}
ghcr.io/${{ github.repository_owner }}/ai-trader:latest
cache-from: type=gha
cache-to: type=gha,mode=max
```
**Key Features:**
- Triggers on `v*` tags (e.g., `git tag v1.0.0 && git push origin v1.0.0`)
- Manual dispatch option for testing
- Uses `GITHUB_TOKEN` (automatically provided, no secrets needed)
- Builds with caching for faster builds
- Tags both version and `latest`
- Multi-platform support possible by adding `platforms: linux/amd64,linux/arm64`
### 5. .dockerignore
```
# Version control
.git/
.gitignore
# Python
__pycache__/
*.py[cod]
*$py.class
*.so
.Python
venv/
env/
ENV/
# IDE
.vscode/
.idea/
*.swp
*.swo
# Environment and secrets
.env
.env.*
!.env.example
# Data files (fetched at runtime)
data/*.json
data/agent_data/
data/merged.jsonl
# Logs
logs/
*.log
# Runtime state
runtime_env.json
# Documentation (not needed in image)
*.md
docs/
!README.md
# CI/CD
.github/
```
**Purpose:**
- Reduces image size
- Keeps secrets out of image
- Excludes generated files
- Keeps only necessary source code and scripts
## Documentation Updates
### New File: docs/DOCKER.md
Create comprehensive Docker usage guide including:
1. **Quick Start**
```bash
cp .env.example .env
# Edit .env with your API keys
docker-compose up
```
2. **Configuration**
- Required environment variables
- Optional configuration overrides
- Custom config file usage
3. **Usage Examples**
```bash
# Run with default config
docker-compose up
# Run with custom config
docker-compose run ai-trader configs/my_config.json
# View logs
docker-compose logs -f
# Stop and clean up
docker-compose down
```
4. **Data Persistence**
- How volume mounts work
- Where data is stored
- How to backup/restore
5. **Troubleshooting**
- MCP services not starting → Check logs, verify ports available
- Missing API keys → Check .env file
- Data fetch failures → API rate limits or invalid keys
- Permission issues → Volume mount permissions
6. **Using Pre-built Images**
```bash
docker pull ghcr.io/hkuds/ai-trader:latest
docker run --env-file .env -v $(pwd)/data:/app/data ghcr.io/hkuds/ai-trader:latest
```
### Update .env.example
Add/clarify Docker-specific variables:
```bash
# AI Model API Configuration
OPENAI_API_BASE=https://your-openai-proxy.com/v1
OPENAI_API_KEY=your_openai_key
# Data Source Configuration
ALPHAADVANTAGE_API_KEY=your_alpha_vantage_key
JINA_API_KEY=your_jina_api_key
# System Configuration (Docker defaults)
RUNTIME_ENV_PATH=/app/data/runtime_env.json
# MCP Service Ports
MATH_HTTP_PORT=8000
SEARCH_HTTP_PORT=8001
TRADE_HTTP_PORT=8002
GETPRICE_HTTP_PORT=8003
# Agent Configuration
AGENT_MAX_STEP=30
```
### Update Main README.md
Add Docker section after "Quick Start":
```markdown
## Docker Deployment
### Using Docker Compose (Recommended)
```bash
# Setup environment
cp .env.example .env
# Edit .env with your API keys
# Run with docker-compose
docker-compose up
```
### Using Pre-built Images
```bash
# Pull latest image
docker pull ghcr.io/hkuds/ai-trader:latest
# Run container
docker run --env-file .env \
-v $(pwd)/data:/app/data \
-v $(pwd)/logs:/app/logs \
ghcr.io/hkuds/ai-trader:latest
```
See [docs/DOCKER.md](docs/DOCKER.md) for detailed Docker usage guide.
```
## Release Process
### For Maintainers
1. **Prepare release:**
```bash
# Ensure main branch is ready
git checkout main
git pull origin main
```
2. **Create and push tag:**
```bash
git tag v1.0.0
git push origin v1.0.0
```
3. **GitHub Actions automatically:**
- Builds Docker image
- Tags with version and `latest`
- Pushes to `ghcr.io/hkuds/ai-trader`
4. **Verify build:**
- Check Actions tab for build status
- Test pull: `docker pull ghcr.io/hkuds/ai-trader:v1.0.0`
5. **Optional: Create GitHub Release**
- Add release notes
- Include Docker pull command
### For Users
```bash
# Pull specific version
docker pull ghcr.io/hkuds/ai-trader:v1.0.0
# Or always get latest
docker pull ghcr.io/hkuds/ai-trader:latest
```
## Implementation Checklist
- [ ] Create Dockerfile with multi-stage build
- [ ] Create docker-compose.yml with volume mounts and environment config
- [ ] Create entrypoint.sh with sequential startup logic
- [ ] Create .dockerignore to exclude unnecessary files
- [ ] Create .github/workflows/docker-release.yml for CI/CD
- [ ] Create docs/DOCKER.md with comprehensive usage guide
- [ ] Update .env.example with Docker-specific variables
- [ ] Update main README.md with Docker deployment section
- [ ] Test local build: `docker-compose build`
- [ ] Test local run: `docker-compose up`
- [ ] Test with custom config
- [ ] Verify data persistence across container restarts
- [ ] Test GitHub Actions workflow (create test tag)
- [ ] Verify image pushed to ghcr.io
- [ ] Test pulling and running pre-built image
- [ ] Update CLAUDE.md with Docker commands
## Future Enhancements
Possible improvements for production use:
1. **Multi-container Architecture**
- Separate containers for each MCP service
- Better isolation and independent scaling
- More complex orchestration
2. **Security Hardening**
- Non-root user in container
- Docker secrets for production
- Read-only filesystem where possible
3. **Monitoring**
- Health checks for MCP services
- Prometheus metrics export
- Logging aggregation
4. **Optimization**
- Multi-platform builds (ARM64 support)
- Smaller base image (alpine)
- Layer caching optimization
5. **Development Tools**
- docker-compose.dev.yml with hot reload
- Debug container with additional tools
- Integration test container
These are deferred to keep initial implementation simple and focused on development/testing use cases.

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Docker Build Test Results
==========================
Date: 2025-10-30
Branch: docker-deployment
Working Directory: /home/bballou/AI-Trader/.worktrees/docker-deployment
Test 1: Docker Image Build
---------------------------
Command: docker-compose build
Status: SUCCESS
Result: Successfully built image 7b36b8f4c0e9
Build Output Summary:
- Base image: python:3.10-slim
- Build stages: Multi-stage build (base + application)
- Dependencies installed successfully from requirements.txt
- Application code copied
- Directories created: data, logs, data/agent_data
- Entrypoint script made executable
- Ports exposed: 8000, 8001, 8002, 8003, 8888
- Environment: PYTHONUNBUFFERED=1 set
- Image size: 266MB
- Build time: ~2 minutes (including dependency installation)
Key packages installed:
- langchain==1.0.2
- langchain-openai==1.0.1
- langchain-mcp-adapters>=0.1.0
- fastmcp==2.12.5
- langgraph<1.1.0,>=1.0.0
- pydantic<3.0.0,>=2.7.4
- openai<3.0.0,>=1.109.1
- All dependencies resolved without conflicts
Test 2: Image Verification
---------------------------
Command: docker images | grep ai-trader
Status: SUCCESS
Result: docker-deployment_ai-trader latest 7b36b8f4c0e9 9 seconds ago 266MB
Image Details:
- Repository: docker-deployment_ai-trader
- Tag: latest
- Image ID: 7b36b8f4c0e9
- Created: Just now
- Size: 266MB (reasonable for Python 3.10 + ML dependencies)
Test 3: Configuration Parsing (Dry-Run)
----------------------------------------
Command: docker-compose --env-file .env.test config
Status: SUCCESS
Result: Configuration parsed correctly without errors
Test .env.test contents:
OPENAI_API_KEY=test
ALPHAADVANTAGE_API_KEY=test
JINA_API_KEY=test
RUNTIME_ENV_PATH=/app/data/runtime_env.json
Parsed Configuration:
- Service name: ai-trader
- Container name: ai-trader-app
- Build context: /home/bballou/AI-Trader/.worktrees/docker-deployment
- Environment variables correctly injected:
* AGENT_MAX_STEP: '30' (default)
* ALPHAADVANTAGE_API_KEY: test
* GETPRICE_HTTP_PORT: '8003' (default)
* JINA_API_KEY: test
* MATH_HTTP_PORT: '8000' (default)
* OPENAI_API_BASE: '' (not set, defaulted to blank)
* OPENAI_API_KEY: test
* RUNTIME_ENV_PATH: /app/data/runtime_env.json
* SEARCH_HTTP_PORT: '8001' (default)
* TRADE_HTTP_PORT: '8002' (default)
- Ports correctly mapped: 8000, 8001, 8002, 8003, 8888
- Volumes correctly configured:
* ./data:/app/data:rw
* ./logs:/app/logs:rw
- Restart policy: unless-stopped
- Docker Compose version: 3.8
Summary
-------
All Docker build tests PASSED successfully:
✓ Docker image builds without errors
✓ Image created with reasonable size (266MB)
✓ Multi-stage build optimizes layer caching
✓ All Python dependencies install correctly
✓ Configuration parsing works with test environment
✓ Environment variables properly injected
✓ Volume mounts configured correctly
✓ Port mappings set up correctly
✓ Restart policy configured
No issues encountered during local Docker build testing.
The Docker deployment is ready for use.
Next Steps:
1. Test actual container startup with valid API keys
2. Verify MCP services start correctly in container
3. Test trading agent execution
4. Consider creating test tag for GitHub Actions CI/CD verification

82
entrypoint.sh Executable file
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@@ -0,0 +1,82 @@
#!/bin/bash
set -e # Exit on any error
echo "🚀 Starting AI-Trader..."
# Validate required environment variables
echo "🔍 Validating environment variables..."
MISSING_VARS=()
if [ -z "$OPENAI_API_KEY" ]; then
MISSING_VARS+=("OPENAI_API_KEY")
fi
if [ -z "$ALPHAADVANTAGE_API_KEY" ]; then
MISSING_VARS+=("ALPHAADVANTAGE_API_KEY")
fi
if [ -z "$JINA_API_KEY" ]; then
MISSING_VARS+=("JINA_API_KEY")
fi
if [ ${#MISSING_VARS[@]} -gt 0 ]; then
echo ""
echo "❌ ERROR: Missing required environment variables:"
for var in "${MISSING_VARS[@]}"; do
echo " - $var"
done
echo ""
echo "Please set these variables in your .env file:"
echo " 1. Copy .env.example to .env"
echo " 2. Edit .env and add your API keys"
echo " 3. Restart the container"
echo ""
echo "See docs/DOCKER.md for more information."
exit 1
fi
echo "✅ Environment variables validated"
# Step 1: Data preparation
echo "📊 Checking price data..."
if [ -f "/app/data/merged.jsonl" ] && [ -s "/app/data/merged.jsonl" ]; then
echo "✅ Using existing price data ($(wc -l < /app/data/merged.jsonl) stocks)"
echo " To refresh data, delete /app/data/merged.jsonl and restart"
else
echo "📊 Fetching and merging price data..."
# Run script from /app/scripts but output to /app/data
# Note: get_daily_price.py now automatically calls merge_jsonl.py after fetching
cd /app/data
python /app/scripts/get_daily_price.py
cd /app
fi
# Step 2: Start MCP services in background
echo "🔧 Starting MCP services..."
cd /app
python agent_tools/start_mcp_services.py &
MCP_PID=$!
# Step 3: Wait for services to initialize
echo "⏳ Waiting for MCP services to start..."
sleep 3
# Step 4: Run trading agent with config file
echo "🤖 Starting trading agent..."
# Smart config selection: custom_config.json takes precedence if it exists
if [ -f "configs/custom_config.json" ]; then
CONFIG_FILE="configs/custom_config.json"
echo "✅ Using custom configuration: configs/custom_config.json"
elif [ -n "$1" ]; then
CONFIG_FILE="$1"
echo "✅ Using specified configuration: $CONFIG_FILE"
else
CONFIG_FILE="configs/default_config.json"
echo "✅ Using default configuration: configs/default_config.json"
fi
python main.py "$CONFIG_FILE"
# Cleanup on exit
trap "echo '🛑 Stopping MCP services...'; kill $MCP_PID 2>/dev/null; exit 0" EXIT SIGTERM SIGINT

View File

@@ -10,8 +10,8 @@ echo "🚀 Launching AI Trader Environment..."
echo "📊 Now getting and merging price data..."
cd ./data
# Note: get_daily_price.py now automatically calls merge_jsonl.py after fetching
python get_daily_price.py
python merge_jsonl.py
cd ../
echo "🔧 Now starting MCP services..."