AI-Trader: Can AI Beat the Market? (Open Source Project)
AI-Trader: Unleashing Autonomous AI in Financial Markets
The AI-Trader project, hosted on GitHub by HKUDS, represents a significant leap into the world of autonomous AI-driven financial trading. This open-source initiative pits five distinct AI models against each other in a fierce competition to generate the highest profits by trading NASDAQ 100 component stocks. What makes this project exceptionally compelling is its commitment to zero human intervention – once launched, the AI agents operate with complete autonomy, making all decisions and executing trades without human input.
The Battle for NASDAQ 100 Supremacy
At its core, AI-Trader is a live competition arena where AI models like DeepSeek, MiniMax-M2, and Claude-3.7 battle for market supremacy. The project’s live trading dashboard transparently showcases the performance of each AI, tracking their total earnings against a baseline. As of the latest update (October 30, 2025), DeepSeek leads the pack with an impressive +13.89% return, demonstrating the tangible potential of advanced AI in capital markets.
Core Features and Architecture
AI-Trader is built on a robust set of features designed to ensure fairness, transparency, and extensibility:
- Fully Autonomous Decision-Making: AI agents independently analyze markets, make decisions, and execute trades.
- Pure Tool-Driven Architecture: Utilizes a Model Context Protocol (MCP) toolchain, allowing AI to perform operations through standardized tool calls.
- Multi-Model Competition: Supports deployment and evaluation of multiple AI models (GPT, Claude, Qwen, etc.) simultaneously.
- Real-Time Performance Analytics: Provides comprehensive trading records, position monitoring, and profit/loss analysis through an enhanced frontend dashboard.
- Intelligent Market Intelligence: Integrates Jina search for real-time market news and financial reports.
- Extensible Strategy Framework: Allows for easy integration of third-party strategies and custom AI agents.
- Historical Replay Capability: Features a time-period replay functionality with crucial anti-look-ahead data controls to prevent future information access.
Each AI model starts with a substantial $10,000 USD to trade NASDAQ 100 stocks within a controlled environment, utilizing real market data from Alpha Vantage and Jina AI.
Competition Rules and Zero Human Intervention
To ensure a fair comparison, all AI models operate under identical conditions: the same starting capital, uniform market data access, identical toolsets, and standardized evaluation metrics. A strict rule of zero human intervention is enforced, meaning no pre-programmed strategies, no human input during trading, and no manual overrides. All operations are executed exclusively through tool calls, empowering AI agents to learn and adapt their strategies independently based on market performance.
Integrating and Contributing
The project maintains a modular design, making it straightforward for developers and researchers to contribute. Users can submit pull requests with their own trading strategies, inheriting from a Basemodel, and the platform will run and continuously update their results. The project provides clear guides for quick start, environment configuration, and data preparation, making it accessible for Python 3.10+ users with necessary API keys (OpenAI, Alpha Vantage, Jina AI).
Roadmap and Community
The AI-Trader team has an ambitious roadmap, including support for A-share markets, advanced post-market statistics, a strategy marketplace, cryptocurrency trading, and minute-level time precision for historical replay. The project fosters community engagement through GitHub Discussions and Issues, welcoming contributions and feedback.
AI-Trader is more than just a trading bot; it's a research framework for empirical studies on market efficiency, decision consistency, and risk management in AI-driven finance. It invites you to explore the full potential of AI in financial markets through complete autonomous decision-making and tool-driven execution. If this project piques your interest, consider giving it a star on GitHub and joining the thriving community exploring the future of finance.