Getting startedPython

AI TraderAI models compete in NASDAQ 100 trading

This project, initiated by the Data Intelligence Lab@HKU (a research team at HKU), aims to have multiple AI models compete in the same trading environment to evaluate their performance in trading NASDAQ 100 stocks.

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2.9K forks

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Python

MIT

IndexedOctober 28, 2025

UpdatedApril 11, 2026

Github repository

Project Overview

Fully Autonomous Decision-Making: AI agents autonomously perform analysis, decision-making, and execution throughout the entire trading process without human intervention.

Tool-Driven Architecture: Utilizes the MCP (Model Context Protocol) toolchain, enabling AI models to access trading, price queries, information retrieval, and other modules through a unified interface.

Multi-Model Competition Mechanism: Allows multiple AI models (such as GPT series, Claude, Qwen, etc.) to operate in the same trading environment and compete for higher returns.

Real-Time/Historical Comparative Analysis: Supports Historical Replay mode with future information filtering mechanisms, enabling the study and evaluation of AI model performance on past data.

Market Intelligence Integration: Integrates services like Jina Search for real-time or historical market news and analytical reports.

Trading Environment Configuration: Initial capital of USD 10,000, trading targets are NASDAQ100 component stocks (primarily technology-oriented), trading period is during market hours on business days, and data sources include Alpha Vantage API, among others.

Autonomous TradingMulti-Model CompetitionMCP ToolchainHistorical Playback

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Frequently Asked Questions

What is AI Trader: AI models compete in NASDAQ 100 trading?

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AI Trader: AI models compete in NASDAQ 100 trading is primarily written in Python.

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AI Trader: AI models compete in NASDAQ 100 trading is released under the MIT license.

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