LLM-Trading-Lab

LLM Trading Lab

This repository started as a 6-month live micro-cap trading experiment in which a large language model (ChatGPT) manages a real-money portfolio under strict, predefined rules.

What began as a single experiment has evolved into a baseline framework for studying how large language models behave as portfolio decision-makers.
All historical data, research artifacts, and logs are preserved for transparency and auditability.

Full research evaluation out now: Evaluating ChatGPT as a Portfolio Decision-Maker in Micro-Cap Equities

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Running Your Own Experiment

If you want to run your own AI-managed trading experiment, check out this framework I created for LLM research: LLM Investor Behavior Benchmark - LIBB

Repository Purpose

This repository serves two primary purposes:

1. A complete, forward-only record of a live AI-managed trading experiment
2. A reusable foundation for future AI-driven trading experiments built on the same structure

Historical artifacts remain unchanged. New experiments, analyses, and methodologies are layered on top without rewriting past results.

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The Concept

Every day, I kept seeing the same ad about having some A.I. pick undervalued stocks. It was obvious it was trying to get me to subscribe to some garbage, so I just rolled my eyes. Then I started wondering, "How well would that actually work?"

So, starting with just $100, I wanted to answer a simple but powerful question: Can powerful large language models like ChatGPT actually generate alpha (or at least make smart trading decisions) using real-time data?

Today, this repo has evolved into so much more than simply chasing alpha.

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Why This Matters

AI is being aggressively marketed as a replacement for human decision-making across industries.
Trading is a domain where mistakes are measurable, irreversible, and costly.

This platform tests those claims using:

• Forward-only decisions
• Full transparency
• Publicly logged results

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Research & Documentation

Here are the artifacts links for the Micro-Cap Experiment:

• Research Index: Deep Research Index

• Decision Logs / Chats: Chats

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Features of This Repository

• 40 page PDF evaluation over results
• Live trading engine used in production
• LLM-driven trade selection under hard constraints
• Daily CSV-based portfolio accounting
• Automated stop-loss enforcement
• Benchmark comparisons (S&P 500, Russell 2000)
• CAPM, Sharpe, Sortino, and drawdown analytics
• Full trade and decision logs

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Tech Stack

• Python 3.11+
• pandas
• yfinance (primary data source)
• Stooq (fallback data source)
• Matplotlib

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Future Work

I am currently designing the future experiment over newly listed IPOs with monthly analysis on my Substack.

Also, I developing the general experimental framework I created for LLM research LIBB for the upcoming and all future experiments.

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Contributing

Contributions are welcome.

• Issues: bugs, edge cases, or design critiques
• Pull Requests: improvements, refactors, or extensions
• Collaboration: high-quality contributors may be invited to help maintain future experiments

Contributing guide:
https://github.com/LuckyOne7777/ChatGPT-Micro-Cap-Experiment/blob/main/Other/CONTRIBUTING.md

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Contact

All my links can be found on my profile, feel free to reach out anywhere!