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

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.

ChatGPT-Micro-Cap-Experiment/
│
├─ README.md
├─ requirements.txt
├─ Makefile
│
├─ experiments/
│  └─ chatgpt_micro_cap/
│     │
│     ├─ trading_script.py
│     │
|     ├─ graphing/
|     │  ├─ daily_returns.py
|     │  ├─ drawdown.py
|     │  └─ ...
|     │ 
│     ├─ csv_files/
│     │  ├─ Daily_Updates.csv
│     │  └─ Trade_Log.csv
│     │
│     ├─ evaluation/
│     │  ├─ evaluation_report.md
│     │  └─ paper.pdf
│     │
│     ├─ collected_artifacts/
│     │  ├─ deep_research_index.md
│     │  ├─ chats.md
│     │  │
│     │  ├─ Weekly_Deep_Research_MD/
│     │  │  ├─ Week_01_Summary.md
│     │  │  ├─ Week_02_Summary.md
│     │  │  └─ ...
│     │  │
│     │  └─ Weekly_Deep_Research_PDF/
│     │     ├─ Starting_Research.pdf
│     │     ├─ Week_01.pdf
│     │     ├─ Week_02.pdf
│     │     └─ ...
│     │
│     ├─ images/
│     │  ├─ equity_vs_baseline.png
│     │  ├─ repeated_exposure.png
│     │  └─ ...
│     │
│     ├─ tables/
│     │  └─ metrics.txt
│     │
│     ├─ metrics/
│     │  ├─ load_dataV3.py
│     │  └─ episode_pcr.py
│     │
│     └─ processing/
│        ├─ ProcessPortfolio.py
|
│
├─

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.

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

Research & Documentation

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

• Research Index: Deep Research Index

• Decision Logs / Chats: Chats

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

Tech Stack

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

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.

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

Contact

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