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Its going viral on Reddit. Somebody let ChatGPT run a $100 live share portfolio, restricted to U.S. micro-cap stocks. Did an LLM really bit the market?. - 4 weeks +23.8% while the Russell 2000 and biotech ETF XBI rose only ~3.9% and 3.5%. Prompt + GitHub posted --- ofcourse its a short‑term outperformance, tiny sample size, and also micro caps are hightly volatile. So much more exahustive analysis is needed with lots or more info (like Sharpe ratios and longer back-testing etc), to explore whether an LLM can truly beat the market.
His original prompt.. The prompt first anchors the model in a clear professional role, then boxes it in with tight, measurable rules ---- “ You are a professional-grade portfolio strategist. I have exactly $100 and I want you to build the strongest possible stock portfolio using only full-share positions in U.S.-listed micro-cap stocks (market cap under $300M). Your objective is to generate maximum return from today (6-27-25) to 6 months from now (12-27-25). This is your timeframe, you may not make any decisions after the end date. Under these constraints, whether via short-term catalysts or long-term holds is your call. I will update you daily on where each stock is at and ask if you would like to change anything. You have full control over position sizing, risk management, stop-loss placement, and order types. You may concentrate or diversify at will. Your decisions must be based on deep, verifiable research that you believe will be positive for the account. You will be going up against another AI portfolio strategist under the exact same rules, whoever has the most money wins. Now, use deep research and create your portfolio.”
All benchmark prices come straight from the Yahoo Finance API, then land in Pandas data frames for simple math and plotting. ChatGPT’s line is different, because the model first chooses a few U.S. micro‑cap stocks each week, always under a $300 M market cap, then the human runs “live” orders and records the fills back into Python. The equity curve is recomputed from those fills and saved to CSV before each new chart.
Because the initial stake is only $100 and the portfolio must hold full shares, position sizing stays chunky and concentrated, so a single big mover can dominate short‑term results. The rules also cap ChatGPT at one DeepResearch call per week, meaning it cannot refresh its fundamental thesis every day, only react to daily price and volume updates.
The author admits the choice of Russell 2000 and XBI as benchmarks is subjective, since the model gravitated to biotech names. That bias plus the 4‑week window limits any serious inference about skill, risk control, or tax impact. Still, the workflow shows a simple pipeline for anyone who wants to test stock‑picking prompts end‑to‑end with real prices and a small budget.
Reddit - <a target="_blank" href="https://www.reddit.com/r/dataisbeautiful/comments/1mc0cz7/oc_4_weeks_of_chatgpt_controlling_a_live_stock/" color="blue">reddit.com/r/dataisbeauti…</a> Github - <a target="_blank" href="https://github.com/LuckyOne7777/ChatGPT-Micro-Cap-Experiment" color="blue">github.com/LuckyOne7777/C…</a>