Recap Hey guys! If you’re new here, I am running a 6 month long experiment to see if a Large Language Model (like ChatGPT) can be a skilled micro-cap portfolio manager. I give it daily closing data at the end of every trading day and it has full control over its assets. Also, once every week it gets to use Deep Research to completely reevaluate it’s account. Can ChatGPT carve consistent alpha in the dangerous world of micro-cap stocks? Lets find out. Overview Happy Thanksgiving everyone! Both the markets and the portfolio had a very strong gains, with ChatGPT now 11% below starting capital. The concentrated positions  lifted the portfolio, now sitting at +$21. No major structural changes were planned during Deep Research, only stoploss updates. $21. Performance Graph [ Risk & Return ] [ Risk & Return ] Max Drawdown:                            -50.33%   on 2025-11-06 Sharpe Ratio (period):                   -0.3293 Sharpe Ratio (annualized):               -0.0865 Sortino Ratio (period):                  -0.3845 Sortino Ratio (annualized):              -0.1009 [ CAPM vs Benchmarks ] Beta (daily) vs ^GSPC:                    0.8807 Alpha (annualized) vs ^GSPC:             -23.48% R² (fit quality):                          0.016     Obs: 105 Note: Short sample and/or low R² — alpha/beta may be unstable. [ Snapshot ] [ Snapshot ] Latest ChatGPT Equity:           $         88.97 $100.0 in S&P 500 (same window): $        110.38 Cash Balance:                    $          0.85 Current Portfolio ticker  shares  buy price  cost basis  stop loss    PnL ticker  shares  buy price  cost basis  stop loss    PnL MIST    14.0       1.75   24.500000        1.5  13.16 VTGN     6.0       4.01   24.060001        3.2   5.34 SLS    13.0       1.41   18.330000        1.1   2.73 Portfolio Review Portfolio Review To see the full report: Click Here Click Here Thesis Review Summary The portfolio is now a concentrated 3-stock biotech catalyst bet, designed for high upside with controlled downside via stop-losses. Each position targets an independent binary event, giving a shot at outperforming the benchmark if even one or two hit. 3-stock biotech catalyst bet 1. Milestone Pharmaceuticals (MIST) 1. Milestone Pharmaceuticals (MIST) Catalyst: FDA decision on etripamil for PSVT – Dec 13 Catalyst: Dec 13 Thesis: Strong Phase 3 data, resolved manufacturing issues, and non-dilutive funding support a high probability of approval. Thesis: Updates: Stock trending upward; management tone positive; no negative signals. Updates: Outlook: Highest-confidence play. Outlook: Upside: $3.50–$4.50 (analysts $6+)Downside: Could halve, softened by cash and stop-lossPlan: Hold through decision; sell most on approvalConfidence: Very high Upside: $3.50–$4.50 (analysts $6+) Upside: Downside: Could halve, softened by cash and stop-loss Downside: Plan: Hold through decision; sell most on approval Plan: Confidence: Very high Confidence: Very high 2. VistaGen Therapeutics (VTGN) 2. VistaGen Therapeutics (VTGN) Catalyst: Phase 3 PALISADE-3 (Social Anxiety) data – end of 2025 Catalyst: Thesis: Redesigned trial addresses prior placebo issues; market opportunity large if successful. Thesis: Updates: Solid cash runway ($77M), stock drifting higher, management confident. Updates: Outlook: True coin flip but strong asymmetric reward. Outlook: Upside: $8–$10Downside: ~$2 if failed; stop-loss at $3.20Plan: Hold; sell half on success, trail the restConfidence: High Upside: $8–$10 Upside: Downside: ~$2 if failed; stop-loss at $3.20 Downside: Plan: Hold; sell half on success, trail the rest Plan: Confidence: High Confidence: High 3. SELLAS Life Sciences (SLS) 3. SELLAS Life Sciences (SLS) Catalyst: Phase 3 REGAL AML survival data – imminent Catalyst: Thesis: Low-probability moonshot, but huge upside if GPS shows survival benefit. Interim analysis not stopping suggests some hope. Thesis: Updates: Raised ~$73M; even failure may not lead to total collapse; KOL enthusiasm noted. Updates: Outlook: Riskier but offers multi-bagger potential. Outlook: Upside: 2–3x or moreDownside: 50%+ dropPlan: Hold; sell most quickly if positiveConfidence: Moderate Upside: 2–3x or more Upside: Downside: 50%+ drop Downside: Plan: Hold; sell most quickly if positive Plan: Confidence: Moderate Confidence: Moderate Portfolio-Level View Portfolio-Level View Deliberately concentrated in three uncorrelated biotechs to maximize recovery potential.One win narrows benchmark gap.Two wins could outperform the S&P.Drawdown risk remains high, but stop-losses and position sizing are active safeguards. Deliberately concentrated in three uncorrelated biotechs to maximize recovery potential. three uncorrelated biotechs One win narrows benchmark gap. One win Two wins could outperform the S&P. Two wins Drawdown risk remains high, but stop-losses and position sizing are active safeguards. stop-losses and position sizing Future Plans Thank you everyone for your suggestions! Here are some of the basic parameters I will implement for the year long version after December: 10,000 dollar paper capitalModels: ChatGPT, DeepSeek, and most likely Gemini.No 1 stock position can exceed 30% of portfolio value.If a stop-loss is activated, the model is required to wait two weeks period before initiating any new buy orders for the same stock. 10,000 dollar paper capital 10,000 dollar paper capital Models: ChatGPT, DeepSeek, and most likely Gemini. Models: ChatGPT, DeepSeek, and most likely Gemini. No 1 stock position can exceed 30% of portfolio value. No 1 stock position can exceed 30% of portfolio value. If a stop-loss is activated, the model is required to wait two weeks period before initiating any new buy orders for the same stock. If a stop-loss is activated, the model is required to wait two weeks period before initiating any new buy orders for the same stock. Keep in mind, important factors like the universe of stocks, prompts etc. are still being decided. Important Announcement I am in the early stages of a long-term project to create a benchmark for LLM trading for U.S stocks. My plan is to create different divisions (microcap, new IPOs, etc.) with major LLMs and publish the live equity curves on a dedicated website. benchmark for LLM trading for U.S stocks It won’t interfere with any current or experiments, just my attempt to broadly analyze the potential for LLM trading in different equity markets. Right now, I’m working on it privately; however, once the roadmap and structure is solid, I’ll open-source the codebase and make the website public. I also created an X account (NathanS729) for dev logs, project updates, and deeper insights on AI and finance. I haven’t made any posts yet, but new content is coming soon! I also created an X account NathanS729 External Links External Links To see all past deep research reports and summaries: Here Here Full chats: Here Here Have a question? Check out: Q&A Q&A If you’d like to see the raw logs and full portfolio simulation code: GitHub Page GitHub Page My X account: NathanS729 NathanS729 If you have any suggestions or advice, my Gmail is: nathanbsmith.business@gmail.com nathanbsmith.business@gmail.com Disclaimer This project is purely educational and research-focused. Nothing here should be taken as financial advice. Full disclaimer: Here Here

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