New Experiment Coming Soon!

Full Experiment Rules/Guidelines Out Now

Hey everyone!

Over the past few months I have been super busy with ACT studying, so I was unable work on LIBB or the upcoming experiment. However, this weekend I have made massive progress on the experiment’s infrastructure (prompts, data functions, workflow, etc). While the code is still being developed, I have fully decided the following rules and guidelines.

Enjoy!

EXPERIMENT RULES — IPO Arena

Primary Research Question: Do LLMs exhibit measurably distinct behavioral, sentiment, and performance patterns when trading IPO-stage equities, and do these patterns differ systematically across model families?

Hypothesis: Model families will exhibit distinct behavioral signatures but no family will consistently outperform the $IPO ETF on a risk-adjusted basis.

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Sub-question 1: Do reasoning models exhibit more conservative behavior than their standard counterparts?

Hypothesis: Reasoning models will exhibit lower turnover and higher order quality due to more deliberate decision-making.

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Sub-question 2: Do reasoning models achieve better risk-adjusted returns in IPO-stage equities?

Hypothesis: Reasoning models will achieve better risk-adjusted returns than their standard counterparts due to more careful position sizing under fundamental uncertainty.

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Sub-question 3: Is sentiment polarity in model reasoning correlated with subsequent portfolio performance?

Hypothesis: Sentiment polarity will show weak to moderate correlation (R ≈ 0.1 to 0.5) with subsequent portfolio performance.

UNIVERSE

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Eligible securities: U.S.-listed equities that IPO’d within the last 3 years

Minimum market cap for new buys: $200M

Maximum market cap for new buys: None

Re-entry: A stock that falls below $200M and recovers above $200M while still within the 3-year IPO window becomes eligible for new buys again.

CAPITAL

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Starting cash: $10,000 per model

No new capital injections at any point.

The models must maintain at least 1 active position at all times. If all positions are exited, a new position must be initiated on the next eligible trading day.

Note: This rule is enforced through instruction only and not at the execution level. Violations are recorded and analyzed as part of behavioral metrics.

POSITION RULES

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Buy eligibility: Ticker must be IPO_ELIGIBLE and MCAP_ELIGIBLE (see eligibility feed).

If a stock becomes ineligible (IPO window expired or market cap below $200M),

existing positions may be held, trimmed, or exited but no new shares may be purchased.

No additional shares may be purchased in a stock that is no longer IPO-eligible, even if market cap recovers above $200M and the stock re-enters eligibility after the IPO window has expired.

ORDERS

Order types: LIMIT and MARKET (DAY only — no GTC)

Share quantities: Integer only (no fractional shares)

Stop losses: Not required. Default to 0.0 if not specified.

Concentration: No limit — model may allocate 100% to a single position.

Risk management: None enforced. Model makes all sizing decisions.

CADENCE

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Initial Prompt: Starting Prompt

Monday–Thursday: Daily Prompt

Friday: Weekly Deep Research Prompt

Duration: 1 year

Blog posts will be provided every month.

MODELS

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All 8 models receive identical inputs on the same day:

Standard: Reasoning counterpart:

DeepSeek V3.2 DeepSeek R1

GPT-4.1 o4-mini

Grok 4.1 Fast Grok 3 Mini

Gemini 2.5 Flash Gemini 2.5 Pro

BASELINES

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Primary: $IPO ETF (Renaissance IPO ETF)

Secondary: SPY

LOGGING

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All prompts and model outputs will be saved to disk.

All orders, executions, and rejections logged via LIBB trade log.

Timeline

I expect to have all the prompts and workflow fully developed by next week; I’ll post a detailed blog post during the weekend.

The experimental design requires some internal components for LIBB to be created, so in approximately 2-3 weeks the experiment should fully begin.

Links

IPO Arena is hosted on LLM Trading Lab, a dedicated personal platform for running controlled LLM trading experiments. All model runs, outputs, and metrics are tracked and will be published as the experiment progresses; check it here.

To stay in the loop, I recommend following on X and LinkedIn

Conclusion

Thank you for reading! I’m excited to be back,

Nathan