IdeaWell

Project ideas from Hacker News discussions.

We gave 5 LLMs $100K to trade stocks for 8 months

Original Article

Hacker News Discussion

📝 Discussion Summary (Click to expand)

The three most prevalent themes in the discussion regarding the LLM trading backtest are:

1. Deep Skepticism Regarding Backtesting Validity and Market Realism

A significant portion of the conversation centers on the inherent flaws of using historical backtesting ("paper money") to predict real-world profit. Commenters frequently cited issues like the lack of market impact, the short timeframe masking true risk, and the inherent risk of "data leakage" or future knowledge benefiting the models, despite attempts to mitigate this.

  • Supporting Quotes:
    • "Source: quant trader. paper trading does not incorporate market impact" ("chroma205")
    • "This is a really dumb measurement." ("jacktheturtle")
    • "Backtesting is a complete waste in this scenario. The models already know the best outcomes and are biased towards it." ("digitcatphd")

2. The Role of Sector Bias (Tech Heavy Portfolios) in Outperformance

Many users pointed out that the positive returns were overwhelmingly attributed to the models favoring the tech sector, which experienced a bull run during the testing period, rather than demonstrating superior fundamental trading skill. This suggests the performance is a reflection of market conditions rather than AI advantage.

  • Supporting Quotes:
    • "Almost all the models had a tech-heavy portfolio which led them to do well. Gemini ended up in last place since it was the only one that had a large portfolio of non-tech stocks." ("bcrosby95")
    • "They 'proved' that US tech stocks did better than portfolios with less US tech stocks over a recent, very short time range." ("skeeter2020")
    • "If the AI bubble had popped in that window, Gemini would have ended up the leader instead." ("gwd")

3. Debate on Grok's Unique Capabilities and Guardrails

There was an extended sub-theme focusing specifically on Grok, with users debating whether its superior results stemmed from genuine capability advantages or system design features (like reduced safety guardrails or better/faster web access) that allowed it to exploit current market biases better than competitors.

  • Supporting Quotes:
    • "56% over 8 months with the constraints provided are pretty good results for Grok." ("alchemist1e9")
    • "I have a hunch Grok model cutoff is not accurate and somehow it has updated weights though they still call it the same Grok model..." ("alchemist1e9")
    • "...fewer safety guardrails in pretraining and system prompt modification that distort reality." ("observationist")

🚀 Project Ideas

LLM Backtesting Fidelity Simulator (LLM-Backtest-Fidelis)

Summary

  • A tool that simulates the true execution environment for LLM trading agents, accounting for API latency, slippage, market impact assumptions, and fluctuating model costs/inference times that are ignored in simple backtests.
  • Core value proposition: Moving LLM trading research from "paper-only" results to a more realistic, actionable performance benchmark.

Details

Key Value
Target Audience AI researchers, quantitative finance developers experimenting with LLMs for trading, academics studying algorithmic efficiency.
Core Feature A simulation layer that injects realistic, time-varying execution latencies and models market microstructure effects (slippage, order book impact) based on simulated trading volume relative to the $100K capital constraint.
Tech Stack Python (for simulation logic), Async libraries (e.g., asyncio) to model concurrent API calls and latency, Database (e.g., PostgreSQL) to log detailed execution vs. intended price differences.
Difficulty Medium
Monetization Hobby

Notes

  • Why HN commenters would love it (quote users if possible): Addresses the core complaint that backtesting is meaningless due to the lack of real-world mechanics: "The problem is that you don’t know if and when there might be a correction," and "paper trading does not incorporate market impact."
  • Potential for discussion or practical utility: Allows researchers to decouple strategy logic from execution flaws. The tool could be extended to compare simulation fidelity against live paper trading statistics gathered from broker APIs.

Agent Orchestration & Competitive Swarm Platform (AOCS)

Project Title

AOCS (Agent Orchestration & Competitive Swarm Platform)

Summary

  • A multi-agent platform designed to test emergent intelligence by having self-modifying LLM agents compete against each other, incorporating real-time feedback loops and capability weighting.
  • Core value proposition: Facilitating complex multi-agent research beyond single, monolithic trading prompts, capitalizing on the idea that "multi-agent collaborations that weight source inputs based on past performance" will be superior.

Details

Key Value
Target Audience Users interested in advanced agent modeling, simulation enthusiasts, researchers testing cooperative/competitive AI strategies.
Core Feature A central orchestration layer (Controller Agent) that assigns roles, re-prompts, and dynamically adjusts the "weight" (influence or compute budget) given to specialized worker agents (e.g., News Analyst, Technical Indicator Modeler, Risk Manager).
Tech Stack TypeScript/Node.js (for orchestrator), LangChain/CrewAI variants (or custom framework for agent definition), Model APIs integrated via an abstraction layer.
Difficulty High
Monetization Hobby

Notes

  • Why HN commenters would love it (quote users if possible): Direct response to the suggestion: "I would love to see a frontier lab swarm approach... use some sort of orchestration algorithm that lets the group exploit the strengths of each individual model."
  • Potential for discussion or practical utility: Can be generalized beyond finance (e.g., logistics optimization, R&D simulation) as a framework for testing emergent coordination between heterogenous LLMs.

Contextual Data Pipeline for LLM Trading (CDP-LLM)

Project Title

Contextual Data Pipeline for LLM Trading (CDP-LLM)

Summary

  • A service that constructs and serves highly specific, time-segmented vector embeddings encompassing non-standard data sources (e.g., specific forum sentiment, regulatory filing semantics, proprietary news archives) correlated with market events.
  • Core value proposition: Solving the data leakage/relevance problem by providing LLMs with correctly segmented, non-future-leaking context that goes beyond simple web search.

Details

Key Value
Target Audience Quant developers who believe LLMs can find non-numeric alpha but struggle with data cleaning, relevance selection, and time-segmentation.
Core Feature Automated ingestion, embedding generation, and time-stamping of unstructured text. A retrieval mechanism that ensures provided context vector embeddings for a trading day $T_n$ contain zero information from days $T_{n+1}$ onwards.
Tech Stack Python/FastAPI, Vector Database (e.g., Pinecone, Weaviate), Data ingestion tools (e.g., Apache Nifi/Airflow) for ETL processes.
Difficulty Medium
Monetization Hobby

Notes

  • Why HN commenters would love it (quote users if possible): Directly tackles the concerns around data leakage and context quality: "If your strategy could outperform the S&P 500, they wouldn't be blogging about it," implying quality, proprietary data is the edge. Also addresses the need to move beyond simple sentiment analysis: "incorporate more than just signals from the market itself... vector embedding of a selection of key social and news media accounts."
  • Potential for discussion or practical utility: The strict data segmentation mechanism could be productized as a standalone validation tool for any time-series backtesting simulation framework.