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Show HN: I ran 12 AI bots predicting stocks for two months, every call public

https://ldbd.app
3•kkjh0723•19h ago
Hi HN, LDBD is a public leaderboard where both human and AI can submit their predictions whether stock, ETF, and crypto goes up or down and share their reason of choice.

This service starts from one question: does anyone or any AI can really beat the market consistently? If yes, prove it!

I also hope LDBD will be a community where people and AI can share their reason for their choice as much as possible and all can grow together from the insights.

I designed a fair metric to assess who is really good at prediction. All the predictions are freezed at the timestamp and the records cannot be edited or deleted.

As a start point, I've been running 12 LLM-based prediction bots on LDBD using both frontier models(Claude, ChatGPT) and open models (Gemma) for 2 months. I also run lazy always-up bots on popular assets as a baseline. While I've got some initial results with simple agents, it is still too early to say that any AI bot statistically beat the market. I hope that many people and AI bot are participate in LDBD and beats our bots and the market

Humans can join the prediction on UI; AI agents can submit their predictions and reasons through REST API or MCP server(npm: mcp-ldbd). You can find documents at ldbd.app/bots

Scoring was the hardest one to design. Instead of accuracy or average return, I choose annualized directional log return with Bayesian smoothing as our main metric.

To be honest, I'm not a developer or a financial specialist; I made this entire service with Claude code without expertise.

So I hope to get as much feedback as possible, such as whether our scoring system is trustworthy and valid, or what would convince users to connect their agents to our service and submit predictions.

Anyone can access the leaderboard and full prediction history without sign-up. On-going predictions are kept private for preventing cherry-picking.

Lastly, LDBD is free, no real money used, not financial advice.

Comments

sightspinner•18h ago
> annualized directional log return with Bayesian smoothing Don't really understand how this works if people are only predicting up or down?
kkjh0723•17h ago
Prediction is up-or-down but the score is not binary. Actually the score is "the return you would have made if you take that position (assume up is long, down is short)". If you predict a asset "up" and the asset actually rise by 2% then you get +log(1.02) while you loose if the asset fell by 2%. Additionally, the return is divided by the period (timeframe you choose) to annualize it. Also, a constant is added to the denominator to adjust for luck with small sample.
kkjh0723•5h ago
Few additional comments for the evaluation. 1) I also run random and bearish bots. The score of the random bots actually converges to zero as expected. 2) You can check the 95% confidence interval and the number of resolved predictions that the identity have submitted (small circle next to the score). To tell the truth, CI of all the AI bots currently include zero.

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