{"slug": "show-hn-i-ran-12-ai-bots-predicting-stocks-for-two-months-every-call-public", "title": "Show HN: I ran 12 AI bots predicting stocks for two months, every call public", "summary": "LDBD, a public leaderboard launched by an independent creator, allows humans and AI to submit predictions on stocks, ETFs, and crypto and has run 12 LLM-based prediction bots using Claude, ChatGPT, and Gemma for two months. The creator reports that it is too early to say any AI bot statistically beats the market, and the service uses annualized directional log return with Bayesian smoothing as its main metric. LDBD is free, uses no real money, and is not financial advice.", "body_md": "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.\n\nThis service starts from one question: does anyone or any AI can really beat the market consistently? If yes, prove it!\n\nI 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.\n\nI 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.\n\nAs 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\n\nHumans 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\n\nScoring 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.\n\nTo be honest, I'm not a developer or a financial specialist; I made this entire service with Claude code without expertise.\n\nSo 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.\n\nAnyone can access the leaderboard and full prediction history without sign-up. On-going predictions are kept private for preventing cherry-picking.\n\nLastly, LDBD is free, no real money used, not financial advice.\n\nComments URL: [https://news.ycombinator.com/item?id=49014412](https://news.ycombinator.com/item?id=49014412)\n\nPoints: 1\n\n# Comments: 0", "url": "https://wpnews.pro/news/show-hn-i-ran-12-ai-bots-predicting-stocks-for-two-months-every-call-public", "canonical_source": "https://ldbd.app", "published_at": "2026-07-22 22:35:21+00:00", "updated_at": "2026-07-22 22:52:05.862397+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-tools", "ai-agents"], "entities": ["LDBD", "Claude", "ChatGPT", "Gemma"], "alternates": {"html": "https://wpnews.pro/news/show-hn-i-ran-12-ai-bots-predicting-stocks-for-two-months-every-call-public", "markdown": "https://wpnews.pro/news/show-hn-i-ran-12-ai-bots-predicting-stocks-for-two-months-every-call-public.md", "text": "https://wpnews.pro/news/show-hn-i-ran-12-ai-bots-predicting-stocks-for-two-months-every-call-public.txt", "jsonld": "https://wpnews.pro/news/show-hn-i-ran-12-ai-bots-predicting-stocks-for-two-months-every-call-public.jsonld"}}