lechmazur/bazaar
The BAZAAR challenges LLMs to navigate the double-auction marketplace, where buyers and sellers must make strategic decisions with incomplete information. Each agent receives a private value and must decide how to quote based solely on the history of previous rounds. A realistic test of market intuition and strategic adaptation.
This project evaluates how well Large Language Models (LLMs) perform in a simulated financial market. It takes an LLM's bidding strategy as input and outputs its performance metrics like profitability, trading frequency, and adaptability. Market researchers, economists, or AI developers interested in the strategic capabilities of LLMs in competitive environments would use this tool.
No commits in the last 6 months.
Use this if you want to understand and compare the economic decision-making abilities of different LLMs in a double-auction marketplace.
Not ideal if you are looking for a tool to build or optimize a real-world trading bot, as this is a research benchmark.
Stars
35
Forks
4
Language
—
License
—
Category
Last pushed
Jul 30, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/transformers/lechmazur/bazaar"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
InternLM/lagent
A lightweight framework for building LLM-based agents
microsoft/rat-sql
A relation-aware semantic parsing model from English to SQL
Mann1988/awesome-claude-skills
📊 Explore high-quality Claude skills focused on business analysis and content creation,...
kevinMEH/keyscan
Keyscan: AI-powered API key scanner for GitHub Gists.
Sakeeb91/text2sql-agent
Self-correcting AI agent for natural language to SQL using HuggingFace smolagents and ReAct framework