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Data & analytics Kalshi Polymarket
PMXT: 'CCXT for prediction markets' Unified API for trading on Polymarket, Kalshi and more.
Last updated Oct 23, 2026 · source link verified
What it does Unified API for trading on Polymarket, Kalshi and more. See the source for setup, usage and current status.
Tools: TypeScript
Difficulty: intermediate
Platforms: Kalshi · Polymarket
FAQ What is PMXT: 'CCXT for prediction markets'? Unified API for trading on Polymarket, Kalshi and more. It is listed under Data & analytics in this directory.
Does PMXT: 'CCXT for prediction markets' work with Kalshi or Polymarket? According to its source, it works with Kalshi and Polymarket.
What tools or languages does PMXT: 'CCXT for prediction markets' use? The source lists: TypeScript.
Who built PMXT: 'CCXT for prediction markets' and where is the source? It was published by pmxt-dev. The public source is at github.com/pmxt-dev/pmxt.
How hard is PMXT: 'CCXT for prediction markets' to set up? This directory rates it intermediate (beginner, intermediate or advanced).
How many GitHub stars does PMXT: 'CCXT for prediction markets' have? It had 2,170 GitHub stars as of Oct 13, 2026.
Is PMXT: 'CCXT for prediction markets' still maintained? The repository was last updated on Sep 30, 2026, as checked on Oct 23, 2026. Check the source for current status.
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Framework for collecting and analyzing prediction-market data, including a large public Kalshi prediction market dataset.
Dataset stat Becker Kalshi dataset: 7.68M markets; 23.9% of settled markets resolved YES. Method on detail page.
Python Parquet
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Scripts to collect Polymarket market and trade data for analysis.
Python
Data & analytics Kalshi
Student research notebook (Rennes School of Business graduating project) that compares the probability distribution implied by SPXW and NDX option prices, using a fitted SVI smile, with the distribution quoted by Kalshi's S&P 500 and Nasdaq-100 range contracts for the same expiry. Uses Yahoo Finance option data and the Kalshi public API, with a saved snapshot in the repo. Findings are self-reported and based on a single snapshot.
Python Jupyter SVI volatility fit +2
Source details Source github.com/pmxt-dev/pmxt ↗ Author pmxt-dev Repo created Jan 8, 2026 Repo updated Sep 30, 2026 Added Oct 13, 2026 Link verified Oct 23, 2026 GitHub stars ★ 2,170 (as of Oct 13, 2026)