Home › Data & analytics › Real-time tick visualizer
Data & analytics Kalshi Polymarket
Real-time tick visualizer WebSocket client that streams and charts tick-by-tick data from Kalshi and Polymarket.
Last updated Oct 23, 2026 · source link verified
What it does WebSocket client that streams and charts tick-by-tick data from Kalshi and Polymarket. See the source for setup, usage and current status.
Tools: Python WebSockets
Difficulty: intermediate
Platforms: Kalshi · Polymarket
FAQ What is Real-time tick visualizer? WebSocket client that streams and charts tick-by-tick data from Kalshi and Polymarket. It is listed under Data & analytics in this directory.
Does Real-time tick visualizer work with Kalshi or Polymarket? According to its source, it works with Kalshi and Polymarket.
What tools or languages does Real-time tick visualizer use? The source lists: Python, WebSockets.
Who built Real-time tick visualizer and where is the source? It was published by RohitDayanand. The public source is at github.com/RohitDayanand/PolyKalshi_Client.
How hard is Real-time tick visualizer to set up? This directory rates it intermediate (beginner, intermediate or advanced).
How many GitHub stars does Real-time tick visualizer have? It had 21 GitHub stars as of Oct 13, 2026.
Is Real-time tick visualizer still maintained? The repository was last updated on Sep 22, 2025, as checked on Oct 23, 2026. Check the source for current status.
Compare all data & analytics tools → · How to build a Kalshi bot · How to build a Polymarket bot
More data: Prediction market datasets and free Kalshi JSON downloads
Data & analytics Kalshi Polymarket
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
Data & analytics Polymarket
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