Telonex gives quant traders, researchers, and academics instant access to historical tick-level prediction market data. Download clean, normalized trades, order books, quotes, and onchain fills - ready for backtesting, research, and analysis. Sign up free and start downloading in minutes.
Features
- Tick-level trades with precise timestamps, prices, and volumes for every Polymarket market
- Full order book depth data (5, 25, and full levels) for reconstructing historical market states
- Best bid/ask quote snapshots for spread analysis and market making research
- Onchain fills with maker and taker wallet addresses for whale tracking and flow analysis
- 450,000+ markets, 20B+ data points, 3+ years of history - the most comprehensive Polymarket dataset available
- Clean, normalized Apache Parquet files - no scraping, no rate limits, no incomplete datasets
- Python SDK and REST API - loads directly into pandas, DuckDB, or Polars
- Free tier with 5 downloads.
Use Cases
- Backtest prediction market trading strategies with realistic slippage, spread costs, and fill probability
- Conduct academic and quantitative research across 450,000+ markets with research-grade data
- Analyze bid-ask spreads, order book depth, and fill patterns for market making research
- Track whale wallets and smart money activity using onchain fills with wallet-level P&L
- Build custom prediction market dashboards and analytics tools using Telonex as the data layer
Comments
Hey everyone! I built Telonex because I needed clean prediction market data for my own research and couldn't find it anywhere. Polymarket has become one of the most interesting financial markets in the world, but getting historical data meant scraping APIs, managing WebSocket connections, and dealing with messy blockchain data. I figured if I was spending weeks just cleaning data instead of actually analyzing it, others were too. So I built the infrastructure to collect, normalize, and serve tick-level trades, order books, quotes, and onchain fills - all in clean Parquet files ready for analysis. Would love to hear what you'd build with this data!
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Comments
Hey everyone! I built Telonex because I needed clean prediction market data for my own research and couldn't find it anywhere. Polymarket has become one of the most interesting financial markets in the world, but getting historical data meant scraping APIs, managing WebSocket connections, and dealing with messy blockchain data. I figured if I was spending weeks just cleaning data instead of actually analyzing it, others were too. So I built the infrastructure to collect, normalize, and serve tick-level trades, order books, quotes, and onchain fills - all in clean Parquet files ready for analysis. Would love to hear what you'd build with this data!
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