What data does outcometick provide?
Tick-level history for Polymarket and Predict.fun crypto Up/Down markets: the Chainlink settlement feeds the markets resolve against (a decimal value, with the relay’s full-precision fixed-point string beside it whenever the relay publishes one), each market’s strike and settled outcome, order-book snapshots and incremental updates, Polymarket trade prints, and OHLC candles from 1 second to 1 month (a tick count per candle, not trade volume).
Which markets and assets are covered?
Polymarket (BNB, BTC, DOGE, ETH, HYPE, SOL, XRP) for the 5-minute and 15-minute Up/Down markets, and Predict.fun (BTC, ETH, BNB) for the 5-minute, 15-minute, hourly and daily ones.
How far back does the data go?
Continuously since 2026-06-08, growing by one archived day at a time. Archives are published two days behind, which leaves a day to correct anything before it ships.
How do I get the data?
Through a self-serve HTTP API. You buy a key, list the files for a date or a range, and download each one; every file carries its sha256 so you can verify it. No sales call and no bespoke export step.
What does it cost?
$10 per month for one venue (more for both), each a rolling 30-day window that moves with the archive. Deeper history is a one-time purchase of any date range you pick, priced per archived day. Card works for every purchase; WeChat Pay (in CNY) and stablecoins (in USD) for one-time purchases. Prices are in USD on the English site and CNY on the Chinese one.
Can I verify the settlement outcomes myself?
Yes, and that is the point of shipping the settlement feed rather than just a label. Each market carries its strike, and each settlement tick carries the relay’s fixed-point figure whenever the relay publishes one, so you can recompute every win/loss from the raw prices instead of trusting our label.
Can I backtest a strategy on this data?
Yes, without downloading anything first. You paste a strategy in Python or Node.js and it runs in a sealed sandbox against the same tick archive we sell — settlement feed, full-depth order books and trade prints. You get back an equity curve, every fill priced against the depth that was really there, and a settlement cross-check. There is also an SDK on npm and PyPI to run it locally. Billed per market-day scanned, from $2.
How does the backtest avoid look-ahead bias?
Structurally, not by filtering. The replay loop pulls one row at a time from the process input, so future rows are not in the sandbox yet when your handler is called — there is nothing to peek at rather than something being hidden from you. A market’s settled outcome is stripped from the market view until the settlement hook fires, and fills are matched against the real book depth at that instant rather than assumed at the midpoint.