How to Get Polymarket Historical Data (Prices, Trades & Outcomes)
Every serious bot project starts with the same unglamorous step: getting good historical data. Polymarket is unusually generous here — prices, trades, and outcomes are all retrievable — but building a clean dataset still takes care. Here is where the data lives and how to assemble it without fooling yourself.
Why historical data is the foundation
You cannot backtest without history, and you cannot evaluate a live bot without a baseline. Historical data answers the questions that decide strategy design: how do prices behave before resolution, how wide do spreads get, and how often would your rules have triggered?
The data sources, by type
Price history
Polymarket's public APIs expose historical price series for market outcomes at various granularities — enough to reconstruct how a market's implied probability evolved from open to resolution.
Market metadata and resolved outcomes
The markets API provides each market's question, rules, category, dates, and — critically for backtesting — its final resolved outcome. Resolved markets are your labeled dataset: thousands of completed experiments with known answers.
Trades and order-book snapshots
Trade-level data shows actual executions; live order-book state comes from the WebSocket feed. Note that full historical order-book depth is much harder to obtain after the fact — if your strategy depends on book state, start recording it yourself now.
On-chain records
Every settlement lives on Polygon, so block explorers and indexers can reconstruct any wallet's full history — the same transparency that powers whale tracking. On-chain data is the ground truth when API sources disagree.
Endpoint names, granularities, and limits evolve. Treat this article as the map, and Polymarket's current API documentation as the territory — verify endpoints there before building anything permanent.
Building a backtest dataset, step by step
- Enumerate resolved markets in your target category and date range.
- Pull each market's price series and store it with UTC timestamps.
- Record the resolved outcome alongside each series — that is your label.
- Capture whatever liquidity information you can (volume, spread estimates).
- Store it simply — CSV or SQLite is plenty at this scale — and version your dataset so results are reproducible.
Respect rate limits while bulk-downloading: batch requests, cache aggressively, and back off on errors. A polite crawler finishes; an aggressive one gets throttled halfway.
The pitfalls that corrupt datasets
Lookahead bias is the silent killer. If your backtest uses any information that was not knowable at that moment — final volume figures, edited metadata, or the resolved outcome leaking into a feature — your results are fiction. Reconstruct what a trader could actually see at each timestamp, nothing more.
- Selection bias: testing only famous, liquid markets overstates how tradeable the average market is.
- Stale prices: in illiquid markets, the “last price” may be hours old — it was not a price you could have traded at size.
- Missing costs: add realistic spread and slippage to every simulated fill, or your edge is imaginary.
- Sparse series: gaps in thin markets need explicit handling, not silent interpolation.
From dataset to decisions
Once the data is clean, the workflow is the standard one: develop rules in-sample, validate out-of-sample, then forward-test small — the full discipline is covered in the backtesting guide. Good data does not make a strategy work, but bad data guarantees you will not know either way.
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Frequently Asked Questions
Disclaimer: This article is for educational purposes only and is not financial, investment, or legal advice. Prediction-market trading carries a real risk of loss. Automation does not guarantee profit, and past performance never guarantees future results. Only trade funds you can afford to lose, and confirm that Polymarket is available and legal in your jurisdiction before trading.