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.

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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

  1. Enumerate resolved markets in your target category and date range.
  2. Pull each market's price series and store it with UTC timestamps.
  3. Record the resolved outcome alongside each series — that is your label.
  4. Capture whatever liquidity information you can (volume, spread estimates).
  5. 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

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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.

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.

Automate Polymarket the self-hosted way

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Frequently Asked Questions

Yes — public API endpoints expose historical prices, market metadata, and resolved outcomes, and every settlement is also recorded on-chain on Polygon. Check Polymarket's current API documentation for exact endpoints, granularities, and rate limits.
At minimum: price series with timestamps, each market's resolved outcome, and realistic cost assumptions for spread and slippage. Liquidity information (volume, book depth) makes results considerably more trustworthy, especially for thin markets.
Only use information that was knowable at each simulated moment. Store point-in-time snapshots, never let resolved outcomes or final statistics leak into features, and be suspicious of any backtest that looks too good — it usually means the future leaked in.
PB
Written by the PolyBot Team

We build self-hosted automation tools for Polymarket and write about prediction-market execution, strategy, and risk management. Our guides are educational, not financial advice.

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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.

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