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To backtest a Polymarket strategy, build a dataset that connects each market’s rules and eventual resolution to its Yes or No token prices and trade history, then test the strategy against that data using an explicit execution model. A historical price series can help test a signal, but it does not by itself show that an order could have filled at each recorded price.
Start by choosing the Polymarket venue and market universe
Decide whether the backtest covers decentralized Polymarket or Polymarket US before collecting data. The Polymarket Institute’s researcher guide, published July 24, 2026, describes them as separate exchanges with distinct APIs and separately managed data. Do not combine their observations unless you have verified that the market definitions, data fields, and trading rules are comparable. Polymarket Institute, “Making Polymarket Exchange Data Accessible for Research”.
Define the strategy’s market universe by the actual question and outcomes it trades, not just by an event’s headline. An event can group several underlying binary markets; those markets may have different questions and resolution criteria. Decide which ones qualify, and record the selection rules so someone else can reproduce the same universe.
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Find markets and preserve the rules that define each outcome
Use Gamma market and event metadata to discover markets and retain the fields needed to interpret them. The Institute guide describes Gamma as a source for market metadata such as the question, end date, resolution criteria, and resolution source. Those details are part of the backtest dataset: a price has little meaning if you cannot establish what “Yes” and “No” refer to or how the market resolves.
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- Market question and event grouping: preserve the exact wording and the event relationship so you can distinguish a market from its broader event.
- Resolution criteria and source: retain the stated rules and source used to determine the final outcome.
- End date: keep the market’s recorded end date rather than inferring it from a headline or event date.
- Identifiers: retain the condition ID and the outcome-token IDs as separate fields. The guide identifies
conditionIdas the unique identifier for a binary-outcome market andtoken_idorclobTokenIdas an identifier for an individual Yes or No token.
When a market resolves, attach the eventual outcome to the same market record and identifiers. Do not infer resolution solely from later news or a similarly worded market; the market’s own criteria govern how its result should be interpreted.
Collect token prices and trade history separately
Outcome-token price history
For CLOB price lookups and history, use the specific Yes or No token ID. Gamma returns clobTokenIds, which lets you associate each outcome with its token identifier. The Institute guide describes the CLOB /prices-history endpoint as returning timestamped price observations. Store the original timestamps, the token ID, the market or condition ID used to connect the observation to a market, and the endpoint that produced it.
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Be precise about what the series represents. The guide discusses price history separately from order-book information; it does not establish that every historical sample is an executable bid or ask, or that the series contains queue position or complete historical depth. A plotted price line is therefore evidence about observed prices, not proof of a fill at every point.
Market trades and user-level history
The Data API is described as providing market trade data and user-level history. The guide says trade history can be filtered by market, user, or time range and gives a market-filtered trades example. Keep this data distinct from the price-history series: trade records and price observations answer different questions. Record the original timestamps and the market or user filter used for each retrieval so that joins and exclusions are auditable.
The guide’s description of these sources and their roles is in the Polymarket Institute researcher guide. It also points to official Orderbook & Pricing documentation for related details such as fees, tick sizes, spreads, and order-book information.
Turn the collected data into a reproducible backtest
- Write down the strategy rule before evaluating results. Specify eligible markets, the signal, when it is evaluated, the entry and exit conditions, and how the strategy handles markets that end or resolve while a position is open.
- Join observations using the correct identifiers. Use the market identifier to associate observations with a market and the outcome-token identifier to associate CLOB prices with Yes or No. Keep condition IDs distinct from token IDs.
- Preserve the source timestamps and sampling method. State which price-history observations or trade records you use, their retrieval source, and the sampling interval or selection rule. Do not silently treat irregular observations as a uniform time series.
- Apply market-specific resolution rules. Link each completed market to its recorded resolution and interpret it using that market’s criteria and source. Use the venue’s applicable settlement and payoff rules when valuing a resolved position; do not substitute an assumed payout rule.
- Account for trading costs only when supported. Record the fee assumptions, price convention, and any slippage assumptions. The guide’s example market record includes fee-related metadata, but an example’s values do not establish a general fee schedule. Verify applicable market- and date-specific costs before using them.
- Separate signal results from execution assumptions. Report what the strategy would indicate from the observed data separately from whether the assumed order could have filled. Label simplified fill assumptions clearly.
Choose and disclose an execution model
Execution is the central limit when moving from a price-based strategy test to a claim about tradable performance. The Institute guide identifies bid-ask spread, midpoint, and order-book depth as relevant execution information, while treating price history as a separate series. A historical price observation alone does not establish the available side of the market, fillable size, queue position, or fill probability for a hypothetical order.
For every backtest, state the order side, the price convention used for entry and exit, the assumed size, any slippage and fee treatment, and the evidence behind the fill assumption. If the test uses a sampled price as a fill price, call that a simplifying assumption rather than an observed execution. The cited guide does not establish the completeness or public API availability of historical order-book depth, so a realistic depth-based simulation requires a separately verified historical order-book source. If you cannot verify one, present results as conditional on the simplified model rather than as proof of executable returns.
Report enough detail for another researcher to reproduce the result
Include the venue, market-selection rules, data sources and endpoints, retrieval date, sampling interval, market and token identifiers, resolution criteria, and execution assumptions. Preserve source data or a reproducible retrieval procedure, along with the joins, exclusions, and transformations applied. The guide says decentralized exchange orders are recorded on Polygon and describes the APIs as convenient views into publicly verifiable market data; that context does not remove the need to document how the backtest dataset was assembled.
A clear report distinguishes three things: the signal computed from the chosen observations, the outcome under the market’s resolution rules, and the assumptions used to turn observations into hypothetical trades. That separation lets readers judge what the data establishes without confusing a price-history result with evidence of achievable fills.
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