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A Polymarket fair-value bot works with three different numbers and one decision rule. Your model produces a forecast: the probability that a clearly defined outcome resolves YES. The market shows a probability-like price. Your executable cost is what the order book would charge you for your intended size, averaged across the price levels that size consumes, plus fees and a slippage allowance. The bot should trade only when the forecast beats that executable cost by a margin you set before the trade. Polymarket’s official documentation explains how to find outcomes and submit orders. It does not show that any model has a durable edge, so whether your model beats the market is something you must test rather than assume.
Keep three prices separate
In this article, fair value means your estimate of the probability that a market resolves YES under its actual rules. A YES token price can be read as a probability-like number, but it is not your forecast. It is what the market is currently offering, and the price you can actually execute is a third figure again. Mixing these up is the most common reason a model that looks profitable on a chart loses money in live trading.
Start from the resolution rule, not the model
The model’s target is the label, and the label comes from the market’s rules. Before you collect a single feature, write down the exact question, the resolution criteria printed on the market page, the source those criteria name, and the cutoff time. Polymarket’s resolution help article explains how markets are resolved in general, but individual markets can carry their own timing, source, and exception language. Read that language for every market you model. This guide does not describe how disputes are handled, so do not assume a dispute procedure exists or how it works.
Every feature also needs a timestamp, and the model may use only information that was public at the decision time. Suppose a market asks whether an event happens before a fixed date, and your decision time is 09:00 UTC on day one. A poll published at 14:00 UTC that same day must be excluded from that decision. A feature that quietly uses later news or a post-close price will make a backtest look excellent and then fail live.
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The modeling loop
A workable build moves through six stages. Each stage should leave an artifact you can audit later.
- Build a point-in-time dataset. Store the market wording and version, the market and outcome identifiers, book snapshots or historical prices, event features with their timestamps, the decision time, and the eventual resolution.
- Set baselines before any model. Compare every candidate with a simple base-rate estimate for the event class and with the market’s price at the same timestamp. A model that cannot beat both has not earned a trade.
- Estimate and calibrate. Produce a probability, log the inputs and model version, and check calibration on outcomes the model did not see during fitting. A raw model score is not a probability until calibration shows that it behaves like one.
- Convert the forecast into a decision. Compare the probability with the executable cost of the side you would buy, at your intended size, using the method in the cost section below.
- Replay fills and costs. Run the strategy against historical data with explicit assumptions for missed, partial, and delayed orders.
- Monitor after deployment. Log data age, market status, order requests and responses, fills, cancellations, positions, and the forecast at each decision.
Neither the official documentation nor the community references behind this guide validate a particular estimator, feature set, or edge threshold. Logistic regression and Bayesian updating are reasonable candidates to test against the baselines above. They are not proven choices.
Reading markets through the API
Polymarket’s official quickstart walks through the end-to-end flow: authenticate, fetch a market, select an outcome identifier, place a market order, wait for on-chain settlement, and check the resulting position. The example also shows that the identifier you trade depends on the market version. CTF markets use a token ID, while Protocol V2 markets use a position ID. Confirm which version a market uses before you wire up order placement, because the identifier you pass determines which outcome you actually trade.
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Which price you are looking at
Four observations are commonly confused. The community guide treats them as answering different questions, and the table below shows what each one can and cannot tell a bot.
| Observation | What it measures | What it does not tell you |
|---|---|---|
| Midpoint | The point halfway between the best bid and the best ask | A price you can fill. A larger order may not trade there. |
| Last trade | The price of the most recent match | What is available now, or at what size |
| Best bid and best ask | The top quote on each side of the book | How much size sits behind that quote |
| Size-weighted executable price | The average price across the levels your order would consume | A guaranteed fill. The book can change before your order arrives. |
Calculate what a buy would actually cost
For a buy, consume the ask levels from lowest price upward until your requested share quantity is covered. Multiply price by shares at each level and add the results. If the displayed depth does not cover your size, the calculation should report a shortfall rather than assume the remainder fills at the last price. The result is an estimate, not a fill guarantee, and fees still apply.
The following example uses a hypothetical book, not a real market. Suppose the ask side shows three levels, and you want to buy 300 shares.
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|---|---|---|---|---|
| 1 | 0.52 | 100 | 100 | 52.00 |
| 2 | 0.54 | 150 | 150 | 81.00 |
| 3 | 0.57 | 200 | 50 | 28.50 |
| Total | 300 | 161.50 |
The average cost is 161.50 ÷ 300, or about 0.5383 per share, even though the best ask is 0.52. A model estimate of 0.60 for YES then leaves a raw gap of about 0.0617 per share before fees and before any slippage allowance. To buy NO instead, use the NO side’s asks and compare your estimate of NO, which is 1 minus your YES probability, with that executable cost. Fee terms are set in Polymarket’s documentation and can change, so look up the current figures before you finalize the threshold.
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Order rules that can invalidate a trade
Market orders versus limit orders
A market order trades against available liquidity. A limit order sets the price you will accept and can rest on the book after you place it. The order documentation describes the limit case this way:
“A limit order specifies the price at which you are willing to trade and can rest on the book until it fills, expires, or you cancel it.”
Check market state, tick size, and minimum size
The order guide asks integrators to check several conditions before submitting anything:
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- Confirm the market is accepting orders at the moment of submission.
- Use the market’s current tick size. A limit price that does not conform to the tick size is not a valid order.
- Use the market’s current minimum order size. Smaller orders are not valid.
- Read the order response instead of assuming that a request was accepted as intended.
Order statuses are not settlement
The order guide documents response statuses that include live, matched, and delayed. These describe the order’s state, not the final outcome of the position. Store every status transition with a timestamp, and reconcile it against trades and on-chain settlement. The quickstart waits for settlement before checking the position, and your bot should follow the same order before it updates its inventory.
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Test the forecast, then test the trades
Keep two evaluations apart. Forecast quality asks whether your probabilities match realized outcomes across many resolved markets. Trading performance asks whether the net money made after fills and costs was positive. A model can be well calibrated and still lose money once spreads and fees are paid. A poorly calibrated model can also look good over a handful of lucky fills.
Forecast quality
- Score the model against the base-rate and market-price baselines on the same held-out outcomes.
- Check calibration by grouping forecasts into probability bands and comparing each band’s average forecast with the observed frequency of YES. Track a proper scoring rule such as the Brier score alongside it.
- Split the data by time, not at random, so that the test set is later than the training data.
Trade replay
- Replay each decision against the book snapshot that existed at decision time, not against the final price.
- Model missed, partial, and delayed fills explicitly, and report results under each assumption.
- Report gross edge and net return separately, with fees and slippage deducted only in the net figure.
An in-sample backtest with no held-out period is not evidence that the model works.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose markets on more than the forecast
Market selection matters as much as the model. Compare candidate markets on these axes:
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- Depth at your size. The executable cost at the size you intend to trade, not the displayed best quote.
- Total cost. The all-in cost per share under current fee terms.
- Resolution clarity. How precisely the rules define the event and its named source.
- Time remaining. How much new information could arrive before resolution.
- Concentration and worst case. The maximum loss if the outcome resolves against your position.
- Operational load. The data freshness, order lifecycle handling, and settlement tracking the market requires.
These axes are a decision framework. The documentation does not show that one market category or execution style is generally more profitable, so none of them should be read as a ranking.
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Controls before live money
The controls below are engineering choices, not findings from the official pages. They reduce operational exposure, but they cannot eliminate model or market risk.
- Run the full pipeline in paper mode first. Exercise data ingestion, cost estimation, order submission, and settlement tracking before any capital is committed, then move to a small, controlled deployment.
- Set position and loss limits. Apply a cap per market, a cap across all markets, and a maximum loss per day. Choose amounts you could lose without operational harm. No source supplies a universal stake size.
- Block decisions on stale or uncertain state. Skip any decision when market data is older than your freshness threshold or when the market is not accepting orders.
- Build a kill switch. It should cancel open orders and stop new submissions, and it should be tested in paper mode before it is ever needed.
Keep the private key out of the code path
Load the signing key from an environment variable or a secrets manager. Never paste it into source code, logs, notebooks, or a third-party service you do not control. For wallet and authentication steps, follow the current official documentation, starting with the authentication section of the quickstart, because those procedures change. This is baseline guidance, not a security audit.
What the evidence does and does not establish
The official pages establish how to identify outcomes, place market and limit orders, read order statuses, and check positions. They do not establish that any probability model produces a profit. No reliable public statistic on fair-value bot accuracy or returns was found, so this article gives no performance figure. Any model you build should be judged by its held-out calibration and its net returns after costs, and both should be measured by you rather than inherited from a tutorial.
Quick Recap
- No estimator, feature set, or edge threshold is validated by the material reviewed here.
- The community guide is not authoritative, and its endpoint descriptions should be confirmed against official documentation before use.
- Fees, tick sizes, minimum order sizes, and order behavior described on official pages can change, so confirm them on the day you implement.
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
