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A fair-value bot estimates the probability that one clearly defined Polymarket outcome resolves YES, compares that estimate with the price it could actually pay for the size it wants, and trades only when the gap is large enough to absorb fees and slippage. The loop is simple to describe. The hard parts are defining the target precisely, keeping every input point-in-time, and showing that the model adds information beyond the market’s own price. The steps below follow that order.
Keep three prices apart
A bot has to keep three numbers separate. The first is your forecast: the probability you estimate that the market resolves YES under its rules. The second is the probability-like price the market displays. The third is the size-aware cost of buying the quantity you want. The table below lists the observations a bot typically reads from the order book and what each one can and cannot tell you.
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| Observation | What it measures | How a bot should use it |
|---|---|---|
| Forecast probability | Your estimate that the market resolves YES under its rules | Fair-value input, after calibration |
| Midpoint | Halfway between the best bid and best ask | Display and monitoring only; not a fill price |
| Last trade | Price of the most recent trade | Context for recent activity; says nothing about current depth |
| Best bid and best ask | Top-of-book quotes, available at the displayed size | Price for a small order on each side; deeper levels may be worse |
| Size-weighted executable price | Average price across the book levels needed to fill your full size | Cost basis for the trade decision; a snapshot that can change before your order arrives |
The community-maintained CLOB API guide draws the same distinction, noting that midpoint, last trade, best bid and ask, and depth-aware executable price each answer a different question.
Define the outcome before you model it
A model can only be scored against the event it was trained to predict. Before collecting any data, write the target as one sentence, then turn the market’s resolution criteria into a label: which event counts, which source or time window the rules name, and how the rules handle the edge cases they address. Save the exact wording you trained against, with a date, so you can later tell which wording each forecast was made under.
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This article does not summarize how Polymarket handles disputed or ambiguous resolutions. The rules printed on each market, together with the resolution help article, govern your label. Build it from the market’s text, not from a general description of how resolutions usually work.
In a standard binary market, a winning share settles at 1 and a losing share settles at 0. Confirm that against the specific market’s rules before you size any position, because every expected-value calculation in the sections below depends on it.
The modeling loop
The sequence below produces the inputs each later step needs. Skipping a step tends to make the final numbers look better than they are.
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Store the market wording and version, the market and outcome identifiers, book snapshots or historical prices, event features, the decision timestamp, and the eventual resolution label. Every feature must have been knowable at its decision timestamp. A figure published after the decision time, or a closing price used to justify an earlier trade, leaks the answer into the model and inflates every metric built on it.
Set baselines before building a model
Compare any candidate with two simple benchmarks: a historical base-rate estimate for the event type, and the market’s own price at the same timestamp. A model earns a place in the bot only if it adds information beyond both on held-out outcomes. A model that does not is restating prices the market already shows.
Estimate and calibrate the probability
Logistic regression, Bayesian updating, and other estimators are all candidates to test. Record the inputs and model version with every forecast. Then calibrate on outcomes the model did not see: group forecasts by their stated probability and check how often those events occurred. A raw model score is not a probability until this check passes. Log loss and the Brier score are standard measures of how well stated probabilities match what happened.
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Turn the forecast into a trade decision
Compare your calibrated probability with the cost of buying the proposed size, using the walkthrough in the next section rather than the midpoint. Deduct the applicable fees and a conservative slippage allowance before you call the remainder an edge. The minimum edge you require before trading is a design choice you set and then test in replay.
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Evaluate forecasts and trades separately
Forecast quality (calibration, log loss) and trading performance (realized net return) answer different questions. Replay past decisions with explicit fill assumptions, and state them: the share of orders assumed missed, the share assumed partially filled, and how long fills are assumed to be delayed. Evaluate on held-out periods. A strategy tuned and tested on the same history can look profitable for reasons that will not survive live trading, so an in-sample backtest is not evidence of profitability by itself.
Monitor the live system
Log data freshness, market status, order requests and responses, fills, cancellations, positions, and the forecast that was current at each decision. Halt or reduce exposure whenever the market state or the data state is uncertain, rather than letting the bot keep trading on the last values it received.
The official workflow and the API map
Polymarket’s quickstart walks through one complete trade. Its order of operations is a useful skeleton for a bot:
- Authenticate the client, loading credentials from an environment variable as the quickstart’s examples do.
- Fetch the market and identify the outcome you intend to trade.
- Select the trading identifier that matches the market version: a token ID for CTF markets, or a position ID for Protocol V2 markets. The unified SDK’s identifier depends on which version the market uses.
- Place a market order.
- Wait for on-chain settlement.
- Check the resulting position.
Confirm the identifier type against the market’s version before submitting any order, since the wrong identifier type is a straightforward way to get an order rejected or to trade a different instrument than intended.
The community API guide maps the services a bot typically touches. Treat it as a secondary reference, and confirm current endpoints and behavior in official documentation before implementing anything.
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| Service | Role in a bot |
|---|---|
| Gamma | Market and event discovery |
| CLOB | Order books and order management |
| Data API | Positions and activity |
| WebSockets | Real-time market data and authenticated account events |
Estimating what a buy will actually cost
The displayed price applies to a small quantity at the top of the book. A buy of several hundred shares consumes deeper levels, and those levels may be more expensive. The procedure below produces the average executable price for a buy.
- Fetch the current ask levels for the outcome you intend to buy.
- Sort the ask levels from lowest price to highest.
- Walk the levels, taking shares at each price until the target quantity is covered. Multiply the price by the shares taken at that level and add the result to a running total.
- Divide the total cost by the target quantity to get the average executable price.
- If the displayed levels do not cover the target quantity, report insufficient depth. Do not extrapolate beyond the book.
- Deduct fees and a slippage allowance, then compare the result with your calibrated probability.
The result is an estimate, not a fill guarantee. The book can move between your read and your order, and execution constraints still apply.
Worked example with illustrative numbers
The figures below are hypothetical, chosen to show the arithmetic. They are not a Polymarket quote. Suppose the ask side holds 200 shares at 0.52, 300 shares at 0.54, and 500 shares at 0.57, and the bot wants to buy 600 shares.
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- 300 × 0.54 = 162.00
- 100 × 0.57 = 57.00
The total cost is 323.00, an average of 0.5383 per share (323 ÷ 600). Suppose the calibrated probability is 0.58. Against the best ask of 0.52, the apparent gap is 0.06 per share. Against the average executable price, the gap is 0.0417 per share, so the top-of-book figure overstates the per-share edge by about 0.018. If the forecast is right, the 600 shares are expected to pay 348.00, which leaves a gross expected value of 25.00 before fees and slippage. That figure is only as good as the 0.58 forecast.
Order rules your bot must respect
The Place Orders documentation states the difference between the two main order types directly:
“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.”
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Polymarket Documentation, Place Orders
A market order, by contrast, trades against whatever liquidity is available. The same guide sets out several checks an integration should make before and after submission:
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- Use the market’s current tick size, and make sure your limit price conforms to it.
- Respect the market’s current minimum order size.
- Read the response status. The guide documents
live,matched, anddelayed.
These are operational states. A matched order is not the same thing as a settled position, which is the subject of the next section.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Track each order to settlement
A successful request is the start of an order’s lifecycle, not the end. Track each order through these steps:
- Submit the order and record the response status:
live,matched, ordelayed. - Update local state from subsequent trade and account events. The community API guide describes authenticated WebSocket channels for this purpose.
- Wait for on-chain settlement before treating the position as final, as the quickstart does.
- Reconcile your local position against the position and activity data from the Data API.
Keys and wallet security
A bot that signs orders controls the funds in its wallet. Keep the private key out of source code, logs, notebooks, and any service you do not control. Load it at runtime from an environment variable or a secrets manager, and make sure no debug output prints configuration values. If a key may have been exposed, move funds to a new wallet and retire the old key.
Wallet and authentication steps change over time, so follow Polymarket’s current official developer documentation for setup rather than older tutorials. This is security guidance, not an audit of any particular setup.
Starting small: controls that limit damage
The controls below are design choices, not requirements imposed by Polymarket. They reduce operational exposure but cannot eliminate market risk or model risk. No source establishes a safe stake size, so set your own limits and test them.
- Paper trade first, then move to a small deployment with a fixed capital allocation.
- Cap exposure per market and in total. A long position’s maximum loss is its cost basis: N shares bought at an average price p cost N × p, and that entire amount is lost if the outcome resolves against you.
- Set a daily loss limit that halts new orders when it is reached.
- Reject any decision whose book or price data is older than a freshness limit you define.
- Check market status and the accepting-orders state before each order.
- Build a kill switch that cancels open orders and blocks new ones, and test it before any live use.
Choose markets on more than headline odds
Not every market suits a fair-value model. Compare candidates on these axes before committing capital:
- Forecast calibration on held-out outcomes for that type of event.
- Depth and spread at your intended order size.
- Fees and other execution costs.
- Clarity and stability of the resolution criteria.
- Time remaining, and how sensitive the price is to new information.
- Concentration, and the loss if the outcome resolves against you.
- Operational complexity: data freshness, order-lifecycle handling, and settlement monitoring.
What the evidence does and does not establish
Polymarket’s official documentation establishes how to find outcomes, submit orders, respect tick size and minimum order size, and track order states. It does not establish that any probability model has an edge over the market. No reliable public figure on the accuracy or net returns of fair-value bots is available to cite, and no particular estimator, feature set, or edge threshold is validated here. The community API guide is dated September 7, 2026 and is not an authoritative reference. Because Polymarket’s pages change, confirm identifiers, fees, tick sizes, market status, and order behavior at the time you implement.
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