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A Polymarket bot can measure whether displayed liquidity currently leans toward bids or asks by summing price-size levels in a defined part of the order book. That imbalance describes the visible book—not trader intent, lasting support, or a reliable forecast of the next price move.
What an order-book imbalance tells you
Polymarket’s central limit order book (CLOB) handles matching and order ordering off-chain, with settlement and execution on-chain, according to Polymarket’s CLOB API documentation, accessed October 7, 2026. A live book is therefore a view of displayed resting liquidity, not a record of settled trades.
For one outcome token, a positive imbalance under a chosen calculation means that the selected slice contains more displayed bid size than ask size. A negative result means it contains more ask size. Neither result establishes who placed the orders, why they did so, whether those orders will remain or be filled, or what the next trade will do.
Use “visible liquidity pressure” to describe this observation. Calling it “real market pressure” without qualification can imply more than the data establishes.
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How to calculate bid-versus-ask imbalance
First define the slice of the book you will measure. For example, include the top five levels on each side, or include levels within a specified price distance of the midpoint. Then calculate:
I = (B - A) / (B + A)
- B is the sum of bid sizes inside the selected slice.
- A is the sum of ask sizes inside the selected slice.
- I ranges from -1 to +1 when B + A is greater than zero. Positive values indicate more displayed bid size; negative values indicate more displayed ask size.
- If both sums are zero, the ratio is undefined. Omit it rather than reporting zero as though the sides were balanced.
This is an analytical convention, not an official Polymarket signal. A result is only interpretable alongside its definition: outcome token, depth band, measurement time, and the sizes included. A top-of-book calculation and a wider-band calculation answer different questions.
Which part of the book should a bot measure?
There is no established universally best imbalance method in the sources reviewed. These are design alternatives to test, not a proven ranking:
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|---|---|---|---|
| Top level | Best bid and best ask sizes | Liquidity nearest the touch | Can change sharply with a single level update and ignores deeper liquidity. |
| Fixed number of levels | The first N price-size levels on each side | Displayed size across a consistent level count | The price distance covered can vary as the book changes. |
| Price band | Levels within a specified distance of the midpoint or another stated reference | Liquidity within a consistent price range | Results depend on the band definition and the spread. |
| Distance-weighted depth | Levels in a selected region, with less weight assigned to levels farther from the touch | Near-touch liquidity while retaining some deeper context | Requires a stated weighting rule; no universal rule is established. |
Near-touch and wider-depth readings may disagree. Report both when useful: the difference shows that liquidity is distributed unevenly across the measured region. Do not silently select whichever reading supports a preferred interpretation.
What data does a Polymarket bot need?
Polymarket’s Real-Time Data documentation, accessed October 7, 2026, describes a market WebSocket at wss://ws-subscriptions-clob.polymarket.com/ws/market. A market subscription includes one or more outcome asset IDs. Book events provide bid and ask levels with price and size; price-change events provide changed levels and best-bid/best-ask information.
A collector should preserve enough context to make each calculated value reproducible:
- The outcome asset ID, so levels from separate outcomes are never combined.
- Price and size for each level used in the calculation.
- Event timestamps and the time the local book was last updated.
- The best bid, best ask, spread, selected depth band, and summed sizes for each side.
- Whether the reading came from a snapshot or from subsequent updates, and how long the imbalance direction has persisted.
Build a local book from an initial snapshot and subsequent updates, then reconcile it when appropriate. On reconnect, do not assume the old local state is still complete: resynchronize before treating its imbalance as current. These safeguards are implementation recommendations, not a strategy endorsed by Polymarket. The documented feed fields make the calculation possible, but do not by themselves guarantee that a bot’s reconstructed book is complete or fresh.
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How to tell a persistent reading from a momentary one
A single snapshot answers only what the selected book slice looked like at that instant. To study persistence, record timestamped observations and define a time window before evaluating them. For example, a bot might track how often the sign stays positive over a stated interval, while separately recording the size of the ratio and the spread. That is a measurement design, not a validated threshold.
Keep inputs visible rather than emitting a bare “bullish” or “bearish” label. A useful record includes:
- Imbalance value and its underlying B and A totals.
- The chosen depth method and band.
- Best bid, best ask, and spread at the observation time.
- Last update time and duration of the current directional reading.
- Whether the near-touch and wider-depth readings agree.
A stale or incomplete book can produce a precise-looking but misleading ratio. A large imbalance can also be concentrated far from the touch, where it is less immediately relevant to the current quote. Show the level distribution so users can see where the size sits.
Does an imbalance predict a price move?
Not by itself. Displayed size can be changed or withdrawn, may never be matched, and does not reveal the identity or motive of an order placer. A measured imbalance is evidence about the visible distribution of resting liquidity, not proof of informed trading, durable buying or selling pressure, or a guaranteed move.
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Be especially careful when inferring intent from changes in the book. A decrease in displayed depth establishes that the visible book changed; it does not establish why. In a 2026 preprint, Maksym Nechepurenko notes that public executed-fill records do not provide the quote lifecycle needed to identify market making, spoofing, or strategic withdrawal. Without separate evidence, describe the observable event—such as “displayed bid depth fell”—rather than assigning a motive.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How reliable are feed-derived signals?
A separate caution applies to inferred trade direction. Philipp D. Dubach’s 2026 preprint, version 2 revised May 14, studied a pre-registered stratified panel of 600 markets. It reports about 59% agreement between trade-direction buckets inferred from Polymarket’s public order-book feed and on-chain ground truth: a panel mean of 0.615, with a 95% confidence interval of 0.58–0.65. The study also describes an archive of 30 billion feed events over 52 days.
Those figures describe that paper’s sample and method. The approximately 59% agreement is not an accuracy estimate for every market, bot, or direct depth-imbalance calculation, and it does not validate a particular imbalance threshold. Treat feed-derived classifications as measurements with limits, not ground truth.
How to validate an imbalance signal responsibly
- Specify the measure. Fix the outcome asset, depth band, calculation, observation interval, and persistence rule before looking for a favorable result.
- Log snapshots and subsequent outcomes. Preserve timestamps and the book inputs, then compare readings with later price and trade outcomes. Do not treat an observed association as proof that imbalance caused a move.
- Compare design choices. Test top-level, fixed-level, or price-band depth; raw size versus a clearly defined distance weighting; and a single snapshot versus persistent readings.
- Evaluate on later data. Choose any thresholds using one period, then evaluate them on a separate later period rather than reporting performance on the same observations used to tune them.
- Include execution frictions. Account for spread, fees, and slippage before drawing conclusions about whether a signal could support a trading decision.
- Check across market types. A result from one set of markets or one test period does not establish that the same relationship holds elsewhere.
No reviewed source validates a particular threshold, look-ahead window, or profitability claim for this imbalance heuristic. A useful bot output is therefore a transparent, time-stamped description of the book; any predictive claim must be established separately against later outcomes.
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