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What the bot is—and is not—doing
Polymarket describes its venue as a central limit order book (CLOB): bids are resting buy orders, asks are resting sell orders, and the spread is the gap between the highest bid and lowest ask. Outcome shares are quoted from $0 to $1 and represent implied probabilities, according to Polymarket’s Prices & Orderbook documentation.
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The bot combines two separate design ideas:
- Order-book imbalance estimates the relative quantity of visible bids and asks. It is a feature you define and evaluate, not an official Polymarket signal.
- TWAP-style execution divides a target order into child orders scheduled across a chosen time window. The schedule is your execution policy; Polymarket’s reviewed documentation does not prescribe a TWAP algorithm.
Keep signal generation separate from order execution. A signal may influence whether to trade, which direction to consider, or how to adjust a schedule, but it does not guarantee that displayed liquidity will remain available or predict the next price move.
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Interpret the book using executable prices
A displayed price is not necessarily a price at which your order can trade. Polymarket says the displayed price is normally the bid-ask midpoint, but when the spread is wider than $0.10, the last traded price is shown instead. A buy generally trades against asks; a sell generally trades against bids. Polymarket’s example explains that a share displayed at $0.37 might cost $0.40 to buy or yield $0.34 to sell.
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That distinction matters in both backtests and live reports. A strategy can appear profitable if it marks positions to a moving midpoint but ignores the price paid or received, the quantity available at that price, fees, and partial fills. Record midpoint movement separately from executable-side results.
Collect and maintain real-time market data
Polymarket’s Real-Time Data documentation describes a market stream subscribed to by outcome token ID. Book events provide bid and ask price-size levels and may include timestamps, tick size, minimum order size, and other market metadata. Price-change events report changed levels and can include best-bid and best-ask fields. The documentation also lists last-trade and tick-size-change events.
Build local state from snapshots and updates
- Identify the outcome token you intend to trade and subscribe to its market data.
- Initialize the local book from a fresh snapshot, preserving price levels and sizes.
- Apply subsequent price-change events in order, updating or removing the affected levels according to the event data.
- Track event timestamps and feed health so the strategy can detect stale or inconsistent local state.
- On disconnect or state divergence, stop execution, obtain a fresh snapshot, and reconcile open orders and recent trades before resuming.
Do not silently continue using a book assembled across a gap in events. Polymarket’s market-making guidance recommends fetching open orders and recent trades after reconnecting; pairing that reconciliation with a fresh book snapshot avoids acting on stale local assumptions.
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Streaming versus polling
| Approach | What it offers | Trade-off |
|---|---|---|
| WebSocket event consumption | Polymarket’s Real-Time Data guide says the stream keeps public market data current without repeated polling. | The client must handle event ordering, disconnects, stale state, and snapshot recovery. |
| Repeated polling | Can provide periodic observations using whatever supported data interface the implementation chooses. | It does not provide the event-driven continuity described for the real-time stream; update timing and stale-state handling need to be explicit. |
Define imbalance as a testable feature
A simple multi-level quantity imbalance is:
I = (B - A) / (B + A)
Here, B is the sum of bid quantities and A is the sum of ask quantities over a defined set of price levels or distance from the best prices. The result is near +1 when the selected bid quantity dominates, near -1 when ask quantity dominates, and near 0 when the sides are similar. This is an implementation example, not a formula specified by Polymarket.
The choices that make the feature meaningful are yours to define and validate:
- Whether to use only best bid and ask or several levels of depth.
- Whether to sum quantities directly or discount levels farther from the best prices.
- How many levels or what price-distance window to include.
- Whether to calculate the feature separately for each outcome token, and how to account for complementary YES/NO positions.
- What, if anything, a given imbalance should change in the trading policy.
Record the input book alongside each feature value. Visible resting size can change quickly, so an imbalance is a description of the observed book at a point in time, not a promise that size will remain available. The official material establishes that the feed supplies book levels and sizes; it does not establish that this formula predicts price changes or is profitable.
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Design the TWAP-style execution policy
Start with a target quantity or notional and a finite execution window. Divide it into child orders on a configurable schedule; equal-sized, evenly timed slices are a simple baseline, not a universal best choice. Before each child order, refresh the relevant conditions and apply the same checks you would use for any live order.
Choose what each slice does
| Execution choice | How it behaves | Main consideration |
|---|---|---|
| Passive resting limit | Places a limit order that may wait for a counterparty. Polymarket’s market-making guide describes GTC and GTD as resting order types. | Execution is uncertain; an order may fill partially or not at all. Changing a quote requires canceling and replacing it rather than editing it in place. |
| Immediate execution or rebalancing | Polymarket’s guide describes FAK and FOK for immediate execution or rebalancing. | Available liquidity, price, and fill outcome can differ from the intended slice. Check each order result rather than assuming the requested amount traded. |
The strategy must specify whether a slice may be passive or marketable, how long a resting order can remain live, what to do with a partial fill, and whether to cancel and replace when the book changes. A batch submission does not remove the need to inspect each order’s result.
Recheck constraints before every child order
- Current book freshness, spread, best prices, and relevant depth.
- Current tick size and minimum order size for the market.
- Available pUSD for buys and corresponding outcome-token inventory for sells.
- Current position, per-order and overall exposure limits, and applicable market fee parameters.
- Market status and price guards, including whether the proposed limit remains within the policy’s permitted range.
- Whether an earlier child order is still unresolved; avoid submitting a replacement that could unintentionally duplicate exposure.
Pause or cancel according to an explicit policy if data is stale, the market stops accepting orders, a guard fails, or an earlier order’s state cannot be established. Adjusting slice size or timing in response to depth or imbalance is a hypothesis to test, not an endorsed Polymarket execution recipe.
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Track orders, fills, inventory, and settlement separately
Polymarket’s first-order flow describes authenticating with a signer and wallet, obtaining an outcome token ID, submitting an order, waiting for settlement, and checking the position. Its quickstart distinguishes matching from settlement: a matched trade settles on-chain asynchronously. A successful submission, an acknowledgement, a match, and confirmed settlement are therefore different states.
Maintain an order ledger that records intent and venue outcomes independently. At minimum, track submitted orders, acknowledgements, partial and complete fills, cancellations, rejections, and settlement status. On reconnect, reconcile against current open orders and recent trades before relying on local order state.
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Include fees and execution costs in evaluation
Polymarket’s Fees documentation says fees apply to some markets and are charged to takers. It gives the formula fee = C × feeRate × p × (1 - p), where C is shares traded and p is share price, and directs users to inspect market-specific fee parameters. Its category information can change, so do not treat one category rate as a universal constant.
For each run, measure executed quantity and average fill price alongside the benchmark. Include applicable taker fees, spread crossing, price impact, partial execution, missed quantity, and cancellation/replacement behavior. A time-matched arrival midpoint can help describe market movement, but also report outcomes against executable-side prices. Neither a high fill rate nor a favorable midpoint move by itself demonstrates profitability.
Validate reliability before testing a strategy
Polymarket’s market-making guide recommends inventory awareness, size limits, price checks against the midpoint, fill monitoring, and a kill switch to cancel open orders. Translate those controls into observable bot behavior before evaluating the signal:
- Begin in read-only or paper mode; save raw feed events and the local book produced from them.
- Reject malformed, crossed, stale, or internally inconsistent states and resynchronize from a fresh snapshot.
- Enforce per-order, per-market, and total exposure limits; verify balance and token inventory before sending orders.
- Validate price and size against current tick and minimum-order rules.
- Handle partial fills, rejected orders, delayed settlement, duplicate events, disconnects, and failed cancellations.
- Stop placing new orders and invoke the defined cancel policy on stale data, a risk-limit breach, unexpected market status, or unresolved state divergence.
- Test order-state reconciliation and cancel-all behavior in failure scenarios, not only on a normal connection.
Can an order-book imbalance bot make money?
The Polymarket documentation reviewed here explains venue mechanics and operational safeguards; it does not provide an independently verified imbalance edge, strategy-performance statistic, or evidence that this particular bot is profitable. Whether a signal has predictive value remains an empirical question.
To investigate it responsibly, preserve point-in-time data and test out of sample with realistic assumptions about executable prices, depth, fees, latency, partial fills, and missed quantity. Keep backtest results distinct from live performance, and do not infer an edge from the signal’s appearance or from midpoint-only marks.
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