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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Clear out junk files and repair common Windows errorsFree Scan →Scan for outdated or missing drivers - takes under a minuteDriver Scan →Whether TWAP mean reversion works on Polymarket is unresolved: the evidence reviewed does not establish that this specific strategy earns positive net returns. TWAP is a way to schedule trades; mean reversion is a separate hypothesis that prices will move back toward a chosen reference. To test the combination, define both parts precisely and model whether the scheduled orders could actually have filled.
What does “TWAP mean reversion” mean?
A time-weighted average price (TWAP) schedule divides a target order across a chosen duration, usually into slices placed at regular intervals. It describes how to execute an order; by itself, it says nothing about whether the trade is likely to make money.
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Mean reversion is the trading hypothesis: after a price moves far enough from a defined reference, it will move back toward that reference. A strategy combines a signal based on this hypothesis with an execution plan such as TWAP. The title alone does not specify a signal, benchmark, holding period, or position-sizing rule, so there is no single reproducible strategy to evaluate until those choices are made.
Polymarket binary-outcome contract prices are expressed in probability units. State whether a strategy trades a market’s Yes or No token and keep the chosen token and price series consistent throughout the test. A move back toward a historical average is not automatically a move toward fair value: news, event resolution, thin liquidity, or changes in the underlying probability distribution can make an old reference obsolete.
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How to turn the idea into a testable strategy
Write down the signal and order schedule separately. The choices below are design requirements, not parameters that the cited studies have validated.
Specify the mean-reversion signal
- Reference mean: Choose a rolling market-price reference or an independently justified fair-probability estimate. Say how it is calculated and over what lookback window.
- Observation frequency: Set how often the signal is evaluated and use timestamps that correspond to data available at that moment.
- Entry threshold: Define how far the selected token’s price must deviate from the reference before an entry is allowed. Specify whether the deviation is measured in probability points or another defined unit.
- Exit rule: State whether the position closes at the reference, at a separate threshold, on a signal reversal, or under another explicit rule.
- Maximum holding time: Set a time limit so a position does not remain open indefinitely if the expected reversion does not occur.
- Position and risk controls: Define sizing, maximum exposure, and any stop or other risk limit. Include the effect of accumulating inventory while an order is only partly filled.
Specify the TWAP schedule
- Target quantity and duration: State how much of the chosen token the schedule attempts to trade and how long it runs.
- Slice cadence: Set the interval or schedule for sending slices.
- Limit-price logic: Define the prices at which slices may be posted or executed, including whether the schedule is allowed to cross the spread.
- Unfilled slices: Specify whether they are canceled, repriced, carried forward, or left unfilled when the schedule ends. State how partial fills change the remaining target.
Without these rules, “TWAP mean reversion” is a label, not an implementable trading system. Changing the signal or the order logic can change the results, so record each version rather than treating all variants as the same strategy.
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What Polymarket data can—and cannot—show
The Polymarket Institute’s July 24, 2026 guide, “Making Polymarket Exchange Data Accessible for Research,” describes three public data surfaces with different uses:
- Gamma: Market discovery and metadata, useful for defining which markets belong in a sample.
- CLOB: Pricing and execution-related information, including order-book data, spreads, depth, and price history. The guide’s examples use a CLOB token ID to retrieve prices and historical prices; it also directs readers to the order-book and pricing documentation for fees, tick sizes, spreads, and other data points.
- Data API: Trade and user-history research.
The guide says Polymarket’s decentralized platform and Polymarket US have separate APIs and separately managed data. Name the venue in every analysis and do not assume data from one represents the other. A price series alone is not an execution record: a historical price or candle does not prove that a particular order could have filled at that price or quantity.
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For every test, record the market identifier, outcome token, venue, timestamp and timezone, endpoint, history resolution, and sample-inclusion rules. Match each signal to the book state that would have been observable at that time. Do not use a later book state or a price derived from the same interval if that would introduce information unavailable when the order was placed.
How to model fills and costs
A credible test needs executable prices, not just apparent price reversion. For each order slice, estimate whether it would have traded against the available bid or ask, or whether it would have rested in the book. Model spread crossing, market depth, partial fills, fees, slippage, and inventory exposure. Where the data does not establish a fill, do not silently count the slice as filled at a midpoint or candle price.
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Public trade archives also do not automatically reveal the full history of quotes and cancellations. The 2026 study “Fill-Side Non-Retail Trading on Polymarket: An Empirical Study of Behavioral Tiers and Microstructure Signatures Under Quote-Attribution Constraints” describes order placement and cancellation events in the off-chain CLOB as a limit on reconstructing address-level quote lifecycles from public on-chain records. Consequently, fills alone cannot establish the complete sequence of quotes or cancellations behind them.
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Report performance both before and after costs, and show how conclusions change under reasonable alternative execution assumptions. A strategy that appears profitable before spread, fees, slippage, or unfilled orders may not remain so after those are included.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to design a defensible backtest
- Fix the market universe and venue. Use market metadata to define eligible markets, and document exclusions and the dates covered. Keep data from the decentralized platform and Polymarket US distinct.
- Build point-in-time observations. Align each signal timestamp with the historical book information that could have been observed then. Preserve the relevant token, price side, depth, and timestamp.
- Apply written entry, exit, and execution rules. Run the signal and TWAP schedule as specified, including the handling of unfilled slices, partial fills, and open inventory.
- Use time-ordered evaluation. Develop parameters on earlier periods and evaluate them on later, held-out periods. Do not tune thresholds on the final test sample.
- Compare meaningful baselines and variants. Use the same market sample when comparing alternatives. The following are useful proposed comparison axes, not results established by the cited studies.
| Test dimension | Variants to compare | Question answered |
|---|---|---|
| Execution aggressiveness | Passive versus aggressive orders | Does any apparent edge survive the trade-off between fill probability and spread cost? |
| Slice timing | Fixed versus adaptive TWAP cadence | Does changing slice timing improve execution under the same signal and sample? |
| Signal price | Midpoint-based versus executable bid/ask-based signals | Does the signal remain useful when evaluated at prices at which the strategy could trade? |
| Mean reference | Price-only reference versus independently estimated fair probability | Does the result depend on treating a past market price as the value to which price should revert? |
| Baseline | Passive holding versus a no-signal execution schedule | Does the signal add value beyond holding or trading on a schedule without it? |
Report net return alongside volatility, drawdown, turnover, and fill rate. Break results out by liquidity and market type, and include sensitivity to fees and slippage. Those measures make it easier to distinguish a robust result from one driven by a narrow market subset or optimistic fill assumptions.
What the published evidence says about profitability
The 2026 paper “Polymarket-v1 Database” reports aggregate tick-rule classifier accuracy of 49.83% and bulk-volume-classification aggregate accuracy of 50.51%. These are classifier accuracy figures in that paper—not mean-reversion strategy win rates, net returns, or evidence that a TWAP schedule is profitable. The paper also argues that positive trade-direction autocorrelation and concentrated market-making can violate mean-reversion assumptions used by classical classifiers.
“Unravelling the Probabilistic Forest: Arbitrage in Prediction Markets,” a separate study using historical order-book data, examines rebalancing and combinatorial arbitrage. It can inform why point-in-time books and market structure matter, but it is not a direct test of TWAP mean reversion and does not establish that this strategy works.
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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallThe evidence reviewed does not provide a directly verifiable out-of-sample backtest or live performance record for this exact strategy. A credible profitability claim would need its own reproducible analysis, including the market universe, dates, venue, fees, execution assumptions, drawdown, and uncertainty. Until then, whether the approach earns positive net returns remains unresolved.
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