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Monte Carlo simulation can show how a Polymarket trading bot might perform across many plausible combinations of forecast error, fills, slippage, latency, fees, and settlement outcomes. It cannot prove that a strategy will be profitable: every result is conditional on the assumptions and historical data used. Polymarket prices are market-implied probabilities shaped by users’ willingness to buy and sell, not guarantees of an event’s true probability or of execution at the displayed price. Polymarket explains its price interpretation here.
What Monte Carlo simulation can tell you
A Monte Carlo test runs a strategy repeatedly while sampling uncertain inputs, then summarizes the resulting range of outcomes. For a trading bot, those inputs might include its forecast error, whether an order fills, execution delay, slippage, and the sequence of market outcomes. The useful result is not a single predicted profit figure; it is a conditional distribution that helps you inspect downside risk and see which assumptions drive performance.
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Polymarket outcome shares are priced from $0.00 to $1.00 USDC. The platform describes prices as probabilities, while noting that they reflect what participants are currently willing to buy and sell. That interpretation is not an assurance that the market price is objectively correct, nor that a bot can transact at that price. Polymarket’s FAQ states, “Prices = Probabilities.”
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| Approach | What it models | Best suited to | Main limitation |
|---|---|---|---|
| Historical price-series simulation | Strategy decisions against historical prices, using the price history available for the selected outcome token. | Early checks of a strategy’s logic and sensitivity to price paths. | A price series alone does not establish whether an order would have filled, how much liquidity was available, or what execution price was achievable. |
| Execution-aware order-book replay | Orders replayed against historical book states and trades, with fill behavior, maker/taker semantics, fees, and settlement modeled where data supports them. | Evaluating strategies whose results depend on execution and liquidity. | It needs more detailed, appropriately timed data and more implementation work; the available sources do not establish complete historical book snapshots for all markets. |
Polymarket Institute documents current-price and historical price-series access. Its CLOB price documentation uses outcome token IDs. PredictionMarketBench describes deterministic, event-driven replay of historical limit-order-book and trade data, including maker/taker behavior and fee modeling; that is a framework description, not evidence that any bot earns a profit. PredictionMarketBench
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Identify outcome tokens and price history
CLOB price requests use an outcome token_id. The Polymarket Institute documentation includes examples for a current price and a historical price series. Check the endpoint’s current semantics, the time resolution returned, and the history available for the specific market and token before treating the series as suitable for a replay. See the CLOB price-history documentation.
Use Data API resources carefully
The Data API v2 documentation describes trades, activity, market state, price history, and cursor pagination. Confirm each endpoint’s scope and paginate through results where required. The existence of trade or price-history resources does not by itself establish that you have a complete history of order-book states for every market.
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Match timestamps to bot decisions
Align the data with the bot’s actual decision schedule. A strategy that acts at fixed intervals should not be tested as if it sees every price change; a strategy that reacts to trades needs appropriately timed trade data. If the data is too coarse to establish what was visible when an order was placed, treat the resulting execution estimate as uncertain rather than a known fill.
Build the simulation in stages
- Specify the strategy. Define the market universe, signal and decision timing, position sizing, and rules for exits or holding through settlement.
- Set the evaluation period. Reserve a held-out period, or use walk-forward evaluation, so that the data used to tune the strategy is distinct from the data used to assess it. Compare the bot with simple baselines.
- Reconstruct each decision. At each decision time, provide only data the bot could have observed then. Apply the strategy’s signal and sizing rules to determine the order it would submit.
- Model execution. Where contemporaneous books and trades are available, replay orders against them. Represent orders that fill fully, fill partially, or do not fill; account for whether the order is maker or taker. Do not assume a displayed quote is an executable fill.
- Apply costs and settlement. Include the applicable fees and the payoff determined by the market’s settlement outcome. Polymarket Institute points readers to separate documentation for fee rates, tick sizes, and spreads; those values can change, so verify the current rules for the relevant market rather than hard-coding an unverified figure. CLOB documentation.
- Sample uncertain inputs. For each trial, draw explicitly defined values for factors such as forecast calibration error, latency, fill probability, or slippage. Use ranges or distributions that you can justify; there are no universal parameter values established by the cited sources.
- Record outcomes. Save return, drawdown, losses, and other measures relevant to the strategy for every trial, along with the sampled assumptions and execution path.
Interpret the results without overstating them
Summarize the distribution, not just its average. Consider the range of returns, drawdown, frequency and size of losses, and how outcomes change under adverse assumptions. A favorable result under one set of inputs is not robust if it disappears when fill rates worsen or slippage rises.
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- Make assumptions visible: state how forecast error, latency, fills, slippage, fees, and settlement were represented.
- Test sensitivity: vary one important assumption at a time, then examine combinations of adverse assumptions.
- Separate evidence from model output: distinguish observed historical prices or trades from simulated fills and sampled future conditions.
- Disclose data limits: describe the markets, time period, resolution, and gaps in the data, especially if complete historical order-book states are unavailable.
- Avoid a guarantee: simulated returns describe what follows from the selected model and inputs; they do not establish future profitability or a guaranteed trading edge.
Common mistakes to avoid
- Filling every order at the displayed price: a quote is not proof of available size or a successful fill. Model book interaction where data permits.
- Ignoring partial and missed fills: strategies can look better when the simulation treats every intended order as completed.
- Leaving out fees or settlement: gross trading returns do not capture costs or the final payoff of a position held to resolution.
- Using the same period to tune and judge: reserve held-out data or use walk-forward evaluation to reduce the risk of mistaking a fit to history for durable performance.
- Treating market-implied probability as ground truth: Polymarket prices reflect supply and demand, not a guaranteed true probability.
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