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Monte Carlo simulation can show how a Polymarket bot’s results might vary under explicitly defined assumptions about forecasts, fills, latency, costs, and settlement. It cannot establish that a strategy will be profitable: simulated outcomes are conditional on the model and data used.
What a Monte Carlo simulation can tell you
A Monte Carlo simulation runs a strategy many times while varying uncertain inputs, then summarizes the resulting distribution of outcomes. For a Polymarket bot, those inputs might include forecast error, whether an order fills, execution delay, and slippage. Useful outputs include return, drawdown, and the frequency or size of losses across the modeled scenarios.
The results answer a conditional question: what outcomes arise if the strategy, data, and sampled assumptions are reasonable representations of trading? They do not prove an edge or guarantee that future conditions will resemble the simulation.
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Polymarket outcome shares are priced between $0.00 and $1.00 USDC. The platform describes this relationship as “Prices = Probabilities,” while explaining that prices reflect what users are willing to buy and sell. Treat a displayed price as a market-implied probability, not as a guarantee of an event’s true probability or an assurance that your bot can trade at that price. See Polymarket’s What is Polymarket?
Choose a simulation approach
| Approach | What it can model | Data and limitations | Complexity |
|---|---|---|---|
| Price-series simulation | Strategy decisions against historical prices, with uncertain assumptions sampled across trials | Polymarket Institute documents current-price and historical-price-series access. A price series alone does not establish that an order would have filled at the quoted price. | Lower than order-book replay |
| Execution-aware replay | Orders replayed against historical book states and trades, with maker/taker behavior, fees, and settlement modeled | PredictionMarketBench describes this event-driven approach. The official sources reviewed do not establish a complete historical order-book archive for every market. | Higher, because it requires richer event data and execution logic |
Choose based on the question you need to answer. A price-series test can help evaluate strategy logic, but it is not evidence of executable performance unless fills and costs are modeled. Replay is more realistic when the necessary historical book and trade data are available and sufficiently aligned with the bot’s decision timing.
Build the simulation in stages
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Specify the strategy before sampling
Write down the market universe, decision timing, entry and exit rules, position sizing, and whether a position is held to settlement. State what counts as a trade and how the bot handles a market that changes or becomes unavailable. Without fixed rules, it is difficult to tell whether outcome differences come from randomness or from changing the strategy during evaluation.
Rank #2
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Gather data that matches the bot’s decisions
Polymarket Institute documents CLOB price requests using an outcome
token_id, including examples for current price and historical price series. Its documentation shows the/priceand/prices-historyendpoints: CLOB price and history documentation. The Data API v2 documents trades, activity, market state, price history, and cursor pagination: Data API v2 documentation.Quick wins for a faster PC:
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Rank #3
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Model order interaction, not just a quoted price
When suitable event data exists, replay orders against contemporaneous book states and trades. Represent whether an order is maker or taker, whether it fills fully, partially, or not at all, and how the bot behaves after a missed fill. A simulation that assumes every order executes at a displayed price can overstate what the strategy could have traded.
PredictionMarketBench describes deterministic, event-driven replay of historical limit-order-book and trade data with maker/taker semantics and fee modeling. That is a framework description, not evidence that any particular bot is profitable: PredictionMarketBench.
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Include costs and settlement
Represent applicable fees and the payoff when a position settles. Polymarket Institute points to separate official documentation for fee rates, tick sizes, and spreads, but current values are not established here. Verify the applicable rules for the market and period you are modeling rather than hard-coding an assumed universal fee or spread. The simulation should make clear which cost inputs came from observed data and which are assumptions.
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Define uncertain inputs and sample them
Choose distributions or scenarios for inputs such as forecast calibration error, latency, fill probability, and slippage. Sample these across trials while keeping the strategy rules fixed. No universal parameter values are established for these inputs; they should reflect the bot, market, data, and execution assumptions being evaluated.
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Separate tuning from evaluation
Use one period to develop or tune the strategy and a separate held-out period—or a walk-forward procedure—to evaluate it. Compare with simple baselines so the result is not judged only against the strategy’s own simulated history. This is evaluation practice, not a Polymarket platform requirement.
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Report distributions and assumptions
Summarize the spread of returns, drawdowns, and loss outcomes rather than presenting only an average or a favorable run. Document the scenario definitions, data coverage, execution assumptions, costs, settlement rules, and sensitivity to changes in uncertain inputs. Readers should be able to distinguish observed historical inputs from assumptions introduced by the model.
How to interpret the results
- Wide outcome ranges indicate that the modeled uncertainty materially affects results; investigate which assumptions drive the spread.
- Results that depend on ideal fills are evidence about the strategy under that fill assumption, not proof that those fills are attainable.
- Results that change sharply with costs or latency are sensitive to execution conditions and should be reported with that qualification.
- Results from limited or mismatched data apply only to the markets, periods, and time resolution represented; they do not establish performance across unobserved conditions.
A Monte Carlo result is most useful as a map of modeled risks and sensitivities. Its credibility depends on how well the strategy, data, execution mechanics, costs, and settlement match the bot’s real operating conditions.
Quick Recap
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