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A similar Kalshi and Polymarket headline is only a lead, not proof that the contracts settle the same way. To compare them responsibly, first find plausible candidates, then verify each market’s rules and outcome structure, and finally compare the prices you could actually execute at your intended size after fees and slippage.
Why matching contracts is more than a search problem
Kalshi and Polymarket create their own market identifiers; there is no shared canonical ID that reliably joins corresponding markets. Titles, categories, dates, and rules can all help identify a possible pair, but none is enough on its own. Similar wording can hide different thresholds or settlement conditions, while different wording can describe the same proposition.
It helps to keep three labels distinct:
- Same headline: The titles look alike or mention the same event. This is a search result, not a verified match.
- Related exposure: The markets respond to similar events, but differ in a way that could affect their outcomes or settlement. They may be useful to compare, but they are not interchangeable.
- Same settlement: The proposition, resolution conditions, outcome definitions, and relevant edge cases align closely enough that the contracts resolve equivalently.
A vendor-authored technical article by Mithril summarizes the priority as: “The resolution rules are the contract.” That is a useful test: settle the meaning of each contract before treating a price gap as meaningful.
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A practical matching pipeline narrows the search in stages. A public project, nanare-sudo/kalshi-polymarket-spreads, describes a method that first blocks by category and close date, then scores token overlap with extra weight for rare terms, and checks numbers, dates, and entities. Optional rule-text verification is used to assess whether resolution criteria align. The method retains confidence and notes, and drops low-confidence pairs.
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1. Narrow by category and time
Start with markets in a compatible subject category and with close dates that plausibly refer to the same event window. This reduces the number of comparisons, but a date match is only a filter: two markets may close at similar times and still ask different questions.
2. Score distinctive words and entities
Compare the meaningful terms, especially rare names and event-specific language. Common words contribute little. The project warns that naïve string similarity can produce confident nonsense: “Trump” is not “Trump Jr.,” and a “nominee” market is not necessarily a “wins election” market.
3. Check numbers, dates, and entities explicitly
Do not leave important details to a general similarity score. Extract and compare the threshold, date, location, person or organization, and other named entities. A mismatch may mean the pair should be rejected or marked as related rather than equivalent.
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One market on one venue may correspond to several contracts on the other. For example, one venue may offer a multi-outcome event while the other offers separate binary contracts or a strike ladder. Forcing these into a one-to-one row can hide missing outcomes or create a false match. Record the relationship honestly: one-to-one, one-to-many, many-to-many, related, or unresolved.
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Verify settlement rules before comparing prices
Read the rules for both contracts, not just their titles or summaries. A comparison workbench described by the source material treats a list generated from title similarity and settlement-time proximity as a reading list, not a verdict. Use the following checklist for each market:
- Exact proposition and threshold: What must happen for the specified outcome to resolve as “Yes”? Check the wording, any numeric cutoff, and the relevant date or event window.
- Resolution source: Which source or authority determines the result? Different sources can report different values or interpret an event differently.
- Settlement date, time, and time zone: Compare the actual settlement clock, including its time zone. Similar event dates do not guarantee identical resolution timing.
- Outcome structure: Confirm whether the market is binary, multi-outcome, or part of a ladder, and how each possible outcome is defined.
- Edge cases: Check how ties, postponements, cancellations, or other unusual outcomes are handled.
A different threshold, resolution source, or clock can turn an apparent match into correlated but non-identical exposure. If the rules do not establish equivalence, preserve that uncertainty in the mapping rather than promoting the pair to a verified match.
Grade equivalence and keep it auditable
A confidence score is useful only if its meaning and supporting evidence are visible. The repository method retains confidence and notes; the following labels are a practical way to make those notes useful to a human reviewer:
- Verified equivalent: The proposition, threshold, source, timing, outcome structure, and relevant edge cases align. Record the rule text or a durable reference to the version checked.
- Related, not equivalent: The markets concern the same broader event but a meaningful condition differs or is not aligned.
- Unresolved: The available rules or data do not establish whether the contracts settle equivalently.
- Rejected: A material mismatch shows that the pair should not be treated as corresponding contracts.
Store the reason for the grade, the time it was checked, and the contract identifiers on both venues. Revisit the mapping if either market is amended, clarified, or relisted. These are not static records: a correct match at discovery can become stale, and an auditable note makes it possible to see why a pair was accepted or rejected.
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Compare executable prices, not just displayed gaps
A midpoint spread is not a price you are guaranteed to trade at. To estimate an executable comparison, normalize the two order books, simulate fills by walking visible price levels at the intended size, and include venue-specific fees and slippage. State how much of the target order the visible depth could absorb. If one side lacks enough displayed liquidity, the target size is not fully supported by the snapshot.
- Choose the intended size. Use the amount you actually want to evaluate, rather than comparing prices without a quantity.
- Read the relevant sides of both books. Use the prices at which the intended transactions could execute, not a midpoint or last-traded price.
- Walk the visible levels. Estimate the average fill across the available depth and identify any unfilled portion.
- Apply the applicable fees and slippage assumptions. Fees differ by venue and can change; do not assume a historical fee schedule is current.
- Record the snapshot time and result. Books move, so a snapshot-based estimate is not a standing or guaranteed price.
The repository’s first public scan, dated 2026-08-19, loaded 1,600 markets—800 per venue—narrowed them to 312 candidate pairs, and retained 17 verified matches at its confidence threshold, with median confidence of 0.83. For those pairs, it reported a median gross midpoint spread of $0.0045, a median executable spread of $0.0006 at a $100 size, and a negative executable spread at that size for 47% of pairs. These are results from one project’s dated scan and method, not a general estimate of current opportunities or an independent benchmark. The project also notes that equivalent pairs may be missed, title-hidden rule differences may remain, and books move.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Build a matching pipeline or use a unified-data service?
There are two implementation paths: maintain your own mapping process, or evaluate a third-party service that provides unified IDs or market data. Neither path removes the need to verify settlement equivalence and executable liquidity.
| Evaluation area | Build and maintain a mapping | Evaluate a unified-data service |
|---|---|---|
| Mapping auditability | Keep your own match records, confidence, and reasons; you control the evidence trail. | Ask how IDs are assigned and whether match decisions and their reasons are inspectable. |
| Rule verification | Implement and review checks against each venue’s rules. | Confirm what rule-level verification the service actually provides; a unified ID alone does not establish equivalent settlement. |
| Updates and revalidation | Build a process to detect amendments, clarifications, and relistings and to revisit affected pairs. | Check how often mappings update and how changes or stale matches are surfaced. |
| Order-book and fee coverage | Collect the books and fee information needed for your own size-specific estimate. | Confirm which books, fee inputs, and fill estimates are included and how current they are. |
| Access and operating cost | Account for the work and infrastructure needed to collect data, verify rules, and maintain mappings. | Verify access requirements, commercial terms, and total cost directly; available evidence does not establish independent vendor performance or current terms. |
Mithril describes a unified-ID API, but that is a vendor’s account of its own product, not an independent performance assessment. Treat it as an option to evaluate against the checks above, not as a substitute for rule verification.
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Collection quirks are not durable API guarantees
The author of the title-specific account reports observing nested Kalshi markets with status: "active" rather than "open", liquidity_dollars at zero, and skipping event tickers beginning KXMVE because they represented multivariate parlay shards. These are that author’s reported ingestion observations. They should not be treated as independently confirmed, current API guarantees; validate the behavior against the data and API documentation available to your implementation.
What a trustworthy comparison record contains
For every pair you keep, retain enough information for someone else to reproduce the decision:
- Both venue identifiers and the relationship shape, including any one-to-many mapping.
- The exact proposition, threshold, resolution source, settlement clock, outcome definitions, and edge-case handling checked on each side.
- An equivalence grade, confidence, reason, reviewer or process, and verification timestamp.
- The order-book snapshot time, intended size, estimated fill on each side, visible unfilled quantity, and fee assumptions.
- A revalidation trigger for rule changes, market relistings, or other updates that could invalidate the mapping.
This workflow is a way to organize market data and expose uncertainty, not financial or legal advice. A candidate match or snapshot-based price difference alone does not establish that two contracts are interchangeable or that a trade can be filled profitably.
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