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Automated Prediction Market Contract Matching Engine
An API service that automatically identifies equivalent contracts across different prediction market platforms using semantic analysis of market descriptions, resolution criteria, and outcome structures. Eliminates the manual bucket-grouping process traders currently endure.
Why this matters
You are building automated trading tools for prediction markets and want to find arbitrage or route orders across platforms. But you quickly discover that seemingly identical contracts on different platforms may have subtly different resolution criteria, different outcome naming, or different expiration dates. There is no automated way to match equivalent contracts — you have to manually read each market's description on every platform and group them into buckets yourself. This is tedious, error-prone, and does not scale as more platforms and markets launch. You need a service that automatically tells you which contracts are truly equivalent and flags resolution risk before you commit capital.
- · Built for Algo traders and developers building cross-platform prediction market tools who need to programmatically identify equivalent contracts.
- · Most likely monetization: API-as-a-service with tiered pricing based on match volume.
The Pain · Narrative
You are building automated trading tools for prediction markets and want to find arbitrage or route orders across platforms. But you quickly discover that seemingly identical contracts on different platforms may have subtly different resolution criteria, different outcome naming, or different expiration dates. There is no automated way to match equivalent contracts — you have to manually read each market's description on every platform and group them into buckets yourself. This is tedious, error-prone, and does not scale as more platforms and markets launch. You need a service that automatically tells you which contracts are truly equivalent and flags resolution risk before you commit capital.
Score Breakdown
Market Signal
Go-to-Market
Developers building cross-platform prediction market trading bots who currently manually map equivalent contracts and want to automate this process
~2,000-5,000 developers actively building prediction market tools globally
Developer communities and open-source prediction market library repositories where developers discuss integration challenges
$49/month for API access with 10,000 match requests
10 API customers within 30 days, with at least 3 integrating into production trading systems
MVP Scope · 1–2 weeks
- Collect and normalize market descriptions from Kalshi and Polymarket APIs for all active contracts
- Build a semantic similarity engine using embeddings to compare market descriptions across platforms
- Create a resolution criteria parser that extracts and compares key resolution terms between matched markets
- Implement confidence scoring algorithm based on description similarity, resolution criteria alignment, and expiration date proximity
- Build a simple REST API endpoint that takes a contract ID from one platform and returns matched contracts from other platforms with confidence scores
- Add a historical resolution database to validate that matched contracts have historically resolved the same way
- Build a conflict detection system that flags matched contracts with material resolution criteria differences
- Create a web dashboard showing all current matches with confidence scores and conflict warnings
- Add webhook notifications for new market listings that match existing contracts on other platforms
- Implement rate limiting, API key authentication, and usage tracking for monetization
Differentiation
Why This Might Fail
Self-rebuttal — the most important trust signal
- 1Contract matching accuracy may never reach the threshold needed for confident trading decisions — false matches could lead to real financial losses, creating liability concerns and reputation damage that kills adoption.
- 2The problem may be small enough that traders prefer manual matching for the small number of cross-platform pairs they actually trade, especially since high-liquidity markets are limited — the TAM for automated matching may be too narrow.
- 3Prediction market platforms could standardize their contract taxonomies or partner with each other to provide official cross-references, making a third-party matching service obsolete.
Evidence Summary
How AI synthesized this insight — no verbatim quotes
Four commenters raised resolution risk and contract matching as a critical concern. One user simply stated resolution risk as a primary worry. Another asked how resolution differences between platforms are handled and suggested it could break routing logic entirely. The original poster confirmed that their router does not attempt automated market matching — traders must manually group equivalent markets into buckets — and admitted that building an automated matcher is very hard. This indicates a clear gap where no solution exists for automated contract equivalence detection.
Action Plan
Validate this opportunity before writing code
Recommended Next Step
Validate
Promising signals, but needs confirmation. Create a landing page, collect email sign-ups, then decide.
Landing Page Copy Kit
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Headline
Automated Prediction Market Contract Matching Engine
Sub-headline
An API service that automatically identifies equivalent contracts across different prediction market platforms using semantic analysis of market descriptions, resolution criteria, and outcome structures. Eliminates the manual bucket-grouping process traders currently endure.
Who It's For
For Algo traders and developers building cross-platform prediction market tools who need to programmatically identify equivalent contracts
Feature List
✓ Semantic similarity matching of market descriptions across platforms ✓ Resolution criteria comparison and conflict detection ✓ Confidence scoring for matched contract pairs ✓ Historical resolution outcome database for validation ✓ REST API with webhooks for new market listing alerts
Where to Validate
Share your landing page in r/r/algotrading — that's exactly where these pain points were discovered.
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