About EVSignals
We build the data layer that prediction market traders, sports bettors, and quants use to compare prices, test ideas, and act without rebuilding market plumbing first.
Evaluating EVSignals
See the scanner, notebooks, and API paths before you commit to a workflow.
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Trying a free workflow
Use the calculators and tracking tools if you want to test the approach first.
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Building right away
Jump into docs and examples if you already know you need raw data access.
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Prediction market data is fragmented — we fix that
Data is scattered across dozens of platforms — Kalshi, Polymarket, sportsbooks, crypto venues — each with different APIs, schemas, and quirks.
We normalize all of it into one clean interface so you can focus on analysis instead of infrastructure. Whether you are building models, scanning for mispricings, or comparing venues, the goal is the same: less plumbing, faster decisions.
Multiple venues, no single source of truth
Kalshi, Polymarket, sportsbooks, and crypto platforms all surface different data
Inconsistent APIs across platforms
Every venue has its own authentication, rate limits, and response formats
Schema differences make comparison impossible
Different naming, odds formats, and market structures across every source
Infrastructure overhead slows you down
Building and maintaining data pipelines takes time away from actual trading
How we build
Four principles behind every EVSignals feature.
Data Integrity
Every data point is validated and sourced. We normalize data from 500+ connected sources into a consistent schema you can trust.
Trader-First Design
Built by traders, for traders. Every feature is designed to help you spot and act on mispricings faster, not just look good in a demo.
Speed Matters
Sub-50ms data feeds, live scanning, and instant alerts. In these markets, speed changes whether a price is still actionable.
Simplicity
Complex markets deserve clean tooling. One SDK, one schema, one notebook — no infrastructure headaches.
Built by traders who needed better tools
We started EVSignals because we were trading prediction markets ourselves — and spending 80% of our time on data plumbing instead of analysis. Every platform had its own API, schema, and rate limits. Cross-referencing Kalshi and Polymarket odds meant writing custom scrapers that broke every week.
So we built what we wished existed: one data layer for live signals, odds history, and cross-platform pricing. EVSignals turns fragmented market feeds into something you can query, analyze, and act on without rebuilding the plumbing every week.
Our team combines quantitative trading experience with infrastructure engineering. We use EVSignals daily — every feature ships because we needed it first.
Our Python SDK is open source. API docs are public. We believe the best way to earn trust is transparency — read the code, test the latency, verify the data.
Looking for engineers and quants passionate about prediction markets. Get in touch →
Built by traders and engineers
We're a small team with experience in quantitative trading, data engineering, and prediction markets. We built EVSignals because we needed these tools ourselves and couldn't find them anywhere else.
Clean Data
Validated, normalized, and sourced. Every data point has been quality-checked against multiple feeds.
Fast APIs
Sub-50ms feeds, real-time WebSocket streams, and endpoints designed for low-latency workflows.
Edge-Finding Tools
Notebooks, scanners, and alerts built specifically to help you find and act on edges.
Ready to find your edge?
Notebooks, scanners, and APIs for prediction market analysis. 14-day free trial — no credit card required.
Free to start · Documentation available · No credit card required