Margin of victory for Democrats in the U.S. Senate election in North Carolina at least 13 percentage points

Kalshi vs Polymarket: Mechanics, Fees, Regulation, Liquidity (2026)

Side-by-side comparison of Kalshi and Polymarket in 2026. Fee math, calibration data, withdrawal speed, and a decision tree for picking the right venue.

Kalshi vs Polymarket: Which Prediction Market Should You Trade?

In-depth comparison of Kalshi and Polymarket for prediction market traders. Regulatory structure, liquidity, fees, API tooling, and cross-venue trading with SimpleFunctions.

US Midterm Elections 2026 Prediction Markets: Trading the Battle for Congress

A deep‑dive guide for prediction market traders on the 2026 US midterm elections: House and Senate control odds, key races, Trump’s impact, economic and approval scenarios, polling accuracy, and data‑driven trading strategies.

Prediction market liquidity: why depth matters more than volume for serious traders

Why orderbook depth matters more than volume for prediction market traders. Real Kalshi examples, liquidity scoring framework, and how to avoid slippage.

Market Making on Polymarket: Why Maker Status Cuts Loss Probability by 36 Points — and Why Spreads Persist Anyway

Akey et al.'s most economically significant finding: moving from pure taker to pure maker status reduces the probability of losing money by ~36 percentage points on Polymarket. Resolution-spec risk is why cross-platform spreads persist at 1.5–4.5% and why even Susquehanna and Jump can't fully arb them.

Prediction Market Orderbook Analysis: Reading Depth, Spread, and Liquidity

How to read prediction market orderbooks. Binary settlement, spread-as-percentage, depth asymmetry, executable edge calculation, and cross-venue arbitrage analysis.