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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.

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.

Kalshi vs Polymarket: A Developer's Comparison of APIs, Orderbooks, and Liquidity

Data-driven comparison of Kalshi and Polymarket APIs, orderbooks, rate limits, and liquidity. Code examples for building on both prediction markets.

Liquidity Availability Is the Real Edge in Prediction Markets

Implied yield, cliff risk, and overround all describe what to trade. Liquidity Availability Score describes whether the orderbook can absorb the trade. Why LAS is the indicator that decides who actually books P&L.

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.

Implied Yield vs Raw Probability: Why Bond-Adjacent Prediction Markets Need a Different Lens

Why fixed-income-adjacent prediction-market contracts need to be priced in implied yield, not raw probability, with two real Kalshi Fed-decision contracts as a case study.