Analyst Outlines 6 Formulas Quant Funds Use in Prediction Markets

Growth in prediction markets is surging as traders, institutions, and even Wall Street rush to capitalize on the growing momentum.

The monthly volume has already exceeded $13.7 billion in March, marking a 599% increase from $1.96 billion last year, led by sector giants like Polymarket and Kalshi.

6 Formulas Driving the Quant Polymarket Playbook

In a recent post, an analyst argued that Polymarket has evolved far beyond a hub for “degen gamblers.” 

“It is quietly becoming a quant battlefield where professional funds harvest edges the way they do in options and futures,” the post read.

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The post also outlined six key formulas hedge funds use to consistently generate returns from prediction markets, noting that retail traders can still replicate parts of these approaches to improve their edge.

The Logarithmic Market Scoring Rule (LMSR) forms the foundation, with quants modeling the pricing engine to forecast how much a trade will move the market before slower participants react.

The Kelly Criterion replaces arbitrary bet sizing with a mathematically derived fraction of bankroll per trade.

Expected Value gap scanning builds independent probability models to identify contracts where implied odds diverge from the trader’s estimates by enough to clear fees.

KL-Divergence flags statistical inconsistencies between related markets, such as competing political candidates, and enables structured hedged positions across them.

Bregman Projection extends this by scanning complex multi-outcome events for pricing inefficiencies that manual traders cannot detect at scale.

Bayesian Updating continuously recalibrates probability estimates as new data arrives. Rather than relying on static views, it keeps positions aligned with the evolving information environment in real time.

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The analyst also shared a basic blueprint to “replicate the system.”

• Data: Get API access from Polygon to pull real-time Polymarket odds and volume data. 
• Environment: Set up Python with the key libraries: numpy, scipy, and cvxpy. These handle the math behind the six formulas.
• Backtesting: Before deploying real money, run the system on 2025 historical data using walk-forward testing, meaning you test it sequentially as if time were moving forward, rather than fitting it to data you already know the outcome of. This guards against overfitting.
• Deployment: Host automated scripts on Railway or GitHub with scheduled jobs, and pipe trade alerts to Telegram so you’re notified in real time.
• Risk Controls: Use fractional Kelly (not full Kelly) to reduce sizing. Set a hard 20% drawdown stop.

The playbook outlines structured quantitative strategies for prediction markets, but its effectiveness depends on execution. Accurate probability estimates, sufficient liquidity, and low fees are critical. 

Practical challenges such as market speed, data quality, and potential overfitting can affect results. Thus, outcomes may vary based on implementation and market conditions.

Disclaimer: This content is for informational purposes only and does not constitute investment advice.