A Market-Making Project You Can Do Today

Key Takeaways

• Polymarket CL contract priced ~73¢ implies 73% probability
• 89.5/90.5 call spread delta gives fair probability
• Compare Poly delta to spread delta to detect mispricing
• If Poly delta steeper, sell contract, hedge with futures
• Building a monitor teaches pricing, delta, and risk management

Summary

The post outlines a hands‑on market‑making experiment using Polymarket’s crude‑oil binary contract and a tight 89.5/90.5 call spread priced with Black‑Scholes. By computing each instrument’s implied delta—how many probability points move per $1 change in CL—you can spot when the Polymarket price reacts more sharply than the option spread. A steeper Polymarket delta signals an over‑priced sensitivity, suggesting you sell the binary and hedge with futures or the spread; the reverse indicates a buying opportunity. The author provides a simple monitoring framework that teaches pricing, delta, and risk without building a full‑blown bot.

Pulse Analysis

Prediction markets like Polymarket provide binary contracts that encode market participants’ collective belief about a future event. In the case of crude‑oil settling above $90, the contract trades around 73 cents, implying roughly a 73 % chance of that outcome. By constructing a narrow call spread—buying the 89.5 strike and selling the 90.5 strike—and pricing it with the Black‑Scholes model at 90 % implied volatility, you obtain a theoretical probability that moves smoothly with the underlying futures price. This spread’s delta, the change in probability per dollar move in CL, serves as a benchmark for what a “fair” market reaction should look like.

The core market‑making insight lies in comparing the Polymarket contract’s implied delta to the option spread’s delta. When the binary’s delta is steeper, the market is over‑reacting to price changes, creating a sell‑side arbitrage: short the binary and hedge the exposure with futures or the call spread. Conversely, a flatter binary delta suggests undervaluation and a buying signal. Implementing this strategy requires only a live feed of CL futures and periodic recalculation of the spread’s theoretical price—volatility input largely cancels out, simplifying the computation. Risk management revolves around balancing hedge costs against exposure, offering a tangible lesson in the trade‑off that market makers constantly navigate.

Beyond the immediate profit potential, this experiment serves as a practical gateway into modern market‑making techniques applicable across emerging platforms such as Kalshi or other decentralized exchanges. It highlights how exchange design—fee structures, anonymity, and order‑book rules—shapes participant behavior and price efficiency. For traders and fintech entrepreneurs, mastering these delta‑based arbitrage signals can inform product development, liquidity provision strategies, and risk‑adjusted pricing models, positioning them to capitalize on the rapid growth of prediction‑market ecosystems.