Use case · Liquidity

Market making on prediction markets

Provide liquidity and earn the spread on Polymarket and Kalshi with polybot's spread-farming strategy — inside a platform that treats inventory risk as a first-class invariant.

The idea

A market maker quotes both a bid and an ask and profits from the spread as traders cross it. On prediction markets this is attractive because contracts are bounded in [0, 1] and resolution provides a natural anchor. The risk is inventory: if the market moves against your accumulated position, the spread you earned can be dwarfed by the mark-to-market loss.

How polybot handles it

• Spread-farming strategy. The built-in spread-farming strategy manages quoting, requoting and cancellation as a BaseStrategy.
• Inventory caps. Per-market and per-category exposure limits stop inventory from running away; daily loss limits auto-disable the strategy on drawdown.
• Async execution. Orders are placed, amended and cancelled over the venue abstraction, with fills reconciled back into position state in real time.
• Shadow first. Test your quoting parameters in paper mode against live order flow before committing capital.

Related

Pair spread farming with statistical arbitrage for correlated hedges, and read how it works for the execution and reconciliation path.

FAQ

What strategy does polybot use for market making?

The built-in spread-farming strategy quotes both sides of a market to earn the bid-ask spread. It is a low-risk, high-touch strategy: inventory and exposure caps keep you from accumulating unbounded directional risk.

How is inventory risk controlled?

Per-market and per-category exposure caps plus daily loss limits are enforced by the platform before quotes are refreshed, so an adverse move cannot push inventory past your configured limits.