Crypto markets never sleep. Neither do the algorithms that keep them running. Market making — the practice of simultaneously offering to buy and sell an asset to capture the spread — has been automated for years. But a new research paper provides the most comprehensive theoretical framework yet for how to do it optimally.
For anyone trading crypto or providing liquidity, the findings matter.
What Market Making Actually Involves
A market maker places both a buy order and a sell order for the same asset at different prices. The difference between them — the spread — is their profit if both orders get filled. Simple enough.
But real market making is not simple. Prices move while your orders are open. You end up holding inventory that loses value. Other traders with better information pick you off. And in crypto specifically, you face funding rates (payments between long and short position holders), cross-exchange arbitrage, and zero-fee venues that change the economics entirely.
The new paper models all of this as a single mathematical problem: choose your bid-ask spread and your hedging decisions adaptively to maximise profit while controlling risk.
The Key Findings at a Glance
The paper, which builds on decades of market microstructure theory, delivers several practically useful results.
First, it decomposes market maker profit-and-loss into five separate components: spread income, adverse selection loss (being traded against by better-informed participants), inventory carrying cost, hedging friction, and funding rate exposure. This decomposition alone is valuable — it tells you exactly where your profit comes from and where it leaks away.
Second, it identifies the conditions under which market making is profitable using five dimensionless parameters. This is a Master Formula approach: plug in your numbers and see whether the market is worth making in.
Third, it analyses zero-fee venues specifically. Many crypto exchanges now offer zero maker fees to attract liquidity. The paper shows the optimal strategies for these environments differ significantly from traditional fee-based markets.
Practical Implications for Traders
If you provide liquidity on crypto exchanges, this research offers a more rigorous way to think about your strategy. The days of setting a fixed spread and hoping for the best are over. Modern market making requires adaptive controls that respond to market conditions in real time.
The paper also addresses a critical practical question: how much of your capital should you risk? It derives Kelly-optimal leverage with ruin boundaries — the mathematically correct bet size that maximises long-term growth while avoiding catastrophic loss. For anyone running a market-making operation, that alone is worth studying.
For smaller traders, the takeaway is simpler: understand your edge. If you cannot identify which of the five PnL components is generating your profit, you are probably not making money — you are just not losing it fast enough to notice.
The Broader Picture
This research unifies several classical market-making models — Avellaneda-Stoikov, Gueant-Lehalle-Fernandez-Tapia, and Glosten-Milgrom — into a single framework adapted for decentralised venues. That is a genuine theoretical contribution.
For most businesses, the direct relevance is limited. Unless you actively make markets in crypto, the details of stochastic optimal control and Hamilton-Jacobi-Bellman equations are not something you will use day-to-day. But the underlying trend is broader: financial markets are being reshaped by AI-driven optimisation at a granular level. The spreads you pay, the slippage you experience, and the liquidity you depend on are all determined by algorithms like the ones this paper analyses.
Understanding how those algorithms work — even at a high level — helps you make better trading decisions. Markets are not magic. They are mathematics running on servers. Papers like this one show you the math.