Not an implementation claim. Market making is an infrastructure business wearing a strategy costume; this guide teaches the forces so you can evaluate the research and recognize why most “market-making backtests” are fiction — not so you can quote tomorrow.
Definition. A market maker posts simultaneous buy and sell quotes, earning the spread from uninformed flow while managing two costs: inventory risk (holding what you were hit on while the price moves) and adverse selection (being hit because the price is about to move). All of market-making research is the study of this triangle.
The P&L identity that organizes everything
Every market-making result decomposes as:
P&L = spread captured − adverse-selection losses − inventory-holding costs − fees + rebates
Measure the terms separately or understand nothing: spread capture scales with uninformed volume share; adverse selection is measured by markouts (mid-price change N seconds/ticks after your fill — the field’s core diagnostic); inventory cost scales with variance held × holding time. A strategy can improve gross capture while dying on markouts, and vice versa — single-number P&L hides which force moved, which is why empirical MM papers that report markout curves outrank those reporting only returns.
Inventory: the Avellaneda–Stoikov intuition
The stochastic-control lineage’s core insight, worth having even without the math: your reservation price is not the mid — it shifts against your inventory (long inventory → quote lower on both sides, shading to sell), and the shift grows with volatility, risk aversion, and remaining horizon. Every practical quoting system embeds some version of this skewing, learned or derived; the RL market-making branch mostly rediscovers it with extra parameters. The model’s known counterfactuals — Poisson arrivals, no informed flow — are exactly the assumption-audit targets when reading descendants.
Adverse selection: the flow you don’t want is the flow you get
Quotes are free options written to the market, and informed traders exercise them. Mechanics that follow: queue position is value (in price-time priority, front-of-queue fills contain less adverse selection — back-of-queue fills at the same price are the toxic residue); cancellation speed is defense (the spread compensates average toxicity, so slower reactions than the venue’s ecology means systematic negative markouts); and spread width is a filter setting (wider quotes select for impatient/uninformed flow but capture less volume — the trade-off is the business). The measurement discipline: bucket markouts by counterparty urgency proxies (trade size, sweep vs single, time-of-day) — toxicity is not uniform, and neither should quotes be.
Where the spread goes: the ecology argument
Displayed spread ≠ your margin. It funds, in order: adverse selection to faster/better-informed participants, exchange fees net of rebates, inventory variance, and then profit. The ecology implication: market-making profitability is relative — to the venue’s other quoters’ speed and models — not absolute. This is why MM research generalizes worse than anything else in microstructure: a 2014 profitability result is a statement about 2014’s ecology. AMM/DeFi research replays the same triangle with the quoting function fixed by the pool curve — impermanent loss is adverse selection in continuous form, LP fees are the spread — which is why that branch reads as classical MM in new notation.
Common ways market making fails in production
- The backtest quoted where the book wouldn’t have let it: simulated fills ignore queue position; real fills at that price would have been the back-of-queue toxic ones. Fill simulation without book reconstruction is the genre’s label-as-return equivalent.
- Inventory death spiral in trends: skewing reduces but doesn’t eliminate accumulation against a trend; without hard position limits and a kill switch, one directional day returns a month of spreads.
- Toxicity regime shifts: a new fast participant arrives; markouts degrade for weeks before cumulative P&L makes it obvious. Monitor markouts daily, not P&L.
- Rebate-dependency: strategies profitable only via maker rebates are fee-schedule bets; venue pricing changes are their regime breaks.
- The uptime tax: quoting obligations and technology failures interact — being down during the volatile hour concentrates your uptime into exactly the adverse periods.
Questions to ask an MM paper
- Are markouts reported, at multiple horizons, by flow type?
- How are fills simulated — and is queue position modeled at all?
- Which venue/era ecology produced the data, and does the paper claim beyond it?
- For RL variants: does the learned policy’s skewing behavior reduce to A-S intuitions, and is the environment’s flow toxicity calibrated to anything real?
What our scoring captures
The map of MM research sorts the field’s branches; within them, the rigor axis rewards markout-level measurement and book-reconstruction honesty — the two practices separating empirical MM work from spread-times-volume arithmetic. Theory branch papers land in Lab Rats by design; the calibrated exceptions top the hub.
The book’s information content: LOB imbalance → · The noise floor: microstructure noise & realized vol → · The field map: market-making research → · From the corpus: VWAP prediction: methods and benchmarks →