Law & Viens (2019) study market making under a weakly consistent limit-order-book model—more book-aware than mid-only toys. Plain-language intro. Not investment advice.
Takeaway. Classic midprice market-making toys ignore book consistency; this paper asks how a market maker should quote when the LOB itself obeys a weakly consistent stochastic structure.
Market Making under a Weakly Consistent Limit Order Book Model (Benjamin Law, Frederi Viens, 2019) sits between "pure mid + inventory" stories and full LOB simulators.
Fit for learners: Market making on a weakly consistent LOB model—bridges book realism and tractable quoting intuition.
This Hub note is a plain-language intro. Not investment advice. Background: Avellaneda–Stoikov, Guéant optimal market making.
If your quoting model only sees a Brownian mid, you miss queue and depth effects that change fill odds and adverse selection. If you jump to a mega agent-based book, you drown in parameters. Learners need a middle rung.
Open the PDF for assumptions and results. Do not paste coefficients into a live venue bot.
You will eventually compare "mid-only AS" vs "book-aware" experiments. This paper names a coherent book-aware setting you can discuss in study notes.
Tiny evening exercise: list five LOB state variables (e.g. best sizes, spread, imbalance) and write which ones a mid-only model ignores—then sketch a fake state tuple in a Python dataclass.
Landing: arXiv:1903.07222
Not investment advice. This Hub note is educational only. The paper is research—not a licence to trade, quote, or execute with real capital.
Single course CTA: SoloQuant Academy's Introduction to Python Programming—get comfortable with functions, files, and plots first; then these microstructure PDFs become much less scary.