Market Microstructure
How prices actually form — order books, market making, adverse selection, and the mechanics of trading. This module builds the theoretical toolkit that every quant trader, market maker, and DeFi protocol designer reaches for, whether they know it or not.
Why This Topic First
If you have ever built a system that needs to price anything in real time — credit scoring, risk engines, order routing — you already think in terms of information asymmetry, adverse selection, and inventory management. Market microstructure is where those intuitions meet rigorous equilibrium models. It also provides the intellectual foundation for understanding AMMs, MEV, and on-chain order books.
Learning Roadmap
The articles below follow a deliberate arc: vocabulary, mechanism design, then the three canonical models (inventory, adverse selection, strategic trading), finishing with empirical decomposition.
| # | Article | Core Idea |
|---|---|---|
| 1 | trading-fundamentals | The fundamental problem of trading and the market maker as intermediary |
| 2 | order-books-and-venues | CLOBs as data structures, RFQ mechanisms, and the venue landscape |
| 3 | ho-stoll-inventory-model | Ho-Stoll (1981): spread as compensation for inventory risk |
| 4 | glosten-milgrom-model | Glosten-Milgrom (1985): spread as defense against informed traders |
| 5 | kyle-lambda | Kyle (1985): price impact, noise traders, and strategic insider behavior |
| 6 | spread-decomposition | Huang-Stoll (1997): decomposing the spread into its three components |
How to Use This Module
Each article is self-contained but rewards sequential reading. The companion Marimo notebooks let you simulate order flow, visualize inventory paths, and replay Bayesian belief updates — use them to build intuition beyond the math.
Key Connections to Other Topics
- Derivatives pricing: delta-hedging is inventory management in disguise. See derivatives-pricing.
- Credit risk: adverse selection in loan markets mirrors Glosten-Milgrom. See credit-risk.
- Portfolio theory: Kyle’s lambda shows up in optimal execution and transaction cost analysis. See portfolio-theory.