Chapter 7 Further Reading

Foundational Texts on Market Microstructure

O'Hara, Maureen. Market Microstructure Theory. Blackwell, 1995. The classic academic treatment of market microstructure. Covers information models, inventory models, and the theory behind order book dynamics. Essential reading for anyone who wants a deep theoretical understanding of how order books work and why spreads exist.
Harris, Larry. Trading and Exchanges: Market Microstructure for Practitioners. Oxford University Press, 2003. A comprehensive and remarkably accessible book covering everything from order types to market design. Written for practitioners rather than academics, making it an excellent complement to O'Hara's more theoretical treatment. Highly recommended as a next step after this chapter.
Hasbrouck, Joel. Empirical Market Microstructure. Oxford University Press, 2007. Focuses on the empirical methods used to study market microstructure. Covers trade classification (including the Lee-Ready algorithm discussed in Section 7.11), spread decomposition, and price discovery measurement. Includes worked examples with data.

Glosten, Lawrence R., and Paul R. Milgrom. "Bid, Ask and Transaction Prices in a Specialist Market with Heterogeneously Informed Traders." Journal of Financial Economics 14, no. 1 (1985): 71-100. The foundational paper explaining why bid-ask spreads exist in the presence of informed traders. Introduces the concept of adverse selection as a determinant of spread width -- directly relevant to understanding why prediction market spreads tend to be wider.
Kyle, Albert S. "Continuous Auctions and Insider Trading." Econometrica 53, no. 6 (1985): 1315-1335. Introduces the Kyle lambda (price impact coefficient) and the concept of market depth as a function of informed trading. Essential for understanding the relationship between order flow, information, and price impact discussed in Section 7.8.
Cont, Rama, Sasha Stoikov, and Rishi Talreja. "A Stochastic Model for Order Book Dynamics." Operations Research 58, no. 3 (2010): 549-563. A quantitative model of order book dynamics that treats order arrivals, cancellations, and executions as stochastic processes. Useful for anyone building simulations of order book behavior.

Biais, Bruno, Larry Glosten, and Chester Spatt. "Market Microstructure: A Survey of Microfoundations, Empirical Results, and Policy Implications." Journal of Financial Markets 8, no. 2 (2005): 217-264. A comprehensive survey of market microstructure research up to 2005. Covers order book theory, empirical findings on spread determinants, and market design implications.
Bouchaud, Jean-Philippe, J. Doyne Farmer, and Fabrizio Lillo. "How Markets Slowly Digest Changes in Supply and Demand." In Handbook of Financial Markets: Dynamics and Evolution, edited by Thorsten Hens and Klaus Schenk-Hoppe, 57-160. North-Holland, 2009. An excellent overview of how order flow drives price changes. Discusses the relationship between order book shape, market impact, and price formation. Particularly relevant to understanding the depth chart dynamics in Section 7.4.
Cao, Charles, Oliver Hansch, and Xiaoxin Wang. "The Information Content of an Open Limit-Order Book." Journal of Futures Markets 29, no. 1 (2009): 16-41. Empirical evidence that order book data beyond the best bid and ask (Level 2 data) contains valuable information for predicting short-term price movements. Supports the discussion in Section 7.9.

Hanson, Robin. "Combinatorial Information Market Design." Information Systems Frontiers 5, no. 1 (2003): 107-119. Hanson's work on market scoring rules and their relationship to order book markets. While focused on automated market makers (covered in Chapter 8), this paper provides important context for understanding why prediction markets sometimes use order books and sometimes use AMMs.
Berg, Joyce E., Forrest D. Nelson, and Thomas A. Rietz. "Prediction Market Accuracy in the Long Run." International Journal of Forecasting 24, no. 2 (2008): 285-300. Analysis of the Iowa Electronic Markets, one of the longest-running prediction markets using an order book mechanism. Provides empirical evidence on how order book-based prediction markets perform over time.
Arrow, Kenneth J., et al. "The Promise of Prediction Markets." Science 320, no. 5878 (2008): 877-878. A brief but influential article by leading economists arguing for the value of prediction markets. Provides context for why efficient market mechanisms (including well-designed order books) matter.

WK Selph. "Building a Trading System." Blog series, 2014-2016. A practical blog series on building matching engines and trading systems. Covers data structures, performance optimization, and real-world engineering challenges. Available online.
Vyukov, Dmitry. "Designing a Lock-Free Concurrent Order Book." Technical discussion of implementing high-performance order books using lock-free data structures. Relevant for anyone interested in scaling the Python implementation from this chapter to production performance.
Johnson, Barry. Algorithmic Trading and DMA: An Introduction to Direct Access Trading Strategies. 4Myeloma Press, 2010. A practitioner-oriented book covering the mechanics of order execution, including detailed discussion of order types, matching algorithms, and market data feeds. Good bridge between theory and practice.

Polymarket API Documentation (https://docs.polymarket.com/) Official documentation for Polymarket's API, including WebSocket feeds for real-time order book data. Useful for applying the concepts from this chapter to live data.
Kalshi API Documentation (https://trading-api.readme.io/) Kalshi's API documentation for accessing order book and trade data on their regulated prediction market platform.
Betfair API Documentation (https://developer.betfair.com/) Betfair's Exchange API provides access to one of the world's most liquid prediction/betting market order books. Their streaming API is particularly well-suited for the depth chart analysis discussed in Section 7.4.
sortedcontainers Python Library (https://grantjenks.com/docs/sortedcontainers/) The sorted data structure library used in our order book implementation. Pure Python, well-documented, and performant. The SortedDict class is particularly useful for maintaining price-sorted order book levels.

Market Making Strategies: Chapter 12 of this textbook will cover market making in depth. For early exploration, see Avellaneda and Stoikov, "High-Frequency Trading in a Limit Order Book" (Quantitative Finance, 2008).
High-Frequency Trading: For understanding how HFT firms interact with order books, see Aldridge, Irene, High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems (Wiley, 2013).
Blockchain-Based Order Books: For understanding how order books work on decentralized exchanges (relevant to some prediction markets), see the documentation for dYdX, Serum, and other on-chain CLOB implementations.
Order Book Machine Learning: For applications of machine learning to order book data, see Sirignano, Justin A., "Deep Learning for Limit Order Books" (Quantitative Finance, 2019), and Zhang, Zihao, Stefan Zohren, and Stephen Roberts, "DeepLOB: Deep Convolutional Neural Networks for Limit Order Books" (IEEE Transactions on Signal Processing, 2019).