Further Reading: Chapter 19

Books

"Algorithmic Trading and DMA" by Barry Johnson -- The definitive reference on execution quality, order types, and market microstructure. While focused on traditional markets, the execution principles apply directly to prediction markets.
"Building Winning Algorithmic Trading Systems" by Kevin Davey -- A practical guide from a competition-winning algorithmic trader. Covers the full cycle from idea to live deployment with emphasis on avoiding common pitfalls.
"Trading and Exchanges: Market Microstructure for Practitioners" by Larry Harris -- Deep treatment of how markets work at the mechanical level: order books, matching engines, and the behavior of different participant types.
"Quantitative Trading: How to Build Your Own Algorithmic Trading Business" by Ernest Chan -- Accessible introduction to systematic trading with practical guidance on backtesting, execution, and risk management.
"The Art of Execution" by Lee Freeman-Shor -- Based on real portfolio manager data, this book examines how execution decisions (when to cut losses, when to add to winners) determine outcomes independent of stock selection skill.

"Trading in the Zone" by Mark Douglas -- The classic text on trading psychology. Covers the mental framework needed to execute a probabilistic edge consistently despite emotional pressures.
"The Daily Trading Coach" by Brett Steenbarger -- 101 practical lessons for building psychological resilience in trading. Useful for developing self-awareness about emotional patterns.
"Thinking in Bets" by Annie Duke -- A former professional poker player applies decision-making frameworks to situations of uncertainty. Directly relevant to prediction market trading where outcomes are probabilistic.
"Fooled by Randomness" by Nassim Nicholas Taleb -- Essential reading on the human tendency to mistake noise for signal. Critical for maintaining discipline during drawdowns and winning streaks.

"Release It! Design and Deploy Production-Ready Software" by Michael Nygard -- Covers stability patterns (circuit breakers, bulkheads, timeouts) and anti-patterns for production systems. Essential for anyone building systems that manage real capital.
"Designing Data-Intensive Applications" by Martin Kleppmann -- While broader than trading, this book covers reliability, scalability, and maintainability principles that apply directly to trading infrastructure.
"Site Reliability Engineering" edited by Betsy Beyer et al. (Google) -- Google's approach to maintaining reliable systems. The chapters on monitoring, alerting, and incident management are directly applicable to trading operations.

Glosten, L. R., & Milgrom, P. R. (1985). "Bid, Ask and Transaction Prices in a Specialist Market with Heterogeneously Informed Traders." Journal of Financial Economics, 14(1), 71-100. Foundational paper on why bid-ask spreads exist and how they relate to information asymmetry.
Kyle, A. S. (1985). "Continuous Auctions and Insider Trading." Econometrica, 53(6), 1315-1335. Seminal model of market impact -- how large orders move prices. Essential for understanding why execution quality matters.
Almgren, R., & Chriss, N. (2001). "Optimal Execution of Portfolio Transactions." Journal of Risk, 3, 5-39. The foundational paper on optimal order splitting to minimize market impact plus timing risk.

Manski, C. F. (2006). "Interpreting the Predictions of Prediction Markets." Economics Letters, 91(3), 425-429. Discusses when and how prediction market prices can be interpreted as probabilities, relevant to signal generation.
Berg, J. E., Nelson, F. D., & Rietz, T. A. (2008). "Prediction Market Accuracy in the Long Run." International Journal of Forecasting, 24(2), 285-300. Long-run analysis of prediction market accuracy, useful for understanding the informational environment you are trading in.

Cartea, A., Jaimungal, S., & Penalva, J. (2015). "Algorithmic and High-Frequency Trading." Cambridge University Press. Comprehensive academic treatment of algorithmic trading strategies, execution algorithms, and market making.

Python asyncio documentation -- For building asynchronous trading systems that handle multiple data feeds and order streams concurrently. https://docs.python.org/3/library/asyncio.html
Redis documentation -- Redis is widely used in trading systems for real-time data caching, pub/sub messaging, and rate limiting. https://redis.io/documentation
Prometheus and Grafana -- The standard open-source stack for monitoring and alerting in production systems. Essential for trading system dashboards. https://prometheus.io/ and https://grafana.com/

"RESTful API Design" by various authors -- Understanding REST API conventions helps you integrate with prediction market platforms more effectively and build better internal APIs for your trading system.
WebSocket protocol specification (RFC 6455) -- For platforms that offer WebSocket-based data feeds, understanding the protocol helps debug connection issues. https://tools.ietf.org/html/rfc6455

"The Checklist Manifesto" by Atul Gawande -- While not about trading, this book makes a compelling case for using checklists in high-stakes operations. Directly applicable to trading deployment and daily operations checklists.
Incident management frameworks (PagerDuty, Opsgenie) -- These tools and their documentation provide best practices for alert management, escalation, and post-incident review that translate well to trading operations.

Polymarket API documentation -- One of the largest prediction market platforms. Understanding its API design provides practical context for the integration patterns discussed in this chapter.
Kalshi API documentation -- A CFTC-regulated prediction market exchange. Its API represents a more traditional exchange-style interface.
Metaculus API -- While primarily a forecasting platform rather than a market, Metaculus provides useful APIs for calibration and forecast data.

Quantitative Finance Stack Exchange (quant.stackexchange.com) -- Active community discussing algorithmic trading, execution, and risk management.
r/algotrading (Reddit) -- Community of algorithmic traders sharing strategies, code, and operational experiences. Variable quality but valuable for practical insights.
QuantConnect and Zipline communities -- Open-source backtesting and live trading platforms with active communities. While focused on traditional markets, the infrastructure discussions are directly relevant.

Platform sandbox/testnet environments -- Most prediction market platforms offer sandbox environments for testing API integration without risking real capital. Always use these before connecting to production.
Paper trading with live data -- As discussed in Section 19.10, building a paper trading engine against live market data is the most realistic way to test before going live.