Learning Prediction Markets -- Complete Outline

This document presents the full table of contents for Learning Prediction Markets, covering 42 chapters organized across seven parts, followed by eight appendices.

Part I: Foundations

Chapter 1 -- What Are Prediction Markets? Introduces the core concept of prediction markets, explains how market prices encode probabilities, and presents real-world examples from elections, sports, and economic forecasting that demonstrate why these markets matter.

Chapter 2 -- A Brief History of Prediction Markets Traces the evolution of prediction markets from informal betting on papal elections in the sixteenth century through early twentieth-century Wall Street curb markets, the Iowa Electronic Markets, the rise and fall of Intrade, and the emergence of modern platforms like Polymarket.

Chapter 3 -- Probability Fundamentals Reviews the mathematical building blocks that every prediction market participant needs: sample spaces, probability axioms, conditional probability, Bayes' theorem, expected value, variance, and the law of large numbers, all implemented in Python.

Chapter 4 -- Contracts, Payoffs, and Market Mechanics Explains the three main contract types found on prediction markets -- binary, multi-outcome, and scalar -- along with their payoff structures, settlement mechanics, and resolution rules.

Chapter 5 -- The Modern Platform Landscape Surveys the major prediction market platforms operating today, including Polymarket, Kalshi, Metaculus, Manifold Markets, and PredictIt, comparing their market types, fee structures, regulatory status, and API capabilities.

Chapter 6 -- Setting Up Your Python Toolkit Walks readers through configuring a complete development environment, installing required libraries, connecting to platform APIs, and building a basic data retrieval pipeline that will be used throughout the book.

Part II: Market Microstructure and Pricing

Chapter 7 -- Order Books and the Limit Order Market Explains how continuous double auctions work, how to read and interpret depth charts, and how order matching algorithms determine trade execution in prediction markets that use limit order books.

Chapter 8 -- Automated Market Makers Presents the theory and implementation of automated market makers including the Logarithmic Market Scoring Rule (LMSR), the Constant Product Market Maker (CPMM), and the Liquidity-Sensitive LMSR, with complete Python implementations of their cost functions.

Chapter 9 -- Scoring Rules and Proper Incentives Covers proper scoring rules -- Brier, logarithmic, and spherical -- that incentivize honest probability reporting, proves their properness, and demonstrates how they connect to the cost functions underlying automated market makers.

Chapter 10 -- Bid-Ask Spreads, Transaction Costs, and Fees Analyzes the components of trading costs in prediction markets, models bid-ask spreads as functions of liquidity and volatility, and derives breakeven edge calculations that traders must clear to achieve profitability.

Chapter 11 -- Information Aggregation Theory Examines how prediction markets aggregate dispersed private information into prices, discusses the efficient market hypothesis as applied to prediction markets, and explores the wisdom-of-crowds phenomenon and its limitations.

Chapter 12 -- Calibration -- Measuring Forecast Quality Teaches readers to evaluate forecast accuracy using calibration plots, reliability diagrams, the Brier skill score, resolution, and reliability decomposition, providing the tools to rigorously assess any forecasting method.

Part III: Trading Strategies and Edge

Chapter 13 -- Finding and Quantifying Your Edge Introduces the concept of expected-value trading, derives the Kelly criterion for optimal bet sizing, and presents systematic methods for estimating and tracking edge over time.

Chapter 14 -- Binary Outcome Trading Strategies Develops concrete strategies for binary prediction markets including fundamental analysis, event-driven trading around news catalysts, mean-reversion approaches for mispriced contracts, and momentum-based trend following.

Chapter 15 -- Multi-Outcome and Scalar Market Strategies Extends trading strategy design to multi-outcome and scalar markets, covering portfolio optimization across correlated contracts, hedging techniques, and strategies for exploiting the richer structure of these market types.

Chapter 16 -- Arbitrage in Prediction Markets Identifies and formalizes arbitrage opportunities in prediction markets, including cross-platform price discrepancies, temporal arbitrage from stale prices, and structural arbitrage arising from overlapping contract definitions.

Chapter 17 -- Portfolio Construction and Risk Management Applies portfolio theory to prediction market positions, covering diversification across uncorrelated events, correlation estimation, Monte Carlo simulation for portfolio risk assessment, and drawdown-based risk limits.

Chapter 18 -- Behavioral Biases and Market Inefficiencies Catalogs the cognitive biases that create persistent mispricings in prediction markets -- including the favorite-longshot bias, anchoring, overconfidence, and neglect of base rates -- and develops strategies to exploit them.

Chapter 19 -- Live Trading, Execution, and Operational Discipline Provides a complete blueprint for live algorithmic trading in prediction markets, including a bot skeleton architecture, robust API error handling, position monitoring, logging, alerting, and the operational discipline required for sustained profitability.

Part IV: Data Science and Modeling

Chapter 20 -- Data Collection and Web Scraping Builds production-grade data ingestion pipelines using platform APIs, web scraping with BeautifulSoup and requests, and designs a relational database schema for storing and querying historical prediction market data.

Chapter 21 -- Exploratory Data Analysis of Market Data Applies exploratory data analysis techniques to prediction market datasets, examining price time-series patterns, volume dynamics, liquidity evolution, and market summary statistics to develop intuition about market behavior.

Chapter 22 -- Statistical Modeling -- Regression and Time Series Fits classical statistical models to prediction market data, including logistic regression for binary outcome probability estimation, ARIMA models for price dynamics, and state-space models for latent probability tracking.

Chapter 23 -- Machine Learning for Probability Estimation Trains modern machine learning models -- XGBoost gradient-boosted trees and feedforward neural networks -- to estimate event probabilities from features, with careful attention to calibration, cross-validation, and avoiding overfitting.

Chapter 24 -- NLP and Sentiment Analysis Applies natural language processing to prediction market trading, using transformer models to extract sentiment from news articles, social media, and expert commentary, and converting sentiment signals into probability adjustments.

Chapter 25 -- Ensemble Methods and Model Combination Combines multiple forecasting models using stacking, Bayesian model averaging, and linear opinion pools, demonstrating that well-constructed ensembles consistently outperform individual models in probability estimation.

Chapter 26 -- Backtesting Prediction Market Strategies Builds a rigorous backtesting framework for prediction market strategies, addresses common pitfalls such as lookahead bias, survivorship bias, and overfitting, and implements walk-forward analysis for realistic performance evaluation.

Chapter 27 -- Feature Stores, Pipelines, and MLOps Operationalizes the modeling workflow using scikit-learn pipelines for reproducible feature engineering, MLflow for experiment tracking and model registry, and deployment patterns for serving predictions in real time.

Part V: Market Design and Mechanism Engineering

Chapter 28 -- Principles of Prediction Market Design Examines the art and science of designing effective prediction markets, covering question formulation, unambiguous resolution criteria, incentive compatibility, and the tradeoffs between expressiveness and tractability.

Chapter 29 -- Liquidity Provision and Market Making Analyzes strategies for providing liquidity to prediction markets, including subsidized market making, managing adverse selection risk, inventory control, and the economics of earning the bid-ask spread.

Chapter 30 -- Combinatorial Prediction Markets Extends prediction market mechanisms to combinatorial outcome spaces where traders can bet on combinations of events, covering LMSR extensions, computational complexity, and practical approximation algorithms.

Chapter 31 -- Decision Markets and Futarchy Explores conditional prediction markets designed to inform decisions, introduces the concept of futarchy (governance by prediction market), and discusses the causal inference challenges that arise when using markets to guide policy.

Chapter 32 -- Building a Platform from Scratch Provides a complete, code-level walkthrough of building a prediction market platform using Flask and FastAPI for the backend, implementing a matching engine and order book, designing a REST API, and creating a basic web frontend.

Chapter 33 -- Scaling, Performance, and Operations Addresses the engineering challenges of running a prediction market platform at scale, covering event sourcing and CQRS architectures, performance optimization, monitoring and alerting, and load testing under realistic traffic patterns.

Part VI: Blockchain and Decentralized Markets

Chapter 34 -- Blockchain Fundamentals for Prediction Markets Provides the blockchain background needed to understand decentralized prediction markets, covering Ethereum architecture, smart contract development in Solidity, and interacting with the blockchain using Web3.py.

Chapter 35 -- Smart Contract Market Mechanisms Examines on-chain prediction market implementations including the Conditional Token Framework, the Augur protocol architecture, and Gnosis conditional tokens, analyzing their mechanism design and tradeoffs.

Chapter 36 -- DeFi Integration and Liquidity Mining Explores how prediction markets integrate with the broader DeFi ecosystem through composability, liquidity pool strategies, yield farming with prediction market positions, and the financial engineering enabled by programmable contracts.

Chapter 37 -- Oracles, Resolution, and the Decentralization Trilemma Confronts the fundamental challenge of bringing real-world information on-chain, surveying oracle designs from centralized reporters to decentralized dispute systems, and analyzing the tensions between decentralization, scalability, and security.

Part VII: Regulation, Ethics, and the Future

Chapter 38 -- The Regulatory Landscape Surveys the legal and regulatory environment for prediction markets across jurisdictions, covering CFTC enforcement actions and no-action letters in the United States, state gambling laws, and Europe's Markets in Crypto-Assets (MiCA) framework.

Chapter 39 -- Ethics of Prediction Markets Examines the ethical dimensions of prediction markets, including moral hazard concerns when people can profit from negative events, manipulation risks, the social impact of normalizing betting on sensitive topics, and frameworks for responsible market design.

Chapter 40 -- Real-World Applications Presents detailed case studies of prediction markets deployed in corporate forecasting, scientific replication prediction, pandemic and public-health monitoring, geopolitical risk assessment, and technology adoption forecasting.

Chapter 41 -- The Frontier -- Research Directions Surveys the cutting edge of prediction market research, including LLM-based automated forecasting, privacy-preserving market mechanisms using zero-knowledge proofs, information elicitation without verification, and emerging application domains.

Chapter 42 -- Capstone: Building a Complete Trading System Integrates every skill from the book into a single, end-to-end project: readers design, implement, backtest, deploy, and monitor a complete prediction market trading system, from data ingestion and modeling through execution and performance evaluation.

Appendices

Appendix A -- Mathematical Foundations Provides reference material on linear algebra, calculus, optimization, and information theory used throughout the book, with proofs of key results and pointers to further reading.

Appendix B -- Statistical Tables Contains standard statistical tables including the normal distribution, chi-squared distribution, t-distribution, and critical values used in hypothesis testing and confidence interval construction.

Appendix C -- Python Reference A quick-reference guide to the Python libraries used in the book -- NumPy, pandas, matplotlib, scikit-learn, and others -- with common patterns, idioms, and code snippets organized by task.

Appendix D -- Data Sources A curated catalog of prediction market data sources, APIs, academic datasets, and third-party data providers, with access instructions and notes on data quality and coverage.

Appendix E -- Glossary Defines key terms from prediction markets, probability, trading, machine learning, blockchain, and market design as used throughout the book.

Appendix F -- Notation Guide Consolidates all mathematical notation and symbol conventions used in the book into a single reference table, organized by topic.

Appendix G -- Answers to Selected Exercises Provides detailed solutions and explanations for selected exercises from each chapter, chosen to reinforce the most important concepts and techniques.

Appendix H -- Bibliography A comprehensive bibliography of academic papers, books, blog posts, and other resources referenced throughout the text, organized by topic.