Part III: Trading Strategies & Edge

Chapters 13--19

Parts I and II gave you the language and the mechanics. Now it is time to put money -- real or play -- on the line. Part III marks the transition from understanding prediction markets to actively participating in them as a trader seeking consistent, positive expected returns. This is where theory meets practice, and where the gap between knowing and doing becomes starkly apparent.

Trading prediction markets well is not gambling, though the superficial resemblance tempts many newcomers into treating it as such. Successful trading requires a disciplined framework: a clear theory of where your edge comes from, a rigorous method for sizing positions, a strategy for managing a portfolio of correlated bets, and an honest accounting of the psychological biases that will work against you at every turn. This part provides that framework.

Chapter 13 begins with the most important question any trader must answer: where does my edge come from? We define edge precisely -- the difference between your assessed probability and the market price, adjusted for transaction costs -- and catalog the most common sources. These include superior domain expertise, faster information processing, better models, and the ability to identify structural inefficiencies in how specific platforms operate. We also address the uncomfortable corollary: if you cannot identify your edge, you probably do not have one.

Chapter 14 introduces the Kelly criterion, the gold standard for bet sizing under uncertainty. We derive the formula, build intuition for what it tells you, and discuss why most experienced traders use a fractional Kelly approach. You will implement a Kelly calculator in Python and see, through simulation, how proper bet sizing can mean the difference between steady growth and ruin even when your probability estimates are accurate.

Chapter 15 focuses on strategies specific to binary outcome markets -- the most common contract type in prediction markets. We cover value betting, momentum signals, mean reversion near resolution, and news-driven trading. Each strategy is presented with concrete examples, Python code, and an honest discussion of when it works and when it fails.

Chapter 16 extends the toolkit to multi-outcome markets, where the strategy space is richer and the pitfalls are deeper. Elections, sporting tournaments, and award ceremonies all produce markets with many mutually exclusive outcomes. We explore how to identify mispriced candidates, how to construct hedged positions, and how the constraint that probabilities must sum to one creates structural arbitrage opportunities.

Chapter 17 is devoted entirely to arbitrage -- the practice of locking in risk-free profit by exploiting price discrepancies. We cover cross-platform arbitrage, intra-platform arbitrage across related contracts, and the practical barriers (withdrawal times, fees, counterparty risk) that make textbook arbitrage messier in the real world than in theory. You will build a simple arbitrage scanner and learn to evaluate whether an apparent opportunity is genuine.

Chapter 18 pulls back to the portfolio level. Most active traders hold positions in dozens or even hundreds of markets simultaneously. We discuss correlation, diversification, risk budgeting, and how to think about the total exposure of a prediction market portfolio. The goal is to move beyond evaluating individual bets in isolation and toward managing a coherent book of positions.

Chapter 19 confronts the enemy within: behavioral biases. Overconfidence, anchoring, the disposition effect, confirmation bias -- these cognitive traps are not abstract curiosities from a psychology textbook. They are the specific, recurring mistakes that drain prediction market accounts. We examine each bias through the lens of prediction market trading and offer concrete mitigation strategies. The chapter closes with a practical section on building a live trading workflow, from signal generation through execution and record-keeping, designed to impose the discipline that good decision-making requires.

By the end of Part III, you will have a complete, actionable trading framework. More importantly, you will understand that consistent profitability comes not from any single clever trick, but from the disciplined integration of edge identification, position sizing, portfolio management, and psychological awareness.

Chapters in This Part