DeFi Meets Machine Learning: Using Predictive Models to Optimize Yield Farming and Decentralized Lending

The intersection of decentralized finance and machine learning represents one of the most consequential frontiers in modern financial technology. This book offers a structured and practical examination of how predictive models can be applied to the rapidly evolving world of DeFi protocols, with particular focus on yield farming strategies and decentralized lending markets. Readers will find a clear progression from foundational concepts — covering the mechanics of liquidity pools, automated market makers, and on-chain lending platforms — to advanced data-driven techniques that enable more informed allocation decisions. The text explains how supervised and unsupervised learning algorithms can process blockchain data, identify behavioral patterns, and forecast risk-adjusted returns across multiple protocols. Special attention is given to the challenges unique to DeFi environments, including smart contract risk, oracle manipulation, impermanent loss modeling, and liquidity fragmentation. Quantitative finance professionals, data scientists entering the crypto space, and sophisticated retail investors will all find relevant material calibrated to their needs. No prior expertise in blockchain technology is assumed; the necessary background is built progressively throughout the chapters. By the final section, readers will have a coherent framework for constructing, backtesting, and deploying machine learning pipelines that interact with DeFi ecosystems in a systematic and risk-aware manner.

───────────────────