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The widespread adoption of AI and machine learning is revolutionizing many industries today. Once these technologies are combined with the programmatic availability of historical and real-time financial data, the financial industry will also change fundamentally. With this practical book, you'll learn how to use AI and machine learning to discover statistical inefficiencies in financial markets and exploit them through algorithmic trading. Author Yves Hilpisch shows practitioners, students, and academics in both finance and data science practical ways to apply machine learning and deep learning algorithms to finance. Thanks to lots of self-contained Python examples, you'll be able to replicate all results and figures presented in the book. In five parts, this guide helps you:Learn central notions and algorithms from AI, including recent breakthroughs on the way to artificial general intelligence (AGI) and superintelligence (SI)Understand why data-driven finance, AI, and machine learning will have a lasting impact on financial theory and practiceApply neural networks and reinforcement learning to discover statistical inefficiencies in financial marketsIdentify and exploit economic inefficiencies through backtesting and algorithmic trading--the automated execution of trading strategiesUnderstand how AI will influence the competitive dynamics in the financial industry and what the potential emergence of a financial singularity might bring about
The widespread adoption of AI and machine learning is revolutionizing many industries today. Once these technologies are combined with the programmatic availability of historical and real-time financial data, the financial industry will also change fundamentally. With this practical book, you'll learn how to use AI and machine learning to discover statistical inefficiencies in financial markets and exploit them through algorithmic trading. Author Yves Hilpisch shows practitioners, students, and academics in both finance and data science practical ways to apply machine learning and deep learning algorithms to finance. Thanks to lots of self-contained Python examples, you'll be able to replicate all results and figures presented in the book. In five parts, this guide helps you:Learn central notions and algorithms from AI, including recent breakthroughs on the way to artificial general intelligence (AGI) and superintelligence (SI)Understand why data-driven finance, AI, and machine learning will have a lasting impact on financial theory and practiceApply neural networks and reinforcement learning to discover statistical inefficiencies in financial marketsIdentify and exploit economic inefficiencies through backtesting and algorithmic trading--the automated execution of trading strategiesUnderstand how AI will influence the competitive dynamics in the financial industry and what the potential emergence of a financial singularity might bring about