Artificial Intelligence in Finance: A Python-Based Guide

Explores the integration of AI, particularly deep learning and reinforcement learning, with financial concepts. It contrasts normative financial theories like CAPM and APT with data-driven approaches, highlighting the limitations of traditional models. The content also details practical applications through Python code examples, covering topics such as data availability and processing, neural network architectures, and the development and backtesting of AI-driven trading bots. Furthermore, it discusses the broader implications of AI in finance, including competition, resource allocation, and the potential for a "financial singularity."

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