Artificial Intelligence in Finance: A Python-Based Guide by Yves Hilpisch

Artificial Intelligence in Finance: A Python-Based Guide by Yves Hilpisch

The core engineering philosophy of Artificial Intelligence in Finance is that modern financial markets are far too complex, fast-moving, and multi-dimensional to be accurately modeled by 18th-century linear regressions or restrictive mathematical assumptions. Hilpisch challenges the long-held foundational cornerstones of traditional finance—such as the Efficient Market Hypothesis (EMH), the Capital Asset Pricing Model (CAPM), and Mean-Variance Portfolio Theory. He argues that these classic frameworks rely on idealized assumptions about rational human behavior and normal asset distributions that break down completely during real-world market shocks.

Instead, the textbook introduces readers to AI-First Finance—an entirely data-driven, model-free approach. Rather than imposing a fixed mathematical theory onto a dataset, developers learn how to let general, highly parameterizable machine learning algorithms discover underlying market patterns autonomously. Through self-contained Python code examples and Jupyter Notebooks, Hilpisch provides step-by-step guides for collecting financial data, processing complex feature sets, training deep feedforward neural networks, and utilizing reinforcement learning agents to optimize real-time trading policies.

As quantitative hedge funds, algorithmic trading shops, and modern fintech platforms look to integrate deep learning into their production strategies, software engineers face unique data hurdles. Treating highly erratic financial time-series data like a standard, predictable data science project often leads to severe overfitting, data leakage, and catastrophic model failures when live capital is deployed.

Artificial Intelligence in Finance provides the precise, mathematical, and code-heavy guide that technical teams require. Dr. Yves J. Hilpisch swaps vague trading abstractions for clean, production-ready Python implementations, clear matrix transformations, and rigorous neural network configurations. By walking developers through the entire development cycle—from raw API data ingestion to advanced reinforcement learning environments—this O'Reilly manual gives programmers, financial analysts, and quantitative researchers the exact skills needed to build resilient data-driven trading infrastructure. It is a mandatory text for any advanced algorithmic engineering library.

Language: English.

Genre:: Quantitative Finance.

Binding: সেলাই করা বাইন্ডিং

Quality: Premium Quality Books.

Printing: High Quality Printing.

Paper: Eye Friendly paper (Cream White)

Cover: Matt cover (Paperback).