Jesse H. Generative AI for Trading and Asset Management 2025 torrent

Textbook in PDF format

Expert guide on using AI to supercharge traders' productivity, optimize portfolios, and suggest new trading strategies.
Generative AI for Trading and Asset Management is an essential guide to understand how Generative AI has emerged as a transformative force in the realm of asset management, particularly in the context of trading, due to its ability to analyze vast datasets, identify intricate patterns, and suggest complex trading strategies. Practically, this book explains how to utilize various types of AI: unsupervised learning, supervised learning, reinforcement learning, and large language models (LLMs) to suggest new trading strategies, manage risks, optimize trading strategies and portfolios, and generally improve the productivity of algorithmic and discretionary traders alike. These techniques converge into an algorithm to trade on the Federal Reserve chair's press conferences in real time.
As Ernie has maintained in Machine Trading, Python is a poor cousin of Matlab—you use it at your own peril. While Matlab’s codes are developed and maintained commercially by a team of full-time professionals and PhDs, Python’s codes are developed and maintained by essentially a group of part-time volunteers. (A bit of grapevine gossip: the Father of Deep Learning and Nobel prize laureate, Dr. Geoff Hinton himself, was known to prefer Matlab to Python in his own research.)
Preface
Part I: Generative AI for Trading and Asset Management: A No-code Introduction
No-code Generative AI for Basic Quantitative Finance
No-code Generative AI for Trading Strategies Development
Whirlwind Tour of ML in Asset Management
Part II: Deep Generative Models for Trading and Asset Management
Understanding Generative AI
Deep Autoregressive Models for Sequence Modeling
Deep Latent Variable Models
Flow Models
Generative Adversarial Networks
Leveraging LLMs for Sentiment Analysis in Trading
Efficient Inference
Afterword