Название: Generative AI for Trading and Asset Management
Автор: Hamlet Jesse Medina Ruiz, Ernest Chan
Издательство: Wiley
Год: 2025
Страниц: 320
Язык: английский
Формат: epub (true)
Размер: 10.9 MB

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.)

In this chapter, we will see if no-code Gen AI can handle more challenging tasks such as turning a trading strategy specification in English into backtest codes, and even more ambitious, suggest a trading strategy based on a vague human insight. As a reminder, Ernie has basic Python programming skills, is much more proficient in MATLAB, and is a hedge fund founder. This means he has extensive experience and domain knowledge in finance. In the following examples, the author approaches the development of the examples as someone who is an expert in the finance field, but not a professional programmer or a specialist in prompt engineering (like many professional asset managers). He needs help coding in a new language. Let’s see if ChatGPT can help him.

Written by Hamlet Medina, chief data scientist Criteo, and Ernie Chan, founder of QTS Capital Management and Predictnow.ai, this book explores topics including:
• How large language models and other Mmachine Learning techniques can improve productivity of algorithmic and discretionary traders from ideation, signal generations, backtesting, risk management, to portfolio optimization
• The pros and cons of tree-based models vs neural networks as they relate to financial applications. How regularization techniques can enhance out of sample performance
• Comprehensive exploration of the main families of explicit and implicit generative models for modeling high-dimensional data, including their advantages and limitations in model representation and training, sampling quality and speed, and representation learning.
• Techniques for combining and utilizing generative models to address data scarcity and enhance data augmentation for training ML models in financial applications like market simulations, sentiment analysis, risk management, and more.
• Application of generative AI models for processing fundamental data to develop trading signals.
• Exploration of efficient methods for deploying large models into production, highlighting techniques and strategies to enhance inference efficiency, such as model pruning, quantization, and knowledge distillation.
• Using existing LLMs to translate Federal Reserve Chair's speeches to text and generate trading signals.

Generative AI for Trading and Asset Management earns a well-deserved spot on the bookshelves of all asset managers seeking to harness the ever-changing landscape of AI technologies to navigate financial markets.