Hands-On AI Trading with Python, QuantConnect, and AWS

Praise for HANDS-ON AI TRADING

“A must-have for algorithmic traders and students, this book emphasizes designing trading strategies with QuantConnect™. Featuring Python™ examples and advanced AI/ML models, it offers a clear and accessible presentation ideal for anyone in quantitative finance.”
—PETTER N. KOLM, Professor, Courant Institute of Mathematical Sciences, New York University; Awarded “Quant of the Year” in 2021

“This concise guide provides a gentle introduction with hands-on examples and expert insights into dissecting and evaluating trades from seasoned traders. The code will make otherwise complex or confusing examples clear. It is an excellent springboard for developing your own strategies.”
—MICHAEL ROBBINS, Author of Quantitative Asset Management

“This is the book I wish I had when starting out, it would have saved me years! It offers rare insights and practical tutorials, allowing the next generation of quants to stand on the shoulders of giants.”
—JACQUES JOUBERT, Quant Researcher and Developer, Co-Founder and CEO of Hudson and Thames Quantitative Research

“The book ties both theory and industry together while providing code, output, and a platform to implement AI models in a trading environment. Cookbook style makes it a great book for those new to machine learning and AI in quantitative finance.”
—DIMITRI BIANCO, Head of Quant Risk and Research, Agora Data, Inc.

“As a novice trader myself, I have been looking for ways to apply AI in real-world trading scenarios. This book does an excellent job in explaining trading concepts and mapping these to AI concepts to build trading strategies. A must-read if you want to use AI for building wealth.”
—RAJNEESH SINGH, Director, Amazon SageMaker

“This book is an excellent resource for learning machine learning and AI for quantitative trading. The authors’ practical guidance helps in creating strategies, building portfolios, and managing risks with QuantConnect’s™ support.”
—JASON JIE SHENG LIM, CFA, FRM, Risk Data Scientist

“This comprehensive guide masterfully bridges the gap between AI technology and practical trading applications, offering finance professionals valuable insights for developing robust, data-driven trading strategies.”
—CHRIS BARTLETT, CEO, Algoseek.com

Master the art of AI-driven algorithmic trading strategies through hands-on examples, in-depth insights, and step-by-step guidance

Hands-On AI Trading with Python, QuantConnect, and AWS explores real-world applications of AI technologies in algorithmic trading. It provides practical examples with complete code, allowing readers to understand and expand their AI toolbelt.

Unlike other books, this one focuses on designing actual trading strategies rather than setting up backtesting infrastructure. It utilizes QuantConnect, providing access to key market data from Algoseek and others. Examples are available on the book's GitHub repository, written in Python, and include performance tearsheets or research Jupyter notebooks.

The book starts with an overview of financial trading and QuantConnect's platform, organized by AI technology used:

• Examples include constructing portfolios with regression models, predicting dividend yields, and safeguarding against market volatility using machine learning packages like SKLearn and MLFinLab.
• Use principal component analysis to reduce model features, identify pairs for trading, and run statistical arbitrage with packages like LightGBM.
• Predict market volatility regimes and allocate funds accordingly.
• Predict daily returns of tech stocks using classifiers.
• Forecast Forex pairs' future prices using Support Vector Machines and wavelets.
• Predict trading day momentum or reversion risk using TensorFlow and temporal CNNs.
• Apply large language models (LLMs) for stock research analysis, including prompt engineering and building RAG applications.
• Perform sentiment analysis on real-time news feeds and train time-series forecasting models for portfolio optimization.
• Better Hedging by Reinforcement Learning and AI: Implement reinforcement learning models for hedging options and derivatives with PyTorch.
• AI for Risk Management and Optimization: Use corrective AI and conditional portfolio optimization techniques for risk management and capital allocation.

Written by domain experts, including Jiri Pik, Ernest Chan, Philip Sun, Vivek Singh, and Jared Broad, this book is essential for hedge fund professionals, traders, asset managers, and finance students. Integrate AI into your next algorithmic trading strategy with Hands-On AI Trading with Python, QuantConnect, and AWS.