I am thrilled to announce the upcoming release of my new book, “Hands-On AI Trading with Python, QuantConnect, and AWS,” co-authored with industry experts Ernest P. Chan, Jared Broad, Philip Sun, and Vivek Singh. Scheduled for release on January 29, 2025, this comprehensive guide will be published by Wiley in full color as both a hardcover and a Kindle edition. You can pre-order your copy here.
A Deep Dive into AI-Powered Algorithmic Trading
In collaboration with my co-authors, we’ve meticulously analyzed the current landscape of AI technologies in algorithmic trading. The result is a collection of over 20 fully worked-out, largely profitable strategies, complete with full Python source code. These strategies are designed to provide practical tools and offer insights into which market conditions favor certain strategies over others.
Key Building Blocks of Modern Trading Strategies
Our focus has been on identifying and incorporating the essential components of modern algorithmic trading strategies. By doing so, we’ve crafted examples that offer diverse perspectives on algorithmic trading, helping readers understand the nuances of different approaches. Rather than delving into exhaustive mathematical details, we’ve emphasized practical insights. We believe that by experimenting with various parameters, readers can deepen their understanding of the methods covered.
Leveraging QuantConnect.com for an Immersive Learning Experience
We’ve selected the QuantConnect.com platform as the ideal playground for learning and mastering algorithmic trading skills. QuantConnect offers an intuitive interface, ready computational resources, and complimentary access to key market data, making it an unparalleled tool for beginners and seasoned traders.
This combination of cutting-edge technologies and hands-on experimentation sets our book apart from others in the field. Readers can instantly apply what they’ve learned on QuantConnect, bridging the gap between theory and practice.
Structured Learning Path
The book begins with an outline of key terms in capital markets and quantitative trading. We then introduce best practices for integrating AI into algorithmic trading, structured into three essential steps:
- Problem Definition
- Dataset Preparation
- Model Choice, Training, and Application
We provide detailed instructions accompanied by full Python examples for each step, enabling readers to apply these methods in real-world scenarios.
Over 20 Fully Worked-Out Examples
We walk you through more than 20 comprehensive examples, each with explained significance and visual aids like tearsheets and charts that highlight their effectiveness under various market conditions. Here’s a glimpse of what you’ll find:
- ML Trend Scanning with MLFinlab
- Factor Preprocessing Techniques for Regime Detection
- Reversion vs. Trending: Strategy Selection by Classification
- Alpha by Hidden Markov Models
- FX SVM Wavelet Forecasting
- Dividend Harvesting Selection of High-Yield Assets
- Effect of Positive-Negative Splits
- Stop Loss Based on Historical Volatility and Drawdown Recovery
- ML Trading Pairs Selection
- Stock Selection through Clustering Fundamental Data
- Inverse Volatility Rank and Allocate to Future Contracts
- Trading Costs Optimization
- PCA Statistical Arbitrage Mean Reversion
- Temporal CNN Prediction
- Gaussian Classifier for Direction Prediction
- LLM Summarization of Tiingo News Articles
- Head and Shoulders Pattern Matching with CNN
- Amazon Chronos Model
- FinBERT Model
- Better Hedging with Reinforcement Learning
- AI for Risk Management and Optimization with Corrective AI and Conditional Parameter Optimization
- Application of Large Language Models and Generative AI in Trading
Each example is crafted to enhance your understanding and provide actionable strategies that you can test and refine.
Endorsements
We’re honored to have received excellent endorsements from leading figures in the industry:
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” 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
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
Upcoming Insights
In the coming weeks, we’ll publish detailed blog posts on key topics covered in the book, offering fresh perspectives and deeper insights into the content.
I couldn’t be more proud of this collaborative effort and the value it offers to anyone interested in AI-driven algorithmic trading. Whether you’re a seasoned professional or just starting, “Hands-On AI Trading with Python, QuantConnect, and AWS” will elevate your trading strategies to the next level.
Don’t miss out—pre-order your copy today!
