Unlock the Future of AI-Powered Trading
We are thrilled to announce the release of Hands-On AI Trading with Python, QuantConnect, and AWS, a groundbreaking book designed to bridge the gap between artificial intelligence (AI) and quantitative trading. This book is your go-to resource for leveraging AI to build, test, and deploy algorithmic trading strategies written by industry leaders Jiri Pik, Ernest Chan, Jared Broad, Philip Sun, and Vivek Singh.
Whether you’re a hedge fund trader, quant researcher, or graduate student looking to break into the world of AI-driven finance, this book provides the tools and insights you need to stay ahead in today’s data-driven markets.
Why This Book Stands Out
Unlike traditional finance books that focus solely on theory, Hands-On AI Trading with Python, QuantConnect, and AWS takes a deeply practical approach. Here’s why this book is a must-read for anyone serious about AI in trading:
1. A Modern, Hands-On Approach to AI in Trading
- The book is full color, making it visually engaging and easy to follow.
- No need to set up complex infrastructure – leverage QuantConnect’s cloud-based backtesting and live trading platform.
- Covers everything from machine learning to deep learning and reinforcement learning, applied directly to trading strategies.
2. A Rich Suite of Practical Examples
- Features 20+ fully implemented AI trading algorithms, with source code available on GitHub.
- Covers techniques such as:
- Machine Learning (Random Forests, SVMs, Gaussian Processes)
- Deep Learning (CNNs, RNNs, Transformers)
- Reinforcement Learning for trading and hedging
- Natural Language Processing for sentiment analysis
- Time Series Forecasting and Clustering
- Examples include:
- ML Trend Scanning with MLFinlab
- Alpha Generation using Hidden Markov Models
- Stock Selection through Clustering of Fundamental Data
- Stop Loss Strategies Based on Volatility and Drawdown Recovery
- Using Large Language Models (LLMs) in Trading
- Optimizing Hedging with Reinforcement Learning
3. Deep Integration of Quantitative Finance Concepts
- Not just AI – also covers risk management, portfolio optimization, and transaction cost modeling.
- Demonstrates AI’s role in adapting trading strategies dynamically to market conditions.
- Shows how AI can predict corporate actions like mergers, acquisitions, and dividend events.
- Discusses real-world challenges such as slippage, execution costs, and market microstructure.
4. Extensive Additional Content and Active Community
- All source code is available in a dedicated GitHub repository.
- Engage with the community via forums, webinars, and discussions with fellow quants and finance professionals.
- Bonus online content, including datasets, video tutorials, and updates on QuantConnect’s latest features.
Who Should Read This Book?
- Hedge Fund Traders & Quantitative Analysts – Learn how to integrate AI into your existing trading models for a competitive edge.
- Graduate Students & Academics – A perfect companion for those studying financial engineering, data science, or algorithmic trading.
- Algorithmic Traders & AI Enthusiasts – Experiment with fully working AI trading strategies without starting from scratch.
- Portfolio Managers & Risk Analysts – Understand how AI can optimize portfolios and dynamically hedge risks in volatile markets.
Order Now and Start Building AI-Driven Trading Strategies
If you’re serious about the intersection of AI and finance, Hands-On AI Trading with Python, QuantConnect, and AWS is an essential addition to your library.
Get your copy today on Amazon and take your trading to the next level!
