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Second Edition
This Second Edition is slightly longer, with improved clarity of code, formatting, text explanations as well as updating the AI for 2021 stock selection onwards.
Build Your Own AI Investor
- Breaks down Value Investing for the AI revolution, whilst being accessible to anyone, even if you’ve never invested in stocks or coded before
- Teaches Python step-by-step, from installation and the basics, all the way to creating your own AI Investor that picks stocks for you
- Watch the AI Portfolio: See AI portfolio performance over time on the website, made with the code in this book.
AI Investing
- Not sure how to approach the stock investing with AI?
- No time to learn these programming skills? Think it sounds daunting?
- Think the investing game is rigged by computer-wielding financial wizards?
Do It All Yourself
- Discover Value Investing, the approach taken by the best investors: Warren Buffett, Joel Greenblatt, Michael Burry, Peter Lynch, John Templeton, Charlie Munger
- Build your own AI! Have your own Value Investing machine provide stock picks for the year
- No time? Set up the AI in a weekend by skipping ahead to Chapter 5
- Anyone can learn the computing tools, every step is in this book to build a value investing AI
Your AI Investor in action
- Make it personal, make your Robo-Investor as conservative or aggressive as you desire
- Watch how your AI would pick stocks over the last 10 years with backtesting, test your AI as thoroughly as you like
Open source tools: All programming tools are available online for free. Working code from the book is available online for readers. Train your AI using free online historical stock market data. (For present day stock selection you will need a SimFin+ online subscription, no affiliation.)
For beginners, exercises are provided in every chapter to develop your Python skills, slowly building competence until you can use Machine Learning tools for general problems.
For advanced readers this books provides a good basis in value investing, framing the stock selection problem in a quantitative way, using Machine Learning algorithms on stock market data.
