Название: Build Your Own AI Investor : With Machine Learning and Python, Step by Step Автор: Damon Lee
Издательство: valueinvestingai.com
Год: 2020
Страниц: 234
Язык: английский
Формат: pdf, epub
Размер: 36.5 MB
This book aims to teach almost anyone to be able to use Machine Learning with Python to build their own AI stock investor with freely available online tools, line by line. The concept being that readers would finish with some knowledge about investing as well as a program of immediate use, made with their own hands, even if they never wrote code before.Investing is a difficult activity fraught with uncertainty, compounded by questionable incentives, and occasionally involving unscrupulous characters. If a layperson can program their own "Robo-investor", they can eliminate the last two issues and help minimise the first. At the very least they will have a better understanding of the strengths and limitations of their creations. The performance benchmark of the AI investor will be the S&P500 index.
This guide is not intended to be an exhaustive course on Python, nor does it teach the intricacies of all Machine Learning algorithms. This guide is intended to be practical and hands-on, where the Machine Learning concepts are explored in intuitively useful code tutorials in a narrative manner toward the goal, building competency along the way. Furthermore it aims to and to show where to look to delve deeper if desired.
Everything is written in plain English where possible, with all the code to create the AI present in print. Readers who want to skip to the end and get the AI investor working immediately can do so. This book assumes some basic knowledge of mathematics, though nothing is present that can’t be looked up on the internet and understood quickly.
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.
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