Overview

Maximize your portfolio, analyze markets, and make data-driven investment decisions using Python and generative AI. Investing for Programmers shows you how you can turn your existing skills as a programmer into a knack for making sharper investment choices. You'll learn how to use the Python ecosystem, modern analytic methods, and cutting-edge AI tools to make better decisions and improve the odds of long-term financial success. In Investing for Programmers you'll learn how to: - Build stock analysis tools and predictive models - Identify market-beating investment opportunities - Design and evaluate algorithmic trading strategies - Use AI to automate investment research - Analyze market sentiments with media data mining In Investing for Programmers you'll learn the basics of financial investment as you conduct real market analysis, connect with trading APIs to automate buy-sell, and develop a systematic approach to risk management. Don't worry--there's no dodgy financial advice or flimsy get-rich-quick schemes. Real-life examples help you build your own intuition about financial markets, and make better decisions for retirement, financial independence, and getting more from your hard-earned money. About the technology A programmer has a unique edge when it comes to investing. Using open-source Python libraries and AI tools, you can perform sophisticated analysis normally reserved for expensive financial professionals. This book guides you step-by-step through building your own stock analysis tools, forecasting models, and more so you can make smart, data-driven investment decisions. About the book Investing for Programmers shows you how to analyze investment opportunities using Python and machine learning. In this easy-to-read handbook, experienced algorithmic investor Stefan Papp shows you how to use Pandas, NumPy, and Matplotlib to dissect stock market data, uncover patterns, and build your own trading models. You'll also discover how to use AI agents and LLMs to enhance your financial research and decision-making process. What's inside - Build stock analysis tools and predictive models - Design algorithmic trading strategies - Use AI to automate investment research - Analyze market sentiment with media data mining About the reader For professional and hobbyist Python programmers with basic personal finance experience. About the author Stefan Papp combines 20 years of investment experience in stocks, cryptocurrency, and bonds with decades of work as a data engineer, architect, and software consultant. Table of Contents 1 The analytical investor 2 Investment essentials 3 Collecting data 4 Growth portfolios 5 Income portfolios 6 Building an asset monitor 7 Risk management 8 AI for financial research 9 AI agents 10 Charts and technical analysis 11 Algorithmic trading 12 Private equity: Investing in start-ups 13 The road goes ever on and on A Setting up the environment Get a free eBook (PDF or ePub) from Manning as well as access to the online liveBook format (and its AI assistant that will answer your questions in any language) when you purchase the print book.

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9781633435803

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Reviews

The book provides the guidance to build a personalized framework for analysis, research, and record keeping for my own financial instruments of interest.  Timothy Beck, Principal Bioinformatics Software Engineer, Simplify Genomics, Inc.  A really engaging and enjoyable read! Sets you up with the tools and correct mindset that will enable you to take advantage of your skillset to start identifying a set of financial instruments that could satisfy your long term financial needs.  Thomas J Heiman, PhD, Senior Data Scientist, 22nd Century Technologies 

Author Information

Stefan Papp is an entrepreneur and data expert known for transforming raw information into profitable investment insights. With over two decades of experience across stocks, cryptocurrency, and bonds, and decades as a data engineer, architect, and consultant for major enterprises, Stefan distils proven finance and coding expertise into practical guides that deliver results. 

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