Investing for Programmers
Understanding markets through data and code
Published on 21. October 2025
368 pages
978-1-63835-778-0 (ISBN)
Description
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
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
More details
Other editions
Additional editions
Steven Papp
Investing for Programmers
Book
11/2025
Manning Publications
€76.94
Shipment within 3-4 weeks
Person
Stefan Papp combines 20+ years of investment experience across stocks, cryptocurrency, and bonds with decades of work as a data engineer, architect, and consultant for major enterprises.
