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| Title & Speakers | Event |
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Investing for Programmers
2025-09-29
Stefan Papp
– author
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. Quotes Especially valuable for anyone looking to improve their investing. - Armen Kherlopian, Covenant Venture Capital A great breadth of topics—from basic finance concepts to cutting-edge technology. - Ilya Kipnis, Quantstrat Trader A top tip for people who want to leverage development skills to improve their investment possibilities. - Michael Zambiasi, Raiffeisen Digital Bank Brilliantly bridges the worlds of coding and finance. - Thomas Wiecki, PyMC Labs |
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Modern Graph Theory Algorithms with Python
2024-06-07
Colleen M. Farrelly
– author
,
Franck Kalala Mutombo
– author
Dive into the fascinating world of graph theory and its applications with 'Modern Graph Theory Algorithms with Python.' Through Python programming and real-world case studies, this book equips you with the tools to transform data into graph structures, apply algorithms, and uncover insights, enabling effective solutions in diverse domains such as finance, epidemiology, and social networks. What this Book will help me do Understand how to wrangle a variety of data types into network formats suitable for analysis. Learn to use graph theory algorithms and toolkits such as NetworkX and igraph in Python. Apply network theory to predict and analyze trends, from epidemics to stock market dynamics. Explore the intersection of machine learning and graph theory through advanced neural network techniques. Gain expertise in database solutions with graph database querying and applications. Author(s) Colleen M. Farrelly, an experienced data scientist, and Franck Kalala Mutombo, a seasoned software engineer, bring years of expertise in network science and Python programming to every page of this book. Their professional experience includes working on cutting-edge problems in data analytics, graph theory, and scalable solutions for real-world issues. Combining their practical know-how, they deliver a resource aimed at both learning and applying techniques effectively. Who is it for? This book is tailored for data scientists, researchers, and analysts with an interest in using graph-based approaches for solving complex data problems. Ideal for those with a basic Python knowledge and familiarity with libraries like pandas and NumPy, the content bridges the gap between theory and application. It also provides insights into broad fields where network science can be impactful, contributing value to both students and professionals. |
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Stock Market Analysis with Python and Machine Learning - Ivan Brigida
2024-01-24 · 10:45
Ivan Brigida
– guest
We talked about: Ivan’s background How Ivan became interested in investing Getting financial data to run simulations Open, High, Low, Close, Volume Risk management strategy Testing your trading strategies Sticking to your strategy Important metrics and remembering about trading fees Important features Deployment How DataTalks.Club courses helped Ivan Ivan’s site and course sign-up Links: Exploring Finance APIs: https://pythoninvest.com/long-read/exploring-finance-apis Python Invest Blog Articles: https://pythoninvest.com/blog Free ML Engineering course: http://mlzoomcamp.com Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html |
DataTalks.Club |
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Analyzing financial data with Python
2023-12-11 · 12:30
Ivan Brigida
– Business Intelligence Analyst
Outline: Stock trading; Starting with trading; Machine learning for stock predictions. |
Stock Market Analysis with
Python and
Machine Learning
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Python for Data Science
2022-08-02
Yuli Vasiliev
– author
Python is an ideal choice for accessing, manipulating, and gaining insights from data of all kinds. Python for Data Science introduces you to the Pythonic world of data analysis with a learn-by-doing approach rooted in practical examples and hands-on activities. Youâ??ll learn how to write Python code to obtain, transform, and analyze data, practicing state-of-the-art data processing techniques for use cases in business management, marketing, and decision support. You will discover Pythonâ??s rich set of built-in data structures for basic operations, as well as its robust ecosystem of open-source libraries for data science, including NumPy, pandas, scikit-learn, matplotlib, and more. Examples show how to load data in various formats, how to streamline, group, and aggregate data sets, and how to create charts, maps, and other visualizations. Later chapters go in-depth with demonstrations of real-world data applications, including using location data to power a taxi service, market basket analysis to identify items commonly purchased together, and machine learning to predict stock prices. |
O'Reilly Data Science Books
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