Python for Finance Cookbook: Over 80 powerful recipes for effective financial data analysis, 2nd Edition 2nd ed. Edition

Really great book with super detailed explanations. I was honestly amazed at how clearly and systematically everything was explained — it made it so much easier to follow and stay interested.

Time series section gave me info and idea to find out a sharpe ratio 0.8 strategies (2020-).But i rate this book as 4 stars as the model factor section makes no sense to me. I am expecting the author shows me cookbook to use stock leading factor (as mentioned in the opening of the section in the book) to put in the model and how to use this model on algo strategy. But it turns out the code example is nothing related to it.

Excellent book. Good discussion on each topic, self-contained code examples and they work! I hope author will come up with an updated version covering newer techniques in detail e.g., using pytorch !! Must have as a reference

Love this book since it is really useful for my study and work!

The Python for Finance Cookbook offers readers a comprehensive introduction to the world of financial data analysis using Python. In addition to being well-structured, the book guides readers through logical progressions of topics from the very beginning. Throughout the book, each chapter is organized in such a manner that it is easy to follow along and comprehend what is being discussed.This book is incredibly helpful in that it breaks down scenarios into sections, beginning with "How to do it," followed by "How it works," and concluding with "There's more.". As a result of this format, readers can gain an understanding of Python techniques, their practical implementation, and additional options. Coding examples provided are clear and concise, making application to real-world financial problems easy.The author's explanations held my attention throughout the reading of the book. Each example presented a well-articulated thought process that guided me through the steps of the analysis and decision-making process. As a result of the author's clear language and the inclusion of practical tips, I was able to avoid common mistakes and gain a greater understanding of financial data analysis.Time series analysis, technical analysis, machine learning, and deep learning are all discussed in the book. In addition, Streamlit is introduced, which is a valuable tool for developing interactive web applications for presenting analysis results. An important aspect of the course was the emphasis on exploratory data analysis, which allowed me to uncover insights and draw meaningful conclusions from the financial information.As a whole, I found the "Python for Finance Cookbook" to be an extremely helpful resource. In this book, you will learn how to analyze financial data using Python to enhance your skills as a financial analyst, data scientist, or Python developer. My proficiency in using Python for financial data analysis has significantly improved as a result of its well-structured outline, detailed explanations, and thought-provoking examples provided in this book. To anyone seeking a deeper understanding of this field, I strongly recommend this book.

As the title states is a Cookbook. It introduces many python libraries to analyzed and apply Machine Learning (ML) applications to financial time series data. Does a great job on how to download financial data. All the code in the book is available from the URLs provided in the book. I found the book very useful and recommend the book, for those getting started in analyzing and forecasting financial time series data in python.

This is one of the worst books I have ever bought. I like to copy over the code and run it myself. Time after time, the code would not work, even after using GITHUB for a most current version.

I recently picked up "Python for Finance Cookbook," which is tailored perfectly for a data analyst like me who studies finance and investing on the side, and it is an understatement to say I was truly impressed! This book seamlessly integrates the complexities of finance with the versatility of Python, offering an invaluable guide to harnessing financial data for insightful analysis.Right from the start, Chapter 1, "Acquiring Financial Data," grabbed my attention. As someone who values accurate data, the step-by-step instructions for gathering data from diverse sources like Yahoo Finance, Nasdaq Data Link, and more were indispensable. Chapter 2's data preprocessing techniques, covering everything from handling missing data to adjusting for inflation, were equally beneficial, streamlining my analysis process.The book's coverage of visualizing financial time series data in Chapter 3 elevated my understanding of plotting financial data. Techniques like creating interactive visualizations and understanding seasonal patterns provided fresh perspectives on market behavior. Chapters 4 to 6 further explored data analysis and forecasting, while Chapters 7 and 8 bridged the gap between data analysis and investment strategy, showing how machine learning can enhance forecasting and estimating models.As I delved deeper, the book's advanced topics, including volatility modeling, Monte Carlo simulations, and deep learning applications, kept pushing my boundaries. Each chapter concluded with a concise summary, reinforcing key takeaways and ensuring I grasped the essentials. In summary, the "Python for Finance Cookbook" is a treasure trove for data analysts with an investing interest. It fuses Python programming with financial concepts seamlessly, empowering readers to confidently analyze data, forecast trends, and make informed investment choices. My skills as a data analyst and investor have undeniably grown through this book, and I'm excited to implement its insights in my future pursuits.