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Overview of Financial Analysis with Python

Since the publication of my previous book Mastering Python for Finance, there have been significant upgrades to Python itself and many third-party libraries. Many tools and features have been deprecated in favor of new ones. This chapter walks you through how to get the latest tools available and how to prepare the environment that will be used throughout the rest of the book.

We will be using Quandl for the majority of datasets covered in this book. Quandl is a platform that serves financial, economic, and alternative data. These sources of data are contributed by various data publishers, including the United Nations, World Bank, central banks, trading exchanges, investment research firms, and even members of the Quandl community. With the Python Quandl module, you can easily download datasets and perform financial analytics to derive...

Key benefits

Explore advanced financial models used by the industry and ways of solving them using Python

Build state-of-the-art infrastructure for modeling, visualization, trading, and more

Empower your financial applications by applying machine learning and deep learning

Description

The second edition of Mastering Python for Finance will guide you through carrying out complex financial calculations practiced in the industry of finance by using next-generation methodologies. You will master the Python ecosystem by leveraging publicly available tools to successfully perform research studies and modeling, and learn to manage risks with the help of advanced examples. You will start by setting up your Jupyter notebook to implement the tasks throughout the book. You will learn to make efficient and powerful data-driven financial decisions using popular libraries such as TensorFlow, Keras, Numpy, SciPy, and scikit-learn. You will also learn how to build financial applications by mastering concepts such as stocks, options, interest rates and their derivatives, and risk analytics using computational methods. With these foundations, you will learn to apply statistical analysis to time series data, and understand how time series data is useful for implementing an event-driven backtesting system and for working with high-frequency data in building an algorithmic trading platform. Finally, you will explore machine learning and deep learning techniques that are applied in finance. By the end of this book, you will be able to apply Python to different paradigms in the financial industry and perform efficient data analysis.

Who is this book for?

If you are a financial or data analyst or a software developer in the financial industry who is interested in using advanced Python techniques for quantitative methods in finance, this is the book you need! You will also find this book useful if you want to extend the functionalities of your existing financial applications by using smart machine learning techniques. Prior experience in Python is required.

What you will learn

Solve linear and nonlinear models representing various financial problems

Perform principal component analysis on the DOW index and its components

Analyze, predict, and forecast stationary and non-stationary time series processes

Create an event-driven backtesting tool and measure your strategies

Build a high-frequency algorithmic trading platform with Python

Replicate the CBOT VIX index with SPX options for studying VIX-based strategies

Perform regression-based and classification-based machine learning tasks for prediction

Use TensorFlow and Keras in deep learning neural network architecture

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

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Alicia M. Sep 05, 2023

Llegó en buen estado y era lo que esperaba.

Joseph R. Jul 19, 2019

This book is a must-read for anyone aspiring or practicing in the field of quantitative finance. James' work is extremely well-written, easy to follow, and detailed.As a financial engineer, I use numerical methods in my daily life; most of these I thought were only possible in lower-level languages. I was incredibly shocked to learn that all of these advanced mathematical methodologies can be written in Python and used for practical applications in algorithmic trading. In addition to walking the reader through these methods step-by-step, James teaches the reader how to build a real, working algorithmic trading strategy (several of them actually) and gives the reader the tools to backtest and implement the strategy for their own use!This is the first book, to my knowledge, that covers these incredibly important steps and does so in a way that programmers of all experience-levels can understand. Absolutely brilliant work!Note: The screenshot is taken from the output of my pairs trading (statistical arbitrage) strategy that I built and traded on InteractiveBrokers - all thanks to James' work!

Robin T. Wernick Dec 09, 2020

Trying to introduce the entire world of Finance Computing is a real stretch or any reference book and this one is short a few critical sections, but I applaud this effort to cover it all. Financial concepts and supporting mathematics are covered decently, but are largely isolated from the full coding examples. This may be because it will save space.Warning: As in the first edition, All code files are of type "ipynb". This will require the use of Jupyter Notebook to display them on screen! So get ready to learn a new IDE to apply this book. See Packt Books for code downloads.API access to Alpha Advantage and Quandl financial data sources are only superficially covered in the book. Critical program coding that explains the API is missing in both cases. I hope that the recommended Jupyter Notebook won't prove to be non intuitive to use to see all the supporting code.Finally, I missed seeing any coding support for access to databases such as MySQL or 'kdb'. I think the author is primarily a concept thinker and leaves the detail work to his assistants. But for us, we need more handholding to make the entire concept framework pay off. So I recommend that you add other references such as "Python GUI Programming Cookbook" and/or "Hands-On Web Scraping with Python" to fill in the missing pieces.

MM Aug 02, 2021

The author maybe has some knowledge of ML, but seems to not really understand financial time series.From using the entire dataset for min/max scaling to predicting price there isn’t much for some to actually learn anything useful.

Mastering Python for Finance: Implement advanced state-of-the-art financial statistical applications using Python , Second Edition

What do you get with Print?

Mastering Python for Finance

Overview of Financial Analysis with Python

Getting Python

Introduction to Quandl

Setting up Quandl for your environment

Plotting a time series chart

Retrieving datasets from Quandl

Performing financial analytics on time series data

Plotting returns

Summary

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Mastering Python for Finance: Implement advanced state-of-the-art financial statistical applications using Python , Second Edition

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