Take your financial skills to the next level by mastering cutting-edge mathematical and statistical financial applications
Key Features
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
Book 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 sklearn. 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.
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
Who this book is 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.
Table of Contents
Overview of Financial Analysis with Python
The Importance of Linearity in Finance
Nonlinearity in Finance
Numerical Methods for Pricing Options
Modeling Interest Rates and Derivates
Statistical Analysis of Time Series Data
Interactive Financial Analytics with VIX
Building an Algorithmic Trading Platform
Implementing a Backtesting System
Machine Learning for Finance
Deep Learning for Finance