Book Image

Mastering Python for Finance - Second Edition

By : James Ma Weiming
Book Image

Mastering Python for Finance - Second Edition

By: James Ma Weiming

Overview of this book

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.
Table of Contents (16 chapters)
Free Chapter
1
Section 1: Getting Started with Python
3
Section 2: Financial Concepts
9
Section 3: A Hands-On Approach

Summary

In this chapter, we took a brief look at the use of the CAPM model and APT model in finance. In the CAPM model, we visited the efficient frontier with the CML to determine the optimal portfolio and the market portfolio. Then, we solved for the SML using regression, which helped us to determine whether an asset is undervalued or overvalued. In the APT model, we explored how various factors affect security returns other than using the mean-variance framework. We performed a multivariate linear regression to help us determine the coefficients of the factors that led to the valuation of our security price.

In portfolio allocation, portfolio managers are typically mandated by investors to achieve a set of objectives while following certain constraints. We can model this problem using linear programming. Using the Pulp Python package, we can define a minimization or maximization...