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

Finite differences in option pricing

Finite difference schemes are very much similar to trinomial tree option pricing, where each node is dependent on three other nodes with an up movement, a down movement, and a flat movement. The motivation behind the finite differencing is the application of the Black-Scholes Partial Differential Equation (PDE) framework (involving functions and their partial derivatives), where price S(t) is a function of f(S,t), with r as the risk-free rate, t as the time to maturity, and σ as the volatility of the underlying security:

The finite difference technique tends to converge faster than lattices and approximates complex exotic options very well.

To solve a PDE by finite differences working backward in time, a discrete-time grid of size M by N is set up to reflect asset prices over a course of time, so that S and t take on the following...