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

Forward rates

An investor who plans to invest at a later time may be curious to know what the future interest rate will look like, as implied by today's term structure of interest rates. For example, you might ask, What is the one-year spot rate one year from now? To answer this question, you can calculate forward rates for the period between T1 and T2 using this formula:

Here, r1 and r2 are the continuously-compounded annual interest rates at time periods T1 and T2, respectively.

The following ForwardRates class helps us generate a list of forward rates from a list of spot rates:

class ForwardRates(object):

def __init__(self):
self.forward_rates = []
self.spot_rates = dict()

def add_spot_rate(self, T, spot_rate):
self.spot_rates[T] = spot_rate

def get_forward_rates(self):
"""
Returns a list of forward rates
...