In this chapter, we looked at a number of numerical procedures in derivative pricing, the most common being options. One such procedure is the use of trees, with binomial trees being the simplest structure to model asset information, where one node extends to two other nodes in each time step, representing an up state and a down state, respectively. In trinomial trees, each node extends to three other nodes in each time step, representing an up state, a down state, and a state with no movement, respectively. As the tree traverses upwards, the underlying asset is computed and represented at each node. The option then takes on the structure of this tree and, starting from the terminal payoffs, the tree traverses backward and toward the root, which converges to the current discounted option price. Besides binomial and trinomial trees, trees can take on the form of the...
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Mastering Python for Finance
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Mastering Python for Finance
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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)
Preface
Overview of Financial Analysis with Python
Section 2: Financial Concepts
The Importance of Linearity in Finance
Nonlinearity in Finance
Numerical Methods for Pricing Options
Modeling Interest Rates and Derivatives
Statistical Analysis of Time Series Data
Section 3: A Hands-On Approach
Interactive Financial Analytics with the VIX
Building an Algorithmic Trading Platform
Implementing a Backtesting System
Machine Learning for Finance
Deep Learning for Finance
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