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Mastering Python for Finance

Mastering Python for Finance

By : James Ma Weiming
2.8 (9)
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Mastering Python for Finance

Mastering Python for Finance

2.8 (9)
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)
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1
Section 1: Getting Started with Python
3
Section 2: Financial Concepts
9
Section 3: A Hands-On Approach

The LU decomposition

The LU decomposition, or also known as lower-upper factorization, is one of the methods that solve square systems of linear equations. As its name implies, the LU factorization decomposes the A matrix into a product of two matrices: a lower triangular matrix, L, and an upper triangular matrix, U. The decomposition can be represented as follows:

Here, we can see a=l11u11, b=l11u12, and so on. A lower triangular matrix is a matrix that contains values in its lower triangle with the remaining upper triangle populated with zeros. The converse is true for an upper triangular matrix.

The definite advantage of the LU decomposition method over the Cholesky decomposition method is that it works for any square matrices. The latter only works for symmetric and positive definite matrices.

Think back to the previous example in Solving linear equations using matrices...

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