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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 QR decomposition

The QR decomposition, also known as the QR factorization, is another method of solving linear systems of equations using matrices, very much like the LU decomposition. The equation to solve is in the form of Ax=B, where matrix A=QR. However, in this case, A is a product of an orthogonal matrix, Q, and upper triangular matrix, R. The QR algorithm is commonly used to solve the linear least-squares problem.

An orthogonal matrix exhibits the following properties:

  • It is a square matrix.
  • Multiplying an orthogonal matrix by its transpose returns the identity matrix:
  • The inverse of an orthogonal matrix equals its transpose:

An identity matrix is also a square matrix, with its main diagonal containing 1s and 0s elsewhere.

The problem of Ax=B can now be restated as follows:

Using the same variables in the LU decomposition example, we will use the qr() method...

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