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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

Summary

In this chapter, we looked at volatility derivatives and their uses by investors to diversify and hedge their risk in equity and credit portfolios. Since long-term investors in equity funds are exposed to downside risk, volatility can be used as a hedge for the tail risk and in replacement for the put options. In the United States, the CBOE VIX measures the short-term volatility implied by SPX option prices. In Europe, the VSTOXX Market Index is based on the market prices of a basket of OESX, and measures the implied market volatility over the next 30 days on the EURO STOXX 50 Index. Many people around the world use the VIX as a popular measurement tool for the stock market volatility over the next 30-day period. To help us better understand how the VIX Index is calculated, we looked at its components and at formulas used in determining its value.

To help us determine...

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