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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

Stationary and non-stationary time series

It is important that time series data that's used for statistical analysis is stationary in order to perform statistical modeling correctly, as such usages may be for prediction and forecasting. This section introduces the concepts of stationarity and non-stationarity in time series data.

Stationarity and non-stationarity

In empirical time series studies, price movements are observed to drift toward some long-term mean, either upwards or downwards. A stationary time series is one whose statistical properties, such as mean, variance, and autocorrelation, are constant over time. Conversely, observations on non-stationary time series data have their statistical properties...

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