Book Image

Mastering Python for Finance - Second Edition

By : James Ma Weiming
Book Image

Mastering Python for Finance - Second Edition

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

Summary

In this chapter, we have been introduced to machine learning in the context of finance. We discussed how AI and machine learning is transforming the financial sector. Machine learning can be supervised or unsupervised, and supervised algorithms can be regression-based and classification-based. The scikit-learn Python library provides various machine learning algorithms and risk metrics.

We discussed the use of regression-based machine learning models such as OLS regression, ridge regression, LASSO regression, and elastic net regularization in predicting continuous values such as security prices. An ensemble of decision trees was also discussed, such as the bagging regressor, gradient tree boosting, and random forests. To measure the performance of regression models, we visited the MSE, MAE, explained variance score, and R2 score.

Classification-based machine learning classifies...