Preface

This second edition of Mastering Python for Finance will guide you through carrying out complex financial calculations practiced in the finance industry, using next-generation methodologies. You will master the Python ecosystem by leveraging publicly available tools to successfully perform research studies and modeling, and learn how to manage risks using advanced examples.

You will start by setting up a Jupyter notebook to implement the tasks throughout the book. You will learn how to make efficient and powerful data-driven financial decisions using popular libraries such as TensorFlow, Keras, NumPy, SciPy, scikit-learn, and so on. 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 how to apply statistical analysis on time series data and understand how to harness high-frequency data to devise trading strategies in building an algorithmic trading platform. You will learn to validate your trading strategies by implementing an event-driven backtesting system and measure its performance. Finally, you will explore machine learning and deep learning techniques that are applied in finance.

By the end of this book, you will have learned how to apply Python to different paradigms in the financial industry and perform efficient data analysis.