Mastering Python for Finance Second Edition: Implement advanced stateoftheart financial statistical applications using Pyth,Used

Take your financial skills to the next level by mastering cuttingedge mathematical and statistical financial applications Key Features Explore advanced financial models used by the industry and ways of solving them using Python Build stateoftheart infrastructure for modeling, visualization, trading, and more Empower your financial applications by applying machine learning and deep learning Book DescriptionThe second edition of Mastering Python for Finance will guide you through carrying out complex financial calculations practiced in the industry of finance by using nextgeneration 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 datadriven financial decisions using popular libraries such as TensorFlow, Keras, Numpy, SciPy, and sklearn. 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 eventdriven backtesting system and for working with highfrequency 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. What you will learn Solve linear and nonlinear models representing various financial problems Perform principal component analysis on the DOW index and its components Analyze, predict, and forecast stationary and nonstationary time series processes Create an eventdriven backtesting tool and measure your strategies Build a highfrequency algorithmic trading platform with Python Replicate the CBOT VIX index with SPX options for studying VIXbased strategies Perform regressionbased and classificationbased machine learning tasks for prediction Use TensorFlow and Keras in deep learning neural network architecture Who this book is forIf you are a financial or data analyst or a software developer in the financial industry who is interested in using advanced Python techniques for quantitative methods in finance, this is the book you need! You will also find this book useful if you want to extend the functionalities of your existing financial applications by using smart machine learning techniques. Prior experience in Python is required. Table of Contents Overview of Financial Analysis with Python The Importance of Linearity in Finance Nonlinearity in Finance Numerical Methods for Pricing Options Modeling Interest Rates and Derivates Statistical Analysis of Time Series Data Interactive Financial Analytics with VIX Building an Algorithmic Trading Platform Implementing a Backtesting System Machine Learning for Finance Deep Learning for Finance