A backtest is a simulation of a model-driven investment strategy's response to historical data. The purpose of performing experiments with backtests is to make discoveries about a process or system. By using historical data, you can save time in testing an investment strategy for the period forward. It helps you test an investment theory based on the movements of the tested period. It is also used to both evaluate and calibrate an investment model. Creating a model is only the first step. The investment strategy will typically employ the model to help you drive simulated trading decisions and compute various factors related to either risk or return. These factors are typically used together to find a combination that is predictive of return.
Mastering Python for Finance - Second Edition
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Mastering Python for Finance - Second Edition
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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.
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Current Title:
Mastering Python for Finance - Second Edition
Table of Contents (16 chapters)
Preface
Overview of Financial Analysis with Python
Section 2: Financial Concepts
The Importance of Linearity in Finance
Nonlinearity in Finance
Numerical Methods for Pricing Options
Modeling Interest Rates and Derivatives
Statistical Analysis of Time Series Data
Section 3: A Hands-On Approach
Interactive Financial Analytics with the VIX
Building an Algorithmic Trading Platform
Implementing a Backtesting System
Machine Learning for Finance
Deep Learning for Finance
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