▶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 non-stationary time series processes
- Create an event-driven backtesting tool and measure your strategies
- Build a high-frequency algorithmic trading platform with Python
- Replicate the CBOT VIX index with SPX options for studying VIX-based strategies
- Perform regression-based and classification-based machine learning tasks for prediction
- Use TensorFlow and Keras in deep learning neural network architecture
▶Key Features
- Explore advanced financial models used by the industry and ways of solving them using Python
- Build state-of-the-art infrastructure for modeling, visualization, trading, and more
- Empower your financial applications by applying machine learning and deep learning
▶Who This Book Is For
If 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.
▶What this book covers
- Chapter 1, Overview of Financial Analysis with Python, goes briefly through setting up a Python environment, including a Jupyter Notebook, so that you can proceed with the rest of the chapters in this book. Within Jupyter, we will perform some time series analysis with pandas, using plots for analysis.
- Chapter 2, The Importance of Linearity in Finance, uses Python to solve systems of linear equations, perform integer programming, and apply matrix algebra to the linear optimization of portfolio allocation.
- Chapter 3, Nonlinearity in Finance, explores some methods that will help us extract information from nonlinear models. You will learn root-finding methods in nonlinear volatility modeling. The optimize module of SciPy contains the root and fsolve functions, which can also help us to perform root finding on non-linear models.
- Chapter 4, Numerical Methods for Pricing Options, explores trees, lattices, and finite differencing schemes for the valuation of options.
- Chapter 5, Modeling Interest Rates and Derivatives, discusses the bootstrapping process of the yield curve and covers some short-rate models for pricing interest rate derivatives with Python.
- Chapter 6, Statistical Analysis of Time Series Data, introduces principal component analysis for identifying principal components. The Dicker-Fuller test is used for testing whether a time series is stationary.
- Chapter 7, Interactive Financial Analytics with VIX, discusses volatility indexes. We will perform analytics on a US stock index and VIX data, and replicate the main index using the options prices of the sub-indexes.
- Chapter 8, Building an Algorithmic Trading Platform, takes a step-by-step approach to developing a mean-reverting and trend-following live trading infrastructure using a broker API.
- Chapter 9, Implementing a Backtesting System, discusses how to design and implement an event-driven backtesting system and helps you to visualize the performance of our simulated trading strategy.
- Chapter 10, Machine Learning for Finance, introduces us to machine learning, allowing us to study its concepts and applications in finance. We will also look at some practical examples for applying machine learning to assist in trading decisions.
- Chapter 11, Deep Learning for Finance, encourages us to take a hands-on approach to learning TensorFlow and Keras by building deep learning prediction models using neural networks.
