What this book covers
Chapter 1, Python for Financial Applications, explores the aspects of Python in judging its suitability as a programming language in finance. The IPython Notebook is introduced as a beneficial tool to visualize data and to perform scientific computing.
Chapter 2, The Importance of Linearity in Finance, uses Python to solve systems of linear equations, perform integer programming, and apply matrix algebra to linear optimization of portfolio allocation.
Chapter 3, Nonlinearity in Finance, discusses the nonlinear models in finance and root-finding methods using Python.
Chapter 4, Numerical Procedures, explores trees, lattices, and finite differencing schemes for valuation of options.
Chapter 5, Interest Rates and Derivatives, discusses the bootstrapping process of the yield curve and covers some short rate models for pricing the interest rate derivatives with Python.
Chapter 6, Interactive Financial Analytics with Python and VSTOXX, discusses the volatility indexes. We will perform analytics on EURO STOXX 50 Index and VSTOXX data, and replicate the main index using options prices of the sub-indexes.
Chapter 7, Big Data with Python, walks you through the uses of Hadoop for big data and covers how to use Python to perform MapReduce operations. Data storage with NoSQL will also be covered.
Chapter 8, Algorithmic Trading, discusses a step-by-step approach to develop a mean-reverting and trend-following live trading infrastructure using Python and the API of a broker. Value-at-risk (VaR) for risk management will also be covered.
Chapter 9, Backtesting, discusses how to design and implement an event-driven backtesting system and helps you visualize the performance of our simulated trading strategy.
Chapter 10, Excel with Python, discusses how to build a Component Object Model (COM) server and client interface to communicate with Excel and to perform numerical pricing on the call and put options on the fly.
