Python is widely practiced in various sectors of finance, such as banking, investment management, insurance, and even real estate, for building tools that help in financial modeling, risk management, and trading. Even big financial corporations embrace Python to build their infrastructure for position management, pricing, risk management, and trading systems.
Throughout this book, theories from academic financial studies will be introduced, accompanied by their mathematical concepts to help you understand their uses in practical situations. You will see how Python is applied to classical pricing models, linearity, and nonlinearity of finance, numerical procedures, and interest rate models, that form the foundations of complex financial models. You will learn about the root-finding methods and finite difference pricing for developing an implied volatility curve with options.
With the advent of advanced computing technologies, methods for the storing and handling of massive amounts of data have to be considered. Hadoop is a popular tool in big data. You will be introduced to the inner workings of Hadoop and its integration with Python to derive analytical insights on financial data. You will also understand how Python supports the use of NoSQL for storing non-structured data.
Many brokerage firms are beginning to offer APIs to customers to trade using their own customized trading software. Using Python, you will learn how to connect to a broker API, retrieve market data, generate trading signals, and send orders to the exchange. The implementation of the mean-reverting and trend-following trading strategies will be covered. Risk management, position tracking, and backtesting techniques will be discussed to help you manage the performance of your trading strategies.
The use of Microsoft Excel is pervasive in the financial industry, from bond trading to back-office operations. You will be taught how to create numerical pricing Component Object Model (COM) servers in Python that will enable your spreadsheets to compute and update model values on the fly.
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.