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.

In this book, the following software will be required:

This book is geared toward students and programmers developing financial applications, consultants offering financial services, financial analysts, and quants who would like to master finance by harnessing Python's strengths in data visualization, interactive analytics, and scientific computing. An intermediate level of Python knowledge and financial concepts is expected. Beginners will receive an introductory background before jumping into the technical process of each chapter.

In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.

Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "The price function of the BinomialEuropeanOption class is a public method that is the entry point for all the instances of this class."

A block of code is set as follows:

    def _traverse_tree_(self, payoffs):
        # Starting from the time the option expires, traverse
        # backwards and calculate discounted payoffs at each node
        for i in range(self.N):
            payoffs = (payoffs[:-1] * self.qu +
                       payoffs[1:] * self.qd) * self.df

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

    Set BinCRRTree = CreateObject("BinomialCRRCOMServer.Pricer")
    answer = BinCRRTree.pricer(S0, K, r, T, N, sigma, isCall, _
        dividend, True)

Any command-line input or output is written as follows:

>>> from FDCnEu import FDCnEu
>>> option = FDCnEu(50, 50, 0.1, 5./12., 0.4, 100, 100,
...                 100, False)
>>> print option.price()

New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "We can compile the code by selecting Debug from the toolbar menu and clicking on Compile VBAProject:"

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