Mastering Pandas for Finance : Master Pandas, an Open Source Python Data Analysis Library, for Financial Data Analysis

This book will teach you to use Python and the Python Data Analysis Library (pandas) to solve real-world financial problems. Starting with a focus on pandas data structures, you will learn to load and manipulate time-series financial data and then calculate common financial measures, leading into more advanced derivations using fixed- and moving-windows. This leads into correlating time-series data to both index and social data to build simple trading algorithms. From there, you will learn about more complex trading algorithms and implement them using open source back-testing tools. Then, you will examine the calculation of the value of options and Value at Risk. This then leads into the modeling of portfolios and calculation of optimal portfolios based upon risk. All concepts will be demonstrated continuously through progressive examples using interactive Python and IPython Notebook. By the end of the book, you will be familiar with applying pandas to many financial problems, giving you the knowledge needed to leverage pandas in the real world of finance.

About This Book

• Learn about distributed indexing and real-time optimization to change index data on fly
• Index data from various sources and web crawlers using built-in analyzers and tokenizers
• This step-by-step guide is packed with real-life examples on indexing data

Who This Book Is For

This book is for developers who want to increase their experience of indexing in Solr by learning about the various index handlers, analyzers, and methods available in Solr. Beginner level Solr development skills are expected.

What You Will Learn

• Get to know the basic features of Solr indexing and the analyzers/tokenizers available
• Index XML/JSON data in Solr using the HTTP Post tool and cURL command
• Work with Data Import Handler to index data from a database
• Use Apache Tika with Solr to index word documents, PDFs, and much more
• Utilize Apache Nutch and Solr integration to index crawled data from web pages
• Update indexes in real-time data feeds
• Discover techniques to index multilanguage and distributed data in Solr
• Combine the various indexing techniques into a real-life working example of an online shopping web application

In Detail

Apache Solr is a widely used, open source enterprise search server that delivers powerful indexing and searching features. These features help fetch relevant information from various sources and documentation.

This fast-paced guide starts by helping you set up Solr. You'll quickly move on to indexing text and boosting the indexing time. Next, you'll focus on basic techniques for indexing a structured data source through Data Import Handler.

Moving on, you will learn techniques to perform real-time indexing and atomic updates. We'll help you set up a cluster of Solr servers that combine fault tolerance and high availability. You will also gain insights into working scenarios of different aspects of Solr and how to use Solr with e-commerce data. By the end of the book, you will be competent and confident working with indexing and will have a good knowledge base to efficiently program elements.

Key FeaturesBook DescriptionWhat you will learnModeling and manipulating financial data using the pandas DataFrameIndexing, grouping, and calculating statistical results on financial informationTimeseries modeling, frequency conversion, and deriving results on fixed and moving windowsCalculating cumulative returns and performing correlations with index and social dataAlgorithmic trading and backtesting using momentum and mean reversion strategiesOption pricing and calculation of Value at RiskModeling and optimization of financial portfoliosWho this book is forIf you are interested in quantitative finance, financial modeling, and trading, or simply want to learn how Python and pandas can be applied to finance, then this book is ideal for you. Some knowledge of Python and pandas is assumed. Interest in financial concepts is helpful, but no prior knowledge is expected. Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Getting Started with pandas Using Wakari.io; What is Wakari?; Creating a Wakari cloud account; Updating existing packages; Installing new packages; Installing the samples in Wakari; Summary; Chapter 2: Introducing the Series and DataFrame; Notebook setup; The main pandas data structures - Series and DataFrame; The Series; The DataFrame; The basics of the Series and DataFrame objects; Creating a Series and accessing elements; Size, shape, uniqueness, and counts of values Obtaining historical stock and index dataFetching historical stock data from Yahoo!; Fetching index data from Yahoo!; Visualizing financial time-series data; Plotting closing prices; Plotting volume-series data; Combined price and volumes; Plotting candlesticks; Fundamental financial calculations; Calculating simple daily percentage change; Calculating simple daily cumulative returns; Analyzing the distribution of returns; Histograms; Q-Q plots; Box-and-whisker plots; Comparison of daily percentage change between stocks; Moving windows; Volatility calculation; Rolling correlation of returns Least-squares regression of returnsComparing stocks to the S & P 500; Summary; Chapter 6: Trading Using Google Trends; Notebook setup; A brief on Quantifying Trading Behavior in Financial Markets Using Google Trends; Data collection; The data from the paper; Gathering our own DJIA data from Quandl; Google Trends data; Generating order signals; Computing returns; Cumulative returns and the result of the strategy; Summary; Chapter 7: Algorithmic Trading; Notebook setup; The process of algorithmic trading; Momentum strategies; Mean-reversion strategies; Moving averages; Simple moving average Organizing the data for the examplesReorganizing and reshaping data; Concatenating multiple DataFrame objects; Merging DataFrame objects; Pivoting; Stacking and unstacking; Melting; Grouping and aggregating; Splitting; Aggregating; Summary; Chapter 4: Time-series; Notebook setup; Time-series data and the DatetimeIndex; Creating time-series with specific frequencies; Representing intervals of time using periods; Shifting and lagging time-series data; Frequency conversion of time-series data; Resampling of time-series; Summary; Chapter 5: Time-series Stock Data; Notebook setup Alignment via index labelsCreating a DataFrame; Example data; Selecting columns of a DataFrame; Selecting rows of a DataFrame using the index; Slicing using the operator; Selecting rows by the index label and location - loc and .iloc; Selecting rows by the index label and/or location - ix; Scalar lookup by label or location using .at and .iat; Selecting rows using the Boolean selection; Arithmetic on a DataFrame; Reindexing the Series and DataFrame objects; Summary; Chapter 3: Reshaping, Reorganizing, and Aggregating; Notebook setup; Loading historical stock data "Mastering pandas, an open source Python Data Analysis Library, for financial data analysis."