Mastering pandas for Finance will teach you how to use Python and pandas to model and solve real-world financial problems using pandas, Python, and several open source tools that assist in various financial tasks, such as option pricing and algorithmic trading.
This book brings together various diverse concepts related to finance in an attempt to provide a unified reference to discover and learn several important concepts in finance and explains how to implement them using a core of Python and pandas that provides a unified experience across the different models and tools.
You will start by learning about the facilities provided by pandas to model financial information, specifically time-series data, and to use its built-in capabilities to manipulate time-series data, group and derive aggregate results, and calculate common financial measurements, such as percentage changes, correlation of time-series, various moving window operations, and key data visualizations for finance.
After establishing a strong foundation from which to use pandas to model financial time-series data, the book turns its attention to using pandas as a tool to model the data that is required as a base for performing other financial calculations. The book will cover diverse areas in which pandas can assist, including the correlations of Google trends with stock movements, creating algorithmic trading systems, and calculating options payoffs, prices, and behaviors. The book also shows how to model portfolios and their risk and to optimize them for specific risk/return tolerances.
Chapter 1, Getting Started with pandas Using Wakari.io, walks you through using Wakari.io, an online collaborative data analytics platform, that utilizes Python, IPython Notebook, and pandas. We will start with a brief overview of Wakari.io and step through how to upgrade the default Python environment and install all of the tools used throughout this text. At the end, you will have a fully functional financial analytics platform supporting all of the examples we will cover.
Chapter 2, Introducing the Series and DataFrame, teaches you about the core pandas data structures—the Series and the DataFrame. You will learn how a Series expands on the functionality of the NumPy array to provide much richer representation and manipulation of sequences of data through the use of high-performance indices. You will then learn about the pandas DataFrame and how to use it to model two-dimensional tabular data.
Chapter 3, Reshaping, Reorganizing, and Aggregating, focuses on how to use pandas to group data, enabling you to perform aggregate operations on grouped data to assist with deriving analytic results. You will learn to reorganize, group, and aggregate stock data and to use grouped data to calculate simple risk measurements.
Chapter 4, Time-series, explains how to use pandas to represent sequences of pricing data that are indexed by the progression of time. You will learn how pandas represents date and time as well as concepts such as periods, frequencies, time zones, and calendars. The focus then shifts to learning how to model time-series data with pandas and to perform various operations such as shifting, lagging, resampling, and moving window operations.
Chapter 5, Time-series Stock Data, leads you through retrieving and performing various financial calculations using historical stock quotes obtained from Yahoo! Finance. You will learn to retrieve quotes, perform various calculations, such as percentage changes, cumulative returns, moving averages, and volatility, and finish with demonstrations of several analysis techniques including return distribution, correlation, and least squares analysis.
Chapter 6, Trading Using Google Trends, demonstrates how to form correlations between index data and trends in searches on Google. You will learn how to gather index data from Quandl along with trend data from Google and then how to correlate this time-series data and use that information to generate trade signals, which will be used to calculate the effectiveness of the trading strategy as compared to the actual market performance.
Chapter 7, Algorithmic Trading, introduces you to the concepts of algorithmic trading through demonstrations of several trading strategies, including simple moving averages, exponentially weighted averages, crossovers, and pairs-trading. You will then learn to implement these strategies with pandas data structures and to use Zipline, an open source back-testing tool, to simulate trading behavior on historical data.
Chapter 8, Working with Options, teaches you to model and evaluate options. You will first learn briefly about options, how they function, and how to calculate their payoffs. You will then load options data from Yahoo! Finance into pandas data structures and examine various options attributes, such as implied volatility and volatility smiles and smirks. We then examine the pricing of options with Black-Scholes using Mibian and finish with an overview of Greeks and how to calculate them using Mibian.
Chapter 9, Portfolios and Risk, will teach you how to model portfolios of multiple stocks using pandas. You will learn about the concepts of Modern Portfolio Theory and how to apply those theories with pandas and Python to calculate the risk and returns of a portfolio, assign different weights to different instruments in a portfolio, derive the Sharpe ratio, calculate efficient frontiers and value at risk, and optimize portfolio instrument allocation.
This book assumes that you have some familiarity with programming concepts, but even those without programming, or specifically Python programming, experience, will be comfortable with the examples as they focus on pandas constructs more than Python or programming. The examples are based on Anaconda Python 2.7 and pandas 0.15.1. If you do not have either installed, guidance is provided in Chapter 1, Getting Started with pandas Using Wakari.io, on installing both on Windows, OS X, and Ubuntu systems. For those interested in not installing any software, instructions are also given on using the Wakari.io online Python data analysis service. Data is either provided with the text or is available for download via pandas from data services such as Yahoo! Finance. We will also use several open source software packages such as Zipline and Mibian, the retrieval, installation, and usage of which will be explained during the appropriate chapters.
If you are interested in quantitative finance, financial modeling, trading, or simply want to learn Python and pandas as applied to finance, then this book is for you. Some knowledge of Python and pandas is assumed, but the book will spend time explaining all of the necessary pandas concepts that are required within the context of application to finance. Interest in financial concepts is helpful, but no prior knowledge is expected.
In this book, you will find a number of styles of text that distinguish between different kinds of information. Here are some examples of these styles, and an explanation of their meaning.
Code words in text are shown as follows: "This information can be easily imported into DataFrame using the pd.read_csv() function as follows."
A block of code entered in a Python interpreter is set as follows:
import pandas as pd
df = pd.DataFrame.from_items([('column1', [1, 2, 3])])
print (df)Any command-line/IPython input or output is written as follows:
In [2]: # create a DataFrame with 5 rows and 3 columns df = pd.DataFrame(np.arange(0, 15).reshape(5, 3), index=['a', 'b', 'c', 'd', 'e'], columns=['c1', 'c2', 'c3']) df Out[2]: c1 c2 c3 a 0 1 2 b 3 4 5 c 6 7 8 d 9 10 11 e 12 13 14
New terms and important words are shown in bold. Words that you see on the screen, in menus or dialog boxes for example, appear in the text like this: "Once dropped, click on the Upload Files button and you will see the following files in your Wakari directory."
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