Mastering pandas for Finance

Master pandas, an open source Python Data Analysis Library, for financial data analysis
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Mastering pandas for Finance

Michael Heydt

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Master pandas, an open source Python Data Analysis Library, for financial data analysis

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Book Details

ISBN 139781783985104
Paperback298 pages

Book Description

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.

Table of Contents

Chapter 1: Getting Started with pandas Using Wakari.io
What is Wakari?
Creating a Wakari cloud account
Installing the samples in Wakari
Summary
Chapter 2: Introducing the Series and DataFrame
Notebook setup
The main pandas data structures – Series and DataFrame
The basics of the Series and DataFrame objects
Reindexing the Series and DataFrame objects
Summary
Chapter 3: Reshaping, Reorganizing, and Aggregating
Notebook setup
Loading historical stock data
Reorganizing and reshaping data
Grouping and 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
Obtaining historical stock and index data
Visualizing financial time-series data
Fundamental financial calculations
Moving windows
Volatility calculation
Comparing 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
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
Moving averages
Technical analysis techniques
Algo trading with Zipline
Summary
Chapter 8: Working with Options
Introducing options
Notebook setup
Calculating payoff on options
Profit and loss calculation
The pricing of options
Summary
Chapter 9: Portfolios and Risk
Notebook setup
An overview of modern portfolio theory
Modeling a portfolio with pandas
Constructing an efficient portfolio
Constructing an optimal portfolio
Visualizing the efficient frontier
Value at Risk
Summary

What You Will Learn

  • Modeling and manipulating financial data using the pandas DataFrame
  • Indexing, grouping, and calculating statistical results on financial information
  • Time-series modeling, frequency conversion, and deriving results on fixed and moving windows
  • Calculating cumulative returns and performing correlations with index and social data
  • Algorithmic trading and backtesting using momentum and mean reversion strategies
  • Option pricing and calculation of Value at Risk
  • Modeling and optimization of financial portfolios

Authors

Table of Contents

Chapter 1: Getting Started with pandas Using Wakari.io
What is Wakari?
Creating a Wakari cloud account
Installing the samples in Wakari
Summary
Chapter 2: Introducing the Series and DataFrame
Notebook setup
The main pandas data structures – Series and DataFrame
The basics of the Series and DataFrame objects
Reindexing the Series and DataFrame objects
Summary
Chapter 3: Reshaping, Reorganizing, and Aggregating
Notebook setup
Loading historical stock data
Reorganizing and reshaping data
Grouping and 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
Obtaining historical stock and index data
Visualizing financial time-series data
Fundamental financial calculations
Moving windows
Volatility calculation
Comparing 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
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
Moving averages
Technical analysis techniques
Algo trading with Zipline
Summary
Chapter 8: Working with Options
Introducing options
Notebook setup
Calculating payoff on options
Profit and loss calculation
The pricing of options
Summary
Chapter 9: Portfolios and Risk
Notebook setup
An overview of modern portfolio theory
Modeling a portfolio with pandas
Constructing an efficient portfolio
Constructing an optimal portfolio
Visualizing the efficient frontier
Value at Risk
Summary

Book Details

ISBN 139781783985104
Paperback298 pages
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From 1 reviews

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