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  • Book Overview & Buying Mastering Pandas for Finance
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Mastering Pandas for Finance

Mastering Pandas for Finance

By : Michael Heydt
3.7 (6)
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Mastering Pandas for Finance

Mastering Pandas for Finance

3.7 (6)
By: Michael Heydt
Buy this Book

Overview of this book

If 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.
Table of Contents (11 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Getting Started with pandas Using Wakari.io
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1. Getting Started with pandas Using Wakari.io
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What is Wakari?
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Creating a Wakari cloud account
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Installing the samples in Wakari
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Summary
2
2. Introducing the Series and DataFrame
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2. Introducing the Series and DataFrame
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Notebook setup
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The main pandas data structures – Series and DataFrame
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The basics of the Series and DataFrame objects
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Reindexing the Series and DataFrame objects
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Summary
3
3. Reshaping, Reorganizing, and Aggregating
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3. Reshaping, Reorganizing, and Aggregating
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Notebook setup
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Loading historical stock data
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Reorganizing and reshaping data
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Grouping and aggregating
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Summary
4
4. Time-series
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4. Time-series
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Notebook setup
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Time-series data and the DatetimeIndex
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Creating time-series with specific frequencies
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Representing intervals of time using periods
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Shifting and lagging time-series data
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Frequency conversion of time-series data
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Resampling of time-series
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Summary
5
5. Time-series Stock Data
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5. Time-series Stock Data
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Notebook setup
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Obtaining historical stock and index data
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Visualizing financial time-series data
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Fundamental financial calculations
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Moving windows
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Volatility calculation
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Comparing stocks to the S&P 500
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Summary
6
6. Trading Using Google Trends
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6. Trading Using Google Trends
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Notebook setup
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A brief on Quantifying Trading Behavior in Financial Markets Using Google Trends
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Data collection
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Generating order signals
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Computing returns
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Cumulative returns and the result of the strategy
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Summary
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7. Algorithmic Trading
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7. Algorithmic Trading
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Notebook setup
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The process of algorithmic trading
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Moving averages
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Technical analysis techniques
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Algo trading with Zipline
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Summary
8
8. Working with Options
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8. Working with Options
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Introducing options
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Notebook setup
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Calculating payoff on options
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Profit and loss calculation
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The pricing of options
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Summary
9
9. Portfolios and Risk
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9. Portfolios and Risk
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Notebook setup
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An overview of modern portfolio theory
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Modeling a portfolio with pandas
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Constructing an efficient portfolio
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Constructing an optimal portfolio
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Visualizing the efficient frontier
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Value at Risk
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Summary
10
Index
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Index

Chapter 7. Algorithmic Trading

In this chapter, we will examine how to use pandas and a library known as Zipline to develop automated trading algorithms. Zipline (http://www.zipline.io/) is a Python-based algorithmic trading library. It provides event-driven approximations of live-trading systems. It is currently used in production as the trading engine that powers Quantopian (https://www.quantopian.com/), a free, community-centered platform for collaborating on the development of trading algorithms with a web browser.

We previously simulated trading based on a historical review of social and stock data, but these examples were naive in that they glossed over many facets of real trading, such as transaction fees, commissions, and slippage, among many others. Zipline provides robust capabilities to include these factors in the trading model.

Zipline also provides a facility referred to as backtesting. Backtesting is the ability to run an algorithm on historical data to determine...

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Programming languages
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Mastering Pandas for Finance
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