Browse Library
Sign In Start Free Trial
Close icon Search icon
Account
Browse Library Sign In Start Free Trial

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying Mastering Pandas for Finance
  • Table Of Contents Toc
Mastering Pandas for Finance

Mastering Pandas for Finance

By : Michael Heydt
3.7 (6)
Buy this Book
close
close
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)
close
close
close
Preface
Icon
Preface
Icon
What this book covers
Icon
What you need for this book
Icon
Who this book is for
Icon
Conventions
Icon
Reader feedback
Icon
Customer support
Lock Free Chapter
1
1. Getting Started with pandas Using Wakari.io
Icon
1. Getting Started with pandas Using Wakari.io
Icon
What is Wakari?
Icon
Creating a Wakari cloud account
Icon
Installing the samples in Wakari
Icon
Summary
2
2. Introducing the Series and DataFrame
Icon
2. Introducing the Series and DataFrame
Icon
Notebook setup
Icon
The main pandas data structures – Series and DataFrame
Icon
The basics of the Series and DataFrame objects
Icon
Reindexing the Series and DataFrame objects
Icon
Summary
3
3. Reshaping, Reorganizing, and Aggregating
Icon
3. Reshaping, Reorganizing, and Aggregating
Icon
Notebook setup
Icon
Loading historical stock data
Icon
Reorganizing and reshaping data
Icon
Grouping and aggregating
Icon
Summary
4
4. Time-series
Icon
4. Time-series
Icon
Notebook setup
Icon
Time-series data and the DatetimeIndex
Icon
Creating time-series with specific frequencies
Icon
Representing intervals of time using periods
Icon
Shifting and lagging time-series data
Icon
Frequency conversion of time-series data
Icon
Resampling of time-series
Icon
Summary
5
5. Time-series Stock Data
Icon
5. Time-series Stock Data
Icon
Notebook setup
Icon
Obtaining historical stock and index data
Icon
Visualizing financial time-series data
Icon
Fundamental financial calculations
Icon
Moving windows
Icon
Volatility calculation
Icon
Comparing stocks to the S&P 500
Icon
Summary
6
6. Trading Using Google Trends
chevron up
Icon
6. Trading Using Google Trends
Icon
Notebook setup
Icon
A brief on Quantifying Trading Behavior in Financial Markets Using Google Trends
Icon
Data collection
Icon
Generating order signals
Icon
Computing returns
Icon
Cumulative returns and the result of the strategy
Icon
Summary
7
7. Algorithmic Trading
Icon
7. Algorithmic Trading
Icon
Notebook setup
Icon
The process of algorithmic trading
Icon
Moving averages
Icon
Technical analysis techniques
Icon
Algo trading with Zipline
Icon
Summary
8
8. Working with Options
Icon
8. Working with Options
Icon
Introducing options
Icon
Notebook setup
Icon
Calculating payoff on options
Icon
Profit and loss calculation
Icon
The pricing of options
Icon
Summary
9
9. Portfolios and Risk
Icon
9. Portfolios and Risk
Icon
Notebook setup
Icon
An overview of modern portfolio theory
Icon
Modeling a portfolio with pandas
Icon
Constructing an efficient portfolio
Icon
Constructing an optimal portfolio
Icon
Visualizing the efficient frontier
Icon
Value at Risk
Icon
Summary
10
Index
Icon
Index

Computing returns

Every week, we will reinvest the entirety of our portfolio. Because of this, the return on the investment over the week will be reflected simply by the percentage change in the DJIA between the close of the first Monday and the close of the following Monday, but with the factor taken into account on whether we went short or long.

We have already accounted for going short or long using -1 or 1 for the signal, respectively. Now, we just need to calculate the percentage change, shift it by one week back in time, and multiply it by the signal value. We shift the percentage change back one week as we want to multiply the signal value for the current week by the next percentage change from the next week:

In [21]:
   base['PctChg'] = base.Close.pct_change().shift(-1)
   base[['Close', 'PctChg', 'signal0', 'signal1']][:5]

Out[21]:
                 Close   PctChg  signal0  signal1
   GoogleWE                                      
   2004-01-10  10485.2  0.00415        0        0...
CONTINUE READING
83
Tech Concepts
36
Programming languages
73
Tech Tools
Icon Unlimited access to the largest independent learning library in tech of over 8,000 expert-authored tech books and videos.
Icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Icon 50+ new titles added per month and exclusive early access to books as they are being written.
Mastering Pandas for Finance
End of Section 6
Next Section
notes
bookmark Notes and Bookmarks search Search in title playlist Add to playlist download Download options
font-size Font size

Change the font size

margin-width Margin width

Change margin width

day-mode Day/Sepia/Night Modes

Change background colour

Close icon Search
Country selected
Close icon Your notes and bookmarks

Confirmation

Modal Close icon
claim successful
Order Page Read Now

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY

Submit Your Feedback

Modal Close icon
Modal Close icon
Modal Close icon