6. Trading Using Google Trends | Mastering Pandas for Finance

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Table Of Contents

Mastering Pandas for Finance

By : Michael Heydt

3.7 (6)

Mastering Pandas for Finance

3.7 (6)

By: Michael Heydt

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.

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

1. Getting Started with pandas Using Wakari.io

1. Getting Started with pandas Using Wakari.io

What is Wakari?

Creating a Wakari cloud account

Installing the samples in Wakari

Summary

2. Introducing the Series and DataFrame

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

3. Reshaping, Reorganizing, and Aggregating

3. Reshaping, Reorganizing, and Aggregating

Notebook setup

Loading historical stock data

Reorganizing and reshaping data

Grouping and aggregating

Summary

4. Time-series

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

5. Time-series Stock Data

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

6. Trading Using Google Trends

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

7. Algorithmic Trading

7. Algorithmic Trading

Notebook setup

The process of algorithmic trading

Moving averages

Technical analysis techniques

Algo trading with Zipline

Summary

8. Working with Options

8. Working with Options

Introducing options

Notebook setup

Calculating payoff on options

Profit and loss calculation

The pricing of options

Summary

9. Portfolios and Risk

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

Index

Index

Generating order signals

In the trading strategy that we will define, we want to be able to decide whether there is enough movement in the volume of searches on debt to go and execute a trade in the market that will make us a profit. The paper defines this threshold as though there is a higher search volume at the end of a Google Trends week than in the previous three-week average of the search volume, and then we will go short. If there is a decline, we will go long the following week.

The first thing we will need to do is reorganize our data by moving the GoogleWE dates into the index. We are going to make our decisions based upon these week-ending dates and use the Close price in an associated record as the basis for our trade as that price represents the Close price at the beginning of the next week. We also drop the DJIAClose column as it is redundant with Close:

In [18]:
   base = final.reset_index().set_index('GoogleWE')
   base.drop(['DJIAClose'], inplace=True, axis=1)
   base[:3...

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Programming languages

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