7. Algorithmic Trading | 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

Technical analysis techniques

We will now cover two categories of technical analysis techniques, which utilize moving averages in different ways to be able to determine trends in market movements and hence give us the information needed to make potentially profitable transactions. We will examine how this works in this section, and in the upcoming section on Zipline, we will see how to implement these strategies in pandas and Zipline.

Crossovers

A crossover is the most basic type of signal for trading. The simplest form of a crossover is when the price of an asset moves from one side of a moving average to the other. This crossover represents a change in momentum and can be used as a point of making the decision to enter or exit the market.

The following command exemplifies several crossovers in the Microsoft data:

In [8]:
   msft['2002-1':'2002-9'][['Adj Close', 
                            'MA30']].plot(figsize=(12,8));

As an example, the cross occurring on July 09, 2002, is a signal of...

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