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Learning Quantitative Finance with R

You're reading from   Learning Quantitative Finance with R Implement machine learning, time-series analysis, algorithmic trading and more

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Product type Paperback
Published in Mar 2017
Publisher Packt
ISBN-13 9781786462411
Length 284 pages
Edition 1st Edition
Languages
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Authors (2):
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PRASHANT VATS PRASHANT VATS
Author Profile Icon PRASHANT VATS
PRASHANT VATS
Dr. Param Jeet Dr. Param Jeet
Author Profile Icon Dr. Param Jeet
Dr. Param Jeet
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Toc

Table of Contents (10) Chapters Close

Preface 1. Introduction to R FREE CHAPTER 2. Statistical Modeling 3. Econometric and Wavelet Analysis 4. Time Series Modeling 5. Algorithmic Trading 6. Trading Using Machine Learning 7. Risk Management 8. Optimization 9. Derivative Pricing

Momentum or directional trading


Momentum trading is trading when the instrument is trending up or down or, in other words, continuation in the trend as like historical winners are expected to be winners and historical losers are expected to lose. You bet on the direction of the instrument and you aim for buying at a low price and selling at a high price. I will not cover the pros and cons and what the different types of momentum trading strategies are. It is left to the trader to devise any idea. I will cover how to implement momentum trading rules and backtest using historical data in R. Stock return depends on various factors, and later in this chapter, I will show you how to use the multifactor model which explains stock return.

Let me start with simple technical indicators.

Technical indicators are implemented in the quantmod package so I will be using quantmod for this:

> library('quantmod') 
>getSymbols("^DJI",src="yahoo") 
[1] "DJI" 
> head(DJI) 

We have...

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