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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
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Dr. Param Jeet
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Toc

Table of Contents (10) Chapters Close

Preface 1. Introduction to R 2. Statistical Modeling FREE CHAPTER 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

Chapter 5. Algorithmic Trading

Algorithmic trading is defined as the buying and selling of financial instruments using predefined rules called algorithms. Traders use predictive modeling, time series modeling, and machine learning to predict the price, return, or direction of movement of the asset.

Algorithms are developed by quantitative traders or quantitative researchers and tested on historical data. Algorithms go through rigorous testing before they are used for live trading. Technical indicator-based trading can also come under algorithm trading if it is fully automated. However, sometimes quantitative traders also use fundamental data such as market capitalization, cash flow, debt to equity ratio, and so on to define rules for algorithms. People are free to use any technique to define rules for algorithms. Very recently, investment or trading firms have started to dive deep into machine learning methods to predict price, return, or direction movement.

I will be covering machine...

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