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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 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

Walk forward testing


Walk forward testing is used in quant finance for identifying the best parameters to be used in a trading strategy. The trading strategy is optimized on a subset of sample data for a specific time window. The rest of the unused data is kept separate for testing purposes. The testing is done on a small window of unused data with the recorded results. Now, the training window is shifted forward to include the testing window and the process is repeated again and again till the testing window is not available.

Walk forward optimization is a method used in finance for determining the best parameters to use in a trading strategy. The trading strategy is optimized with in-sample data for a time window in a data series. The remainder of the data is reserved for out-of-sample testing. A small portion of the reserved data following the in-sample data is tested with the results recorded. The in-sample time window is shifted forward by the period covered by the out-of-sample test...

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