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Learning Quantitative Finance with R

You're reading from  Learning Quantitative Finance with R

Product type Book
Published in Mar 2017
Publisher Packt
ISBN-13 9781786462411
Pages 284 pages
Edition 1st Edition
Languages
Authors (2):
Dr. Param Jeet Dr. Param Jeet
Profile icon Dr. Param Jeet
PRASHANT VATS PRASHANT VATS
Profile icon PRASHANT VATS
View More author details

Table of Contents (16) Chapters

Learning Quantitative Finance with R
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
1. Introduction to R 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

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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