Statistically Sound Machine Learning for Algorithmic Trading of Financial Instruments: Developing Predictive-Model-Based Trading Systems Using TSSB

ISBN-13: 9781489507716
ISBN-10: 148950771X
Author: David Aronson, Timothy Masters
Publication date: 2013
Publisher: CreateSpace Independent Publishing Platform
Format: Paperback 520 pages
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Book details

ISBN-13: 9781489507716
ISBN-10: 148950771X
Author: David Aronson, Timothy Masters
Publication date: 2013
Publisher: CreateSpace Independent Publishing Platform
Format: Paperback 520 pages
Category: Accounting , Business , Economics , Macroeconomics , Statistics , Computers , Finance

Summary

Acknowledged authors David Aronson, Timothy Masters wrote Statistically Sound Machine Learning for Algorithmic Trading of Financial Instruments: Developing Predictive-Model-Based Trading Systems Using TSSB comprising 520 pages back in 2013. Textbook and eTextbook are published under ISBN 148950771X and 9781489507716. Since then Statistically Sound Machine Learning for Algorithmic Trading of Financial Instruments: Developing Predictive-Model-Based Trading Systems Using TSSB textbook was available to sell back to BooksRun online for the top buyback price of $ 11.59 or rent at the marketplace.

Description

This book serves two purposes. First, it teaches the importance of using sophisticated yet accessible statistical methods to evaluate a trading system before it is put to real-world use. In order to accommodate readers having limited mathematical background, these techniques are illustrated with step-by-step examples using actual market data, and all examples are explained in plain language. Second, this book shows how the free program TSSB (Trading System Synthesis & Boosting) can be used to develop and test trading systems. The machine learning and statistical algorithms available in TSSB go far beyond those available in other off-the-shelf development software. Intelligent use of these state-of-the-art techniques greatly improves the likelihood of obtaining a trading system whose impressive backtest results continue when the system is put to use in a trading account. Among other things, this book will teach the reader how to: Estimate future performance with rigorous algorithms Evaluate the influence of good luck in backtests Detect overfitting before deploying your system Estimate performance bias due to model fitting and selection of seemingly superior systems Use state-of-the-art ensembles of models to form consensus trade decisions Build optimal portfolios of trading systems and rigorously test their expected performance Search thousands of markets to find subsets that are especially predictable Create trading systems that specialize in specific market regimes such as trending/flat or high/low volatility More information on the TSSB program can be found at TSSBsoftware dot com.

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