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Timothy Masters - Testing and Tuning Market Trading Systems: Algorithms in C++

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Timothy Masters Testing and Tuning Market Trading Systems: Algorithms in C++
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Build, test, and tune financial, insurance or other market trading systems using C++ algorithms and statistics. Youve had an idea and have done some preliminary experiments, and it looks promising. Where do you go from here? Well, this book discusses and dissects this case study approach. Seemingly good backtest performance isnt enough to justify trading real money. You need to perform rigorous statistical tests of the systems validity. Then, if basic tests confirm the quality of your idea, you need to tune your system, not just for best performance, but also for robust behavior in the face of inevitable market changes. Next, you need to quantify its expected future behavior, assessing how bad its real-life performance might actually be, and whether you can live with that. Finally, you need to find its theoretical performance limits so you know if its actual trades conform to this theoretical expectation, enabling you to dump the system if it does not live up to expectations. This book does not contain any sure-fire, guaranteed-riches trading systems. Those are a dime a dozen... But if you have a trading system, this book will provide you with a set of tools that will help you evaluate the potential value of your system, tweak it to improve its profitability, and monitor its on-going performance to detect deterioration before it fails catastrophically. Any serious market trader would do well to employ the methods described in this book. What You Will Learn See how the spaghetti-on-the-wall approach to trading system development can be done legitimately Detect overfitting early in development Estimate the probability that your systems backtest results could have been due to just good luck Regularize a predictive model so it automatically selects an optimal subset of indicator candidates Rapidly find the global optimum for any type of parameterized trading system Assess the ruggedness of your trading system against market changes Enhance the stationarity and information content of your proprietary indicators Nest one layer of walkforward analysis inside another layer to account for selection bias in complex trading systems Compute a lower bound on your systems mean future performance Bound expected periodic returns to detect on-going system deterioration before it becomes severe Estimate the probability of catastrophic drawdown Who This Book Is For Experienced C++ programmers, developers, and software engineers. Prior experience with rigorous statistical procedures to evaluate and maximize the quality of systems is recommended as well.

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Timothy Masters Testing and Tuning Market Trading Systems Algorithms in C - photo 1
Timothy Masters
Testing and Tuning Market Trading Systems Algorithms in C++
Timothy Masters Ithaca NY USA Any source code or other supplementary - photo 2
Timothy Masters
Ithaca, NY, USA

Any source code or other supplementary material referenced by the author in this book is available to readers on GitHub via the books product page, located at www.apress.com/9781484241721 . For more detailed information, please visit www.apress.com/source-code .

ISBN 978-1-4842-4172-1 e-ISBN 978-1-4842-4173-8
https://doi.org/10.1007/978-1-4842-4173-8
Library of Congress Control Number: 2018961186
Timothy Masters 2018
This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed.
Trademarked names, logos, and images may appear in this book. Rather than use a trademark symbol with every occurrence of a trademarked name, logo, or image we use the names, logos, and images only in an editorial fashion and to the benefit of the trademark owner, with no intention of infringement of the trademark. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights.
While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein.
Distributed to the book trade worldwide by Springer Science+Business Media New York, 233 Spring Street, 6th Floor, New York, NY 10013. Phone 1-800-SPRINGER, fax (201) 348-4505, e-mail orders-ny@springer-sbm.com, or visit www.springeronline.com. Apress Media, LLC is a California LLC and the sole member (owner) is Springer Science + Business Media Finance Inc (SSBM Finance Inc). SSBM Finance Inc is a Delaware corporation.
Table of Contents
About the Author and About the Technical Reviewer
About the Author
Timothy Masters

received a PhD in mathematical statistics with a specialization in numerical computing. Since then he has continuously worked as an independent consultant for government and industry. His early research involved automated feature detection in high-altitude photographs while he developed applications for the prediction of floods and droughts, the detection of hidden missile silos, and the identification of threatening military vehicles. Later he worked with medical researchers in the development of computer algorithms for distinguishing between benign and malignant cells in needle biopsies. For the last 20 years he has focused primarily on methods for evaluating automated financial market trading systems. He has authored the following books on practical applications of predictive modeling: Deep Belief Nets in C++ and CUDA C: Volumes 13 (Apress, 2018); Assessing and Improving Prediction and Classification (Apress, 2018), Data Mining Algorithms in C++ (Apress, 2018); Neural, Novel, and Hybrid Algorithms for Time Series Prediction (Wiley, 1995); Advanced Algorithms for Neural Networks (Wiley, 1995); Signal and Image Processing with Neural Networks (Wiley, 1994); and Practical Neural Network Recipes in C++ (Academic Press, 1993).

About the Technical Reviewer
Jason Whitehorn
is an experienced entrepreneur and software developer and has helped many oil - photo 3

is an experienced entrepreneur and software developer and has helped many oil and gas companies automate and enhance their oilfield solutions through field data capture, SCADA, and machine learning. Jason obtained his BS in computer science from Arkansas State University, but he traces his passion for development back many years before then, having first taught himself to program BASIC on his familys computer while still in middle school.

When hes not mentoring and helping his team at work, writing, or pursuing one of his many side projects, Jason enjoys spending time with his wife and four children and living in the Tulsa, Oklahoma, region. You can learn more about Jason at https://jason.whitehorn.us .

Timothy Masters 2018
Timothy Masters Testing and Tuning Market Trading Systems https://doi.org/10.1007/978-1-4842-4173-8_1
1. Introduction
Timothy Masters
(1)
Ithaca, NY, USA

Before we delve into the meat (or tofu, if you prefer) of this book, we should be clear on what you will and will not find here, as well as what degree of preparation is expected of readers.

The Target Audience, and Overview of Contents

This book is intended for readers who have a modest statistics background (Statistics 101 is plenty), have some programming skill in any language (C++ with a strong bent toward traditional C is used in the examples here), and are interested in trading financial markets with a degree of mathematical rigor far beyond that of most traders. Here you will find a useful collection of algorithms , including sample code, that will help you tweak your ideas into trading systems that have above-average likelihood of profitability. But there are many things that you will not find in this book. We begin with an overview of the material included in this book.

Whats in This Book
The following topics are covered in this book:
  • If your system involves optimization of parameters, and most do, you will learn how to determine whether your optimized system has captured authentic market patterns or whether it has simply learned random noise patterns that will never again appear.

  • You will learn how to modify linear regression in a way that makes it even less susceptible to overfitting than it already is and that, as a bonus, separates predictors into those that are valuable and those that are worthless. You will also learn how to modify linear regression to enable its use in moderately nonlinear situations.

  • You will discover an extremely general and powerful nonlinear optimization algorithm that is applicable to both predictive-model-based trading systems and traditional algorithmic systems.

  • All trading systems assume a degree of consistency in the market being traded ; if the pattern on which your system is based has occurred regularly over recent history, we must assume that this same pattern will continue into at least the near future. Some trading systems are robust against moderate changes in market patterns, while other systems are rendered worthless by even tiny changes in market patterns. You will learn how to assess the degree to which your system is robust against such changes.

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