Permutation and Randomization Tests for Trading System Development PDF Download

Author: Timothy Masters
Publisher:
ISBN:
Category :
Languages : en
Pages : 173

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Book Description
This book provides the trading system developer with a powerful set of statistical tools for measuring vital aspects of performance that are ignored by most developers. All algorithms include intuitive justification, basic theory, all relevant equations, and highly commented C++ code for complete programs that run in a Windows Command Console. Reprogramming them in other languages should be easy, given the detailed explanations of each algorithm. The following topics are covered: Testing for overfitting at the earliest possible stage Evaluating the luckiness-versus-skill of a fully developed system before deploying it Testing the effectiveness and reliability of a trading system factory Removing selection bias when screening a large number of indicators Probability bounds for future mean returns Bounding typical and catastrophic future drawdowns Is the best indicator or model in a competition truly the best, or just the luckiest? Which markets provide truly superior profits for your trading system? What holding time for your system provides the best risk/return performance?

Author: Timothy Masters
Publisher: Apress
ISBN: 1484241738
Category : Computers
Languages : en
Pages : 325

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Book Description
Build, test, and tune financial, insurance or other market trading systems using C++ algorithms and statistics. You’ve 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 isn't enough to justify trading real money. You need to perform rigorous statistical tests of the system's 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 legitimatelyDetect overfitting early in developmentEstimate the probability that your system's backtest results could have been due to just good luckRegularize a predictive model so it automatically selects an optimal subset of indicator candidatesRapidly find the global optimum for any type of parameterized trading systemAssess the ruggedness of your trading system against market changesEnhance the stationarity and information content of your proprietary indicatorsNest one layer of walkforward analysis inside another layer to account for selection bias in complex trading systemsCompute a lower bound on your system's mean future performanceBound expected periodic returns to detect on-going system deterioration before it becomes severeEstimate 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.

Author: Eugene S. Edgington
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 310

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Book Description
Random assignment; Calculating significance values; One-way analysis of variance and the independent t test; Repeated-measures analysis of variance and the correlated t test; Factorial designs; Multivariate designs; Correlation; Trend tests; One-subject randomization tests.

Author: David Aronson
Publisher: Createspace Independent Publishing Platform
ISBN: 9781489507716
Category : Algorithmus
Languages : en
Pages : 0

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Book 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.

Author: Murray A. Ruggiero
Publisher: John Wiley & Sons
ISBN: 9780471149200
Category : Business & Economics
Languages : en
Pages : 344

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Book Description
Ein Überblick über die aktuellsten Technologien zum Aufbau einer Handelsstrategie: neuronale Netzwerke, genetische Algorithmen, Expertensysteme, Fuzzy logic und statistische Mustererkennung. Gezeigt wird, wie diese neuen Methoden in klassische Analysenverfahren integriert werden können. Auch Erläuterungen zur Prüfung und Bewertung existierender Systeme kommen nicht zu kurz.

Author: Timothy Masters
Publisher: Apress
ISBN: 1484259882
Category : Computers
Languages : en
Pages : 233

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Book Description
Discover a variety of data-mining algorithms that are useful for selecting small sets of important features from among unwieldy masses of candidates, or extracting useful features from measured variables. As a serious data miner you will often be faced with thousands of candidate features for your prediction or classification application, with most of the features being of little or no value. You’ll know that many of these features may be useful only in combination with certain other features while being practically worthless alone or in combination with most others. Some features may have enormous predictive power, but only within a small, specialized area of the feature space. The problems that plague modern data miners are endless. This book helps you solve this problem by presenting modern feature selection techniques and the code to implement them. Some of these techniques are: Forward selection component analysis Local feature selection Linking features and a target with a hidden Markov modelImprovements on traditional stepwise selectionNominal-to-ordinal conversion All algorithms are intuitively justified and supported by the relevant equations and explanatory material. The author also presents and explains complete, highly commented source code. The example code is in C++ and CUDA C but Python or other code can be substituted; the algorithm is important, not the code that's used to write it. What You Will Learn Combine principal component analysis with forward and backward stepwise selection to identify a compact subset of a large collection of variables that captures the maximum possible variation within the entire set. Identify features that may have predictive power over only a small subset of the feature domain. Such features can be profitably used by modern predictive models but may be missed by other feature selection methods. Find an underlying hidden Markov model that controls the distributions of feature variables and the target simultaneously. The memory inherent in this method is especially valuable in high-noise applications such as prediction of financial markets.Improve traditional stepwise selection in three ways: examine a collection of 'best-so-far' feature sets; test candidate features for inclusion with cross validation to automatically and effectively limit model complexity; and at each step estimate the probability that our results so far could be just the product of random good luck. We also estimate the probability that the improvement obtained by adding a new variable could have been just good luck. Take a potentially valuable nominal variable (a category or class membership) that is unsuitable for input to a prediction model, and assign to each category a sensible numeric value that can be used as a model input. Who This Book Is For Intermediate to advanced data science programmers and analysts.

Author: Eugene Edgington
Publisher: CRC Press
ISBN: 9780824796693
Category : Mathematics
Languages : en
Pages : 444

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Book Description
The material in this work is organized in such as way as to illustrate how randomization tests are related to topics in parametric and traditional nonparametric statistics. The work extends the scope of applications by freeing tests from parametric assumptions without reducing data to ranks. This edition provides many new features, including more accessible terminology to clarify understanding, a current analysis of single-unit experiments as well as single-subject experiments, a discussion on how single-subject experiments relate to repeated-measures experiments and the use of randomized tests in single-patient research, and more.

Author: Victor Kaptelinin
Publisher: MIT Press
ISBN: 0262513315
Category : Computers
Languages : en
Pages : 347

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Book Description
A systematic presentation of activity theory, its application to interaction design, and an argument for the development of activity theory as a basis for understanding how people interact with technology. Activity theory holds that the human mind is the product of our interaction with people and artifacts in the context of everyday activity. Acting with Technology makes the case for activity theory as a basis for understanding our relationship with technology. Victor Kaptelinin and Bonnie Nardi describe activity theory's principles, history, relationship to other theoretical approaches, and application to the analysis and design of technologies. The book provides the first systematic entry-level introduction to the major principles of activity theory. It describes the accumulating body of work in interaction design informed by activity theory, drawing on work from an international community of scholars and designers. Kaptelinin and Nardi examine the notion of the object of activity, describe its use in an empirical study, and discuss key debates in the development of activity theory. Finally, they outline current and future issues in activity theory, providing a comparative analysis of the theory and its leading theoretical competitors within interaction design: distributed cognition, actor-network theory, and phenomenologically inspired approaches.

Author: Xin-She Yang
Publisher: John Wiley & Sons
ISBN: 1119490545
Category : Mathematics
Languages : en
Pages : 384

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Book Description
A guide to modern optimization applications and techniques in newly emerging areas spanning optimization, data science, machine intelligence, engineering, and computer sciences Optimization Techniques and Applications with Examples introduces the fundamentals of all the commonly used techniques in optimization that encompass the broadness and diversity of the methods (traditional and new) and algorithms. The author—a noted expert in the field—covers a wide range of topics including mathematical foundations, optimization formulation, optimality conditions, algorithmic complexity, linear programming, convex optimization, and integer programming. In addition, the book discusses artificial neural network, clustering and classifications, constraint-handling, queueing theory, support vector machine and multi-objective optimization, evolutionary computation, nature-inspired algorithms and many other topics. Designed as a practical resource, all topics are explained in detail with step-by-step examples to show how each method works. The book’s exercises test the acquired knowledge that can be potentially applied to real problem solving. By taking an informal approach to the subject, the author helps readers to rapidly acquire the basic knowledge in optimization, operational research, and applied data mining. This important resource: Offers an accessible and state-of-the-art introduction to the main optimization techniques Contains both traditional optimization techniques and the most current algorithms and swarm intelligence-based techniques Presents a balance of theory, algorithms, and implementation Includes more than 100 worked examples with step-by-step explanations Written for upper undergraduates and graduates in a standard course on optimization, operations research and data mining, Optimization Techniques and Applications with Examples is a highly accessible guide to understanding the fundamentals of all the commonly used techniques in optimization.

Author: Nicola Santoro
Publisher: John Wiley & Sons
ISBN: 0470072636
Category : Computers
Languages : en
Pages : 730

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Book Description
This text is based on a simple and fully reactive computational model that allows for intuitive comprehension and logical designs. The principles and techniques presented can be applied to any distributed computing environment (e.g., distributed systems, communication networks, data networks, grid networks, internet, etc.). The text provides a wealth of unique material for learning how to design algorithms and protocols perform tasks efficiently in a distributed computing environment.