{"product_id":"testing-and-tuning-market-trading-systems-algorithms-in-c-paperback","title":"Testing and Tuning Market Trading Systems: Algorithms in C++ - Paperback","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eTimothy Masters\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eBuild, 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. \u003c\/p\u003e 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.\u003cp\u003e\u003c\/p\u003eThis 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.\u003cp\u003e\u003cb\u003eWhat You Will Learn\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eSee how the 'spaghetti-on-the-wall' approach to trading system development can be done legitimately\u003c\/li\u003e\n\u003cli\u003eDetect overfitting early in development\u003c\/li\u003e\n\u003cli\u003eEstimate the probability that your system's backtest results could have been due to just good luck\u003c\/li\u003e\n\u003cli\u003eRegularize a predictive model so it automatically selects an optimal subset of indicator candidates\u003c\/li\u003e\n\u003cli\u003eRapidly find the global optimum for any type of parameterized trading system\u003c\/li\u003e\n\u003cli\u003eAssess the ruggedness of your trading system against market changes\u003c\/li\u003e\n\u003cli\u003eEnhance the stationarity and information content of your proprietary indicators\u003c\/li\u003e\n\u003cli\u003eNest one layer of walkforward analysis inside another layer to account for selection bias in complex trading systems\u003c\/li\u003e\n\u003cli\u003eCompute a lower bound on your system's mean future performance\u003c\/li\u003e\n\u003cli\u003eBound expected periodic returns to detect on-going system deterioration before it becomes severe\u003c\/li\u003e\n\u003cli\u003eEstimate the probability of catastrophic drawdown\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003c\/p\u003e \u003cp\u003e \u003cb\u003eWho This Book Is For\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eExperienced C++ programmers, developers, and software engineers. Prior experience with rigorous statistical procedures to evaluate and maximize the quality of systems is recommended as well. \u003c\/p\u003e\u003cbr\u003e\u003ch3\u003eBack Jacket\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003ci\u003e\"The algorithms in this book are essential tools for any serious trading system developer.\"\u003c\/i\u003e\u003c\/p\u003e\u003cp\u003e--David R. Aronson, Hood River Research Inc.\u003c\/p\u003e\u003cp\u003eBuild, 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. \u003cbr\u003e\u003c\/p\u003eSeemingly 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.\u003cp\u003e\u003c\/p\u003eThis 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.\u003cp\u003e\u003cb\u003eYou will: \u003c\/b\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eSee how the 'spaghetti-on-the-wall' approach to trading system development can be done legitimately\u003c\/li\u003e\n\u003cli\u003eDetect overfitting early in development\u003c\/li\u003e\n\u003cli\u003eEstimate the probability that your system's backtest results could have been due to just good luck\u003c\/li\u003e\n\u003cli\u003eRegularize a predictive model so it automatically selects an optimal subset of indicator candidates\u003c\/li\u003e\n\u003cli\u003eRapidly find the global optimum for any type of parameterized trading system\u003c\/li\u003e\n\u003cli\u003eAssess the ruggedness of your trading system against market changes\u003c\/li\u003e\n\u003cli\u003eEnhance the stationarity and information content of your proprietary indicators\u003c\/li\u003e\n\u003cli\u003eNest one layer of walkforward analysis inside another layer to account for selection bias in complex trading systems\u003c\/li\u003e\n\u003cli\u003eCompute a lower bound on your system's mean future performance\u003c\/li\u003e\n\u003cli\u003eBound expected periodic returns to detect on-going system deterioration before it becomes severe\u003c\/li\u003e\n\u003cli\u003eEstimate the probability of catastrophic drawdown\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003eTimothy 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 flood and drought prediction, detection of hidden missile silos, and 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 twenty years he has focused primarily on methods for evaluating automated financial market trading systems. He has authored five books on practical applications of predictive modeling: Practical Neural Network Recipes in C++ (Academic Press, 1993); Signal and Image Processing with Neural Networks (Wiley, 1994); Advanced Algorithms for Neural Networks (Wiley, 1995); Neural, Novel, and Hybrid Algorithms for Time Series Prediction (Wiley, 1995); Data Mining Algorithms in C++ (Apress, 2018); Assessing and Improving Prediction and Classification (Apress, 2018); Deep Belief Nets in C++ and CUDA C: Volume 1 (Apress, 2018); and Deep Belief Nets in C++ and CUDA C: Volume 2 (Apress, 2018).\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 321\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.69 x 10 x 7 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eIllustrated:\u003c\/strong\u003e Yes\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e October 27, 2018\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":44287722356838,"sku":"9781484241721","price":74.37,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0599\/7255\/0758\/files\/UkVIWCtEQWtzR2pEd0MzMFBmN0xhZz09.webp?v=1766668069","url":"https:\/\/infinitylightwa.com\/products\/testing-and-tuning-market-trading-systems-algorithms-in-c-paperback","provider":"Infinity Light","version":"1.0","type":"link"}