Expert Trading Systems – John R. Wolberg

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About this book

With the proliferation of computer programs to predict market direction, professional traders and sophisticated individual investors have increasingly turned to mathematical modeling to develop predictive systems. Kernel regression is a popular data modeling technique that can yield useful results fast.

Provides data modeling methodology used to develop trading systems.

Shows how to design, test, and measure the significance of results

John R. Wolberg (Haifa, Israel) is professor of mechanical engineering at the Haifa Institute in Israel. He does research and consulting in data modeling in the financial services area.

From the Inside Flap

Expert Trading Systems This book is an excellent introduction to advanced statistical modeling of financial markets. Wolberg’s explanation of kernel regression is lucid and direct. The author carefully leads readers through each stage of a trade system design and points out to them any potential difficulties they might encounter along the way. In addition, the examples give a concrete grasp of the subject without getting tangled up in any lengthy mathematical derivation.

-Peter F. Borish, President, Computer Trading Corporation The successful application of advanced modeling methods to the development of expert trading systems and financial market forecasting models requires both theoretical and practical knowledge. Wolberg was a pioneer in the development and application of kernel regression modeling to this area, and his book displays both deep theoretical understanding and practical knowledge in a highly readable how-to manner.

Moreover, Wolberg’s advanced kernel regression algorithm is orders of magnitude faster than existing methods, thus broadening its application tremendously. I highly recommend this book to any practitioner in this area. -David Aronson, President, Raden Research Group Inc. Kernel regression is a powerful statistical modeling technique that gives excellent performance in a wide variety of applications, including financial market prediction.

Its use has traditionally been limited by its potentially overwhelming computational requirements, but Wolberg provides an effective algorithm that speeds computation by orders of magnitude, making it universally available.

-Timothy Masters, author of Neural, Novel & Hybrid Algorithms for Time Series Prediction This book presents an excellent overview of nonlinear modeling techniques used to build predictive models for financial time series. It is suitable both as a text for a financial modeling course or for a financial analyst who wants to use kernel methods for modeling. Wolberg describes his innovative approach to speeding up kernel regression, which allows these methods to be applied to a more complex set of problems. His software can be used to develop, test, and generate technical trading systems with more flexibility than other software that is commonly available. -Sandor Straus, PhD, Merfin, LLC, former partner of Renaissance Technology Corp.

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