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Sinopsis
You have an idea for a market trading system. You do some preliminary experiments, and it looks promising. Where do you go from here?
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 covers all of these topics, and much more. It does not contain any sure-fire, guaranteed-riches trading systems. Those are a dime a dozen and worth the price. But if you have a trading system, or the germ of an idea for one, this book will show you how to maximize its potential, tuning it to perfection, applying rigorous statistical tests of its validity, and estimating important aspects of expected future performance.
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Acerca del autor
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 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 nine books on practical applications of predictive modeling.
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