Testing and Tuning Market Trading Systems: Algorithms in C++ - Tapa blanda

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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 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).

De la contraportada

"The algorithms in this book are essential tools for any serious trading system developer."

--David R. Aronson,  Hood River Research Inc.

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.  

You will:

• 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 system's 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 system's mean future performance
• Bound expected periodic returns to detect on-going system deterioration before it becomes severe
• Estimate the probability of catastrophic drawdown