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In walk-forward testing, a system is optimized on several years of data and then traded the next year. The system is then reoptimized on several more years of data, moving the window forward to include the year just traded. The system is then traded for another year. This process is repeated again and again, walking forward through the data series. Although very computationally intensive, this is an excellent way to study and test a trading system. In a sense, even though optimization is occurring, all trades are taken on what is essentially out-of-sample test data. All the statistics discussed above, such as the t-tests, can be used on walkforward test results in a simple manner that does not require any corrections for optimization. In addition, the tests will very closely simulate the process that occurs during real trading--first optimization occurs, next the system is traded on data not used during the optimization, and then every so often the system is reoptimized to update it. Sophisticated developers can build the optimization process into the system, producing what might be called an adaptive trading model. Meyers (1997) wrote an article illustrating the process of walk-forward testing. asp.net pdf 417 ASP . NET PDF-417 Barcode Generator - Generate 2D PDF417 in ...
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