Abstract: This chapter focuses on performing proper in-sample and out-of-sample tests, which is perhaps the most critical step in the trading/investment system development process. If the system is profitable both in sample and out of sample, it is likely to receive capital to begin implementation and trading as soon as possible. If a system is profitable only in sample, it may be allocated additional resources for continued research and/or sent back to Stage 1. If, however, the system proves to be unprofitable both in sample as well as out of sample, management will likely scrap the project altogether mainly due to the nonscalability of the trading idea. In-sample testing is very time intensive, as the team manually checks the calculations and results. During the in-sample test, algorithms may calculate the averages and standard deviations for trades. This is the step where the team converts all the prototype examples into prototype production-level code. In-sample testing also exposes irregularities in the data, so that the development team can make necessary modifications. Out-of-sample testing is done to ensure everything is working properly, with no adjustments and with almost real-world data and samples. During the out-of-sample testing, no more modifications can be made. Trading algorithms and quantitative models are examined against both in-sample and out-of-sample data before progressing to the implementation stage, so it is of utmost importance to save some of the historical data for out-of-sample testing.
Publication Year: 2008
Publication Date: 2008-01-01
Language: en
Type: book-chapter
Indexed In: ['crossref']
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Cited By Count: 1
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