![]() This logic is applied both in the validation and testing period. Operates under the assumption that the stratégy trading is turned off when the percentage value of the maximum drawdown from the training period is reached. ![]() Training/Validation/Testing WHAT-IF snippet The drawdown in What If Out Of Sample is lower. At the bottom of the database, we can see how the statistics of each strategy have changed. The strategy in Out of sample will end the trade early. We can see that the strategy reaches the maximum drawdown in the out-of-sample period.Īfter using the WHAT-IF snippet, we can see what happens when the strategy exits the trade after reaching 50% of the maximum drawdown from the sample part of the data. In the figure above, we see a simple RSI strategy backtest divided into an in-sample period and an out-of-sample period. Let’s take a look at the use of these snippets in practice: More information about how to use and set training/validation/testing you can find here. In Strategy Quant X you can set the training/validation/testing in the Data section. The WHAT-IF snippet determines the maximum drawdown in the validation part of the data and stops trading in the testing part of the data after reaching the percentage drawdown measured in the validation data part.The maximum drawdown is measured in the training part of the data, and the strategy stops trading in the validation and testing part of the data after reaching the maximum training drawdown.In the hold-out method for model selection, the dataset is split into three different sets – training, validation, and test dataset. This way of analysis is based on the hold-out method. This WHAT-IF snippet works with training/validation test logic. ![]() Finally, the strategy turns off during the out-of-sample period when it reaches the percentage drawdown measured in the DD sample. For example, you can choose 50% of IS DD or 150% of IS DD. I’ve prepared two versions of the WHAT-IF AnalysisĮ WHAT-IF snippet, you can set the percentage threshold of the percentage drawdown in the sample. In today’s part, I have prepared a snippet that allows the strategy developer to measure the maximum drawdown in the in-sample period (the training period) and then terminate the strategy when it reaches the percentage value of this drawdown in the out-of-sample period. In the following episodes, we will also introduce snippets based on statistical quality control logic, and finally, we will conduct a study comparing whether these snippets statistically reduce the risk profile of strategies. This episode is the introduction to a series of articles in which I will introduce new WHAT-IF snippets to help us deal with this problem. Strategies generated by data mining can experience performance degradation quite quickly, and these snippets can help analyze such situations. Evaluating the trading performance of strategiesĪ common problem for a trader is to know when his strategy has lost the advantage, or in short, when there is such a situation that does not fit today’s strategies. ![]()
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