Adaptive Modeler - Technology

Technology

The software creates an agent-based model that consist of a population of agents representing traders or investors that trade on a virtual market. The agents use real market data as input to their technical trading rules that evolve through an adaptive form of genetic programming. The forecasts are based on the behavior of the entire virtual market instead of only the best performing agent. This aims to increase the robustness of the model and its ability to adapt to changing market behavior.

Contrary to many other techniques used in technical trading software (such as repeated optimizing and back-testing of trading rules, genetic algorithms and neural networks), Adaptive Modeler does not optimize or overfit (curve-fit) trading rules to historical training data. Instead, its models evolve incrementally over the available price data so that agents experience every price change only once (as in the real world). Also there is no difference in the processing of historical and new price data. Therefore there is no specific reason to expect that a model's back-tested historical performance is better than its future performance (unlike when optimization or overfitting is used). The historical results can therefore be considered more meaningful than results demonstrated on historical data by techniques based on optimization or overfitting.

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