Altreva Adaptive Modeler is a software application for creating agent-based financial market simulation models for the purpose of forecasting prices of real world market traded stocks or other securities.[1] The technology it uses is based on the theory of Agent-based computational economics (ACE), the computational study of economic processes modeled as dynamic systems of interacting heterogeneous agents.
Altreva's Adaptive Modeler and other agent-based models are used to simulate financial markets to capture the complex dynamics of a large diversity of investors and traders with different strategies, different trading time frames, and different investment goals.[2]
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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.
In an example model,[3] Adaptive Modeler shows significant risk-adjusted excess returns after transaction costs over the S&P 500 index. On historical price data covering a period of 60 years (1950–2010) a compounded average annual return of over 22% has been achieved, which is an excess annual return of 15%.
Adaptive Modeler was used in a study to demonstrate increased complexity of trading rules in an evolutionary forecasting model during a critical period of a company's history.[4]
As an example of virtual intelligent life in a complex system (such as a stock market), Adaptive Modeler is said to be an illustration of simple agents interacting in a complex (nonlinear) way to forecast stock prices.[5]
Adaptive Modeler was created by Jim Witkam and was first released to the public in August 2005. Several updated versions have been released since then.