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Adaptive Modeler
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.〔(【引用サイトリンク】url=http://www.econ.iastate.edu/tesfatsi/acedemos.htm#ACE )〕 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.〔(【引用サイトリンク】url=http://readingthemarkets.blogspot.com/2009/10/agent-based-models.html )〕 Agent-based models based on heterogeneous and boundedly rational (learning) agents have shown to be able to explain the empirical features of financial markets better than traditional financial models that are based on representative rational agents.〔(Agent-based Financial Markets: Matching Stylized Facts with Style ). Blake LeBaron, in "Post Walrasian Macroeconomics: Beyond the DSGE Model", edited by D. Colander, Cambridge University Press, 2006: 221-235.〕
== Technology ==
The software creates an agent-based model for a particular stock, consisting of a population of trader agents and a virtual market. Each agent represents a virtual trader/investor and has its own trading rule and funds. The model is then evolved step by step in the following way: At every step a new (historical) real market price is imported. All agents evaluate their trading rule and place orders on the virtual market. The virtual market then determines the clearing price and executes all matching orders. The clearing price is taken as the forecast for the next step real market price. (So the virtual market serves as a one-step-ahead prediction market for the real market). This process is repeated for every new received real market price. Meanwhile the trading rules evolve through a special adaptive form of genetic programming. The forecasts are thus based on the behavior of the entire market instead of only on the best performing trading rule. This intends to increase the robustness of the model and its ability to adapt to changing market circumstances.〔(【引用サイトリンク】url=http://www.altreva.com/technology.html )〕
To avoid overfitting (or curve-fitting) to historical data -and unlike many other techniques used in trading software such as optimizing of trading rules by repeated backtesting, genetic algorithms and neural networks- Adaptive Modeler does not optimize trading rules on historical 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 trading rules have been optimized on historical data). The historical results can therefore be considered more meaningful than results demonstrated by techniques based on optimization.〔(【引用サイトリンク】url=http://www.altreva.com/advantages.html )〕
抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』
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