Adaptive Modeler
Encyclopedia
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. 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.
. 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.
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.
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.
Financial market
In economics, a financial market is a mechanism that allows people and entities to buy and sell financial securities , commodities , and other fungible items of value at low transaction costs and at prices that reflect supply and demand.Both general markets and...
simulation models for the purpose of forecasting prices of real world market traded stocks or other securities. The technology it uses is based on the theory of Agent-based computational economics
Agent-Based Computational Economics
Agent-based computational economics is the major aspect of computational economics that studies economic processes, including whole economies, as dynamic systems of interacting agents. As such, it falls in paradigm of complex adaptive systems...
(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.
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 programmingGenetic programming
In artificial intelligence, genetic programming is an evolutionary algorithm-based methodology inspired by biological evolution to find computer programs that perform a user-defined task. It is a specialization of genetic algorithms where each individual is a computer program...
. 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
Backtesting
Backtesting is the process of evaluating a strategy, theory, or model by applying it to historical data. Backtesting can be used in situations like studying how a trading method would have performed in past stock markets or how a model of climate and weather patterns would have matched past...
of trading rules, genetic algorithms and neural networks
Neural Networks
Neural Networks is the official journal of the three oldest societies dedicated to research in neural networks: International Neural Network Society, European Neural Network Society and Japanese Neural Network Society, published by Elsevier...
), Adaptive Modeler does not optimize or overfit
Overfitting
In statistics, overfitting occurs when a statistical model describes random error or noise instead of the underlying relationship. Overfitting generally occurs when a model is excessively complex, such as having too many parameters relative to the number of observations...
(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.
Examples and use cases
In an example model, Adaptive Modeler shows significant risk-adjusted excess returns after transaction costs over the S&P 500S&P 500
The S&P 500 is a free-float capitalization-weighted index published since 1957 of the prices of 500 large-cap common stocks actively traded in the United States. The stocks included in the S&P 500 are those of large publicly held companies that trade on either of the two largest American stock...
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.
As an example of virtual intelligent life in a complex system
Complex system
A complex system is a system composed of interconnected parts that as a whole exhibit one or more properties not obvious from the properties of the individual parts....
(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.