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Home Product Download News Faq Forums Support Store About Altreva ™ introduces Adaptive Modeler ™Adaptive Modeler is a tool for creating agent-based market simulation models for price forecasting of real world securities such as stocks, ETFs or forex currencies. Its models evolve and adapt incrementally over time without optimizing or overfitting on historical data. This results in better adaptability to changing market conditions and more consistent and reliable performance.
What are agent-based models?
An agent-based
model is a dynamic system of interacting autonomous entities. More
specifically, an agent-based model of a financial market consists of a
population of agents (representing investors with their own assets and
trading strategy) and a price discovery mechanism (representing a market).
(Learn
more about agent-based models in finance) Why use an agent-based model for price forecasting?
Agent-based models
have shown to be able to simulate complex systems such as stock markets
better than traditional mathematical finance. Conventionally, financial
markets have been
Research has shown
that complex behavior as seen in actual markets can emerge from simulations of agents with relatively simple decision rules.
Furthermore, commonly observed “stylized facts” of financial time series
(such as fat tails in return distributions and volatility clustering)
that have confronted the Efficient Market Hypothesis, have been
reproduced in agent-based market models. How does Adaptive Modeler use agent-based models for price forecasting?
Adaptive Modeler
creates an agent-based market model for a given real world security. The
model is popula
Self-organization through
the evolution of agents and the resulting price dynamics drives the
model to learn to recognize and anticipate recurring price patter How does Adaptive Modeler differ from other Trading Software?Most conventional trading software based on technical trading rules supports the user in finding or creating a (mostly static) rule-based trading strategy by optimizing or training on historical data. If one searches long enough, this approach will always produce a trading strategy that seems highly profitable on historical data. This however doesn't mean that this strategy will also perform well in the future when price behavior may be different. The apparent past success of the strategy has in fact merely been caused by repeatedly optimizing or training on the same historical data. This tends to lead to overfitting (or curve fitting) and is likely to produce trading rules that fail when exposed to new price data. More advanced software may provide adaptive trading rules that automatically adapt to price developments using neural networks, genetic algorithms or other techniques. However, one adaptive trading rule will still not be able to capture the complex price behavior of a financial market caused by the interaction of various heterogeneous investors, and this approach still carries the risk of overfitting.
Instead of optimizing one or a few trading rules by back-testing them over and over again on the same historical data, Adaptive Modeler lets a multitude of trading strategies compete and evolve on a virtual market in real time. This means that every historical price is only used once for "testing" the trading rules (as in the real world). This process is sometimes also called unoptimized, walk-forward or out-of-sample tested. The overall behavior of the virtual market is the basis for trading signals.
Though technical
trading rules still form the basic building blocks, Adaptive Modeler
automates the process of creating new trading rules to adapt to market
changes and also diversifies the risk of a single trading rule by using
many different trading rules simultaneously to generate trading signals. Advantages
Key Features (extensive feature list)
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