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Home Product Download News Faq Forums About Altreva™ introduces Adaptive Modeler™Adaptive Modeler™ is a tool for creating agent-based market simulation models for price forecasting of real world market-traded securities such as stocks, ETFs or forex currencies.
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 studied using analytical mathematics based on a generalization of market participants and other simplifications and idealizations. However, the behavior of financial markets as observed in reality can not be fully described by such mathematical models. In reality, market prices are established by a large diversity of investors with different decision making methods and different investment goals (such as risk preference and time horizon). The complex dynamics of these heterogeneous investors and the resulting price formation process require a simulation model of multiple heterogeneous agents and a virtual market.
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 populated with thousands of agents each with their own technical trading rule (initially created randomly). Adaptive Modeler then evolves this model step-by-step while feeding it with historical prices of the security. After every received price, the agents evaluate their trading rule and place buy or sell orders on the virtual market where an order matching and price formation process takes place. Agent and their trading rules evolve through a special adaptive form of genetic programming. Agents with poor performance are being replaced by new agents whose trading rules have been created through crossover of trading rules of agents with good performance. Self-organization through the evolution of agents and the resulting price dynamics drives the model to learn to recognize and anticipate recurring price patterns while adapting to changing market behavior. Model evolution never ends. When all historical prices have been processed, the model waits for new price data to become available and then evolves further. The model thus evolves in parallel with the real world market. After every processed price bar the model generates a bar-ahead price forecast based on the behavior of the virtual market. Trading signals are generated based on the forecasts and the user’s trading preferences. (more product information)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 and back-testing 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 and back-testing 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. In financial markets no single trading rule continues to beat the market for any long period of time. Financial markets are constantly changing and new trading strategies come and go, affecting price behavior and each other’s returns. As the market evolves, trading strategies need to evolve as well in order to stay profitable. Instead of optimizing one or a few trading rules by back-testing them over and over 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 also said to be unoptimized and walk-forward. 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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