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Automated Trading with Genetic-Algorithm Neural-Network Risk Cybernetics: An Application on FX Markets
Finamatrix, Jan 2011, Pages: 28
Recent years have witnessed the advancement of automated algorithmic trading systems as institutional solutions in the form of autobots or black box. However, little research has been done in this area with sufficient evidence to show the efficiency of these systems. This paper builds an automated trading system which implements an optimized genetic-algorithm neural-network (GANN) model with cybernetic concepts and evaluates the success using a modified value-at-risk (MVaR) framework.
The cybernetic engine includes a circular causal feedback control feature and a developed golden-ratio estimator, which can be applied to any form of market data in the development of risk-pricing models. The paper applies the Euro and Yen currency rates as data inputs. It is shown that the technique is useful as a trading and volatility control system for institutions including central bank monetary policy as a risk-minimizing strategy. Furthermore, the results are achieved within a 30 second timeframe for a intra-week trading strategy, offering relative low latency performance.
The results show that risk exposures are reduced by four to five times with a maximum possible success rate of 96%, providing evidence of further research and development on this area.
Key words
- Automated trading
- Autobot
- Genetic-algorithm neural-network
- Risk cybernetics
- Risk-pricing
- Algorithmic trading
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