茨城大学
Automated Trading System Using the Nonlinear Portfolio Model Implemented by Matlab and MetaTrader
Algorithmic trading systems have become popular recently, and some specialistic softwares like MetaTrader have been released. However, they have some inconveniences especially for personal traders. In order to solve inconveniences, we developed a more user-friendly system by connecting MetaTrader with Matlab, which helps us to program complex trading algorithms more easily. It is said that there are only about 20% of the investors who benefits from the forex and stock exchange market. This could be explained by the prospect theory in behavioral economics, which says human emotions work so as to expand losses by the lose aversion bias. Namely, objective judgment is required for investments. In this regard, algorithmic trading systems have become popular recently. For example, MetaTrader[1] is very famous among personal traders because it is free to obtain historical price data and trade through a broker automatically. However, there are some week points. Although MetaTrader adopts its own MQL language, MQL is not common for personal traders. Moreover, the way of using this system and programing is often changed by releasing a new version. So we use MetaTrader only for getting historical price data and sending orders for trading. Besides, these orders are decided by a trading algorithm on Matlab, which is a high-level technical computing language and an interactive environment for algorithm development. In the present system, we apply the nonlinear portfolio model[2] as a trading algorithm. Namely, by using Matlab with MetaTrader, our trading system can help us to develop complex trading algorithms and modify them freely without any technical limitations of a specialized software like MetaTrader, and then it can help us to perform automated trading by MetaTrader.