The hardware structure of a processing element used for optimization of an investment strategy for financial markets is presented. It is shown how this processing element can be multiply implemented on the massively parallel FPGA-machine RIVYERA. This leads to a speedup of a factor of about 17,000 in comparison to one single high-performance PC, while saving more than 99% of the consumed energy. Furthermore, it is shown for a special security and different time periods that the optimized investment strategy delivers an outperformance between 2 and 14 percent in relation to a buy and hold strategy. 1. Introduction The goal of technical financial market analysis is to predict the development of indices, stocks, funds, and other securities by evaluating the charts of the past. A method to find such predictions can lead to an investment strategy. Many well-known chart-analysis methods (e.g., Elliot waves [1], Bollinger Bands [2]) try to extract patterns from the charts, expecting that such patterns will come up in similar ways again in the future. There are more than 100 different chart-analysis methods but their success is doubted [3]. In most cases, the current development of the markets significantly affects the quality of the different investment strategies. Since the business volume per year on the worldwide stock markets is more than USD 35 trillions [4], it is not surprising that successful investment strategies are in the focus of intensive research. In general, there are lots of indicators influencing the chart of a security. Those are not only economical and political indicators but also psychological ones. It is very difficult to decide which weight should be assigned to which indicator, the more so as there are known and unknown tradeoffs between different indicators. Furthermore, weights change in time. Recent papers [5–8] try to apply data mining methods on historical market rates, in order to find investment strategies that perform significantly above the average. This approach is extreme compute-intensive since every day there are millions of quotations that are fixed worldwide. Even with the use of high-performance computers the reduction of this amount of data is required. But as shown in the literature [5–8], data mining helps to keep the essential information contents in order to come to successful investment strategies. In this paper, we present an investment strategy using a novel data mining method, which is discussed in Section 2. It results in a performance significantly above average for certain periods. It is based on the idea of
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