Abstract
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.
Highlights
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
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
We disclaim to investigate more indicators since, on the one hand, these 8 indicators represent the activities on the international stock markets to some high degree, and on the other hand, the results with this restriction are already remarkable
Summary
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. Recent papers [5,6,7,8] 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. It results in a performance significantly above average for certain periods.
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