Suchergebnisse

New technological advancements with high performance computing in electronical markets and the need for fast trading decision making leads the market members to high frequency trading. The analysis of the literature showed the lack of a tested formalized method and technological solution that allows testing algorithmic high-frequency trading-HFT strategies in financial markets. Testing of HFT strategies is also provided in the European Union's MiFID II Directive, which aims to oblige HFT trading operators to test all trading algorithms and HFT strategies for possible errors. In this paper we wished to create and implement the testing method for the automated high frequency statistical arbitrage trading that would be able to analyse big amount of tick-by-tick data received from electronic market in nanosecond time-stamp precision. The initial motivation of this paper was to prepare testing method for automated HFT statistical arbitrage trading that would work with big amount of high frequency data and suggest the hardware for this type of system. To achieve the above mentioned aim the HFT statistical arbitrage testing algorithm prototype was created based on the proposed method. The proposed method demonstrates how the use of the GPU memory, code vectorization, parallel calculations and multidimensional arrays brings impressive speedups in the HFT trading and analyzation of the high frequency data.

New technological advancements with high performance computing in electronical markets and the need for fast trading decision making leads the market members to high frequency trading. The analysis of the literature showed the lack of a tested formalized method and technological solution that allows testing algorithmic high-frequency trading-HFT strategies in financial markets. Testing of HFT strategies is also provided in the European Union's MiFID II Directive, which aims to oblige HFT trading operators to test all trading algorithms and HFT strategies for possible errors. In this paper we wished to create and implement the testing method for the automated high frequency statistical arbitrage trading that would be able to analyse big amount of tick-by-tick data received from electronic market in nanosecond time-stamp precision. The initial motivation of this paper was to prepare testing method for automated HFT statistical arbitrage trading that would work with big amount of high frequency data and suggest the hardware for this type of system. To achieve the above mentioned aim the HFT statistical arbitrage testing algorithm prototype was created based on the proposed method. The proposed method demonstrates how the use of the GPU memory, code vectorization, parallel calculations and multidimensional arrays brings impressive speedups in the HFT trading and analyzation of the high frequency data.

New technological advancements with high performance computing in electronical markets and the need for fast trading decision making leads the market members to high frequency trading. The analysis of the literature showed the lack of a tested formalized method and technological solution that allows testing algorithmic high-frequency trading-HFT strategies in financial markets. Testing of HFT strategies is also provided in the European Union's MiFID II Directive, which aims to oblige HFT trading operators to test all trading algorithms and HFT strategies for possible errors. In this paper we wished to create and implement the testing method for the automated high frequency statistical arbitrage trading that would be able to analyse big amount of tick-by-tick data received from electronic market in nanosecond time-stamp precision. The initial motivation of this paper was to prepare testing method for automated HFT statistical arbitrage trading that would work with big amount of high frequency data and suggest the hardware for this type of system. To achieve the above mentioned aim the HFT statistical arbitrage testing algorithm prototype was created based on the proposed method. The proposed method demonstrates how the use of the GPU memory, code vectorization, parallel calculations and multidimensional arrays brings impressive speedups in the HFT trading and analyzation of the high frequency data.

New technological advancements with high performance computing in electronical markets and the need for fast trading decision making leads the market members to high frequency trading. The analysis of the literature showed the lack of a tested formalized method and technological solution that allows testing algorithmic high-frequency trading-HFT strategies in financial markets. Testing of HFT strategies is also provided in the European Union's MiFID II Directive, which aims to oblige HFT trading operators to test all trading algorithms and HFT strategies for possible errors. In this paper we wished to create and implement the testing method for the automated high frequency statistical arbitrage trading that would be able to analyse big amount of tick-by-tick data received from electronic market in nanosecond time-stamp precision. The initial motivation of this paper was to prepare testing method for automated HFT statistical arbitrage trading that would work with big amount of high frequency data and suggest the hardware for this type of system. To achieve the above mentioned aim the HFT statistical arbitrage testing algorithm prototype was created based on the proposed method. The proposed method demonstrates how the use of the GPU memory, code vectorization, parallel calculations and multidimensional arrays brings impressive speedups in the HFT trading and analyzation of the high frequency data.

New technological advancements with high performance computing in electronical markets and the need for fast trading decision making leads the market members to high frequency trading. The analysis of the literature showed the lack of a tested formalized method and technological solution that allows testing algorithmic high-frequency trading-HFT strategies in financial markets. Testing of HFT strategies is also provided in the European Union's MiFID II Directive, which aims to oblige HFT trading operators to test all trading algorithms and HFT strategies for possible errors. In this paper we wished to create and implement the testing method for the automated high frequency statistical arbitrage trading that would be able to analyse big amount of tick-by-tick data received from electronic market in nanosecond time-stamp precision. The initial motivation of this paper was to prepare testing method for automated HFT statistical arbitrage trading that would work with big amount of high frequency data and suggest the hardware for this type of system. To achieve the above mentioned aim the HFT statistical arbitrage testing algorithm prototype was created based on the proposed method. The proposed method demonstrates how the use of the GPU memory, code vectorization, parallel calculations and multidimensional arrays brings impressive speedups in the HFT trading and analyzation of the high frequency data.

New technological advancements with high performance computing in electronical markets and the need for fast trading decision making leads the market members to high frequency trading. The analysis of the literature showed the lack of a tested formalized method and technological solution that allows testing algorithmic high-frequency trading-HFT strategies in financial markets. Testing of HFT strategies is also provided in the European Union's MiFID II Directive, which aims to oblige HFT trading operators to test all trading algorithms and HFT strategies for possible errors. In this paper we wished to create and implement the testing method for the automated high frequency statistical arbitrage trading that would be able to analyse big amount of tick-by-tick data received from electronic market in nanosecond time-stamp precision. The initial motivation of this paper was to prepare testing method for automated HFT statistical arbitrage trading that would work with big amount of high frequency data and suggest the hardware for this type of system. To achieve the above mentioned aim the HFT statistical arbitrage testing algorithm prototype was created based on the proposed method. The proposed method demonstrates how the use of the GPU memory, code vectorization, parallel calculations and multidimensional arrays brings impressive speedups in the HFT trading and analyzation of the high frequency data.

New technological advancements with high performance computing in electronical markets and the need for fast trading decision making leads the market members to high frequency trading. The analysis of the literature showed the lack of a tested formalized method and technological solution that allows testing algorithmic high-frequency trading-HFT strategies in financial markets. Testing of HFT strategies is also provided in the European Union's MiFID II Directive, which aims to oblige HFT trading operators to test all trading algorithms and HFT strategies for possible errors. In this paper we wished to create and implement the testing method for the automated high frequency statistical arbitrage trading that would be able to analyse big amount of tick-by-tick data received from electronic market in nanosecond time-stamp precision. The initial motivation of this paper was to prepare testing method for automated HFT statistical arbitrage trading that would work with big amount of high frequency data and suggest the hardware for this type of system. To achieve the above mentioned aim the HFT statistical arbitrage testing algorithm prototype was created based on the proposed method. The proposed method demonstrates how the use of the GPU memory, code vectorization, parallel calculations and multidimensional arrays brings impressive speedups in the HFT trading and analyzation of the high frequency data.

New technological advancements with high performance computing in electronical markets and the need for fast trading decision making leads the market members to high frequency trading. The analysis of the literature showed the lack of a tested formalized method and technological solution that allows testing algorithmic high-frequency trading-HFT strategies in financial markets. Testing of HFT strategies is also provided in the European Union's MiFID II Directive, which aims to oblige HFT trading operators to test all trading algorithms and HFT strategies for possible errors. In this paper we wished to create and implement the testing method for the automated high frequency statistical arbitrage trading that would be able to analyse big amount of tick-by-tick data received from electronic market in nanosecond time-stamp precision. The initial motivation of this paper was to prepare testing method for automated HFT statistical arbitrage trading that would work with big amount of high frequency data and suggest the hardware for this type of system. To achieve the above mentioned aim the HFT statistical arbitrage testing algorithm prototype was created based on the proposed method. The proposed method demonstrates how the use of the GPU memory, code vectorization, parallel calculations and multidimensional arrays brings impressive speedups in the HFT trading and analyzation of the high frequency data.