Abstract: A method for high frequency trading is provided, which is performed by one or more processors, and includes calculating a latency for a market order for each of a plurality of candidate batch sizes, selecting a batch size from among the plurality of candidate batch sizes based on the calculated latency, generating input data corresponding to the selected batch size using market data for a target item, using a machine learning model, generating prediction data for the target item at a future time point associated with the selected batch size, based on the generated input data, and generating order data for the target item based on the generated prediction data.

Abstract: A method for adjusting a hardware performance for high frequency trading is provided, which is performed by one or more processors, and includes receiving context information including at least one of a traffic of market data for one or more target items or a computational complexity of using a machine learning model performed based on the market data, determining whether or not to change a processing performance of the hardware based on the received context information, and changing a resource supplied to the hardware based on the determination result for changing the processing performance.

Abstract: A method for adjusting a hardware performance for high frequency trading is provided, which is performed by one or more processors, and includes receiving context information including at least one of a traffic of market data for one or more target items or a computational complexity of using a machine learning model performed based on the market data, determining whether or not to change a processing performance of the hardware based on the received context information, and changing a resource supplied to the hardware based on the determination result for changing the processing performance.

Abstract: The disclosure relates to an apparatus for high frequency trading. The apparatus includes one or more memories, at least one reconfigurable processor coupled to the one or more memories, and a dedicated accelerator preconfigured for the machine learning model. The one or more processors receive market-related information from one or more market-related information servers and generates market prediction reference data based on the market-related information. The dedicated accelerator performs operations for the machine learning model with the market prediction reference data to generate future market prediction data. The at least one reconfigurable processor generates an order signal based on the future market prediction data and transmits the order signal to a target exchange server.

Abstract: A method for adjusting a hardware performance for high frequency trading is provided, which is performed by one or more processors, and includes receiving context information including at least one of a traffic of market data for one or more target items or a computational complexity of using a machine learning model performed based on the market data, determining whether or not to change a processing performance of the hardware based on the received context information, and changing a resource supplied to the hardware based on the determination result for changing the processing performance.

Abstract: A method for high frequency trading is provided, which is performed by one or more processors, and includes calculating a latency for a market order for each of a plurality of candidate batch sizes, selecting a batch size from among the plurality of candidate batch sizes based on the calculated latency, generating input data corresponding to the selected batch size using market data for a target item, using a machine learning model, generating prediction data for the target item at a future time point associated with the selected batch size, based on the generated input data, and generating order data for the target item based on the generated prediction data.

Abstract: A method for high frequency trading is provided, which is performed by one or more processors, and includes calculating a latency for a market order for each of a plurality of candidate batch sizes, selecting a batch size from among the plurality of candidate batch sizes based on the calculated latency, generating input data corresponding to the selected batch size using market data for a target item, using a machine learning model, generating prediction data for the target item at a future time point associated with the selected batch size, based on the generated input data, and generating order data for the target item based on the generated prediction data.

Abstract: A method for high frequency trading is provided, which is performed by one or more processors, and includes generating input data based on market data for a target item, generating prediction data for the target item for each of a plurality of future time points by inputting the generated input data to a machine learning model, and generating order data for the target item based on the generated prediction data.

Abstract: The disclosure relates to an apparatus for high frequency trading. The apparatus includes one or more memories, at least one reconfigurable processor coupled to the one or more memories, and a dedicated accelerator preconfigured for the machine learning model. The one or more processors receive market-related information from one or more market-related information servers and generates market prediction reference data based on the market-related information. The dedicated accelerator performs operations for the machine learning model with the market prediction reference data to generate future market prediction data. The at least one reconfigurable processor generates an order signal based on the future market prediction data and transmits the order signal to a target exchange server.

Abstract: The disclosure relates to an apparatus for high frequency trading. The apparatus includes one or more memories, at least one reconfigurable processor coupled to the one or more memories, and a dedicated accelerator preconfigured for the machine learning model. The one or more processors receive market-related information from one or more market-related information servers and generates market prediction reference data based on the market-related information. The dedicated accelerator performs operations for the machine learning model with the market prediction reference data to generate future market prediction data. The at least one reconfigurable processor generates an order signal based on the future market prediction data and transmits the order signal to a target exchange server.