Abstract: A system, method and computer program product are described for providing the execution probability of a limit order within a given time period based on historical and current information and for adaptively and dynamically adjusting to intra-day trade data. For a given financial interest, the frequency of trade execution, the time evolution of the price, the time evolution of the trade volume, and the current state of the market, among other parameters, are captured and/or calculated. A probability function is generated based on the parameters corresponding to various time spans, and the execution probability of a limit order within a given time period is provided. Embodiments of the invention can be employed to estimate the probability of a limit order being executed within a given time period in the future, e.g., the next two minutes, based on the parameter data of a given time period in the past, e.g., the previous five minutes.

Abstract: Systems, methods, and computer program products for constructing and weighting a currency index for a currency basket. The weights of the components of the currency basket can be determined using only past statistical time series behaviors of the currency pairs.

Abstract: A system, method and computer program product are described for providing the execution probability of a limit order within a given time period based on historical and current information and for adaptively and dynamically adjusting to intra-day trade data. For a given financial interest, the frequency of trade execution, the time evolution of the price, the time evolution of the trade volume, and the current state of the market, among other parameters, are captured and/or calculated. A probability function is generated based on the parameters corresponding to various time spans, and the execution probability of a limit order within a given time period is provided. Embodiments of the invention can be employed to estimate the probability of a limit order being executed within a given time period in the future, e.g., the next two minutes, based on the parameter data of a given time period in the past, e.g., the previous five minutes.