Object modeling for exploring large data sets

Abstract

An object model is used to facilitate performing financial analysis and that includes certain zero-order objects or building blocks that lend themselves particularly well to doing financial analysis. The object model comprises a universe of data items, relationships between the data items, higher-order objects generated based on one or more data items in the universe, higher-order objects generated based on other objects, and auxiliary entities related to the universe of data items.

1 . A computer-implemented method, comprising:
 creating and storing in computer memory a programmatic object model that facilitates performing financial analysis and comprising a plurality of zero-order objects that are not decomposable into other objects;   wherein the object model comprises a universe of data items and relationships between the data items, and a plurality of higher-order objects that are generated based on the zero objects;   wherein the zero objects comprise a plurality of time series objects, a plurality of metric objects, and a plurality of financial instrument objects.   
     
     
         2 . The method of  claim 1 , wherein the higher-order objects can be decomposed into other building blocks, and wherein the higher-order objects comprise date set objects, index objects, portfolio objects, strategy objects, instrument group objects, and regression objects. 
     
     
         3 . The method of  claim 2 , wherein instrument group objects comprise one or more instruments selected from a universe of instruments using a filter chain; wherein index objects indicate a collective value of one or more instruments; wherein regression objects transform one or more first time series into a predicted time series and compares the predicted time series with a second time series; wherein portfolio objects comprise zero or more time series each of which represents an instrument, a particular date set, and one or more trades that refer to times represented in the particular date set; wherein strategy objects comprise a date set that represents a time period and a statement block that can be executed to determines one or more trades of the instrument; wherein date set objects comprises time values that satisfy one or more selection criteria. 
     
     
         4 . The method of  claim 2 , wherein each of the index objects comprises an instrument group object, a metric object, and a date set object. 
     
     
         5 . The method of  claim 2 , wherein each of the instrument group objects comprises a plurality of filter objects. 
     
     
         6 . The method of  claim 5 , wherein each of the filter objects comprises a set of instrument objects and a metric object. 
     
     
         7 . The method of  claim 1 , further comprising:
 receiving, at runtime, user input specifying a custom metric object name and identifying an ordered or concatenated plurality of function tokens for association with the custom metric object name;   creating and storing, in the object model, a custom metric object based on the name and the tokens.   
     
     
         8 . A computer-implemented method comprising:
 identifying a metric that transforms one or more time series into an output object;   determining, based on one or more input objects, the one or more time series;   applying the metric using the one or more time series, thereby generating a particular value for the output object;   storing, in a physical storage device, one of the metric and the particular value for the output object.   
     
     
         9 . The method of  claim 8 , wherein the output object is one of a) an instrument group that comprises one or more instruments selected from a universe of instruments using a filter chain, b) an index that indicates a collective value of one or more instruments, c) a regression that transforms one or more first time series into a predicted time series and compares the predicted time series with a second time series, d) a portfolio that comprises i) zero or more time series each of which represents an instrument, ii) a particular date set, and iii) one or more trades that refer to times represented in the particular date set, e) a strategy that comprises i) a date set that represents a time period and ii) a statement block that can be executed to determines one or more trades of the instrument, or f) a date set comprising time values that satisfy one or more selection criteria. 
     
     
         10 . The method of  claim 8 , wherein the output object, the one or more input objects, and the metric are specified in a document that specifies a tree comprising a plurality of objects and a plurality of metrics. 
     
     
         11 . The method of  claim 8 , wherein the first metric is a custom metric specified as a token by a user after a data analysis system is deployed, and wherein the custom metric can be immediately accessed by referring to the token after the custom metric is dynamically loaded into the data analysis system as a part of computing logic of the data analysis system. 
     
     
         12 . The method of  claim 8 , wherein the one or more time series include at least one time series whose value is not associated with an instrument. 
     
     
         13 . The method of  claim 8 , wherein at least one of the one or more time series is associated with an instrument in a universe of instruments. 
     
     
         14 . The method of  claim 13 , wherein the universe of instruments comprises one or more ontological relationships among all instruments in the universe of instruments. 
     
     
         15 . The method of  claim 8 , wherein generating the particular value of the output object occurs at a first time, wherein the metric generates another value of the output object at a second time different from the first time, and wherein the particular value of the output object is different from said another value of the output object. 
     
     
         16 . The method of  claim 8 , wherein the metric includes one or more input arguments whose runtime values influence runtime behaviors of the metric. 
     
     
         17 . A machine-readable storage medium comprising one or more program instructions recorded thereon, which instructions, when executed by one or more processors, cause the one or more processors to perform the steps of:
 creating and storing in computer memory a programmatic object model that facilitates performing financial analysis and comprising a plurality of zero-order objects that are not decomposable into other objects;   wherein the object model comprises a universe of data items and relationships between the data items, and a plurality of higher-order objects that are generated based on the zero-order objects;   wherein the zero-order objects comprise a plurality of time series objects, a plurality of metric objects, and a plurality of financial instrument objects.   
     
     
         18 . The medium of  claim 17 , wherein the higher-order objects can be decomposed into other building blocks, and wherein the complex objects comprise date set, index, portfolio, strategy, instrument group, and regression objects. 
     
     
         19 . The medium of  claim 18 , wherein instrument group objects comprise one or more instruments selected from a universe of instruments using a filter chain; wherein index objects indicate a collective value of one or more instruments; wherein regression objects transform one or more first time series into a predicted time series and compares the predicted time series with a second time series; wherein portfolio objects comprise zero or more time series each of which represents an instrument, a particular date set, and one or more trades that refer to times represented in the particular date set; wherein strategy objects comprise a date set that represents a time period and a statement block that can be executed to determines one or more trades of the instrument; wherein date set objects comprises time values that satisfy one or more selection criteria. 
     
     
         20 . The medium of  claim 18 , wherein each of the index objects comprises an instrument group object, a metric object, and a date set object. 
     
     
         21 . The medium of  claim 18 , wherein each of the instrument group objects comprises a plurality of filter objects. 
     
     
         22 . The medium of  claim 21 , wherein each of the filter objects comprises a set of instrument objects and a metric object. 
     
     
         23 . The medium of  claim 17 , wherein the one or more program instructions further comprise instructions which, when executed by one or more processors, cause the one or more processors to perform:
 receiving, at runtime, user input specifying a custom metric object name and identifying an ordered or concatenated plurality of function tokens for association with the custom metric object name;   creating and storing, in the object model, a custom metric object based on the name and the tokens.   
     
     
         24 . A machine-readable storage medium comprising one or more program instructions recorded thereon, which instructions, when executed by one or more processors, cause the one or more processors to perform the steps of:
 identifying a metric that transforms one or more time series into an output object;   determining, based on one or more input objects, the one or more time series;   applying the metric using the one or more time series, thereby generating a particular value for the output object;   storing, in a physical storage device, one of the metric and the particular value for the output object.   
     
     
         25 . The medium of  claim 24 , wherein the output object is one of a) an instrument group that comprises one or more instruments selected from a universe of instruments using a filter chain, b) an index that indicates a collective value of one or more instruments, c) a regression that transforms one or more first time series into a predicted time series and compares the predicted time series with a second time series, d) a portfolio that comprises i) zero or more time series each of which represents an instrument, ii) a particular date set, and iii) one or more trades that refer to times represented in the particular date set, e) a strategy that comprises i) a date set that represents a time period and ii) a statement block that can be executed to determines one or more trades of the instrument, or f) a date set comprising time values that satisfy one or more selection criteria. 
     
     
         26 . The medium of  claim 24 , wherein the output object, the one or more input objects, and the metric are specified in a document that specifies a tree comprising a plurality of objects and a plurality of metrics. 
     
     
         27 . The medium of  claim 24 , wherein the first metric is a custom metric specified as a token by a user after a data analysis system is deployed, and wherein the custom metric can be immediately accessed by referring to the token after the custom metric is dynamically loaded into the data analysis system as a part of computing logic of the data analysis system. 
     
     
         28 . The medium of  claim 24 , wherein the one or more time series include at least one time series whose value is not associated with an instrument. 
     
     
         29 . The medium of  claim 24 , wherein at least one of the one or more time series is associated with an instrument in a universe of instruments. 
     
     
         30 . The medium of  claim 29 , wherein the universe of instruments comprises one or more ontological relationships among all instruments in the universe of instruments. 
     
     
         31 . The medium of  claim 24 , wherein generating the particular value of the output object occurs at a first time, wherein the metric generates another value of the output object at a second time different from the first time, and wherein the particular value of the output object is different from said another value of the output object. 
     
     
         32 . The medium of  claim 24 , wherein the metric include one or more input arguments whose runtime values influence runtime behaviors of the metric. 
     
     
         33 . An application server comprising:
 a network interface that is coupled to a data network for receiving one or more packet flows therefrom;   a processor; and   one or more stored program instructions which, when executed by the processor, cause the processor to carry out the steps of:
 creating and storing in computer memory a programmatic object model that facilitates performing financial analysis and comprising a plurality of zero-order objects that are not decomposable into other objects; 
 wherein the object model comprises a universe of data items and relationships between the data items, and a plurality of higher-order objects that are generated based on the zero-order objects; 
 wherein the zero-order objects comprise a plurality of time series objects, a plurality of metric objects, and a plurality of financial instrument objects. 
   
     
     
         34 . The application server of  claim 33 , wherein the higher-order objects can be decomposed into other building blocks, and wherein the higher-order objects comprise date set, index, portfolio, strategy, instrument group, and regression objects. 
     
     
         35 . The application server of  claim 34 , wherein instrument group objects comprise one or more instruments selected from a universe of instruments using a filter chain; wherein index objects indicate a collective value of one or more instruments; wherein regression objects transform one or more first time series into a predicted time series and compares the predicted time series with a second time series; wherein portfolio objects comprise zero or more time series each of which represents an instrument, a particular date set, and one or more trades that refer to times represented in the particular date set; wherein strategy objects comprise a date set that represents a time period and a statement block that can be executed to determines one or more trades of the instrument; wherein date set objects comprises time values that satisfy one or more selection criteria. 
     
     
         36 . The application server of  claim 34 , wherein each of the index objects comprises an instrument group object, a metric object, and a date set object. 
     
     
         37 . The application server of  claim 34 , wherein each of the instrument group objects comprises a plurality of filter objects. 
     
     
         38 . The application server of  claim 37 , wherein each of the filter objects comprises a set of instrument objects and a metric object. 
     
     
         39 . The application server of  claim 33 , wherein the one or more program instructions further comprise instructions which, when executed by one or more processors, cause the one or more processors to perform:
 receiving, at runtime, user input specifying a custom metric object name and identifying an ordered or concatenated plurality of function tokens for association with the custom metric object name;   creating and storing, in the object model, a custom metric object based on the name and the tokens.   
     
     
         40 . An application server comprising:
 a network interface that is coupled to a data network for receiving one or more packet flows therefrom;   a processor; and   one or more stored program instructions which, when executed by the processor, cause the processor to carry out the steps of:
 identifying a metric that transforms one or more time series into an output object; 
 determining, based on one or more input objects, the one or more time series; 
 applying the metric using the one or more time series, thereby generating a particular value for the output object; 
 storing, in a physical storage device, one of the metric and the particular value for the output object. 
   
     
     
         41 . The application server of  claim 40 , wherein the output object is one of a) an instrument group that comprises one or more instruments selected from a universe of instruments using a filter chain, b) an index that indicates a collective value of one or more instruments, c) a regression that transforms one or more first time series into a predicted time series and compares the predicted time series with a second time series, d) a portfolio that comprises i) zero or more time series each of which represents an instrument, ii) a particular date set, and iii) one or more trades that refer to times represented in the particular date set, e) a strategy that comprises i) a date set that represents a time period and ii) a statement block that can be executed to determines one or more trades of the instrument, or f) a date set comprising time values that satisfy one or more selection criteria. 
     
     
         42 . The application server of  claim 40 , wherein the output object, the one or more input objects, and the metric are specified in a document that specifies a tree comprising a plurality of objects and a plurality of metrics. 
     
     
         43 . The application server of  claim 40 , wherein the first metric is a custom metric specified as a token by a user after a data analysis system is deployed, and wherein the custom metric can be immediately accessed by referring to the token after the custom metric is dynamically loaded into the data analysis system as a part of computing logic of the data analysis system. 
     
     
         44 . The application server of  claim 40 , wherein the one or more time series include at least one time series whose value is not associated with an instrument. 
     
     
         45 . The application server of  claim 40 , wherein at least one of the one or more time series is associated with an instrument in a universe of instruments. 
     
     
         46 . The application server of  claim 45 , wherein the universe of instruments comprises one or more ontological relationships among all instruments in the universe of instruments. 
     
     
         47 . The application server of  claim 40 , wherein generating the particular value of the output object occurs at a first time, wherein the metric generates another value of the output object at a second time different from the first time, and wherein the particular value of the output object is different from said another value of the output object. 
     
     
         48 . The application server of  claim 40 , wherein the metric include one or more input arguments whose runtime values influence runtime behaviors of the metric. 
     
     
         49 . The method of  claim 2 , wherein each of the regression objects comprises a set of time series and a date set object. 
     
     
         50 . The method of  claim 2 , wherein each of the date set objects is associated with a metric that is configured to receive a time series as a first input and one or more selection criteria as a second input and to generate one or more dates in the time series that are within the specified range.