Searching pre-generated data structures for event impact discovery

Abstract

Techniques for prediction of financial instrument returns, identifying statistical history, the discovery of pricing anomalies, and financial instrument visualization are disclosed. In one particular exemplary embodiment, the techniques may be realized as a method for identifying financial instrument returns and pricing anomalies including matching, using at least one computer processor one or more portions of current market data associated with a financial instrument with historical market data, averaging outcomes of matched historical market data, and providing a probabilistic outcome for financial instrument returns, pricing anomalies, or other metrics based on the matched historical market data and the current market data. Techniques for financial instrument analysis may also include processing event data, correlating the event data using a large volume of historical market data to identify a predicted impact on returns of a financial instrument and/or pricing anomalies, and presenting the predicted impact to a user (e.g., in near real time).

1 . A data processing system for near real-time processing of data records, the system comprising:
 a database storing one or more first pre-generated data structures, wherein each of the one or more first pre-generated data structures includes data representing one or more pre-computed precedents;   volatile memory that receives a stream of data records;   a parser that parses at least portions of contents of data records in the stream to detect one or more data items in the stream, wherein the one or more data items identify a near real-time event;   an impact module that accesses, from the non-volatile memory, the one or more first pre-generated data structures and determines an impact of the identified near real-time event on a first instrument based on (i) one or more pre-computed precedents represented by the one or more first pre-generated data structures, and (ii) the one or more data items that identify the near real-time event; and   an output module that is configured to generate visualization data that, when rendered on a client device, displays a visualization of the impact of the near real-time event on the first instrument.   
     
     
         2 . The data processing system of  claim 1 , wherein each of the one or more first pre-generated data structures is a data record storing data representing a pre-computed precedent. 
     
     
         3 . The data processing system of  claim 2 , further comprising:
 a pre-computation module to compute each pre-computed precedent that is stored in each of the one or more first pre-generated data structures.   
     
     
         4 . The data processing system of  claim 1 , wherein the impact module is further configured to identify one or more pre-computed precedents represented by the one or more pre-generated data structures stored in the database based on comparing (i) an attribute associated with the one or more data items from the feed of data that identify the real-time event and (ii) an attribute of the one or more pre-computed precedents. 
     
     
         5 . The data processing system of  claim 4 , wherein each pre-computed precedent includes data that identifies (i) an event type, and (ii) a probabilistic impact of an event on one or more instruments. 
     
     
         6 . The data processing system of  claim 3 , wherein the probabilistic impact of the event is a pre-computed standard deviation. 
     
     
         7 . The data processing system of  claim 1 , wherein the notification comprises a confidence indicator. 
     
     
         8 . The data processing system of  claim 1 , wherein notification comprises an email or a text message. 
     
     
         9 . The data processing system of  claim 1 , further comprising:
 an instrument identification module that obtains data identifying the first instrument.   
     
     
         10 . A method for near real-time processing of data records, the method comprising:
 storing one or more first pre-generated data structures, wherein each of the one or more first pre-generated data structures includes data representing one or more pre-computed precedents;   receiving a stream of data records;   parsing at least portions of contents of data records in the stream to detect one or more data items in the stream, wherein the one or more data items identify a near real-time event;   accessing the one or more first pre-generated data structures and determining an impact of the identified near real-time event on a first instrument based on (i) one or more pre-computed precedents represented by the one or more first pre-generated data structures, and (ii) the one or more data items that identify the near real-time event; and   generating visualization data that, when rendered on a client device, displays a visualization of the impact of the near real-time event on the first instrument.   
     
     
         11 . The method of  claim 10 , wherein each of the one or more first pre-generated data structures is a data record storing data representing a pre-computed precedent. 
     
     
         12 . The method of  claim 11 , the method further comprising:
 computing each pre-computed precedent that is stored in each of the one or more first pre-generated data structures.   
     
     
         13 . The method of  claim 10 , wherein accessing the one or more first data records and determining an impact of the identified near real-time event on the first instrument further comprises identifying one or more pre-computed precedents represented by the one or more pre-generated data structures stored in the database based on comparing (i) an attribute associated with the one or more data items from the feed of data that identify the real-time event and (ii) an attribute of the one or more pre-computed precedents. 
     
     
         14 . The method of  claim 13 , wherein each pre-computed precedent includes data that identifies (i) an event type, and (ii) a probabilistic impact of an event on one or more instruments. 
     
     
         15 . The method of  claim 12 , wherein the probabilistic impact of the event is a pre-computed standard deviation. 
     
     
         16 . The method of  claim 10 , wherein the notification comprises a confidence indicator. 
     
     
         17 . The method of  claim 10 , wherein notification comprises an email or a text message. 
     
     
         18 . The method of  claim 10 , the method further comprising:
 obtaining data identifying a first instrument.