TECHNICAL FIELD

The present disclosure relates generally towards improving electronic data conversion and distribution, and, in particular to systems and methods for electronic data conversion and distribution of electronic data sensitivities and projections where electronic data is sparse, whether from high volume data sources and/or differently formatted electronic data sources.

BACKGROUND

Problems exist in the field of electronic data conversion and distribution. Users of data classes with sparse electronic data often seek additional data and information in order to analyze or otherwise utilize theses data classes. One utilization of electronic data is in the creation of data projections (or other statistical analyses/applications) for those data classes having sparse electronic data (e.g., limited historical data). Since the electronic data is sparse, it may be a challenge to obtain the additional electronic data and information needed, at desired time(s) and/or in desired data types and volumes, to generate accurate data projections. Indeed, accurate projections (and other forms of statistical analysis) typically require a large amount of historic electronic data and/or information for analysis. In the absence of such data and information, conventional projections (based on the sparse data and information) are often very inaccurate and unreliable. Accordingly, there is a need for improved data conversion and distribution systems which are able to generate accurate projections and yield other data analysis results that are accurate and timely, even if the data being projected is sparse.

SUMMARY

The present disclosure is related to data conversion and distribution systems which are able to process and utilize any amount of data, received at different volumes, frequencies, and/or formats, from any number of different data sources in order to generate data that is usable for creating accurate data sensitivities, projections and/or yielding other statistical analyses associated with a data class having sparse data, all in a timely manner.

Aspects of the present disclosure include systems, methods and non-transitory computer-readable storage media specially configured for data conversion and distribution. The systems, methods, and non-transitory computer readable media may further include a data subscription unit and a virtual machine. The data subscription unit may have at least one data interface communicatively coupled to a plurality of data source devices and may be configured to obtain data from the plurality of data source devices. The data subscription unit may also be configured to transmit the data via secure communication over a network. The virtual machine of the present disclosure may include one or more servers, a non-transitory memory, and/or one or more processors including machine readable instructions. The virtual machine may be communicatively coupled to the data subscription unit. The virtual machine may include a data receiver module, a data unification module, and a data conversion module.

The data receiver module may be configured to receive the data from the data subscription unit. The data unification module may be configured to reformat and aggregate the data from the data subscription unit to generate unified data. The data conversion module may comprise a backtesting utility that is configured to run the unified data through one or more of filters and conditions to generate backtesting data. The backtesting utility may be further configured to run the backtesting data through one or more statistical algorithms to generate one or more metrics of the unified data and run the backtesting data through one or more non-statistical algorithms to determine one or more relationships amongst the backtesting data. The backtesting utility may generate backtesting results based on the one or more metrics and the one or more relationships, translate the backtesting results into one or more interactive visuals, and generate a graphical user interface (GUI) for displaying the backtesting results and the one or more interactive visuals on a user device. The backtesting utility may be configured to update one or more of the displayed backtesting results and the one or more interactive visuals in response to one or more of user input via the GUI or updates to the unified data, the update being processed without re-running the one or more statistical algorithms and the one or more non-statistical algorithms.

