Data Showcasing Service

Data Owners and Data Distributors

We demonstrate the value of data with our ability to incorporate time-series data into our backtesting and research tools. In CloudQuant, showcasing and exploring data is the best way to see the value in data.

Our showcase provides portfolio managers and quantitative traders with:

  • Functioning backtesting algorithms that allow quantitative analysts to see backtesting logic
  • Quantitative experts who discuss the use of datasets
  • Access to the institutional grade Mariner™ backtesting and visualization tools used by professional quantitative portfolio managers
  • Time-synced historical tick market data
  • Dataset Catalog with featured datasets from our CloudQuant partners
  • Fully enabled backtesting
  • Sample algorithmic trading strategies
  • Trading scorecards
  • Online detailed demonstration results
  • Trade history files
  • Working algorithms that showcase your datasets
  • Full source code to all algorithms
  • Industry standard ticker symbology
  • Point-in-time historical symbols
  • AI driven symbology maps
  • Point-in-time API access to data
  • Specify the symbol and time range to query data
  • Results in JSON or DataFrame
Data Security
  • No one can download or copy your datasets
  • Only authorized users have access to the data through our Liberator API

The Difficulties Of Dataset Onboarding

The industry knows that the onboarding a new data set is a difficult process. There are numerous problems that include*:

  • Data not compatible with current data analysis systems
  • Can’t find the correct resource
  • Lack of time
  • Difficulty understanding the data’s value
  • Human Capital not available
  • Management not convinced of the value
  • Prohibitively High Fees

For those in the trading industry, we have additional problems that include:

  • Difficulty symbology mapping.
  • Dealing with point-in-time changes to symbol changes.
  • Correlating time series data correctly.

*Source: Greenwich Associates, Alternative Data Usage Study, 2017

For more information on this dataset or other datasets, Contact Us.
See also our Data Catalog and our Repository of White Papers.