CyMetica | Public Company Hidden Relationship Discovery



Create unique Nasdaq, NYSE, OTCBB, ETF & Options datasets
with custom columns/feature labels for additional signal boosting


Rows contain stock symbols. Columns contain fundamental data and 1 year (2008 - 2014) of minute-by-minute price data for S&P 500 stocks or EOD data for all equities + up to 5 columns you can define, represented by keywords or concepts you choose. Dataset generation is based on public & private databases, an army of human curators and data triangulation.

What kind of things can be done with custom concept columns/features?

  • Create unique sectors or clusters based on concepts and hidden relationships and compare their gains to the S&P
  • Determine if price correlations have similar concept or keyword correlations
  • Examine symbiotic, parasitic and sympathetic relationships between equities
  • Automatically create baskets of stocks based on concepts and/or keywords
  • Detach the custom columns and append them to other proprietary inhouse datasets
  • Select a Data Context (e.g. Biological, Chemical, GeoPhysical and others) to derive different signals
  • Use stock symbols as custom concept column labels and model cross-correlations between equities
  • Create features using trending terms anywhere on the internet
  • Enter 1 to 5 concepts, keywords or Google Search Trends & we'll build the dataset

    example: Playstation, Helium, Korea, Shampoo, Coffee


    Your email (so we can let you know when it's ready)


    Example use case 1: Explore hidden relationship networks similar to those described here

    Example use case 2: Returns for Playstation, Helium, Korea, Shampoo, & Coffee in comparison to the S&P 500: (interactive)


    Google Search Trends for Playstation, Helium, Korea, Shampoo, & Coffee



    Tools:

  • Google Trends as concepts
  • Concept Explorer


    Data, feature reqeusts or suggestions: cymetica@gmail.com