CyMetica | Public Company Hidden Relationship Discovery

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


Rows contain stock symbols. Columns contain scores that represent known and hidden relationships between stocks & data streams below.




Option 1.  Download a Dataset


  • Elements in the Periodic Table(CSV)




  • Option 2.  Build a Dataset


    Step 1.
    Real-time Search Trends: Google, Bing, Facebook, Twitter
    Emerging Technologies
    Bitcoin, price relationships
    Chemicals
    Pharmaceuticals
    Context-controlled Sentiment
    Top Gainers, Top Losers
    Metals
    Foods
    Human Genes
    Real Estate
    Commodities
    Global Geography, Cities
    Botanicals, Phytochemicals, Micronutrients


    Step 2.




    Option 3.  Create a Dataset


    Step 1.
    Equity Type; 
    Data Stream:

    Step 2.
    Enter 1 to 5 features, concepts or keywords:

    example: Playstation, Helium, Korea, Shampoo, Coffee

    Step 3.






    Data streaming & feature scoring

    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

    How do the concepts (or trends) correlate to stocks, options or ETFs?

    Scores range from 0 to 1 and represent strength of known and hidden relationships between a concept and a stock, option or ETF. The score is calculated based on a series of algorithms that monitor data surrounding each company associated to the underlying security where each score is combined with scores from human curation teams. These concepts can then be factored or parameterized for exploring new signals or building new models.




  • Example use cases



    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:

  • Concept %Return Explorer
  • Google Trends as concepts
  • Concept Explorer


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