Explore Returns

  • Generate returns for clusters of cryptocurrencies based on their correlations to a global trend, concept or topic
  • Build long & short baskets, generate alpha

Data source: Cryptocurrency | NYSE & Nasdaq

Enter 1 to 5 concepts, keywords or global search trends to generate basket returns
example baskets: machine learning, solar, music, real estate, cars

Basket returns for Machine Learning, Solar, Music, Real Estate & Cars (interactive)

Basket: Machine learning

Component (score): Cindicator, (CND, 1.000), Cube, (AUTO, 0.769), Everex, (EVX, 0.573), Sovereign-hero, (HERO, 0.565), Leverj, (LEV, 0.059)

Basket: Solar

Component (score): Solar, (SDAO, 1.000), Cube, (AUTO, 0.050), Woodcoin, (LOG, 0.025), Auctus, (AUC, 0.021), Storm, (STORM, 0.020), Bloom, (BLT, 0.019), Power-ledger, (POWR, 0.017)

Basket: Cars

Component (score): Cube, (AUTO, 1.000), Streamr-datacoin, (DATA, 0.986), Glasscoin, (GLS, 0.051), Singularitynet, (AGI, 0.024), Storj, (STORJ, 0.019)

Basket: Music

Component (score): Voise, (VOISE, 1.000), Musiconomi, (MCI, 0.725), Florincoin, (FLO, 0.343), Vibe, (VIBE, 0.337)

Basket: Real estate

Component (score): Real, (REAL, 1.000), Propy, (PRO, 0.187), Digitaldevelopersfund, (DDF, 0.087)

Use Cases:

What kind of things can be done with a NLP-based correlation matrix like this?

  • Create unique sectors or clusters based on concepts and hidden relationships and compare their gains to the S&P (see below)
  • 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 & trends correlate to crypto, stocks 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. [Ref: Equity Correlations - J.P. Morgan]

    Data Engineering Pipeline Overview:

  • API

    Correlated cryptocurrencies via context-controlled NLP

    Pass a URL, global trend, concept, keyword or any text and get related cryptocurrencies based on an algorithm used for detecting hidden relationships in data

    JSON API endpoint:

    POST /recommend/app/correlated_cryptos


    query=[URL or text]

    curl: Returns:

    Partners & Collaborators:

    Selected references and acknowledgements: