● Free to all developers
● Use Equeum’s proprietary language to predict Bitcoin price trend
● Awards totalling $35,000
December 6, 2021 – Equeum, a fintech startup, today announced its first Crypto challenge, with 100 awards of $250 each and 400 awards of $25 each. There is no charge for developers to enter the Challenge. Equeum has developed the world’s first decentralized creator economy platform for crypto, where top data scientists, quants, and coders compete to create crypto investment resources.
Developers create prediction models using EQL (Equeum Query Language), a high-level language that streamlines the development process. “As with blockchain itself, decentralization of development is transformative”, said Mark Nelson, Equeum Founder and CEO. “Creator economies are intrinsically decentralized…and explosively powerful.
On the Roblox creator platform, for example, 10M+ developers drive billions in revenue, and collectively draw an astounding $500M. We believe Equeum will be of similar or greater scale.” The Equeum platform is powered by:
● EQL (Equeum Query Language)
● A proprietary, quant-based time-series engine
● A library of advanced developer resources
Decentralization: a Radical Transformation
What makes decentralization vital to the crypto space is that it makes it possible to source virtually all relevant crypto data, including on/off-blockchain, sentiment, centralized and decentralized exchange, news, liquidity, reference rates, etc.
“As with open-source, it is a matter of scale”, said Nelson.. “There are tens of thousands of talented developers in the world who can source and analyze massive amounts of data, and this is only feasible with decentralization.” “Data is the lifeblood of investing, and the Equeum software makes it easy to normalize petabytes of disparate data, an indispensable step.
You can then select analytical tools from the ever-growing Resource Library to extract signals that in themselves may be weak, but in aggregate become a strong signal, and help form the basis of high-probability predictive models.” For more information, contact Kelsey Monaghan at [email protected]
