Signals Network provides sophisticated machine learning algorithms to help cryptotraders build their investment strategies.
Crypto trading strategies are about to become a lot smarter. Signals ( https://signals.network/), a Prague based startup, is building a platform to connect traders with data scientists. Signals will have an interface where traders will be able to assemble machine learning-powered trading strategy with a few clicks.
Signals is going to offer sophisticated machine learning algorithms to anyone, and its team wants to achieve that by building a network open to cryptotraders and data science developers. Soon traders will be able to build strategies from specific trading indicators, ranging from technical analysis to crowd wisdom insights, train it on historical data, and monetize strategies by offering it for copytrading. Here’s how it’s going to work.
To process extreme amounts of data and complex algorithmic computations within a short time, the Signals company has just partnered with iExec, a French company specializing in blockchain-based cloud computing.
The alpha version of Signals should be released before the end of 2017 to the public, and it will include a Strategy Marketplace. All paid features of the Signals platform – like purchasing indicators, data sets, or leasing a strategy for copytrading – will be available for users via a custom currency called Signals Token (SGN). After every payment made in the Signals Platform, a small amount of the currency will be burned, making SGN token effectively a deflationary currency. As the usage of SGN will increase, its supply will slowly shrink, making it more valuable.
Signals is offering purchasing the SGN token for Ethereum cryptocurrency in Signals Token Presale, which starts on Wednesday, November 22, 2017, at 14:00 UTC. The price of one token unit will start at approximately 0.26 USD, which will be set in Ethereum on the day of the token sale start. Before buying the SGN tokens, one needs to register with a name, e-mail and an ETH wallet address.
White paper: https://cdn.signals.network/docs/Signals-Whitepaper.pdf
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