Tickeron, an artificial and human intelligence platform delivering unparalleled trading insights and analysis, introduces its Intraday Pattern Feed for cryptocurrency trading. Paired with Tickeron’s Trend Prediction Engine, the Intraday Pattern Feed uses artificial intelligence to scan hundreds of cryptocurrencies in search of patterns which indicate buy or sell signals.
Tickeron’s technology has an established track record in the cryptocurrency market, which includes accurately predicting the 2018 Bitcoin crash within two percent of the actual decline. Three weeks in advance of the crash, Tickeron projected with 88.78 percent confidence that Bitcoin, which had been trading at over $11,000, would experience a 40.53 percent decline to prices below $6,000. The actual crash was remarkably close to their target, with prices at $6,914 resulting in a 39 percent decline. In this instance, the artificial intelligence was able to predict the crash by accurately identifying a “Broadening Bottom Pattern” that signaled the substantial drop in price that took place. All of Tickeron’s Bitcoin predictions, past and present, are available here.
The same valuable technology is making cryptocurrency trading analysis much more accessible to investors. Once the user selects patterns and choses their minimum confidence level, the work is done for them. Tickeron’s artificial intelligence then predicts breakout and target prices, backtests the pattern and provides other valuable cryptocurrency trading information. Founded by Sergey Savastiouk, who serves as the company’s CEO, Tickeron is a subscription-based market intelligence platform providing industry news and artificial intelligence-generated predictions.
“Our artificial intelligence has precisely predicted several major events in the cryptocurrency market,” said Savastiouk. “Crypto traders can now have the same artificial intelligence at their disposal to make informed trading decisions by discovering these patterns within the context of our exclusive, sophisticated algorithms and data analysis.”