Tickeron, the quant-sourced marketplace for AI-powered stock trading tools, is pleased to introduce a new type of AI robot—an algorithm trained to generate stock trading ideas within specific sectors.
Tickeron’s quant team has developed a new method for analyzing the behavior of stocks within a sector or group of sectors, based on a thorough study of correlation relationships between stock prices and various combinations of sectors and industries.
To evaluate each model, our team performed multilevel back-tests and forward tests based on advanced mathematical methods driven by neural networks. This work spawned a wide range of robots capable of generating trading signals for a single sector or industry, or for a diversified set of sectors that the robots or investors can determine.
Swing traders who prefer to concentrate on a single industry when trading stocks can follow AI-generated trade signals for the industry or specific stocks within it. Traders with a broader range of investable stocks can use a ‘diversification model’ robot, or the investor can build their own model from individual robots. Day traders can use this line of robots to identify stocks across industries that are exhibiting strong medium-term trends, and perhaps use this information to make a directional trade. The list goes on for hedge funds too.
“Our main goal is to enable the maximum possible number of traders to find the most convenient and effective trading signals for them. We have robots using the methods of technical analysis, fundamental analysis, and their combination. We have robots for beginners and a set of tools (Pattern Search Engine, Real-Time Patterns, Trend Prediction Engine) that allow advanced traders to conduct sophisticated financial markets analysis. On the Tickeron.com, every type of investor can find what they need,” said Sergey Savastiouk, Ph.D., CEO and Founder of Tickeron.
Visit AITechPark for cutting-edge Tech Trends around AI, ML, Cybersecurity, along with AITech News, and timely updates from industry professionals!
