Official news

Deep-Learning Startup Alpaca Raises $1M to Bring A.I. into Personal Investing

October 19th, 2015 02:10

Deep-Learning Trading Platform Lets People Build Algorithms With Few Clicks On Historical Chart

Oct. 19, 2015 (San Mateo, CA) — Alpaca, a San Mateo based Deep-Learning startup enabling AI technology to automate professional human tasks, announced today the closing of $1,000,000 seed funding from venture capitals and angel investors to focus its business in FinTech. The company also announced the private beta launch of its Deep-Learning trading platform, Capitalico, that lets people build trading algorithms with few clicks and visually from historical chart.

Alpaca fully leverages its Deep-Learning technology into personal investing sector with Capitalico, where even beginner traders can build their own trading algorithms intuitively without any programming. Existing algorithm building services always have involved programming which is not familiar for most discretionary traders and beginner traders.

“We saw an opportunity to support traders and to-be traders who understand and process the market data visually from charts and technical indicators, and for them to backtest and automate their trading strategies,” said Yoshi Yokokawa, Alpaca’s co-founder and CEO. “I am surprised myself how easy and simple the experience is to develop complex trading algorithms on Capitalico by simply selecting a portion on historical chart without worrying anything about programming at all.”

Capitalico is now accepting signups to the waiting list for the early access to the service. Capitalico is scheduled to be available public as beta beginning of next year.

About Alpaca

Alpaca is a San Mateo based Deep-Learning startup enabling AI technology to automate professional human tasks. 2015 June, Alpaca made its debut with the easiest deep-learning SaaS for computer-vision “Labellio,” which was featured in NVIDIA’s technical blog “PARALLEL FORALL” for its advanced backend technology used in the application to enhance the existing deep-learning capability to be faster and parallel. For more information, visit .




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