Kaiju ETF Advisors today announced the start of trading for the BTD Capital Fund (NYSE Arca: DIP), an actively managed AI-driven fund designed to eliminate the guesswork in finding dips. Rather than track sectors, DIP seeks to capitalize on quick-return opportunities in the market regardless of sector or market conditions.
The company’s AI identifies dips, initiates buys, and then instructs when to sell rebounded shares in short order, replacing a significant portion of the ETF’s holdings every day. The AI behind DIP accounts for more than 25 factors — applying scientific methods to a volume of data on a massive scale — to optimize trading decisions for short-term gain.
“Using artificial intelligence, this new ETF will seek to find out the oversold stocks, benefit from a reversion to the mean, and quickly exit. This offers a potentially smarter way to take advantage of active management through an ETF,” said Todd Rosenbluth, head of research at VettaFi.
DIP’s investment strategy is designed to operate efficiently in all market conditions. DIP’s AI does this by searching for needles in the haystack (the “haystack” being the entirety of the S&P 500 and Nasdaq 100). Its goal is to identify and capitalize on short-term buying opportunities and then sell once the equity increases in price. While there may be fewer opportunities in a downturn, DIP’s AI was built with the goal of finding true dips in individual stocks, which can occur regardless of the overall market performance.
“Buy the Dip is a simple concept — purchase an asset when it’s oversold, then sell when its value bounces back,” said Ryan Pannell, CEO of Kaiju ETF Advisors, in a news release. “Our proprietary algorithm is the basis for an AI that can identify authentic dips in nanoseconds. And now we’re making that technology available to everyone.”
Pannell added that the “type of systematic trading” that DIP employs can “potentially find those needle-in-a-haystack opportunities by parsing data at a rate that exceeds human ability.”
DIP’s AI is based on machine learning techniques that leverage ongoing and emerging peer-reviewed research from academia and the financial industry. Each of these systems is trained on more than 15 years of intra-day market data and contributes to the generation of the entry and exit signals for potentially lucrative opportunities while simultaneously determining how to control and mitigate risk.
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