Introducing Binatix: A Deep Learning Trading Firm That’s Already Profitable
While Silicon Valley’s brand names use “deep learning” tools to help computers recognize cat images and play games, one startup has been quietly applying the artificial intelligence technology to more straightforward aims: Making money.Previously:
Binatix is effectively a deep-learning trading firm, possibly the first to use the state-of-the-art machine learning algorithms to spot patterns that offer an edge in investing. The seven-year-old Palo Alto, Calif., company, which is emerging from stealth mode with the publication of this story, says it’s already nicely profitable.
“It’s been working very well for over three years,” said Itamar Arel, chief technology officer and co-founder.
“It’s beyond luck; it’s clear we’ve got an edge.”
The company declined to discuss financials in detail.
Leveraging software, data and quantitative analysis to spot investment opportunities is far from a new phenomenon on Wall Street. And while elegant algorithms have minted millionaires in boom times, they’ve also been implicated in the unraveling of high-flying hedge funds and near-meltdowns of the market.
So at this point it’s hard to say just how novel Binatix’s approach is and whether it will stand the test of more volatile times. But the company’s co-founders believe they’ve made a genuine leap by applying their particular flavor of deep learning to this problem.
What is deep learning? As Re/code explained in an earlier piece:
Deep learning is a form of machine learning in which researchers attempt to train computer algorithms to spot meaningful patterns by showing them lots of data, rather than trying to program in every rule about the world. Taking inspiration from the way neurons work in the human brain, deep learning uses layers of algorithms that successively recognize increasingly complex features — going from, say, edges to circles to an eye in an image.Unlike most deep-learning approaches to date, Binatix’s software doesn’t just learn from static data points, but incorporates “temporal signals,” essentially how the information continually changes over time, season by season, day by day, minute by minute. That provides them with something closer to a three-dimensional view of financial trends....MORE
Notably, these techniques have allowed researchers to train algorithms using unstructured data, where features haven’t been laboriously labeled by human beings ahead of time. It’s not a new concept, but recent refinements have resulted in significant advances over traditional AI approaches.
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