Facing a similar big data issue, Deep Value, a developer of algorithms, backtests its algorithms “on a multitude of orders across many months of historic data,” says CEO Harish Devarajan. To gain an edge, it also must simulate how the algos would have worked across hundreds of days of trading. The next phase is to ask “what if” questions of the data from hundreds of machines, which actually creates a new problem–“storing an ocean of data,” Devarajan says.
Two and a half years ago, Deep Value, a Chicago-based provider of algorithms to buy- and sell-side firms that also has a broker-dealer affiliate, began using big data analytics to improve executions. Despite the depressed environment in U.S. cash equities, CEO Harish Devarajan says, the firm’s business has grown well — it executed nearly 3 percent of overall market volume on its highest-volume day this summer.
Traders Magazine Online News, September 10, 2012
Taken together, the perception is the industry is losing control. “The complexity of some systems overcomes the best efforts of designers to keep them under control,” says Harish Devarajan, chief executive of Deep Value, a developer of trading algorithms used at the New York Stock Exchange and elsewhere. “All systems start off as things that do our bidding. But some rise in complexity to the point where we masters become the servants of the system.”
Traders Magazine Online News, April 10, 2012
Harish Devarajan, CEO of algorithmic firm Deep Value, said Reg NMS and the fragmentation it created had already delivered a huge blow to upstairs block desks before the financial crisis happened, and the thinning out of the Street after the crisis continued to push the buyside toward algos. It was those factors as much as improvements in algorithms that led to algos’ increased share in small-cap trades.