Algorithmic trading firms were put on notice last Monday when Commissioner Bart Chilton of the Commodity Futures Trading Commission (CFTC) said that high frequency trading (HFT) and algorithmic trading firms should be made legally responsible for maintaining a minimum set of testing and monitoring standards to prevent future flash crashes.
The possibility that algorithmic trading firms may need to support new regulatory requirements highlights the rapidly growing importance of the market data cloud and new cloud services like NASDAQ Data-On-Demand for back testing purposes.
In a bid to resuscitate the heroes of 1980's sitcoms, Mr. Chilton declared, "I want to be like MacGyver. Remember, he was always trying to prevent crime before it happened." So if you're a criminally culpable algorithmic trading firm, you'd best beware-especially if Mr. Chilton gets his hands on a paper clip and a stick of chewing gum. Household items aside, Mr. Chilton also plans to rely on tools such as a "kind of Good Housekeeping Seal of Approval." He'd like algorithmic trading systems to be tested by either exchanges or regulators before going live. And after going live, he'd like algorithmic trading systems to be monitored on an on-going basis.
So, if you're engaged in algorithmic trading, you'll probably want to make sure you've got systems in place to test your algorithms so you're prepared for any regulatory requirements that might come down the pike. That's where the market data cloud comes in. If you want to future proof your algorithmic trading systems against any possible testing requirements, you'll want to look into how you can use the market data cloud to back test your algorithms with minimal expense, time and effort put into building new technology infrastructure.
On-demand market data cloud services like NASDAQ Data-On-Demand offer a new way to hold down both the cost and time investments of getting historical tick data to back test algorithmic trading systems. You can get only the slice of data you need for your specific test conditions, and you only pay for what you use, not the entire universe of data, which dramatically reduces your data costs. On-demand cloud services also let you get highly customized data sets in a variety of common formats (XML, CSV, JSON and so on) which reduces your need to build new technology infrastructure just to load the data into your algorithmic trading systems.
So if Mr. Chilton knocks at your door with the latest issue of Good Housekeeping under one arm, never fear. Just check out NASDAQ Data-On-Demand and rest assured you can handle any algorithmic trading regulatory requirements for back testing that he might have in mind without breaking the bank on new technology infrastructure.
(For more details on Mr. Chilton's public statements, see the recent Securities Industry News coverage.)