Communications of the ACM

ifying kernels and/or running real-time kernels. I left HFT in late 2005 and returned in 2009, only to discover the world was approaching absurdity: by 2009 we were required to operate well below the one-millisecond barrier, and were looking at tick-to-trade requirements of 250 microseconds. Tick to trade is the time it takes to:

1. Receive a packet at the network interface;

2.Process the packet and run through the business logic of trading;

3. Send a trade packet back out on the network interface.

To do this, we used real-time kernels and in my shop, we had begun implementing functionality on the switches themselves (the Arista switch was Linux based, and we had root access). We must not have been alone in implementing custom code on the switch, because shortly after, Arista made a 24-port switch with a built-in field-programma-ble gate array (FPGA). 1 FPGAs were becoming more common in trading— especially in dealing with the increasing onslaught of market-data processing.

As with all great technology, using it became easier over time, allowing more and more complicated systems to be built. By 2010, the barriers to entry into HFT began to fall as many of the more esoteric technologies developed over the previous few years became commercially available. Strategy development, or the big-data problem of analyzing market data, was a great example. Hadoop was not common in many HFT shops, but the influx of talent in distributed data mining meant a number of products were becoming more available. Software companies (often started by former HFT traders) were now offering amazing solutions for messaging, market-data capture, and networking. Perhaps as a result of the inevitable lowering of the barriers to entry, HFT was measurably more difficult by 2010. Most of our models at that time were running at half-lives of three to six months.

I remember coming home late one night, and my mother, a math teach-er, asked why I was so depressed and exhausted. I said, “Imagine every day you have to figure out a small part of the world. You develop fantastic machines, which can measure everything, and you deploy them to track an object (minimum price increment) from

1/16th of a dollar to $0.01 per share. What this meant was that “overnight the minimum spread a market-maker (someone who electronically offered to both buy and sell a security) stood to pocket between a bid and offer was compressed from 6. 25 cents…down to a penny.” 5

This led to an explosion in revenue
for U.S. equity-based HFT firms, as
they were the only shops capable of
operating at such small incremental
margins through the execution of mas-
sive volume. Like the plot of Superman
III, HFT shops could take over market-
making in U.S. equities by collecting
pennies (or fractions of a penny) mil-
lions of times a day. I was not trading
stocks, however; I was trading futures
and bonds. Tucked inside a large Wall
Street partnership, I was tackling mar-
kets that were electronic but outside
the purview of the average algorithmic
shop. This is important as it meant we
could start with a tractable goal: build
an automated market-making system
that executes trades in under 10 milli-
seconds on a 3.2GHz Xeon (130nm). By
2004, this was halved to five millisec-
onds, and we were armed with a 3.6GHz
Nocona. By 2005 we were approaching
the one-millisecond barrier for latency
arbitrage and were well into the over-
clocking world. I remember bricking a
brand-new HP server in an attempt to
break the 4.1GHz barrier under air.