Recently we read an excellent article on investing from Alpha Architect entitled One way to beat the market? Be different! In the article, the author shows how thinking differently from the pack can provide better performance for your investment portfolio.
As often happens with lateral thinking, this article stimulated our thinking from an HFT point of view. How can thinking differently help you at the other end of the speed spectrum?
There are a lot of well-trodden paths in HFT. Many, many shops host their trading software on machines running the Linux OS. The vast majority of strategies are written in some descendant of C. Many firms use FPGA technology to speed up their processing of network messages. There are good technical reasons to do this. Linux is free, open source, provides lots of customization, and is extremely extensible. C and its ilk are fast, while FPGA’s offload tasks to hardware which used to be performed by software, minimizing reaction time considerably.
Likewise, quant’s can follow the road more traveled and use R to analyze and manipulate their high frequency datasets. Those frustrated by R’s limitations — or those who want greater flexibility — can plump for Python and its PyData stack. We can all use big data tech like Hadoop or Spark for big data processing.
On the other hand, some choose to be wildly different.
Some people prefer to remain on the fringe, using technologies that haven’t been adopted by the broader market. Consider the prop trading firm Jane Street, which utilizes functional programming languages like oCaml to run their trading desk.
Quants who prefer to be different can use tools like Scala for data processing or HDF for big data storage (we are partial to HDF, might be partially because of the UIUC connection). Neither technology has experienced wide-spread adoption, despite active development from their backers.
In the context of High Frequency Trading, what are the benefits of walking the road less traveled?
While there are many arguments we could make, the biggest benefit comes from synchronicity, or the simultaneous occurrence of events that appear significantly related but have no discernible causal connection. This definition brings to mind events like the equity flash crash in May of 2010 or the bond flash rally in October 2014. But it’s perhaps subtler than that: the technology we use shapes the way our minds think in the same way human language shapes individual thought.
Flash events like this are examples of extreme synchronicity in the markets. This sync can occur from many sources. Does everyone shut off their strategies when the market has moved X% in Y seconds? Then a move bigger than X% will synchronize everyone’s strategies. Do you trade the same market with the same technology off the same datafeed as your competitor? Then your strategies will share a latent synchronicity with your competitors. A great example would be two HFT competitors using the same microwave or undersea line to connect two exchanges which have a wide geographical separation. There were days when it rained in Ohio and rendered a multi-million dollar microwave line useless. Firms which built their lines on a slightly different path could be unaffected, and thus enjoy outsized gains from increased market share.
Moreover, a quant’s choice of technology has a direct impact on their strategy’s implementation. This includes mathematical models. For example, a lot of statistical arbitrage traders are fading relative value relationships between different assets. All of these strategies accept a certain amount of risk, but if enough other traders are fading the same spread, the relationship can become a self-reinforcing blow up.
Consider the situation where many traders are short the 10-year and long the 30-year US treasury futures; this often occurs after large moves in a spread. Market makers become increasingly loaded up in one direction by definition: they provide liquidity in the spread at all times. As such they get selected by the market, so to speak, and at some point they have to cut risk to trade another day.
As they exit their positions it pushes the spread further out of equilibrium, further exacerbating the pain of trader’s who have yet to liquidate their positions. This can snowball, creating a liquidity hole on the exit side of the trade.
In a world where automation becomes imperative to survival, technological choices have a profound influence on the correlation of overall strategies in the market. When we cluster around certain technologies we can experience a clustering of performance as well.
H/T to Quantocracy for providing the forum for this kind of quant-minded discussion.
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