Recently, Man AHL, a “Trend Following Wizard”, announced in this press release some of the benefits from their “hard work to increase trade efficiency”:
In the past two years, the Asia desk has reduced Asian trading costs by more than 20%, and 98 per cent of regional trading can now be processed electronically […].
We have honed our execution style and algorithmic trading services to boost risk management and performance […]. These improvements have enabled us to add new asset classes to our trading models and we’ve recently expanded trading of several instruments including Asian stock index futures, interest rate swaps, relative value strategies, and a number of Asian currencies.
Algorithmic Trading: Really a Novelty?
You will have noticed the use of “algorithmic trading” in the copy above.
The recent rise of High-Frequency Trading (HFT) has placed algorithmic trading in the spotlight, making it appear as the new trendy (and controversial) development in financial markets.
But algorithms are not necessarily the overly complex and sophisticated processes used in HFT. As Merriam-Webster defines it, an algorithm is simply:
“a step-by-step procedure for solving a problem or accomplishing some end, especially by a computer”.
For example, a simple traffic light could be operated based on a fairly straight-forward algorithm (cycling colors in a predetermined sequence and time interval), in the same way as a trading system can have simple rules for Buy and Sell signals, like MA cross-overs. In practice, the algorithms are obviously more complex, for both traffic lights and trading systems.
But any mechanical (a.k.a. rule-based) trading system, such as those Trend Following programs employed by most CTAs would fit in the “algorithmic trading” category. In effect, any system that can be back-tested has to be based on algorithms.
A Duality in Algorithmic Trading Applications
There is a distinction between alpha-generation models and order execution models. The former has been implemented by CTAs with algorithmic trading for decades (using rule-based systems), whereas the latter is usually what the term algorithmic trading refers to nowadays (computerisation and automation of order flow in financial markets – as defined in wikipedia).
And this is naturally an area that also applies to CTAs. Whereas small retail traders face the opposite problem, CTAs’ larger size can represent an issue in terms of market impact and slippage, especially when dealing in less liquid markets.
As the Man press release highlights, not only does execution algorithmic trading benefits CTAs in reducing trade friction, it also allows them to tap extra markets, usually less liquid.
Example of Algorithmic Trading for Execution: Aspect Capital
Employing seasoned traders to execute orders dictated by the “alpha model” is one way to increase efficiency in trade friction. In this Automated Trader interview, Robert Wakefield, then COO of Aspect Capital – another London-based Trend Following Wizard – explains how their approach of mixing algorithmic execution with a desk of human traders allowed Aspect to reduce their net trading costs from 400 to 80 basis points.
The article is a few years old (2007) but gives an interesting example of the evolution of execution algorithmic trading within the CTA space.
Some excerpts (emphasis mine):
Ultimately, the focus for us is to use the execution strategies to reduce our footprint in the market. Our order flow is now definitely more difficult to identify and prey upon. An important input into the algorithm design stage is that our counterparties are becoming more sophisticated at exploiting systematic order flows. So delivering an order flow that is invisible is a key requirement for us in building up capacity in the trading models we are running and also protecting the returns generated from them. Minimising predation of our order flow is absolutely critical for us.
At the moment about 90% of our electronic trading is managed by our algorithmic execution model.
Commodities have stood out over the past year in terms of reduced trading costs. That has been partly due to the fact that some commodities are trading side by side on the floor and electronically, which has thrown up opportunities for price improvement.
In addition, we built some liquidity-seeking algorithms […] that allow us to trade instantly once our size and price conditions have been met. I think these algorithms have given us a real edge in commodity markets […] because we have been able to respond immediately while other participants have been looking at the floor price and the screen price and taking time to decide which price to trade.
We already have the capability to completely automate the execution process right from our “alpha models” (which generate the raw trading signals) through to our execution systems and the back office. However, while we have this capability, we still prefer to take advantage of the value that human traders bring to the trading process.
All signals generated by the alpha models are published as core position requests on the trading floor. However, we allow the traders a degree of freedom around these core positions, which we refer to as a “risk corridor”. Intriguingly, you sometimes find that the traders who outperform are those who do less trading and are at the lower boundary of the corridor. In range bound conditions the alpha model may wish to trade more than is actually ideal for the market conditions, so the trader can actually add value by doing less.
You can read the full interview there.