|By Toby Cole, managing director FX client advisory group and Jeremy Smart, global head of electronic distribution at RBS.|
Firms are increasingly under pressure to provide the best all-in price for clients and high quality execution.
The recent spate of law suits brought by pension funds relating to excessive charging for FX trades brings further resonance to an Asset Managers desire to receive competitive pricing and high quality execution.
As a result, it has never been more important to think about your current FX execution policy and grasp the fundamental concepts underpinning FX algorithms. Already widespread, use of these techniques is set to grow further.
The term algorithmic trading is a catch-all covering two different types of strategies.
1. Trading strategies
This is where technology is used to determine a trading requirement. It can be programmed to follow different trading strategies to suit the users risk appetite.
An example would be asking the system to buy CHFJPY when the 30-day moving average of USDJPY crosses the 30-day moving average of USDCHF. Algorithmic trading can be high or low frequency. High frequency trading is a subset of algorithmic trading focused on market-making.
2. Execution strategies
These are used to execute trades into the market in a manner which helps to minimise market impact and transaction costs. The technology can offer a huge range of different execution styles, with strategies selected according to the risk appetite of the client and the market conditions at the point of execution.
A simple example would be selling a currency pair using a time slice order. The algorithm will slice the order into pre-determined pieces of risk to trade at specified time intervals. Although this spreads the time over which the execution takes place, the smaller order sizes help to limit the transaction costs involved.
To fully appreciate the need for algorithmic execution and the opportunities for algorithmic trading it is necessary to understand how technology has already transformed the FX world. Once the FX trading between dealers was dominated by only two systems, EBS and Reuters. Now there are many trading venues, some open to clients as well as banks, such as CME, Currenex, Hotspot and Lava.
The development of this market structure and the availability of liquidity in electronic form has encouraged banks to invest in their own single-dealer platforms and in the automated engines that manage the risk. There is an increasing trend among major liquidity providers to internalise flow, matching buyers and sellers and warehousing risk.
As a result while there is a proliferation of different trading venues the market impact of trading in different places can be very varied. Furthermore, structural changes and the increased automation of markets have led to the introduction of high-frequency trading (HFT) into the FX market.
Some HFT strategies are specifically designed to monitor market activity for signs of larger orders being placed. This can mean that observable liquidity is not always available and order placing needs to be managed to prevent signalling risk.
In some respects it is the growth in automated trading and HFT that has driven the increased demand for more algorithmic execution as clients look for ways to keep their orders confidential, helping to minimise market impact.
Alongside regulatory and customer pressures, these drivers combined with the current world of relatively low investment returns and low market volatility all combine to promote algorithmic execution.
We live in a time when saving a few basis points of execution costs can make a real difference. Spending time to determine the right execution approach and analysing post trade transaction costs become an integral part of the investment process.
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