e-Forex Magazine | Retail Forex Client | High frequency automated FX trading

Retail Forex Client

Andy Webb

High frequency automated FX trading

First Published in e-Forex Magazine January 2005

Andy Webb

Freelance writer

The concept of automated trading has attracted rapidly growing interest in recent years. Andy Webb looks at a key focus for this growing interest which is high frequency autotrading.

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The concept of automated trading has attracted rapidly growing interest in recent years. In certain markets, such as exchange-traded futures, it has already become an everyday fact of life. In others, such as interbank spot FX, the party is just beginning to get underway.

Irrespective of the specific market, a key focus for this growing interest has been high frequency autotrading. While technology advances have certainly been a factor in this, another major driver has been the inexorable decline in transaction costs.

Lower cost, higher frequency
Assume for a moment that the average profit per trade for a trading system should be at least ten times the cost of trade execution. On that basis, a fall in transaction costs from $50 to $5 cuts the minimum acceptable profit per trade by $450 - from $500 to $50. This makes it viable to deploy trading systems with a smaller profit target per trade but a higher trade frequency. Typically such systems will also be using very short timeframe prices (e.g. single ticks) as a data input and will typically be handling smaller deal sizes.

Yet sooner or later this increasing trade frequency runs up against human limitations. There comes a point when it is simply no longer physically possible for the trader to hit the keypad or click the mouse fast enough, not to mention managing the resulting positions.

This conflict has been a further factor in the growth of high frequency autotrading. Even where an automated trading environment generates fewer trades per market than a human trader can handle, it can of course replicate its actions across multiple markets and timeframes. Furthermore, it is far less restricted in the number of intermarket opportunities it can observe and act upon.

An automated system is also unaffected by the psychological swings that human traders are prey to. This is particularly relevant when trading with a mechanical model, which is typically developed on the assumption that all the trade entries flagged will actually be taken in real time trading.

This is sometimes hard for a human trader to do - and not just because they may be away from their desk when a trade signal is triggered. A mechanical trading system can experience long runs of losing trades, so a human trader contemplating placing a new order after suffering six losing trades in a row may be tempted to withhold the order. Mechanical systems often depend for their overall profitability on a relatively small number of winning trades outweighing a larger number of smaller losers, so this can be critical. In futures markets this has prompted some technology vendors to deploy customer trading models on the broker/clearers servers within the exchange, rather than the traders workstation.

Furthermore, while the execution of the trading model may be automated, its design and coding are still performed by humans. Any errors undetected in the development stages will sooner or later emerge (probably with expensive consequences) in real time trading. Therefore it is essential to have a robust risk management infrastructure capable of terminating the activities of a rogue trading model that has run amok.

Some automated trading environments already offer this infrastructure, with a broad range of controls that can be applied to the trading systems. The FX broker EBS has created a laboratory facility which allows customers to test their model trading algorithms in a secure environment using historical FX market data and live market rates as part of its Spot Ai trading offering.

Spreading the risk, smoothing the curve
One of the most important advantages of automated trading is ease of diversification. While diversification by market is widespread, diversification by timeframe and trading model are far less common. A high frequency automated trading environment is ideally suited to these additional diversification opportunities. In such an environment it becomes relatively straightforward to deploy the same (or differing) trading models across a portfolio of timeframes.

The benefits of time diversification in terms of reducing correlation (even when using the same basic trade system rules) can be striking. Figure 1 shows the equity curve for a very simple reversal trading system applied to 20-minute GBPUSD data over twenty trading sessions. (Twelve hour trading sessions, 0600 to 1800 UK time). Applying the same trading logic in a 1-minute timeframe obviously generates a far higher trade count than the 20-minute data (approx 5000 trades versus 270). If the 1-minute trades closest in time to the 20-minute trades are sampled to produce two sets of results of the same size, the correlation between the two data sets is just over 0.1 a very small positive correlation. This also highlights the need for access to the most accurate and timely market data directly from its purest source.

Although the trade system in Fig 1 is profitable, it has two notable drawdowns at A and B, and an underwater period at C immediately after trading commences. However, by virtue of the larger number of data points it generates, high frequency trading can be added to the mix to improve the smoothness of the longer time frames equity curve and to reduce its standard deviation of returns. Figure 2 shows the effect of adding the 5000 results for the 1-minute time frame mentioned above to the 20-minute time frame results in Figure 1. In addition to largely removing the drawdowns at letters A and B, the higher frequency results also eradicate the initial underwater period at letter C. The standard deviation of returns was also reduced by a factor of seven.

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