High frequency automated FX trading
Andy WebbFreelance 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.
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
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
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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