Forex Automaton

This GBP/USD back-testing analysis continues the series which began with EUR/USD. Simulated track records of six best Step Two algorithmic traders are studied graphically. For a more numbers-oriented approach to performance, see the article explaining the trading system optimization process which led to the selection of these six robots.

Table 1. Various modes of the trading system operation. The present report deals with performance of selected Step Two algorithmic traders.

A run of the back-testing program created simulated track-records for just the six independent "virtual traders" (forex robots), each of them being an incarnation of the same algorithm, differing by the setting of the adjustable knobs. The robots had been selected on the basis of Step One vs Step Two performance comparison, and are believed to balance risk with return well while taking advantage of the multi-market capital allocation available in the Step Two algorithm. (The article linked above gives details of the traders' input parameters and the performance figures of merit). The algorithm learns continuously, using only data from the past of the time series. Therefore, every trading step, even during the simulated trading, tests applicability of the past experience to the present moment. In Step Two mode, co-existing trading opportunities in markets other than the market under study may influence which trades are placed. There is a competition between the markets.

Fig.1. GBP/USD bar charts for the period under study. Day scale. The chart includes training time when no trades could be placed.

As in all other studies posted here so far, the trading is performed on one-decision-a-day time scale, with 1:100 leverage, risking no more than 10% of the trading capital at any point in time. Historical EUR/USD, USD/JPY, GBP/USD (Fig.1), USD/CAD, USD/CHF and AUD/USD day scale data are used, covering the time interval from August 20, 2002 to August 21, 2009, with the actual trading starting in April 2006 (when the initial "training" of the system was completed). This report covers the trading in GBP/USD, for all six traders.

		Trader position
		long	short
Market agrees	yes
	no

Fig.3. Performance track record chart showcasing T588, one of the most successful robots trading GBP/USD. Day scale. Bars in the charts are color-coded according to the type of trading position held, as explained in the table. Black corresponds to no trading position. The time range is restricted to highlight the high volatility phase when the trades were placed.

Discussion of GBP/USD performance

The selected robots trade GBP/USD only during the period of highest volatility. No trades have been placed in 2006 and 2007. While most money is made by shorting GBP/USD, the system is not shy of occasionally changing the direction from short to long as it did in October 2008 -- although not with such a spectacular opportunism as was demonstrated in trading EUR/USD.

The adherence of the trading system to high volatility in GBP/USD is not much different from what has been seen already with EUR/USD and USD/JPY. The fact that the optimized systems make money in what turned to be a deadly environment for so many investors should be taken very cautiosly, for although there is no direct "benefit of hindsight" as the algorithms do not know the future, the "benefit of hindsight" may enter via selection bias. I dislike the fact that the systems do not trade unless the volatility is extreme, since now there seems to be a risk of launching a system which will never place a trade if the volatility storm brought about by the financial panic of 2007-2008 finally abates. This is a topic of special discussion, but the core problem is now easy to identify: the way the entry and exit parameters are formulated, unlike the stop-loss parameter, is non-invariant with respect to volatility. In other words, the threshold is applied to a quantity (relative expected price change) which is large when volatility is large and small when the volatility is small. That this quanity is a relative price change does not change the fact that the threshold effectively favors high volatility. The reason I end up with strategies trading only high volatility is because I try to minimize the risk, and the risk is minimized by minimizing the number of trades, by being selective about them. This is not too difficult to fix by changing the way selectivity threshold is calculated. This problem is also completely different from the problem of obtaining the market direction forecasts on which the trades are based: it has to do with what fraction of the trade ideas that get accepted, not with the composition of the pool of trade ideas subject to this filter.

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