However, the FX markets have their own non-random behaviour.  It’s generally accepted among traders that each currency cross is different, with each having its own particular nuances.  To some extent, this is true, and therefore, if one finds that a certain set of parameters work well for EURUSD but not for GBPJPY, then it’s easy to find arguments to explain why the two crosses may behave differently, with economic data and news events being reported in different time zones etc. 

There are also moves specific to certain currency pairs, as FX is involved in every cross-border transaction across the world.  As a spot trader, I recall a certain, oil oriented, corporate customer always selling a market-moving amount of GBPNOK at a specific time every Friday.

Historic price data analysis may well have revealed that non-random behaviour, but without knowing why it occurred, it would have been foolish to trade it, as one may have lost a huge amount of money if the corporate customer changed its trading habits. 

There are also much broader characteristics of the FX markets. It is very well known that Europe is the largest trading centre by volume, followed by the US, with a very illiquid trading period, as the sun crosses the Pacific, until Asia comes in.

Although genuine FX volume data is impossible to quantify exactly, being so fragmented, and with no central exchange, we can use the CME currency futures as a proxy.  We find that their volume distribution is very different to the distribution of a typical futures market, as discussed above. The chart above shows a similar average hourly volume for the Canadian Dollar Futures contract, over a three-month trading period (UK Time).

TRADING SYSTEMS Building Robust FX Trading Systems

Just as with the futures markets, although volume analysis may not produce a robust trading system, it does illustrate that FX clearly isn’t entirely random and there is a very predictable, robust pattern, repeated by traders every day.

Fool’s Gold

With all of that in mind, it’s relatively easy to find systems that work well for specific instruments, on historic data, which would appear to have huge ‘edges’ and to come up with explanations as to why those parameters would work for a certain cross.

When testing enough parameters though, one will always find parameters that work for any indicator on a given market.  Take, for example, just testing a simple two moving average crossover combination, between 1 and 50.  This will return 2,450 different equity curves (assuming we count the 10 event crossing above the 20 event moving average, as a buy, and vice versa for a counter trend trade).

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