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SEASONALITY PLAYS ON FOREX CURRENCIES
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When evaluating systems, the power behind the software is certainly im portant in providing you with as many details as possible. One such aspect is determining if the performance of some months is better than others. Seasonality factors do have a tendency to influence price behavior in currency because money flows change at fiscal year-ends as multiconglomerate corporations move money to balance their books to tax season in April.

If you can determine which months perform best, you can enhance your performance or at least be aware that the system performs at it best or worst in various months. There are many analysts who contend that there are historic performances in currencies with a high degree of accuracy. I just want to review my back-test results to see how my system fairs at certain times of the year, and then I can look into each month to see if it was a situation that may reoccur (such as at the end of a fiscal year).

The graph in Figure 8.3 shows March as having a vicious drawdown pe riod. Granted, there are not enough sample years to determine if there is a high probability that the same phenomenon will reoccur, but the results certainly will help me to look at trading during that time period the follow ing year.

My philosophy on trading is to use a method that provides consistency and a better-than-normal degree of accuracy with a good risk/reward ratio.

The method should be built on time-tested strategies that are slightly com plex yet simple. I believe this stochastics-pivot point method is one such method and a system that you can build on yourself. So as the song goes, “Shine on, you crazy diamond!”

Now let's examine the MACD Histogram Trading System. The moving average convergence/divergence indicator is based on a series of moving averages and, therefore, can be considered more of a lagging indicator than stochastics is. Since many forex traders use MACD, I wanted to run a sys tem based on the same parameters, under the same conditions, and for the same length of time as the stochastics to see which one would produce bet ter results or a more reliable system.

The parameters used in the MACD settings were 12- and 26-day expo nential moving averages (EMA). To determine the two moving averages, the “fast line” is calculated out as follows: Take the 26-day EMA and sub tract it from the 12-day EMA. The slow-line average is the 9-day EMA of the fast line. The histogram is figured out when the fast line is above the slow line; the bar will read above the zero level. Inversely, when the fast line is below the slow line, the histogram will cross below the zero level.

These crossover features are what trigger buy and sell signals. The time frame in which this system was performed was a 15-minute time period and for all trading sessions, meaning a 24-hour period from a 5:05 P . M . (EST) open and using a 5 P . M . (EST) close. The pivot points were calculated at the 5 P . M . (EST) Bank of New York settlement time. The parameters for the MACD system sell signals were programmed to trigger within 10 PIPs above or below the pivot point R-1, R-2, and R-3 levels, once the histogram bar crossed and closed below the zero line.

The testing signals are included at the R-1 level, but it does not define a wide enough price extreme or departure from the means as defined by the typical price or the actual pivot point. When prices have had an extreme price move from the prior session, R-1 would be calculated at a significant enough distance that I would consider a move as being in a short-term over bought state. Once the sell signal is generated and triggers a short position, we use a 100-PIP initial stop from entry price with a 40-point profit target. The 100-PIP risk level is mainly used as an emergency stop, which can be programmed to be trailed or moved as prices move in the desired direction.

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