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THE PIVOT POINT MOVING AVERAGE FOREX TRADING SYSTEM
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I call the pivot point moving average system the Defcon III system; its de sign was based mostly on pivot point theory. First, I use the pivot point moving average to help filter the projected support and resistance levels based on the closing price in relationship to the actual pivot point [(High + Low + Close)/3]. If prices settle above the pivot point by a certain percent age or PIP basis, then the market is determined to be in a bullish mode. Therefore, I look for the range of the next time period to be between S-1 and R-2. The opposite is true for a bearish outlook: If the market is bearish, then I look for the market to stay between R-1 and S-2; and that is what I look to be the projected range for the next time period. The next dimension I use to help determine trading signals is the use of two moving average components that generate buy and sell signals as prices cross and close above and below the averages. The moving averages are based on pivot points, and I use a short-term and a longer-term time period for these values. You can determine the best time periods and experiment with a pivot point moving average (H + L + C/3) of various time frames on the markets of interest you choose. You need to scan and test various markets to detect the ultimate time frame for that select market as a function of volatility.

Remember that a slower-moving, less-volatile market will respond bet ter with lower time frame settings. A market with heightened volatility with shorter time frame settings will generate too many signals. Let's face facts: The euro and the British pound have larger swings on a daily basis than do the Japanese yen or the Canadian dollar. You want to mechanically and also visually back-test and optimize your settings for the various currencies. Genesis Software allows for the mechanical analysis; and just by setting up the parameters on your moving average settings, you can scroll back on the charts to see what the market did at these cross-over spots. This is what I consider a multidimensional trading method because I am looking for more than just one function in order to trigger a trade. As I stated earlier, I in corporate a moving average approach to help automatically filter out the projected support and resistance levels and use the pivot point value as a moving average. Next, I need one more criteria to confirm a sell signal at re sistance or a buy signal at support. This is determined by the programming of a high close doji or a low close doji. I also have a few other patterns that help generate buy and sell signals at or near the project pivot support or re sistance levels.

Generally speaking, I am looking for a confirmation of a shift in momentum by spotting a conditional change with a higher closing high for a buy signal or a lower closing low for a sell signal. The programming has fil tered how many past time periods it will use for these signals. In other words, if the market is in a steep decline, with the pattern formation of lower closes than opens, lower highs, lower lows, and lower closing lows, a conditional change would be what? A conditional change would be a com bination of those events to reverse, such as prices forming higher closing highs, with higher highs and higher lows as the closes of each candle are above the open. The sample trade signal shown in Figure 8.7 illustrates how prices drifted lower and then fell in a steep decline. You will see that this downtrend consisted of the sequence of lower closes than opens, lower highs, lower lows, and, most important, lower closing lows (the close was below the prior one or two time frames' lows). That is what really defines bearish momentum. Now as prices reach the projected pivot point support target level, the trained professional or candlestick aficionado will notice that the exact low was formed by a doji; then two candles later, an inverted hammer formed. The buy signal is generated as the moving average crosses; and we see, as I described earlier, a conditional change occurred.

The system kicks in a nice buy signal; and as Figure 8.7 shows, a bullish trend develops and gives significantly more than our 40-PIP profit tar get. This leads me to explain that a mechanical trading system can be enhanced by discretionary input if you have the discipline to follow through with trailing stops and to monitor the price action rather than forming an opinion based on greed with respect to increasing your expectations on the outcome for the trade. Let me be more specific: The system generates a buy signal, and you enter multiple lots. As the market moves in the desired direction, you scale out of half or two-thirds of your positions and then enter a trailing stop. What was intended to be a day trade might now be carried into an overnight or even a swing trade lasting several days. As long as you do not start to build an opinion that the current trend might last forever and stick with a risk mechanism, then you can milk the trade and increase your performance. The trouble is—and the breakdown in a trading plan occurs—when traders stop following a system-driven plan.

Let's examine the performance statistics shown in Table 8.4 and see how a moving average method fared compared to the previous two most popular indicators, stochastics and MACD. The overall performance is pretty good, with a 63 percent winning accuracy rate and a net return of $46,750. The moving average system recommends start-up capital per posi tion based on a $100,000 lot size of $7,963. That is slightly more than the stochastics system ($6,050), which had a winning percentage of 67 percent and produced a net profit of $40,400, and less than the MACD system ($10,820), which had a winning percentage of 57 percent and a net profit of $34,180. The best performer was, indeed, the moving average system, from the standpoint of generating the most profits. It even ranked the best in the profit factor category, with a reading of 2.03, as well as in the Kelly ratio, with a ranking of 0.3191.

Looking at the equity curve shown in Figure 8.8, notice that the draw down periods are less dramatic and that equity growth is on a steeper curve. One more item that stands out here is that the moving average sys tem tends to not demonstrate as many nor as significant drawdowns against peak equity performance. Apart from a one-time maximum intraday drawdown in November 2004 of $4,400, this system shows decent consistency in profitable results, as well as fewer drawdowns.

Looking at the monthly performance statistics in Figure 8.9, we uncover the most profitable months and, best of all, the worst month. I say “best of all” because running a back-test as I reviewed earlier lets us see, at least from a historic perspective, what the weakest link in the armor is. In the moving average system we see that April is the worst month; and now we can draw a few conclusions and possibly defend our trading capital by dissecting the signals more closely when trading during April. Seasonality might be a factor, as the market may be choppy and sloppy because April is tax month and the first month of the second quarter. Capital flows may be a factor that influences the ebb and flow of the currency markets at that time as it applies with the moving average system. September and Novem ber, while profitable, seem less so; and, therefore, by studying a system, perhaps you will be able to make better decisions or decide not to trade during those periods.

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