Maximum Favorable Excursion (MFE) is the greatest distance that the market moves for a trade during the time the trade is open. Our strategy for optimizing the profit target is similar to that used for optimizing the ISL. In this case, however, we are conducting the profit target analysis with the stop loss already in place.
Here are the MFE data gathered from the initial backtest:
Mean: Wins: 0.01055 (105.5 pips); losses: 0.00355 (35.5 pips)
Median: Wins: 0.00973 (97.3 pips); losses: 0.00303 (30.3 pips)
Std. dev.: Wins: 0.00563 (56.3 pips); losses: 0.00307 (30.7 pips)
The average winning trade amount was $70.98, or about 71 pips. It appears that the average winning trade is giving back about 34 pips before exiting at the end of the session. Because our goal is to try to capture those pips, 71 pips is a logical value for the lower boundary of the test range. For the upper boundary, we’ll add two standard deviations to the mean win MFE; the sum is about 218 pips. Though it may seem like wishful thinking to imagine profits this large, note that the largest winning trade with the ISL was 293 pips. Rounding to the nearest multiple of five, we have:
Lower boundary of test range: 70 pips
Upper boundary of test range: 220 pips
If we optimize in increments of five pips, the optimizer will test 31 possible values for the profit target. We’ll reuse our boundaries for optimizing the initial stop loss, keeping the increment at two pips. The total number of combinations is the product of the possible values for each variable: 25 x 31 = 775 possible combinations of initial stop loss and profit target values. This time, each optimization run will take a bit of time, perhaps several minutes or more, depending upon the speed of your computer. The optimization runs will be performed over the same time periods as before. The results are shown in “Merged results” (below).
Adding a profit target to the stop-equipped system caused profitability to drop from $775.92 to $103.05; expectancy remained positive, but dropped to 0.264. The results from the walk-forward period of November through December 2011 wiped out nearly all the profit that had accumulated during the prior walk-forward periods. Such a result calls into question the robustness of the system.
Although these results may not appear promising, they have a purpose. This is precisely why testing is so important before trading a system with real money. In the last part of this series, we’ll address this as we apply money management to the system and perform Monte Carlo analysis as the final test of our system.
Neil Rosenthal is a retired dentist who trades his own account. He also is an experienced computer programmer. He can be reached at firstname.lastname@example.org.