The system produced 1,038 trades, of which 505 (48.65%) were long trades and 533 (51.35%) were short. As expected, this result approaches the theoretical 50/50 heads/tails split of an unlimited number of coin flips.
Winners and losers were nearly an even split, as well: 513 winners (49.42%) and 525 losers (51.58%). Consecutive wins and consecutive losses both averaged two in a row. The average winning trade amount was $64.38 and the average losing trade amount was $64.51 — again, nearly equal. Due to this small difference, the system generated a net loss of $838.60, representing an 8.386% loss of equity (see “Strategy tester report,” below). If we can reduce the amount of the average loss (with a stop loss) and increase the amount of the average win (with a profit target), the system can be made profitable. MAE and MFE should help us to do that.
Using Excel’s sorting ability, the winning trades are separated from the losing trades. Excel can calculate the mean, median and standard deviation of MAE and MFE using its built-in functions, AVERAGE, MEDIAN and STDEV. Click here to view more detailed statistics.
The results of the calculations are shown in “Statistical analysis” (below). (The calculations for all trades combined also are shown for completeness.) The mean MAE is 0.00291, or 29.1 pips. For trades that exit ultimately profitably, the market averages about 29 pips against the position prior to the exit. Contrast that with the mean MAE for losing trades: 95.6 pips. Clearly, there is no need to tolerate a move of 96 pips against the trade. The MAE data indicates that the trade is unlikely to exit profitably after such an adverse excursion. On the other hand, it would be unwise to set the ISL blindly at 29 pips because too many good trades will be stopped out prematurely. The optimal stop loss likely lies somewhere in between.
Turning to the MFE data, with a mean MFE of 97.1 pips, the market averages 97 pips of open profit prior to the exit. Note the difference between the average MFE of 97 pips and the average win of approximately 64 pips (one pip = $1 when trading a single mini-lot). The typical winning trade is, apparently, giving back at least 30 pips of profit before the trade is exited at the close of the session. If we can capture those additional pips, then combined with the stop loss, we should be able to increase the ratio of average win to average loss for a net profitable system.
Here, we have laid the statistical foundation for improving our core system. In the next article, we will add the ISL to the system and use our MAE findings to optimize it. The next step will be to do the same on the profit side: Add the PT to the system and use MFE to optimize that. Each step is vital groundwork for our ultimate goal of developing a complete trading system.