There are many money management techniques, but for our purposes we’ll employ a simple fixed-fraction scheme. This method involves using a constant fraction of the account balance (usually expressed as a risk percent) to calculate the size of the trade. Because our model system trades the forex market, trades are sized in terms of lots (in the stock market, trades would be sized in terms of shares of stock, and in the futures market, in terms of contracts). The formula for calculating the trade size is:
Lots = (Account Balance x Risk Percent) / Initial Stop Loss
For example, assume a starting account balance of $10,000, a risk percent of 2% and an initial stop loss of 40 pips (because we are trading with mini-lots at $1/pip, this is equal to $40):
Lots = ($10,000 x 2%) / $40
Lots = $200 / $40
Lots = 5
The trade would be entered with a trade size of five lots.
As noted previously, the general advice to traders is to restrict per-trade risk within a range of 1% to 3% of the account balance. Thus, we will run our optimization using this range, and will use increments of 0.25% to fine-tune our trade size. This results in nine possible values of our risk percent.
Our optimization runs will follow the pattern set down in the second part of our series: A six-month in-sample optimization followed by a two-month walk-forward test, using the data from January 2010 through December 2011 as before. This time, we will optimize on two variables simultaneously: The initial stop loss and the risk percent. For the initial stop loss, we will use the same range as before (40-88 pips in increments of two). This results in 25 possible values of our initial stop loss. The total number of combinations that must be tested is equal to the product of both sets of values (9 x 25 = 225 combinations). Fortunately, because of the speed of modern computers, this will take two minutes or less.
In our previous optimizations, we held the lot size constant at one lot. As such, the drawdowns experienced during the optimization runs were not excessive, and we focused on net profit as the criterion by which to choose the best parameter from that optimization set. However, when using lot sizes greater than one, drawdowns can, and do, become excessive. Therefore, we will attempt to find the sweet spot in our optimization set: The best compromise between net profit and a drawdown that does not exceed 10% of our account. (We can stretch that up to about 12% if the net profit warrants doing so.)
The results of the full optimization are far better than were expected: Net profit increased from $775.92 to $4,431.48. Expectancy was slightly less than before at 1.164 vs. the previous 1.19, but that was more than compensated for by proper trade sizing and money management. Also, despite a winning percentage of only 45%, the system still turned a profit. The full results are shown in “Strategy tester report” (below).