From the July/August 2013 issue of Futures Magazine • Subscribe!

Unlocking the mystery of building trading strategies

Trade management

Most traders would agree that a system’s exit rules are equally as important as its entry conditions. It is possible to test a variety of exits to see which type of exit will yield the best results. Exits include those based on price, such as profit targets, stop losses and trailing stops; and those based on other factors, including time-based exits (such as end-of-day) or the passage of a specific number of price bars.

Our example strategy uses a stop-and-reverse methodology; essentially, it is the trigger of a new trade that generates an exit signal for the previous trade. In an attempt to improve the performance of the strategy, we test a variety of exit conditions.

The addition of a profit target seems to show promise, and we perform an optimization study to determine the best value. Instead of being a stop-and-reverse strategy, the system now exits at either a profit target (optimized at 12.3% of the trade entry price) or with an opposing signal, whichever comes first. “Banking profits” (below) shows this version of the strategy, with both the optimized trade filter and an optimized profit target in place. 

The addition of a profit target makes a modest improvement to many of the metrics in the strategy performance report. With the trade filter and a profit target, the strategy has a total net profit of $55,562 and a profit factor of 1.99; the maximum drawdown remains unchanged at $9,894. It is important to remember that these are not the only important metrics found within a performance report, and attention should be paid to the overall report when analyzing and comparing results.

Stop losses protect traders from large losses by signaling an exit if a trade moves in the wrong direction by a specified amount. Stop losses can be incorporated into any strategy, and again, traders can try different types of stops (such as one based on percentage of the trade or a fixed dollar amount), and optimized to find the value that yields the best results.

Optimizing and backtesting on historical data, as we have done here, provide valuable information. However, these steps are only part of the evaluation process, and the results can be misleading. While outside the scope of this article, out-of-sample testing and forward performance testing can offer a more realistic assessment of the system. Good correlation between backtesting, out-of-sample and forward performance testing results is vital in determining the viability of a trading system.

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