In implementing the candlestick method, if the model is in a long position and the four-hour candlestick closes positively, then we assume that the limit order was met before the stop order executes. However, if the candlestick closes negatively, then the stop order hits before the limit order is met. If the model is in a short position and the four-hour candlestick closely negatively, then the limit order hits before the stop order is met. However, if the candlestick closes positively, then the stop order hits before the limit order is met. Finally, if the four-hour candlestick closes in a neutral position, the opening price equals the closing price, we assume that the limit order is met before the stop order is hit.
The table “Method comparison” (below) displays the results of the three tests to resolve the ambiguity issue. Again, we use the candlestick method when displaying our results. We feel this is the most logical use of the data. In addition, because our model is actually a “lagging” model (we do not enter a trade until after the EMA cross signal), the model itself has a built-in degree of profit conservatism.
This simple model is an attempt to catch the self-similar trends located within the four-hour candlestick price set of the spot EUR/USD. We know that these trends occur, but we are not sure of their magnitude or their temporal periodicity. Using exponential moving averages as our trend indicator, we capture a small bite of each trend. Our goals are low cost of execution, low risk and stable returns. This model is meant to provide consistent returns to add to the net return of the firm; think trading this spot four-hour model on five currencies at an average yearly return of more than 15% with low to no human capital cost.
Leslie K. McNew is the executive in residence, University of Scranton, Kania School of Management. Reach her at firstname.lastname@example.org. Backtesting of this model was provided by Hussien O. Saleh and Peyton E. Veith of the University of Dayton School of Business Administration. Model validation and supervisory work were provided by Christine Muench, credit and risk analyst at E.ON Global Commodities North America. Additional model validation was provided by Zachary Hadaway.