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Data Mining a Daniel’s Strategy

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1Data Mining a Daniel’s Strategy Empty Data Mining a Daniel’s Strategy Tue Oct 16, 2018 12:47 pm



Daniel uses a data mining approach to develop a strategy for trading the four Forex majors.

In order to construct his system, Daniel used his data mining software to define entry and exit signals that would have produced a profitable trading strategy on each of the four currency pairs over the past 20 years. What he comes up with is a combination of three price-based rules that form the foundation of his Forex Majors strategy.

Daniel’s Forex Majors strategy is very simple in that it always has a position, either long or short, in each of the four currency pairs that it trades. It bases all of its trades on daily charts.

The strategy goes long when the following three conditions are met:

  • Close[9] > Close[10]
  • Open[158] > Low[130]
  • Close[156] > Close[173]

The strategy goes short when the following three conditions are met:

  • Close[9] < Close[10]
  • Open[158] < Low[130]
  • Close[156] < Close[173]

As you can see, the strategy is basically an optimized trend following strategy. This makes sense, because Daniel states at the beginning of his article that long-term trend following strategies are generally the best strategies for trading multiple markets.

One additional rule that Daniel’s strategy makes use of is an ATR-based stop-loss. The fixed stop-loss is set at 180% of the 20-day ATR. If the stop-loss is triggered, the strategy remains out of the market until a signal is generated in the opposite direction. Testing indicates that re-entering on a signal in the same direction negatively affected performance.

The backtesting results that Daniel included in his post show that the strategy was quite profitable. It produced a win ratio of 45%, a profit factor of 1.38, and a reward to risk ratio of 1.68. Daniel’s biggest concern about the strategy was that the maximum drawdown period represented a very long time.

According to Daniel’s numbers, the mean annual return was 9.67%. This consisted of 16 profitable years, 4 losing years, and one year that basically broke even. The best year was a return of 37.76%, and the worst year was a loss of 20.2%.

Daniel notes that this system would not represent a good standalone strategy because of its returns relative to maximum drawdowns. However, he suggests that it could be an interesting piece of a larger, multi-system strategy.

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