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IMO, the main objective of the optimization phase is to select what appears to be the most robust set of parameter settings, which may not necessarily be the most profitable setting. This relates specifically to the sensitivity of these parameter values which as ASXG mentioned in a previous post means a lookout for a relative stable plateau or platform as opposed to a sharp point with steep fallouts in all directions. The most robust settings would be right bang in the middle of the plateau.The less sensitive the parameter values the greater the scope of these parameter values to change without significantly impacting performance. Therefore, outside of giving us valuable information regarding the “pockets” of outperformance within the in-sample data, I am not sure whether there are any information that can be gleened from the optimization exercise. If there are more “general characteristics that precede profitable trading opportunities” that can be extracted, I would certainly like to hear about them.OK, some clarification here. The Monte Carlo analysis that I suggested being performed on the out of sample data is purely for verification purposes only, by using optimized parameter values created from running the optimization and parameter value sensitivity testing on the in-sample data. At no stage am I advocating that we convert what was previously out of sample data into in-sample data by re-optimising the previously out of sample data and extracting new optimized parameter values. Without the re-optimisation process, one really does not convert out of sample data to in-sample data.As part of the walk forward process, we would use optimized parameter values to test against out of sample data. The point is that while we are performing this walk forward process, there is really nothing stopping us performing Monte Carlo testing at the same time. If the system gives more signals than the trader has to trade then Monte Carlo just give the testing procedure more “credibility” by subjecting the out of sample data to a more comprehensive level of testing than a single walk-through could ever provide. This then provides more level of “confidence” should the results come out as expected …..
IMO, the main objective of the optimization phase is to select what appears to be the most robust set of parameter settings, which may not necessarily be the most profitable setting. This relates specifically to the sensitivity of these parameter values which as ASXG mentioned in a previous post means a lookout for a relative stable plateau or platform as opposed to a sharp point with steep fallouts in all directions. The most robust settings would be right bang in the middle of the plateau.The less sensitive the parameter values the greater the scope of these parameter values to change without significantly impacting performance. Therefore, outside of giving us valuable information regarding the “pockets” of outperformance within the in-sample data, I am not sure whether there are any information that can be gleened from the optimization exercise. If there are more “general characteristics that precede profitable trading opportunities” that can be extracted, I would certainly like to hear about them.
OK, some clarification here. The Monte Carlo analysis that I suggested being performed on the out of sample data is purely for verification purposes only, by using optimized parameter values created from running the optimization and parameter value sensitivity testing on the in-sample data. At no stage am I advocating that we convert what was previously out of sample data into in-sample data by re-optimising the previously out of sample data and extracting new optimized parameter values. Without the re-optimisation process, one really does not convert out of sample data to in-sample data.
As part of the walk forward process, we would use optimized parameter values to test against out of sample data. The point is that while we are performing this walk forward process, there is really nothing stopping us performing Monte Carlo testing at the same time. If the system gives more signals than the trader has to trade then Monte Carlo just give the testing procedure more “credibility” by subjecting the out of sample data to a more comprehensive level of testing than a single walk-through could ever provide. This then provides more level of “confidence” should the results come out as expected …..
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