Tradestation Update – New Monte Carlo Walk-Forward Analysis
The Walk-Forward Optimizer (WFO) now allows users to perform Monte Carlo Walk-Forward Analysis, which is a combination of Walk-Forward Analysis and Monte Carlo Analysis. This feature provides a different and potentially more conservative view of the original Walk-Forward Analysis.
By looking at the strategy from different angles, it provides a greater understanding of the true robustness of the underlying strategy. If the results of different walk-forward views confirm each other, they increase your confidence in the strategy – that is, in the likelihood that our strategy will continue to generate profits in the future.
The TradeStation WFO is the very first trading software supporting Monte Carlo Analysis performed concurrently with the Walk-Forward Analysis. Monte Carlo Walk-Forward Analysis is selected by checking the Monte Carlo option on the Setup Optimization settings dialog, as shown below:
Whereas the existing Monte Carlo Analysis feature (available on the Monte Carlo tab) is performed after a Walk-Forward Analysis has already been completed, the new Monte Carlo Walk-Forward Analysis method allows one to apply Monte Carlo sampling while a Walk-Forward Analysis is being calculated.
To explain how this feature works,let’s refer to the figure below:
The Monte Carlo Analysis, as per the Monte Carlo tab, calculates the analysis on the in- and out-of-sample trades (rectangle B in the figure) of the final Walk-Forward Equity graph, which is also displayed on the Graphs tab.
The new concurrent Monte Carlo Walk-Forward performs a Monte Carlo Analysis on each of the in-sample runs, i.e., on Run# 1-8 in parallelogram A in the figure.
With a traditional Walk-Forward Analysis, for each in-sample run, the WFO will scan once through the list of optimization tests and determine the best solution (based on the walk-forward selection rule) for the given run. This solution is then displayed in the Optimization Summary (In-Sample) and Walk-Forward Summary (Out-Of-Sample) report.
With a Monte Carlo Walk-Forward Analysis, the WFO will scan multiple times through the list of optimization tests (i.e., the total number of tests that were initially performed within TradeStation) and calculate the average of all the best solutions. The WFO will then display the average values in the Optimization Summary (In-Sample) and Walk-Forward Summary (Out-Of-Sample) report. See example report below:
As such, the Monte Carlo Walk-Forward method cannot be used for periodic re-optimization purposes because the method does not list any input values to be used (i.e., the Inputs column of the Optimization and Walk-Forward Summary reports does not contain any values).
In essence, the Monte Carlo Walk-Forward Analysis show the average results that can be expected if the same Walk-Forward Analysis is performed multiple times, yet with variations introduced by the Monte Carlo sampling procedure. (By default, the simulation method being used is Monte Carlo with Normal Distribution.)
The Net Profit displayed in the Walk-Forward Summary represents averages of multiple simulations. Thus, it cannot be traced back to a specific trade list and accordingly the Graphs, P/L History and Performance Summary tabs are not available for a Monte Carlo Walk-Forward.
Since several similar walk-forwards are performed, the computing time will be inevitably much longer than if the Monte Carlo option was not selected on the Setup Optimization settings dialog. If the Monte Carlo option was selected, then the Walk-Forward Analysis will take X times longer to compute, where X equals the number of simulations specified in the Monte Carlo Walk-Forward box on the Setup Environment dialog:
While the default setting is 10 simulations, most users will probably settle for a value ranging between 5 and 20, so as to produce a realistic computation time, depending on the speed of the computer available and number of tests per WFA. Because of the extended computation times involved, we need to warn users upfront that you need to carefully consider if Monte Carlo Walk-Forward Analysis is suitable for your own use. In terms of efficiency, the WFO is fully multi-threaded and can utilize a multi-core CPU to its full potential; thus, a long waiting time is a clear indication of how computationally intensive this method is.
It must also be noted that the Monte Carlo Walk-Forward feature is completely optional. If you feel that you have the actual time and computing power available, then you may want to experiment with this new method. As such, we would not recommend using this method unless you are an experienced user of the WFO.
The purpose of Monte Carlo Walk-Forward Analysis is to provide a different and more computing intensive (and potentially more conservative) view of the original Walk-Forward Analysis. Ideally, we would want the Optimization Summary and Walk-Forward Summary reports produced by Monte Carlo Walk-Forward Analysis to convey a message similar to that of the same reports produced by a standard Walk-Forward Analysis. However, since the Monte Carlo Walk-Forward Analysis reports are based on averages, it should be expected that the overall result may deteriorate from the original.