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Running searches

To start a search click Run after you create and save the workspace.  The progress bars will indicate the progress of the search:

The first progress bar, Applying Feature Sub-Clusters, shows the sub-cluster from the major cluster that the program is using at that moment and the particular search line number.

The second progress bar, Processing Data, shows the progress of the search in the specified Test Sample Size and the data file processed.

The third progress bar, Applying Target and Stop, shows which profit target and stop-loss pair is applied from the T/S file in the relevant search line.

Useful information about the learning process is found below the progress bars.

The main cluster type is shown and the percent of search completed that takes into account all active search lines.

The deep learning efficiency can be low, average, high and very high.  If percent completed is more than 50% and efficiency remains low, this should be a warning that it may be advisable to terminate the search.

Suggestion can be None or Abort, in case there is indication that the learning process does not converge.

Total features tested refers to strategies tested and Total accepted features refers to strategies that fulfill the performance constraints. In addition that total number of accepted long and short strategies is shown.

Total predictions is related to the number of signals generated by accepted strategies. Total successful predictions is the number of signals that reached their profit target. In addition, the combined hit rate of all accepted strategies is displayed and the approximate (expected) profit factor of all strategies combined.

The above information allows the user to evaluate the search progress in real-time and decide whether to continue or abort to save development time.

Clicking Abort terminates the search. Results are NOT not saved.