Training method; affects the way how parameters and factors are generated in the
The following flags can be set :
||Ascend optimization (default). Walks through all
parameter ranges and seeks for 'plateaus' in the parameter space, while
ignoring single peaks. This is normally the best algorithm for generating a robust strategy.
||Brute force optimization, for special
purposes (Zorro S required). Evaluates all
possible parameter combinations and selects the most profitable combination
that is not a single peak. This algorithm will likely overfit the strategy, so out-of-sample or walk-forward testing
||Genetic optimization, for special
purposes (Zorro S required). A population of parameter combinations is evolved toward the best
solution in an iterative process. In each iteration, the fittest - i.e. most
profitable - individuals are stochastically selected, and their parameters
are then pair-wise recombined and randomly mutated to form a new generation. This algorithm
can be useful when a large number of parameters per component must be
||Optimize considering the trade sizes (Lots). Large trades get more weight. Otherwise trade sizes are ignored in the training process.
||Optimize ignoring phantom trades. Otherwise phantom trades are
treated as normal trades in the training process.
||Optimize toward the highest peak in the parameter space, rather than
toward hills or plateaus. This can generate unstable strategies and is for
special purposes only.
||Generate individual OptimalF
factor files for all WFO cycles, instead of a single file for the whole
simulation. This reduces the factor quality due to less trades, but
prevents backtest bias.
Number of parameters to optimize in the current loop in training mode (read/only).
Current parameter or current generation, runs from 1 to NumParameters
in Ascend mode, or from 1 to Generations in
Genetic mode (read/only).
Number of the optimize cycle, starting with 1 (read/only).
Set this to 0 for aborting the optimization.
Maximum population size for the genetic algorithm (default = 50). Any parameter
combination is an individual of the population. The population
size reduces automatically when the algorithm converges and only the fittest
individuals and the mutants remain.
Maximum number of generations for the genetic algorithm (default = 50).
Evolution terminates when this number is reached or when the overall fitness does not increase for 10
Average number of mutants in any generation, in percent (default =
Average number of parameter recombinations in any generation, in
percent (default = 80%).
- Brute force and Genetic optimizations do not produce parameter charts.
It is recommended to train at first with the default Ascent algorithm for
determining the parameter dependence of a strategy.
- In Genetic optimization, parameter combinations that were already
evaluated in previous generations are not evaluated again and don't appear
in the log.
- Genetic optimization is also possible with the free Zorro version using
the Z Optimizer tool from the Download page.
TrainCycle, setf, resf
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