519
edits
Changes
Different traders use different criteria to define strategy performance. Some traders use the highest net profit, while other traders use the lowest drawdown. MultiCharts lets the trader define his own criteria.
Optimization can have detrimental effects if the user searches for the combination of inputs based solely on the best performance over a period of historical data and focuses to too much on market conditions that may never occur again. This approach is known as over-optimization or curve-fitting. Performance will not be the same in real trading, since historical patterns are highly unlikely to be repeated.
<br>
The drawback of the GA approach is that the solution found will be a solution approaching the absolute optimum solution, but not necessarily the absolute optimum solution itself. This drawback, however, is handsomely offset by the processing power and time savings in cases with a large number of possible solutions.
In general, GA's work is primarily about two abstracts: an Individual (or Genome) and an Algorithm (i.e. Genetic Algorithm itself). Each Genome instance represents a single unique inputs combination, while GA itself defines how the evolution should take place. The GA uses a given trading strategy to determine how 'fit' a genome is for survival, e.g. how much Net Profit does an inputs combination generates in case Net Profit was selected as an Optimization Criteria.
<br>Here are some GA definitions that help in understanding the process:
# After a number of all possible combinations is determined, an optimal number of individuals is selected.
# Each individual is selected at random. These individuals form the first Generation. The optimal number of individuals is automatically placed in next to the '''Set Population Size''' field box and can be changed manually.<br><div style="background-color: #E5F6FF;">Tip: An excessively large Population Size value will result in an increase in calculation time, while an overly small Population Size value will result in a decrease in calculation accuracy.</div><br><div style="background-color: #E3FBE5;">Note: MultiCharts' GA support artificially exclusive population. This means that identical individuals cannot exist inside the same population, and thus the population size can not exceed the total number of input combinations. The population size is constant for each generation.</div>
# The fitness of each individual is evaluated and the least fit individuals discarded.
# A new population of individuals is generated from the remaining members of the previous population by applying the crossover and mutation operations, as well as selection and/or replacement strategies that depend on the GA subtype: <br>
#: '''GA Subtypes and Replacement Schemas'''
#: GA subtype defines the way that GA creates new individuals and replaces old individuals when creating next generations.
#: GA subtype can be set in the '''Genetic Algorithm Subtype''' drop-down listsection.
#: Two GA subtypes are available: '''Basic''' and '''Incremental'''.
#: '''Basic''' subtype is the standard so-called “simple genetic algorithm”. This algorithm uses non-overlapping generations and Elitism mode (optional). For each generation, the algorithm creates an entirely new population of individuals (if the '''Elitism''' option is selected, the most fit individuals move on to the next generation).<br>
#: '''Worst''' – least fit individuals are replaced
#: '''Parent''' – parent individuals are replaced
#: '''Random''' – individuals are replaced randomly<br>#: '''Offspring Number''' #: Offspring Number is the number of children to be added each time that a new generation is created. '''One''' or '''Two''' children can be added. #: * The fitness of each individual is evaluated and the least fit individuals discarded.#: * The process is repeated, until the specified degree of convergence or generation number is reached (depends on GA setting selected).<br>
#: '''GA Convergence Type'''
#: Genetic Algorithms optimization process has no implicit final result and thus can proceed forever. Therefore, an "ending-point" must be specified, indicating when the optimization process must come to an end.
#: '''Terminate-Upon-Generation''' will stop the optimization process once the specified '''Maximum Number of Generations''' is reached.
#: '''Terminate-Upon-Convergence''' will stop the optimization process once the defined '''Convergence Rate''' is reached, or once the defined '''Maximum Number of Generations''' is reached.<br>
#: GA optimization "ending-point" criterion is selected in the '''Conversion Type''' drop-down listsection.
#: The desired Maximum Number of Generations, Minimum Number of Generations, and Conversion Rate can be set in the corresponding text boxes.<br>
#: '''Convergence Rate'''
#: P - convergence rate; values used are usually close to 1, with the default value of 0.99.
#: <div style="background-color: #E3FBE5;">Note: Convergence Rate is not calculated for generations that have an ordinal number less than the defined minimal number of the generations.</div><br>
<br>
[[Category:Optimization]]