Use the trading strategy parameters optimization tab to set up the optimization objective and other optimization parameters.
- Select the Objective to determine the best entry, stop, and exit conditions during optimization of the trading strategy
This is the objective that the trading strategy uses to determine what the “best” trading strategy is during optimization. For more information see Trading Objective Functions.
- Setup the Optimization parameters.
Shortest desired average trade span – causes the optimization to give preferences to trading strategies with an average trade span greater than or equal to the Shortest Average Trade Span over the optimization period (specified on the Dates tab). Use this option is to decrease the number of trades if you find that optimization produces too many trades over the optimization period. It is recommended that you choose this option only if you are unable to achieve your goals using other methods.
Longest desired average trade span – causes the optimization to give preference to trading strategies with an average trade span less than or equal to the Longest Average Trade Span over the optimization period (specified on the Dates tab). Use this option to increase the number of trades if you find that optimization produces too few trades over the optimization period. It is recommended that you choose this option only if you are unable to achieve your goals using other methods.
Optimize for exactly – Forces the optimization to take a specified amount of time. Because of the nature of genetic optimization, it is impossible to determine the exact optimization time or even when the best trading strategy has been found. Without this parameter selected, optimization will automatically stop after it has decided that a better trading strategy is unlikely to be found in the future. Set this parameter to a longer to make absolutely sure that the best trading strategy has been found. Set this parameter to a shorter time to stop optimization before the best trading strategy has been found.
Optimize across all chart pages – Forces the optimization to choose the same rules and parameters for each chart page. The optimization will find the rules and parameters that perform best across all chart pages by using the average result across all chart pages instead of trying to optimize the individual results for each chart page. Use of this parameter will result in worse results than optimizing each chart page individually, but will provide more consistent and generalized rules and parameters across the chart pages.
- Setup the Optimization Algorithm.
Gene Hunter Optimization ‘ The classic genetic algorithm used in previous NeuroShell Trader versions. For more information about genetic algorithms, see the help topic What are Genetic Algorithms?
Evolution Strategy Optimization ‘ Evolution Strategies are variants of genetic algorithms that use real numbers instead of integers in chromosomes, and therefore do not cross segments of a chromosome, but instead cross whole chromosomes. The individuals represent potential solutions to a problem. The individuals are tested by a fitness function and the results are used to determine if the individual will be included in the next generation of potential solutions. For more information refer to the following book: Michalewicz, Z., “Genetic Algorithms + Data Structures = Evolution Programs”, Second, Extended Edition, Springer-Verlag, New York, NY, 1992, chapter 8, Evolution Strategies and Other Methods.
Swarm Optimization ‘ Like genetic algorithms, Particle Swarm Optimization begins with a random population of solutions in the form of individuals. (Individuals represent a set of problem values that are being optimized.) As time progresses, the individuals “swarm” generally towards the best individuals, but not directly as some randomness is involved. The best individuals are judged by a fitness function relevant to the problem, e.g., maximize the number of correct classifications or minimize the number of false negatives. For more information refer to the following paper: Eberhart, R. C. and Kennedy, J., A New Optimizer Using Particle Swarm Theory. Proceedings of the Sixth International Symposium on Micromachine and Human Science, Nagoya, Japan, pp 39-43, 1995
Brute Optimization ‘ This is an exhaustive brute force search of all possible parameter combinations as used in most other technical analysis packages. Note that if the parameter range is a floating point parameter range (1.1 to 1.5), then the brute force algorithm splits the range into 20 increments instead of searching the unlimited floating point precision possibilities). Note also that if you have a large number of parameters or a very wide parameter search space, brute force effectively becomes useless as it could take weeks, months or years to search every parameter combination of a large parameter space.
- (Power User only) Setup Walk-forward Optimization.
Walk-forward optimization – This option allows for the evaluation of the out of sample performance of a strategy reoptimized regularly on newer data. After selecting this option, enter the number of walk-forwards to be performed in the corresponding text box. Each subsequent “walk-forward” optimization is applied to new Actual Trading data, until the last date is reached. The amount that each walkforward is shifted forward in time is controlled by the size of the actual trading period setup on the “Dates” tab.
Note that there will be one more optimization than the number of walkforwards specified in the text box. The final optimization is labeled walkforward 0. It is optimized using the very latest data and thus has no out of sample data. It is also the optimization that gets used when the trading strategy trades into the future.
Carry forward actual trading account balances to next walkforward – This option allows simulating the real world scenario where a trading strategy starts with a given account balance, is reoptimized regularly, but more money is never added to the account balance to offset losses as it trades. When this option is selected, each walkforward’s actual trading starts with the account balance that remained after the subsequent walkforward’s actual trading. If this option is not chosen, then each walkforward resets it’s actual trading starting account balance to the original account balance.
When you are satisfied with the Trading Strategy Parameters press the OK button to return to the Trading Strategy Wizard.
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