Optimization runs multiple backtest procedures and compares their performance. How to start Optimization Back to top. Build your cBot Add an Instance Click the Optimization tab Click the Parameters button and select parameters to be optimized Define your time period by using the dropdown menus or by dragging the slider Click Play.
Optimization results Back to top. Optimization may take a long time to complete. Optimization process runs backtesting multiple times. Once backtesting for specific set of parameters passed, new row is added to the Passes grid. Passes grid contains the following columns: Pass - number of pass in current Optimization session Fitness - shows how well current pass fits the Optimization Criteria Equity - value of equity at the end of backtesting Balance - value of balance at the end of backtesting Net Profit - the difference between ending balance and starting balance Trades - amount of closed positions Profit factor - total profit divided by total loss Equity drawdown - the maximum amount of equity drawdown Parameters - by pressing Apply button in the parameters column you can apply parameters of the pass to the current cBot instance.
Bottom part of optimization window shows detailed information for selected pass. After you find a pass that shows desired performance, you can press Apply button next to it to apply parameters to current cBot instance. Settings Back to top. All optimization settings are splitted to 5 groups: For every selected parameter you must specify set of values to be used during optimization. Optimization Criteria group provides an ability to specify the criteria to be used during optimization.
Standard Criteria can be combined from several criteria. For every criterion you must specify a direction: Fitness value will be calculated for every pass based on the set of selected criteria. The higher fitness value, the better. If a custom criterion is chosen you need to override GetFitness method in your cBot and provide a fitness value based on GetFitnessArgs.
Two options are available: Grid that simply backtests each possible set of parameters and Genetic Algorithm that finds the optimal parameters faster. Resources group provides an ability to allocate CPU resources for optimization. The more resources you allocate, the faster optimization will be done. CPU resources can be adjusted during optimization. Ideas and Suggestions page. Optimization cAlgo advanced optimization functionality lets you find the optimal set of parameters for your cBot.
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