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  Strategy Optimization
Strategy Builder
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Strategy Optimization
Tradecision Strategy Optimization
Advanced Strategy Optimization

Improve Your Profitable Trading Ideas

Optimization is the process of performing multiple tests while modifying the parameters used in strategy rules or indicators. The purpose of optimization is to discover the best and most profitable settings for a specific indicator or price pattern traded on a specific security.

Optimization can greatly improve your knowledge of each strategy and your competence about what works in different types of markets. It allows you to experiment with the parameters of your strategy without changing its main functions.

When Should Optimization Be Used?

Many traders wrongly use optimization to force an unfinished strategy toward completion. However, if optimization is used properly, it can become the key step in preparing your strategy for real trading.

Optimization should only be started after you have tested a strategy and made sure that it is profitable.

Strategy Optimization Wizard

You can optimize a strategy for a variety of time periods and markets, maintaining its peak performance. The Strategy Optimization Wizard will provide step-by-step guidance for the optimization process.

Finding the Optimal Parameters

You can use optimization to make sure that you are using the optimal parameters for the indicators applied to your strategy and entry/exit conditions.

Automatic Walk-Forward Testing

Tradecision performs automatic walk-forward testing. This kind of testing is important because it helps you make sure that you are doing successful trading using your optimized strategy. The walk-forward testing capability enables you to define the test date ranges, thus giving you the power to select the stock parts to be used for the optimization and out-of-sample testing.

Specifying the Range and Step

Additionally, you need to specify the range (minimum, maximum) and step (increment) for each optimization variable. Please note, that the more optimization variables you have, and the more of them are tested for each variable, the longer the optimization process will take.

Applying the Algorithm that Suits You the Best

The optimization process can take seconds, minutes or hours, depending on the number of the simulations being run. To reduce the time required for optimization, you can either reduce the number of the optimization parameters, or use Genetic Algorithms for the optimization.

In those cases, when you have multiple optimization variables and wide search ranges, the Genetic Algorithms work much faster than the exhaustive search, while remaining very robust.

Genetic Algorithms are search algorithms based on the mechanics of natural selection and natural genetics. They combine the survival of the fittest rule with structured yet randomized information exchange.

Genetic algorithms possess the best characteristics of the other optimization methods, such as robustness and fast convergence, which does not depend on any of the optimization criteria (for instance, on smoothness).

Exhaustive Search
verifies all the possible combinations of the optimized parameters, thus ensuring that the best possible solution will be found. However, the time required for conducting exhaustive search is dramatically increased when the number of the parameters increases.

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