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Market Strategy Optimization

Market Strategy Optimization is a system that substantially improves the probability that the statistical characteristics of a trading strategy will be repeatable and reliable in the future through a sophisticated optimization process.

It practically guarantees that the statistical analysis will remain stable in the future, characteristic that no other statistical model for Trading currently does.

The module Market Strategy Optimization is used within the osMarkets ecosystem both for the design of more complex strategies and for the optimization of already developed RoboAdvisors strategies.

Example of the returns generated by a portfolio

Focuses the use of financial RoboAdvisors under the concept of service, where the value of the product resides not only in the intelligence of the algorithm implemented in the robot, but in the ability of osMarkets to provide configurations optimized for the market at all times.

Market Strategy Optimization

System that substantially improves the chances that your statistical analysis is repeatable and reliable in the future.

This module works with the following criteria


Profitability, pursues the objective of obtaining a high profitability in comparison with other products.


Continuity, seeks a stable profitability, that profits are continuous and without ups and downs, which allows to manage the risk and expectations.


Prediction ability, is focused on the results work in the future, not only to the justification that they are consistent with the backtesting system.

Long term

Long term, commitment to sustainable strategies over time and long duration, protecting the cost of exit of a strategy.

To achieve these objectives we developed a model based on the following elements:

Optimization strategy

Optimization of each strategy separately or together, through the use of backtest, optimization models and walk forward (real-time optimization)


Strategies Portfolio

It allows to combine several strategies with the objective of executing them at the same time, diversifying and dividing the risk among them, penalizing the use of similar strategies, looking for strategies that are not correlated with each other and obtaining a more linear result of the set (continuity).

Optimization Portfolio

We apply optimization models (walk forward) on the portfolio as a whole, so that each strategy is not optimized separately, but rather all of them.

Process of Selection of optimal strategies

Low correlation

Accept low correlation with portfolio strategies

Healthy neighbors

Selection of strategies with «healthy» neighbors

Linearity and stability

Objective of linearity and stability instead of absolute benefit

Increase robustness

Guarantee certain randomness to increase robustness

Genetic Algorithms

Application it for the search of strategies and parameters

Machine Learning

Machine Learning application as a filter in market entries, and for position adjustment based on results