Optimisation

Optimisation is the real deal when it comes to randomly picking values to derive something useful. We use weights to achieve the desired balance between risk and return. Techniques such as evolutionary algorithms, gradient descent and portfolio optimisation methods (In our case we heavily depend on Markowitz portfolio theory) can be integrated depending on the complexity and constraints of the problem.

Here we do cross Machine learning strategies, so eventually we want our strategies to come in correlation with each other, this gives us huge maintainability bonus over all our distribution techniques.