First pKantuML results: Starting the building of a machine learning repository

Last month I posted about the great improvements that we were likely to achieve thanks to the implementation of machine learning mining software (pKantuML) that used an OpenCL/C hybrid process for the mining of machine learning trading strategies. After spending a lot of time building and testing the software plus doing all the server side implementations for the cloud mining and the processing of cloud mining results today I am glad to say that we now have a fully functioning machine learning cloud mining operation using the power of mixed OpenCL/C calculations. On today's blog post I want to talk a bit about what we have achieved, how this will evolve and why this will bring a significant level of diversification to our current trading operations. The idea with pKantuML is to take advantage of GPU simulations to mine machine learning trading strategies, however since the ML part cannot be easily translated into OpenCL code -especially in a way in which results are the same between OpenCL and C/C for live trading – we decided to generate all the needed ML predictions within our C/C tester and then use these generated prediction files to perform massive amounts of simulations in OpenCL code varying things that do not depend on the machine learning code (such as the filtering hours, stoploss values, trailing stop types, etc). The idea is to do a small amount of computationally intensive work in C/C that can then be expanded greatly by using OpenCL.