Hello my good folks,

Am also relatively new in the algo world, but a bit okay with python.

I have discovered a strategy where technical indicators are combined or used with machine learning as features to design trading strategies.

I found this very fascinating and decided to pursue it..but unfortunately the author could not conclude the project in a manner that is explanatory enough, also the author can no longer be traced for any comments.

But i want to narrate the strategy whether someone is familiar and can help.

Five technical indicators have been correlated against the closing price of a stock and various correlation figures and charts generated.

One of the indicators eg. EMA was found to be the most correlated to the closing price.

The author went further to apply the same five technical indicators in machine learning strategy where he used them as multivariant features (independent variables) and the closing price as the dependent variable. He then modeled and fit algorithms to determine which models best predict the  prices.

He is no longer interested in the best indicator but rather the best machine learning model eg. Logistic regresion, forest trees, SVM etc.

He could also not explain how this strategy can br applied in practical manner.

Please if you find these to make some sense to you kindly help me understand the strategy and eventually backtest the result in QuantConnect. I will provide link to the original project whenever necessary.