Machine Learning Implementation - Adaptive EA to market conditions

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Kishan Bhatt
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Hello

Just from my own learning recently (I am learning in my spare time using datacamp for a data science course); Python and C++ I have been reading up about machine learning. How feasible is this approach to the EA to implement. The idea would be to have something that would learn to be adaptive to different conditions. For example to self optimise to get a signal, or to try to use itself different oscillators and trend filters to make a trade. 

Maybe adopting a particular trading methodology, so looking at H4 timeframe, then looking at an M5 time frame for entry exit points for intraday trading. 

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Andrey Khatimlianskyi
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Machine Learning — is very deep topic.

And I am not sure it can be implemented to the CP.

Also I'm not sure it can help to make profits on the market ;)

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Kishan Bhatt

Hi, 


No problem. This is already an excellent project and I am happy just to be involved. No need to change anything if it is already excellent. 

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Belpu Prajwal Rao
Quote from Andrey Khatimlianskyi

Machine Learning — is very deep topic.

And I am not sure it can be implemented to the CP.

Also I'm not sure it can help to make profits on the market ;)

Actually is profitable if it can see seconds timeframe markets and find explosive moves based on EMAs like GMMA strategy with volumes(tick or real) + Main Monthly/weekly/daily/hourly pivot points as in Cammerila rules or something close to it.
(You may need the closest VPS to your broker to be profitable) anyways AI will understand what to do when to do and how much time after which trade needs to be closed.