Stock price prediction using SVM and Random Forest with Python

I am trying to predict the S&P 500 and Nasdaq 100 indexes with Support Vector machines and random forest algorithms using Python. However my accuracy scores are low. I only used the technical indicators as features in my model but I want to improve my model using inter market features as in the example [login to view URL] ([login to view URL]) I want to group my features like technical indicators; inter-market features and all features and see how accuracy score improves. Also i want to add another targets like open-close and return and see if open close or return target is good for prediction as in the case of [login to view URL] . Lastly i want to add a trading strategy to see if my prediction is good for return. my code so far is attached also

Compétences : Exploitation de Données, Java, Machine Learning (ML), Python, Architecture Logicielle

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Concernant l'employeur :
( 0 commentaires ) Istanbul, Turkey

Nº du projet : #19787135

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8 freelance font une offre moyenne de $72 pour ce travail


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