Machine Learning and Prediction of US Stock Index

[rex.fan]

Background
Assuming that the historical daily-level SPX Ohlcv data is known, and I want to predict the price one month later, it is enough to know the probability of increase or decrease. It is better if I can predict the increase or decrease percentage.

Question
1 Which ML algorithm should be chosen? and why?

• Logistic Regression
• Random Forests
• Gradient Boosting Machines (GBM)
• Support Vector Machines (SVM)
• Recurrent Neural Networks (RNNs) / Long Short-Term Memory (LSTM)

2 What details should be paid attention to when using algorithm prediction?
3 Are there any other feasible ideas? For example: Will the historical OHLCV data combined with news improve accuracy?
4 Are there any related blogs recommended for reading?

Finally, any course to learn to know the get the answer?

Thanks

 

[rex.fan]

After the brief investigation, I found part of the result
1 LSTM with 3 - 5 years historical daily data is a good choice
2 Indicators, e.g. RSI, ADX, are suitable selections.

Further question, any article/course to learn more detail to reproduce the prediction?