Machine Learning and Prediction of US Stock Index

R

rex.fan

Joined
9/18/23
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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
 
Last edited:
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?
 
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