The project aim is to build a model to predict Stock Market prices, using a combination of Machine Learning Algorithms.
The output of the prediction are the daily returns of S&P-500 index. I'm exploring two possible different problems:
- Binary Classification Problem: predict positive (Up) or negative (Down) return respect to the previous day.
- Regression Problem: predict the exact return; more useful to feed an hypothetical trading algorithm.
The language I picked to implement the analysis is Python (numpy, scipy, pandas, matplotlib, scikit), although the first exploratory stuff has been done in R.
The main file is Stocks.py. The script calls several functions contained in the functions.py.
I'm actively working on the project meaning that the repo is going to be updated quite often.