I am new to time series analysis and I am currently tackling a stock market prediction problem.
I have a set of market indicators (such as Bollinger Bands, ADX etc) which are derived from the time dependent Open, High, Low and Close prices ( in Dollars ).
I need to find correlations of this indicators with the Open, Low, High and Close prices over time and drop the features which are not correlated enough ( less than 0.70 )
I am working on python. Using Pandas I have tried the
pandas.dataframe.corr() method as well, but I want to know if Pearson and Spearman correlation fucntions in pandas serve my purpose ? Is it the right way or is there another way of finding correct correlations ?