Hi, i’m preparing data (in Pandas) for a machine learning Forex strategy. Data comes from FRED, FXCM, Alpha vantage ecc. How could different features be aggregated in a pandas dataframe? For example fundamental data with price time series (GBPUSD + technical indicators + GDP + interest rates ecc). There is a problem with date adaptation, a feature is daily while others generally monthly. I know scaling and features selection/reduction with PCA but i’m interested in preprocessing and joining of features with different scale/values/timeframe. Please tell me a detailed process in pandas or Scikit Learn to obtain fundamental and price features perfectly merged and ready for a machine learning training/test. From cleaning to scaling. Then many ML models like Random trees or Svm will be compared choosing the best performer. Thank you very much.