Technical Analysis library to financial datasets with Python Pandas
Dario Lopez Padial

Great work Dario. Especially think about the high learning curve of using TA-lib (understanding the indicators, etc) and installation overhead of TA-lib, I’d definitely apply your library into my project.

I have a few questions though:

  1. The example of add_all_features has fillNA=True, how does it work by default? Any possible negative impact to the features generation?
  2. How many data points you need to look back in order to construct the vector X of features for the current day/hour/minutes? Example, to get the current day’s SMA as one of feature column you probably need 15 consecutive days data.

Looking forward to hearing from you, Sir.