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I have the following list of stocks:

For each one I would like to separate by day of month as this explanatory drawing:

With this separation I can perform the cumulative return for each day and separate by max and min cumulative returns for each stock symbol.

I am doing the following (example from another stock list) from SO: Call a report from a dictionary of dataframes :

data_dict = dict()  # create an empty dict here
for k, df in df_dict.items():
    df_dict[k]['Return %'] = df.iloc[:, 0].pct_change(-1)*100

    # aggregate the max and min of Return
    mm = df_dict[k]['Return %'].agg(['max', 'min'])  

    # add it to the dict, with ticker as the key
    data_dict[k] = {'max': mm.max(), 'min': mm.min()}  

# convert to a dataframe if you want
mm_df = pd.DataFrame.from_dict(data_dict, orient='index')

# display(mm_df)
          max      min
aapl  8.70284 -4.90070
msft  6.60377 -4.08443

This results in a linear analysis of the stocks in the list and do not separate by day as I wish to do as per drawing above..

Question:

  • How can I insert a step to split by day of month and then perform the above code?
Daniel Bittencourt
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1 Answers1

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You can use pandas.groupby and use datetime.date() as the grouping field. Then you can use sum operator on the group object to calculate daily return. This post shows using groupby with datetime

kyc12
  • 349
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