Removed print

cuemacro · Mar 30, 2021 · e790450 · e790450
1 parent 6b139fd
commit e790450
Showing 1 changed file with 9 additions and 37 deletions.
diff --git a/finmarketpy/economics/eventstudy.py b/finmarketpy/economics/eventstudy.py
@@ -87,7 +87,6 @@ def get_intraday_moves_over_custom_event(self, data_frame_rets, ef_time_frame, v
  minute_start=5, mins=3 * 60, min_offset=0, create_index=False,
  resample=False, freq='minutes', cumsum=True, adj_cumsum_zero_point=False,
  adj_zero_point=2):
-
  filter = Filter()
 
  ef_time_frame = filter.filter_time_series_by_date(data_frame_rets.index[0], data_frame_rets.index[-1],
@@ -114,61 +113,34 @@ def get_intraday_moves_over_custom_event(self, data_frame_rets, ef_time_frame, v
 
  # All data needs to be equally spaced
  if resample:
- # make sure time series is properly sampled at 1 min intervals
+ # Make sure time series is properly sampled at 1 min intervals
  if freq == 'minutes':
  data_frame_rets = data_frame_rets.resample('1min').last()
  data_frame_rets = data_frame_rets.fillna(value=0)
  data_frame_rets = filter.remove_out_FX_out_of_hours(data_frame_rets)
  elif freq == 'daily':
  data_frame_rets = data_frame_rets.resample('B').last()
  data_frame_rets = data_frame_rets.fillna(value=0)
- elif freq == 'daily':
+ elif freq == 'weekly':
  data_frame_rets = data_frame_rets.resample('W').last()
  data_frame_rets = data_frame_rets.fillna(value=0)
 
- # data_frame_rets['Ind'] = numpy.nan
-
  start_index = data_frame_rets.index.searchsorted(ef_time_start)
  finish_index = data_frame_rets.index.searchsorted(ef_time_end)
 
- # Not all observation windows will be same length (eg. last one?)
-
- # # fill the indices which represent minutes
- # # TODO vectorise this!
- # for i in range(0, len(ef_time_frame.index)):
- # try:
- # data_frame_rets['Ind'][start_index[i]:finish_index[i]] = ords
- # except:
- # data_frame_rets['Ind'][start_index[i]:finish_index[i]] = ords[0:(finish_index[i] - start_index[i])]
- #
- # data_frame_rets['Rel'] = numpy.nan
- #
- # # Set the release dates
- # data_frame_rets['Rel'][start_index] = ef_time # set entry points
- # data_frame_rets['Rel'][finish_index + 1] = numpy.zeros(len(start_index)) # set exit points
- # data_frame_rets['Rel'] = data_frame_rets['Rel'].fillna(method='pad') # fill down signals
- #
- # data_frame_rets = data_frame_rets[pandas.notnull(data_frame_rets['Ind'])] # get rid of other
- #
- # data_frame = data_frame_rets.pivot(index='Ind',
- # columns='Rel', values=data_frame_rets.columns[0])
-
  data_frame = pandas.DataFrame(index=ords, columns=ef_time_frame.index)
 
  for i in range(0, len(ef_time_frame.index)):
- #try:
- vals = data_frame_rets.iloc[start_index[i]:finish_index[i]].values
-
- print(vals.size)
 
- if len(vals) < len(lst_ords):
- extend = np.zeros((len(lst_ords) - len(vals), 1)) * np.nan
- vals = np.append(vals, extend)
+ vals = data_frame_rets.iloc[start_index[i]:finish_index[i]].values
 
- data_frame[ef_time_frame.index[i]] = vals
+ # Add extra "future" history in case we are doing an event study which goes outside our data window
+ # (will just be filled with NaN)
+ if len(vals) < len(lst_ords):
+ extend = np.zeros((len(lst_ords) - len(vals), 1)) * np.nan
+ vals = np.append(vals, extend)
 
- #except:
- # data_frame_rets['Ind'][start_index[i]:finish_index[i]] = ords[0:(finish_index[i] - start_index[i])]
+ data_frame[ef_time_frame.index[i]] = vals
 
  data_frame.index.names = [None]