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import datetime |
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import numpy as np |
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import pandas as pd |
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from six import iteritems |
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from six.moves import zip |
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from zipline.utils.numpy_utils import np_NaT |
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def next_date_frame(dates, events_by_sid): |
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""" |
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Make a DataFrame representing the simulated next known date for an event. |
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Parameters |
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---------- |
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dates : pd.DatetimeIndex. |
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The index of the returned DataFrame. |
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events_by_sid : dict[int -> pd.Series] |
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Dict mapping sids to a series of dates. Each k:v pair of the series |
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represents the date we learned of the event mapping to the date the |
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event will occur. |
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Returns |
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------- |
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next_events: pd.DataFrame |
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A DataFrame where each column is a security from `events_by_sid` where |
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the values are the dates of the next known event with the knowledge we |
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had on the date of the index. Entries falling after the last date will |
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have `NaT` as the result in the output. |
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See Also |
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-------- |
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previous_date_frame |
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""" |
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cols = { |
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equity: np.full_like(dates, np_NaT) for equity in events_by_sid |
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} |
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raw_dates = dates.values |
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for equity, event_dates in iteritems(events_by_sid): |
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data = cols[equity] |
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if not event_dates.index.is_monotonic_increasing: |
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event_dates = event_dates.sort_index() |
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# Iterate over the raw Series values, since we're comparing against |
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# numpy arrays anyway. |
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iterkv = zip(event_dates.index.values, event_dates.values) |
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for knowledge_date, event_date in iterkv: |
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date_mask = ( |
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(knowledge_date <= raw_dates) & |
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(raw_dates <= event_date) |
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) |
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value_mask = (event_date <= data) | (data == np_NaT) |
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data[date_mask & value_mask] = event_date |
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return pd.DataFrame(index=dates, data=cols) |
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def previous_date_frame(date_index, events_by_sid): |
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""" |
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Make a DataFrame representing simulated next earnings date_index. |
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Parameters |
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---------- |
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date_index : DatetimeIndex. |
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The index of the returned DataFrame. |
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events_by_sid : dict[int -> DatetimeIndex] |
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Dict mapping sids to a series of dates. Each k:v pair of the series |
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represents the date we learned of the event mapping to the date the |
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event will occur. |
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Returns |
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------- |
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previous_events: pd.DataFrame |
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A DataFrame where each column is a security from `events_by_sid` where |
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the values are the dates of the previous event that occured on the date |
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of the index. Entries falling before the first date will have `NaT` as |
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the result in the output. |
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See Also |
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-------- |
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next_date_frame |
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""" |
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sids = list(events_by_sid) |
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out = np.full((len(date_index), len(sids)), np_NaT, dtype='datetime64[ns]') |
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dn = date_index[-1].asm8 |
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for col_idx, sid in enumerate(sids): |
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# events_by_sid[sid] is Series mapping knowledge_date to actual |
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# event_date. We don't care about the knowledge date for |
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# computing previous earnings. |
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values = events_by_sid[sid].values |
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values = values[values <= dn] |
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out[date_index.searchsorted(values), col_idx] = values |
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frame = pd.DataFrame(out, index=date_index, columns=sids) |
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frame.ffill(inplace=True) |
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return frame |
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def normalize_data_query_time(dt, time, tz): |
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"""Apply the correct time and timezone to a date. |
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Parameters |
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---------- |
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dt : pd.Timestamp |
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The original datetime that represents the date. |
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time : datetime.time |
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The time of day to use as the cutoff point for new data. Data points |
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that you learn about after this time will become available to your |
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algorithm on the next trading day. |
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tz : tzinfo |
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The timezone to normalize your dates to before comparing against |
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`time`. |
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Returns |
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------- |
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query_dt : pd.Timestamp |
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The timestamp with the correct time and date in utc. |
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""" |
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# merge the correct date with the time in the given timezone then convert |
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# back to utc |
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return pd.Timestamp( |
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datetime.datetime.combine(dt.date(), time), |
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tz=tz, |
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).tz_convert('utc') |
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def normalize_timestamp_to_query_time(df, |
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time, |
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tz, |
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inplace=False, |
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ts_field='timestamp'): |
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"""Update the timestamp field of a dataframe to normalize dates around |
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some data query time/timezone. |
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Parameters |
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---------- |
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df : pd.DataFrame |
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The dataframe to update. This needs a column named ``ts_field``. |
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time : datetime.time |
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The time of day to use as the cutoff point for new data. Data points |
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that you learn about after this time will become available to your |
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algorithm on the next trading day. |
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tz : tzinfo |
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The timezone to normalize your dates to before comparing against |
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`time`. |
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inplace : bool, optional |
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Update the dataframe in place. |
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ts_field : str, optional |
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The name of the timestamp field in ``df``. |
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Returns |
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------- |
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df : pd.DataFrame |
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The dataframe with the timestamp field normalized. If ``inplace`` is |
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true, then this will be the same object as ``df`` otherwise this will |
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be a copy. |
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""" |
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if not inplace: |
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# don't mutate the dataframe in place |
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df = df.copy() |
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dtidx = pd.DatetimeIndex(df.loc[:, ts_field], tz='utc') |
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dtidx_local_time = dtidx.tz_convert(tz) |
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to_roll_forward = dtidx_local_time.time > time |
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# for all of the times that are greater than our query time add 1 |
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# day and truncate to the date |
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df.loc[to_roll_forward, ts_field] = ( |
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dtidx_local_time[to_roll_forward] + datetime.timedelta(days=1) |
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).normalize().tz_localize(None).tz_localize('utc') # cast back to utc |
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df.loc[~to_roll_forward, ts_field] = dtidx[~to_roll_forward].normalize() |
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return df |
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quantopian /
zipline
