The only downstream contex that was using batch_order_target_percent
already had all necessary prices, so calling batch_order_target_percent
was wasteful.
This allows us to remove the check for whether the provided dt had a
time of midnight, which was a flimsy way to infer if the data frequency
was 'daily'. Besides the explicit check being preferable, this method
was broken on the futures calendar, since midnight is a valid market
minute.
Added as a minimal subclass of DailyEquityHistoryTestCase, swapping out
just the primary calendar. This requires significant modifications to
DailyEquityHistoryTestCase, to allow for a generic primary calendar.
Previously, a dataframe passed into BcolzDailyBarWriter.write that was
missing an expected session between its first and last sessions would be
written incorrectly. Upon converting the dataframe to a ctable, the
values for all days following the gap would be shifted backwards, and
nans would be shifted in at the end.
This commit handles the issue by asserting that the number of rows in
the input table matches the number of sessions in the calendar between
the table's first and last sessions.
Also fixes a test that was mistakenly using minutes_in_range where it
should have been using sessions_in_range (uncovered by this change).
The March, June, September, and December contracts for these futures
contain most of the trading activity, so we exclude the other more
sparsely traded contracts from the chain.
For futures, we need to divide the position’s commission by the
contract size to get a per-unit commission in order to properly update
the position’s cost basis.
Optimize session close lookups in MinuteResampleSessionBarReader:
- Adds `session_closes_in_range` method (along with
`session_opens_in_range`) to TradingCalendar to allow vectorized
retrieval of all values in a range of sessions.
- Improves code path for resampling a single session's worth of data (as
is the case when calling `get_value`), since we don't actually need to
look up the close minute.
When retrieving the open and close for a given session, we only care
about the scalar values, so using DataFrame.at instead of DataFrame.loc
is significantly faster.
Ana Ruelas