MAINT: Remove environment as an argument to benchmark source.
To allow the BenchmarkSource class to be more easily used in contexts other than
a TradingAlgorithm, remove the TradingEnvironment as an argument to the
benchmark source.
Instead:
- Pass a benchmark Asset, instead of a bencmark sid; so that the asset_finder
does not need to be passed to the benchmark source.
- Pass the pre-calculated benchmark_returns instead of an env,
which contains the benchmark_returns; a consumer can let the benchmark_returns
stay as the default of `None` when using an asset.
We may want to further refactor and make two different classes, instead of
relying on a combination of existence/non-existence of benchmark_asset and
benchmark_returns. That refactoring should be easier to do with this change.
A TypeError exception was raised with message "Cannot join tz-naive with
tz-aware DatetimeIndex". Removing old unnecessary workaround in
`holidays_at_time` function (Pandas already fixed that before 0.18)
fixes this issue.
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 subclass of MinuteEquityHistoryTestCase, where the primary
calendar is 'us_futures'.
Notes on modifications to MinuteEquityHistoryTestCase:
- To work on generic calendars, many tests now use set minutes for
window start and end, and check the values on active equity minutes.
- test_minute_regular should test against active equity minutes
- Adapts test_minute_midnight to work with futures calendar
- Use a method of getting the last open minute that works with
calendars that are open at midnight
- Test against Sunday at midnight, since the real intention of this
test is to check that given a non-open minute, we fall back to the
last open 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).
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.
Instead of recursively calling `DailyHistoryAggregator.closes` until we
find a non-nan close, we can instead call `load_raw_arrays` once, and
find the value from the returned array.
Ana Ruelas