Instead of using the difference between the session close of the front
contract before the roll and and the open of back contract on the
beginning of the roll, use the close of both at the end of the session
before the roll.
The closes of the session prior to roll is in lieu of settlement data.
Add roll style which takes the volume of the contracts into account.
If the volume moves from the front to the back before the auto close
date, the roll is put at that session.
Also, factors out some of the common logic shared with calendar based rolls.
Match the behavior of the minute bar reader, now that the session and
minute bar readers share a common interface.
isnull is slightly slower than checking against -1; however, n cases
where we check against illiquid trades in a tight loop, volume is
checked which is not using nan. The change here should be marginal with
regards to performance.
Move dates queried near beginning of test data so that the range of data
covered does not extend beyond the beginning of the range.
i.e. the windows were covering 2016-01-25, which had no test data generated.
(Does not matter for the calendar based rolls, but is needed for volume
based rolls.)
Also, make room for having the first roll to be a day before the first auto
close by moving the first contracts auto close date back a day.
In preparation for testing volume rolls.
The last traded dt provided from the session bar reader which resamples
from minutes should provide a dt that is a session label, not one that
is at the minute frequency.
The minute to session sampling reading was creating two DataFrame
objects, the first to hold the minute data, and then a second returned
by the `DataFrame.groupby` to sample down to sessions.
Instead use the arrays returned by the minute readers `load_raw_arrays`
and implement sampling logic which takes advantage that the minutes
being passed start with the first minute of the first session and end
with the last minute of the last session.
On my machine this takes the tests in `test/test_continuous_futures`
from ~4.0 to about ~0.1 seconds.
Add `.adj('mul')` and `.adj('add')` methods on ContinuousFuture, which
when used with `history`, will calculate and apply adjustments so that
the values are adjusted to account for discounts and premiums during
rolls.
Example usage in an algo:
```
from zipline.api import continuous_future
def initialize(context):
context.cl_add = continuous_future('CL', offset=0, roll='calendar').adj('add')
context.cl_mul = continuous_future('CL', offset=0, roll='calendar').adj('mul')
context.cl = continuous_future('CL', offset=0, roll='calendar')
schedule_function(print_history)
def print_history(context, data):
frame = data.history([context.cl, context.cl_add, context.cl_mul],
['price', 'sid'],
20,
'1d')
print 'unadjusted'
print frame.loc[:, :, context.cl]
print 'adjusted add'
print frame.loc[:, :, context.cl_add]
print 'adjusted mul'
print frame.loc[:, :, context.cl_mul]
```
Include minutes (in addition to the days) in the price encoding for
continuous futures tests.
Need for different values minute to minute arose when working on tests
for adjusted values.
Limit the perspective offset to 1. There is a possibility that if a
consumer of the AdjustedArrayWindow does not fetch adjustments between
the end of the data window and the vantage points beyond the end of the
window.
Until that case has a solution, e.g. having the consumer of the
AdjustedArrayWindow include the perspective offset when calculating the
query for adjustments, limit the offsets to 1.
- Refactor `test_adjusted_array` to test a range of perspective_offsets in
all tests.
- Make perspective_offset a parameter to `AdjustedArray.traverse`
instead of `AdjustedArray`.
Add a perspective offset to `AdjustedArrayWindow` and `AdjustedArray`,
so that `HistoryLoader` does not need to twiddle with offsets to support
viewing the data from the bar after end of the window, (Which is the
case when a '1d' history window is retrieved in minute mode, which is
explained in the docstring for `HistoryLoader.history`)
Presently, this simplifies the logic in
`HistoryLoader._get_adjustments_in_range`, and other incoming
AdjustmentReader's, (e.g. the roll based adjustment reader for continous
futures.) This patch should also make it easier for history and pipeline
to converge on a singular `load_adjustments` method.
Enable unadjusted history for continuous futures.
The history array is filled by the values for the underlying contracts,
where the contract used changes based on rolls.
e.g., if a `1d` history window was over the range
`2016-01-20` -> `2016-02-29` with contracts with a suffix of `F16` that
rolls at the beginning of the session on `2016-01-26`, `G16` on
`2016-02-26`, and `H16` on `2016-03-26`. The `2016-01-20` ->
`2016-01-25` portion would use the values for `F16', the `2016-01-26` ->
`2016-02-25` portion would use `G16` and the `2016-02-26` ->
`2016-02-29` portion would use `H16`.
Using the same contracts as above, a `1m` history window over the range
(using a timezone of US/Eastern) `2016-01-25 4:00PM` -> `2016-01-25
7:00PM` would fill the `4:00PM` -> `6:00PM` portion with data for `F16`
and the `6:01PM` -> `7:00PM` portion with data for `G16`, since the
beginning of the `2016-01-26` session is `2016-01-25 6:01PM`.
Supports `1d` and `1m`.
Also adds the `sid` field to `history` to assist in showing the active
contract at each dt in the window.
Allows ``__funcname`` to be passed to preprocessors like expect_types
and expect_dtypes to override the name displayed in error messages.
This is useful for providing clearer errors for ``__init__`` and
``__new__`` methods in classes.
Add `chain`field to current, as well as supporting methods in DataPortal
and OrderedContracts.
Enables the following example:
```
from zipline.api import continuous_future
def initialize(context):
context.primary_cl = continuous_future('CL', offset=0, roll='calendar')
schedule_function(print_current_chain)
def print_current_chain(context, data):
chain = data.current_chain(context.primary_cl)
print 'datetime={0}'.format(get_datetime())
print 'primary={0}'.format(chain[0])
print 'secondary={0}'.format(chain[1])
print 'tertiary={0}'.format(chain[2])
```
```
datetime=2015-12-23 14:31:00+00:00
primary=Future(1058201602 [CLG16])
secondary=Future(1058201603 [CLH16])
tertiary=Future(1058201604 [CLJ16])
```
Also:
- make return types of OrderedContracts methods compatible across
architectures. (Noticed while adding `active_chain` method.)
- Add year suffix to future contract names in test data.
Add the ability for an algorithm to request the current contract for a
future chain via `data.current`.
e.g.:
```
data.current(ContinuousFuture('CL', offset=0, roll='calendar'),
'contract')
```
This allows optionally setting the last available dts in the DataPortal
explicitly. If these args aren't provided, we fall back to inferring
these from the underlying readers, which was the previous behavior.
- Use parameter_space instead of `parameterized.expand`.
- Use a timedelta instead of concatenating strings.
- Use a (possibly no-op) scramble function instead of reordering list
literals.
- Use `freeze_dt, unfreeze_dt, re_freeze_dt` instead of `dts[n]`.
- Rename `assert_vectorized_results` to `assert_all_restrictions`.
In favor of a new method `set_restrictions` which takes a Restrictions
object. Calls to `set_do_not_order_list` should raise a deprecation
warning and create an equivalent Restrictions object, with which
`set_restrictions` will be called. For convenience, create a
RestrictionsSet from which the "restrictions" version of a security
list can be accessed
Introducing a WithCreateBarData fixture which allows for the
creation of a BarData using only the `simulation_dt_func` and
`restrictions` params. Assumes that each suite uses the same
`data_portal`, `data_frequency` and `trading_calendar`
The SecurityList implements a non-exposed method
`current_securities(dt)` which SecurityListRestrictions calls to
determine if an asset is restricted. Deprecate the `__iter__` and
`__contains__` methods of security lists in favor of
`current_securities(dt)`
An ABC Restrictions defines a group of restrictions responsible
for returning restriction information for sids on certain dts. An
InMemoryRestrictions is a point-in-time group of such restrictions,
with all restrictions and their dates passed in upon instantiation.
A StaticRestrictedList takes a list of sids, restricting them at all
dates
MAINT: optimization - only look at assets appearing in data
TST: simplify test
DOC: add documentation for checkpoints
MAINT: explicitly cast event date field to datetime
MAINT: add back import
TST: fix indexing to remove setting wtih copy warning
TST: fix quarter normalization test
TST: change test name
BUG: remove arg
BUG: look at dict keys
TST: add test for windowing
MAINT: raise ValueError instead of asserting
TST: add assertion to check windowing
TST: parametrize test over number of quarters forward/back.
BUG: fix adjustment calculation logic for quarter crossovers.
TST: add test for previous quarter windows
BUG: fix bugs in calculating previous windows
BUG: fix missing value for datetime
TST: add test case for missing quarter
Eddie Hebert