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.
If a KeyError occurred in the adjustment logic, the exception would be
swallowed by the try block, which was intended to just check whether or
not there was an adjustment reader adjusted.
Discovered when some logic in a futures adjustment reader were failing
because of a mismatch of minute and session labels, which resulted in no
adjustments during windows when there should have been.
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]
```
Start making the equity adjustments calculations for the history loader
conform to the same method signature as `load_adjustments` provided by
`SQLiteAdjustmentReader, so that an `AdjustmentReader` interface can
begin to take form.
This prepares for creating a `DispatchAdjustmentReader` which will route
adjustment calculations for equities to the
`HistoryCompatibleUSEquityAdjustmentReader` and continuous futures to a
not yet implemented adjustment reader. All of these readers will share
the `load_adjustments` method.
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.
When dispatching to sub readers in dispatch reader, pass along the asset
object, instead of extracting the sid.
The in development reader for continuous futures values besides `sid`
are needed from the `ContinuousFuture` object.
Scott Sanderson