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Merge pull request #101 from quantopian/fix_1516

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Fix 1516
This commit is contained in:
fawce
2013-03-06 14:26:33 -08:00
parent e53622c930 a12eeb2383
commit 6a3e402a32
2 changed files with 70 additions and 29 deletions
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zipline/test_algorithms.py
+34 -8
View File
@@ -273,6 +273,11 @@ def uses_ufunc(data, *args, **kwargs):
return np.log(data)
@batch_transform
def price_multiple(data, multiplier, extra_arg=1):
return data.price * multiplier * extra_arg
class BatchTransformAlgorithm(TradingAlgorithm):
def initialize(self, *args, **kwargs):
self.refresh_period = kwargs.pop('refresh_period', 1)
@@ -342,12 +347,6 @@ class BatchTransformAlgorithm(TradingAlgorithm):
clean_nans=True
)
self.return_ticks = return_data(
refresh_period=self.refresh_period,
window_length=self.window_length,
create_panel=False
)
self.return_not_full = return_data(
refresh_period=0,
window_length=self.window_length,
@@ -360,6 +359,12 @@ class BatchTransformAlgorithm(TradingAlgorithm):
clean_nans=False
)
self.price_multiple = price_multiple(
refresh_period=self.refresh_period,
window_length=self.window_length,
clean_nans=False
)
self.iter = 0
self.set_slippage(FixedSlippage())
@@ -372,12 +377,33 @@ class BatchTransformAlgorithm(TradingAlgorithm):
self.history_return_args.append(
self.return_args_batch.handle_data(
data, *self.args, **self.kwargs))
self.history_return_ticks.append(
self.return_ticks.handle_data(data))
self.history_return_not_full.append(
self.return_not_full.handle_data(data))
self.uses_ufunc.handle_data(data)
# check that calling transforms with the same arguments
# is idempotent
self.price_multiple.handle_data(data, 1, extra_arg=1)
if self.price_multiple.full:
pre = len(self.price_multiple.ticks)
result1 = self.price_multiple.handle_data(data, 1, extra_arg=1)
post = len(self.price_multiple.ticks)
assert pre == post, "batch transform is appending redundant events"
result2 = self.price_multiple.handle_data(data, 1, extra_arg=1)
assert result1 is result2, "batch transform is not idempotent"
# check that calling transform with the same data, but
# different supplemental arguments results in new
# results.
result3 = self.price_multiple.handle_data(data, 2, extra_arg=1)
assert result1 is not result3, \
"batch transform is not updating for new args"
result4 = self.price_multiple.handle_data(data, 1, extra_arg=2)
assert result1 is not result4,\
"batch transform is not updating for new kwargs"
new_data = deepcopy(data)
for sid in new_data:
new_data[sid]['arbitrary'] = 123
zipline/transforms/utils.py
+36 -21
View File
@@ -228,8 +228,6 @@ class EventWindow(object):
# Subclasses should override handle_add to define behavior for
# adding new ticks.
self.handle_add(event)
#if len(self.ticks) > self.window_length:
# import nose.tools; nose.tools.set_trace()
# Clear out any expired events.
#
# oldest newest
@@ -313,12 +311,11 @@ class BatchTransform(EventWindow):
def __init__(self,
func=None,
refresh_period=None,
refresh_period=0,
window_length=None,
clean_nans=True,
sids=None,
fields=None,
create_panel=True,
compute_only_full=True):
"""Instantiate new batch_transform object.
@@ -329,7 +326,7 @@ class BatchTransform(EventWindow):
with the data panel and all args and kwargs supplied
to the handle_data() call.
refresh_period : int
Interval to call batch_transform function.
Interval to wait between advances in the window.
window_length : int
How many days the trailing window should have.
clean_nans : bool <default=True>
@@ -342,12 +339,6 @@ class BatchTransform(EventWindow):
Which fields to include in the moving window
(e.g. 'price'). If not supplied, fields will be
extracted from incoming events.
create_panel : bool <default=True>
If True, will create a pandas panel every refresh
period and pass it to the user-defined function.
If False, will pass the underlying deque reference
directly to the function which will be significantly
faster.
compute_only_full : bool <default=True>
Only call the user-defined function once the window is
full. Returns None if window is not full yet.
@@ -361,7 +352,6 @@ class BatchTransform(EventWindow):
self.compute_transform_value = self.get_value
self.clean_nans = clean_nans
self.create_panel = create_panel
self.compute_only_full = compute_only_full
self.sids = sids
@@ -376,12 +366,15 @@ class BatchTransform(EventWindow):
self.window_length = window_length
self.trading_days_since_update = 0
self.trading_days_total = 0
self.window = None
self.full = False
self.last_dt = None
self.updated = False
self.cached = None
self.last_args = None
self.last_kwargs = None
# Data panel that provides bar information to fill in the window,
# when no bar ticks are available from the data source generator
@@ -411,9 +404,19 @@ class BatchTransform(EventWindow):
# functionality to zipline
if len(v)}
# append data frame to window. update() will call handle_add() and
# handle_remove() appropriately
self.update(event)
# only modify the trailing window if this is
# a new event. This is intended to make handle_data
# idempotent.
if event not in self.ticks:
# append data frame to window. update() will call handle_add() and
# handle_remove() appropriately, and self.updated
# will be modified based on the refresh_period
self.update(event)
else:
# we are recalculating based on an old event, so
# there is no change in the contents of the trailing
# window
self.updated = False
# return newly computed or cached value
return self.get_transform_value(*args, **kwargs)
@@ -454,7 +457,6 @@ class BatchTransform(EventWindow):
# to call the user-defined batch-transform with the most
# recent datapanel
self.updated = True
self.trading_days_since_update = 0
else:
self.updated = False
@@ -518,13 +520,26 @@ class BatchTransform(EventWindow):
if self.compute_only_full and not self.full:
return None
recalculate_needed = False
if self.updated:
# Either create new pandas panel or pass ticks dequeue
# directly
data = self.get_data() if self.create_panel else self.ticks
self.cached = self.compute_transform_value(data, *args,
**kwargs)
# Create new pandas panel
self.window = self.get_data()
# reset our counter for refresh_period
self.trading_days_since_update = 0
recalculate_needed = True
else:
recalculate_needed = \
args != self.last_args or kwargs != self.last_kwargs
if recalculate_needed:
self.cached = self.compute_transform_value(
self.window,
*args,
**kwargs
)
self.last_args = args
self.last_kwargs = kwargs
return self.cached
def __call__(self, f):