def __init__(self, data, **kwargs):
"""
Data must be a DataFrame formatted like this:
#################################################################################################
# # GS # TW #
# # N10 # H10 # G14 # H14 #
# # Price # Volume # Price # Volume # Price # Metric3 # Price # Metric3 #
# 2013-12-20 00:09:15 # 101.00 # 1000 # 60.34 # 2500 # 400.00 # -0.0034 # Price # -5.0 #
# 2013-12-20 00:09:17 # 201.00 # 2000 # 20.34 # 2500 # 200.00 # -2.0034 # Price # -2.0 #
# etc... #
#################################################################################################
"""
assert isinstance(data.index, pd.tseries.index.DatetimeIndex)
self.data = data
# Unpack config dictionary with default values.
self.sids = kwargs.get(
'sids', list(set(['.'.join(tup[:2]) for tup in data.columns])))
self.start = kwargs.get('start', data.index[0])
self.end = kwargs.get('end', data.index[-1])
# Hash_value for downstream sorting.
self.arg_string = hash_args(data, **kwargs)
self._raw_data = None
def __init__(self, tnfm_class, *args, **kwargs):
assert isinstance(tnfm_class, (types.ObjectType, types.ClassType)), \
"Stateful transform requires a class."
assert hasattr(tnfm_class, 'update'), \
"Stateful transform requires the class to have an update method"
# Create an instance of our transform class.
if isinstance(tnfm_class, TransformMeta):
# Classes derived TransformMeta have their __call__
# attribute overridden. Since this is what is usually
# used to create an instance, we have to delegate the
# responsibility of creating an instance to
# TransformMeta's parent class, which is 'type'. This is
# what is implicitly done behind the scenes by the python
# interpreter for most classes anyway, but here we have to
# be explicit because we've overridden the method that
# usually resolves to our super call.
self.state = super(TransformMeta, tnfm_class).__call__(
*args, **kwargs)
# Normal object instantiation.
else:
self.state = tnfm_class(*args, **kwargs)
# save the window_length of the state for external access.
self.window_length = self.state.window_length
# Create the string associated with this generator's output.
self.namestring = tnfm_class.__name__ + hash_args(*args, **kwargs)
def __init__(self, data, **kwargs):
"""
Data must be a DataFrame formatted like this:
#################################################################################################
# # GS # TW #
# # N10 # H10 # G14 # H14 #
# # Price # Volume # Price # Volume # Price # Metric3 # Price # Metric3 #
# 2013-12-20 00:09:15 # 101.00 # 1000 # 60.34 # 2500 # 400.00 # -0.0034 # Price # -5.0 #
# 2013-12-20 00:09:17 # 201.00 # 2000 # 20.34 # 2500 # 200.00 # -2.0034 # Price # -2.0 #
# etc... #
#################################################################################################
"""
assert isinstance(data.index, pd.tseries.index.DatetimeIndex)
self.data = data
# Unpack config dictionary with default values.
self.sids = kwargs.get('sids', list(set(['.'.join(tup[:2]) for tup in data.columns])))
self.start = kwargs.get('start', data.index[0])
self.end = kwargs.get('end', data.index[-1])
# Hash_value for downstream sorting.
self.arg_string = hash_args(data, **kwargs)
self._raw_data = None
def __init__(self, data, **kwargs):
assert isinstance(data.major_axis, pd.tseries.index.DatetimeIndex)
self.data = data
# Unpack config dictionary with default values.
self.sids = kwargs.get('sids', data.items)
self.start = kwargs.get('start', data.major_axis[0])
self.end = kwargs.get('end', data.major_axis[-1])
# Hash_value for downstream sorting.
self.arg_string = hash_args(data, **kwargs)
self._raw_data = None
def __init__(self, data, **kwargs):
assert isinstance(data.index, pd.tseries.index.DatetimeIndex)
self.data = data
# Unpack config dictionary with default values.
self.sids = kwargs.get('sids', data.columns)
self.start = kwargs.get('start', data.index[0])
self.end = kwargs.get('end', data.index[-1])
# Hash_value for downstream sorting.
self.arg_string = hash_args(data, **kwargs)
self._raw_data = None
def __init__(self, *args, **kwargs):
# We shouldn't get any positional arguments.
assert len(args) == 0
# Default to None for event_list and filter.
self.event_list = kwargs.get('event_list')
self.filter = kwargs.get('filter')
if self.event_list is not None:
# If event_list is provided, extract parameters from there
# This isn't really clean and ultimately I think this
# class should serve a single purpose (either take an
# event_list or autocreate events).
self.count = kwargs.get('count', len(self.event_list))
self.sids = kwargs.get(
'sids',
np.unique([event.sid for event in self.event_list]).tolist())
self.start = kwargs.get('start', self.event_list[0].dt)
self.end = kwargs.get('start', self.event_list[-1].dt)
self.delta = kwargs.get(
'delta',
self.event_list[1].dt - self.event_list[0].dt)
self.concurrent = kwargs.get('concurrent', False)
else:
# Unpack config dictionary with default values.
self.count = kwargs.get('count', 500)
self.sids = kwargs.get('sids', [1, 2])
self.start = kwargs.get(
'start',
datetime(2008, 6, 6, 15, tzinfo=pytz.utc))
self.delta = kwargs.get(
'delta',
timedelta(minutes=1))
self.concurrent = kwargs.get('concurrent', False)
# Hash_value for downstream sorting.
self.arg_string = hash_args(*args, **kwargs)
self.generator = self.create_fresh_generator()
def get_hash(self):
"""
There should only ever be one TSC in the system, so
we don't bother passing args into the hash.
"""
return self.__class__.__name__ + hash_args()
