INSTRUCTION stringlengths 1 8.43k | RESPONSE stringlengths 75 104k |
|---|---|
Adapt OHLCV columns into uint32 columns. | def convert_cols(cols, scale_factor, sid, invalid_data_behavior):
"""Adapt OHLCV columns into uint32 columns.
Parameters
----------
cols : dict
A dict mapping each column name (open, high, low, close, volume)
to a float column to convert to uint32.
scale_factor : int
Factor ... |
Write the metadata to a JSON file in the rootdir. | def write(self, rootdir):
"""
Write the metadata to a JSON file in the rootdir.
Values contained in the metadata are:
version : int
The value of FORMAT_VERSION of this class.
ohlc_ratio : int
The default ratio by which to multiply the pricing data to
... |
Open an existing rootdir for writing. | def open(cls, rootdir, end_session=None):
"""
Open an existing ``rootdir`` for writing.
Parameters
----------
end_session : Timestamp (optional)
When appending, the intended new ``end_session``.
"""
metadata = BcolzMinuteBarMetadata.read(rootdir)
... |
Parameters ---------- sid: int Asset identifier. | def sidpath(self, sid):
"""
Parameters
----------
sid : int
Asset identifier.
Returns
-------
out : string
Full path to the bcolz rootdir for the given sid.
"""
sid_subdir = _sid_subdir_path(sid)
return join(self._r... |
Parameters ---------- sid: int Asset identifier. | def last_date_in_output_for_sid(self, sid):
"""
Parameters
----------
sid : int
Asset identifier.
Returns
-------
out : pd.Timestamp
The midnight of the last date written in to the output for the
given sid.
"""
... |
Create empty ctable for given path. | def _init_ctable(self, path):
"""
Create empty ctable for given path.
Parameters
----------
path : string
The path to rootdir of the new ctable.
"""
# Only create the containing subdir on creation.
# This is not to be confused with the `.bcolz... |
Ensure that a ctable exists for sid then return it. | def _ensure_ctable(self, sid):
"""Ensure that a ctable exists for ``sid``, then return it."""
sidpath = self.sidpath(sid)
if not os.path.exists(sidpath):
return self._init_ctable(sidpath)
return bcolz.ctable(rootdir=sidpath, mode='a') |
Fill sid container with empty data through the specified date. | def pad(self, sid, date):
"""
Fill sid container with empty data through the specified date.
If the last recorded trade is not at the close, then that day will be
padded with zeros until its close. Any day after that (up to and
including the specified date) will be padded with `... |
Write all the supplied kwargs as attributes of the sid s file. | def set_sid_attrs(self, sid, **kwargs):
"""Write all the supplied kwargs as attributes of the sid's file.
"""
table = self._ensure_ctable(sid)
for k, v in kwargs.items():
table.attrs[k] = v |
Write a stream of minute data. | def write(self, data, show_progress=False, invalid_data_behavior='warn'):
"""Write a stream of minute data.
Parameters
----------
data : iterable[(int, pd.DataFrame)]
The data to write. Each element should be a tuple of sid, data
where data has the following form... |
Write the OHLCV data for the given sid. If there is no bcolz ctable yet created for the sid create it. If the length of the bcolz ctable is not exactly to the date before the first day provided fill the ctable with 0s up to that date. | def write_sid(self, sid, df, invalid_data_behavior='warn'):
"""
Write the OHLCV data for the given sid.
If there is no bcolz ctable yet created for the sid, create it.
If the length of the bcolz ctable is not exactly to the date before
the first day provided, fill the ctable with... |
Write the OHLCV data for the given sid. If there is no bcolz ctable yet created for the sid create it. If the length of the bcolz ctable is not exactly to the date before the first day provided fill the ctable with 0s up to that date. | def write_cols(self, sid, dts, cols, invalid_data_behavior='warn'):
"""
Write the OHLCV data for the given sid.
If there is no bcolz ctable yet created for the sid, create it.
If the length of the bcolz ctable is not exactly to the date before
the first day provided, fill the cta... |
Internal method for write_cols and write. | def _write_cols(self, sid, dts, cols, invalid_data_behavior):
"""
Internal method for `write_cols` and `write`.
Parameters
----------
sid : int
The asset identifier for the data being written.
dts : datetime64 array
The dts corresponding to values... |
Return the number of data points up to and including the provided day. | def data_len_for_day(self, day):
"""
Return the number of data points up to and including the
provided day.
"""
day_ix = self._session_labels.get_loc(day)
# Add one to the 0-indexed day_ix to get the number of days.
num_days = day_ix + 1
return num_days * ... |
Truncate data beyond this date in all ctables. | def truncate(self, date):
"""Truncate data beyond this date in all ctables."""
truncate_slice_end = self.data_len_for_day(date)
glob_path = os.path.join(self._rootdir, "*", "*", "*.bcolz")
sid_paths = sorted(glob(glob_path))
for sid_path in sid_paths:
file_name = os... |
Calculate the minutes which should be excluded when a window occurs on days which had an early close i. e. days where the close based on the regular period of minutes per day and the market close do not match. | def _minutes_to_exclude(self):
"""
Calculate the minutes which should be excluded when a window
occurs on days which had an early close, i.e. days where the close
based on the regular period of minutes per day and the market close
do not match.
Returns
-------
... |
Build an interval tree keyed by the start and end of each range of positions should be dropped from windows. ( These are the minutes between an early close and the minute which would be the close based on the regular period if there were no early close. ) The value of each node is the same start and end position stored... | def _minute_exclusion_tree(self):
"""
Build an interval tree keyed by the start and end of each range
of positions should be dropped from windows. (These are the minutes
between an early close and the minute which would be the close based
on the regular period if there were no ea... |
Returns ------- List of tuples of ( start stop ) which represent the ranges of minutes which should be excluded when a market minute window is requested. | def _exclusion_indices_for_range(self, start_idx, end_idx):
"""
Returns
-------
List of tuples of (start, stop) which represent the ranges of minutes
which should be excluded when a market minute window is requested.
"""
itree = self._minute_exclusion_tree
... |
Retrieve the pricing info for the given sid dt and field. | def get_value(self, sid, dt, field):
"""
Retrieve the pricing info for the given sid, dt, and field.
Parameters
----------
sid : int
Asset identifier.
dt : datetime-like
The datetime at which the trade occurred.
field : string
... |
Internal method that returns the position of the given minute in the list of every trading minute since market open of the first trading day. Adjusts non market minutes to the last close. | def _find_position_of_minute(self, minute_dt):
"""
Internal method that returns the position of the given minute in the
list of every trading minute since market open of the first trading
day. Adjusts non market minutes to the last close.
ex. this method would return 1 for 2002-... |
Parameters ---------- fields: list of str open high low close or volume start_dt: Timestamp Beginning of the window range. end_dt: Timestamp End of the window range. sids: list of int The asset identifiers in the window. | def load_raw_arrays(self, fields, start_dt, end_dt, sids):
"""
Parameters
----------
fields : list of str
'open', 'high', 'low', 'close', or 'volume'
start_dt: Timestamp
Beginning of the window range.
end_dt: Timestamp
End of the window ra... |
Write the frames to the target HDF5 file using the format used by pd. Panel. to_hdf | def write(self, frames):
"""
Write the frames to the target HDF5 file, using the format used by
``pd.Panel.to_hdf``
Parameters
----------
frames : iter[(int, DataFrame)] or dict[int -> DataFrame]
An iterable or other mapping of sid to the corresponding OHLCV
... |
Construct an index array that when applied to an array of values produces a 2D array containing the values associated with the next event for each sid at each moment in time. | def next_event_indexer(all_dates,
data_query_cutoff,
all_sids,
event_dates,
event_timestamps,
event_sids):
"""
Construct an index array that, when applied to an array of values, produces
a 2D a... |
Construct an index array that when applied to an array of values produces a 2D array containing the values associated with the previous event for each sid at each moment in time. | def previous_event_indexer(data_query_cutoff_times,
all_sids,
event_dates,
event_timestamps,
event_sids):
"""
Construct an index array that, when applied to an array of values, produces
a 2D array con... |
Determine the last piece of information known on each date in the date | def last_in_date_group(df,
data_query_cutoff_times,
assets,
reindex=True,
have_sids=True,
extra_groupers=None):
"""
Determine the last piece of information known on each date in the date
index... |
Forward fill values in a DataFrame with special logic to handle cases that pd. DataFrame. ffill cannot and cast columns to appropriate types. | def ffill_across_cols(df, columns, name_map):
"""
Forward fill values in a DataFrame with special logic to handle cases
that pd.DataFrame.ffill cannot and cast columns to appropriate types.
Parameters
----------
df : pd.DataFrame
The DataFrame to do forward-filling on.
columns : lis... |
Shift dates of a pipeline query back by shift days. | def shift_dates(dates, start_date, end_date, shift):
"""
Shift dates of a pipeline query back by `shift` days.
load_adjusted_array is called with dates on which the user's algo
will be shown data, which means we need to return the data that would
be known at the start of each date. This is often l... |
Template formatters into docstring. | def format_docstring(owner_name, docstring, formatters):
"""
Template ``formatters`` into ``docstring``.
Parameters
----------
owner_name : str
The name of the function or class whose docstring is being templated.
Only used for error messages.
docstring : str
The docstri... |
Decorator allowing the use of templated docstrings. | def templated_docstring(**docs):
"""
Decorator allowing the use of templated docstrings.
Examples
--------
>>> @templated_docstring(foo='bar')
... def my_func(self, foo):
... '''{foo}'''
...
>>> my_func.__doc__
'bar'
"""
def decorator(f):
f.__doc__ = format_d... |
Add a column. | def add(self, term, name, overwrite=False):
"""
Add a column.
The results of computing `term` will show up as a column in the
DataFrame produced by running this pipeline.
Parameters
----------
column : zipline.pipeline.Term
A Filter, Factor, or Class... |
Set a screen on this Pipeline. | def set_screen(self, screen, overwrite=False):
"""
Set a screen on this Pipeline.
Parameters
----------
filter : zipline.pipeline.Filter
The filter to apply as a screen.
overwrite : bool
Whether to overwrite any existing screen. If overwrite is F... |
Compile into an ExecutionPlan. | def to_execution_plan(self,
domain,
default_screen,
start_date,
end_date):
"""
Compile into an ExecutionPlan.
Parameters
----------
domain : zipline.pipeline.domain.Domain
... |
Helper for to_graph and to_execution_plan. | def _prepare_graph_terms(self, default_screen):
"""Helper for to_graph and to_execution_plan."""
columns = self.columns.copy()
screen = self.screen
if screen is None:
screen = default_screen
columns[SCREEN_NAME] = screen
return columns |
Render this Pipeline as a DAG. | def show_graph(self, format='svg'):
"""
Render this Pipeline as a DAG.
Parameters
----------
format : {'svg', 'png', 'jpeg'}
Image format to render with. Default is 'svg'.
"""
g = self.to_simple_graph(AssetExists())
if format == 'svg':
... |
A list of terms that are outputs of this pipeline. | def _output_terms(self):
"""
A list of terms that are outputs of this pipeline.
Includes all terms registered as data outputs of the pipeline, plus the
screen, if present.
"""
terms = list(six.itervalues(self._columns))
screen = self.screen
if screen is n... |
Get the domain for this pipeline. | def domain(self, default):
"""
Get the domain for this pipeline.
- If an explicit domain was provided at construction time, use it.
- Otherwise, infer a domain from the registered columns.
- If no domain can be inferred, return ``default``.
Parameters
----------... |
Create a tuple containing all elements of tup plus elem. | def _ensure_element(tup, elem):
"""
Create a tuple containing all elements of tup, plus elem.
Returns the new tuple and the index of elem in the new tuple.
"""
try:
return tup, tup.index(elem)
except ValueError:
return tuple(chain(tup, (elem,))), len(tup) |
Ensure that our expression string has variables of the form x_0 x_1... x_ ( N - 1 ) where N is the length of our inputs. | def _validate(self):
"""
Ensure that our expression string has variables of the form x_0, x_1,
... x_(N - 1), where N is the length of our inputs.
"""
variable_names, _unused = getExprNames(self._expr, {})
expr_indices = []
for name in variable_names:
... |
Compute our stored expression string with numexpr. | def _compute(self, arrays, dates, assets, mask):
"""
Compute our stored expression string with numexpr.
"""
out = full(mask.shape, self.missing_value, dtype=self.dtype)
# This writes directly into our output buffer.
numexpr.evaluate(
self._expr,
lo... |
Return self. _expr with all variables rebound to the indices implied by new_inputs. | def _rebind_variables(self, new_inputs):
"""
Return self._expr with all variables rebound to the indices implied by
new_inputs.
"""
expr = self._expr
# If we have 11+ variables, some of our variable names may be
# substrings of other variable names. For example, ... |
Merge the inputs of two NumericalExpressions into a single input tuple rewriting their respective string expressions to make input names resolve correctly. | def _merge_expressions(self, other):
"""
Merge the inputs of two NumericalExpressions into a single input tuple,
rewriting their respective string expressions to make input names
resolve correctly.
Returns a tuple of (new_self_expr, new_other_expr, new_inputs)
"""
... |
Compute new expression strings and a new inputs tuple for combining self and other with a binary operator. | def build_binary_op(self, op, other):
"""
Compute new expression strings and a new inputs tuple for combining
self and other with a binary operator.
"""
if isinstance(other, NumericalExpression):
self_expr, other_expr, new_inputs = self._merge_expressions(other)
... |
Short repr to use when rendering Pipeline graphs. | def graph_repr(self):
"""Short repr to use when rendering Pipeline graphs."""
# Replace any floating point numbers in the expression
# with their scientific notation
final = re.sub(r"[-+]?\d*\.\d+",
lambda x: format(float(x.group(0)), '.2E'),
... |
Get the last modified time of path as a Timestamp. | def last_modified_time(path):
"""
Get the last modified time of path as a Timestamp.
"""
return pd.Timestamp(os.path.getmtime(path), unit='s', tz='UTC') |
Get the root directory for all zipline - managed files. | def zipline_root(environ=None):
"""
Get the root directory for all zipline-managed files.
For testing purposes, this accepts a dictionary to interpret as the os
environment.
Parameters
----------
environ : dict, optional
A dict to interpret as the os environment.
Returns
-... |
Build a dict of Adjustment objects in the format expected by AdjustedArray. | def format_adjustments(self, dates, assets):
"""
Build a dict of Adjustment objects in the format expected by
AdjustedArray.
Returns a dict of the form:
{
# Integer index into `dates` for the date on which we should
# apply the list of adjustments.
... |
Load data from our stored baseline. | def load_adjusted_array(self, domain, columns, dates, sids, mask):
"""
Load data from our stored baseline.
"""
if len(columns) != 1:
raise ValueError(
"Can't load multiple columns with DataFrameLoader"
)
column = columns[0]
self._v... |
Make sure a passed column is our column. | def _validate_input_column(self, column):
"""Make sure a passed column is our column.
"""
if column != self.column and column.unspecialize() != self.column:
raise ValueError("Can't load unknown column %s" % column) |
To resolve the symbol in the LEVERAGED_ETF list the date on which the symbol was in effect is needed. | def load_from_directory(list_name):
"""
To resolve the symbol in the LEVERAGED_ETF list,
the date on which the symbol was in effect is needed.
Furthermore, to maintain a point in time record of our own maintenance
of the restricted list, we need a knowledge date. Thus, restricted lists
are dict... |
Users should only access the lru_cache through its public API: cache_info cache_clear The internals of the lru_cache are encapsulated for thread safety and to allow the implementation to change. | def _weak_lru_cache(maxsize=100):
"""
Users should only access the lru_cache through its public API:
cache_info, cache_clear
The internals of the lru_cache are encapsulated for thread safety and
to allow the implementation to change.
"""
def decorating_function(
user_function, tu... |
Weak least - recently - used cache decorator. | def weak_lru_cache(maxsize=100):
"""Weak least-recently-used cache decorator.
If *maxsize* is set to None, the LRU features are disabled and the cache
can grow without bound.
Arguments to the cached function must be hashable. Any that are weak-
referenceable will be stored by weak reference. Once... |
Checks if name is a final object in the given mro. We need to check the mro because we need to directly go into the __dict__ of the classes. Because final objects are descriptor we need to grab them _BEFORE_ the __call__ is invoked. | def is_final(name, mro):
"""
Checks if `name` is a `final` object in the given `mro`.
We need to check the mro because we need to directly go into the __dict__
of the classes. Because `final` objects are descriptor, we need to grab
them _BEFORE_ the `__call__` is invoked.
"""
return any(isin... |
Bind a Column object to its name. | def bind(self, name):
"""
Bind a `Column` object to its name.
"""
return _BoundColumnDescr(
dtype=self.dtype,
missing_value=self.missing_value,
name=name,
doc=self.doc,
metadata=self.metadata,
) |
Specialize self to a concrete domain. | def specialize(self, domain):
"""Specialize ``self`` to a concrete domain.
"""
if domain == self.domain:
return self
return type(self)(
dtype=self.dtype,
missing_value=self.missing_value,
dataset=self._dataset.specialize(domain),
... |
Look up a column by name. | def get_column(cls, name):
"""Look up a column by name.
Parameters
----------
name : str
Name of the column to look up.
Returns
-------
column : zipline.pipeline.data.BoundColumn
Column with the given name.
Raises
------
... |
Construct a new dataset given the coordinates. | def _make_dataset(cls, coords):
"""Construct a new dataset given the coordinates.
"""
class Slice(cls._SliceType):
extra_coords = coords
Slice.__name__ = '%s.slice(%s)' % (
cls.__name__,
', '.join('%s=%r' % item for item in coords.items()),
)
... |
Take a slice of a DataSetFamily to produce a dataset indexed by asset and date. | def slice(cls, *args, **kwargs):
"""Take a slice of a DataSetFamily to produce a dataset
indexed by asset and date.
Parameters
----------
*args
**kwargs
The coordinates to fix along each extra dimension.
Returns
-------
dataset : Data... |
Check that the raw value for an asset/ date/ column triple is as expected. | def expected_bar_value(asset_id, date, colname):
"""
Check that the raw value for an asset/date/column triple is as
expected.
Used by tests to verify data written by a writer.
"""
from_asset = asset_id * 100000
from_colname = OHLCV.index(colname) * 1000
from_date = (date - PSEUDO_EPOCH)... |
Return an 2D array containing cls. expected_value ( asset_id date colname ) for each date/ asset pair in the inputs. | def expected_bar_values_2d(dates,
assets,
asset_info,
colname,
holes=None):
"""
Return an 2D array containing cls.expected_value(asset_id, date,
colname) for each date/asset pair in the inputs.
M... |
Load by delegating to sub - loaders. | def load_adjusted_array(self, domain, columns, dates, sids, mask):
"""
Load by delegating to sub-loaders.
"""
out = {}
for col in columns:
try:
loader = self._loaders.get(col)
if loader is None:
loader = self._loader... |
Make a random array of shape ( len ( dates ) len ( sids )) with dtype. | def values(self, dtype, dates, sids):
"""
Make a random array of shape (len(dates), len(sids)) with ``dtype``.
"""
shape = (len(dates), len(sids))
return {
datetime64ns_dtype: self._datetime_values,
float64_dtype: self._float_values,
int64_dtyp... |
Return uniformly - distributed floats between - 0. 0 and 100. 0. | def _float_values(self, shape):
"""
Return uniformly-distributed floats between -0.0 and 100.0.
"""
return self.state.uniform(low=0.0, high=100.0, size=shape) |
Return uniformly - distributed integers between 0 and 100. | def _int_values(self, shape):
"""
Return uniformly-distributed integers between 0 and 100.
"""
return (self.state.randint(low=0, high=100, size=shape)
.astype('int64')) |
Return uniformly - distributed dates in 2014. | def _datetime_values(self, shape):
"""
Return uniformly-distributed dates in 2014.
"""
start = Timestamp('2014', tz='UTC').asm8
offsets = self.state.randint(
low=0,
high=364,
size=shape,
).astype('timedelta64[D]')
return start +... |
Compute rowwise array quantiles on an input. | def quantiles(data, nbins_or_partition_bounds):
"""
Compute rowwise array quantiles on an input.
"""
return apply_along_axis(
qcut,
1,
data,
q=nbins_or_partition_bounds, labels=False,
) |
Handles the close of the given minute in minute emission. | def handle_minute_close(self, dt, data_portal):
"""
Handles the close of the given minute in minute emission.
Parameters
----------
dt : Timestamp
The minute that is ending
Returns
-------
A minute perf packet.
"""
self.sync_l... |
Handles the start of each session. | def handle_market_open(self, session_label, data_portal):
"""Handles the start of each session.
Parameters
----------
session_label : Timestamp
The label of the session that is about to begin.
data_portal : DataPortal
The current data portal.
"""
... |
Handles the close of the given day. | def handle_market_close(self, dt, data_portal):
"""Handles the close of the given day.
Parameters
----------
dt : Timestamp
The most recently completed simulation datetime.
data_portal : DataPortal
The current data portal.
Returns
-------... |
When the simulation is complete run the full period risk report and send it out on the results socket. | def handle_simulation_end(self, data_portal):
"""
When the simulation is complete, run the full period risk report
and send it out on the results socket.
"""
log.info(
'Simulated {} trading days\n'
'first open: {}\n'
'last close: {}',
... |
Encapsulates a set of custom command line arguments in key = value or key. namespace = value form into a chain of Namespace objects where each next level is an attribute of the Namespace object on the current level | def create_args(args, root):
"""
Encapsulates a set of custom command line arguments in key=value
or key.namespace=value form into a chain of Namespace objects,
where each next level is an attribute of the Namespace object on the
current level
Parameters
----------
args : list
A... |
Converts argument strings in key = value or key. namespace = value form to dictionary entries | def parse_extension_arg(arg, arg_dict):
"""
Converts argument strings in key=value or key.namespace=value form
to dictionary entries
Parameters
----------
arg : str
The argument string to parse, which must be in key=value or
key.namespace=value form.
arg_dict : dict
... |
A recursive function that takes a root element list of namespaces and the value being stored and assigns namespaces to the root object via a chain of Namespace objects connected through attributes | def update_namespace(namespace, path, name):
"""
A recursive function that takes a root element, list of namespaces,
and the value being stored, and assigns namespaces to the root object
via a chain of Namespace objects, connected through attributes
Parameters
----------
namespace : Namespa... |
Create a new registry for an extensible interface. | def create_registry(interface):
"""
Create a new registry for an extensible interface.
Parameters
----------
interface : type
The abstract data type for which to create a registry,
which will manage registration of factories for this type.
Returns
-------
interface : ty... |
Construct an object from a registered factory. | def load(self, name):
"""Construct an object from a registered factory.
Parameters
----------
name : str
Name with which the factory was registered.
"""
try:
return self._factories[name]()
except KeyError:
raise ValueError(
... |
If there is a minimum commission: If the order hasn t had a commission paid yet pay the minimum commission. | def calculate_per_unit_commission(order,
transaction,
cost_per_unit,
initial_commission,
min_trade_cost):
"""
If there is a minimum commission:
If the order hasn't had ... |
Pay commission based on dollar value of shares. | def calculate(self, order, transaction):
"""
Pay commission based on dollar value of shares.
"""
cost_per_share = transaction.price * self.cost_per_dollar
return abs(transaction.amount) * cost_per_share |
Creates a dictionary representing the state of the risk report. | def risk_metric_period(cls,
start_session,
end_session,
algorithm_returns,
benchmark_returns,
algorithm_leverages):
"""
Creates a dictionary representing the state of th... |
For the given root symbol find the contract that is considered active on a specific date at a specific offset. | def _get_active_contract_at_offset(self, root_symbol, dt, offset):
"""
For the given root symbol, find the contract that is considered active
on a specific date at a specific offset.
"""
oc = self.asset_finder.get_ordered_contracts(root_symbol)
session = self.trading_cale... |
Parameters ---------- root_symbol: str The root symbol for the contract chain. dt: Timestamp The datetime for which to retrieve the current contract. offset: int The offset from the primary contract. 0 is the primary 1 is the secondary etc. | def get_contract_center(self, root_symbol, dt, offset):
"""
Parameters
----------
root_symbol : str
The root symbol for the contract chain.
dt : Timestamp
The datetime for which to retrieve the current contract.
offset : int
The offset ... |
Get the rolls i. e. the session at which to hop from contract to contract in the chain. | def get_rolls(self, root_symbol, start, end, offset):
"""
Get the rolls, i.e. the session at which to hop from contract to
contract in the chain.
Parameters
----------
root_symbol : str
The root symbol for which to calculate rolls.
start : Timestamp
... |
r Return the active contract based on the previous trading day s volume. | def _active_contract(self, oc, front, back, dt):
r"""
Return the active contract based on the previous trading day's volume.
In the rare case that a double volume switch occurs we treat the first
switch as the roll. Take the following case for example:
| +++++ _____... |
Parameters ---------- root_symbol: str The root symbol for the contract chain. dt: Timestamp The datetime for which to retrieve the current contract. offset: int The offset from the primary contract. 0 is the primary 1 is the secondary etc. | def get_contract_center(self, root_symbol, dt, offset):
"""
Parameters
----------
root_symbol : str
The root symbol for the contract chain.
dt : Timestamp
The datetime for which to retrieve the current contract.
offset : int
The offset ... |
Coerce buffer data for an AdjustedArray into a standard scalar representation returning the coerced array and a dict of argument to pass to np. view to use when providing a user - facing view of the underlying data. | def _normalize_array(data, missing_value):
"""
Coerce buffer data for an AdjustedArray into a standard scalar
representation, returning the coerced array and a dict of argument to pass
to np.view to use when providing a user-facing view of the underlying data.
- float* data is coerced to float64 wi... |
Merge lists of new and existing adjustments for a given index by appending or prepending new adjustments to existing adjustments. | def _merge_simple(adjustment_lists, front_idx, back_idx):
"""
Merge lists of new and existing adjustments for a given index by appending
or prepending new adjustments to existing adjustments.
Notes
-----
This method is meant to be used with ``toolz.merge_with`` to merge
adjustment mappings.... |
Return the input as a numpy ndarray. | def ensure_ndarray(ndarray_or_adjusted_array):
"""
Return the input as a numpy ndarray.
This is a no-op if the input is already an ndarray. If the input is an
adjusted_array, this extracts a read-only view of its internal data buffer.
Parameters
----------
ndarray_or_adjusted_array : nump... |
Check that a window of length window_length is well - defined on data. | def _check_window_params(data, window_length):
"""
Check that a window of length `window_length` is well-defined on `data`.
Parameters
----------
data : np.ndarray[ndim=2]
The array of data to check.
window_length : int
Length of the desired window.
Returns
-------
... |
Merge adjustments with existing adjustments handling index collisions according to method. | def update_adjustments(self, adjustments, method):
"""
Merge ``adjustments`` with existing adjustments, handling index
collisions according to ``method``.
Parameters
----------
adjustments : dict[int -> list[Adjustment]]
The mapping of row indices to lists of... |
The iterator produced when traverse is called on this Array. | def _iterator_type(self):
"""
The iterator produced when `traverse` is called on this Array.
"""
if isinstance(self._data, LabelArray):
return LabelWindow
return CONCRETE_WINDOW_TYPES[self._data.dtype] |
Produce an iterator rolling windows rows over our data. Each emitted window will have window_length rows. | def traverse(self,
window_length,
offset=0,
perspective_offset=0):
"""
Produce an iterator rolling windows rows over our data.
Each emitted window will have `window_length` rows.
Parameters
----------
window_length : int... |
Return a string representation of the data stored in this array. | def inspect(self):
"""
Return a string representation of the data stored in this array.
"""
return dedent(
"""\
Adjusted Array ({dtype}):
Data:
{data!r}
Adjustments:
{adjustments}
"""
).format(
... |
Map a function over baseline and adjustment values in place. | def update_labels(self, func):
"""
Map a function over baseline and adjustment values in place.
Note that the baseline data values must be a LabelArray.
"""
if not isinstance(self.data, LabelArray):
raise TypeError(
'update_labels only supported if da... |
Handle a TradingControlViolation either by raising or logging and error with information about the failure. | def handle_violation(self, asset, amount, datetime, metadata=None):
"""
Handle a TradingControlViolation, either by raising or logging and
error with information about the failure.
If dynamic information should be displayed as well, pass it in via
`metadata`.
"""
... |
Fail if we ve already placed self. max_count orders today. | def validate(self,
asset,
amount,
portfolio,
algo_datetime,
algo_current_data):
"""
Fail if we've already placed self.max_count orders today.
"""
algo_date = algo_datetime.date()
# Reset order c... |
Fail if the asset is in the restricted_list. | def validate(self,
asset,
amount,
portfolio,
algo_datetime,
algo_current_data):
"""
Fail if the asset is in the restricted_list.
"""
if self.restrictions.is_restricted(asset, algo_datetime):
... |
Fail if the magnitude of the given order exceeds either self. max_shares or self. max_notional. | def validate(self,
asset,
amount,
portfolio,
algo_datetime,
algo_current_data):
"""
Fail if the magnitude of the given order exceeds either self.max_shares
or self.max_notional.
"""
if self.asse... |
Fail if the given order would cause the magnitude of our position to be greater in shares than self. max_shares or greater in dollar value than self. max_notional. | def validate(self,
asset,
amount,
portfolio,
algo_datetime,
algo_current_data):
"""
Fail if the given order would cause the magnitude of our position to be
greater in shares than self.max_shares or greater in do... |
Fail if we would hold negative shares of asset after completing this order. | def validate(self,
asset,
amount,
portfolio,
algo_datetime,
algo_current_data):
"""
Fail if we would hold negative shares of asset after completing this
order.
"""
if portfolio.positions[asset].a... |
Fail if the algo has passed this Asset s end_date or before the Asset s start date. | def validate(self,
asset,
amount,
portfolio,
algo_datetime,
algo_current_data):
"""
Fail if the algo has passed this Asset's end_date, or before the
Asset's start date.
"""
# If the order is for ... |
Fail if the leverage is greater than the allowed leverage. | def validate(self,
_portfolio,
_account,
_algo_datetime,
_algo_current_data):
"""
Fail if the leverage is greater than the allowed leverage.
"""
if _account.leverage > self.max_leverage:
self.fail() |
Make validation checks if we are after the deadline. Fail if the leverage is less than the min leverage. | def validate(self,
_portfolio,
account,
algo_datetime,
_algo_current_data):
"""
Make validation checks if we are after the deadline.
Fail if the leverage is less than the min leverage.
"""
if (algo_datetime > sel... |
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