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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 c... |
df.ffill(inplace=True)
# Fill in missing values specified by each column. This is made
# significantly more complex by the fact that we need to work around
# two pandas issues:
# 1) When we have sids, if there are no records for a given sid for any
# dates, pandas will generate a column fu... |
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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 ... |
try:
start = dates.get_loc(start_date)
except KeyError:
if start_date < dates[0]:
raise NoFurtherDataError(
msg=(
"Pipeline Query requested data starting on {query_start}, "
"but first known date is {calendar_start}"
... |
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def format_docstring(owner_name, docstring, formatters):
""" Template ``formatters`` into ``docstring``. Parameters owner_name : str The name of the function or ... |
# Build a dict of parameters to a vanilla format() call by searching for
# each entry in **formatters and applying any leading whitespace to each
# line in the desired substitution.
format_params = {}
for target, doc_for_target in iteritems(formatters):
# Search for '{name}', with optional ... |
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def templated_docstring(**docs):
""" Decorator allowing the use of templated docstrings. Examples -------- 'bar' """ |
def decorator(f):
f.__doc__ = format_docstring(f.__name__, f.__doc__, docs)
return f
return decorator |
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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 ... |
self.validate_column(name, term)
columns = self.columns
if name in columns:
if overwrite:
self.remove(name)
else:
raise KeyError("Column '{}' already exists.".format(name))
if not isinstance(term, ComputableTerm):
rai... |
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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. ov... |
if self._screen is not None and not overwrite:
raise ValueError(
"set_screen() called with overwrite=False and screen already "
"set.\n"
"If you want to apply multiple filters as a screen use "
"set_screen(filter1 & filter2 & ...).\n"
... |
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def to_execution_plan(self, domain, default_screen, start_date, end_date):
""" Compile into an ExecutionPlan. Parameters domain : zipline.pipeline.domain.Domain ... |
if self._domain is not GENERIC and self._domain is not domain:
raise AssertionError(
"Attempted to compile Pipeline with domain {} to execution "
"plan with different domain {}.".format(self._domain, domain)
)
return ExecutionPlan(
do... |
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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 |
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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':
return g.svg
elif format == 'png':
return g.png
elif format == 'jpeg':
return g.jpeg
else:
# We should never get here because of the expect_element decorator
#... |
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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, i... |
terms = list(six.itervalues(self._columns))
screen = self.screen
if screen is not None:
terms.append(screen)
return terms |
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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 f... |
# Always compute our inferred domain to ensure that it's compatible
# with our explicit domain.
inferred = infer_domain(self._output_terms)
if inferred is GENERIC and self._domain is GENERIC:
# Both generic. Fall back to default.
return default
elif infe... |
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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) |
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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,
local_dict={
"x_%d" % idx: array
for idx, array in enumerate(arrays)
},
globa... |
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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, we might have x_1,
# x_10, and x_100. By enumerating in reverse order, we ensure that
# every variable name which is a substring of another variabl... |
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def _merge_expressions(self, other):
""" Merge the inputs of two NumericalExpressions into a single input tuple, rewriting their respective string expressions to... |
new_inputs = tuple(set(self.inputs).union(other.inputs))
new_self_expr = self._rebind_variables(new_inputs)
new_other_expr = other._rebind_variables(new_inputs)
return new_self_expr, new_other_expr, new_inputs |
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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)
elif isinstance(other, Term):
self_expr = self._expr
new_inputs, other_idx = _ensure_element(self.inputs, other)
other_expr = "x_%d" % other_idx
... |
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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'),
self._expr)
# Graphviz interprets `\l` as "divide label into lines, left-ju... |
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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') |
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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 ... |
if environ is None:
environ = os.environ
root = environ.get('ZIPLINE_ROOT', None)
if root is None:
root = expanduser('~/.zipline')
return root |
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def format_adjustments(self, dates, assets):
""" Build a dict of Adjustment objects in the format expected by AdjustedArray. Returns a dict of the form: { # Inte... |
make_adjustment = partial(make_adjustment_from_labels, dates, assets)
min_date, max_date = dates[[0, -1]]
# TODO: Consider porting this to Cython.
if len(self.adjustments) == 0:
return {}
# Mask for adjustments whose apply_dates are in the requested window of
... |
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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._validate_input_column(column)
date_indexer = self.dates.get_indexer(dates)
assets_indexer = self.assets.get_indexer(si... |
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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) |
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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 ma... |
data = {}
dir_path = os.path.join(SECURITY_LISTS_DIR, list_name)
for kd_name in listdir(dir_path):
kd = datetime.strptime(kd_name, DATE_FORMAT).replace(
tzinfo=pytz.utc)
data[kd] = {}
kd_path = os.path.join(dir_path, kd_name)
for ld_name in listdir(kd_path):
... |
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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 w... |
class desc(lazyval):
def __get__(self, instance, owner):
if instance is None:
return self
try:
return self._cache[instance]
except KeyError:
inst = ref(instance)
@_weak_lru_cache(maxsize)
@w... |
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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,
) |
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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),
name=self._name,
doc=self.__doc__,
metadata=self._metadata,
... |
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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.Bound... |
clsdict = vars(cls)
try:
maybe_column = clsdict[name]
if not isinstance(maybe_column, _BoundColumnDescr):
raise KeyError(name)
except KeyError:
raise AttributeError(
"{dset} has no column {colname!r}:\n\n"
"Poss... |
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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()),
)
return Slice |
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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 ... |
coords, hash_key = cls._canonical_key(args, kwargs)
try:
return cls._slice_cache[hash_key]
except KeyError:
pass
Slice = cls._make_dataset(coords)
cls._slice_cache[hash_key] = Slice
return Slice |
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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._loaders[col.unspecialize()]
except KeyError:
raise ValueError("Couldn't find loader for %s" % col)
... |
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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) |
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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')) |
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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 + offsets |
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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,
) |
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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... |
self.sync_last_sale_prices(dt, data_portal)
packet = {
'period_start': self._first_session,
'period_end': self._last_session,
'capital_base': self._capital_base,
'minute_perf': {
'period_open': self._market_open,
'period_c... |
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def handle_market_open(self, session_label, data_portal):
"""Handles the start of each session. Parameters session_label : Timestamp The label of the session tha... |
ledger = self._ledger
ledger.start_of_session(session_label)
adjustment_reader = data_portal.adjustment_reader
if adjustment_reader is not None:
# this is None when running with a dataframe source
ledger.process_dividends(
session_label,
... |
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def handle_market_close(self, dt, data_portal):
"""Handles the close of the given day. Parameters dt : Timestamp The most recently completed simulation datetime.... |
completed_session = self._current_session
if self.emission_rate == 'daily':
# this method is called for both minutely and daily emissions, but
# this chunk of code here only applies for daily emissions. (since
# it's done every minute, elsewhere, for minutely emissi... |
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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: {}',
self._session_count,
self._trading_calendar.session_open(self._first_session),
self._trading_calendar.session_close(self._last_session),
)
packet... |
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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, ... |
extension_args = {}
for arg in args:
parse_extension_arg(arg, extension_args)
for name in sorted(extension_args, key=len):
path = name.split('.')
update_namespace(root, path, extension_args[name]) |
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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 arg... |
match = re.match(r'^(([^\d\W]\w*)(\.[^\d\W]\w*)*)=(.*)$', arg)
if match is None:
raise ValueError(
"invalid extension argument '%s', must be in key=value form" % arg
)
name = match.group(1)
value = match.group(4)
arg_dict[name] = value |
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def update_namespace(namespace, path, name):
""" A recursive function that takes a root element, list of namespaces, and the value being stored, and assigns name... |
if len(path) == 1:
setattr(namespace, path[0], name)
else:
if hasattr(namespace, path[0]):
if isinstance(getattr(namespace, path[0]), six.string_types):
raise ValueError("Conflicting assignments at namespace"
" level '%s'" % path[0])... |
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def create_registry(interface):
""" Create a new registry for an extensible interface. Parameters interface : type The abstract data type for which to create a r... |
if interface in custom_types:
raise ValueError('there is already a Registry instance '
'for the specified type')
custom_types[interface] = Registry(interface)
return interface |
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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(
"no %s factory registered under name %r, options are: %r" %
(self.interface.__name__, name, sorted(self._factories)),
) |
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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 |
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def risk_metric_period(cls, start_session, end_session, algorithm_returns, benchmark_returns, algorithm_leverages):
""" Creates a dictionary representing the sta... |
algorithm_returns = algorithm_returns[
(algorithm_returns.index >= start_session) &
(algorithm_returns.index <= end_session)
]
# Benchmark needs to be masked to the same dates as the algo returns
benchmark_returns = benchmark_returns[
(benchmark_ret... |
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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 ... |
oc = self.asset_finder.get_ordered_contracts(root_symbol)
session = self.trading_calendar.minute_to_session_label(dt)
front = oc.contract_before_auto_close(session.value)
back = oc.contract_at_offset(front, 1, dt.value)
if back is None:
return front
primary =... |
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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... |
oc = self.asset_finder.get_ordered_contracts(root_symbol)
front = self._get_active_contract_at_offset(root_symbol, end, 0)
back = oc.contract_at_offset(front, 1, end.value)
if back is not None:
end_session = self.trading_calendar.minute_to_session_label(end)
firs... |
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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 volum... |
front_contract = oc.sid_to_contract[front].contract
back_contract = oc.sid_to_contract[back].contract
tc = self.trading_calendar
trading_day = tc.day
prev = dt - trading_day
get_value = self.session_reader.get_value
# If the front contract is past its auto clos... |
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def _normalize_array(data, missing_value):
""" Coerce buffer data for an AdjustedArray into a standard scalar representation, returning the coerced array and a d... |
if isinstance(data, LabelArray):
return data, {}
data_dtype = data.dtype
if data_dtype in BOOL_DTYPES:
return data.astype(uint8), {'dtype': dtype(bool_)}
elif data_dtype in FLOAT_DTYPES:
return data.astype(float64), {'dtype': dtype(float64)}
elif data_dtype in INT_DTYPES:
... |
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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 adjust... |
if len(adjustment_lists) == 1:
return list(adjustment_lists[0])
else:
return adjustment_lists[front_idx] + adjustment_lists[back_idx] |
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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 ad... |
if isinstance(ndarray_or_adjusted_array, ndarray):
return ndarray_or_adjusted_array
elif isinstance(ndarray_or_adjusted_array, AdjustedArray):
return ndarray_or_adjusted_array.data
else:
raise TypeError(
"Can't convert %s to ndarray" %
type(ndarray_or_adjuste... |
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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] ... |
if window_length < 1:
raise WindowLengthNotPositive(window_length=window_length)
if window_length > data.shape[0]:
raise WindowLengthTooLong(
nrows=data.shape[0],
window_length=window_length,
) |
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def update_adjustments(self, adjustments, method):
""" Merge ``adjustments`` with existing adjustments, handling index collisions according to ``method``. Parame... |
try:
merge_func = _merge_methods[method]
except KeyError:
raise ValueError(
"Invalid merge method %s\n"
"Valid methods are: %s" % (method, ', '.join(_merge_methods))
)
self.adjustments = merge_with(
merge_func,
... |
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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] |
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def traverse(self, window_length, offset=0, perspective_offset=0):
""" Produce an iterator rolling windows rows over our data. Each emitted window will have `win... |
data = self._data.copy()
_check_window_params(data, window_length)
return self._iterator_type(
data,
self._view_kwargs,
self.adjustments,
offset,
window_length,
perspective_offset,
rounding_places=None,
... |
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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(
dtype=self.dtype.name,
data=self.data,
adjustments=self.adjustments,
) |
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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 data is of type LabelArray.'
)
# Map the baseline values.
self._data = self._data.map(func)
# Map each of the adjustments.
for _, row_adjustments... |
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def handle_violation(self, asset, amount, datetime, metadata=None):
""" Handle a TradingControlViolation, either by raising or logging and error with information... |
constraint = self._constraint_msg(metadata)
if self.on_error == 'fail':
raise TradingControlViolation(
asset=asset,
amount=amount,
datetime=datetime,
constraint=constraint)
elif self.on_error == 'log':
log.... |
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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 count if it's a new day.
if self.current_date and self.current_date != algo_date:
self.orders_placed = 0
self.current_date = algo_date
if self.orders_placed >= self.max_count:
self.handle_violation(asset, a... |
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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):
self.handle_violation(asset, amount, algo_datetime) |
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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 se... |
if self.asset is not None and self.asset != asset:
return
if self.max_shares is not None and abs(amount) > self.max_shares:
self.handle_violation(asset, amount, algo_datetime)
current_asset_price = algo_current_data.current(asset, "price")
order_value = amount... |
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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 great... |
if self.asset is not None and self.asset != asset:
return
current_share_count = portfolio.positions[asset].amount
shares_post_order = current_share_count + amount
too_many_shares = (self.max_shares is not None and
abs(shares_post_order) > self.m... |
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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].amount + amount < 0:
self.handle_violation(asset, amount, algo_datetime) |
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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 star... |
# If the order is for 0 shares, then silently pass through.
if amount == 0:
return
normalized_algo_dt = pd.Timestamp(algo_datetime).normalize()
# Fail if the algo is before this Asset's start_date
if asset.start_date:
normalized_start = pd.Timestamp(ass... |
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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() |
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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... |
if (algo_datetime > self.deadline and
account.leverage < self.min_leverage):
self.fail() |
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def alter_columns(op, name, *columns, **kwargs):
"""Alter columns from a table. Parameters name : str The name of the table. *columns The new columns to have. se... |
selection_string = kwargs.pop('selection_string', None)
if kwargs:
raise TypeError(
'alter_columns received extra arguments: %r' % sorted(kwargs),
)
if selection_string is None:
selection_string = ', '.join(column.name for column in columns)
tmp_name = '_alter_colum... |
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def downgrade(engine, desired_version):
"""Downgrades the assets db at the given engine to the desired version. Parameters engine : Engine An SQLAlchemy engine t... |
# Check the version of the db at the engine
with engine.begin() as conn:
metadata = sa.MetaData(conn)
metadata.reflect()
version_info_table = metadata.tables['version_info']
starting_version = sa.select((version_info_table.c.version,)).scalar()
# Check for accidental u... |
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def downgrades(src):
"""Decorator for marking that a method is a downgrade to a version to the previous version. Parameters src : int The version this downgrades... |
def _(f):
destination = src - 1
@do(operator.setitem(_downgrade_methods, destination))
@wraps(f)
def wrapper(op, conn, version_info_table):
conn.execute(version_info_table.delete()) # clear the version
f(op)
write_version_info(conn, version_info... |
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def _downgrade_v1(op):
""" Downgrade assets db by removing the 'tick_size' column and renaming the 'multiplier' column. """ |
# Drop indices before batch
# This is to prevent index collision when creating the temp table
op.drop_index('ix_futures_contracts_root_symbol')
op.drop_index('ix_futures_contracts_symbol')
# Execute batch op to allow column modification in SQLite
with op.batch_alter_table('futures_contracts') ... |
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def _downgrade_v2(op):
""" Downgrade assets db by removing the 'auto_close_date' column. """ |
# Drop indices before batch
# This is to prevent index collision when creating the temp table
op.drop_index('ix_equities_fuzzy_symbol')
op.drop_index('ix_equities_company_symbol')
# Execute batch op to allow column modification in SQLite
with op.batch_alter_table('equities') as batch_op:
... |
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def _downgrade_v3(op):
""" Downgrade assets db by adding a not null constraint on ``equities.first_traded`` """ |
op.create_table(
'_new_equities',
sa.Column(
'sid',
sa.Integer,
unique=True,
nullable=False,
primary_key=True,
),
sa.Column('symbol', sa.Text),
sa.Column('company_symbol', sa.Text),
sa.Column('share_class_sy... |
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def _downgrade_v4(op):
""" Downgrades assets db by copying the `exchange_full` column to `exchange`, then dropping the `exchange_full` column. """ |
op.drop_index('ix_equities_fuzzy_symbol')
op.drop_index('ix_equities_company_symbol')
op.execute("UPDATE equities SET exchange = exchange_full")
with op.batch_alter_table('equities') as batch_op:
batch_op.drop_column('exchange_full')
op.create_index('ix_equities_fuzzy_symbol',
... |
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def _make_metrics_set_core():
"""Create a family of metrics sets functions that read from the same metrics set mapping. Returns ------- metrics_sets : mappingpro... |
_metrics_sets = {}
# Expose _metrics_sets through a proxy so that users cannot mutate this
# accidentally. Users may go through `register` to update this which will
# warn when trampling another metrics set.
metrics_sets = mappingproxy(_metrics_sets)
def register(name, function=None):
... |
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def validate_column_specs(events, columns):
""" Verify that the columns of ``events`` can be used by a EarningsEstimatesLoader to serve the BoundColumns describe... |
required = required_estimates_fields(columns)
received = set(events.columns)
missing = required - received
if missing:
raise ValueError(
"EarningsEstimatesLoader missing required columns {missing}.\n"
"Got Columns: {received}\n"
"Expected Columns: {required}"... |
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def get_requested_quarter_data(self, zero_qtr_data, zeroth_quarter_idx, stacked_last_per_qtr, num_announcements, dates):
""" Selects the requested data for each ... |
zero_qtr_data_idx = zero_qtr_data.index
requested_qtr_idx = pd.MultiIndex.from_arrays(
[
zero_qtr_data_idx.get_level_values(0),
zero_qtr_data_idx.get_level_values(1),
self.get_shifted_qtrs(
zeroth_quarter_idx.get_level_valu... |
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def get_split_adjusted_asof_idx(self, dates):
""" Compute the index in `dates` where the split-adjusted-asof-date falls. This is the date up to which, and includ... |
split_adjusted_asof_idx = dates.searchsorted(
self._split_adjusted_asof
)
# The split-asof date is after the date index.
if split_adjusted_asof_idx == len(dates):
split_adjusted_asof_idx = len(dates) - 1
elif self._split_adjusted_asof < dates[0].tz_locali... |
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def collect_overwrites_for_sid(self, group, dates, requested_qtr_data, last_per_qtr, sid_idx, columns, all_adjustments_for_sid, sid):
""" Given a sid, collect al... |
# If data was requested for only 1 date, there can never be any
# overwrites, so skip the extra work.
if len(dates) == 1:
return
next_qtr_start_indices = dates.searchsorted(
group[EVENT_DATE_FIELD_NAME].values,
side=self.searchsorted_side,
)
... |
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def merge_into_adjustments_for_all_sids(self, all_adjustments_for_sid, col_to_all_adjustments):
""" Merge adjustments for a particular sid into a dictionary cont... |
for col_name in all_adjustments_for_sid:
if col_name not in col_to_all_adjustments:
col_to_all_adjustments[col_name] = {}
for ts in all_adjustments_for_sid[col_name]:
adjs = all_adjustments_for_sid[col_name][ts]
add_new_adjustments(col_to... |
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def get_adjustments(self, zero_qtr_data, requested_qtr_data, last_per_qtr, dates, assets, columns, **kwargs):
""" Creates an AdjustedArray from the given estimat... |
zero_qtr_data.sort_index(inplace=True)
# Here we want to get the LAST record from each group of records
# corresponding to a single quarter. This is to ensure that we select
# the most up-to-date event date in case the event date changes.
quarter_shifts = zero_qtr_data.groupby(... |
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def create_overwrites_for_quarter(self, col_to_overwrites, next_qtr_start_idx, last_per_qtr, quarters_with_estimates_for_sid, requested_quarter, sid, sid_idx, col... |
for col in columns:
column_name = self.name_map[col.name]
if column_name not in col_to_overwrites:
col_to_overwrites[column_name] = {}
# If there are estimates for the requested quarter,
# overwrite all values going up to the starting index of
... |
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def get_last_data_per_qtr(self, assets_with_data, columns, dates, data_query_cutoff_times):
""" Determine the last piece of information we know for each column o... |
# Get a DataFrame indexed by date with a MultiIndex of columns of
# [self.estimates.columns, normalized_quarters, sid], where each cell
# contains the latest data for that day.
last_per_qtr = last_in_date_group(
self.estimates,
data_query_cutoff_times,
... |
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def get_zeroth_quarter_idx(self, stacked_last_per_qtr):
""" Filters for releases that are on or after each simulation date and determines the previous quarter by... |
previous_releases_per_date = stacked_last_per_qtr.loc[
stacked_last_per_qtr[EVENT_DATE_FIELD_NAME] <=
stacked_last_per_qtr.index.get_level_values(SIMULATION_DATES)
].groupby(
level=[SIMULATION_DATES, SID_FIELD_NAME],
as_index=False,
# Here we ... |
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def get_adjustments_for_sid(self, group, dates, requested_qtr_data, last_per_qtr, sid_to_idx, columns, col_to_all_adjustments, split_adjusted_asof_idx=None, split... |
all_adjustments_for_sid = {}
sid = int(group.name)
self.collect_overwrites_for_sid(group,
dates,
requested_qtr_data,
last_per_qtr,
sid_... |
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def get_adjustments(self, zero_qtr_data, requested_qtr_data, last_per_qtr, dates, assets, columns, **kwargs):
""" Calculates both split adjustments and overwrite... |
split_adjusted_cols_for_group = [
self.name_map[col.name]
for col in columns
if self.name_map[col.name] in self._split_adjusted_column_names
]
# Add all splits to the adjustment dict for this sid.
split_adjusted_asof_idx = self.get_split_adjusted_asof... |
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def determine_end_idx_for_adjustment(self, adjustment_ts, dates, upper_bound, requested_quarter, sid_estimates):
""" Determines the date until which the adjustme... |
end_idx = upper_bound
# Find the next newest kd that happens on or after
# the date of this adjustment
newest_kd_for_qtr = sid_estimates[
(sid_estimates[NORMALIZED_QUARTERS] == requested_quarter) &
(sid_estimates[TS_FIELD_NAME] >= adjustment_ts)
][TS_FIEL... |
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def collect_pre_split_asof_date_adjustments( self, split_adjusted_asof_date_idx, sid_idx, pre_adjustments, requested_split_adjusted_columns ):
""" Collect split ... |
col_to_split_adjustments = {}
if len(pre_adjustments[0]):
adjustment_values, date_indexes = pre_adjustments
for column_name in requested_split_adjusted_columns:
col_to_split_adjustments[column_name] = {}
# We need to undo all adjustments that happ... |
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def collect_post_asof_split_adjustments(self, post_adjustments, requested_qtr_data, sid, sid_idx, sid_estimates, requested_split_adjusted_columns):
""" Collect s... |
col_to_split_adjustments = {}
if post_adjustments:
# Get an integer index
requested_qtr_timeline = requested_qtr_data[
SHIFTED_NORMALIZED_QTRS
][sid].reset_index()
requested_qtr_timeline = requested_qtr_timeline[
requested_... |
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def merge_split_adjustments_with_overwrites( self, pre, post, overwrites, requested_split_adjusted_columns ):
""" Merge split adjustments with the dict containin... |
for column_name in requested_split_adjusted_columns:
# We can do a merge here because the timestamps in 'pre' and
# 'post' are guaranteed to not overlap.
if pre:
# Either empty or contains all columns.
for ts in pre[column_name]:
... |
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def collect_split_adjustments(self, adjustments_for_sid, requested_qtr_data, dates, sid, sid_idx, sid_estimates, split_adjusted_asof_idx, pre_adjustments, post_ad... |
(pre_adjustments_dict,
post_adjustments_dict) = self._collect_adjustments(
requested_qtr_data,
sid,
sid_idx,
sid_estimates,
split_adjusted_asof_idx,
pre_adjustments,
post_adjustments,
requested_split_adjust... |
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def collect_split_adjustments(self, adjustments_for_sid, requested_qtr_data, dates, sid, sid_idx, sid_estimates, split_adjusted_asof_idx, pre_adjustments, post_ad... |
(pre_adjustments_dict,
post_adjustments_dict) = self._collect_adjustments(
requested_qtr_data,
sid,
sid_idx,
sid_estimates,
split_adjusted_asof_idx,
pre_adjustments,
post_adjustments,
requested_split_adjust... |
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def from_span(cls, inputs, window_length, span, **kwargs):
""" Convenience constructor for passing `decay_rate` in terms of `span`. Forwards `decay_rate` as `1 -... |
if span <= 1:
raise ValueError(
"`span` must be a positive number. %s was passed." % span
)
decay_rate = (1.0 - (2.0 / (1.0 + span)))
assert 0.0 < decay_rate <= 1.0
return cls(
inputs=inputs,
window_length=window_length,
... |
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def from_halflife(cls, inputs, window_length, halflife, **kwargs):
""" Convenience constructor for passing ``decay_rate`` in terms of half life. Forwards ``decay... |
if halflife <= 0:
raise ValueError(
"`span` must be a positive number. %s was passed." % halflife
)
decay_rate = exp(log(.5) / halflife)
assert 0.0 < decay_rate <= 1.0
return cls(
inputs=inputs,
window_length=window_length... |
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def from_center_of_mass(cls, inputs, window_length, center_of_mass, **kwargs):
""" Convenience constructor for passing `decay_rate` in terms of center of mass. F... |
return cls(
inputs=inputs,
window_length=window_length,
decay_rate=(1.0 - (1.0 / (1.0 + center_of_mass))),
**kwargs
) |
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def tolerant_equals(a, b, atol=10e-7, rtol=10e-7, equal_nan=False):
"""Check if a and b are equal with some tolerance. Parameters a, b : float The floats to chec... |
if equal_nan and isnan(a) and isnan(b):
return True
return math.fabs(a - b) <= (atol + rtol * math.fabs(b)) |
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Description:
def round_if_near_integer(a, epsilon=1e-4):
""" Round a to the nearest integer if that integer is within an epsilon of a. """ |
if abs(a - round(a)) <= epsilon:
return round(a)
else:
return a |
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Description:
def binop_return_dtype(op, left, right):
""" Compute the expected return dtype for the given binary operator. Parameters op : str left : numpy.dtype Dtype of lef... |
if is_comparison(op):
if left != right:
raise TypeError(
"Don't know how to compute {left} {op} {right}.\n"
"Comparisons are only supported between Factors of equal "
"dtypes.".format(left=left, op=op, right=right)
)
return boo... |
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Description:
def binary_operator(op):
""" Factory function for making binary operator methods on a Factor subclass. Returns a function, "binary_operator" suitable for impleme... |
# When combining a Factor with a NumericalExpression, we use this
# attrgetter instance to defer to the commuted implementation of the
# NumericalExpression operator.
commuted_method_getter = attrgetter(method_name_for_op(op, commute=True))
@with_doc("Binary Operator: '%s'" % op)
@with_name(me... |
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Description:
def reflected_binary_operator(op):
""" Factory function for making binary operator methods on a Factor. Returns a function, "reflected_binary_operator" suitable ... |
assert not is_comparison(op)
@with_name(method_name_for_op(op, commute=True))
@coerce_numbers_to_my_dtype
def reflected_binary_operator(self, other):
if isinstance(self, NumericalExpression):
self_expr, other_expr, new_inputs = self.build_binary_op(
op, other
... |
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Description:
def unary_operator(op):
""" Factory function for making unary operator methods for Factors. """ |
# Only negate is currently supported.
valid_ops = {'-'}
if op not in valid_ops:
raise ValueError("Invalid unary operator %s." % op)
@with_doc("Unary Operator: '%s'" % op)
@with_name(unary_op_name(op))
def unary_operator(self):
if self.dtype != float64_dtype:
raise T... |
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