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quantopian/zipline | zipline/utils/run_algo.py | _run | def _run(handle_data,
initialize,
before_trading_start,
analyze,
algofile,
algotext,
defines,
data_frequency,
capital_base,
bundle,
bundle_timestamp,
start,
end,
output,
trading_calendar,
... | python | def _run(handle_data,
initialize,
before_trading_start,
analyze,
algofile,
algotext,
defines,
data_frequency,
capital_base,
bundle,
bundle_timestamp,
start,
end,
output,
trading_calendar,
... | [
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quantopian/zipline | zipline/utils/run_algo.py | load_extensions | def load_extensions(default, extensions, strict, environ, reload=False):
"""Load all of the given extensions. This should be called by run_algo
or the cli.
Parameters
----------
default : bool
Load the default exension (~/.zipline/extension.py)?
extension : iterable[str]
The pat... | python | def load_extensions(default, extensions, strict, environ, reload=False):
"""Load all of the given extensions. This should be called by run_algo
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Parameters
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default : bool
Load the default exension (~/.zipline/extension.py)?
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quantopian/zipline | zipline/utils/run_algo.py | run_algorithm | def run_algorithm(start,
end,
initialize,
capital_base,
handle_data=None,
before_trading_start=None,
analyze=None,
data_frequency='daily',
bundle='quantopian-quandl',
... | python | def run_algorithm(start,
end,
initialize,
capital_base,
handle_data=None,
before_trading_start=None,
analyze=None,
data_frequency='daily',
bundle='quantopian-quandl',
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quantopian/zipline | zipline/data/data_portal.py | DataPortal.handle_extra_source | def handle_extra_source(self, source_df, sim_params):
"""
Extra sources always have a sid column.
We expand the given data (by forward filling) to the full range of
the simulation dates, so that lookup is fast during simulation.
"""
if source_df is None:
retu... | python | def handle_extra_source(self, source_df, sim_params):
"""
Extra sources always have a sid column.
We expand the given data (by forward filling) to the full range of
the simulation dates, so that lookup is fast during simulation.
"""
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quantopian/zipline | zipline/data/data_portal.py | DataPortal.get_last_traded_dt | def get_last_traded_dt(self, asset, dt, data_frequency):
"""
Given an asset and dt, returns the last traded dt from the viewpoint
of the given dt.
If there is a trade on the dt, the answer is dt provided.
"""
return self._get_pricing_reader(data_frequency).get_last_trade... | python | def get_last_traded_dt(self, asset, dt, data_frequency):
"""
Given an asset and dt, returns the last traded dt from the viewpoint
of the given dt.
If there is a trade on the dt, the answer is dt provided.
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quantopian/zipline | zipline/data/data_portal.py | DataPortal._is_extra_source | def _is_extra_source(asset, field, map):
"""
Internal method that determines if this asset/field combination
represents a fetcher value or a regular OHLCVP lookup.
"""
# If we have an extra source with a column called "price", only look
# at it if it's on something like p... | python | def _is_extra_source(asset, field, map):
"""
Internal method that determines if this asset/field combination
represents a fetcher value or a regular OHLCVP lookup.
"""
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quantopian/zipline | zipline/data/data_portal.py | DataPortal.get_spot_value | def get_spot_value(self, assets, field, dt, data_frequency):
"""
Public API method that returns a scalar value representing the value
of the desired asset's field at either the given dt.
Parameters
----------
assets : Asset, ContinuousFuture, or iterable of same.
... | python | def get_spot_value(self, assets, field, dt, data_frequency):
"""
Public API method that returns a scalar value representing the value
of the desired asset's field at either the given dt.
Parameters
----------
assets : Asset, ContinuousFuture, or iterable of same.
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quantopian/zipline | zipline/data/data_portal.py | DataPortal.get_scalar_asset_spot_value | def get_scalar_asset_spot_value(self, asset, field, dt, data_frequency):
"""
Public API method that returns a scalar value representing the value
of the desired asset's field at either the given dt.
Parameters
----------
assets : Asset
The asset or assets who... | python | def get_scalar_asset_spot_value(self, asset, field, dt, data_frequency):
"""
Public API method that returns a scalar value representing the value
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Parameters
----------
assets : Asset
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quantopian/zipline | zipline/data/data_portal.py | DataPortal.get_adjustments | def get_adjustments(self, assets, field, dt, perspective_dt):
"""
Returns a list of adjustments between the dt and perspective_dt for the
given field and list of assets
Parameters
----------
assets : list of type Asset, or Asset
The asset, or assets whose adj... | python | def get_adjustments(self, assets, field, dt, perspective_dt):
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Returns a list of adjustments between the dt and perspective_dt for the
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quantopian/zipline | zipline/data/data_portal.py | DataPortal.get_adjusted_value | def get_adjusted_value(self, asset, field, dt,
perspective_dt,
data_frequency,
spot_value=None):
"""
Returns a scalar value representing the value
of the desired asset's field at the given dt with adjustments applie... | python | def get_adjusted_value(self, asset, field, dt,
perspective_dt,
data_frequency,
spot_value=None):
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quantopian/zipline | zipline/data/data_portal.py | DataPortal._get_history_daily_window | def _get_history_daily_window(self,
assets,
end_dt,
bar_count,
field_to_use,
data_frequency):
"""
Internal method that returns a dataf... | python | def _get_history_daily_window(self,
assets,
end_dt,
bar_count,
field_to_use,
data_frequency):
"""
Internal method that returns a dataf... | [
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quantopian/zipline | zipline/data/data_portal.py | DataPortal._get_history_minute_window | def _get_history_minute_window(self, assets, end_dt, bar_count,
field_to_use):
"""
Internal method that returns a dataframe containing history bars
of minute frequency for the given sids.
"""
# get all the minutes for this window
try:
... | python | def _get_history_minute_window(self, assets, end_dt, bar_count,
field_to_use):
"""
Internal method that returns a dataframe containing history bars
of minute frequency for the given sids.
"""
# get all the minutes for this window
try:
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quantopian/zipline | zipline/data/data_portal.py | DataPortal.get_history_window | def get_history_window(self,
assets,
end_dt,
bar_count,
frequency,
field,
data_frequency,
ffill=True):
"""
Public A... | python | def get_history_window(self,
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"""
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quantopian/zipline | zipline/data/data_portal.py | DataPortal._get_minute_window_data | def _get_minute_window_data(self, assets, field, minutes_for_window):
"""
Internal method that gets a window of adjusted minute data for an asset
and specified date range. Used to support the history API method for
minute bars.
Missing bars are filled with NaN.
Paramet... | python | def _get_minute_window_data(self, assets, field, minutes_for_window):
"""
Internal method that gets a window of adjusted minute data for an asset
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Missing bars are filled with NaN.
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quantopian/zipline | zipline/data/data_portal.py | DataPortal._get_daily_window_data | def _get_daily_window_data(self,
assets,
field,
days_in_window,
extra_slot=True):
"""
Internal method that gets a window of adjusted daily data for a sid
and specified date... | python | def _get_daily_window_data(self,
assets,
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quantopian/zipline | zipline/data/data_portal.py | DataPortal._get_adjustment_list | def _get_adjustment_list(self, asset, adjustments_dict, table_name):
"""
Internal method that returns a list of adjustments for the given sid.
Parameters
----------
asset : Asset
The asset for which to return adjustments.
adjustments_dict: dict
A... | python | def _get_adjustment_list(self, asset, adjustments_dict, table_name):
"""
Internal method that returns a list of adjustments for the given sid.
Parameters
----------
asset : Asset
The asset for which to return adjustments.
adjustments_dict: dict
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quantopian/zipline | zipline/data/data_portal.py | DataPortal.get_splits | def get_splits(self, assets, dt):
"""
Returns any splits for the given sids and the given dt.
Parameters
----------
assets : container
Assets for which we want splits.
dt : pd.Timestamp
The date for which we are checking for splits. Note: this is
... | python | def get_splits(self, assets, dt):
"""
Returns any splits for the given sids and the given dt.
Parameters
----------
assets : container
Assets for which we want splits.
dt : pd.Timestamp
The date for which we are checking for splits. Note: this is
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quantopian/zipline | zipline/data/data_portal.py | DataPortal.get_stock_dividends | def get_stock_dividends(self, sid, trading_days):
"""
Returns all the stock dividends for a specific sid that occur
in the given trading range.
Parameters
----------
sid: int
The asset whose stock dividends should be returned.
trading_days: pd.Dateti... | python | def get_stock_dividends(self, sid, trading_days):
"""
Returns all the stock dividends for a specific sid that occur
in the given trading range.
Parameters
----------
sid: int
The asset whose stock dividends should be returned.
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quantopian/zipline | zipline/data/data_portal.py | DataPortal.get_fetcher_assets | def get_fetcher_assets(self, dt):
"""
Returns a list of assets for the current date, as defined by the
fetcher data.
Returns
-------
list: a list of Asset objects.
"""
# return a list of assets for the current date, as defined by the
# fetcher sou... | python | def get_fetcher_assets(self, dt):
"""
Returns a list of assets for the current date, as defined by the
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Returns
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list: a list of Asset objects.
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quantopian/zipline | zipline/data/data_portal.py | DataPortal.get_current_future_chain | def get_current_future_chain(self, continuous_future, dt):
"""
Retrieves the future chain for the contract at the given `dt` according
the `continuous_future` specification.
Returns
-------
future_chain : list[Future]
A list of active futures, where the firs... | python | def get_current_future_chain(self, continuous_future, dt):
"""
Retrieves the future chain for the contract at the given `dt` according
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Returns
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future_chain : list[Future]
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quantopian/zipline | zipline/utils/numpy_utils.py | make_kind_check | def make_kind_check(python_types, numpy_kind):
"""
Make a function that checks whether a scalar or array is of a given kind
(e.g. float, int, datetime, timedelta).
"""
def check(value):
if hasattr(value, 'dtype'):
return value.dtype.kind == numpy_kind
return isinstance(va... | python | def make_kind_check(python_types, numpy_kind):
"""
Make a function that checks whether a scalar or array is of a given kind
(e.g. float, int, datetime, timedelta).
"""
def check(value):
if hasattr(value, 'dtype'):
return value.dtype.kind == numpy_kind
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quantopian/zipline | zipline/utils/numpy_utils.py | coerce_to_dtype | def coerce_to_dtype(dtype, value):
"""
Make a value with the specified numpy dtype.
Only datetime64[ns] and datetime64[D] are supported for datetime dtypes.
"""
name = dtype.name
if name.startswith('datetime64'):
if name == 'datetime64[D]':
return make_datetime64D(value)
... | python | def coerce_to_dtype(dtype, value):
"""
Make a value with the specified numpy dtype.
Only datetime64[ns] and datetime64[D] are supported for datetime dtypes.
"""
name = dtype.name
if name.startswith('datetime64'):
if name == 'datetime64[D]':
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quantopian/zipline | zipline/utils/numpy_utils.py | repeat_first_axis | def repeat_first_axis(array, count):
"""
Restride `array` to repeat `count` times along the first axis.
Parameters
----------
array : np.array
The array to restride.
count : int
Number of times to repeat `array`.
Returns
-------
result : array
Array of shape... | python | def repeat_first_axis(array, count):
"""
Restride `array` to repeat `count` times along the first axis.
Parameters
----------
array : np.array
The array to restride.
count : int
Number of times to repeat `array`.
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result : array
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quantopian/zipline | zipline/utils/numpy_utils.py | repeat_last_axis | def repeat_last_axis(array, count):
"""
Restride `array` to repeat `count` times along the last axis.
Parameters
----------
array : np.array
The array to restride.
count : int
Number of times to repeat `array`.
Returns
-------
result : array
Array of shape a... | python | def repeat_last_axis(array, count):
"""
Restride `array` to repeat `count` times along the last axis.
Parameters
----------
array : np.array
The array to restride.
count : int
Number of times to repeat `array`.
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quantopian/zipline | zipline/utils/numpy_utils.py | rolling_window | def rolling_window(array, length):
"""
Restride an array of shape
(X_0, ... X_N)
into an array of shape
(length, X_0 - length + 1, ... X_N)
where each slice at index i along the first axis is equivalent to
result[i] = array[length * i:length * (i + 1)]
Parameters
--... | python | def rolling_window(array, length):
"""
Restride an array of shape
(X_0, ... X_N)
into an array of shape
(length, X_0 - length + 1, ... X_N)
where each slice at index i along the first axis is equivalent to
result[i] = array[length * i:length * (i + 1)]
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quantopian/zipline | zipline/utils/numpy_utils.py | isnat | def isnat(obj):
"""
Check if a value is np.NaT.
"""
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raise ValueError("%s is not a numpy datetime or timedelta")
return obj.view(int64_dtype) == iNaT | python | def isnat(obj):
"""
Check if a value is np.NaT.
"""
if obj.dtype.kind not in ('m', 'M'):
raise ValueError("%s is not a numpy datetime or timedelta")
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quantopian/zipline | zipline/utils/numpy_utils.py | is_missing | def is_missing(data, missing_value):
"""
Generic is_missing function that handles NaN and NaT.
"""
if is_float(data) and isnan(missing_value):
return isnan(data)
elif is_datetime(data) and isnat(missing_value):
return isnat(data)
return (data == missing_value) | python | def is_missing(data, missing_value):
"""
Generic is_missing function that handles NaN and NaT.
"""
if is_float(data) and isnan(missing_value):
return isnan(data)
elif is_datetime(data) and isnat(missing_value):
return isnat(data)
return (data == missing_value) | [
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quantopian/zipline | zipline/utils/numpy_utils.py | busday_count_mask_NaT | def busday_count_mask_NaT(begindates, enddates, out=None):
"""
Simple of numpy.busday_count that returns `float` arrays rather than int
arrays, and handles `NaT`s by returning `NaN`s where the inputs were `NaT`.
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quantopian/zipline | zipline/utils/numpy_utils.py | changed_locations | def changed_locations(a, include_first):
"""
Compute indices of values in ``a`` that differ from the previous value.
Parameters
----------
a : np.ndarray
The array on which to indices of change.
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"""
Compute indices of values in ``a`` that differ from the previous value.
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The array on which to indices of change.
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quantopian/zipline | zipline/utils/date_utils.py | compute_date_range_chunks | def compute_date_range_chunks(sessions, start_date, end_date, chunksize):
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Parameters
----------
sessions : DatetimeIndex
The available dates.
start_date : pd.Timestamp
The first date in the pipeline.
end_date : pd.Timesta... | python | def compute_date_range_chunks(sessions, start_date, end_date, chunksize):
"""Compute the start and end dates to run a pipeline for.
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The available dates.
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quantopian/zipline | zipline/pipeline/engine.py | SimplePipelineEngine.run_pipeline | def run_pipeline(self, pipeline, start_date, end_date):
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The pipeline to run.
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Start date of the computed matrix.
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"""
Compute a pipeline.
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The pipeline to run.
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Start date of the computed matrix.
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quantopian/zipline | zipline/pipeline/engine.py | SimplePipelineEngine._compute_root_mask | def _compute_root_mask(self, domain, start_date, end_date, extra_rows):
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Compute a lifetimes matrix from our AssetFinder, then drop columns that
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----------
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quantopian/zipline | zipline/pipeline/engine.py | SimplePipelineEngine.compute_chunk | def compute_chunk(self, graph, dates, sids, initial_workspace):
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Compute the Pipeline terms in the graph for the requested start and end
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Compute the Pipeline terms in the graph for the requested start and end
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quantopian/zipline | zipline/pipeline/engine.py | SimplePipelineEngine._to_narrow | def _to_narrow(self, terms, data, mask, dates, assets):
"""
Convert raw computed pipeline results into a DataFrame for public APIs.
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terms : dict[str -> Term]
Dict mapping column names to terms.
data : dict[str -> ndarray[ndim=2]]
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Convert raw computed pipeline results into a DataFrame for public APIs.
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terms : dict[str -> Term]
Dict mapping column names to terms.
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quantopian/zipline | zipline/pipeline/engine.py | SimplePipelineEngine._validate_compute_chunk_params | def _validate_compute_chunk_params(self,
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quantopian/zipline | zipline/pipeline/engine.py | SimplePipelineEngine.resolve_domain | def resolve_domain(self, pipeline):
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quantopian/zipline | zipline/utils/api_support.py | require_initialized | def require_initialized(exception):
"""
Decorator for API methods that should only be called after
TradingAlgorithm.initialize. `exception` will be raised if the method is
called before initialize has completed.
Examples
--------
@require_initialized(SomeException("Don't do that!"))
de... | python | def require_initialized(exception):
"""
Decorator for API methods that should only be called after
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quantopian/zipline | zipline/utils/api_support.py | disallowed_in_before_trading_start | def disallowed_in_before_trading_start(exception):
"""
Decorator for API methods that cannot be called from within
TradingAlgorithm.before_trading_start. `exception` will be raised if the
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Examples
--------
@disallowed_in_before_trading_start(... | python | def disallowed_in_before_trading_start(exception):
"""
Decorator for API methods that cannot be called from within
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quantopian/zipline | zipline/lib/normalize.py | naive_grouped_rowwise_apply | def naive_grouped_rowwise_apply(data,
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func,
func_args=(),
out=None):
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Simple implementation of grouped row-wise function application.
Parameters
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... | python | def naive_grouped_rowwise_apply(data,
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Simple implementation of grouped row-wise function application.
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items : sequence
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quantopian/zipline | zipline/assets/synthetic.py | make_rotating_equity_info | def make_rotating_equity_info(num_assets,
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frequency,
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exchange='TEST'):
"""
Create a DataFrame representing li... | python | def make_rotating_equity_info(num_assets,
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quantopian/zipline | zipline/assets/synthetic.py | make_simple_equity_info | def make_simple_equity_info(sids,
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quantopian/zipline | zipline/assets/synthetic.py | make_simple_multi_country_equity_info | def make_simple_multi_country_equity_info(countries_to_sids,
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end_date):
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bet... | python | def make_simple_multi_country_equity_info(countries_to_sids,
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quantopian/zipline | zipline/assets/synthetic.py | make_jagged_equity_info | def make_jagged_equity_info(num_assets,
start_date,
first_end,
frequency,
periods_between_ends,
auto_close_delta):
"""
Create a DataFrame representing assets that all begin... | python | def make_jagged_equity_info(num_assets,
start_date,
first_end,
frequency,
periods_between_ends,
auto_close_delta):
"""
Create a DataFrame representing assets that all begin... | [
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quantopian/zipline | zipline/assets/synthetic.py | make_future_info | def make_future_info(first_sid,
root_symbols,
years,
notice_date_func,
expiration_date_func,
start_date_func,
month_codes=None,
multiplier=500):
"""
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month_codes=None,
multiplier=500):
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quantopian/zipline | zipline/assets/synthetic.py | make_commodity_future_info | def make_commodity_future_info(first_sid,
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years,
month_codes=None,
multiplier=500):
"""
Make futures testing data that simulates the notice/expiration date
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month_codes=None,
multiplier=500):
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Make futures testing data that simulates the notice/expiration date
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quantopian/zipline | zipline/pipeline/classifiers/classifier.py | Classifier.eq | def eq(self, other):
"""
Construct a Filter returning True for asset/date pairs where the output
of ``self`` matches ``other``.
"""
# We treat this as an error because missing_values have NaN semantics,
# which means this would return an array of all False, which is almos... | python | def eq(self, other):
"""
Construct a Filter returning True for asset/date pairs where the output
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quantopian/zipline | zipline/pipeline/classifiers/classifier.py | Classifier.startswith | def startswith(self, prefix):
"""
Construct a Filter matching values starting with ``prefix``.
Parameters
----------
prefix : str
String prefix against which to compare values produced by ``self``.
Returns
-------
matches : Filter
... | python | def startswith(self, prefix):
"""
Construct a Filter matching values starting with ``prefix``.
Parameters
----------
prefix : str
String prefix against which to compare values produced by ``self``.
Returns
-------
matches : Filter
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quantopian/zipline | zipline/pipeline/classifiers/classifier.py | Classifier.endswith | def endswith(self, suffix):
"""
Construct a Filter matching values ending with ``suffix``.
Parameters
----------
suffix : str
String suffix against which to compare values produced by ``self``.
Returns
-------
matches : Filter
Fil... | python | def endswith(self, suffix):
"""
Construct a Filter matching values ending with ``suffix``.
Parameters
----------
suffix : str
String suffix against which to compare values produced by ``self``.
Returns
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matches : Filter
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quantopian/zipline | zipline/pipeline/classifiers/classifier.py | Classifier.has_substring | def has_substring(self, substring):
"""
Construct a Filter matching values containing ``substring``.
Parameters
----------
substring : str
Sub-string against which to compare values produced by ``self``.
Returns
-------
matches : Filter
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"""
Construct a Filter matching values containing ``substring``.
Parameters
----------
substring : str
Sub-string against which to compare values produced by ``self``.
Returns
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matches : Filter
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quantopian/zipline | zipline/pipeline/classifiers/classifier.py | Classifier.matches | def matches(self, pattern):
"""
Construct a Filter that checks regex matches against ``pattern``.
Parameters
----------
pattern : str
Regex pattern against which to compare values produced by ``self``.
Returns
-------
matches : Filter
... | python | def matches(self, pattern):
"""
Construct a Filter that checks regex matches against ``pattern``.
Parameters
----------
pattern : str
Regex pattern against which to compare values produced by ``self``.
Returns
-------
matches : Filter
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quantopian/zipline | zipline/pipeline/classifiers/classifier.py | Classifier.element_of | def element_of(self, choices):
"""
Construct a Filter indicating whether values are in ``choices``.
Parameters
----------
choices : iterable[str or int]
An iterable of choices.
Returns
-------
matches : Filter
Filter returning Tru... | python | def element_of(self, choices):
"""
Construct a Filter indicating whether values are in ``choices``.
Parameters
----------
choices : iterable[str or int]
An iterable of choices.
Returns
-------
matches : Filter
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quantopian/zipline | zipline/pipeline/classifiers/classifier.py | Classifier.to_workspace_value | def to_workspace_value(self, result, assets):
"""
Called with the result of a pipeline. This needs to return an object
which can be put into the workspace to continue doing computations.
This is the inverse of :func:`~zipline.pipeline.term.Term.postprocess`.
"""
if self.... | python | def to_workspace_value(self, result, assets):
"""
Called with the result of a pipeline. This needs to return an object
which can be put into the workspace to continue doing computations.
This is the inverse of :func:`~zipline.pipeline.term.Term.postprocess`.
"""
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quantopian/zipline | zipline/pipeline/classifiers/classifier.py | Classifier._to_integral | def _to_integral(self, output_array):
"""
Convert an array produced by this classifier into an array of integer
labels and a missing value label.
"""
if self.dtype == int64_dtype:
group_labels = output_array
null_label = self.missing_value
elif sel... | python | def _to_integral(self, output_array):
"""
Convert an array produced by this classifier into an array of integer
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"""
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quantopian/zipline | zipline/pipeline/classifiers/classifier.py | CustomClassifier._allocate_output | def _allocate_output(self, windows, shape):
"""
Override the default array allocation to produce a LabelArray when we
have a string-like dtype.
"""
if self.dtype == int64_dtype:
return super(CustomClassifier, self)._allocate_output(
windows,
... | python | def _allocate_output(self, windows, shape):
"""
Override the default array allocation to produce a LabelArray when we
have a string-like dtype.
"""
if self.dtype == int64_dtype:
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quantopian/zipline | zipline/utils/input_validation.py | verify_indices_all_unique | def verify_indices_all_unique(obj):
"""
Check that all axes of a pandas object are unique.
Parameters
----------
obj : pd.Series / pd.DataFrame / pd.Panel
The object to validate.
Returns
-------
obj : pd.Series / pd.DataFrame / pd.Panel
The validated object, unchanged.
... | python | def verify_indices_all_unique(obj):
"""
Check that all axes of a pandas object are unique.
Parameters
----------
obj : pd.Series / pd.DataFrame / pd.Panel
The object to validate.
Returns
-------
obj : pd.Series / pd.DataFrame / pd.Panel
The validated object, unchanged.
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quantopian/zipline | zipline/utils/input_validation.py | optionally | def optionally(preprocessor):
"""Modify a preprocessor to explicitly allow `None`.
Parameters
----------
preprocessor : callable[callable, str, any -> any]
A preprocessor to delegate to when `arg is not None`.
Returns
-------
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"""Modify a preprocessor to explicitly allow `None`.
Parameters
----------
preprocessor : callable[callable, str, any -> any]
A preprocessor to delegate to when `arg is not None`.
Returns
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quantopian/zipline | zipline/utils/input_validation.py | ensure_dtype | def ensure_dtype(func, argname, arg):
"""
Argument preprocessor that converts the input into a numpy dtype.
Examples
--------
>>> import numpy as np
>>> from zipline.utils.preprocess import preprocess
>>> @preprocess(dtype=ensure_dtype)
... def foo(dtype):
... return dtype
.... | python | def ensure_dtype(func, argname, arg):
"""
Argument preprocessor that converts the input into a numpy dtype.
Examples
--------
>>> import numpy as np
>>> from zipline.utils.preprocess import preprocess
>>> @preprocess(dtype=ensure_dtype)
... def foo(dtype):
... return dtype
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Examples
--------
>>> import numpy as np
>>> from zipline.utils.preprocess import preprocess
>>> @preprocess(dtype=ensure_dtype)
... def foo(dtype):
... return dtype
...
>>> foo(float)
dtype('float64') | [
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quantopian/zipline | zipline/utils/input_validation.py | ensure_timezone | def ensure_timezone(func, argname, arg):
"""Argument preprocessor that converts the input into a tzinfo object.
Examples
--------
>>> from zipline.utils.preprocess import preprocess
>>> @preprocess(tz=ensure_timezone)
... def foo(tz):
... return tz
>>> foo('utc')
<UTC>
"""
... | python | def ensure_timezone(func, argname, arg):
"""Argument preprocessor that converts the input into a tzinfo object.
Examples
--------
>>> from zipline.utils.preprocess import preprocess
>>> @preprocess(tz=ensure_timezone)
... def foo(tz):
... return tz
>>> foo('utc')
<UTC>
"""
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quantopian/zipline | zipline/utils/input_validation.py | ensure_timestamp | def ensure_timestamp(func, argname, arg):
"""Argument preprocessor that converts the input into a pandas Timestamp
object.
Examples
--------
>>> from zipline.utils.preprocess import preprocess
>>> @preprocess(ts=ensure_timestamp)
... def foo(ts):
... return ts
>>> foo('2014-01-0... | python | def ensure_timestamp(func, argname, arg):
"""Argument preprocessor that converts the input into a pandas Timestamp
object.
Examples
--------
>>> from zipline.utils.preprocess import preprocess
>>> @preprocess(ts=ensure_timestamp)
... def foo(ts):
... return ts
>>> foo('2014-01-0... | [
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quantopian/zipline | zipline/utils/input_validation.py | expect_dtypes | def expect_dtypes(__funcname=_qualified_name, **named):
"""
Preprocessing decorator that verifies inputs have expected numpy dtypes.
Examples
--------
>>> from numpy import dtype, arange, int8, float64
>>> @expect_dtypes(x=dtype(int8))
... def foo(x, y):
... return x, y
...
>... | python | def expect_dtypes(__funcname=_qualified_name, **named):
"""
Preprocessing decorator that verifies inputs have expected numpy dtypes.
Examples
--------
>>> from numpy import dtype, arange, int8, float64
>>> @expect_dtypes(x=dtype(int8))
... def foo(x, y):
... return x, y
...
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... def foo(x, y):
... return x, y
...
>>> foo(arange(3, dtype=int8), 'foo')
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quantopian/zipline | zipline/utils/input_validation.py | expect_kinds | def expect_kinds(**named):
"""
Preprocessing decorator that verifies inputs have expected dtype kinds.
Examples
--------
>>> from numpy import int64, int32, float32
>>> @expect_kinds(x='i')
... def foo(x):
... return x
...
>>> foo(int64(2))
2
>>> foo(int32(2))
2
... | python | def expect_kinds(**named):
"""
Preprocessing decorator that verifies inputs have expected dtype kinds.
Examples
--------
>>> from numpy import int64, int32, float32
>>> @expect_kinds(x='i')
... def foo(x):
... return x
...
>>> foo(int64(2))
2
>>> foo(int32(2))
2
... | [
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quantopian/zipline | zipline/utils/input_validation.py | expect_types | def expect_types(__funcname=_qualified_name, **named):
"""
Preprocessing decorator that verifies inputs have expected types.
Examples
--------
>>> @expect_types(x=int, y=str)
... def foo(x, y):
... return x, y
...
>>> foo(2, '3')
(2, '3')
>>> foo(2.0, '3') # doctest: +NO... | python | def expect_types(__funcname=_qualified_name, **named):
"""
Preprocessing decorator that verifies inputs have expected types.
Examples
--------
>>> @expect_types(x=int, y=str)
... def foo(x, y):
... return x, y
...
>>> foo(2, '3')
(2, '3')
>>> foo(2.0, '3') # doctest: +NO... | [
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>>> foo(2, '3')
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quantopian/zipline | zipline/utils/input_validation.py | make_check | def make_check(exc_type, template, pred, actual, funcname):
"""
Factory for making preprocessing functions that check a predicate on the
input value.
Parameters
----------
exc_type : Exception
The exception type to raise if the predicate fails.
template : str
A template stri... | python | def make_check(exc_type, template, pred, actual, funcname):
"""
Factory for making preprocessing functions that check a predicate on the
input value.
Parameters
----------
exc_type : Exception
The exception type to raise if the predicate fails.
template : str
A template stri... | [
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quantopian/zipline | zipline/utils/input_validation.py | expect_element | def expect_element(__funcname=_qualified_name, **named):
"""
Preprocessing decorator that verifies inputs are elements of some
expected collection.
Examples
--------
>>> @expect_element(x=('a', 'b'))
... def foo(x):
... return x.upper()
...
>>> foo('a')
'A'
>>> foo('b... | python | def expect_element(__funcname=_qualified_name, **named):
"""
Preprocessing decorator that verifies inputs are elements of some
expected collection.
Examples
--------
>>> @expect_element(x=('a', 'b'))
... def foo(x):
... return x.upper()
...
>>> foo('a')
'A'
>>> foo('b... | [
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>>> foo('a')
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quantopian/zipline | zipline/utils/input_validation.py | expect_bounded | def expect_bounded(__funcname=_qualified_name, **named):
"""
Preprocessing decorator verifying that inputs fall INCLUSIVELY between
bounds.
Bounds should be passed as a pair of ``(min_value, max_value)``.
``None`` may be passed as ``min_value`` or ``max_value`` to signify that
the input is onl... | python | def expect_bounded(__funcname=_qualified_name, **named):
"""
Preprocessing decorator verifying that inputs fall INCLUSIVELY between
bounds.
Bounds should be passed as a pair of ``(min_value, max_value)``.
``None`` may be passed as ``min_value`` or ``max_value`` to signify that
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quantopian/zipline | zipline/utils/input_validation.py | expect_dimensions | def expect_dimensions(__funcname=_qualified_name, **dimensions):
"""
Preprocessing decorator that verifies inputs are numpy arrays with a
specific dimensionality.
Examples
--------
>>> from numpy import array
>>> @expect_dimensions(x=1, y=2)
... def foo(x, y):
... return x[0] + y... | python | def expect_dimensions(__funcname=_qualified_name, **dimensions):
"""
Preprocessing decorator that verifies inputs are numpy arrays with a
specific dimensionality.
Examples
--------
>>> from numpy import array
>>> @expect_dimensions(x=1, y=2)
... def foo(x, y):
... return x[0] + y... | [
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quantopian/zipline | zipline/utils/input_validation.py | coerce | def coerce(from_, to, **to_kwargs):
"""
A preprocessing decorator that coerces inputs of a given type by passing
them to a callable.
Parameters
----------
from : type or tuple or types
Inputs types on which to call ``to``.
to : function
Coercion function to call on inputs.
... | python | def coerce(from_, to, **to_kwargs):
"""
A preprocessing decorator that coerces inputs of a given type by passing
them to a callable.
Parameters
----------
from : type or tuple or types
Inputs types on which to call ``to``.
to : function
Coercion function to call on inputs.
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quantopian/zipline | zipline/utils/input_validation.py | coerce_types | def coerce_types(**kwargs):
"""
Preprocessing decorator that applies type coercions.
Parameters
----------
**kwargs : dict[str -> (type, callable)]
Keyword arguments mapping function parameter names to pairs of
(from_type, to_type).
Examples
--------
>>> @coerce_types... | python | def coerce_types(**kwargs):
"""
Preprocessing decorator that applies type coercions.
Parameters
----------
**kwargs : dict[str -> (type, callable)]
Keyword arguments mapping function parameter names to pairs of
(from_type, to_type).
Examples
--------
>>> @coerce_types... | [
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quantopian/zipline | zipline/utils/input_validation.py | validate_keys | def validate_keys(dict_, expected, funcname):
"""Validate that a dictionary has an expected set of keys.
"""
expected = set(expected)
received = set(dict_)
missing = expected - received
if missing:
raise ValueError(
"Missing keys in {}:\n"
"Expected Keys: {}\n"
... | python | def validate_keys(dict_, expected, funcname):
"""Validate that a dictionary has an expected set of keys.
"""
expected = set(expected)
received = set(dict_)
missing = expected - received
if missing:
raise ValueError(
"Missing keys in {}:\n"
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quantopian/zipline | zipline/utils/enum.py | enum | def enum(option, *options):
"""
Construct a new enum object.
Parameters
----------
*options : iterable of str
The names of the fields for the enum.
Returns
-------
enum
A new enum collection.
Examples
--------
>>> e = enum('a', 'b', 'c')
>>> e
<enum... | python | def enum(option, *options):
"""
Construct a new enum object.
Parameters
----------
*options : iterable of str
The names of the fields for the enum.
Returns
-------
enum
A new enum collection.
Examples
--------
>>> e = enum('a', 'b', 'c')
>>> e
<enum... | [
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quantopian/zipline | zipline/utils/data.py | RollingPanel.oldest_frame | def oldest_frame(self, raw=False):
"""
Get the oldest frame in the panel.
"""
if raw:
return self.buffer.values[:, self._start_index, :]
return self.buffer.iloc[:, self._start_index, :] | python | def oldest_frame(self, raw=False):
"""
Get the oldest frame in the panel.
"""
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quantopian/zipline | zipline/utils/data.py | RollingPanel.get_current | def get_current(self, item=None, raw=False, start=None, end=None):
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Get a Panel that is the current data in view. It is not safe to persist
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"""
item_indexer = slice(None)
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Get a Panel that is the current data in view. It is not safe to persist
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quantopian/zipline | zipline/utils/data.py | RollingPanel._roll_data | def _roll_data(self):
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"""
self.buffer.values[:, :self._window, :] = \
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Roll window worth of data up to position zero.
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quantopian/zipline | zipline/utils/data.py | MutableIndexRollingPanel.oldest_frame | def oldest_frame(self, raw=False):
"""
Get the oldest frame in the panel.
"""
if raw:
return self.buffer.values[:, self._oldest_frame_idx(), :]
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"""
Get the oldest frame in the panel.
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if raw:
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quantopian/zipline | zipline/finance/order.py | Order.check_triggers | def check_triggers(self, price, dt):
"""
Update internal state based on price triggers and the
trade event's price.
"""
stop_reached, limit_reached, sl_stop_reached = \
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"""
Update internal state based on price triggers and the
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quantopian/zipline | zipline/finance/order.py | Order.check_order_triggers | def check_order_triggers(self, current_price):
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quantopian/zipline | zipline/finance/order.py | Order.triggered | def triggered(self):
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"""
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quantopian/zipline | zipline/__init__.py | setup | def setup(self,
np=np,
numpy_version=numpy_version,
StrictVersion=StrictVersion,
new_pandas=new_pandas):
"""Lives in zipline.__init__ for doctests."""
if numpy_version >= StrictVersion('1.14'):
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np.set_printoptions(leg... | python | def setup(self,
np=np,
numpy_version=numpy_version,
StrictVersion=StrictVersion,
new_pandas=new_pandas):
"""Lives in zipline.__init__ for doctests."""
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quantopian/zipline | zipline/__init__.py | teardown | def teardown(self, np=np):
"""Lives in zipline.__init__ for doctests."""
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"""Lives in zipline.__init__ for doctests."""
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quantopian/zipline | zipline/gens/utils.py | hash_args | def hash_args(*args, **kwargs):
"""Define a unique string for any set of representable args."""
arg_string = '_'.join([str(arg) for arg in args])
kwarg_string = '_'.join([str(key) + '=' + str(value)
for key, value in iteritems(kwargs)])
combined = ':'.join([arg_string, kwarg... | python | def hash_args(*args, **kwargs):
"""Define a unique string for any set of representable args."""
arg_string = '_'.join([str(arg) for arg in args])
kwarg_string = '_'.join([str(key) + '=' + str(value)
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quantopian/zipline | zipline/gens/utils.py | assert_datasource_protocol | def assert_datasource_protocol(event):
"""Assert that an event meets the protocol for datasource outputs."""
assert event.type in DATASOURCE_TYPE
# Done packets have no dt.
if not event.type == DATASOURCE_TYPE.DONE:
assert isinstance(event.dt, datetime)
assert event.dt.tzinfo == pytz.u... | python | def assert_datasource_protocol(event):
"""Assert that an event meets the protocol for datasource outputs."""
assert event.type in DATASOURCE_TYPE
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quantopian/zipline | zipline/gens/utils.py | assert_trade_protocol | def assert_trade_protocol(event):
"""Assert that an event meets the protocol for datasource TRADE outputs."""
assert_datasource_protocol(event)
assert event.type == DATASOURCE_TYPE.TRADE
assert isinstance(event.price, numbers.Real)
assert isinstance(event.volume, numbers.Integral)
assert isinst... | python | def assert_trade_protocol(event):
"""Assert that an event meets the protocol for datasource TRADE outputs."""
assert_datasource_protocol(event)
assert event.type == DATASOURCE_TYPE.TRADE
assert isinstance(event.price, numbers.Real)
assert isinstance(event.volume, numbers.Integral)
assert isinst... | [
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quantopian/zipline | zipline/gens/composites.py | date_sorted_sources | def date_sorted_sources(*sources):
"""
Takes an iterable of sources, generating namestrings and
piping their output into date_sort.
"""
sorted_stream = heapq.merge(*(_decorate_source(s) for s in sources))
# Strip out key decoration
for _, message in sorted_stream:
yield message | python | def date_sorted_sources(*sources):
"""
Takes an iterable of sources, generating namestrings and
piping their output into date_sort.
"""
sorted_stream = heapq.merge(*(_decorate_source(s) for s in sources))
# Strip out key decoration
for _, message in sorted_stream:
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quantopian/zipline | zipline/utils/factory.py | create_daily_trade_source | def create_daily_trade_source(sids,
sim_params,
asset_finder,
trading_calendar):
"""
creates trade_count trades for each sid in sids list.
first trade will be on sim_params.start_session, and daily
thereafter for e... | python | def create_daily_trade_source(sids,
sim_params,
asset_finder,
trading_calendar):
"""
creates trade_count trades for each sid in sids list.
first trade will be on sim_params.start_session, and daily
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quantopian/zipline | zipline/data/bundles/quandl.py | load_data_table | def load_data_table(file,
index_col,
show_progress=False):
""" Load data table from zip file provided by Quandl.
"""
with ZipFile(file) as zip_file:
file_names = zip_file.namelist()
assert len(file_names) == 1, "Expected a single file from Quandl."
... | python | def load_data_table(file,
index_col,
show_progress=False):
""" Load data table from zip file provided by Quandl.
"""
with ZipFile(file) as zip_file:
file_names = zip_file.namelist()
assert len(file_names) == 1, "Expected a single file from Quandl."
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quantopian/zipline | zipline/data/bundles/quandl.py | fetch_data_table | def fetch_data_table(api_key,
show_progress,
retries):
""" Fetch WIKI Prices data table from Quandl
"""
for _ in range(retries):
try:
if show_progress:
log.info('Downloading WIKI metadata.')
metadata = pd.read_csv(
... | python | def fetch_data_table(api_key,
show_progress,
retries):
""" Fetch WIKI Prices data table from Quandl
"""
for _ in range(retries):
try:
if show_progress:
log.info('Downloading WIKI metadata.')
metadata = pd.read_csv(
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quantopian/zipline | zipline/data/bundles/quandl.py | quandl_bundle | def quandl_bundle(environ,
asset_db_writer,
minute_bar_writer,
daily_bar_writer,
adjustment_writer,
calendar,
start_session,
end_session,
cache,
show_progress... | python | def quandl_bundle(environ,
asset_db_writer,
minute_bar_writer,
daily_bar_writer,
adjustment_writer,
calendar,
start_session,
end_session,
cache,
show_progress... | [
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quantopian/zipline | zipline/data/bundles/quandl.py | download_with_progress | def download_with_progress(url, chunk_size, **progress_kwargs):
"""
Download streaming data from a URL, printing progress information to the
terminal.
Parameters
----------
url : str
A URL that can be understood by ``requests.get``.
chunk_size : int
Number of bytes to read a... | python | def download_with_progress(url, chunk_size, **progress_kwargs):
"""
Download streaming data from a URL, printing progress information to the
terminal.
Parameters
----------
url : str
A URL that can be understood by ``requests.get``.
chunk_size : int
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quantopian/zipline | zipline/data/bundles/quandl.py | download_without_progress | def download_without_progress(url):
"""
Download data from a URL, returning a BytesIO containing the loaded data.
Parameters
----------
url : str
A URL that can be understood by ``requests.get``.
Returns
-------
data : BytesIO
A BytesIO containing the downloaded data.
... | python | def download_without_progress(url):
"""
Download data from a URL, returning a BytesIO containing the loaded data.
Parameters
----------
url : str
A URL that can be understood by ``requests.get``.
Returns
-------
data : BytesIO
A BytesIO containing the downloaded data.
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quantopian/zipline | zipline/data/resample.py | minute_frame_to_session_frame | def minute_frame_to_session_frame(minute_frame, calendar):
"""
Resample a DataFrame with minute data into the frame expected by a
BcolzDailyBarWriter.
Parameters
----------
minute_frame : pd.DataFrame
A DataFrame with the columns `open`, `high`, `low`, `close`, `volume`,
and `d... | python | def minute_frame_to_session_frame(minute_frame, calendar):
"""
Resample a DataFrame with minute data into the frame expected by a
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Parameters
----------
minute_frame : pd.DataFrame
A DataFrame with the columns `open`, `high`, `low`, `close`, `volume`,
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quantopian/zipline | zipline/data/resample.py | minute_to_session | def minute_to_session(column, close_locs, data, out):
"""
Resample an array with minute data into an array with session data.
This function assumes that the minute data is the exact length of all
minutes in the sessions in the output.
Parameters
----------
column : str
The `open`, ... | python | def minute_to_session(column, close_locs, data, out):
"""
Resample an array with minute data into an array with session data.
This function assumes that the minute data is the exact length of all
minutes in the sessions in the output.
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----------
column : str
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quantopian/zipline | zipline/data/resample.py | DailyHistoryAggregator.opens | def opens(self, assets, dt):
"""
The open field's aggregation returns the first value that occurs
for the day, if there has been no data on or before the `dt` the open
is `nan`.
Once the first non-nan open is seen, that value remains constant per
asset for the remainder ... | python | def opens(self, assets, dt):
"""
The open field's aggregation returns the first value that occurs
for the day, if there has been no data on or before the `dt` the open
is `nan`.
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quantopian/zipline | zipline/data/resample.py | DailyHistoryAggregator.highs | def highs(self, assets, dt):
"""
The high field's aggregation returns the largest high seen between
the market open and the current dt.
If there has been no data on or before the `dt` the high is `nan`.
Returns
-------
np.array with dtype=float64, in order of ass... | python | def highs(self, assets, dt):
"""
The high field's aggregation returns the largest high seen between
the market open and the current dt.
If there has been no data on or before the `dt` the high is `nan`.
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-------
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quantopian/zipline | zipline/data/resample.py | DailyHistoryAggregator.lows | def lows(self, assets, dt):
"""
The low field's aggregation returns the smallest low seen between
the market open and the current dt.
If there has been no data on or before the `dt` the low is `nan`.
Returns
-------
np.array with dtype=float64, in order of assets... | python | def lows(self, assets, dt):
"""
The low field's aggregation returns the smallest low seen between
the market open and the current dt.
If there has been no data on or before the `dt` the low is `nan`.
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quantopian/zipline | zipline/data/resample.py | DailyHistoryAggregator.closes | def closes(self, assets, dt):
"""
The close field's aggregation returns the latest close at the given
dt.
If the close for the given dt is `nan`, the most recent non-nan
`close` is used.
If there has been no data on or before the `dt` the close is `nan`.
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... | python | def closes(self, assets, dt):
"""
The close field's aggregation returns the latest close at the given
dt.
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... | 77ad15e6dc4c1cbcdc133653bac8a63fc704f7fe | https://github.com/quantopian/zipline/blob/77ad15e6dc4c1cbcdc133653bac8a63fc704f7fe/zipline/data/resample.py#L372-L446 | train |
quantopian/zipline | zipline/data/resample.py | DailyHistoryAggregator.volumes | def volumes(self, assets, dt):
"""
The volume field's aggregation returns the sum of all volumes
between the market open and the `dt`
If there has been no data on or before the `dt` the volume is 0.
Returns
-------
np.array with dtype=int64, in order of assets pa... | python | def volumes(self, assets, dt):
"""
The volume field's aggregation returns the sum of all volumes
between the market open and the `dt`
If there has been no data on or before the `dt` the volume is 0.
Returns
-------
np.array with dtype=int64, in order of assets pa... | [
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... | The volume field's aggregation returns the sum of all volumes
between the market open and the `dt`
If there has been no data on or before the `dt` the volume is 0.
Returns
-------
np.array with dtype=int64, in order of assets parameter. | [
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... | 77ad15e6dc4c1cbcdc133653bac8a63fc704f7fe | https://github.com/quantopian/zipline/blob/77ad15e6dc4c1cbcdc133653bac8a63fc704f7fe/zipline/data/resample.py#L448-L510 | train |
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