BoghdadyJR/codesearch_python · Data Studio

text stringlengths 75 104k
def _run(handle_data, initialize, before_trading_start, analyze, algofile, algotext, defines, data_frequency, capital_base, bundle, bundle_timestamp, start, end, output, trading_calendar, ...
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...
def run_algorithm(start, end, initialize, capital_base, handle_data=None, before_trading_start=None, analyze=None, data_frequency='daily', bundle='quantopian-quandl', ...
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...
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...
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...
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. ...
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...
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...
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...
def _get_history_daily_window(self, assets, end_dt, bar_count, field_to_use, data_frequency): """ Internal method that returns a dataf...
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: ...
def get_history_window(self, assets, end_dt, bar_count, frequency, field, data_frequency, ffill=True): """ Public A...
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...
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...
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...
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 ...
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...
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...
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...
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...
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) ...
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...
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...
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 --...
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") return obj.view(int64_dtype) == iNaT
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)
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`. Doesn't support custom weekdays or calendars, but probably should in the future. S...
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. include_first : bool Whether or not to consider the first index of the array as "cha...
def compute_date_range_chunks(sessions, start_date, end_date, chunksize): """Compute the start and end dates to run a pipeline for. Parameters ---------- sessions : DatetimeIndex The available dates. start_date : pd.Timestamp The first date in the pipeline. end_date : pd.Timesta...
def run_pipeline(self, pipeline, start_date, end_date): """ Compute a pipeline. Parameters ---------- pipeline : zipline.pipeline.Pipeline The pipeline to run. start_date : pd.Timestamp Start date of the computed matrix. end_date : pd.Time...
def _compute_root_mask(self, domain, start_date, end_date, extra_rows): """ Compute a lifetimes matrix from our AssetFinder, then drop columns that didn't exist at all during the query dates. Parameters ---------- domain : zipline.pipeline.domain.Domain Domai...
def compute_chunk(self, graph, dates, sids, initial_workspace): """ Compute the Pipeline terms in the graph for the requested start and end dates. This is where we do the actual work of running a pipeline. Parameters ---------- graph : zipline.pipeline.graph.Exe...
def _to_narrow(self, terms, data, mask, dates, assets): """ Convert raw computed pipeline results into a DataFrame for public APIs. Parameters ---------- terms : dict[str -> Term] Dict mapping column names to terms. data : dict[str -> ndarray[ndim=2]] ...
def _validate_compute_chunk_params(self, graph, dates, sids, initial_workspace): """ Verify that the values passed to compute_chunk are well-formed....
def resolve_domain(self, pipeline): """Resolve a concrete domain for ``pipeline``. """ domain = pipeline.domain(default=self._default_domain) if domain is GENERIC: raise ValueError( "Unable to determine domain for Pipeline.\n" "Pass domain=<des...
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...
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 method is called inside `before_trading_start`. Examples -------- @disallowed_in_before_trading_start(...
def naive_grouped_rowwise_apply(data, group_labels, func, func_args=(), out=None): """ Simple implementation of grouped row-wise function application. Parameters ---------- ...
def bulleted_list(items, indent=0, bullet_type='-'): """Format a bulleted list of values. Parameters ---------- items : sequence The items to make a list. indent : int, optional The number of spaces to add before each bullet. bullet_type : str, optional The bullet type t...
def make_rotating_equity_info(num_assets, first_start, frequency, periods_between_starts, asset_lifetime, exchange='TEST'): """ Create a DataFrame representing li...
def make_simple_equity_info(sids, start_date, end_date, symbols=None, names=None, exchange='TEST'): """ Create a DataFrame representing assets that exist for the full durat...
def make_simple_multi_country_equity_info(countries_to_sids, countries_to_exchanges, start_date, end_date): """Create a DataFrame representing assets that exist for the full duration bet...
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...
def make_future_info(first_sid, root_symbols, years, notice_date_func, expiration_date_func, start_date_func, month_codes=None, multiplier=500): """ Create a DataFra...
def make_commodity_future_info(first_sid, root_symbols, years, month_codes=None, multiplier=500): """ Make futures testing data that simulates the notice/expiration date behavior of ph...
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...
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 ...
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...
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 ...
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 ...
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...
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....
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...
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, ...
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. ...
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 ------- optional_preprocessor : callable[callable, str, any -> a...
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 ....
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> """ ...
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...
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 ... >...
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 ...
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...
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...
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...
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...
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...
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. ...
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...
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" ...
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...
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, :]
def extend_back(self, missing_dts): """ Resizes the buffer to hold a new window with a new cap_multiple. If cap_multiple is None, then the old cap_multiple is used. """ delta = len(missing_dts) if not delta: raise ValueError( 'missing_dts must...
def get_current(self, item=None, raw=False, start=None, end=None): """ Get a Panel that is the current data in view. It is not safe to persist these objects because internal data might change """ item_indexer = slice(None) if item: item_indexer = self.items.ge...
def set_current(self, panel): """ Set the values stored in our current in-view data to be values of the passed panel. The passed panel must have the same indices as the panel that would be returned by self.get_current. """ where = slice(self._start_index, self._pos) ...
def _roll_data(self): """ Roll window worth of data up to position zero. Save the effort of having to expensively roll at each iteration """ self.buffer.values[:, :self._window, :] = \ self.buffer.values[:, -self._window:, :] self.date_buf[:self._window] = se...
def oldest_frame(self, raw=False): """ Get the oldest frame in the panel. """ if raw: return self.buffer.values[:, self._oldest_frame_idx(), :] return self.buffer.iloc[:, self._oldest_frame_idx(), :]
def get_current(self): """ Get a Panel that is the current data in view. It is not safe to persist these objects because internal data might change """ where = slice(self._oldest_frame_idx(), self._pos) major_axis = pd.DatetimeIndex(deepcopy(self.date_buf[where]), tz='ut...
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 = \ self.check_order_triggers(price) if (stop_reached, limit_reached) \ != (sel...
def check_order_triggers(self, current_price): """ Given an order and a trade event, return a tuple of (stop_reached, limit_reached). For market orders, will return (False, False). For stop orders, limit_reached will always be False. For limit orders, stop_reached will al...
def triggered(self): """ For a market order, True. For a stop order, True IFF stop_reached. For a limit order, True IFF limit_reached. """ if self.stop is not None and not self.stop_reached: return False if self.limit is not None and not self.limit_re...
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'): self.old_opts = np.get_printoptions() np.set_printoptions(leg...
def teardown(self, np=np): """Lives in zipline.__init__ for doctests.""" if self.old_err is not None: np.seterr(self.old_err) if self.old_opts is not None: np.set_printoptions(self.old_opts)
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...
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...
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...
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
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...
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." ...
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( ...
def quandl_bundle(environ, asset_db_writer, minute_bar_writer, daily_bar_writer, adjustment_writer, calendar, start_session, end_session, cache, show_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...
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. ...
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...
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`, ...
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 ...
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...
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...
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`. Returns ...
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...

def _run(handle_data, initialize, before_trading_start, analyze, algofile, algotext, defines, data_frequency, capital_base, bundle, bundle_timestamp, start, end, output, trading_calendar, ...

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...

def run_algorithm(start, end, initialize, capital_base, handle_data=None, before_trading_start=None, analyze=None, data_frequency='daily', bundle='quantopian-quandl', ...

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...

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...

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...

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. ...

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...

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...

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...

def _get_history_daily_window(self, assets, end_dt, bar_count, field_to_use, data_frequency): """ Internal method that returns a dataf...

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: ...

def get_history_window(self, assets, end_dt, bar_count, frequency, field, data_frequency, ffill=True): """ Public A...

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...

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...

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...

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 ...

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...

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...

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...

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...

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) ...

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...

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...

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 --...

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") return obj.view(int64_dtype) == iNaT

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)

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`. Doesn't support custom weekdays or calendars, but probably should in the future. S...

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. include_first : bool Whether or not to consider the first index of the array as "cha...

def compute_date_range_chunks(sessions, start_date, end_date, chunksize): """Compute the start and end dates to run a pipeline for. Parameters ---------- sessions : DatetimeIndex The available dates. start_date : pd.Timestamp The first date in the pipeline. end_date : pd.Timesta...

def run_pipeline(self, pipeline, start_date, end_date): """ Compute a pipeline. Parameters ---------- pipeline : zipline.pipeline.Pipeline The pipeline to run. start_date : pd.Timestamp Start date of the computed matrix. end_date : pd.Time...

def _compute_root_mask(self, domain, start_date, end_date, extra_rows): """ Compute a lifetimes matrix from our AssetFinder, then drop columns that didn't exist at all during the query dates. Parameters ---------- domain : zipline.pipeline.domain.Domain Domai...

def compute_chunk(self, graph, dates, sids, initial_workspace): """ Compute the Pipeline terms in the graph for the requested start and end dates. This is where we do the actual work of running a pipeline. Parameters ---------- graph : zipline.pipeline.graph.Exe...

def _to_narrow(self, terms, data, mask, dates, assets): """ Convert raw computed pipeline results into a DataFrame for public APIs. Parameters ---------- terms : dict[str -> Term] Dict mapping column names to terms. data : dict[str -> ndarray[ndim=2]] ...

def _validate_compute_chunk_params(self, graph, dates, sids, initial_workspace): """ Verify that the values passed to compute_chunk are well-formed....

def resolve_domain(self, pipeline): """Resolve a concrete domain for ``pipeline``. """ domain = pipeline.domain(default=self._default_domain) if domain is GENERIC: raise ValueError( "Unable to determine domain for Pipeline.\n" "Pass domain=<des...

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...

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 method is called inside `before_trading_start`. Examples -------- @disallowed_in_before_trading_start(...

def naive_grouped_rowwise_apply(data, group_labels, func, func_args=(), out=None): """ Simple implementation of grouped row-wise function application. Parameters ---------- ...

def bulleted_list(items, indent=0, bullet_type='-'): """Format a bulleted list of values. Parameters ---------- items : sequence The items to make a list. indent : int, optional The number of spaces to add before each bullet. bullet_type : str, optional The bullet type t...

def make_rotating_equity_info(num_assets, first_start, frequency, periods_between_starts, asset_lifetime, exchange='TEST'): """ Create a DataFrame representing li...

def make_simple_equity_info(sids, start_date, end_date, symbols=None, names=None, exchange='TEST'): """ Create a DataFrame representing assets that exist for the full durat...

def make_simple_multi_country_equity_info(countries_to_sids, countries_to_exchanges, start_date, end_date): """Create a DataFrame representing assets that exist for the full duration bet...

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...

def make_future_info(first_sid, root_symbols, years, notice_date_func, expiration_date_func, start_date_func, month_codes=None, multiplier=500): """ Create a DataFra...

def make_commodity_future_info(first_sid, root_symbols, years, month_codes=None, multiplier=500): """ Make futures testing data that simulates the notice/expiration date behavior of ph...

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...

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 ...

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...

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 ...

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 ...

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...

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....

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...

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, ...

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. ...

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 ------- optional_preprocessor : callable[callable, str, any -> a...

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 ....

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> """ ...

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...

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 ... >...

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 ...

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...

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...

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...

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...

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...

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. ...

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...

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" ...

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...

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, :]

def extend_back(self, missing_dts): """ Resizes the buffer to hold a new window with a new cap_multiple. If cap_multiple is None, then the old cap_multiple is used. """ delta = len(missing_dts) if not delta: raise ValueError( 'missing_dts must...

def get_current(self, item=None, raw=False, start=None, end=None): """ Get a Panel that is the current data in view. It is not safe to persist these objects because internal data might change """ item_indexer = slice(None) if item: item_indexer = self.items.ge...

def set_current(self, panel): """ Set the values stored in our current in-view data to be values of the passed panel. The passed panel must have the same indices as the panel that would be returned by self.get_current. """ where = slice(self._start_index, self._pos) ...

def _roll_data(self): """ Roll window worth of data up to position zero. Save the effort of having to expensively roll at each iteration """ self.buffer.values[:, :self._window, :] = \ self.buffer.values[:, -self._window:, :] self.date_buf[:self._window] = se...

def oldest_frame(self, raw=False): """ Get the oldest frame in the panel. """ if raw: return self.buffer.values[:, self._oldest_frame_idx(), :] return self.buffer.iloc[:, self._oldest_frame_idx(), :]

def get_current(self): """ Get a Panel that is the current data in view. It is not safe to persist these objects because internal data might change """ where = slice(self._oldest_frame_idx(), self._pos) major_axis = pd.DatetimeIndex(deepcopy(self.date_buf[where]), tz='ut...

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 = \ self.check_order_triggers(price) if (stop_reached, limit_reached) \ != (sel...

def check_order_triggers(self, current_price): """ Given an order and a trade event, return a tuple of (stop_reached, limit_reached). For market orders, will return (False, False). For stop orders, limit_reached will always be False. For limit orders, stop_reached will al...

def triggered(self): """ For a market order, True. For a stop order, True IFF stop_reached. For a limit order, True IFF limit_reached. """ if self.stop is not None and not self.stop_reached: return False if self.limit is not None and not self.limit_re...

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'): self.old_opts = np.get_printoptions() np.set_printoptions(leg...

def teardown(self, np=np): """Lives in zipline.__init__ for doctests.""" if self.old_err is not None: np.seterr(**self.old_err) if self.old_opts is not None: np.set_printoptions(**self.old_opts)

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...

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...

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...

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

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...

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." ...

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( ...

def quandl_bundle(environ, asset_db_writer, minute_bar_writer, daily_bar_writer, adjustment_writer, calendar, start_session, end_session, cache, show_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...

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. ...

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...

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`, ...

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 ...

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...

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...

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`. Returns ...

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...