BoghdadyJR/codesearch_python · Data Studio

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def risk_metric_period(cls, start_session, end_session, algorithm_returns, benchmark_returns, algorithm_leverages): """ Creates a dictionary representing the state of th...
def _get_active_contract_at_offset(self, root_symbol, dt, offset): """ For the given root symbol, find the contract that is considered active on a specific date at a specific offset. """ oc = self.asset_finder.get_ordered_contracts(root_symbol) session = self.trading_cale...
def get_contract_center(self, root_symbol, dt, offset): """ Parameters ---------- root_symbol : str The root symbol for the contract chain. dt : Timestamp The datetime for which to retrieve the current contract. offset : int The offset ...
def get_rolls(self, root_symbol, start, end, offset): """ Get the rolls, i.e. the session at which to hop from contract to contract in the chain. Parameters ---------- root_symbol : str The root symbol for which to calculate rolls. start : Timestamp ...
def _active_contract(self, oc, front, back, dt): r""" Return the active contract based on the previous trading day's volume. In the rare case that a double volume switch occurs we treat the first switch as the roll. Take the following case for example: \| +++++ _____...
def get_contract_center(self, root_symbol, dt, offset): """ Parameters ---------- root_symbol : str The root symbol for the contract chain. dt : Timestamp The datetime for which to retrieve the current contract. offset : int The offset ...
def _normalize_array(data, missing_value): """ Coerce buffer data for an AdjustedArray into a standard scalar representation, returning the coerced array and a dict of argument to pass to np.view to use when providing a user-facing view of the underlying data. - float* data is coerced to float64 wi...
def _merge_simple(adjustment_lists, front_idx, back_idx): """ Merge lists of new and existing adjustments for a given index by appending or prepending new adjustments to existing adjustments. Notes ----- This method is meant to be used with ``toolz.merge_with`` to merge adjustment mappings....
def ensure_ndarray(ndarray_or_adjusted_array): """ Return the input as a numpy ndarray. This is a no-op if the input is already an ndarray. If the input is an adjusted_array, this extracts a read-only view of its internal data buffer. Parameters ---------- ndarray_or_adjusted_array : nump...
def _check_window_params(data, window_length): """ Check that a window of length `window_length` is well-defined on `data`. Parameters ---------- data : np.ndarray[ndim=2] The array of data to check. window_length : int Length of the desired window. Returns ------- ...
def update_adjustments(self, adjustments, method): """ Merge ``adjustments`` with existing adjustments, handling index collisions according to ``method``. Parameters ---------- adjustments : dict[int -> list[Adjustment]] The mapping of row indices to lists of...
def _iterator_type(self): """ The iterator produced when `traverse` is called on this Array. """ if isinstance(self._data, LabelArray): return LabelWindow return CONCRETE_WINDOW_TYPES[self._data.dtype]
def traverse(self, window_length, offset=0, perspective_offset=0): """ Produce an iterator rolling windows rows over our data. Each emitted window will have `window_length` rows. Parameters ---------- window_length : int...
def inspect(self): """ Return a string representation of the data stored in this array. """ return dedent( """\ Adjusted Array ({dtype}): Data: {data!r} Adjustments: {adjustments} """ ).format( ...
def update_labels(self, func): """ Map a function over baseline and adjustment values in place. Note that the baseline data values must be a LabelArray. """ if not isinstance(self.data, LabelArray): raise TypeError( 'update_labels only supported if da...
def handle_violation(self, asset, amount, datetime, metadata=None): """ Handle a TradingControlViolation, either by raising or logging and error with information about the failure. If dynamic information should be displayed as well, pass it in via `metadata`. """ ...
def validate(self, asset, amount, portfolio, algo_datetime, algo_current_data): """ Fail if we've already placed self.max_count orders today. """ algo_date = algo_datetime.date() # Reset order c...
def validate(self, asset, amount, portfolio, algo_datetime, algo_current_data): """ Fail if the asset is in the restricted_list. """ if self.restrictions.is_restricted(asset, algo_datetime): ...
def validate(self, asset, amount, portfolio, algo_datetime, algo_current_data): """ Fail if the magnitude of the given order exceeds either self.max_shares or self.max_notional. """ if self.asse...
def validate(self, asset, amount, portfolio, algo_datetime, algo_current_data): """ Fail if the given order would cause the magnitude of our position to be greater in shares than self.max_shares or greater in do...
def validate(self, asset, amount, portfolio, algo_datetime, algo_current_data): """ Fail if we would hold negative shares of asset after completing this order. """ if portfolio.positions[asset].a...
def validate(self, asset, amount, portfolio, algo_datetime, algo_current_data): """ Fail if the algo has passed this Asset's end_date, or before the Asset's start date. """ # If the order is for ...
def validate(self, _portfolio, _account, _algo_datetime, _algo_current_data): """ Fail if the leverage is greater than the allowed leverage. """ if _account.leverage > self.max_leverage: self.fail()
def validate(self, _portfolio, account, algo_datetime, _algo_current_data): """ Make validation checks if we are after the deadline. Fail if the leverage is less than the min leverage. """ if (algo_datetime > sel...
def alter_columns(op, name, columns, kwargs): """Alter columns from a table. Parameters ---------- name : str The name of the table. columns The new columns to have. selection_string : str, optional The string to use in the selection. If not provided, it will select ...
def downgrade(engine, desired_version): """Downgrades the assets db at the given engine to the desired version. Parameters ---------- engine : Engine An SQLAlchemy engine to the assets database. desired_version : int The desired resulting version for the assets database. """ ...
def downgrades(src): """Decorator for marking that a method is a downgrade to a version to the previous version. Parameters ---------- src : int The version this downgrades from. Returns ------- decorator : callable[(callable) -> callable] The decorator to apply. ""...
def _downgrade_v1(op): """ Downgrade assets db by removing the 'tick_size' column and renaming the 'multiplier' column. """ # Drop indices before batch # This is to prevent index collision when creating the temp table op.drop_index('ix_futures_contracts_root_symbol') op.drop_index('ix_fu...
def _downgrade_v2(op): """ Downgrade assets db by removing the 'auto_close_date' column. """ # Drop indices before batch # This is to prevent index collision when creating the temp table op.drop_index('ix_equities_fuzzy_symbol') op.drop_index('ix_equities_company_symbol') # Execute batc...
def _downgrade_v3(op): """ Downgrade assets db by adding a not null constraint on ``equities.first_traded`` """ op.create_table( '_new_equities', sa.Column( 'sid', sa.Integer, unique=True, nullable=False, primary_key=True, ...
def _downgrade_v4(op): """ Downgrades assets db by copying the `exchange_full` column to `exchange`, then dropping the `exchange_full` column. """ op.drop_index('ix_equities_fuzzy_symbol') op.drop_index('ix_equities_company_symbol') op.execute("UPDATE equities SET exchange = exchange_full")...
def _make_metrics_set_core(): """Create a family of metrics sets functions that read from the same metrics set mapping. Returns ------- metrics_sets : mappingproxy The mapping of metrics sets to load functions. register : callable The function which registers new metrics sets in...
def validate_column_specs(events, columns): """ Verify that the columns of ``events`` can be used by a EarningsEstimatesLoader to serve the BoundColumns described by `columns`. """ required = required_estimates_fields(columns) received = set(events.columns) missing = required - received ...
def get_requested_quarter_data(self, zero_qtr_data, zeroth_quarter_idx, stacked_last_per_qtr, num_announcements, dates): """ Sele...
def get_split_adjusted_asof_idx(self, dates): """ Compute the index in `dates` where the split-adjusted-asof-date falls. This is the date up to which, and including which, we will need to unapply all adjustments for and then re-apply them as they come in. After this date, adjustm...
def collect_overwrites_for_sid(self, group, dates, requested_qtr_data, last_per_qtr, sid_idx, columns, ...
def merge_into_adjustments_for_all_sids(self, all_adjustments_for_sid, col_to_all_adjustments): """ Merge adjustments for a particular sid into a dictionary containing adjustments for all sids. Param...
def get_adjustments(self, zero_qtr_data, requested_qtr_data, last_per_qtr, dates, assets, columns, **kwargs): """ Creates an AdjustedArr...
def create_overwrites_for_quarter(self, col_to_overwrites, next_qtr_start_idx, last_per_qtr, quarters_with_estimates_for_sid, requ...
def get_last_data_per_qtr(self, assets_with_data, columns, dates, data_query_cutoff_times): """ Determine the last piece of information we know for each column on each date in ...
def get_zeroth_quarter_idx(self, stacked_last_per_qtr): """ Filters for releases that are on or after each simulation date and determines the previous quarter by picking out the most recent release relative to each date in the index. Parameters ---------- stacked...
def get_adjustments_for_sid(self, group, dates, requested_qtr_data, last_per_qtr, sid_to_idx, columns, ...
def get_adjustments(self, zero_qtr_data, requested_qtr_data, last_per_qtr, dates, assets, columns, **kwargs): """ Calculates both split ...
def determine_end_idx_for_adjustment(self, adjustment_ts, dates, upper_bound, requested_quarter, sid_estimates): ...
def collect_pre_split_asof_date_adjustments( self, split_adjusted_asof_date_idx, sid_idx, pre_adjustments, requested_split_adjusted_columns ): """ Collect split adjustments that occur before the split-adjusted-asof-date. All those a...
def collect_post_asof_split_adjustments(self, post_adjustments, requested_qtr_data, sid, sid_idx, si...
def retrieve_split_adjustment_data_for_sid(self, dates, sid, split_adjusted_asof_idx): """ dates : pd.DatetimeIndex The calendar dates. sid : i...
def merge_split_adjustments_with_overwrites( self, pre, post, overwrites, requested_split_adjusted_columns ): """ Merge split adjustments with the dict containing overwrites. Parameters ---------- pre : dict[str -> dict[int -> list]] ...
def collect_split_adjustments(self, adjustments_for_sid, requested_qtr_data, dates, sid, sid_idx, sid_estimates, ...
def collect_split_adjustments(self, adjustments_for_sid, requested_qtr_data, dates, sid, sid_idx, sid_estimates, ...
def from_span(cls, inputs, window_length, span, **kwargs): """ Convenience constructor for passing `decay_rate` in terms of `span`. Forwards `decay_rate` as `1 - (2.0 / (1 + span))`. This provides the behavior equivalent to passing `span` to pandas.ewma. Examples -----...
def from_halflife(cls, inputs, window_length, halflife, **kwargs): """ Convenience constructor for passing ``decay_rate`` in terms of half life. Forwards ``decay_rate`` as ``exp(log(.5) / halflife)``. This provides the behavior equivalent to passing `halflife` to pandas.ewma. ...
def from_center_of_mass(cls, inputs, window_length, center_of_mass, **kwargs): """ Convenience constructor for passing `decay_rate` in terms of center of mass. Forwards `decay...
def tolerant_equals(a, b, atol=10e-7, rtol=10e-7, equal_nan=False): """Check if a and b are equal with some tolerance. Parameters ---------- a, b : float The floats to check for equality. atol : float, optional The absolute tolerance. rtol : float, optional The relative ...
def round_if_near_integer(a, epsilon=1e-4): """ Round a to the nearest integer if that integer is within an epsilon of a. """ if abs(a - round(a)) <= epsilon: return round(a) else: return a
def coerce_numbers_to_my_dtype(f): """ A decorator for methods whose signature is f(self, other) that coerces ``other`` to ``self.dtype``. This is used to make comparison operations between numbers and `Factor` instances work independently of whether the user supplies a float or integer literal...
def binop_return_dtype(op, left, right): """ Compute the expected return dtype for the given binary operator. Parameters ---------- op : str Operator symbol, (e.g. '+', '-', ...). left : numpy.dtype Dtype of left hand side. right : numpy.dtype Dtype of right hand sid...
def binary_operator(op): """ Factory function for making binary operator methods on a Factor subclass. Returns a function, "binary_operator" suitable for implementing functions like __add__. """ # When combining a Factor with a NumericalExpression, we use this # attrgetter instance to defer...
def reflected_binary_operator(op): """ Factory function for making binary operator methods on a Factor. Returns a function, "reflected_binary_operator" suitable for implementing functions like __radd__. """ assert not is_comparison(op) @with_name(method_name_for_op(op, commute=True)) @...
def unary_operator(op): """ Factory function for making unary operator methods for Factors. """ # Only negate is currently supported. valid_ops = {'-'} if op not in valid_ops: raise ValueError("Invalid unary operator %s." % op) @with_doc("Unary Operator: '%s'" % op) @with_name(u...
def function_application(func): """ Factory function for producing function application methods for Factor subclasses. """ if func not in NUMEXPR_MATH_FUNCS: raise ValueError("Unsupported mathematical function '%s'" % func) @with_doc(func) @with_name(func) def mathfunc(self): ...
def winsorize(row, min_percentile, max_percentile): """ This implementation is based on scipy.stats.mstats.winsorize """ a = row.copy() nan_count = isnan(row).sum() nonnan_count = a.size - nan_count # NOTE: argsort() sorts nans to the end of the array. idx = a.argsort() # Set value...
def demean(self, mask=NotSpecified, groupby=NotSpecified): """ Construct a Factor that computes ``self`` and subtracts the mean from row of the result. If ``mask`` is supplied, ignore values where ``mask`` returns False when computing row means, and output NaN anywhere the mask ...
def zscore(self, mask=NotSpecified, groupby=NotSpecified): """ Construct a Factor that Z-Scores each day's results. The Z-Score of a row is defined as:: (row - row.mean()) / row.stddev() If ``mask`` is supplied, ignore values where ``mask`` returns False when compu...
def rank(self, method='ordinal', ascending=True, mask=NotSpecified, groupby=NotSpecified): """ Construct a new Factor representing the sorted rank of each column within each row. Parameters ---------- method : str, {'or...
def pearsonr(self, target, correlation_length, mask=NotSpecified): """ Construct a new Factor that computes rolling pearson correlation coefficients between `target` and the columns of `self`. This method can only be called on factors which are deemed safe for use as inputs to o...
def spearmanr(self, target, correlation_length, mask=NotSpecified): """ Construct a new Factor that computes rolling spearman rank correlation coefficients between `target` and the columns of `self`. This method can only be called on factors which are deemed safe for use as inpu...
def linear_regression(self, target, regression_length, mask=NotSpecified): """ Construct a new Factor that performs an ordinary least-squares regression predicting the columns of `self` from `target`. This method can only be called on factors which are deemed safe for use as inp...
def winsorize(self, min_percentile, max_percentile, mask=NotSpecified, groupby=NotSpecified): """ Construct a new factor that winsorizes the result of this factor. Winsorizing changes values ranked less than the minimum per...
def quantiles(self, bins, mask=NotSpecified): """ Construct a Classifier computing quantiles of the output of ``self``. Every non-NaN data point the output is labelled with an integer value from 0 to (bins - 1). NaNs are labelled with -1. If ``mask`` is supplied, ignore data p...
def top(self, N, mask=NotSpecified, groupby=NotSpecified): """ Construct a Filter matching the top N asset values of self each day. If ``groupby`` is supplied, returns a Filter matching the top N asset values for each group. Parameters ---------- N : int ...
def bottom(self, N, mask=NotSpecified, groupby=NotSpecified): """ Construct a Filter matching the bottom N asset values of self each day. If ``groupby`` is supplied, returns a Filter matching the bottom N asset values for each group. Parameters ---------- N : in...
def percentile_between(self, min_percentile, max_percentile, mask=NotSpecified): """ Construct a new Filter representing entries from the output of this Factor that fall within the percentile range defined by min_pe...
def _validate(self): """ Verify that the stored rank method is valid. """ if self._method not in _RANK_METHODS: raise UnknownRankMethod( method=self._method, choices=set(_RANK_METHODS), ) return super(Rank, self)._validate()
def _compute(self, arrays, dates, assets, mask): """ For each row in the input, compute a like-shaped array of per-row ranks. """ return masked_rankdata_2d( arrays[0], mask, self.inputs[0].missing_value, self._method, se...
def _time_to_micros(time): """Convert a time into microseconds since midnight. Parameters ---------- time : datetime.time The time to convert. Returns ------- us : int The number of microseconds since midnight. Notes ----- This does not account for leap seconds or...
def mask_between_time(dts, start, end, include_start=True, include_end=True): """Return a mask of all of the datetimes in ``dts`` that are between ``start`` and ``end``. Parameters ---------- dts : pd.DatetimeIndex The index to mask. start : time Mask away times less than the sta...
def find_in_sorted_index(dts, dt): """ Find the index of ``dt`` in ``dts``. This function should be used instead of `dts.get_loc(dt)` if the index is large enough that we don't want to initialize a hash table in ``dts``. In particular, this should always be used on minutely trading calendars. ...
def nearest_unequal_elements(dts, dt): """ Find values in ``dts`` closest but not equal to ``dt``. Returns a pair of (last_before, first_after). When ``dt`` is less than any element in ``dts``, ``last_before`` is None. When ``dt`` is greater any element in ``dts``, ``first_after`` is None. ``...
def categorical_df_concat(df_list, inplace=False): """ Prepare list of pandas DataFrames to be used as input to pd.concat. Ensure any columns of type 'category' have the same categories across each dataframe. Parameters ---------- df_list : list List of dataframes with same columns....
def check_indexes_all_same(indexes, message="Indexes are not equal."): """Check that a list of Index objects are all equal. Parameters ---------- indexes : iterable[pd.Index] Iterable of indexes to check. Raises ------ ValueError If the indexes are not all the same. """...
def required_event_fields(next_value_columns, previous_value_columns): """ Compute the set of resource columns required to serve ``next_value_columns`` and ``previous_value_columns``. """ # These metadata columns are used to align event indexers. return { TS_FIELD_NAME, SID_FIELD...
def validate_column_specs(events, next_value_columns, previous_value_columns): """ Verify that the columns of ``events`` can be used by an EventsLoader to serve the BoundColumns described by ``next_value_columns`` and ``previous_value_columns``. """ required = required_event_fields(next_value_co...
def split_next_and_previous_event_columns(self, requested_columns): """ Split requested columns into columns that should load the next known value and columns that should load the previous known value. Parameters ---------- requested_columns : iterable[BoundColumn] ...
def compare_arrays(left, right): "Eq check with a short-circuit for identical objects." return ( left is right or ((left.shape == right.shape) and (left == right).all()) )
def from_codes_and_metadata(cls, codes, categories, reverse_categories, missing_value): """ Rehydrate a LabelArray from the codes and metadata. Parameters ----...
def as_int_array(self): """ Convert self into a regular ndarray of ints. This is an O(1) operation. It does not copy the underlying data. """ return self.view( type=ndarray, dtype=unsigned_int_dtype_with_size_in_bytes(self.itemsize), )
def as_categorical(self): """ Coerce self into a pandas categorical. This is only defined on 1D arrays, since that's all pandas supports. """ if len(self.shape) > 1: raise ValueError("Can't convert a 2D array to a categorical.") with ignore_pandas_nan_catego...
def as_categorical_frame(self, index, columns, name=None): """ Coerce self into a pandas DataFrame of Categoricals. """ if len(self.shape) != 2: raise ValueError( "Can't convert a non-2D LabelArray into a DataFrame." ) expected_shape = (le...
def set_scalar(self, indexer, value): """ Set scalar value into the array. Parameters ---------- indexer : any The indexer to set the value at. value : str The value to assign at the given locations. Raises ------ ValueErr...
def _equality_check(op): """ Shared code for __eq__ and __ne__, parameterized on the actual comparison operator to use. """ def method(self, other): if isinstance(other, LabelArray): self_mv = self.missing_value other_mv = other.missin...
def empty_like(self, shape): """ Make an empty LabelArray with the same categories as ``self``, filled with ``self.missing_value``. """ return type(self).from_codes_and_metadata( codes=np.full( shape, self.reverse_categories[self.missin...
def map_predicate(self, f): """ Map a function from str -> bool element-wise over ``self``. ``f`` will be applied exactly once to each non-missing unique value in ``self``. Missing values will always return False. """ # Functions passed to this are of type str -> bool. ...
def map(self, f): """ Map a function from str -> str element-wise over ``self``. ``f`` will be applied exactly once to each non-missing unique value in ``self``. Missing values will always map to ``self.missing_value``. """ # f() should only return None if None is our mi...
def asymmetric_round_price(price, prefer_round_down, tick_size, diff=0.95): """ Asymmetric rounding function for adjusting prices to the specified number of places in a way that "improves" the price. For limit prices, this means preferring to round down on buys and preferring to round up on sells. F...
def check_stoplimit_prices(price, label): """ Check to make sure the stop/limit prices are reasonable and raise a BadOrderParameters exception if not. """ try: if not isfinite(price): raise BadOrderParameters( msg="Attempted to place an order with a {} price " ...
def csvdir_bundle(environ, asset_db_writer, minute_bar_writer, daily_bar_writer, adjustment_writer, calendar, start_session, end_session, cache, show_progress...
def restrict_to_dtype(dtype, message_template): """ A factory for decorators that restrict Term methods to only be callable on Terms with a specific dtype. This is conceptually similar to zipline.utils.input_validation.expect_dtypes, but provides more flexibility for providing error messages th...
def daily_returns(self, start, end=None): """Returns the daily returns for the given period. Parameters ---------- start : datetime The inclusive starting session label. end : datetime, optional The inclusive ending session label. If not provided, treat ...
def _initialize_precalculated_series(self, asset, trading_calendar, trading_days, data_portal): """ Internal method that pre-calculates the ...

def risk_metric_period(cls, start_session, end_session, algorithm_returns, benchmark_returns, algorithm_leverages): """ Creates a dictionary representing the state of th...

def _get_active_contract_at_offset(self, root_symbol, dt, offset): """ For the given root symbol, find the contract that is considered active on a specific date at a specific offset. """ oc = self.asset_finder.get_ordered_contracts(root_symbol) session = self.trading_cale...

def get_contract_center(self, root_symbol, dt, offset): """ Parameters ---------- root_symbol : str The root symbol for the contract chain. dt : Timestamp The datetime for which to retrieve the current contract. offset : int The offset ...

def get_rolls(self, root_symbol, start, end, offset): """ Get the rolls, i.e. the session at which to hop from contract to contract in the chain. Parameters ---------- root_symbol : str The root symbol for which to calculate rolls. start : Timestamp ...

def _active_contract(self, oc, front, back, dt): r""" Return the active contract based on the previous trading day's volume. In the rare case that a double volume switch occurs we treat the first switch as the roll. Take the following case for example: | +++++ _____...

def get_contract_center(self, root_symbol, dt, offset): """ Parameters ---------- root_symbol : str The root symbol for the contract chain. dt : Timestamp The datetime for which to retrieve the current contract. offset : int The offset ...

def _normalize_array(data, missing_value): """ Coerce buffer data for an AdjustedArray into a standard scalar representation, returning the coerced array and a dict of argument to pass to np.view to use when providing a user-facing view of the underlying data. - float* data is coerced to float64 wi...

def _merge_simple(adjustment_lists, front_idx, back_idx): """ Merge lists of new and existing adjustments for a given index by appending or prepending new adjustments to existing adjustments. Notes ----- This method is meant to be used with ``toolz.merge_with`` to merge adjustment mappings....

def ensure_ndarray(ndarray_or_adjusted_array): """ Return the input as a numpy ndarray. This is a no-op if the input is already an ndarray. If the input is an adjusted_array, this extracts a read-only view of its internal data buffer. Parameters ---------- ndarray_or_adjusted_array : nump...

def _check_window_params(data, window_length): """ Check that a window of length `window_length` is well-defined on `data`. Parameters ---------- data : np.ndarray[ndim=2] The array of data to check. window_length : int Length of the desired window. Returns ------- ...

def update_adjustments(self, adjustments, method): """ Merge ``adjustments`` with existing adjustments, handling index collisions according to ``method``. Parameters ---------- adjustments : dict[int -> list[Adjustment]] The mapping of row indices to lists of...

def _iterator_type(self): """ The iterator produced when `traverse` is called on this Array. """ if isinstance(self._data, LabelArray): return LabelWindow return CONCRETE_WINDOW_TYPES[self._data.dtype]

def traverse(self, window_length, offset=0, perspective_offset=0): """ Produce an iterator rolling windows rows over our data. Each emitted window will have `window_length` rows. Parameters ---------- window_length : int...

def inspect(self): """ Return a string representation of the data stored in this array. """ return dedent( """\ Adjusted Array ({dtype}): Data: {data!r} Adjustments: {adjustments} """ ).format( ...

def update_labels(self, func): """ Map a function over baseline and adjustment values in place. Note that the baseline data values must be a LabelArray. """ if not isinstance(self.data, LabelArray): raise TypeError( 'update_labels only supported if da...

def handle_violation(self, asset, amount, datetime, metadata=None): """ Handle a TradingControlViolation, either by raising or logging and error with information about the failure. If dynamic information should be displayed as well, pass it in via `metadata`. """ ...

def validate(self, asset, amount, portfolio, algo_datetime, algo_current_data): """ Fail if we've already placed self.max_count orders today. """ algo_date = algo_datetime.date() # Reset order c...

def validate(self, asset, amount, portfolio, algo_datetime, algo_current_data): """ Fail if the asset is in the restricted_list. """ if self.restrictions.is_restricted(asset, algo_datetime): ...

def validate(self, asset, amount, portfolio, algo_datetime, algo_current_data): """ Fail if the magnitude of the given order exceeds either self.max_shares or self.max_notional. """ if self.asse...

def validate(self, asset, amount, portfolio, algo_datetime, algo_current_data): """ Fail if the given order would cause the magnitude of our position to be greater in shares than self.max_shares or greater in do...

def validate(self, asset, amount, portfolio, algo_datetime, algo_current_data): """ Fail if we would hold negative shares of asset after completing this order. """ if portfolio.positions[asset].a...

def validate(self, asset, amount, portfolio, algo_datetime, algo_current_data): """ Fail if the algo has passed this Asset's end_date, or before the Asset's start date. """ # If the order is for ...

def validate(self, _portfolio, _account, _algo_datetime, _algo_current_data): """ Fail if the leverage is greater than the allowed leverage. """ if _account.leverage > self.max_leverage: self.fail()

def validate(self, _portfolio, account, algo_datetime, _algo_current_data): """ Make validation checks if we are after the deadline. Fail if the leverage is less than the min leverage. """ if (algo_datetime > sel...

def alter_columns(op, name, *columns, **kwargs): """Alter columns from a table. Parameters ---------- name : str The name of the table. *columns The new columns to have. selection_string : str, optional The string to use in the selection. If not provided, it will select ...

def downgrade(engine, desired_version): """Downgrades the assets db at the given engine to the desired version. Parameters ---------- engine : Engine An SQLAlchemy engine to the assets database. desired_version : int The desired resulting version for the assets database. """ ...

def downgrades(src): """Decorator for marking that a method is a downgrade to a version to the previous version. Parameters ---------- src : int The version this downgrades from. Returns ------- decorator : callable[(callable) -> callable] The decorator to apply. ""...

def _downgrade_v1(op): """ Downgrade assets db by removing the 'tick_size' column and renaming the 'multiplier' column. """ # Drop indices before batch # This is to prevent index collision when creating the temp table op.drop_index('ix_futures_contracts_root_symbol') op.drop_index('ix_fu...

def _downgrade_v2(op): """ Downgrade assets db by removing the 'auto_close_date' column. """ # Drop indices before batch # This is to prevent index collision when creating the temp table op.drop_index('ix_equities_fuzzy_symbol') op.drop_index('ix_equities_company_symbol') # Execute batc...

def _downgrade_v3(op): """ Downgrade assets db by adding a not null constraint on ``equities.first_traded`` """ op.create_table( '_new_equities', sa.Column( 'sid', sa.Integer, unique=True, nullable=False, primary_key=True, ...

def _downgrade_v4(op): """ Downgrades assets db by copying the `exchange_full` column to `exchange`, then dropping the `exchange_full` column. """ op.drop_index('ix_equities_fuzzy_symbol') op.drop_index('ix_equities_company_symbol') op.execute("UPDATE equities SET exchange = exchange_full")...

def _make_metrics_set_core(): """Create a family of metrics sets functions that read from the same metrics set mapping. Returns ------- metrics_sets : mappingproxy The mapping of metrics sets to load functions. register : callable The function which registers new metrics sets in...

def validate_column_specs(events, columns): """ Verify that the columns of ``events`` can be used by a EarningsEstimatesLoader to serve the BoundColumns described by `columns`. """ required = required_estimates_fields(columns) received = set(events.columns) missing = required - received ...

def get_requested_quarter_data(self, zero_qtr_data, zeroth_quarter_idx, stacked_last_per_qtr, num_announcements, dates): """ Sele...

def get_split_adjusted_asof_idx(self, dates): """ Compute the index in `dates` where the split-adjusted-asof-date falls. This is the date up to which, and including which, we will need to unapply all adjustments for and then re-apply them as they come in. After this date, adjustm...

def collect_overwrites_for_sid(self, group, dates, requested_qtr_data, last_per_qtr, sid_idx, columns, ...

def merge_into_adjustments_for_all_sids(self, all_adjustments_for_sid, col_to_all_adjustments): """ Merge adjustments for a particular sid into a dictionary containing adjustments for all sids. Param...

def get_adjustments(self, zero_qtr_data, requested_qtr_data, last_per_qtr, dates, assets, columns, **kwargs): """ Creates an AdjustedArr...

def create_overwrites_for_quarter(self, col_to_overwrites, next_qtr_start_idx, last_per_qtr, quarters_with_estimates_for_sid, requ...

def get_last_data_per_qtr(self, assets_with_data, columns, dates, data_query_cutoff_times): """ Determine the last piece of information we know for each column on each date in ...

def get_zeroth_quarter_idx(self, stacked_last_per_qtr): """ Filters for releases that are on or after each simulation date and determines the previous quarter by picking out the most recent release relative to each date in the index. Parameters ---------- stacked...

def get_adjustments_for_sid(self, group, dates, requested_qtr_data, last_per_qtr, sid_to_idx, columns, ...

def get_adjustments(self, zero_qtr_data, requested_qtr_data, last_per_qtr, dates, assets, columns, **kwargs): """ Calculates both split ...

def determine_end_idx_for_adjustment(self, adjustment_ts, dates, upper_bound, requested_quarter, sid_estimates): ...

def collect_pre_split_asof_date_adjustments( self, split_adjusted_asof_date_idx, sid_idx, pre_adjustments, requested_split_adjusted_columns ): """ Collect split adjustments that occur before the split-adjusted-asof-date. All those a...

def collect_post_asof_split_adjustments(self, post_adjustments, requested_qtr_data, sid, sid_idx, si...

def retrieve_split_adjustment_data_for_sid(self, dates, sid, split_adjusted_asof_idx): """ dates : pd.DatetimeIndex The calendar dates. sid : i...

def merge_split_adjustments_with_overwrites( self, pre, post, overwrites, requested_split_adjusted_columns ): """ Merge split adjustments with the dict containing overwrites. Parameters ---------- pre : dict[str -> dict[int -> list]] ...

def collect_split_adjustments(self, adjustments_for_sid, requested_qtr_data, dates, sid, sid_idx, sid_estimates, ...

def from_span(cls, inputs, window_length, span, **kwargs): """ Convenience constructor for passing `decay_rate` in terms of `span`. Forwards `decay_rate` as `1 - (2.0 / (1 + span))`. This provides the behavior equivalent to passing `span` to pandas.ewma. Examples -----...

def from_halflife(cls, inputs, window_length, halflife, **kwargs): """ Convenience constructor for passing ``decay_rate`` in terms of half life. Forwards ``decay_rate`` as ``exp(log(.5) / halflife)``. This provides the behavior equivalent to passing `halflife` to pandas.ewma. ...

def from_center_of_mass(cls, inputs, window_length, center_of_mass, **kwargs): """ Convenience constructor for passing `decay_rate` in terms of center of mass. Forwards `decay...

def tolerant_equals(a, b, atol=10e-7, rtol=10e-7, equal_nan=False): """Check if a and b are equal with some tolerance. Parameters ---------- a, b : float The floats to check for equality. atol : float, optional The absolute tolerance. rtol : float, optional The relative ...

def round_if_near_integer(a, epsilon=1e-4): """ Round a to the nearest integer if that integer is within an epsilon of a. """ if abs(a - round(a)) <= epsilon: return round(a) else: return a

def coerce_numbers_to_my_dtype(f): """ A decorator for methods whose signature is f(self, other) that coerces ``other`` to ``self.dtype``. This is used to make comparison operations between numbers and `Factor` instances work independently of whether the user supplies a float or integer literal...

def binop_return_dtype(op, left, right): """ Compute the expected return dtype for the given binary operator. Parameters ---------- op : str Operator symbol, (e.g. '+', '-', ...). left : numpy.dtype Dtype of left hand side. right : numpy.dtype Dtype of right hand sid...

def binary_operator(op): """ Factory function for making binary operator methods on a Factor subclass. Returns a function, "binary_operator" suitable for implementing functions like __add__. """ # When combining a Factor with a NumericalExpression, we use this # attrgetter instance to defer...

def reflected_binary_operator(op): """ Factory function for making binary operator methods on a Factor. Returns a function, "reflected_binary_operator" suitable for implementing functions like __radd__. """ assert not is_comparison(op) @with_name(method_name_for_op(op, commute=True)) @...

def unary_operator(op): """ Factory function for making unary operator methods for Factors. """ # Only negate is currently supported. valid_ops = {'-'} if op not in valid_ops: raise ValueError("Invalid unary operator %s." % op) @with_doc("Unary Operator: '%s'" % op) @with_name(u...

def function_application(func): """ Factory function for producing function application methods for Factor subclasses. """ if func not in NUMEXPR_MATH_FUNCS: raise ValueError("Unsupported mathematical function '%s'" % func) @with_doc(func) @with_name(func) def mathfunc(self): ...

def winsorize(row, min_percentile, max_percentile): """ This implementation is based on scipy.stats.mstats.winsorize """ a = row.copy() nan_count = isnan(row).sum() nonnan_count = a.size - nan_count # NOTE: argsort() sorts nans to the end of the array. idx = a.argsort() # Set value...

def demean(self, mask=NotSpecified, groupby=NotSpecified): """ Construct a Factor that computes ``self`` and subtracts the mean from row of the result. If ``mask`` is supplied, ignore values where ``mask`` returns False when computing row means, and output NaN anywhere the mask ...

def zscore(self, mask=NotSpecified, groupby=NotSpecified): """ Construct a Factor that Z-Scores each day's results. The Z-Score of a row is defined as:: (row - row.mean()) / row.stddev() If ``mask`` is supplied, ignore values where ``mask`` returns False when compu...

def rank(self, method='ordinal', ascending=True, mask=NotSpecified, groupby=NotSpecified): """ Construct a new Factor representing the sorted rank of each column within each row. Parameters ---------- method : str, {'or...

def pearsonr(self, target, correlation_length, mask=NotSpecified): """ Construct a new Factor that computes rolling pearson correlation coefficients between `target` and the columns of `self`. This method can only be called on factors which are deemed safe for use as inputs to o...

def spearmanr(self, target, correlation_length, mask=NotSpecified): """ Construct a new Factor that computes rolling spearman rank correlation coefficients between `target` and the columns of `self`. This method can only be called on factors which are deemed safe for use as inpu...

def linear_regression(self, target, regression_length, mask=NotSpecified): """ Construct a new Factor that performs an ordinary least-squares regression predicting the columns of `self` from `target`. This method can only be called on factors which are deemed safe for use as inp...

def winsorize(self, min_percentile, max_percentile, mask=NotSpecified, groupby=NotSpecified): """ Construct a new factor that winsorizes the result of this factor. Winsorizing changes values ranked less than the minimum per...

def quantiles(self, bins, mask=NotSpecified): """ Construct a Classifier computing quantiles of the output of ``self``. Every non-NaN data point the output is labelled with an integer value from 0 to (bins - 1). NaNs are labelled with -1. If ``mask`` is supplied, ignore data p...

def top(self, N, mask=NotSpecified, groupby=NotSpecified): """ Construct a Filter matching the top N asset values of self each day. If ``groupby`` is supplied, returns a Filter matching the top N asset values for each group. Parameters ---------- N : int ...

def bottom(self, N, mask=NotSpecified, groupby=NotSpecified): """ Construct a Filter matching the bottom N asset values of self each day. If ``groupby`` is supplied, returns a Filter matching the bottom N asset values for each group. Parameters ---------- N : in...

def percentile_between(self, min_percentile, max_percentile, mask=NotSpecified): """ Construct a new Filter representing entries from the output of this Factor that fall within the percentile range defined by min_pe...

def _validate(self): """ Verify that the stored rank method is valid. """ if self._method not in _RANK_METHODS: raise UnknownRankMethod( method=self._method, choices=set(_RANK_METHODS), ) return super(Rank, self)._validate()

def _compute(self, arrays, dates, assets, mask): """ For each row in the input, compute a like-shaped array of per-row ranks. """ return masked_rankdata_2d( arrays[0], mask, self.inputs[0].missing_value, self._method, se...

def _time_to_micros(time): """Convert a time into microseconds since midnight. Parameters ---------- time : datetime.time The time to convert. Returns ------- us : int The number of microseconds since midnight. Notes ----- This does not account for leap seconds or...

def mask_between_time(dts, start, end, include_start=True, include_end=True): """Return a mask of all of the datetimes in ``dts`` that are between ``start`` and ``end``. Parameters ---------- dts : pd.DatetimeIndex The index to mask. start : time Mask away times less than the sta...

def find_in_sorted_index(dts, dt): """ Find the index of ``dt`` in ``dts``. This function should be used instead of `dts.get_loc(dt)` if the index is large enough that we don't want to initialize a hash table in ``dts``. In particular, this should always be used on minutely trading calendars. ...

def nearest_unequal_elements(dts, dt): """ Find values in ``dts`` closest but not equal to ``dt``. Returns a pair of (last_before, first_after). When ``dt`` is less than any element in ``dts``, ``last_before`` is None. When ``dt`` is greater any element in ``dts``, ``first_after`` is None. ``...

def categorical_df_concat(df_list, inplace=False): """ Prepare list of pandas DataFrames to be used as input to pd.concat. Ensure any columns of type 'category' have the same categories across each dataframe. Parameters ---------- df_list : list List of dataframes with same columns....

def check_indexes_all_same(indexes, message="Indexes are not equal."): """Check that a list of Index objects are all equal. Parameters ---------- indexes : iterable[pd.Index] Iterable of indexes to check. Raises ------ ValueError If the indexes are not all the same. """...

def required_event_fields(next_value_columns, previous_value_columns): """ Compute the set of resource columns required to serve ``next_value_columns`` and ``previous_value_columns``. """ # These metadata columns are used to align event indexers. return { TS_FIELD_NAME, SID_FIELD...

def validate_column_specs(events, next_value_columns, previous_value_columns): """ Verify that the columns of ``events`` can be used by an EventsLoader to serve the BoundColumns described by ``next_value_columns`` and ``previous_value_columns``. """ required = required_event_fields(next_value_co...

def split_next_and_previous_event_columns(self, requested_columns): """ Split requested columns into columns that should load the next known value and columns that should load the previous known value. Parameters ---------- requested_columns : iterable[BoundColumn] ...

def compare_arrays(left, right): "Eq check with a short-circuit for identical objects." return ( left is right or ((left.shape == right.shape) and (left == right).all()) )

def from_codes_and_metadata(cls, codes, categories, reverse_categories, missing_value): """ Rehydrate a LabelArray from the codes and metadata. Parameters ----...

def as_int_array(self): """ Convert self into a regular ndarray of ints. This is an O(1) operation. It does not copy the underlying data. """ return self.view( type=ndarray, dtype=unsigned_int_dtype_with_size_in_bytes(self.itemsize), )

def as_categorical(self): """ Coerce self into a pandas categorical. This is only defined on 1D arrays, since that's all pandas supports. """ if len(self.shape) > 1: raise ValueError("Can't convert a 2D array to a categorical.") with ignore_pandas_nan_catego...

def as_categorical_frame(self, index, columns, name=None): """ Coerce self into a pandas DataFrame of Categoricals. """ if len(self.shape) != 2: raise ValueError( "Can't convert a non-2D LabelArray into a DataFrame." ) expected_shape = (le...

def set_scalar(self, indexer, value): """ Set scalar value into the array. Parameters ---------- indexer : any The indexer to set the value at. value : str The value to assign at the given locations. Raises ------ ValueErr...

def _equality_check(op): """ Shared code for __eq__ and __ne__, parameterized on the actual comparison operator to use. """ def method(self, other): if isinstance(other, LabelArray): self_mv = self.missing_value other_mv = other.missin...

def empty_like(self, shape): """ Make an empty LabelArray with the same categories as ``self``, filled with ``self.missing_value``. """ return type(self).from_codes_and_metadata( codes=np.full( shape, self.reverse_categories[self.missin...

def map_predicate(self, f): """ Map a function from str -> bool element-wise over ``self``. ``f`` will be applied exactly once to each non-missing unique value in ``self``. Missing values will always return False. """ # Functions passed to this are of type str -> bool. ...

def map(self, f): """ Map a function from str -> str element-wise over ``self``. ``f`` will be applied exactly once to each non-missing unique value in ``self``. Missing values will always map to ``self.missing_value``. """ # f() should only return None if None is our mi...

def asymmetric_round_price(price, prefer_round_down, tick_size, diff=0.95): """ Asymmetric rounding function for adjusting prices to the specified number of places in a way that "improves" the price. For limit prices, this means preferring to round down on buys and preferring to round up on sells. F...

def check_stoplimit_prices(price, label): """ Check to make sure the stop/limit prices are reasonable and raise a BadOrderParameters exception if not. """ try: if not isfinite(price): raise BadOrderParameters( msg="Attempted to place an order with a {} price " ...

def csvdir_bundle(environ, asset_db_writer, minute_bar_writer, daily_bar_writer, adjustment_writer, calendar, start_session, end_session, cache, show_progress...

def restrict_to_dtype(dtype, message_template): """ A factory for decorators that restrict Term methods to only be callable on Terms with a specific dtype. This is conceptually similar to zipline.utils.input_validation.expect_dtypes, but provides more flexibility for providing error messages th...

def daily_returns(self, start, end=None): """Returns the daily returns for the given period. Parameters ---------- start : datetime The inclusive starting session label. end : datetime, optional The inclusive ending session label. If not provided, treat ...

def _initialize_precalculated_series(self, asset, trading_calendar, trading_days, data_portal): """ Internal method that pre-calculates the ...