Universe#

class sigtech.framework.instruments.equities.Universe

Defines a timeseries of constituents of a some grouping of instruments (for example constituents of S&P 500, or all stocks in a region, or something else)

ticker: Optional[str]
db_ticker: Optional[str]
db_sector: Optional[str]
issuer: Optional[str]
db_history_end_date: Optional[datetime.date]
property data_source_all

Data source of the instrument.

get_differences(date_newer=None, date_older=None)

List the data differences between two data points.

Parameters

  • date_newer – A point in time (optional, if not provided, the most recent date will be used).

  • date_older – A point in time (optional, if not provided, the second most recent date will be used).

Returns

dates, data differences between date_newer and date_older, data differences between             ``date_older and date_newer.

add_custom_history(history: pandas.core.series.Series, field: str = 'LastPrice', data_point: Optional[sigtech.framework.infra.data_adapter.common.DataPoint] = None)

Add custom history data for this object. If multiple, conflicting custom histories are added only the last addition will be returned from history.

Parameters

  • history – pd.Series of custom history with index as tz-naive, pd.DatetimeIndex dates.

  • field – Name of history field to overwrite.

  • data_point – Optional DataPoint to specify timing of custom history.

after_history_func()
property allowed_clean_sparse_fields

List of fields that are allowed to have na values on clean history.

property asset_description: str

String representing the underlying asset, or None if no assets exist.

property available_data_points: Optional[list[sigtech.framework.infra.data_adapter.common.DataPoint]]

Available data points for this object.

available_data_providers(entitled_only=True) → Optional[List[str]]

Available data providers for this object. Bool to return entitled providers only

property base_name

Primary name used to identify the object.

cache_history(fields: list, data_point: sigtech.framework.infra.data_adapter.common.DataPoint, start_dt: Optional[datetime.datetime] = None, end_dt: Optional[datetime.datetime] = None, **kwargs)

Ensure history data is cached

property cache_name: str

Cache name of this object.

calendar_schedule() → sigtech.framework.schedules.schedule.Schedule

A calendar schedule - a schedule that is corresponding to a history schedule, but without min and max dates set.

Returns

Calendar schedule instance for the instrument

property class_name

Class name of this object.

property class_short_name

Short name of the object class.

clear_cached_data()

Clear all the cached data of the object.

clone_object(params=None)

Return a clone of the object with amended parameters.

Parameters

params – Optional dictionary of parameters to override.

Returns

New object.

compute_dependencies(root_dependency: Optional[sigtech.framework.internal.infra.mu.graph.registry.factory.Dependency] = None)

Compute these dependencies - triggers MDS requests

static convert_dtypes(clz)

Class decorator to convert classes’ BaseType into variable annotations (PEP-526), and to generate __aliases__ based on BaseType.db_name when required.

data_available(d: Optional[datetime.date] = None) → bool

Method to indicate if pricing data is available for a given date.

Parameters

d – Date of interest (optional), if not provided it will take as of date of the environment.

data_df(data_point: Optional[sigtech.framework.infra.data_adapter.common.DataPoint] = None, multi_index: bool = False, drop_nan_cols: bool = False, pretty_print=False) → pandas.core.frame.DataFrame

Return a DataFrame containing all data available for this object.

Parameters

  • data_point – Optional data point used to load the object history.

  • multi_index – If set to True, rows are uniquely indexed by a multi index (if applicable). Default is False.

  • drop_nan_cols – If set to True, all-NaN columns are dropped. Default is False.

  • pretty_print – If set to True, formatting is added to columns names and data values. Rates will be represented as percentage number instead of decimal number (e.g. 3.5 instead of 0.035).

Returns

pandas DataFrame.

data_dict()

Return the object attributes in a dict.

property data_format

Return the data format associated with sparse history, i.e. ROWISE or COLUMNAR.

property data_point

Field used when retrieving history.

property data_source

Data provider of the instrument.

data_validation(sparse_series)

Additional data checks can be implemented in sub classes here.

property default_data_point

Default data point for this object.

dependencies(input_dependency: Optional[sigtech.framework.internal.infra.mu.graph.registry.factory.Dependency] = None, valuation_currency: Optional[str] = None, use_start: bool = True) → list[sigtech.framework.internal.infra.mu.graph.registry.factory.Dependency]

Returns a list of Dependency

Parameters

  • input_dependency – A Dependency object representing the root of the dependency tree to be returned.

  • valuation_currency – If supplied, include the dependencies required to output the data in this currency.

  • use_start – If False, return only dependencies required to calculate an earliest start date - self.start_date will be None in this case.

Returns

List of Dependency objects representing the current level in the dependency tree.

property dependency_type

Should return GraphNodeType. Return None to autodetect based on result of dependencies()

property env: sigtech.framework.config.config.ConfiguredEnvironment

Return the configured environment.

env_date_change(old_env_dt: datetime.datetime, new_env_dt: datetime.datetime, live_data_update=None)

Routine called after the environment date is changed.

Parameters

  • old_env_dt – Old datetime (tz aware).

  • new_env_dt – New datetime (tz aware).

  • live_data_update – Live streaming data associated with the environment date change.

finalize_for_comparison()

Method to call to ensure all data dict values are finalized prior to doing object comparisons.

classmethod from_dictionary(dct: dict[str, Any], cache: bool = True, identifier: Optional[sigtech.framework.infra.data_adapter.identifier.Identifier] = None, env: Optional[sigtech.framework.config.config.ConfiguredEnvironment] = None, **kwargs)

Factory method to create object using data dictionary

classmethod get_names(sort_by_group: Optional[bool] = False, include_db: Optional[bool] = True, include_local: Optional[bool] = True, include_children: Optional[bool] = False, ignore_unmapped: Optional[bool] = True) → list[str]

Return an ordered list of object names associated with the class.

Parameters

  • sort_by_group – If set, the list is first ordered by sector/group, if applies, e.g. commodity or index futures (default is False).

  • include_db – If set, include objects available from the database (default is True).

  • include_local – If set, include objects available in the local environment (default is True).

  • include_children – If set, include objects available from child classes (default is False).

  • ignore_unmapped – If set, ignore errors due to unmapped database objects (default is True).

Returns

List of object names.

get_universe(d)

History at one point in time.

Parameters

d – A point in time.

Returns

Historical data at one point in time.

group() → any

Group of this object.

has_preloaded_history(fields=None, data_point=None)

Check if the object has preloaded history.

history(field: str = None, adjust_for_delay: bool = False, date_index: bool = False, data_point: Optional[sigtech.framework.infra.data_adapter.common.DataPoint] = None, datetime_index: bool = False) → pandas.core.series.Series

Method to retrieve time series for objects overlaid by history schedule and publication delay (optional). If the given field does not exist, an empty series will be returned.

Parameters

  • field – Optional - Returns time series for field - defaults to prime history field.

  • adjust_for_delay – Optional - Returns adjusted index if there is a publication delay.

  • date_index – Optional - Convert the timestamp index to dates.

  • data_point – data point to retrieve - from self.available_data_points

  • datetime_index – Optional - if True, populate the time and UTC timezone information in the index.

Returns

pd.Series.

history_dates()

Return all the dates we should have actual history values.

history_dates_actual()

Return all the dates we have actual history values.

history_dates_eventual()

Return all the dates we will eventually have history values.

history_df(fields: list[str] = None, data_point: sigtech.framework.infra.data_adapter.common.DataPoint = None, multicolumn: bool = None) deprecated

Return the object history dataframe.

Parameters

  • fields – list of fields. If not supplied, self.history_fields will be used

  • data_point – optional data point used to load the object history.

  • multicolumn – Deprecated input. This will be removed in future releases.

Returns

pd.DataFrame.

history_end_date() → datetime.date

Eventual history end date, truncated to the as of date - the day on which history should end.

history_end_date_actual()

The last date we have an actual value for.

history_end_date_eventual() → datetime.date

Last date for which data will be available - typically date.max if no end date set.

history_end_timestamp() → pandas._libs.tslibs.timestamps.Timestamp
Returns

First timestamp for which data should be available

property history_fields: list[str]

The fields of history retrieved for this instrument.

history_schedule() → sigtech.framework.schedules.schedule.Schedule

Object describing dates on which history is available and the corresponding delivery times.

Returns

History schedule instance for the instrument.

history_start_date() → datetime.date

First date for which data should be available.

history_start_timestamp() → pandas._libs.tslibs.timestamps.Timestamp
Returns

First timestamp for which data should be available

property identifier: sigtech.framework.infra.data_adapter.identifier.Identifier

Return the object identifier.

info() → dict

Return a dictionary with useful object information.

property input_parameters

Values of initial input parameters entered when creating the instance.

property internal_id

Ticker - i.e. part of name determining content of the class. For types with saved reference data - ticker will be self._ticker. For software types - ticker should be calculated.

intraday_history(field=None, period=None, start_dt=None, end_dt=None, daily_timeseries_time=None, daily_timeseries_tz=None, filter_by_trading_sessions=False, timezone=None, convert_yield_to_price=True)

Retrieve intraday history for this instrument as a Series.

Parameters

  • field – field name in self.intraday_history_fields.

  • period – a dtm.timedelta giving the desired periodicity of the data.

  • start_dt – optional tz-aware dtm.datetime giving a desired start point.

  • end_dt – optional tz-aware dtm.datetime giving a desired end point.

  • daily_timeseries_time – if supplied, the intraday data will be down-sampled to daily data snapped at this time.

  • daily_timeseries_tz – timezone for the above. Optional, if not supplied defaults to self.valuation_tzinfo

  • filter_by_trading_sessions – If False ``(default), return all available data. If ``True return data during the main trading session. If a string/list(string), return data during the trading session(s) of that name according to self.group().session_data(). See intraday_trading_sessions() for list of sessions.

  • timezone – set the timezone of the returned series to this value

  • convert_yield_to_price – applies yield to price conversion for yield quoted futures, e.g. AUD bond futures

Returns

A (possibly sparse) pd.Series with a DateTimeIndex. Times should be tz-aware UTC.

intraday_history_df(fields=None, period=None, start_dt=None, end_dt=None, daily_timeseries_time=None, daily_timeseries_tz=None, filter_by_trading_sessions=False, timezone=None, convert_yield_to_price=True)

Retrieve intraday history for this instrument as a dataframe.

Parameters

  • fields – a list of field names all in self.intraday_history_fields.

  • period – a dtm.timedelta giving the desired periodicity of the data.

  • start_dt – optional tz-aware dtm.datetime giving a desired start point.

  • end_dt – optional tz-aware dtm.datetime giving a desired end point.

  • daily_timeseries_time – if supplied, the intraday data will be down-sampled to daily data snapped at this time.

  • daily_timeseries_tz – timezone for the above. Optional, if not supplied defaults to self.valuation_tzinfo

  • filter_by_trading_sessions – If False ``(default), return all available data. If ``True return data during the main trading session. If a string/list(string), return data during the trading session(s) of that name according to self.group().session_data()

  • timezone – set the timezone of the returned dataframe to this value

  • convert_yield_to_price – applies yield to price conversion for yield quoted futures, e.g. AUD bond futures

Returns

A (possibly sparse) pd.DataFrame with a DateTimeIndex. Times should be tz-unaware UTC.

property intraday_history_fields: list[str]

The intraday fields of history retrieved for this instrument.

intraday_trading_sessions()

Gets ths list of trading sessions available as per the Copp-Clarke session data for this product group

property is_unmapped

Check if the object is not mapped.

property live_supported: bool

Flag for live supported objects.

property name

Primary name by which the FrameworkObject is identified. If reference information (or e.g. corresponding time series) are stored in the DB, then this name will be used.

property prime_history_field: str

The main history field returned by default.

property prime_intraday_history_field

The main intraday history field returned by default.

print_dependencies(root_dependency: Optional[sigtech.framework.internal.infra.mu.graph.registry.factory.Dependency] = None, resolve_future_dependencies: bool = True, fields: Optional[list] = None)

Pretty print dependency tree :param root_dependency: starting dependency of the tree. :param resolve_future_dependencies: resolve future dependencies before printing. If ‘false’ nothing get printed. :param fields: additional fields to extract from dependencies object. Default fields are product_type, currency, frequency and data_source. To get the list of available fields for an object you can use the data_dict() method.

property product_type

Return the product_type property of this object.

publication_delay() → str

Publication delay of data for this instrument.

realign_history(series)

Realign history series to proper business days.

Parameters

series – Input series.

Returns

Realigned history series.

property sector

Sector used on BBG, or the class ID for SIG instruments.

set_preloaded_history(data) → None

Set the preloaded history of the object.

Parameters

data – History data.

set_sparse_history(data) → None

Set the sparse history of the object.

Parameters

data – History data.

static sort_key_static(name)

Convert a name to the sort key as of Portfolio Presentation Guidelines for performance reasons.

Parameters

name – Input name.

Returns

Tuple (sort key, length of sort key, input name).

property supplementary_fields: list[str]

Additional fields available for the group.

textual_representation()

Return a printable representation of this object.

trade_schedule() → sigtech.framework.schedules.schedule.Schedule

Schedule giving trade dates + last time at which we can trade for that date (i.e. notice time for funds).

Returns

Trade schedule instance for the instrument

validate()

Validation routine adding checks that will be run on object creation.

wait_for_live_data(period: Optional[Union[datetime.datetime, str]] = None)

Waits until live data is collected up to and including env.asofdatetime :param period: period of the live intraday data to wait for (defaults to 1 minute)

price_factor: Optional[float]
use_price_factor: Optional[bool]
intraday_times: Optional[list]
intraday_tz_str: Optional[str]
instrument_id: Optional[int]
get_full_universe()

Union of the universe over all dates. Return a set for quick look-ups.