CrossCurrencySwap#

class sigtech.framework.instruments.xccy_swap.CrossCurrencySwap

Vanilla cross currency swap 'FloatFloat' receiving main currency currency and paying pay_currency with zero spread on receiving leg.

The swap type is inferred from fixed_rate field. If a fixed_rate is set, the swap type will default in 'FixedFloat'. This can be overridden by setting swap_type parameter.

The receiving leg is fixed in FixedFloat case

Keyword arguments:

  • currency: Currency of the swap and the receiving leg

  • pay_currency: Currency of the paying leg

  • tenor: Tenor (e.g. 5Y) or maturity date (e.g. dtm.date(2025,1,3)) of the swap

  • trade_date: Trade date of the swap. Used for starting the history and inferring un-supplied parameters.

  • start_date: Start date of the swap, or tenor for forward starting swap (e.g. '1Y' for swap starting in 1 year). If no date or tenor is given, T+2 is used

  • pay_notional: Notional of the paying leg. Notional of the receiving leg is always assumed 1. If no value is passed, the start_date FX forward on the trade_date is used

  • fixed_rate: Fixed rate (of the receiving leg) in case of fixed-floating swap. Will be set to the fair rate if left None, and swap_type is set to 'FixedFloat'. Expected in decimal format, e.g. 0.05 for 5%

  • spread: Spread (of the pay leg). In case of floating-floating swap, if left blank, fair spread will be set. Expected in the decimal format, e.g. 0.002 for 20bp

  • fixed_frequency: Fixed leg frequency ('Q', 'SA', or 'A') in case of fixed-floating swap quarterly if left blank (used only in 'FixedFloat' swap case)

  • receive_tenor: Receiving floating leg tenor ('1M', '3M', '6M', '1Y') in case of floating-floating swap. '3M' if left blank (used only in 'FloatFloat' swap case)

  • pay_tenor: Paying floating leg tenor ('1M', '3M', '6M', '1Y') . '3M' if left blank

  • receive_daycount: day count of the receiving leg (e.g. '30/360'). Currency interest rate swap default is used if left blank

  • pay_daycount: Day count of the paying leg (e.g. '30/360'). Currency interest rate swap default is used if left blank

  • swap_type: 'FixedFloat' or 'FloatFloat' to specify explicitly the type of swap (e.g. if fixed rate is not input and has to be inferred as the market fair rate)

  • pay_fixes_index: Optional override for default floating index fixing object of the pay leg

  • receive_fixes_index: Optional override for default floating index fixing object of the receive leg in 'FloatFloat' swap case

  • is_ois: Optional flag to use OIS floating leg(s) instead of IBOR if True. (Default swaps are IBOR swaps).

  • ois_params: Optional dictionary of OIS swap additional parameters overrides. Supported parameters are 'fixing_lag', 'pay_delay', 'pay_fixing_lag', 'rec_fixing_lag', 'ois_legs' - fixing_lag can be set for both floating legs via 'fixing_lag', or for each leg individually via 'pay_fixing_lag' and 'rec_fixing_leg' correspondingly, if both legs are floating OIS legs. 'ois_legs' is an optional parameter to allow only one of the legs be ois based, while the second - IBOR based. Allowed values are 'both' (default), 'pay' or 'rec'.

  • use_notional_reset: Optional flag to reset receiving leg notional to fair fx equivalent of the pay leg notional at each coupon end date. (Also known as MTM swaps). (False by default, i.e. notionals are fixed through life of the swap).

  • ignore_future_notional_reset: Optional flag for the resettable notional case. If True, only actually happened notional resets are taken into account, and legs are assumed bullet for the remaining life for valuation purposes - this reduces the valuation time without much sacrifice in precision as effect of proper future resets is minimal (True by default).

Example object creation:

swap = CrossCurrencySwap(
            currency='USD',
            pay_currency='EUR',
            tenor=dtm.date(2025, 7, 5),
            pay_notional=0.89,
            start_date=dtm.date(2021, 7, 5),
            fixed_rate=0.0132)

This will create a swap, starting on 5-Jul-2021, ending on 5-Jul-2025, paying 3-month EURIBOR and receiving 1.32% fixed USD quarterly leg (with notional exchanges of 1 USD and 0.89 EUR at the beginning and the end)

swap = CrossCurrencySwap(
            currency='USD',
            pay_currency='EUR',
            tenor='1Y',
            trade_date=dtm.date(2021, 7, 1),
        )

This will create a 1 year swap, starting on 5-Jul-2021 (\(T+2\)), paying 3-month EURIBOR plus spread (making the trade date value of the swap 0), and receiving 3-month USD LIBOR (with notional exchanges of 1 USD and spot USDEUR on 1-Jul-2021 of EUR at the beginning and the end)

swap = CrossCurrencySwap(
            currency='USD',
            pay_currency='EUR',
            tenor='1Y',
            trade_date=dtm.date(2021, 7, 1),
            swap_type='FixedFloat',
        )

This will again create a 1 year swap, starting on 5-Jul-2021 (\(T+2\)), paying 3-month EURIBOR (without spread), and receiving quarterly USD fixed coupon, that makes swap value 0 on 1-Jul-2021 (with notional exchanges of 1 USD and spot USDEUR on 1-Jul-2021 of EUR at the beginning and the end).

Minor point to note - valuation data point rules for XCCY swaps is different from FXForwards - we try to always use the discounting/forecasting curve data point for FX as well, unless they are inconsistent, but even in that case, fx forward curves data points are used, even if more dense spot fx data is available (to get better overall consistency).

currency: str
pay_currency: str
tenor: Union[datetime.date, str]
start_date: Optional[Union[datetime.date, str]]
trade_date: Optional[datetime.date]
pay_notional: Optional[float]
execution_datetime: Optional[datetime.datetime]
fixed_rate: Optional[float]
spread: Optional[float]
fixed_frequency: Optional[Literal['Q', 'SA', 'A']]
receive_tenor: Optional[Literal['1M', '3M', '6M', '1Y']]
pay_tenor: Optional[Literal['1M', '3M', '6M', '1Y']]
receive_daycount: Optional[str]
pay_daycount: Optional[str]
swap_type: Optional[Literal['FixedFloat', 'FloatFloat']]
pay_fixes_index: Optional[str]
receive_fixes_index: Optional[str]
is_ois: Optional[bool]
ois_params: Optional[dict]
use_notional_reset: Optional[bool]
ignore_future_notional_reset: Optional[bool]
static schedule_stub(currency, pay_currency)

Schedule stub for all XCCY swaps.

Parameters

  • currency – Input receive leg currency.

  • pay_currency – Input pay leg currency.

Returns

Schedule stub.

property maturity

Maturity date.

static spot_date(asof_date: Union[datetime.date, pandas.core.indexes.datetimes.DatetimeIndex, numpy.ndarray], currency_par: str)

Standard swap start date if traded on asof_date. Vectorised form is also supported.

swap_metrics(data_dates: list[datetime.date] = None, fields: Union[str, list[str]] = None, data_point=None) → pandas.core.frame.DataFrame deprecated

Deprecated method to calculate the PV in both currency and pay_currency, fair spread/rate, pvbp of the pay leg, and fixed leg (if applicable) of this swap for given dates.

metrics(data_dates: Optional[list[datetime.date]] = None, fields: Optional[Union[str, list[str]]] = None, data_point=None) → pandas.core.frame.DataFrame

Calculate the PV in both currency and pay_currency, fair spread/rate, pvbp of the pay leg, and fixed leg (if applicable) of this swap for given dates. Supported fields values are 'LastPrice', 'PV Pay Currency', 'FairSpread', 'FairRate', 'PayLegPvbp', 'FixedPvbp'.

swap_details(d: Optional[datetime.date] = None) → dict

Return swap info.

Parameters

d – Reference date to return info for (env asof_date used if omitted).

Returns

Dictionary of swap info.

coupon_payment_dates() → list[datetime.date]

List of all dated on which coupons are being paid.

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 alias: Optional[str]

Return an alias string used to represent the instrument in the portfolio table or other widgets.

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.

property bbg_ticker: str

Ticker for Bloomberg data vendor.

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

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.

property data_source_all: Optional[list[str]]

Available data points for this object.

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

Dependency type

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.

exchange() → Any

Object for exchange (or substitute, e.g. 'OTCLN') on which instrument trades.

property exchange_code: str

Exchange code for traded instruments belonging to this group.

property expiry_date: datetime.date

Date when the contract expires, or last date on which we can trade.

property expiry_dt: datetime.datetime

Datetime when the contract expires, or last datetime at which we can trade.

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.

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 instrument_type: str
Returns

Classification using class name of instruments.

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

is_cash() → bool

Return True if the instrument represents a cash/margin amount.

is_exchange_traded() → bool

Return True if this instrument is traded on an exchange.

is_expandable() → bool

Return True if the instrument can be expressed in terms of a portfolio of other instruments.

is_margin() → bool

Check if the instrument represents a margin amount.

is_margin_accountable() → bool

Check if an artificial margin is created to account for price changes in instruments that do not require immediate payment, but have a nominal value, e.g. futures. The actual P&L is computed through this artificial margin for such instruments.

is_notional_exposure_calc_needed() → bool

Check if the notional exposure weight should be computed and included in the interactive portfolio table, e.g. for options, FX forwards, IR swaps.

is_option() → bool

Return True if the instrument is an option.

is_otc() → bool

Is this instrument OTC traded?

is_strategy() → bool

Return True if this object is a strategy.

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.

order_class() → Any

Return the order class for timeline processing.

property order_rounding

The value to which the units should be rounded to when calculating target units. Eg, 1000 would round to the nearest thousand :return: order rounding factor

otc_fields_to_persist = []
property position_type: str

Display trade type.

positions_to_units(position, dt)

Strategy scaling function: convert positions to number of units.

Parameters

  • position – Number of positions.

  • dt – Input scaling date.

Returns

Number of units.

positions_to_units_multiplier(dt)

Strategy scaling function: multiplier to convert positions to number of units.

Parameters

dt – Input scaling date.

Returns

multiplier

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.

classmethod print_add_trade_kwargs()

Print the keyword arguments for the Strategy add_trade method.

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.

rounded_units(units, dt, to_trade=True)

The number of units allowed to trade for a specified target number of units, at the provided time.

This should be overwritten for instrument specific rounding logic.

The ‘to_trade’ flag switches between the use case of rounding the position for trade execution or for analysis The trading case can be different because the position is held for an extended time, while in the case of analysis you can consider the instantaneous amount of rounding allowed. An example of this applied is for the strategies where the underlying positions change over time.

Parameters

  • units – Target number of units.

  • dt – Datetime.

  • to_trade – Boolean flag to switch between allowed to open for an extended time (True) or at that instant.

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.

property settlement_type: str

Stub indicating how final cash flows are exchanged.

property size_type: str

Type of trade size for the instrument.

sizing_price(sizing_dt: datetime.datetime, ccy: Optional[str] = None, execution_dt: Optional[datetime.datetime] = None) → float

On the base class this is just the valuation price, but can be overwritten in derived classes For specific instrument, price on sizing date (eg T-1), isn’t necessarily the best approximation for today’s price if the market doesn’t move.

For instance, for bonds, if coupon is to be paid on Settlement(T), then today’s price would be approximately yesterday price - coupon.

Parameters

  • sizing_dt – Sizing datetime

  • ccy – Currency string identifier

  • execution_dt – Execution datetime

Returns

Sizing price used in weight to model unit calculations

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_line(units, dt, rounded_units=True)

Trade entry containing information about units and string representation.

Parameters

  • units – Number of units.

  • dt – Size date.

  • rounded_units – If True, use rounded units to get positions (optional True by default).

Returns

Scaled units and trade description.

property trade_name: str

String identifier used for order/trade generation.

trade_price(trade_dt: datetime.datetime, trade_sign: float, include_trading_costs: Optional[bool] = True, transaction_type: Optional[str] = None, currency: Optional[str] = None, cache_trade_date: Optional[bool] = True) → float

Price Traded - gives either bid or ask depending on trade type.

Parameters

  • trade_dt – Trading datetime

  • trade_sign – Integer/float to determine if it is a buy or sell

  • include_trading_costs – If True will apply trading cost adjustment otherwise ignores it

  • transaction_type – String identifier to indicate type of transaction, e.g. ‘outright’, ‘roll’

  • currency – Currency string stub

  • cache_trade_date – Optional parameter to cache trading dates of instrument

Returns

Trade/fill price

trade_price_adjustment(d: datetime.datetime, include_trading_costs: Optional[bool] = True, transaction_type: Optional[str] = None, cache_trade_date: Optional[bool] = True) → float

Trade price adjustment (TPA) formula:

\[TPA_t = \alpha * S_t^{Mid} + \beta\]
Parameters

  • d – Trading date

  • include_trading_costs – If True will apply trading cost adjustment otherwise ignores it

  • transaction_type – String identifier to indicate type of transaction, e.g. ‘outright’, ‘roll’

  • cache_trade_date – Optional parameter to cache trading dates of instrument

Returns

Trade price adjustment

trade_price_adjustment_add(d: datetime.datetime, transaction_type: Optional[str] = None, cache_trade_date: Optional[bool] = True) → float

Absolute adjustment to the mid price value in the trade price adjustment.

Parameters

  • d – Trade date

  • transaction_type – String identifier to indicate type of transaction, e.g. ‘outright’, ‘roll’

  • cache_trade_date – Optional parameter to cache trading dates of instrument

trade_price_adjustment_multiply(d: datetime.datetime, transaction_type: Optional[str] = None, cache_trade_date: Optional[bool] = True) → float

Multiplier to the mid price value in the trade price adjustment

Parameters

  • d – Trade date

  • transaction_type – String identifier to indicate type of transaction, e.g. ‘outright’, ‘roll’

  • cache_trade_date – Optional parameter to cache trading dates of instrument

trade_price_ask(trade_dt: datetime.datetime, include_trading_costs: Optional[bool] = True, transaction_type: Optional[str] = None, currency: Optional[str] = None, cache_trade_date: Optional[bool] = True) → float

Ask price at a point in time.

Parameters

  • trade_dt – Trading datetime

  • include_trading_costs – If True will apply trading cost adjustment otherwise ignores it

  • transaction_type – String identifier to indicate type of transaction, e.g. ‘outright’, ‘roll’

  • currency – Currency string stub

  • cache_trade_date – Optional parameter to cache trading dates of instrument

Returns

Ask trade price

trade_price_bid(trade_dt: datetime.datetime, include_trading_costs: Optional[bool] = True, transaction_type: Optional[str] = None, currency: Optional[str] = None, cache_trade_date: Optional[bool] = True) → float

Bid price at a point in time.

Parameters

  • trade_dt – Trading datetime

  • include_trading_costs – If True will apply trading cost adjustment otherwise ignores it

  • transaction_type – String identifier to indicate type of transaction, e.g. ‘outright’, ‘roll’

  • currency – Currency string stub

  • cache_trade_date – Optional parameter to cache trading dates of instrument

Returns

Bid trade price

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.

units_to_positions(units, dt)

Strategy scaling function: convert number of units to positions.

Parameters

  • units – Number of units.

  • dt – Input scaling date.

Returns

Number of positions.

validate()

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

valuation_dt(d: datetime.date) → datetime.datetime

Valuation dt - datetime for a given valuation date.

valuation_point() → str

Valuation Point - from this we can infer the field, time and timezone for the valuation.

valuation_price(dt: datetime.datetime, ccy: Optional[str] = None) → float

Price on given valuation date.

Parameters

  • dt – Input valuation date

  • ccy – Currency string identifier

Returns

Valuation price for given input date dt

valuation_price_base(dt: Union[datetime.datetime, datetime.date]) → float

Return the price in base currency on a given valuation date.

Parameters

dt – Input date/datetime.

Returns

float.

valuation_price_history(ccy: Optional[str] = None, field: Optional[str] = None, history_fill: Optional[bool] = None) → pandas.core.frame.DataFrame

Return a history of valuation (available for each weekday) prices.

Parameters

  • ccy – Currency string identifier.

  • field – History field.

  • history_fill – Should gaps in history be forward filled?

Returns

Valuation history.

valuation_price_series(dts: list[datetime.datetime], ccy: Optional[str] = None, intraday_data: Optional[bool] = True) → pandas.core.series.Series

Series of prices at the gives date-times.

Parameters

  • dts – List of date times

  • ccy – Currency string identifier

  • intraday_data – Use intraday data (optional, default True).

Returns

Valuation price series

property valuation_time

Valuation time for the instrument.

property valuation_tzinfo

Valuation timezone info.

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)

activity_fields: Optional[list[str]]
group_name: Optional[str]
description: Optional[str]
ticker: Optional[str]
db_ticker: Optional[str]
db_sector: Optional[str]
price_factor: Optional[float]
use_price_factor: Optional[bool]
intraday_times: Optional[list]
intraday_tz_str: Optional[str]
instrument_id: Optional[int]