InflationGovernmentBond#

class sigtech.framework.instruments.bonds.InflationGovernmentBond

Inflation Government Bond. Similar functionality to Government Bond, but all coupons and notional redemptions are adjusted with the inflation index index change over its issue time value index_base_value. Only USD CPI bonds are supported at the moment, but that can be extended with time.

index: Optional[str]
index_base_value: float
index_interpolation: Optional[Literal['flat', 'linear']]
observation_lag: Optional[str]
cashflows(d: datetime.date)

Returns the cashflows incurred from a given date to the maturity of the bond. Cashflows after the last inflation fixing are computed assuming zero inflation (i.e. no future inflation forecast is made)

Parameters

d – The date from which to return cashflows

Returns

List of cashflows (coupons + redemption)

inflation_scale(trade_date: Optional[datetime.date] = None, settlement_date: Optional[datetime.date] = None) → float

Inflation index scale of the bond price (multiplier to translate real bond price into nominal). Can be computed either on the trade date or settlement date.

Parameters

  • trade_date – trade date for which the scale is needed (not needed if settlement_date is passed.

  • settlement_date – settlement date of the trade (not needed if trade_date is passed).

Returns

inflation scale for the date.

accrued_interest(d)

Accrued interest given a settlement date.

Parameters

d – Settlement date.

Returns

Accrued interest.

accrued_interests(ds)

Accrued interest given a list of settlement dates.

Parameters

ds – List of settlement dates.

Returns

List of accrued interests.

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.

asw_spread(trade_date, clean_price=None, index_tenor=None, discount_curve_name=None, forecasting_curve_name=None)

Calculates bond asset swap spread with regards to the input discount and forecasting curve. (Standard swap market ones are taken by default, but can be overwritten). Swap assumes to start on the bond settlement day, which is also used as the floating leg accrual start and the corresponding IBOR rate setting (at the standard reset lag). Floating rate is taken to be the standard swap market frequency, but can be overwritten. The spread is given in basis points (23 means 23 basis points)

Parameters

  • trade_date – date on which the asset swap spread is calculated

  • clean_price – optional - clean price with usual bond conventions (100 for par bond)

  • index_tenor – optional - floating index frequency (e.g. ‘3M’ or ‘6M’)

  • discount_curve_name – optional: swap discounting curve

  • forecasting_curve_name – optional: floating index forecasting curve

Returns

asw_spreads(trade_dates=None, index_tenor=None, discount_curve_name=None, forecasting_curve_name=None)

Calculates bond asset swap spread with regards to the input discount and forecasting curve for the given set of trade_dates. If not dates are given, the maximal available series of asw-spreads is returned (from the latest of bond history start and forecasting curve history start to the last bond trade date with settlement prior to maturity) Standard swap market curves are taken by default, but can be overwritten. Swaps assume to start on the bond settlement day, which is also used as the floating leg accrual start and the corresponding IBOR rate setting (at the standard reset lag). Floating rate is taken to be the standard swap market frequency, but can be overwritten. The spread is given in basis points (23 means 23 basis points)

Parameters

  • trade_dates – dates on which the asset swap spread is calculated. All history dates if None

  • index_tenor – optional - floating index frequency (e.g. ‘3M’ or ‘6M’)

  • discount_curve_name – optional: swap discounting curve

  • forecasting_curve_name – optional: floating index forecasting curve

Returns

property available_data_points

The data points available for this instrument

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

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

property available_history_fields

Combined fields available for history retrieval -> clean/dirty prices + calculated fields.

property base_name

Primary name used to identify the object.

property bbg_ticker: str

Ticker for Bloomberg data vendor.

bond_dependency()

Returns a Dependency object suitable for this Bond

bond_info() deprecated

Bond info summary, required by BondMixin.

bond_px(settlement_date, yield_)

Clean bond price.

Parameters

  • settlement_date – Settlement date.

  • yield – Quoted yield.

Returns

Clean bond price.

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()

This is when we expect data to arrive.

carry_roll_down(d: datetime.date, dates: Union[str, datetime.date, list[Union[str, datetime.date]]], clean_price: Optional[float] = None, discount_curve_name: Optional[str] = None, repo_rate: Union[float, list[float]] = 0.00035, otr_bond_yields: Optional[pandas.core.frame.DataFrame] = None)

Compute carry and roll-down of bond by moving the valuation date forward to the requested dates, keeping discount curve constant, i.e. discount_factor(from_original_date, for_x_number_of_days) equals discount_factor(from_the_forward_date, for_x_number_of_days), and keeping the z-spread to that curve constant as well

Parameters

  • d – valuation date

  • dates – string/date or a list of strings/dates for which the carry/roll-down is needed

  • clean_price – optional - clean price with usual bond conventions (100 for par bond)

  • discount_curve_name – optional: discounting curve (swap market standard one is used if None is provided)

  • repo_rate – optional: repo rate (or list of repo rates - one per horizon). Absolute (0.01 means 1%)

  • otr_bond_yields – optional: DataFrame of two columns of the ref bonds - first columns maturities of reference bonds, and second column - their yields. If omitted, the default OTR bonds yields curve is used. 'disable' or 'no' would disable computation of yield roll-down altogether

Returns

DataFrame of 6 columns - carry - cumulative cashflows from d to date, roll_down- change in bond pv, and the same numbers scaled by bond DV01. Additional 2 columns are yield carry (based on the input repo rate) and yield roll down, based on the input on-the-run bonds yields for d (or the default OTR yields curve)

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

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

property dirty_fields

Field identifier for dirty quoted prices.

dv01(settlement_date, yield_, expiry_date=None)

DV01, i.e. derivative of bond price with respect to yield. Scaling unit of yield change is 1, i.e. if change in bond value per 1 basis point change in yield is needed, the value should be multiplied by 0.0001.

Parameters

  • settlement_date – Settlement date.

  • yield – Yield quote.

  • expiry_date – Expiry date (optional).

Returns

Dollar duration.

dv01s(settlement_dates, yields_, expiry_date=None)

DV01, i.e. derivative of bond price with respect to yield, for a list of settlement dates and yield quotes. Scaling unit of yield change is 1, i.e. if change in bond value per 1 basis point change in yield is needed, the value should be multiplied by 0.0001.

Parameters

  • settlement_dates – List of settlement dates.

  • yields – List of yield quotes.

  • expiry_date – Expiry date (optional).

Returns

List of dollar durations.

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.

ex_div_date(d)

Ex-dividend date given a trade date.

Parameters

d – Trade date.

Returns

Ex-dividend date.

ex_div_dates(ds)

Ex-dividend dates given a list of trade dates.

Parameters

ds – Trade dates.

Returns

Ex-dividend dates.

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 instrument.

property expiry_date

Date 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.

property extra_fields

Extra fields one wants to compute in the history -> order is important, check retrieve_history()!

finalize_for_comparison()

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

forward_price(settlement, horizon, price, repo_rate)

Forward price.

Parameters

  • settlement – Settlement date.

  • horizon – Forward date.

  • price – Bond price.

  • repo_rate – Repo rate in percentage.

Returns

Forward price.

forward_prices(settlements, horizons, prices, repo_rates)

Forward price for a list of settlement dates, forward dates, prices and repo rates.

Parameters

  • settlements – List of settlement dates.

  • horizons – List of forward dates.

  • prices – List of bond prices.

  • repo_rates – List of repo rates in percentage.

Returns

List of forward prices.

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

get_bond_repo()

Generate a government bond repo from the current bond instrument.

get_bond_swap()

Generate a government bond swap from the current bond instrument.

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_limits()

Get reduced history limits, if they were set by the set_history_limits function.

Returns

Dictionary of history start_date and end_date overrides

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.

last_proper_trade_date()

Last date on which bond can be traded to settle before the maturity.

property live_supported: bool

Flag for live supported objects.

matching_swap(trade_date=None, start_date=None)

Returns a receiver spot starting swap (start date is usually 2BD after trade_date) with IBOR rate (+ asset swap spread) based floating leg that matches the bond’s maturity, ccy and coupon (as fixed rate).

Parameters

  • trade_date – Swap trade date, if NA or before the bond’s issue date - defaults to the bond’s issue date.

  • start_date – Swap start date, if NA - defaults to swap spot date (usually 2BD after swap trade date).

modified_duration(settlement_date, yield_, expiry_date=None)

Modified duration.

Parameters

  • settlement_date – Settlement date.

  • yield – Yield quote.

  • expiry_date – Expiry date (optional).

Returns

Modified duration.

modified_durations(settlement_dates, yields_, expiry_date=None)

Modified duration for a list of settlement dates and yield quotes.

Parameters

  • settlement_dates – List of settlement dates.

  • yields – List of yield quotes.

  • expiry_date – Expiry date (optional).

Returns

List of modified durations.

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.

next_cashflow(d: datetime.date)

Returns the next cashflow after a given date.

Parameters

d – The date after which to return next cashflow

Returns

numpy.array() representing cashflow in a form of [payment dtm.date(), coupon amount] or empty numpy.array() if there are no cashflows after the date d

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

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.

static schedule_stub(country)

Schedule stub.

property sector

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

set_history_limits(start_date=None, end_date=None, reset_all=False)

Truncate bond history by supplied start_date and end_date (to speed up history() call, if history outside is not needed). If history already has been pre-cached, and new limits are inside, they are ignored, unless reset_all is set to True. If start_date or end_date is None, they are also ignored.

Parameters

  • start_date – Date for history to start (or last business day before, when data was available). Optional, None by default.

  • end_date – Last date for history (or last business day prior). Optional, None by default.

  • reset_all – Boolean flag, telling if the parameters should be set even if they don’t add any new available data. (e.g. start_date=None would be ignored if reset_all=False, but previously set start_date will be deleted if reset_all=True). Optional, False by default.

Returns

no return, function modifies bond internally.

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.

settlement_date(d)

Settlement date given a trade date.

Parameters

d – Trade date.

Returns

Settlement date.

settlement_dates(ds)

Settlement dates given a list of trade dates.

Parameters

ds – Trade dates.

Returns

Settlement dates.

property settlement_type

Type of settlement, e.g. ‘Cash’ or ‘Physical’.

property size_type

Type of trade size for tradable instrument, either units or notional.

sizing_price(sizing_dt, ccy=None, execution_dt=None)

Price on given valuation date.

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()

Perform validation checks on the instrument.

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)

ytm(settlement_date, price, expiry_date=None, yield_quote=None)

Yield to maturity.

Parameters

  • settlement_date – Settlement date.

  • price – Bond price.

  • expiry_date – Expiry date (optional).

  • yield_quote – Yield quote (optional).

Returns

Yield to maturity.

ytms(settlement_dates, prices, expiry_date=None, yield_quote=None)

Yield to maturity for a list of settlement dates and prices.

Parameters

  • settlement_dates – List of settlement dates.

  • prices – List of bond prices.

  • expiry_date – Expiry date (optional).

  • yield_quote – Yield quote (optional).

Returns

List of yield to maturity.

z_spread(trade_date, clean_price=None, settlement_date=None, curve_name=None, day_count=None, frequency=None)

Calculates bond z-spread with regards to the input discount curve. Bond day count and frequency taken by d efault, but can be overwritten for different bonds comparison. Settlement date can be overwritten if non standard, and clean price input can be changed from the historic one in the database. The spread is given in basis points (23 means 23 basis points)

Parameters

  • trade_date – date on which the Z-spread is calculated

  • clean_price – optional - clean price with usual bond conventions (100 for par bond)

  • settlement_date – optional - bond settlement date

  • curve_name – curve to which the Z-spread is to be calculated

  • day_count – optional - the one used in bond is taken by default

  • frequency – optional - the one used in bond is taken by default

Returns

z_spreads(trade_dates=None, curve_name=None, day_count=None, frequency=None)

Calculates bond z-spread with regards to the input discount curve for the given set of trade_dates. If not dates are given, the maximal available series of z-spreads is returned (from the latest of bond history start and curve history start to the last bond trade date with settlement prior to maturity). Bond day count and frequency taken by default, but can be overwritten for different bonds comparison. The spread is given in basis points (23 means 23 basis points)

Parameters

  • trade_dates – dates for which z-spread is to be calculated. All history dates if None

  • curve_name – optional - discount curve to which z-spread is calculated

  • day_count – optional - bond day_count override for z-spread

  • frequency – optional - bond frequency override for z-spread

Returns

Series of z-spreads for all input or available dates

country: Literal['US', 'DE', 'GB', 'JP', 'FR', 'IT', 'ES', 'BE', 'CA', 'SE', 'NO']
coupon: float
coupon_type: Literal['FIXED', 'FLOATING', 'ZERO_COUPON', 'INDEX_LINKED']
coupon_frequency: Literal['ANNUAL', 'SEMI_ANNUAL', 'QUARTERLY']
issue_date: dtm.date
issuer: str
maturity_date: dtm.date
first_coupon_date: dtm.date
int_acc_date: dtm.date
redemption_amount: float
day_count: Literal['ACT/ACT', 'ACT/360', 'ACT/365F', '30/360', '30E/360', 'ACT/365_NL', 'ACT/366']
isin: Optional[str]
description: Optional[str]
days_to_settle: Optional[int]
first_settle_date: Optional[dtm.date]
series_number: Optional[str]
calc_type: Optional[int]
db_history_end_date: Optional[dtm.date]
tax_withholding: Optional[float]
activity_fields: Optional[list[str]]
group_name: Optional[str]
currency: 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]