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OISSwap

OISSwap#

class sigtech.framework.instruments.ois_swap.OISSwap

Baseclasses: InterestRateSwapBase

OIS swap receiving fixed rate and paying OIS rate.

Keyword arguments:

  • currency: Currency of the swap.

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

  • fixed_rate: Fixed rate (of the receiving leg). Will be set to the fair rate if left None. Expected in decimal format, e.g. 0.05 for 5%.

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

  • start_date: Start date of the swap. If no date is given, T+``settlement_days`` is used.

  • settlement_days: Number of business days to wait after the trade_date to start the swap, Currency default is used if left blank (same as for LIBOR swaps).

  • daycount: Day count of the legs, same for both (e.g. '30/360'). Currency default is used if left blank.

  • frequency: Legs frequency ('Q', 'SA', or 'A') (same for both legs). Currency default is used if left blank ('A' for most currencies).

  • pay_delay: Delay (number of business days) between accrual end and payment dates (same for both legs). Currency default is used if left blank (same as settlement_days for most currencies).

  • index: Optional override for default OIS index fixing object.

Example object creation:

swap =sig.OISSwap(
               currency='USD',
               tenor=dtm.date(2025, 7, 5),
               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 SOFR and receiving 1.32% fixed USD annually, using ACT/360 daycount, with payment delay of 2 business days

swap = sig.OISSwap(
               currency='USD',
               tenor='1Y',
               trade_date=dtm.date(2019, 7, 1),
               )

This will create a 1 year swap, starting on 5-Jul-2021 (\(T+2\)), paying FedFunds and receiving a fixed rate, making the trade date value of the swap 0.

day_count: Optional[Literal['ACT/ACT', 'ACT/360', 'ACT/365F', '30/360', '30E/360', 'ACT/365_NL', 'ACT/366']]
fixed_rate: Optional[float]
frequency: Optional[Literal['Q', 'SA', 'A']]
index: Optional[str]
property maturity

Maturity date.

pay_delay: Optional[int]
settlement_days: Optional[int]
start_date: Optional[date]
tenor: Union[date, str]
trade_date: Optional[date]
carry_roll_down(d: date, dates: Union[str, date, list[Union[str, datetime.date]]], data_point: DataPoint = None)

Compute carry and roll-down of swap by moving the valuation date forward to the requested dates, keeping discount and forecasting curves 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 assuming that all the forward required IBOR fixings are equal to the valuation date (d) implied fixing.

Parameters:

  • d – Valuation date.

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

  • data_point – Data point for the original curves.

Returns:

DataFrame of 4 columns - carry - cumulative cashflows from d to date, roll_down - change in swap PV, and the same numbers scaled by swap PV01.

fair_rate(d=None, mode: str = 'mid', transaction_type: Optional[str] = None, start_date=None, data_point: DataPoint = None, bid_ask_to_mid_spread_override: Optional[dict] = None) → float

Calculate the fair swap rate for a given tenor on a given date.

If no start_date is passed as an input, swap start date is used.

Parameters:

  • d – Reference date to calculate fair swap rate (optional, defaults to instrument’s trade_date).

  • mode – Quote to return, defaults to 'mid' but can be 'bid' or 'ask' (optional).

  • transaction_type – Type of transaction to assume, defaults to outright but can be set to roll (optional).

  • start_date – Start date of swap to assume (optional).

  • data_point – Data point of the underlying curves (optional).

  • bid_ask_to_mid_spread_override – Mid to bid/ask to use for each transaction type (optional).

Returns:

Fair swap rate given the inputs.

fixing_date(start_date)

Index fixing date for the coupon starting at start_date

maturity_from_tenor(start_date, tenor, bdc='NONE_RULE')

Compute maturity date from tenor.

Parameters:

  • start_date – Start date.

  • tenor – Tenor.

  • bdc – Business day convention, e.g. 'FOLLOWING', 'MOD_FOL', 'PRECEDE'.

Returns:

Maturity date.

risk_ladder(d: datetime, shift_bps: float = 1.0, field: str = 'NPV', interpolation_type: str = 'CS', notional: float = 1.0) → DataFrame

Return forward and zero rate shift risk ladder for specified field. Output is change in value for this field.

Parameters:

  • d – Reference date to return info for.

  • shift_bps – Fwd and zero rate shift amount in bps (optional), 1bps used as default.

  • field – Field to calculate risk ladder for (optional). 'NPV' or 'PV01', 'NPV' used as default.

  • notional – Notional (optional).

  • interpolation_type – Interpolation type (optional). 'FF', 'MCF', 'FFMCF' or 'CS', 'CS' used as default

Returns:

DataFrame risk ladder for field input.

static schedule_stub(currency)

Schedule stub for all OIS swaps.

Parameters:

currency – Input swap currency.

Returns:

Schedule stub.

static spot_date(asof_date: date, currency: str, settlement_days: int = None)

Standard swap start date if traded on asof_date.

swap_details(d: datetime, notional: float = 1.0, as_df: bool = True) → Union[dict, DataFrame]

Return swap info.

Parameters:

  • d – Reference date to return info for.

  • notional – Notional (optional).

  • as_df – Return swap info as DataFrame (optional).

Returns:

Dictionary or DataFrame of swap info.

swap_rate_shift_details(d: datetime, shift_bps: float = 1.0, tenor: str = 'tenors', notional: float = 1.0) → DataFrame

Return swap rate shift scenario analysis info.

Parameters:

  • d – Reference date to return info for.

  • shift_bps – Swap rate shift amount in bps (optional), 1bps used as default.

  • tenor – Swap rate tenor to shift (optional), e.g. '1Y', '5Y' or 'tenors' to shift all available swap rate tenors, 'tenors' used as default.

  • notional – Notional (optional).

Returns:

DataFrame of scenario analysis info.