ENH add fixing table to SBS

docs/source/i_whatsnew.rst

@@ -43,6 +43,7 @@ email contact, see `rateslib <https://rateslib.com>`_.
        (`467 <https://github.com/attack68/rateslib/pull/467>`_)
        (`470 <https://github.com/attack68/rateslib/pull/470>`_)
        (`490 <https://github.com/attack68/rateslib/pull/490>`_)
+       (`493 <https://github.com/attack68/rateslib/pull/493>`_)
    * - Instruments
      - Add a :meth:`~rateslib.instruments.Portfolio.fixings_table` method to *Portfolio*  to
        aggregate fixings tables on contained and applicable *Instruments*.

python/rateslib/instruments/core.py

@@ -992,3 +992,36 @@ def _upper(val: str | NoInput):
     if isinstance(val, str):
         return val.upper()
     return val
+
+
+def _composit_fixings_table(df_result, df):
+    """
+    Add a DataFrame to an existing fixings table by extending or adding to relevant columns.
+
+    Parameters
+    ----------
+    df_result: The main DataFrame that will be updated
+    df: The incoming DataFrame with new data to merge
+
+    Returns
+    -------
+    DataFrame
+    """
+    # reindex the result DataFrame
+    df_result = df_result.reindex(index=df_result.index.union(df.index))
+
+    # update existing columns with missing data from the new available data
+    for c in [c for c in df.columns if c in df_result.columns and c[1] in ["dcf", "rates"]]:
+        df_result[c] = df_result[c].combine_first(df[c])
+
+    # merge by addition existing values with missing filled to zero
+    m = [c for c in df.columns if c in df_result.columns and c[1] in ["notional", "risk"]]
+    if len(m) > 0:
+        df_result[m] = df_result[m].add(df[m], fill_value=0.0)
+
+    # append new columns without additional calculation
+    a = [c for c in df.columns if c not in df_result.columns]
+    if len(a) > 0:
+        df_result[a] = df[a]
+
+    return df_result

python/rateslib/instruments/generics.py

@@ -15,6 +15,7 @@
 from rateslib.instruments.core import (
     BaseMixin,
     Sensitivities,
+    _composit_fixings_table,
     _get_curves_fx_and_base_maybe_from_solver,
     _get_vol_maybe_from_solver,
 )
@@ -692,27 +693,11 @@ def fixings_table(
         """
         df_result = DataFrame()
         for inst in self.instruments:
-            df1 = inst.fixings_table(
-                curves=curves, solver=solver, fx=fx, base=base, approximate=approximate
-            )
-
-            # reindex the result DataFrame
-            df_result = df_result.reindex(index=df_result.index.union(df1.index))
-
-            # update existing columns with missing data from the new available data
-            for c in [
-                c for c in df1.columns if c in df_result.columns and c[1] in ["dcf", "rates"]
-            ]:
-                df_result[c] = df_result[c].combine_first(df1[c])
-
-            # merge by addition existing values with missing filled to zero
-            m = [c for c in df1.columns if c in df_result.columns and c[1] in ["notional", "risk"]]
-            if len(m) > 0:
-                df_result[m] = df_result[m].add(df1[m], fill_value=0.0)
-
-            # append new columns without additional calculation
-            a = [c for c in df1.columns if c not in df_result.columns]
-            if len(a) > 0:
-                df_result[a] = df1[a]
-
+            try:
+                df = inst.fixings_table(
+                    curves=curves, solver=solver, fx=fx, base=base, approximate=approximate
+                )
+            except AttributeError:
+                continue
+            df_result = _composit_fixings_table(df_result, df)
         return df_result

python/rateslib/instruments/rates_derivatives.py

@@ -3,7 +3,7 @@
 from abc import ABCMeta, abstractmethod
 from datetime import datetime
 
-from pandas import DataFrame, Series, concat
+from pandas import DataFrame, Series
 
 from rateslib import defaults
 from rateslib.calendars import CalInput
@@ -14,6 +14,7 @@
 from rateslib.instruments.core import (
     BaseMixin,
     Sensitivities,
+    _composit_fixings_table,
     _get,
     _get_curves_fx_and_base_maybe_from_solver,
     _inherit_or_negate,
@@ -2239,7 +2240,7 @@ def fixings_table(
         df2 = self.leg2.fixings_table(
             curve=curves[2], approximate=approximate, disc_curve=curves[3]
         )
-        return concat([df1, df2], keys=("leg1", "leg2"), axis=1)
+        return _composit_fixings_table(df1, df2)
 
 
 class FRA(Sensitivities, BaseMixin):

python/rateslib/instruments/rates_multi_ccy.py

@@ -13,6 +13,7 @@
 from rateslib.instruments.core import (
     BaseMixin,
     Sensitivities,
+    _composit_fixings_table,
     _get,
     _get_curves_fx_and_base_maybe_from_solver,
     _update_not_noinput,
@@ -755,6 +756,78 @@ def cashflows(
             self.leg2._do_not_repeat_set_periods = False  # reset the mtm calc
         return ret
 
+    def fixings_table(
+        self,
+        curves: Curve | str | list | NoInput = NoInput(0),
+        solver: Solver | NoInput = NoInput(0),
+        fx: float | FXRates | FXForwards | NoInput = NoInput(0),
+        base: str | NoInput = NoInput(0),
+        approximate: bool = False,
+    ):
+        """
+        Return a DataFrame of fixing exposures on any :class:`~rateslib.legs.FloatLeg` or
+        :class:`~rateslib.legs.FloatLegMtm` associated with the *XCS*.
+
+        Parameters
+        ----------
+        curves : Curve, str or list of such
+            A list defines the following curves in the order:
+
+            - Forecasting :class:`~rateslib.curves.Curve` for leg1 (if floating).
+            - Discounting :class:`~rateslib.curves.Curve` for leg1.
+            - Forecasting :class:`~rateslib.curves.Curve` for leg2 (if floating).
+            - Discounting :class:`~rateslib.curves.Curve` for leg2.
+
+        solver : Solver, optional
+            The numerical :class:`~rateslib.solver.Solver` that constructs
+            :class:`~rateslib.curves.Curve` from calibrating instruments.
+
+            .. note::
+
+               The arguments ``fx`` and ``base`` are unused and results are returned in
+               local currency of each *Leg*.
+
+        approximate : bool, optional
+            Perform a calculation that is broadly 10x faster but potentially loses
+            precision upto 0.1%.
+
+        Returns
+        -------
+        DataFrame
+        """
+        curves, fx_, base_ = _get_curves_fx_and_base_maybe_from_solver(
+            self.curves,
+            solver,
+            curves,
+            fx,
+            base,
+            self.leg1.currency,
+        )
+
+        try:
+            df1 = self.leg1.fixings_table(
+                curve=curves[0],
+                disc_curve=curves[1],
+                fx=fx_,
+                base=base_,
+                approximate=approximate,
+            )
+        except AttributeError:
+            df1 = DataFrame()
+
+        try:
+            df2 = self.leg2.fixings_table(
+                curve=curves[2],
+                disc_curve=curves[3],
+                fx=fx_,
+                base=base_,
+                approximate=approximate,
+            )
+        except AttributeError:
+            df2 = DataFrame()
+
+        return _composit_fixings_table(df1, df2)
+
 
 # Licence: Creative Commons - Attribution-NonCommercial-NoDerivatives 4.0 International
 # Commercial use of this code, and/or copying and redistribution is prohibited.

python/tests/test_instruments.py

@@ -1300,7 +1300,7 @@ def test_fixings_table(self, curve):
         result = inst.fixings_table()
         assert isinstance(result, DataFrame)
 
-    def test_fixings_table_3s1s(self, curve):
+    def test_fixings_table_3s1s(self, curve, curve2):
         inst = SBS(
             dt(2022, 1, 15),
             "6m",
@@ -1310,10 +1310,12 @@ def test_fixings_table_3s1s(self, curve):
             leg2_method_param=1,
             frequency="Q",
             leg2_frequency="m",
-            curves=curve,
+            curves=[curve, curve, curve2, curve],
         )
         result = inst.fixings_table()
         assert isinstance(result, DataFrame)
+        assert len(result.columns) == 8
+        assert len(result.index) == 8
 
 
 class TestFRA:
@@ -2800,6 +2802,34 @@ def test_initialisation_nonmtm_xcs_leg_notional_unused(self) -> None:
         )
         assert abs(xcs.leg2.notional + 100e6) < 1e-8  # not 20e6
 
+    @pytest.mark.parametrize("fixed1", [True, False])
+    @pytest.mark.parametrize("fixed2", [True, False])
+    @pytest.mark.parametrize("mtm", [True, False])
+    def test_fixings_table(self, curve, curve2, fixed1, fixed2, mtm):
+        curve.id = "c1"
+        curve2.id = "c2"
+        fxf = FXForwards(
+            FXRates({"eurusd": 1.1}, settlement=dt(2022, 1, 3)),
+            {"usdusd": curve, "eurusd": curve2, "eureur": curve2},
+        )
+
+        xcs = XCS(
+            dt(2022, 2, 1),
+            "8M",
+            frequency="M",
+            payment_lag=0,
+            currency="eur",
+            leg2_currency="usd",
+            payment_lag_exchange=0,
+            fixed=fixed1,
+            leg2_fixed=fixed2,
+            leg2_mtm=mtm,
+            fixing_method="ibor",
+            leg2_fixing_method="ibor",
+        )
+        result = xcs.fixings_table(curves=[curve, curve, curve2, curve2], fx=fxf)
+        assert isinstance(result, DataFrame)
+
 
 class TestFixedFloatXCS:
     def test_mtmfixxcs_rate(self, curve, curve2) -> None:
@@ -3462,6 +3492,12 @@ def test_fixings_table(self, curve, curve2):
         # c2 has DCF
         assert abs(result["c2", "dcf"][dt(2022, 1, 22)] - 0.50277) < 1e-3
 
+    def test_fixings_table_null_inst(self, curve):
+        irs = IRS(dt(2022, 1, 15), "6m", spec="eur_irs3", curves=curve)
+        frb = FixedRateBond(dt(2022, 1, 1), "5y", "A", fixed_rate=2.0, curves=curve)
+        pf = Portfolio([irs, frb])
+        assert isinstance(pf.fixings_table(), DataFrame)
+
 
 class TestFly:
     @pytest.mark.parametrize("mechanism", [False, True])