Bond portfolio valuation in Quantlib python

Question

I have a table of bonds which I imported into python using pandas.

Is there a way I can simultaneously price all of them in python using the Quantlib library. I know how to price one bond but not in a table.

Answer 1

QuantLib doesn't really have the concept of portfolio but since you're using pandas, you can play around with that to price your bonds at once. Here is an example:

data = [
    [ "15-06-2018", "15-06-2022", 4.75, 500 ],
    [ "21-07-2017", "21-07-2027", 0.25, 100 ],
    [ "17-02-2015", "17-02-2045", 1.50, 250 ],   
]
bonds = pd.DataFrame(data, columns=["start", "maturity", "coupon", "notional"])


def makeBond(row):
    start, maturity, coupon, notional = row
    startDate = ql.Date(start, "%d-%m-%Y")
    maturityDate = ql.Date(maturity, "%d-%m-%Y")
    return ql.FixedRateBond(2, ql.TARGET(), 100, startDate, maturityDate, ql.Period("1Y"), [coupon], ql.ActualActual())

yts = ql.YieldTermStructureHandle(
    ql.FlatForward(2, ql.TARGET(), 0.05, ql.Actual360())
)
engine = ql.DiscountingBondEngine(yts)
bonds['bond'] = bonds.apply(makeBond, axis=1)
bonds['bond'].apply(lambda x: x.setPricingEngine(engine))
bonds['bond'].apply(lambda x: x.NPV()).sum()

3347.2767053215985

Or you could even make a bond portfolio object from a pandas DataFrame:

class BondPortfolio(pd.DataFrame):
    def makeBond(self, row):
        start, maturity, coupon, notional = row
        startDate = ql.Date(start, "%d-%m-%Y")
        maturityDate = ql.Date(maturity, "%d-%m-%Y")
        return ql.FixedRateBond(2, ql.TARGET(), 100, startDate, maturityDate, ql.Period("1Y"), [coupon], ql.ActualActual())
    def makeBonds(self):
        self['bond'] = self.apply(self.makeBond, axis=1)
    def setPricingEngine(self, engine):
        self['bond'].apply(lambda x: x.setPricingEngine(engine))
    def NPV(self):
        return self['bond'].apply(lambda x: x.NPV()).sum()
        
portfolio = BondPortfolio(data, columns=["start", "maturity", "coupon", "notional"])
portfolio.makeBonds()
portfolio.setPricingEngine(engine)
portfolio.NPV()

3347.2767053215985