Quantlib SOFR swap repricing across 2 different dates

I am trying to price SOFR swaps in two different dates (the same swaps, just different curves and dates)

This are my initial parameters:

curve_date =ql.Date (9,5,2022) ql.Settings.instance().evaluationDate = curve_date sofr = ql.Sofr() #overnightIndex swaps_calendar = ql.UnitedStates(ql.UnitedStates.FederalReserve)#calendar day_count = ql.Actual360() #day count convention settlement_days = 2 #t+2 settlement convention for SOFR swaps 

this is the SOFR curve as of May 9th, 2022:

This data is stored in a df called:swap_data and I use it to build tuples (rate, (tenor)) for the OISRateHelper objects

swaps= [(row.quote,(row.n, row.tenor)) for row in swap_data.itertuples(index=True, name='Pandas')] def zero_curve(settlement_days,swaps,day_count): ois_helpers = [ ql.OISRateHelper(settlement_days, #settlementDays ql.Period(*tenor), #tenor -> note that `tenor` in the list comprehension are (n,units), so uses * to unpack when calling ql.Period(n, units) ql.QuoteHandle(ql.SimpleQuote(rate/100)), #fixedRate sofr) #overnightIndex for rate, tenor in swaps] #for now I have chosen to use a logCubicDiscount term structure to ensure continuity in the inspection sofrCurve = ql.PiecewiseLogCubicDiscount(settlement_days, #referenceDate swaps_calendar,#calendar ois_helpers, #instruments day_count, #dayCounter ) sofrCurve.enableExtrapolation() #allows for extrapolation at the ends return sofrCurve 

using this function I build a zero curve, a sofr object linked to that curve and a swap pricing engine

sofrCurve = zero_curve(settlement_days,swaps,day_count) valuation_Curve = ql.YieldTermStructureHandle(sofrCurve) sofrIndex = ql.Sofr(valuation_Curve) swapEngine = ql.DiscountingSwapEngine(valuation_Curve) 

With this I create OIS swaps and price them using this curve to ensure that it's correctly calibrated:

effective_date = swaps_calendar.advance(curve_date, settlement_days, ql.Days) notional = 10_000_000 ois_swaps = [] for rate, tenor in swaps: schedule = ql.MakeSchedule(effective_date, swaps_calendar.advance(effective_date, ql.Period(*tenor)), ql.Period('1Y'), calendar = swaps_calendar) fixedRate = rate/100 oisSwap = ql.MakeOIS(ql.Period(*tenor), sofrIndex, fixedRate, nominal=notional) oisSwap.setPricingEngine(swapEngine) ois_swaps.append(oisSwap) 

the NPVs on all the swaps is zero so they seem. I went a step further to confirm that I was getting the PV of the legs correctly by constructing a function that yields a table with the leg relevant information

def leg_information(effective_date, day_count,ois_swap, leg_type, sofrCurve): leg_df=pd.DataFrame(columns=['date','yearfrac','CF','discountFactor','PV','totalPV']) cumSum_pv= 0 leg = ois_swap.leg(0) if leg_type == "fixed" else ois_swap.leg(1) for index, cf in enumerate(leg): yearfrac = day_count.yearFraction(effective_date,cf.date()) df = sofrCurve.discount(yearfrac) pv = df * cf.amount() cumSum_pv += pv row={'date':datetime.datetime(cf.date().year(), cf.date().month(), cf.date().dayOfMonth()),'yearfrac':yearfrac, 'CF':cf.amount() ,'discountFactor':df,'PV':pv,'totalPV':cumSum_pv} leg_df.loc[index]=row return leg_df 

Then I proceeded to view the fixed and float legs for the 30y swap:

fixed_leg = leg_information(effective_date, day_count,ois_swaps[-3], 'fixed', sofrCurve) fixed_leg.tail() 

float_leg = leg_information(effective_date, day_count,ois_swaps[-3], 'Float', sofrCurve) float_leg.tail() 

Also, the DV01 on the swap lines up with what I see in bloomberg:

ois_swaps[-3].fixedLegBPS() = $20462.68. So at this point, I feel comfortable with what the swap object because it seems to match what I see on Bloomberg using SWPM

Now, when I change the date:

curve_date =ql.Date (9,5,2023) ql.Settings.instance().evaluationDate = curve_date effective_date = swaps_calendar.advance(curve_date, settlement_days, ql.Days) 

and pull the new curve:

store the above data in swap_data and proceed again to recalibrate the zero curve:

swaps= [(row.quote,(row.n, row.tenor)) for row in swap_data.itertuples(index=True, name='Pandas')] sofrCurve_2023 = zero_curve(settlement_days,swaps,day_count) valuation_Curve2023 = ql.YieldTermStructureHandle(sofrCurve_2023) sofrIndex2023 = ql.Sofr(valuation_Curve2023) swapEngine2023 = ql.DiscountingSwapEngine(valuation_Curve2023) ois_swaps[-3].setPricingEngine(swapEngine2023) 

and try to get the NPV of the swap

ois_swaps[-3].NPV() 

It yields a value of $60968.42 .

I know that the NPV after changing the date forward is wrong. I did I simple calculation: the 30y swap rate moved from 2.60 to 3.12 ( I know it's a 29y swap 1 year later, but for illustration purposes the P&L is more less -20k* 52bps = -$1,040,000.

and If I try to view the floating leg by calling:

float_leg = leg_information(effective_date, day_count,ois_swaps[-3], 'Float', sofrCurve) float_leg.tail() 

I get the following:

RuntimeError: Missing SOFRON Actual/360 fixing for May 11th, 2022 

Which makes me think that I need to relink to the OvernightIndex to sofrIndex2023 on that 30y swap (I just don't know how to do this, I have looked at the documentation and there's no hints about how to do this)

So what am I doing wrong?