1 files changed, 50 insertions, 16 deletions

diff --git a/python/analytics/tranche_functions.py b/python/analytics/tranche_functions.py
index 00fd0ec1..606457cd 100644
--- a/python/analytics/tranche_functions.py
+++ b/python/analytics/tranche_functions.py
@@ -1,17 +1,21 @@
 import numpy as np
 from ctypes import POINTER, c_int, c_double, byref
 from numpy.ctypeslib import ndpointer
-from quantlib.time.schedule import Schedule, CDS2015
+from pyisda.legs import FeeLeg
+from pyisda.date import previous_twentieth
+from quantlib.time.schedule import Schedule, CDS2015, OldCDS
 from quantlib.time.api import (
     Actual360,
+    Date,
     Period,
     WeekendsOnly,
     ModifiedFollowing,
     Unadjusted,
+    pydate_from_qldate,
 )
-from quantlib.util.converter import pydate_to_qldate
 import pandas as pd
 from scipy.special import h_roots
+from .utils import next_twentieth
 import os
 
 
@@ -441,32 +445,62 @@ def tranche_pv(L, R, cs, K1, K2):
     return tranche_pl(L, cs, K1, K2) + tranche_cl(L, R, cs, K2, K2)
 
 
-def credit_schedule(tradedate, tenor, coupon, yc, enddate=None):
-    tradedate = pydate_to_qldate(tradedate)
+def credit_schedule(tradedate, coupon, yc, enddate=None, tenor=None, rule=CDS2015):
+    tradedate = Date.from_datetime(tradedate)
     if enddate is None:
         enddate = tradedate + Period(tenor)
     else:
-        enddate = pydate_to_qldate(enddate)
+        enddate = Date.from_datetime(enddate)
     cal = WeekendsOnly()
     DC = Actual360()
     start_date = tradedate + 1
     sched = Schedule.from_rule(
-        tradedate, enddate, Period("3M"), cal, ModifiedFollowing, Unadjusted, CDS2015
+        start_date, enddate, Period("3M"), cal, ModifiedFollowing, Unadjusted, rule
     )
-    dates = sched.to_npdates()
-    pydates = dates.astype("O")
-    df = [yc.discount_factor(d) for d in pydates if d > start_date]
+    payment_dates = [pydate_from_qldate(cal.adjust(d)) for d in sched[1:]]
+    df = [yc.discount_factor(d) for d in payment_dates]
     coupons = [
-        DC.year_fraction(d1, d2) * coupon
-        for d1, d2 in zip(sched[:-2], sched[1:-1])
-        if d2 > start_date
+        DC.year_fraction(d1, d2) * coupon for d1, d2 in zip(sched[:-2], sched[1:-1])
     ]
     coupons.append(Actual360(True).year_fraction(sched[-2], sched[-1]) * coupon)
-    if dates[1] <= start_date:
-        dates = dates[2:]
+    dates = sched.to_npdates()
+    start_dates = dates[:-1]
+    end_dates = dates[1:]
+    return pd.DataFrame(
+        {
+            "df": df,
+            "coupons": coupons,
+            "start_dates": start_dates,
+            "payment_dates": payment_dates,
+        },
+        index=end_dates,
+    )
+
+
+def credit_schedule_pyisda(
+    tradedate, coupon, yc, enddate=None, tenor=None, rule=CDS2015
+):
+    tradedate = Date.from_datetime(tradedate)
+    if enddate is None:
+        enddate = tradedate + Period(tenor)
     else:
-        dates = dates[1:]
-    return pd.DataFrame({"df": df, "coupons": coupons}, index=dates)
+        enddate = Date.from_datetime(enddate)
+    start_date = pydate_from_qldate(tradedate + 1)
+    if (next_twentieth(start_date) - start_date).days < 30:
+        stub = "f/l"
+    else:
+        stub = "f/s"
+    print(stub)
+    if rule is CDS2015:
+        start_date = previous_twentieth(start_date)
+    fl = FeeLeg(start_date, pydate_from_qldate(enddate), True, 1.0, coupon, stub=stub)
+    df = pd.DataFrame({"coupons": [t[1] for t in fl.cashflows], **fl.inspect()})
+    df["df"] = [yc.discount_factor(d) for d in df.pay_dates]
+
+    df = df.rename(
+        columns={"acc_start_dates": "start_dates", "pay_dates": "payment_dates"}
+    ).set_index("acc_end_dates")
+    return df
 
 
 def cds_accrued(tradedate, coupon):