1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
|
import datetime
import numpy as np
import pandas as pd
from serenitas.analytics.dates import tenor_t
from pandas.tseries.offsets import BDay
from dateutil.relativedelta import relativedelta
from serenitas.analytics.yieldcurve import get_curve
from serenitas.utils.db import serenitas_engine
from pyisda.legs import FeeLeg, ContingentLeg
from pyisda.curve import SpreadCurve
from pyisda.date import previous_twentieth
tenors = {
"IG": ("3yr", "5yr", "7yr", "10yr"),
"HY": ("3yr", "5yr", "7yr"),
"EU": ("3yr", "5yr", "7yr", "10yr"),
"XO": ("3yr", "5yr", "7yr", "10yr"),
"HYBB": ("5yr",),
}
sql_str = "INSERT INTO index_risk VALUES(%s, %s, %s)"
def get_legs(index, series, tenors):
fee_legs = {}
contingent_legs = {}
df = pd.read_sql_query(
"SELECT tenor, maturity, coupon, issue_date "
"FROM index_maturity "
"WHERE index=%s AND series=%s and tenor IN %s "
"ORDER BY maturity",
serenitas_engine,
params=(index, series, tenors),
parse_dates=["maturity", "issue_date"],
)
df.coupon *= 1e-4
for tenor, maturity, coupon, issue_date in df.itertuples(index=False):
maturity_short = maturity - relativedelta(years=1)
fee_legs[tenor] = (
FeeLeg(issue_date, maturity, True, 1.0, 1.0),
FeeLeg(issue_date, maturity_short, True, 1.0, coupon),
)
contingent_legs[tenor] = (
ContingentLeg(issue_date, maturity, True),
ContingentLeg(issue_date, maturity_short, True),
)
# number of seconds since epoch
# number of days between 1900-1-1 and epoch
df.maturity = df.maturity.to_numpy("M8[D]").astype(int) + 134774
return fee_legs, contingent_legs, df
def index_pv(fl, cl, value_date, step_in_date, cash_settle_date, yc, sc, recovery):
dl_pv = cl.pv(value_date, step_in_date, cash_settle_date, yc, sc, recovery)
cl_pv = fl.pv(value_date, step_in_date, cash_settle_date, yc, sc, True)
return dl_pv - cl_pv
if __name__ == "__main__":
conn = serenitas_engine.raw_connection()
for index in ["IG", "HY", "EU", "XO", "HYBB"]:
if index in ["HY", "HYBB"]:
recoveries = np.full(len(tenors[index]), 0.3)
else:
recoveries = np.full(len(tenors[index]), 0.4)
for series in range(18, 39):
if index in ["EU", "XO"] and series == 38:
continue
fee_legs, contingent_legs, df = get_legs(index, series, tenors[index])
index_quotes = pd.read_sql_query(
"SELECT distinct on (date, tenor) id, date, tenor, version, close_price "
"FROM index_quotes_pre "
"LEFT JOIN index_risk USING (id) "
"WHERE index=%s AND series=%s "
"AND source='MKIT' AND duration is NULL AND tenor IN %s "
"ORDER BY date, tenor, version desc",
serenitas_engine,
params=(index, series, tenors[index]),
parse_dates=["date"],
index_col="id",
)
if index_quotes.empty:
continue
index_quotes.tenor = index_quotes.tenor.astype(tenor_t)
index_quotes = index_quotes.sort_values("tenor")
index_quotes["close_price"] = 1.0 - index_quotes["close_price"] / 100
with conn.cursor() as c:
for value_date, data in index_quotes.groupby("date"):
yc = get_curve(
value_date, "USD" if index in ["IG", "HY"] else "EUR"
)
data = data.reset_index().merge(df, on="tenor").set_index("id")
step_in_date = value_date + datetime.timedelta(days=1)
cash_settle_date = value_date + 3 * BDay()
start_date = previous_twentieth(value_date)
try:
sc = SpreadCurve(
value_date,
yc,
start_date,
step_in_date,
cash_settle_date,
data.maturity.values,
data.coupon.values,
data.close_price.values,
recoveries,
)
except ValueError:
break
for r in data[["coupon", "tenor", "close_price"]].itertuples():
fl, fl_short = fee_legs[r.tenor]
cl, cl_short = contingent_legs[r.tenor]
duration = fl.pv(
value_date, step_in_date, cash_settle_date, yc, sc, True
)
if cl_short.end_date <= value_date.date():
theta = None
else:
pv = index_pv(
fl_short,
cl_short,
value_date,
step_in_date,
cash_settle_date,
yc,
sc,
recoveries[0],
)
theta = r.close_price - pv + r.coupon
c.execute(
"INSERT INTO index_risk VALUES(%s, %s, %s)",
(r.Index, theta, duration),
)
conn.commit()
conn.close()
|
