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import datetime
import pandas as pd
from analytics.index import CreditIndex
from db import serenitas_pool
from statistics import median
def get_refids(conn, index, series, expiry, value_date=datetime.date.today(),
sources=["GS", "MS", "CITI"]):
sql_str = ("SELECT ref_id, ref, quotedate FROM swaption_ref_quotes "
"WHERE quotedate::date=%s "
" AND quote_source=%s "
" AND index=%s AND series=%s"
" AND expiry=%s "
"ORDER BY quotedate DESC LIMIT 1")
d = {}
with conn.cursor() as c:
for s in sources:
c.execute(sql_str, (value_date, s, index, series, expiry))
d[s] = c.fetchone()
return d
def adjust_stacks(index_type, series, expiry,
value_date=datetime.date.today(),
sources=["GS", "MS", "CITI"], common_ref=None):
conn = serenitas_pool.getconn()
d = get_refids(conn, index_type, series, expiry, value_date, sources)
if all(v is None for v in d.values()):
raise ValueError("no quotes")
if common_ref is None:
common_ref = median(v[1] for v in d.values())
index = CreditIndex(index_type, series, "5yr", value_date=value_date,
notional=10000.)
index.ref = common_ref
old_pv = index.pv
quotes = {}
for s, (ref_id, ref, _) in d.items():
index.ref = ref
dindex_pv = index.pv - old_pv
df = pd.read_sql_query("SELECT strike, pay_bid, pay_offer, delta_pay, "
"rec_bid, rec_offer, delta_rec FROM swaption_quotes "
"WHERE ref_id=%s ORDER BY strike",
conn,
params=(ref_id,),
index_col=['strike'])
if s == "GS":
df['delta_rec'] = 1 - df['delta_pay']
if index_type == "HY":
df[['pay_bid', 'pay_offer', 'rec_bid', 'rec_offer']] *=100
if s == "CITI":
df['delta_rec'] *= -1
if dindex_pv != 0.:
df[['pay_bid', 'pay_offer']] = df[['pay_bid', 'pay_offer']].sub(
df.delta_pay * dindex_pv, axis=0)
df[['rec_bid', 'rec_offer']] = df[['rec_bid', 'rec_offer']].add(
df.delta_rec * dindex_pv, axis=0)
quotes[s] = df
quotes = pd.concat(quotes, names=['source'])
quotes = quotes.swaplevel('source', 'strike').sort_index()
inside_quotes = pd.concat([
quotes[['pay_bid', 'rec_bid']].groupby(level='strike').max(),
quotes[['pay_offer', 'rec_offer']].groupby(level='strike').min()],
axis=1
).sort_index(axis=1)
quotes = quotes.unstack('source')
d = {}
for k in ['pay_bid', 'rec_bid']:
#quotes[k].style.apply(highlight_max, axis=1)
df = pd.concat([quotes[k], inside_quotes[k]], axis=1)
d[k] = df.rename(columns={k: 'Best'})
for k in ['pay_offer', 'rec_offer']:
#quotes[k].style.apply(highlight_min, axis=1)
df = pd.concat([inside_quotes[k], quotes[k]], axis=1)
d[k] = df.rename(columns={k: 'Best'})
serenitas_pool.putconn(conn)
return common_ref, pd.concat(d, axis=1)
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