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from db import dbengine, dbconn
from dates import bond_cal
import datetime
import pandas as pd
serenitasdb = dbengine('serenitasdb')
def insert_quotes():
"""
backpopulate some version i+1 quotes one day before they start trading so that
we get continuous time series when we compute returns.
We can also do it in sql as follows:
INSERT INTO index_quotes(date, index, series, version, tenor, closeprice)
SELECT date, index, series, version+1, tenor, (factor1*closeprice-100*0.355)/factor2
FROM index_quotes
WHERE index='HY' and series=23 and date='2017-02-02'
"""
dates = pd.DatetimeIndex(['2014-05-21', '2015-02-19', '2015-03-05','2015-06-23'])
df = pd.read_sql_query("SELECT DISTINCT ON (date) * FROM index_quotes " \
"WHERE index='HY' AND tenor='5yr' " \
"ORDER BY date, series DESC, version DESC",
serenitasdb, parse_dates=['date'], index_col=['date'])
df = df.loc[dates]
for tup in df.itertuples():
result = serenitasdb.execute("SELECT indexfactor, cumulativeloss FROM index_version " \
"WHERE index = 'HY' AND series=%s AND version in (%s, %s)" \
"ORDER BY version",
(tup.series, tup.version, tup.version+1))
factor1, cumloss1 = result.fetchone()
factor2, cumloss2 = result.fetchone()
recovery = 1-(cumloss2-cumloss1)
version2_price = (factor1 * tup.closeprice - 100*recovery)/factor2
print(version2_price)
serenitasdb.execute("INSERT INTO index_quotes(date, index, series, version, tenor, closeprice)" \
"VALUES(%s, %s, %s, %s, %s, %s)",
(tup.Index, 'HY', tup.series, tup.version+1, tup.tenor, version2_price))
def get_index_quotes(index=None, series=None, tenor=None, from_date=None, years=3):
args = locals().copy()
if args['years'] is not None:
args['date'] = (pd.Timestamp.now() - pd.DateOffset(years=years)).date()
del args['years']
if args['from_date']:
args['date'] = args['from_date']
del args['from_date']
def make_str(key, val):
if isinstance(val, list):
op = "IN"
return "{} IN %({})s".format(key, key)
elif isinstance(val, datetime.date):
op = ">="
else:
op = "="
return "{} {} %({})s".format(key, op, key)
where_clause = " AND ".join(make_str(k, v)
for k, v in args.items() if v is not None)
sql_str = "SELECT * FROM index_quotes"
if where_clause:
sql_str = " WHERE ".join([sql_str, where_clause])
def make_params(args):
return {k: tuple(v) if isinstance(v, list) else v
for k, v in args.items() if v is not None}
df = pd.read_sql_query(sql_str, serenitasdb, parse_dates=['date'],
index_col=['date', 'index', 'series', 'version', 'tenor'],
params = make_params(args))
df.sort_index(inplace=True)
## get rid of US holidays
dates = df.index.levels[0]
if index in ['IG', 'HY']:
holidays = bond_cal().holidays(start=dates[0], end=dates[-1])
df = df.loc(axis=0)[dates.difference(holidays),:,:]
return df
def index_returns(df=None, index=None, series=None, tenor=None, from_date=None, years=3):
"""computes daily spreads and price returns
Parameters
----------
df : pandas.DataFrame
index : str or List[str], optional
index type, one of 'IG', 'HY', 'EU', 'XO'
series : int or List[int], optional
tenor : str or List[str], optional
tenor in years e.g: '3yr', '5yr'
date : datetime.date, optional
starting date
years : int, optional
limits many years do we go back starting from today.
"""
if df is None:
df = get_index_quotes(index, series, tenor, from_date, years)
df = (df.
groupby(level=['index', 'series', 'tenor', 'version'])
[['closespread','closeprice']].
pct_change())
df.columns = ['spread_return', 'price_return']
df = df.groupby(level=['date', 'index', 'series', 'tenor']).nth(0)
coupon_data = pd.read_sql_query("SELECT index, series, tenor, coupon FROM " \
"index_maturity WHERE coupon is NOT NULL", serenitasdb,
index_col=['index', 'series', 'tenor'])
def add_accrued(df):
coupon = coupon_data.loc[df.index[0][1:],'coupon'] * 1e-4
accrued = (df.index.levels[0].to_series().diff().
astype('timedelta64[D]')/360 * coupon)
return df + accrued
df['price_return'] = (df.
groupby(level=['index', 'series', 'tenor'])['price_return'].
transform(add_accrued))
return df
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