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import os
from common import root
import csv
import datetime
from db import serenitasdb
import re, sys
from pandas.tseries.offsets import BDay
import pandas as pd
import numpy as np
import psycopg2
def convert(x):
try:
return float(x[:-1])
except ValueError:
return None
def get_current_tickers(database, workdate):
sqlstr = "SELECT markit_ticker, markit_tier, cds_curve from index_members(%s, %s)"
markit_bbg_mapping = {}
all_tickers = set([])
if workdate >= datetime.date(2014, 9, 19):
doc_clause = 'XR14'
else:
doc_clause = 'XR'
for index in ['HY9', 'HY10', 'HY15', 'HY17', 'HY19', 'HY21', 'HY22',
'IG9', 'IG19', 'IG21', 'IG22', 'IG23']:
spread=0.05 if 'HY' in index else 0.01
with database.cursor() as c:
c.execute(sqlstr, (index, workdate))
for line in c:
all_tickers.add((line['markit_ticker'], line['markit_tier']))
key = (line['markit_ticker'], line['markit_tier'], 'USD', doc_clause, spread)
hykey = key[:-1]+(0.05,)
if hykey in markit_bbg_mapping:
del markit_bbg_mapping[hykey] ## we only keep the tightest quote
markit_bbg_mapping[key] = line['cds_curve']
return (all_tickers, markit_bbg_mapping)
def insert_cds(database, workdate):
all_tickers, markit_bbg_mapping = get_current_tickers(database, workdate)
filename = "cds eod {0}.csv".format(datetime.datetime.strftime(workdate, "%Y%m%d"))
colnames = ['Upfront'+tenor for tenor in ['6m', '1y', '2y', '3y', '4y', '5y', '7y', '10y']]
sqlstr = "INSERT INTO cds_quotes(date, curve_ticker, upfrontbid, upfrontask," \
"runningbid, runningask, source, recovery) VALUES(%s, %s, %s, %s, %s, %s, %s, %s)"
tickers_found = set([])
with database.cursor() as c:
c.execute("DELETE from cds_quotes where date=%s", (workdate,))
database.commit()
with open(os.path.join(root, "Tranche_data", "CDS", filename)) as fh:
csvreader = csv.DictReader(fh)
with database.cursor() as c:
for line in csvreader:
tickers_found.add((line['Ticker'], line['Tier']))
k = (line['Ticker'], line['Tier'], line['Ccy'], line['DocClause'], float(line['RunningCoupon']))
try:
c.executemany(sqlstr,
[(workdate, t, convert(line[colnames[i]]), convert(line[colnames[i]]),
float(line['RunningCoupon'])*10000, float(line['RunningCoupon'])*10000,
'MKIT', convert(line['RealRecovery'])/100)
for i, t in enumerate(markit_bbg_mapping[k])])
except KeyError:
continue
database.commit()
print(all_tickers-tickers_found)
def insert_cds_single(database, workdate, bbg_tickers, mkt_tuple):
filename = "cds eod {0}.csv".format(datetime.datetime.strftime(workdate, "%Y%m%d"))
colnames = ['Upfront'+tenor for tenor in ['6m', '1y', '2y', '3y', '4y', '5y', '7y', '10y']]
sqlstr = "INSERT INTO cds_quotes(date, curve_ticker, upfrontbid, upfrontask," \
"runningbid, runningask, source, recovery) VALUES(%s, %s, %s, %s, %s, %s, %s, %s)"
with open(os.path.join(root, "Tranche_data", "CDS", filename)) as fh:
csvreader = csv.DictReader(fh)
with database.cursor() as c:
for line in csvreader:
if (line['Ticker'], line['Tier'], line['DocClause'], line['RunningCoupon'], line['Ccy']) == \
mkt_tuple:
try:
c.executemany(sqlstr,
[(workdate, t, convert(line[colnames[i]]), convert(line[colnames[i]]),
float(line['RunningCoupon'])*10000, float(line['RunningCoupon'])*10000,
'MKIT', convert(line['RealRecovery'])/100)
for i, t in enumerate(bbg_tickers)])
except psycopg2.IntegrityError:
database.rollback()
database.commit()
def insert_index(database, workdate):
basedir = os.path.join(root, 'Tranche_data', 'Composite_reports')
filenames = [os.path.join(basedir, f) for f in os.listdir(basedir) if 'Indices' in f]
name_mapping = {"CDXNAHY":"HY", "CDXNAIG":"IG",'ITraxx Eur': "EU"}
sqlstr = "INSERT INTO index_quotes VALUES(%s, %s, %s, %s, %s, %s, %s, %s, %s)"
for f in filenames:
if datetime.datetime.fromtimestamp(os.path.getmtime(f)).date()==(workdate+BDay(1)).date():
data = pd.read_csv(f, skiprows=2, parse_dates=[0,7])
data.dropna(subset=['Composite Price'], inplace=True)
cols = ['Composite Price', 'Composite Spread', 'Model Price', 'Model Spread']
data[cols] = data[cols].applymap(lambda x: float(x[:-1]) if x.endswith('%') else x)
data['Term']=data['Term'].apply(lambda x: x.lower()+'r')
data['Name'] = data['Name'].apply(lambda x: name_mapping[x] if x in name_mapping else np.NaN)
data = data.dropna(subset=['Name'])
data.set_index('Name', drop=False, inplace=True)
data['Composite Spread'] = data['Composite Spread']*100
data['Model Spread'] = data['Model Spread']*100
toinsert = [tuple(r) for r in data[["Date", "Name", "Series", "Version", "Term"]+cols].values]
with database.cursor() as c:
c.executemany(sqlstr, toinsert)
database.commit()
if __name__=="__main__":
if len(sys.argv)>=2:
workdate = datetime.datetime.strptime(sys.argv[1], "%Y-%m-%d")
else:
workdate = datetime.datetime.today()-BDay(1)
workdate = workdate.date()
#insert_cds(serenitasdb, workdate)
#insert_index(serenitasdb, workdate)
## backpopulate single ticker
company_id = 16632863
with serenitasdb.cursor() as c:
c.execute("select cds_curve, markit_ticker, markit_tier from cds_issuers where company_id=%s",
(company_id,))
tup=c.fetchone()
bbg_tickers= tup[0]
for f in os.listdir(os.path.join(root, "Tranche_data", "CDS")):
if f.endswith("csv"):
workdate = datetime.datetime.strptime(f.split(" ")[2].split(".")[0], "%Y%m%d")
workdate = workdate.date()
if workdate >= datetime.date(2014, 9, 19):
mkt_tuple = (tup[1], tup[2]) + ('XR14', '0.05', 'USD')
else:
mkt_tuple = (tup[1], tup[2]) + ('XR', '0.05', 'USD')
insert_cds_single(serenitasdb, workdate, bbg_tickers, mkt_tuple)
serenitasdb.close()
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