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import csv
import datetime
import logging
import numpy as np
import pandas as pd
import os
from collections import defaultdict
from common import root
from pandas.tseries.offsets import BDay
logger = logging.getLogger(__name__)
def convert(x):
try:
return float(x[:-1])
except ValueError:
return None
index_list = ['HY9', 'HY10'] + ['HY' + str(s) for s in range(15, 29)] + ['IG9'] + \
['IG' + str(s) for s in range(16, 29)] + \
['XO' + str(s) for s in range(22, 28)] + \
['EU9'] + \
['EU' + str(s) for s in range(19, 28)]
DOC_CLAUSE_MAPPING14 = {'Full Restructuring': 'MM14',
'No Restructuring': 'XR14',
'Modified Modified Restructurin': 'MM14'}
DOC_CLAUSE_MAPPING = {'Full Restructuring': 'MM',
'No Restructuring': 'XR',
'Modified Modified Restructurin': 'MM'}
def get_markit_bbg_mapping(database, basketid_list, workdate):
if workdate>=datetime.date(2014, 9, 19):
doc_clause_mapping = DOC_CLAUSE_MAPPING14
else:
doc_clause_mapping = DOC_CLAUSE_MAPPING
markit_bbg_mapping = defaultdict(set)
all_tickers = set([])
with database.cursor() as c:
c.execute("SELECT markit_ticker, markit_tier, spread, currency, cds_curve, " \
" doc_clause FROM historical_cds_issuers(%s) where index_list && %s",
(workdate, basketid_list))
for line in c:
all_tickers.add((line['markit_ticker'], line['markit_tier']))
key = (line['markit_ticker'], line['markit_tier'], line['currency'],
doc_clause_mapping[line['doc_clause']], float(line['spread'])/10000)
if key==('CESEOP', 'SNRFOR', 'USD', 'XR14', 0.05):
key=('CESEOP', 'SNRFOR', 'USD', 'XR', 0.05)
## each markit ticker can be mapped to multiple bbg tickers
## these bbg tickers can have different curves (ok)
## or same curves (not ok since date, curve_ticker needs to be unique)
## therefore we keep them in a set structure
markit_bbg_mapping[key].add(tuple(line['cds_curve']))
database.commit()
return (all_tickers, markit_bbg_mapping)
def get_basketids(database, index_list, workdate):
r = []
with database.cursor() as c:
for index in index_list:
c.execute("SELECT * FROM nameToBasketID(%s, %s)", (index, workdate))
r.append(c.fetchone()[0])
database.commit()
return r
def get_current_tickers(database, workdate):
basketid_list = get_basketids(database, index_list, workdate)
return get_markit_bbg_mapping(database, basketid_list, workdate)
def insert_cds(database, workdate):
"""insert Markit index quotes into the database
:param database: psycopg2 connection to the database.
:param workdate:
"""
all_tickers, markit_bbg_mapping = get_current_tickers(database, workdate)
filename = "cds eod {0:%Y%m%d}.csv".format(workdate)
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) " \
"ON CONFLICT DO NOTHING"
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:
k = (line['Ticker'], line['Tier'], line['Ccy'], line['DocClause'], float(line['RunningCoupon']))
if k in markit_bbg_mapping:
for curves in markit_bbg_mapping[k]:
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(curves)])
tickers_found.add((line['Ticker'], line['Tier']))
database.commit()
logger.warning('missing_quotes for {0}'.format(all_tickers-tickers_found))
def insert_index(engine, workdate=None):
"""insert Markit index quotes into the database
:param engine: sqlalchemy engine to the database
:param workdate: date. If None, we will try to reinsert all files
"""
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", 'iTraxx Eur Xover': "XO"}
cols = ['closeprice', 'closespread', 'modelprice', 'modelspread']
colmapping={'Date':'date', 'Name': 'index', 'Series': 'series', 'Version': 'version',
'Term': 'tenor', 'Composite Price': 'closeprice', 'Composite Spread': 'closespread',
'Model Price': 'modelprice', 'Model Spread': 'modelspread'}
ext_cols = ['date', 'index', 'series', 'version', 'tenor'] + cols + \
['adjcloseprice', 'adjmodelprice']
for f in filenames:
if workdate is None or \
datetime.datetime.fromtimestamp(os.path.getmtime(f)).date()==(workdate+BDay(1)).date():
data = pd.read_csv(f, skiprows=2, parse_dates=[0,7], engine='python')
data.rename(columns=colmapping, inplace=True)
data.dropna(subset=['closeprice'], inplace=True)
for col in cols:
data[col] = data[col].str.replace('%', '').astype('float')
data['tenor'] = data['tenor'].apply(lambda x: x.lower()+'r')
data['index'] = data['index'].apply(lambda x: name_mapping[x] if x in name_mapping else np.NaN)
data = data.dropna(subset=['index'])
data.set_index('index', drop=False, inplace=True)
data['closespread'] *= 100
data['modelspread'] *= 100
## we renumbered the version for HY9, 10 and 11
data.loc[data.series.isin([9, 10, 11]) & (data.index=='HY'),'version'] -= 3
data['adjcloseprice'] = data['closeprice']
data['adjmodelprice'] = data['modelprice']
data = data.groupby(['index', 'series', 'tenor', 'date']).last()
data.reset_index(inplace=True)
data[ext_cols].to_sql('index_quotes', engine, if_exists='append', index=False)
def insert_tranche(engine, workdate = None):
"""insert Markit index quotes into the database
:param engine: sqlalchemy engine to the database
:param workdate: If None, we will try to reinsert all files
:type workdate: pd.Timestamp
"""
basedir = os.path.join(root, 'Tranche_data', 'Composite_reports')
filenames = [os.path.join(basedir, f) for f in os.listdir(basedir) if f.startswith('Tranche Composites')]
index_version = pd.read_sql_table("index_version", engine, index_col='redindexcode')
for f in filenames:
if workdate is None or \
datetime.datetime.fromtimestamp(os.path.getmtime(f)).date()==(workdate+BDay(1)).date():
df = pd.read_csv(f, skiprows=2, parse_dates=['Date'])
df.rename(columns={'Date':'quotedate',
'Index Term':'tenor',
'Attachment':'attach',
'Detachment':'detach',
'Tranche Upfront Bid': 'upfront_bid',
'Tranche Upfront Mid': 'upfront_mid',
'Tranche Upfront Ask': 'upfront_ask',
'Index Price Mid': 'index_price',
'Tranche Spread Mid': 'tranche_spread',
'Red Code':'redindexcode'}, inplace=True)
df.attach = df.attach *100
df.detach = df.detach * 100
df.tranche_spread = df.tranche_spread*10000
df.tenor = df.tenor.str.lower() + 'r'
df.set_index('redindexcode', inplace=True)
df = df.join(index_version)
df = df.filter(['basketid', 'quotedate', 'tenor', 'attach', 'detach',
'upfront_bid', 'upfront_ask', 'upfront_mid',
'tranche_spread', 'index_price'])
df.to_sql('markit_tranche_quotes', engine, if_exists='append', index=False)
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