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import os
import os.path
import pandas as pd
from intex_scenarios import dealname_from_cusip
from zipfile import ZipFile
import re
import datetime
from dateutil.relativedelta import relativedelta
from db import query_db
import yaml
from quantlib.time.api import Schedule, Actual360, Period, Months, Calendar, Unadjusted
from quantlib.util.converter import pydate_to_qldate, qldate_to_pydate
from quantlib.settings import Settings
from yieldcurve import YC
import numpy as np
from optimization import KLfit
import pdb
if os.name =='nt':
root = "//WDsentinel/share/CorpCDOs"
elif os.name == 'posix':
root = '/home/share/CorpCDOs'
def sanitize_float(string):
try:
string = string.replace(",","")
if "(" in string:
return - float(string[1:-1])
else:
return float(string)
except AttributeError:
return string
def processzipfiles(workdate=datetime.date.today()):
pricesdir = os.path.join(root, "Scenarios", "Prices_" + str(workdate))
zipfiles = [os.path.join(pricesdir, f) for f in os.listdir(pricesdir) \
if f.endswith(".zip")]
zipfiles = sorted(zipfiles, key=lambda x: os.stat(x).st_ctime)
cusips_dict = {}
dealnames_dict = {}
for i, zip in enumerate(zipfiles):
with ZipFile(zip) as myzip:
allfiles = set([f.filename.split("-")[0] for f in myzip.infolist()])
allfiles = allfiles - {"Total"}
dealnames = set([f.filename.split("-")[0] for f in myzip.infolist() \
if re.search("COLLAT.*Scen100", f.filename)])
cusips = allfiles - dealnames
dealnames = [d.lower() for d in dealnames]
cusips_dict.update((cusip, i) for cusip in cusips)
dealnames_dict.update((dealname, i) for dealname in dealnames)
return {"dealnames": dealnames_dict, "cusips":cusips_dict, "zipfiles": zipfiles}
def get_configfile(dealname, workdate):
configfile = os.path.join(root, "Scenarios", "_".join(["Intex curves", str(workdate)]),
"csv", dealname + ".config")
try:
with open(configfile) as fh:
config = yaml.load(fh)
except FileNotFoundError:
config = {"reinvflag": True}
return config
def basic_schedule(day_of_month, enddate, startdate=datetime.date.today()):
return Schedule(startdate, enddate, Period('1Mo'), Unadjusted)
#return pd.date_range(startdate, end=enddate, freq='MS') + datetime.timedelta(days=day_of_month-1)
def getdealcf(dealnames, zipfiles, workdate = datetime.date.today()):
fiels = ["Cashflow", "Principal", "Interest"]
n_scenarios = 100
us_cal = Calendar.from_name('USA')
cfdata = {}
m = YC(workdate)
fields = ["Cashflow", "Principal", "Interest"]
for dealname, i in dealnames.items():
zip = zipfiles[i]
cfdata[dealname]={}
mv = query_db("select marketvalue from latest_deal_model_numbers where dealname = %s",
(dealname,))[0]
sqlstr = "select \"Curr Collat Bal\", \"Reinv End Date\", \"Deal Next Pay Date\"," \
"maturity, \"Principal Bal\", \"Pay Day\" from historical_clo_universe(%s, %s)"
sqldata = query_db(sqlstr, (dealname, str(workdate)))
if not sqldata["Pay Day"]:
sqldata["Pay Day"] = sqldata["Deal Next Pay Date"] + relativedelta(months=-3)
if abs((sqldata["Deal Next Pay Date"]- sqldata["Pay Day"]).days-90)>10:
sqldata["Pay Day"] = sqldata["Deal Next Pay Date"] + relativedelta(months=-6)
cfdata[dealname] = {"mv":mv, "currbal": sqldata["Curr Collat Bal"]}
config = get_configfile(dealname, workdate)
sched = Schedule(pydate_to_qldate(sqldata["Pay Day"]), pydate_to_qldate(sqldata["maturity"]),
Period('1Mo'), us_cal, Unadjusted, Unadjusted)
pd_sched = [pd.to_datetime(str(day), format="%d/%m/%Y") for day in sched]
discounts = pd.Series([m.discount(day) if day > m.settle_date else 1 for day in sched],
index = pd_sched, name='df')
if (not sqldata["Reinv End Date"] or not config["reinvflag"]):
tranches = ["COLLAT"]
else:
tranches = ["COLLAT_INITIAL", "COLLAT_REINVEST"]
cf = {}
with ZipFile(zip) as myzip:
for tranche in tranches:
scen = {}
for j in range(1, n_scenarios+1):
filename = "{0}-{1}-CF-Scen{2}.txt".format(dealname.upper(), tranche, j)
data = pd.read_table(myzip.open(filename), skiprows=[1, 2], parse_dates=[0],
thousands=",",
date_parser = lambda x: datetime.datetime.strptime(x, "%b %d, %Y"),
index_col=0)
for c in fields:
if data.dtypes[c] != np.dtype('float64'):
data[c] = data[c].apply(sanitize_float)
data = data[fields].join(discounts)
scen[j] = np.dot(data["df"], data[fields])
cf[tranche] = pd.DataFrame(scen).T
cf[tranche].columns = fields
cfdata[dealname]["panel"] = pd.Panel(cf)
cf = cfdata[dealname]["panel"].minor_xs("Cashflow").T.sum()
cfdata[dealname]["wapbasis"] = (cf.mean() - mv)/mv
program = KLfit(cf.values/1e8, np.ones(n_scenarios)/n_scenarios, mv/1e8)
cfdata[dealname]["weight"] = program["weight"]
print(dealname)
return cfdata
if __name__=="__main__":
#workdate = datetime.date.today()
workdate = datetime.datetime.strptime("2014-03-19", "%Y-%m-%d").date()
params = processzipfiles(workdate)
pomme = getdealcf(params["dealnames"], params["zipfiles"], workdate)
pdb.set_trace()
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