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from common import root
import os
import datetime
from quantlib.settings import Settings
from quantlib.time.api import (WeekendsOnly, Date, Period, Days, Schedule, Annual,
Semiannual, today, Actual360, Months, Years,
ModifiedFollowing, Thirty360, Actual365Fixed,
calendar_from_name)
from quantlib.currency.api import USDCurrency, EURCurrency
from quantlib.indexes.ibor_index import IborIndex
from quantlib.termstructures.yields.api import (
PiecewiseYieldCurve, DepositRateHelper, SwapRateHelper, BootstrapTrait, Interpolator)
from quantlib.time.date import pydate_from_qldate
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from quantlib.quotes import SimpleQuote
from db import dbconn
from pyisda.curve import YieldCurve
def getMarkitIRData(effective_date = datetime.date.today(),
currency = "USD"):
conn = dbconn("serenitasdb")
sql_str = "SELECT * FROM {}_rates WHERE effective_date = %s".format(currency)
with conn.cursor() as c:
c.execute(sql_str, (effective_date,))
col_names = [col[0] for col in c.description]
r = c.fetchone()
MarkitData = {'effectiveasof': r[0],
'deposits': [(t, r[i]) for i, t in \
enumerate(col_names[1:7], 1) if r[i] is not None],
'swaps': [(t, r[i]) for i, t in enumerate(col_names[7:], 7)]}
return MarkitData
def get_futures_data(date = datetime.date.today()):
futures_file = os.path.join(root, "data", "Yield Curves",
"futures-{0:%Y-%m-%d}.csv".format(date))
with open(futures_file) as fh:
quotes = [float(line.split(",")[1]) for line in fh]
return quotes
def rate_helpers(currency="USD", MarkitData=None):
"""Util function to build a list of RateHelpers
Parameters
----------
currency : str, optional
One of `USD`, `EUR` at the moment, defaults to `USD`
MarkitData : dict, optional
MarkitData for the current evaluation_date
Returns
-------
helpers : list
List of QuantLib RateHelpers
"""
settings = Settings()
if not MarkitData:
MarkitData = getMarkitIRData(pydate_from_qldate(settings.evaluation_date), currency)
if MarkitData['effectiveasof'] != pydate_from_qldate(settings.evaluation_date):
raise RuntimeError("Yield curve effective date: {0} doesn't " \
"match the evaluation date: {1}".format(
MarkitData['effectiveasof'],
pydate_from_qldate(settings.evaluation_date)))
calendar = WeekendsOnly()
if currency == "USD":
isda_ibor = IborIndex("IsdaIbor", Period(3, Months), 2, USDCurrency(), calendar,
ModifiedFollowing, False, Actual360())
fix_freq = Semiannual
elif currency == "EUR":
isda_ibor = IborIndex("IsdaIbor", Period(6, Months), 2, EURCurrency(), calendar,
ModifiedFollowing, False, Actual360())
fix_freq = Annual
# we use SimpleQuotes, rather than just float to make it updateable
deps = [DepositRateHelper(SimpleQuote(q), Period(t), 2, calendar, ModifiedFollowing, False, Actual360())
for t, q in MarkitData['deposits']]
# this matches with bloomberg, but according to Markit, maturity should be unadjusted
swaps = [SwapRateHelper.from_tenor(SimpleQuote(q), Period(t), calendar, fix_freq, ModifiedFollowing,
Thirty360(), isda_ibor) for t, q in MarkitData['swaps']]
return deps + swaps
def get_dates(date, currency="USD"):
"""computes the list of curve dates on a given date"""
if currency == "USD":
month_periods = [1, 2, 3, 6, 12]
year_periods = [2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 15, 20, 25, 30]
calendar = WeekendsOnly()
settle_date = calendar.advance(Date.from_datetime(date), 2, 0)
deposit_dates = [calendar.advance(settle_date, period = Period(m, Months),
convention=ModifiedFollowing) \
for m in month_periods]
swap_dates = [calendar.advance(settle_date, period = Period(y, Years),
convention=ModifiedFollowing) \
for y in year_periods]
dates = deposit_dates + swap_dates
return [pydate_from_qldate(d) for d in dates]
def roll_yc(yc, forward_date):
"""returns the expected forward yield cuve on a forward_date"""
dates = get_dates(forward_date)
dfs = np.array([yc.discount_factor(d, forward_date) for d in dates])
return YieldCurve.from_discount_factors(forward_date, dates, dfs, 'ACT/365F')
def YC(helpers = None, currency="USD", MarkitData=None):
if helpers is None:
helpers = rate_helpers(currency, MarkitData)
calendar = WeekendsOnly()
return PiecewiseYieldCurve(BootstrapTrait.Discount, Interpolator.LogLinear,
0, calendar, helpers, Actual365Fixed())
def ql_to_jp(ql_yc):
"""convert a QuantLib yield curve to a JP's one"""
if ql_yc._trait == BootstrapTrait.Discount:
dfs = np.array(ql_yc.data[1:])
dates = [pydate_from_qldate(d) for d in ql_yc.dates[1:]]
trade_date = pydate_from_qldate(ql_yc.dates[0])
return YieldCurve.from_discount_factors(trade_date, dates, dfs, 'ACT/365F')
else:
raise RuntimeErrror('QuantLib curve needs to use Discount trait')
if __name__=="__main__":
#evaluation_date = Date(29, 4, 2014)
Settings.instance().evaluation_date = today()
ts = YC()
cal = calendar_from_name('USA')
p1 = Period('1Mo')
p2 = Period('2Mo')
p3 = Period('3Mo')
p6 = Period('6Mo')
p12 = Period('12Mo')
sched = Schedule(ts.reference_date, ts.reference_date+Period('5Yr'), Period('3Mo'), cal)
days = [pydate_from_qldate(d) for d in sched]
f3 = [ts.forward_rate(d, d+p3, Actual360(), 0).rate for d in sched]
f6 = [ts.forward_rate(d, d+p6, Actual360(), 0).rate for d in sched]
f2 = [ts.forward_rate(d, d+p2, Actual360(), 0).rate for d in sched]
plt.plot(days, f2, days, f3, days, f6)
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