diff options
Diffstat (limited to 'python/exploration/test_cms.py')
| -rw-r--r-- | python/exploration/test_cms.py | 137 |
1 files changed, 27 insertions, 110 deletions
diff --git a/python/exploration/test_cms.py b/python/exploration/test_cms.py index 205ca670..31c96029 100644 --- a/python/exploration/test_cms.py +++ b/python/exploration/test_cms.py @@ -1,113 +1,30 @@ -from analytics.cms_spread import ( - quantlib_model, globeop_model, build_spread_index, VolatilityType, - get_swaption_vol_data, get_swaption_vol_matrix) -from yieldcurve import YC +from analytics.cms_spread import CmsSpread +from pathlib import Path +import os import pandas as pd -from quantlib.time.api import Date, Period, Years, Days, ModifiedFollowing -from quantlib.cashflows.cms_coupon import CmsCoupon -from quantlib.experimental.coupons.cms_spread_coupon import ( - CmsSpreadCoupon, CappedFlooredCmsSpreadCoupon) -import datetime -import math -# swap_index_30y2y, yc = build_spread_index(30, 2) -# cap = 0.00758 -# corr = 0.8 -# r = [] -# maturity = pd.Timestamp("2020-01-19") -# today = pd.Timestamp.today() -# for d in pd.bdate_range("2018-01-19", today, closed="left", normalize=True): -# d = pd.Timestamp("2018-01-19") -# yc.link_to(YC(evaluation_date=d.date())) -# yc.extrapolation = True -# if d == pd.Timestamp("2018-02-16"): -# continue -# capped_floored_cms_spread_coupon_ln = \ -# quantlib_model(d, swap_index_30y2y, yc, cap, corr, maturity) -# rate1 = capped_floored_cms_spread_coupon_ln.rate -# cms_spread_coupon_n = quantlib_model(d, swap_index_30y2y, yc, cap, corr, maturity, -# VolatilityType.Normal) -# rate2 = cms_spread_coupon_n.rate -# rate3 = globeop_model(d, swap_index_30y2y, yc, cap, corr - 0.075, maturity) -# # df = pd.DataFrame(r, columns=['date', 'QL_ln', 'QL_n', 'Globeop']).set_index('date') +DAILY_DIR = Path(os.environ["DAILY_DIR"]) +r = [] +today = pd.Timestamp.today() +trade = CmsSpread.from_tradeid(1) +dr = pd.bdate_range("2018-01-19", today, closed="left", normalize=True) +for d in dr: + trade.value_date = d + r.append(trade.pv) -# try to get convexity adjustement -from quantlib.indexes.swap.usd_libor_swap import UsdLiborSwapIsdaFixAm -from quantlib.cashflows.conundrum_pricer import AnalyticHaganPricer, YieldCurveModel -from quantlib.quotes import SimpleQuote -from quantlib.time.api import Actual365Fixed -# trade_date = datetime.date(2018, 1, 19) -trade_date = datetime.date(2018, 8, 23) -maturity = Date.from_datetime(trade_date) + Period(2, Years) -spread_index, yc = build_spread_index(30, 2) -yc.link_to(YC(evaluation_date=trade_date)) -yc.extrapolation = True -fixing_date = spread_index.fixing_calendar.adjust(maturity, ModifiedFollowing) -payment_date = spread_index.fixing_calendar.advance(fixing_date, 2, Days) -accrued_end_date = payment_date -accrued_start_date = accrued_end_date - Period(1, Years) - -cap = 0.00758 -cms2y30y_cap = CappedFlooredCmsSpreadCoupon( - payment_date, - 100_000_000, - start_date=accrued_start_date, - end_date=accrued_end_date, - fixing_days=spread_index.fixing_days, - index=spread_index, - gearing=1., - spread=-cap, - floor=0., - day_counter=Actual365Fixed(), - is_in_arrears=True) - -date, surf = get_swaption_vol_data(date=trade_date, vol_type=VolatilityType.ShiftedLognormal) -atm_vol = get_swaption_vol_matrix(trade_date, surf) -μ = SimpleQuote(0.1) -from quantlib.experimental.coupons.lognormal_cmsspread_pricer import LognormalCmsSpreadPricer -corr = SimpleQuote(0.8) -cms_pricer = AnalyticHaganPricer(atm_vol, YieldCurveModel.Standard, μ) -spread_pricer = LognormalCmsSpreadPricer( - cms_pricer, - corr, - integration_points=20) -cms2y30y_cap.set_pricer(spread_pricer) -fixing_time = atm_vol.time_from_reference(cms2y30y_cap.fixing_date) -cms30y = CmsCoupon(payment_date, - 100_000_000, - start_date=accrued_start_date, - end_date=accrued_end_date, - fixing_days=2, - index=spread_index.swap_index1, - is_in_arrears=True) -cms2y = CmsCoupon(payment_date, - 100_000_000, - start_date=accrued_start_date, - end_date=accrued_end_date, - fixing_days=2, - index=spread_index.swap_index2, - is_in_arrears=True) -cms30y.set_pricer(cms_pricer) -cms2y.set_pricer(cms_pricer) -s1 = cms30y.index_fixing -s2 = cms2y.index_fixing -adjusted1 = cms30y.rate -adjusted2 = cms2y.rate -import math -T_alpha = atm_vol.time_from_reference(cms2y30y_cap.fixing_date) -mu1 = 1 / T_alpha * math.log(adjusted1 / s1) -mu2 = 1 / T_alpha * math.log(adjusted2 / s2) -vol1 = atm_vol.volatility(cms2y.fixing_date, spread_index.swap_index1.tenor, s1) -vol2 = atm_vol.volatility(cms30y.fixing_date, spread_index.swap_index2.tenor, s2) -mu_x = (mu1 - 0.5 * vol1 ** 2) * T_alpha -mu_y = (mu2 - 0.5 * vol2 ** 2) * T_alpha -sigma_x = vol1 * math.sqrt(T_alpha) -sigma_y = vol2 * math.sqrt(T_alpha) - -from scipy.special import roots_hermitenorm -from analytics.cms_spread import h_call, h_put -import numpy as np -x, w = roots_hermitenorm(16) -val_put = 1/math.sqrt(2*math.pi) * np.dot(w, h_put(x, cap, s1, s2, mu_x, mu_y, sigma_x, sigma_y, corr.value)) -val_call = 1/math.sqrt(2*math.pi) * np.dot(w, h_call(x, cap, s1, s2, mu_x, mu_y, sigma_x, sigma_y, corr.value)) -print(cms2y30y_cap.rate, cms2y30y_cap.underlying.rate + val_put, val_call) +def gs_navs(trade_id='LTAAB4ZN3333L6TTSH7.0.0.0'): + # load gs navs for a given trade + dates = [] + r = [] + for fname in (DAILY_DIR / "GS_reports").glob("Trade_Detail*.xls"): + m = re.match("[^\d]*(\d{2}_.{3}_\d{4})", fname.name) + if m: + date_string, = m.groups() + dates.append(datetime.datetime.strptime(date_string, "%d_%b_%Y")) + df = pd.read_excel(fname, skiprows=9, skipfooter=77) + r.append(df.set_index('Trade Id').loc[trade_id, 'NPV (USD)']) + s = pd.Series(r, dates) + s = s.sort_index() + #remove the IA until it settled + s[:2] -= 68750.00 + return -s |