diff options
Diffstat (limited to 'python/analytics/option.py')
| -rw-r--r-- | python/analytics/option.py | 70 |
1 files changed, 65 insertions, 5 deletions
diff --git a/python/analytics/option.py b/python/analytics/option.py index 6f52bf3d..2ffbe462 100644 --- a/python/analytics/option.py +++ b/python/analytics/option.py @@ -8,6 +8,7 @@ import pandas as pd from db import dbengine from .black import black, Nx +from .sabr import sabr from .utils import GHquad, build_table from .index import g, ForwardIndex, Index, engine from yieldcurve import roll_yc @@ -26,6 +27,7 @@ import matplotlib.pyplot as plt from multiprocessing import Pool from functools import partial from itertools import chain +from scipy.optimize import least_squares from scipy.special import logit, expit @@ -437,13 +439,16 @@ def compute_vol(option, strike, mid): else: return np.array([option.sigma, option.tail_prob, option.vega, option.moneyness, option.strike]) -def _get_keys(df): +def _get_keys(df, models=["black", "precise"]): for quotedate, source in (df[['quotedate', 'quote_source']]. drop_duplicates(). itertuples(index=False)): for option_type in ["payer", "receiver"]: - for model in ["black", "precise"]: - yield (quotedate, source, option_type, model) + if models: + for model in models: + yield (quotedate, source, option_type, model) + else: + yield (quotedate, source, option_type) class VolatilitySurface(ForwardIndex): def __init__(self, index_type, series, tenor='5yr', trade_date=datetime.date.today()): @@ -529,9 +534,9 @@ class VolatilitySurface(ForwardIndex): swaption_class = Swaption moneyness, T, r = [], [], [] if option_type == "payer": - quotes = quotes.assign(mid = quotes[['pay_bid','pay_offer']].mean(1) * 1e-4) + quotes = quotes.assign(mid=quotes[['pay_bid','pay_offer']].mean(1) * 1e-4) else: - quotes = quotes.assign(mid = quotes[['rec_bid','rec_offer']].mean(1) * 1e-4) + quotes = quotes.assign(mid=quotes[['rec_bid','rec_offer']].mean(1) * 1e-4) quotes = quotes.dropna(subset=['mid']) with Pool(4) as p: for expiry, df in quotes.groupby(['expiry']): @@ -556,3 +561,58 @@ class VolatilitySurface(ForwardIndex): else: return self._surfaces[surface_id] +def calib_sabr(x, option, strikes, pv, beta): + alpha, rho, nu = x + F = option.forward_spread + T = option.T + r = np.empty_like(strikes) + for i, K in enumerate(strikes): + option.strike = K + option.sigma = sabr(alpha, beta, rho, nu, F, option._strike, T) + r[i] = option.pv - pv[i] + return r + +class SABRVolatilitySurface(VolatilitySurface): + def __init__(self, index_type, series, tenor='5yr', + trade_date=datetime.date.today(), beta=None): + VolatilitySurface.__init__(self, index_type, series, tenor='5yr', + trade_date=datetime.date.today()) + if index_type == "HY": + self.beta = 3.19 + elif index_type == "IG": + self.beta = 1.84 + + def list(self, source=None, option_type=None): + """returns list of vol surfaces""" + r = [] + for k in _get_keys(self._quotes, []): + if (source is None or k[1] == source) and \ + (option_type is None or k[2] == option_type): + r.append(k) + return r + + def __getitem__(self, surface_id): + if surface_id not in self._surfaces: + quotedate, source, option_type = surface_id + quotes = self._quotes[(self._quotes.quotedate == quotedate) & + (self._quotes.quote_source == source)] + self._index.ref = quotes.ref.iat[0] + + T, r = [], [] + if option_type == "payer": + quotes = quotes.assign(mid=quotes[['pay_bid','pay_offer']].mean(1) * 1e-4) + else: + quotes = quotes.assign(mid=quotes[['rec_bid','rec_offer']].mean(1) * 1e-4) + quotes = quotes.dropna(subset=['mid']) + for expiry, df in quotes.groupby(['expiry']): + option = BlackSwaption(self._index, expiry.date(), 100, option_type) + prog = least_squares(calib_sabr, (0.01, 0.3, 3.5), + bounds=([0, -1, 0], [np.inf, 1, np.inf]), + args=(option, df.strike.values, df.mid.values, self.beta)) + T.append(option.T) + r.append(prog.x) + print(prog) + self._surfaces[surface_id] = (T, r) + return self._surfaces[surface_id] + else: + return self._surfaces[surface_id] |