diff options
Diffstat (limited to 'python/analytics/option.py')
| -rw-r--r-- | python/analytics/option.py | 86 |
1 files changed, 63 insertions, 23 deletions
diff --git a/python/analytics/option.py b/python/analytics/option.py index cf02cbeb..314c269b 100644 --- a/python/analytics/option.py +++ b/python/analytics/option.py @@ -471,16 +471,17 @@ def _get_keys(df, models=["black", "precise"]): class QuoteSurface(): def __init__(self, index_type, series, tenor='5yr', value_date=datetime.date.today()): self._quotes = pd.read_sql_query( - "SELECT quotedate, index, series, ref, fwdspread, expiry, " \ + "SELECT quotedate, index, series, ref, fwdspread, fwdprice, expiry, " \ "swaption_quotes.*, quote_source " \ "FROM swaption_quotes " \ "JOIN swaption_ref_quotes USING (ref_id)" \ "WHERE quotedate::date = %s AND index= %s AND series = %s " \ "AND quote_source != 'SG' " \ - "ORDER BY quotedate", + "ORDER BY quotedate, strike", _engine, parse_dates=['quotedate', 'expiry'], params=(value_date, index_type.upper(), series)) + self._quote_is_price = index_type == "HY" self._quotes.loc[(self._quotes.quote_source == "GS") & (self._quotes['index'] =="HY"), ["pay_bid", "pay_offer", "rec_bid", "rec_offer"]] *=100 if self._quotes.empty: @@ -510,11 +511,19 @@ class VolSurface(QuoteSurface): quotedate, source = surface_id quotes = self._quotes[(self._quotes.quotedate == quotedate) & (self._quotes.quote_source == source)] - quotes = quotes.assign(moneyness=quotes.strike / quotes.fwdspread, - time=((quotes.expiry - self.value_date).dt.days + 0.25) / 365) - spline = lambda df: CubicSpline(np.log(df.moneyness), df.vol, bc_type="natural") - h = quotes.sort_values('moneyness').groupby('time').apply(spline) - self._surfaces[surface_id] = MyInterp(h.index.values, h.values) + quotes = quotes.assign( + time=((quotes.expiry - + pd.Timestamp(self.value_date)).dt.days + 0.25) / 365) + if self._quote_is_price: + quotes = quotes.assign(moneyness=np.log(quotes.strike / quotes.fwdprice)) + else: + quotes = quotes.assign(moneyness=np.log(quotes.strike / quotes.fwdspread)) + + h = (quotes. + sort_values('moneyness'). + groupby('time'). + apply(lambda df: CubicSpline(df.moneyness, df.vol, bc_type="natural"))) + self._surfaces[surface_id] = BivariateLinearFunction(h.index.values, h.values) return self._surfaces[surface_id] else: return self._surfaces[surface_id] @@ -523,13 +532,14 @@ class VolSurface(QuoteSurface): """computes the vol for a given moneyness and term.""" if isinstance(T, datetime.date): T = ((T - self.value_date).days + 0.25) / 365 - return self[surface_id].ev(T, moneyness) + return self[surface_id](T, moneyness) def plot(self, surface_id): fig = plt.figure() ax = fig.gca(projection='3d') surf = self[surface_id] time = surf.T + # TODO: need to adjust the range for price based quotes y = np.arange(-0.15, 0.7, 0.01) x = np.arange(time[0], time[-1], 0.01) xx, yy = np.meshgrid(x, y) @@ -549,40 +559,61 @@ def _compute_vol(option, strike, mid): else: return np.array([option.sigma, option.moneyness]) -def _calibrate_model(index, quotes, option_type, option_model): +def _calibrate_model(index, quotes, option_type, option_model, + interp_method="bivariate_spline"): + """ + interp_method : one of 'bivariate_spline', 'bivariate_linear' + """ T, r = [], [] column = 'pay_mid' if option_type == 'payer' else 'rec_mid' + if index.index_type == "HY": + quotes = quotes.sort_values('strike', ascending=False) with Pool(4) as p: for expiry, df in quotes.groupby(['expiry']): option = option_model(index, expiry.date(), 100, option_type) - T.append(option.T * np.ones(df.shape[0])) + T.append(option.T) r.append(np.stack(p.starmap(partial(_compute_vol, option), df[['strike', column]].values))) - r = np.concatenate(r) - vol = r[:,0] - non_nan = ~np.isnan(vol) - vol = vol[non_nan] - time = np.hstack(T) - time = time[non_nan] - moneyness = r[non_nan,1] - return SmoothBivariateSpline(time, moneyness, vol) + if interp_method == "bivariate_spline": + T = [np.full(len(data), t) for t, data in zip(T, r)] + r = np.concatenate(r) + vol = r[:,0] + non_nan = ~np.isnan(vol) + vol = vol[non_nan] + time = np.hstack(T)[non_nan] + moneyness = np.log(r[non_nan,1]) + return SmoothBivariateSpline(time, moneyness, vol, s=1e-3) + elif interp_method == "bivariate_linear": + h = [] + for data in r: + vol = data[:,0] + non_nan = ~np.isnan(vol) + vol = vol[non_nan] + moneyness = np.log(data[non_nan,1]) + h.append(CubicSpline(moneyness, vol, bc_type="natural")) + return BivariateLinearFunction(T, h) + else: + raise ValueError("interp_method needs to be one of 'bivariate_spline' or 'bivariate_linear'") def _calibrate(index, quotes, option_type, **kwargs): if 'option_model' in kwargs: - return _calibrate_model(index, quotes, option_type, kwargs['option_model']) + return _calibrate_model(index, quotes, option_type, **kwargs) elif 'beta' in kwargs: return _calibrate_sabr(index, quotes, option_type, kwargs['beta']) class ModelBasedVolSurface(VolSurface): - def __init__(self, index_type, series, tenor='5yr', value_date=datetime.date.today()): + def __init__(self, index_type, series, tenor='5yr', value_date=datetime.date.today(), + interp_method='bivariate_spline'): super().__init__(index_type, series, tenor, value_date) self._index = Index.from_name(index_type, series, tenor, value_date, notional=1.) self._surfaces = {} + self._index_refs = {} self._quotes = self._quotes.assign( pay_mid=self._quotes[['pay_bid', 'pay_offer']].mean(1) * 1e-4, rec_mid=self._quotes[['rec_bid', 'rec_offer']].mean(1) * 1e-4) if type(self) is BlackSwaptionVolSurface: - self._opts = {'option_model': BlackSwaption} + self._opts = {'option_model': BlackSwaption, + 'interp_method': interp_method} elif type(self) is SwaptionVolSurface: self._opts = {'option_model': Swaption} elif type(self) is SABRVolSurface: @@ -606,13 +637,21 @@ class ModelBasedVolSurface(VolSurface): quotes = quotes.dropna(subset= ['pay_mid' if option_type == "payer" else 'rec_mid']) self._index.ref = quotes.ref.iat[0] + self._index_refs[surface_id] = quotes.ref.iat[0] self._surfaces[surface_id] = _calibrate(self._index, quotes, option_type, **self._opts) return self._surfaces[surface_id] else: + self._index.ref = self._index_refs[surface_id] return self._surfaces[surface_id] + def index_ref(self, surface_id): + if surface_id not in self._surfaces: + self[surface_id] + return self._index_refs[surface_id] + + def plot(self, surface_id): fig = plt.figure() ax = fig.gca(projection='3d') @@ -749,9 +788,10 @@ class ProbSurface(QuoteSurface): ax.set_ylabel("Strike") ax.set_zlabel("Tail Probability") -class MyInterp: +class BivariateLinearFunction: + """Linear interpolation between a set of functions""" def __init__(self, T, f): - self.T = T + self.T = np.asarray(T) self.f = f self._dgrid = np.diff(self.T) |