diff options
Diffstat (limited to 'python')
| -rw-r--r-- | python/analytics/option.py | 37 |
1 files changed, 22 insertions, 15 deletions
diff --git a/python/analytics/option.py b/python/analytics/option.py index 13522a59..d7d9942f 100644 --- a/python/analytics/option.py +++ b/python/analytics/option.py @@ -370,10 +370,10 @@ class Swaption(BlackSwaption): b *= eta self.sigma = brentq(handle, a, b) -def compute_vol(option, strike, mid, pv_type): +def compute_vol(option, strike, mid): option.strike = strike try: - setattr(option, pv_type, mid) + option.pv = mid except ValueError as e: return None else: @@ -385,7 +385,8 @@ class VolatilitySurface(ForwardIndex): self._quotes = pd.read_sql_query( "SELECT swaption_quotes.*, ref FROM swaption_quotes " \ "JOIN swaption_ref_quotes USING (quotedate, index, series, expiry)" \ - "WHERE quotedate::date = %s and index= %s and series = %s", + "WHERE quotedate::date = %s and index= %s and series = %s" \ + "ORDER BY quotedate DESC", engine, parse_dates = ['quotedate', 'expiry'], params=(trade_date, index_type, series)) @@ -396,16 +397,23 @@ class VolatilitySurface(ForwardIndex): self._surfaces = {} for k, g in self._quotes.groupby(['quotedate', 'quote_source']): quotedate, source = k - for surface_type in ["payer", "receiver", "payer_black", "receiver_black"]: - self._surfaces[(quotedate, source, surface_type)] = None + for option_type in ["payer", "receiver"]: + for model in ["black", "precise"]: + self._surfaces[(quotedate, source, option_type, model)] = None def vol(self, T, moneyness, surface_id): """computes the vol for a given moneyness and term.""" return self._surfaces[surface_id](T, moneyness) - def list(self): + def list(self, source=None, option_type=None, model=None): """returns list of vol surfaces""" - return list(self._surfaces.keys()) + r = [] + for k in self._surfaces.keys(): + if (source is None or k[1] == source) and \ + (option_type is None or k[2] == option_type) and \ + (model is None or k[3] == model): + r.append(k) + return r def __iter__(self): return self._surfaces.items() @@ -423,17 +431,15 @@ class VolatilitySurface(ForwardIndex): def __getitem__(self, surface_id): if self._surfaces[surface_id] is None: - quotedate, source, surface_type = surface_id + quotedate, source, option_type, model = surface_id quotes = self._quotes[(self._quotes.quotedate == quotedate) & (self._quotes.quote_source == source)] self._index.ref = quotes.ref.iat[0] - if "_" in surface_type: - option_type, _ = surface_type.split("_") - pv_type = "pv_black" + if model == "black": + swaption_class = BlackSwaption else: - option_type = surface_type - pv_type = "pv" + swaption_class = Swaption moneyness, T, r = [], [], [] if option_type == "payer": quotes = quotes.assign(mid = quotes[['pay_bid','pay_offer']].mean(1) * 1e-4) @@ -441,11 +447,12 @@ class VolatilitySurface(ForwardIndex): quotes = quotes.assign(mid = quotes[['rec_bid','rec_offer']].mean(1) * 1e-4) for expiry, df in quotes.groupby(['expiry']): atm_strike = ATMstrike(self._index, expiry.date()) - option = Swaption(self._index, expiry.date(), 100, option_type) + option = swaption_class(self._index, expiry.date(), 100, option_type) + T.append(option.T * np.ones(df.shape[0])) moneyness.append(df.strike.values / atm_strike) r.append(Parallel(n_jobs=4)( - delayed(compute_vol)(option, strike, mid, pv_type) for strike, mid in + delayed(compute_vol)(option, strike, mid) for strike, mid in df[['strike', 'mid']].values)) r = np.fromiter(chain(*r), np.float, quotes.shape[0]) f = SmoothBivariateSpline(np.hstack(T), np.hstack(moneyness), r) |