diff options
Diffstat (limited to 'python/exploration/option_trades.py')
| -rw-r--r-- | python/exploration/option_trades.py | 102 |
1 files changed, 63 insertions, 39 deletions
diff --git a/python/exploration/option_trades.py b/python/exploration/option_trades.py index e3217e07..aadbddfd 100644 --- a/python/exploration/option_trades.py +++ b/python/exploration/option_trades.py @@ -26,7 +26,7 @@ def realized_vol(index, series, tenor='5yr', date=None, years=None, return_type= return (res.conditional_volatility * math.sqrt(252), res) def lr_var(res): - """ computes long run variance of the garch process + r""" computes long run variance of the garch process .. math:: @@ -38,21 +38,38 @@ def lr_var(res): var = res.params[names[0]]/(1 - res.params[names[1:]]) return math.sqrt(var * 252) -def atm_vol_fun(v, moneyness=0.2, index=None): - f = interp1d(v.strike.values, v.vol.values, fill_value='extrapolate') - if index is None: - atm_val = v['fwdspread'].iat[0] - otm_val = atm_val * (1 + moneyness) - else: - index.ref = v['ref'].iat[0] - atm_val = ForwardIndex(index, v.index.get_level_values('expiry')[0]).forward_spread - otm_val = atm_val * (1 + moneyness) - if index._quote_is_price: - index.spread = atm_val - atm_val = index.price - index.spread = otm_val - otm_val = index.price - return pd.Series(f(np.array([atm_val, otm_val])), index = ['atm_vol', 'otm_vol']) +def atm_vol_calc(df, index_type, moneyness): + r = np.empty((len(df.index.unique()), 3)) + i = 0 + index_keys = [] + for s, g1 in df.groupby(level='series'): + index = Index.from_name(index_type, s, '5yr') + for date, g2 in g1.groupby(pd.Grouper(level='quotedate', freq='D')): + if not g2.empty: + index.trade_date = date.date() + for (ref, expiry), g3 in g2.reset_index('expiry').groupby(['ref', 'expiry']): + index.ref = ref + atm_val = forward_spread = ForwardIndex(index, expiry, False).forward_spread + otm_val = atm_val * (1 + moneyness) + if index._quote_is_price: + index.spread = atm_val + atm_val = index.price + index.spread = otm_val + otm_val = index.price + for quotedate, v in g3.groupby(level='quotedate'): + f = interp1d(v.strike.values, v.vol.values, fill_value='extrapolate') + r[i, 0] = forward_spread + r[i, 1:] = f([atm_val, otm_val]) + i += 1 + index_keys.append((quotedate, expiry, s)) + df = pd.DataFrame(data=r, + index=pd.MultiIndex.from_tuples(index_keys, + names=['quotedate', 'expiry', 'series']), + columns=['forward_spread', 'atm_vol', 'otm_vol']) + df['T'] = df.index.get_level_values('expiry').values.astype('datetime64[D]') - \ + df.index.get_level_values('quotedate').values.astype('datetime64[D]') + df['T'] = df['T'].dt.days / 365 + return df def atm_vol(index, date, series=None, moneyness=0.2): sql_str = "SELECT * from swaption_ref_quotes JOIN swaption_quotes " \ @@ -65,21 +82,7 @@ def atm_vol(index, date, series=None, moneyness=0.2): df = pd.read_sql_query(sql_str, serenitasdb, index_col=['quotedate', 'expiry', 'series'], params=params, parse_dates=['quotedate']) - df1 = atm_vol_calc(df, index) - return df1 - -def atm_vol_calc(df, index): - g_temp = {} - for s, g1 in df.groupby(level='series'): - index_obj = Index.from_name(index, s, '5yr') - for date, g2 in g1.groupby(g1.index.get_level_values(0)): - index_obj.trade_date = date.date() - for expiry, g3 in g2.groupby(g2.index.get_level_values(1)): - g_temp[(date, expiry, s)] = atm_vol_fun(g3, index=index_obj) - df = pd.concat(g_temp, names=['quotedate', 'expiry', 'series']) - df = df.unstack(-1) - df = df.reset_index(level=['expiry', 'series']) - return df + return atm_vol_calc(df, index, moneyness) def rolling_vol(df, col='atm_vol', term=[3]): """compute the rolling volatility for various terms""" @@ -224,11 +227,32 @@ def compute_allocation(df): return (W, fund_return, fund_vol) if __name__ == "__main__": - d1 = sell_vol_strategy(months=1) - d2 = sell_vol_strategy(months=2) - d3 = sell_vol_strategy(months=3) - all_tenors = pd.concat([aggregate_trades(d) for d in [d1, d2, d3]], axis=1) - all_tenors.columns = ['1m', '2m', '3m'] - all_tenors['optimal'] = ((1.2*all_tenors['1m']). - sub(1.2*all_tenors['2m'], fill_value=0). - add(all_tenors['3m'], fill_value=0)) + # d1 = sell_vol_strategy(months=1) + # d2 = sell_vol_strategy(months=2) + # d3 = sell_vol_strategy(months=3) + # all_tenors = pd.concat([aggregate_trades(d) for d in [d1, d2, d3]], axis=1) + # all_tenors.columns = ['1m', '2m', '3m'] + # all_tenors['optimal'] = ((1.2*all_tenors['1m']). + # sub(1.2*all_tenors['2m'], fill_value=0). + # add(all_tenors['3m'], fill_value=0)) + import datetime + import statsmodels.formula.api as smf + ## HY + df = atm_vol("HY", datetime.date(2017, 3, 20)) + df['forward_spread'] *= 1e-4 + df['log_forward_spread'] = np.log(df['forward_spread']) + df['log_atm_vol'] = np.log(df['atm_vol']) + df_hy28 = df.xs(28, level='series') + results = smf.ols('log_atm_vol ~ log_forward_spread + T', data=df_hy28).fit() + beta_hy28 = 1 + results.params.log_forward_spread + print(results.summary()) + + ## IG + df = atm_vol("IG", datetime.date(2017, 3, 20)) + df['forward_spread'] *= 1e-4 + df['log_forward_spread'] = np.log(df['forward_spread']) + df['log_atm_vol'] = np.log(df['atm_vol']) + df_ig28 = df.xs(28, level='series') + results = smf.ols('log_atm_vol ~ log_forward_spread + T', data=df_ig28).fit() + beta_ig28 = 1 + results.params.log_forward_spread + print(results.summary()) |