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Diffstat (limited to 'python/option_trades.py')
| -rw-r--r-- | python/option_trades.py | 146 |
1 files changed, 146 insertions, 0 deletions
diff --git a/python/option_trades.py b/python/option_trades.py new file mode 100644 index 00000000..1b4f18d3 --- /dev/null +++ b/python/option_trades.py @@ -0,0 +1,146 @@ +import pandas as pd +from arch import arch_model +import math +from db import dbengine +import numpy as np +from scipy.interpolate import interp1d + +serenitasdb = dbengine('serenitasdb') + +def get_daily_pnl(index, series, tenor, coupon=1): + sql_str = "SELECT date, adjcloseprice AS close, closespread AS spread, duration, theta FROM index_quotes " \ + "WHERE index=%s and series=%s and tenor = %s" + df = pd.read_sql_query(sql_str, serenitasdb, parse_dates=['date'], + index_col=['date'], params=(index, series, tenor)) + df.sort_index(inplace=True) + df['dt'] = df.index.to_series().diff().astype('timedelta64[D]') + df['pnl'] = df['close'].ffill().diff() + df.dt/360*coupon + return df + +def daily_spreads(index, series, tenor): + """computes daily spreads returns + + Parameters + ---------- + index : string + series : int + tenor : string + """ + sql_str = "SELECT date, closespread AS spread FROM index_quotes " \ + "WHERE index=%s and series=%s and tenor = %s" + df = pd.read_sql_query(sql_str, serenitasdb, parse_dates=['date'], index_col=['date'], + params=(index.upper(), series, tenor)) + df.sort_index(inplace=True) + return df.spread.pct_change().dropna() + +def index_returns(date=None, years=3, index="IG", tenor="5yr"): + """computes on the run returns""" + if date is None: + date = (pd.Timestamp.now() - pd.DateOffset(years=years)).date() + sql_str = "SELECT date, series, closespread AS spread FROM index_quotes " \ + "WHERE index=%s and date>=%s and tenor = %s" + df = pd.read_sql_query(sql_str, serenitasdb, parse_dates=['date'], index_col=['date', 'series'], + params=(index.upper(), date, tenor)) + df.sort_index(inplace=True) + return (df.groupby(level='series'). + transform(lambda x: x.pct_change()). + groupby(level='date'). + last()) + +def realized_vol(index, series, tenor, date=None, years=None): + """computes the realized spread volatility""" + if date is no + returns = daily_spreads(index, series, tenor) + am = arch_model(returns) + res = am.fit(update_freq=0, disp='off') + return (res.conditional_volatility * math.sqrt(252), res) + +def atm_vol_fun(v, ref_is_price=False, moneyness=0.2): + f = interp1d(v.strike.values, v.vol.values, fill_value='extrapolate') + atm_val = v['fwdspread'].iat[0] + otm_val = atm_val * (1 + moneyness) ## doesn't make sense for HY + return pd.Series(f(np.array([atm_val, otm_val])), index = ['atm_vol', 'otm_vol']) + +def atm_vol(index, series, moneyness=0.2): + df = pd.read_sql_query('SELECT quotedate, expiry, strike, vol from swaption_quotes ' \ + 'WHERE index = %s and series = %s', + serenitasdb, index_col=['quotedate', 'expiry'], + params = (index.upper(), series)) + index_data = pd.read_sql_query( + 'SELECT quotedate, expiry, fwdspread from swaption_ref_quotes ' \ + 'WHERE index= %s and series = %s', + serenitasdb, index_col = ['quotedate', 'expiry'], + params = (index.upper(), series)) + + df = df.join(index_data) + df = df.groupby(level=['quotedate', 'expiry']).filter(lambda x: len(x)>=2) + df = df.groupby(level=['quotedate', 'expiry']).apply(atm_vol_fun, index=="HY", moneyness) + df = df.reset_index(level=-1) #move expiry back to the column + return df + +def atm_vol_date(index, date): + df = pd.read_sql_query('SELECT quotedate, series, expiry, strike, vol ' \ + 'FROM swaption_quotes ' \ + 'WHERE index = %s and quotedate >= %s', + serenitasdb, + index_col=['quotedate', 'expiry', 'series'], + params=(index.upper(), date)) + index_data = pd.read_sql_query( + 'SELECT quotedate, expiry, series, fwdspread FROM swaption_ref_quotes ' \ + 'WHERE index= %s and quotedate >= %s', + serenitasdb, index_col=['quotedate', 'expiry', 'series'], + params = (index.upper(), date)) + df = df.join(index_data) + df = df.groupby(df.index).filter(lambda x: len(x)>=2) + df = df.groupby(level=['quotedate', 'expiry', 'series']).apply(atm_vol_fun) + df = df.reset_index(level=['expiry', 'series']) #move expiry and series back to the columns + return df + +def rolling_vol(df, col='atm_vol', term=[3]): + """compute the rolling volatility for various terms""" + df = df.groupby(df.index).filter(lambda x: len(x)>2) + def aux(s, col, term): + k = s.index[0] + f = interp1d(s.expiry.values.astype('float'), s[col].values, fill_value='extrapolate') + x = np.array([(k + pd.DateOffset(months=t)).to_datetime64().astype('float') \ + for t in term]) + return pd.Series(f(x), index=[str(t)+'m' for t in term]) + + df = df.groupby(level='quotedate').apply(aux, col, term) + # MS quotes don't have fwdspread so they end up as NA + return df.dropna() + +def vol_var(percentile=0.99, index='IG'): + df = atm_vol_date("IG", datetime.date(2014, 6, 11)) + df = rolling_vol(df, term=[1,2,3]) + df = df.sort_index() + df = df.groupby(df.index.date).last() + return df.pct_change().quantile(percentile) + +def lr_var(res): + """ computes long run variance of the garch process""" + var = res.params.omega/(1 - res.params['alpha[1]'] - res.params['beta[1]']) + return math.sqrt(var) * math.sqrt(252) + +if __name__ == "__main__": + series = 23 + rv, res = realized_vol("ig", series, "5yr") + rv = pd.DataFrame(rv) + rv = rv.reset_index() + df_vol = atm_vol("ig", series) + df_vol = rolling_vol(df_vol, term=[1, 2, 3]) + realized_vs_atm = pd.merge_asof(rv, df_vol, on='date') + realized_vs_atm.set_index('date', inplace=True) + fig = realized_vs_atm[['cond_vol', '1m', '2m', '3m']].plot() + + #compute series + top10 = pd.DataFrame() + for series in [23, 24, 25, 26, 27]: + df_vol = atm_vol("ig", series) + df_vol = rolling_vol(df_vol, term=[1, 2, 3]) + df_vol.set_index('date', inplace=True) + daily_vol = df_vol.resample('D').last() + daily_vol['series'] = series + daily_vol = pd.DataFrame(daily_vol['3m'].diff().abs().nlargest(10)) + daily_vol['series'] = series + top10 = top10.append(daily_vol) |