diff options
Diffstat (limited to 'python')
| -rw-r--r-- | python/option_trades.py | 31 |
1 files changed, 30 insertions, 1 deletions
diff --git a/python/option_trades.py b/python/option_trades.py index c3f9cb77..7aae31ba 100644 --- a/python/option_trades.py +++ b/python/option_trades.py @@ -9,7 +9,7 @@ from arch import arch_model from db import dbengine, dbconn from scipy.interpolate import interp1d from analytics import Index - +from dates import bond_cal serenitasdb = dbengine('serenitasdb') def get_daily_pnl(index, series, tenor, coupon=1): @@ -38,6 +38,29 @@ def daily_spreads(index, series, tenor): df.sort_index(inplace=True) return df.spread.pct_change().dropna() +def insert_quotes(): + # backpopulate some version i+1 quotes one day before they start trading so that + # we get continuous time series in the returns + dates = pd.DatetimeIndex(['2014-05-21', '2015-02-19', '2015-03-05','2015-06-23']) + df = pd.read_sql_query("SELECT DISTINCT ON (date) * FROM index_quotes " \ + "WHERE index='HY' AND tenor='5yr' " \ + "ORDER BY date, series DESC, version DESC", + serenitasdb, parse_dates=['date'], index_col=['date']) + df = df.loc[dates] + for tup in df.itertuples(): + result = serenitasdb.execute("SELECT indexfactor, cumulativeloss FROM index_version " \ + "WHERE index = 'HY' AND series=%s AND version in (%s, %s)" \ + "ORDER BY version", + (tup.series, tup.version, tup.version+1)) + factor1, cumloss1 = result.fetchone() + factor2, cumloss2 = result.fetchone() + recovery = 1-(cumloss2-cumloss1) + version2_price = (factor1 * tup.closeprice - 100*recovery)/factor2 + print(version2_price) + serenitasdb.execute("INSERT INTO index_quotes(date, index, series, version, tenor, closeprice)" \ + "VALUES(%s, %s, %s, %s, %s, %s)", + (tup.Index, 'HY', tup.series, tup.version+1, tup.tenor, version2_price)) + def index_returns(date=None, years=3, index="IG", tenor="5yr"): """computes on the run spread returns""" if date is None: @@ -50,6 +73,7 @@ def index_returns(date=None, years=3, index="IG", tenor="5yr"): df.sort_index(inplace=True) return (df.groupby(level=['series', 'version']). transform(lambda x: x.pct_change()). + dropna(). groupby(level='date'). last()) @@ -64,6 +88,11 @@ def index_price_returns(date=None, years=3, index="IG", tenor="5yr"): index_col=['date', 'series', 'version'], params=(index.upper(), date, tenor)) df.sort_index(inplace=True) + ## get rid of holidays + dates = df.index.levels[0] + holidays = bond_cal().holidays(start=dates[0], end=dates[-1]) + df = df.loc(axis=0)[dates.difference(holidays),:,:] + def returns(df, coupon=1): df['returns'] = df.price.pct_change() + \ coupon * df.index.levels[0].to_series().diff().dt.days/360/df.price |