from .db import _engine, dbconn from dates import bond_cal import numpy as np from .utils import tenor_t from functools import lru_cache from pyisda.curve import SpreadCurve from multiprocessing import Pool from yieldcurve import get_curve import datetime import pandas as pd def insert_quotes(): """ backpopulate some version i+1 quotes one day before they start trading so that we get continuous time series when we compute returns. We can also do it in sql as follows: INSERT INTO index_quotes(date, index, series, version, tenor, closeprice) SELECT date, index, series, version+1, tenor, (factor1*closeprice-100*0.355)/factor2 FROM index_quotes WHERE index='HY' and series=23 and date='2017-02-02' """ 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", _engine, parse_dates=['date'], index_col=['date']) df = df.loc[dates] for tup in df.itertuples(): result = _engine.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) _engine.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 get_index_quotes(index=None, series=None, tenor=None, from_date=None, years=3, remove_holidays=True, source='MKIT'): args = locals().copy() del args['remove_holidays'] if args['years'] is not None: args['date'] = (pd.Timestamp.now() - pd.DateOffset(years=years)).date() del args['years'] if args['from_date']: args['date'] = args['from_date'] del args['from_date'] def make_str(key, val): if isinstance(val, list): op = "IN" return "{} IN %({})s".format(key, key) elif isinstance(val, datetime.date): op = ">=" else: op = "=" return "{} {} %({})s".format(key, op, key) where_clause = " AND ".join(make_str(k, v) for k, v in args.items() if v is not None) sql_str = "SELECT * FROM index_quotes_pre LEFT JOIN index_risk2 USING (id)" if where_clause: sql_str = " WHERE ".join([sql_str, where_clause]) def make_params(args): return {k: tuple(v) if isinstance(v, list) else v for k, v in args.items() if v is not None} df = pd.read_sql_query(sql_str, _engine, parse_dates=['date'], index_col=['date', 'index', 'series', 'version'], params=make_params(args)) df.tenor = df.tenor.astype(tenor_t) df = df.set_index('tenor', append=True) df.sort_index(inplace=True) # get rid of US holidays if remove_holidays: dates = df.index.levels[0] if index in ['IG', 'HY']: holidays = bond_cal().holidays(start=dates[0], end=dates[-1]) df = df.loc(axis=0)[dates.difference(holidays), :, :] return df def index_returns(df=None, index=None, series=None, tenor=None, from_date=None, years=3, per=1): """computes spreads and price returns Parameters ---------- df : pandas.DataFrame index : str or List[str], optional index type, one of 'IG', 'HY', 'EU', 'XO' series : int or List[int], optional tenor : str or List[str], optional tenor in years e.g: '3yr', '5yr' date : datetime.date, optional starting date years : int, optional limits many years do we go back starting from today. per: int, optional calculate returns across different time frames """ if df is None: df = get_index_quotes(index, series, tenor, from_date, years) spread_return = (df. groupby(level=['index', 'series', 'tenor', 'version']). close_spread. pct_change(periods=per)) price_return = (df. groupby(level=['index', 'series', 'tenor', 'version']). close_price. diff() / 100) df = pd.concat([spread_return, price_return], axis=1, keys=['spread_return', 'price_return']) df = df.groupby(level=['date', 'index', 'series', 'tenor']).nth(0) coupon_data = pd.read_sql_query("SELECT index, series, tenor, coupon * 1e-4 AS coupon, " "maturity FROM " "index_maturity WHERE coupon is NOT NULL", _engine, index_col=['index', 'series', 'tenor']) df = df.reset_index('date').join(coupon_data).reset_index('tenor') # for some reason pandas doesn't keep the categories, so we have to # do this little dance df.tenor = df.tenor.astype(tenor_t) df = df.set_index('tenor', append=True) df['day_frac'] = (df.groupby(level=['index', 'series', 'tenor'])['date']. transform(lambda s: s. diff(). astype('timedelta64[D]') / 360)) df['price_return'] += df.day_frac * df.coupon df = df.drop(['day_frac', 'coupon', 'maturity'], axis=1) return df.set_index(['date'], append=True) def get_singlenames_quotes(indexname, date, tenors): conn = dbconn('serenitasdb') with conn.cursor() as c: c.execute("SELECT * FROM curve_quotes2(%s, %s, %s)", vars=(indexname, date, list(tenors))) return list(c) def build_curve(r, tenors, currency="USD"): if r['date'] is None: raise ValueError(f"Curve for {r['cds_ticker']} is missing") spread_curve = 1e-4 * np.array(r['spread_curve'], dtype='float') upfront_curve = 1e-2 * np.array(r['upfront_curve'], dtype='float') recovery_curve = np.array(r['recovery_curve'], dtype='float') yc = get_curve(r['date'], currency) try: sc = SpreadCurve(r['date'], yc, None, None, None, tenors, spread_curve, upfront_curve, recovery_curve, ticker=r['cds_ticker'], defaulted=r['event_date']) except ValueError as e: print(r[0], e) return None return sc def build_curves(quotes, args): return [build_curve(q, *args) for q in quotes if q is not None] def build_curves_dist(quotes, args, workers=4): # about twice as fast as the non distributed version # non thread safe for some reason so need ProcessPool with Pool(workers) as pool: r = pool.starmap(build_curve, [(q, *args) for q in quotes], 30) return r @lru_cache(maxsize=16) def _get_singlenames_curves(index_type, series, trade_date, tenors): sn_quotes = get_singlenames_quotes(f"{index_type.lower()}{series}", trade_date, tenors) currency = "EUR" if index_type in ["XO", "EU"] else "USD" args = (np.array(tenors), currency) return build_curves_dist(sn_quotes, args) def get_singlenames_curves(index_type, series, trade_date, tenors=(0.5, 1, 2, 3, 4, 5, 7, 10)): # tenors need to be a subset of (0.5, 1, 2, 3, 4, 5, 7, 10) if isinstance(trade_date, pd.Timestamp): trade_date = trade_date.date() return _get_singlenames_curves(index_type, series, min(datetime.date.today(), trade_date), tenors) def get_tranche_quotes(index_type, series, tenor, date=datetime.date.today()): conn = dbconn('serenitasdb') with conn.cursor() as c: c.callproc("get_tranche_quotes", (index_type, series, tenor, date)) return pd.DataFrame.from_records(dict(d) for d in c)