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from analytics.curve_trades import on_the_run
from analytics.index_data import get_index_quotes, index_returns
from db import dbengine
import pandas as pd
import math
import datetime
dawndb = dbengine('dawndb')
serenitasdb = dbengine('serenitasdb')
def hist_var(portf, index_type='IG', quantile=.05, years=5):
df = index_returns(index=index_type, years=years,
tenor=['3yr', '5yr', '7yr', '10yr'])
df = (df.reset_index(['index'], drop=True).
reorder_levels(['date', 'series', 'tenor']))
returns = df.spread_return.dropna().reset_index('series')
returns['dist_on_the_run'] = (returns.
groupby('date')['series'].
transform(lambda x: x.max() - x))
del returns['series']
returns = returns.set_index('dist_on_the_run', append=True).unstack('tenor')
returns.columns = returns.columns.droplevel(0)
portf.reset_pv()
otr = on_the_run(index_type)
spreads = pd.DataFrame({'spread': portf.spread,
'tenor': [ind.tenor for ind in portf.indices],
'dist_on_the_run': [otr - ind.series for ind in portf.indices]})
spreads = spreads.set_index(['dist_on_the_run', 'tenor'])
r = []
for k, g in returns.groupby(level='date', as_index=False):
shocks = g.reset_index('date', drop=True).stack('tenor')
shocks.name = 'shocks'
portf.spread = spreads.spread * (1 + spreads.join(shocks).shocks)
r.append((k, portf.pnl))
pnl = pd.DataFrame.from_records(r, columns=['date', 'pnl'], index=['date'])
return pnl.quantile(quantile) * math.sqrt(12)
def rel_spread_diff(report_date=datetime.date.today(), index='HY', rolling=10):
otr = on_the_run(index)
## look at spreads
df = get_index_quotes(index, list(range(otr - rolling, otr + 1)),
tenor=['3yr', '5yr', '7yr', '10yr'], years=5)
duration = df.duration.xs((report_date, '5yr', otr), level=['date', 'tenor', 'series'])[-1]
df = df.xs('5yr', level='tenor')['closespread'].groupby(['date']).last()
df = df.loc['2013-01-15':report_date]
curr_spread = df.iloc[-1]
df = df.pct_change(freq='22B').dropna()
return df.groupby('date').last(), curr_spread, duration
def get_pos(report_date, strategy=None):
if strategy is None:
return pd.read_sql_query("SELECT * from list_cds_marks(%s)",
dawndb, params=(report_date,))
else:
return pd.read_sql_query("SELECT * from list_cds_marks(%s, %s)",
dawndb, params=(report_date, strategy))
def cleared_cds_margins(report_date=datetime.date.today()):
df = get_pos(report_date)
#Cap Allocation for Deltas
percentile = .95 #monthly 90%tile case...
shocks, widen, tighten, onTR_dur, onTR_spread = {}, {}, {}, {}, {}
for ind in ['IG', 'HY', 'EU']:
shocks[ind], onTR_spread[ind], onTR_dur[ind] = rel_spread_diff(report_date, index=ind)
widen[ind] = shocks[ind].quantile(percentile)
tighten[ind] = shocks[ind].quantile(1-percentile)
df['onTR_notional'] = df.apply(lambda df:
df.notional * df.factor * df.duration / onTR_dur[df.p_index], axis=1)
df['widen'] = df.apply(lambda df:
df.onTR_notional * onTR_spread[df.p_index] * onTR_dur[df.p_index] * widen[df.p_index]/10000, axis=1)
df['tighten'] = df.apply(lambda df:
df.onTR_notional * onTR_spread[df.p_index] * onTR_dur[df.p_index] * tighten[df.p_index]/10000, axis=1)
delta_alloc = df.groupby('strategy').sum()
delta_alloc['total'] = delta_alloc.apply(lambda df: max(abs(df.widen), abs(df.tighten)), axis=1)
return delta_alloc
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