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import pandas as pd
import datetime
from db import dbengine
from yieldcurve import YC
def ver_one():
date = (datetime.date.today() + pd.offsets.BMonthEnd(-1)).date()
df = pd.read_sql_query("select * from risk_positions(%s, 'Subprime') where price >0 and length(identifier) = 9"
, dbengine('dawndb'), params = [date], index_col=['identifier'])
v1_temp = {}
for cusip in df.index:
v1_temp[cusip] = pd.read_sql_query("select * from priced_percentiles where date(timestamp) = %s and cusip = %s and model_version = 1"
, dbengine('dawndb'), params = [date, cusip])
v1 = pd.concat(v1_temp)
v1 = v1.set_index('cusip')
v1 = v1.merge(df, left_index=True, right_index=True)
yc = YC(evaluation_date=date)
v1 = v1.replace('M_MTG_GOOD','M_MTG_LMG')
v1['return'] = v1.apply(lambda df: (df.pv/(df.price/100)) ** (1/df.moddur) - 1 + yc.zero_rate(df.moddur).rate, axis = 1)
port_ret = v1.groupby('percentile').apply(wavg, 'return', 'usd_market_value')
strat_ret = v1.groupby(['percentile','strategy']).apply(wavg, 'return', 'usd_market_value').unstack(-1)
port_ret = port_ret.rename('Portfolio')
strat_ret = strat_ret.join(port_ret)
return strat_ret
def plot_strat():
import seaborn as sns
strat_ret = ver_one()
strat_ret = strat_ret.rename(columns={ \
'M_MTG_FP': 'First Pay',\
'M_MTG_IO': 'Interest Only',\
'M_MTG_LMG': 'Long Duration Par',\
'M_MTG_PR': 'Pro-Rata Pay',\
'M_MTG_RW': 'R&W Dependent',\
'M_MTG_SD': 'Stepdown',\
'M_MTG_THRU': 'Pass-through'
})
sns.set(style="darkgrid")
ax = strat_ret.plot(figsize = [10, 3.5])
ax.set_ylim([-.2,.6])
vals = ax.get_yticks()
ax.set_yticklabels(['{:3.0f}%'.format(x*100) for x in vals])
ax.set_ylabel('return')
ax.legend(loc='upper center', bbox_to_anchor=(0.5, -.05), shadow=True, ncol=3)
def wavg(group, avg_name, weight_name):
d = group[avg_name]
w = group[weight_name]
try:
return (d * w).sum() / w.sum()
except ZeroDivisionError:
return d.mean()
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