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import sys
sys.path.append("..")
from graphics import plot_time_color_map
import analytics.tranche_functions as tch
import analytics.tranche_basket as bkt
import analytics.basket_index as idx_bkt
import numpy as np
import pandas as pd
from analytics import Swaption, BlackSwaption, Index, BlackSwaptionVolSurface, Portfolio
from analytics.scenarios import run_swaption_scenarios, run_index_scenarios, run_portfolio_scenarios
import exploration.swaption_calendar_spread as spread
from scipy.interpolate import interp1d
from datetime import date
from db import dbengine
engine = dbengine('serenitasdb')
def rv_calc1():
#let's do IG27 from IG29, need to get the quotes from risk_numbers_new not just random ones
#Get IG29-1 year shortened rho with TLP, compare to IG27 5y rho
index = 'IG'
series = 29
series2 = series -2
tenor = '5yr'
shortened = 4
method = 'TLP'
#Read existing results, find which ones need to run
try:
results = pd.read_csv(f"/home/serenitas/edwin/Python/rv_{index}{series}.csv",
parse_dates=['date'], index_col=['date'])
except IOError:
results = pd.DataFrame()
sql_string = "select distinct date from risk_numbers_new where index = %s and series = %s order by date desc"
df = pd.read_sql_query(sql_string, engine, params=(index, series), parse_dates=['date'])
df1 = pd.read_sql_query(sql_string, engine, params=(index, series2), parse_dates=['date'])
df = df.merge(df1, on=['date'])
df = df[~df.date.isin(results.index)]
rho_tlp, pv_tlp, rho_prev_index, pv_prev_index = [], [], [], []
tranche = bkt.TrancheBasket('IG', series, '5yr')
tranche2 = bkt.TrancheBasket('IG', series2, '5yr')
for trade_date in df.date:
tranche.trade_date = trade_date
tranche2.trade_date = trade_date
tranche.build_skew()
tranche.rho = tranche.map_skew(tranche, method, 4)
pv = tranche.tranche_pvs().bond_price
rho_tlp.append(tranche.rho[1:-1])
pv_tlp.append(pv)
tranche2.build_skew()
rho_prev_index.append(tranche2.rho[1:-1])
tranche.rho = tranche2.rho
pv = tranche.tranche_pvs(shortened=4).bond_price
pv_prev_index.append(pv)
temp1 = pd.DataFrame(rho_tlp, index=df.date, columns=['3_rho_tlp','7_rho_tlp','15_rho_tlp'])
temp2 = pd.DataFrame(pv_tlp, index=df.date, columns=['03_pv_tlp','37_pv_tlp','715_pv_tlp','15100_pv_tlp'])
temp3 = pd.DataFrame(rho_prev_index, index=df.date, columns=['3_rho_ig27','7_rho_ig27','15_rho_ig27'])
temp4 = pd.DataFrame(pv_prev_index, index=df.date, columns=['03_pv_ig27','37_pv_ig27','715_pv_ig27','15100_pv_ig27'])
results = results.append(pd.concat([temp1, temp2, temp3, temp4], axis=1))
result.to_csv("/home/serenitas/edwin/Python/rv_" + index + series + ".csv")
def dispersion():
from quantlib.time.api import Schedule, Rule, Date, Period, WeekendsOnly
from quantlib.settings import Settings
curves = {}
maturities = {}
settings = Settings()
for series in [24, 25, 26, 27, 28, 29]:
index_temp = idx_bkt.MarkitBasketIndex('IG', series, ["5yr",], trade_date=trade_date)
maturities[series] = index_temp.maturities[0]
cds_schedule = Schedule.from_rule(settings.evaluation_date, Date.from_datetime(maturities[series]),
Period('3M'), WeekendsOnly(), date_generation_rule=Rule.CDS2015)
sm, tickers = index_temp.survival_matrix(cds_schedule.to_npdates().view('int') + 134774)
curves[series] = pd.DataFrame(1 - sm, index=tickers, columns=cds_schedule)
#temp = (pd.to_datetime(maturities[series]) - datetime.datetime(1970,1,1)).days + 134774
#curves[series] = pd.concat([c.to_series() for _,_, c in index_temp.items()], axis=1)
curve_df = pd.concat(curves).stack()
curve_df.index.rename(['series', 'maturity', 'name'], inplace=True)
disp = {}
for series in [24, 25, 26, 27, 28, 29]:
temp = curve_df.xs([series, maturities[series].strftime('%Y-%m-%d')])
temp = temp[pd.qcut(temp, 10, labels=False) == 9]
disp[series] = temp.std()/temp.mean()
dispersion = pd.concat(disp)
curve_df.groupby(['series', 'maturity']).mean()
curve_df.groupby(['series', 'maturity']).std()
def scenarios(tranche, shock_range=None, roll_corr=False):
from copy import deepcopy
tranche.build_skew()
orig_tranche_cl, _, orig_tranche_pv = tranche.tranche_pvs()
if shock_range is None:
shock, step = 1, 10
shock_range = (1 + np.linspace(-.3, shock, step)) * tranche.tranche_quotes.indexrefspread[0]
#create empty lists
shock_index_pv_calc = np.empty(len(shock_range))
shock_tranche_pv = np.empty((len(shock_range), tranche.K.size - 1))
shock_tranche_delta = np.empty((len(shock_range), tranche.K.size - 1))
shock_tranche_cl = np.empty((len(shock_range), tranche.K.size - 1))
shock_tranche_carry = np.empty((len(shock_range), tranche.K.size - 1))
results = pd.DataFrame()
for shortened in [0,1,2]:
temp_tranche = deepcopy(tranche)
if shortened > 0:
temp_tranche.cs = temp_tranche.cs[:-shortened]
for i, shock in enumerate(shock_range):
temp_tranche.tweak(shock)
if roll_corr is True:
temp_tranche.rho = tranche.map_skew(temp_tranche, 'TLP')
shock_index_pv_calc[i] = temp_tranche._snacpv(shock * 1e-4, temp_tranche.coupon(temp_tranche.maturity), temp_tranche.recovery)
shock_tranche_cl[i], _, shock_tranche_pv[i] = temp_tranche.tranche_pvs()
shock_tranche_delta[i] = temp_tranche.tranche_deltas()['delta']
shock_tranche_carry[i] = temp_tranche.tranche_quotes.running
temp1 = pd.DataFrame(shock_tranche_pv, index=shock_range, columns=[s + "_pv" for s in tranche._row_names])
temp2 = pd.DataFrame(shock_tranche_delta, index=shock_range, columns=[s + "_delta" for s in tranche._row_names])
temp3 = pd.DataFrame(np.subtract(shock_tranche_pv, orig_tranche_pv), index=shock_range, columns=[s + "_pnl" for s in tranche._row_names])
temp4 = pd.DataFrame(shock_index_pv_calc, index=shock_range, columns=['index_price_snacpv'])
temp5 = pd.DataFrame(shock_tranche_carry, index=shock_range, columns=[s + "_carry" for s in tranche._row_names])
#temp5 = pd.DataFrame(np.subtract(shock_tranche_cl, orig_tranche_cl), index=shock_range, columns=[s + "_coupon_pnl" for s in tranche._row_names])
df = pd.concat([temp1, temp2, temp3, temp4, temp5], axis=1)
if shortened > 0:
df['days'] = ((tranche.cs.index[-1] - tranche.cs.index[-shortened-1])/ np.timedelta64(1, 'D')).astype(int)
else:
df['days'] = 0
for column in [s + "_carry" for s in tranche._row_names]:
df[column] *= df['days']/365
results = results.append(df)
return results
def run_scen(trade_date = pd.Timestamp.today().normalize()- pd.offsets.BDay()):
option_delta = Index.from_tradeid(910)
option1 = BlackSwaption.from_tradeid(13, option_delta)
option2 = BlackSwaption.from_tradeid(12, option_delta)
portf = Portfolio([option1, option2, option_delta])
#Start with swaptions
portf.reset_pv()
portf.mark()
earliest_date = min(portf.swaptions, key=lambda x: x.exercise_date).exercise_date
#date_range = pd.bdate_range(portf.indices[0].trade_date, earliest_date - BDay(), freq = '3B')
date_range = pd.date_range(trade_date, periods=4, freq = '5B')
vol_shock = np.arange(-0.01, 0.01, 0.01)
shock_min=-.3
shock_max=.8
spread_shock = np.arange(shock_min, shock_max, 0.05)
index = portf.indices[0].name.split()[1]
series = portf.indices[0].name.split()[3][1:]
vs = BlackSwaptionVolaSurface(index, series, trade_date=trade_date)
vol_surface = vs[vs.list(option_type='payer')[-1]]
df = run_portfolio_scenarios(portf, date_range, spread_shock, vol_shock, vol_surface,
params=["pnl","delta"])
df = df[df.vol_shock == 0]
df['days'] = ((df.index - trade_date)/ np.timedelta64(1, 'D')).astype(int)
#now do the tranches
tranche = bkt.TrancheBasket('IG', 29, '5yr', trade_date=trade_date)
shock_range = (1 + spread_shock) * portf.indices[0].spread
results = scenarios(tranche, shock_range, date_range)
results.set_index('days', append=True)
notional = 10000000
results['delta'] = -notional * (results['0-3_delta'] - 6* results['7-15_delta'])
results['pnl'] = notional* (results['0-3_pnl'] + results['0-3_carry'] - 6* (results['7-15_pnl'] + results['7-15_carry']))
results['date'] = tranche.trade_date + results.days * pd.offsets.Day()
results.index.name = 'spread'
#now combine the results
f = {}
for i, g in results.groupby('spread'):
f[i] = interp1d(g.days, g.pnl)
df['total_pnl'] = df.apply(lambda df: f[df.spread](df.days), axis = 1)
df.total_pnl = df.total_pnl.astype(float)
return results, df, shock_range
def plot_pnl():
a, b, shock_range = run_scen()
a.reset_index(inplace=True)
a.set_index('date', inplace=True)
#plot Tranche only PNL
plot_time_color_map(a, shock_range, attr="pnl")
#plot swaption only PNL
plot_time_color_map(b, shock_range, attr="pnl")
#plot Tranche and Swaption PNL
plot_time_color_map(b, shock_range, attr="total_pnl")
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