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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, ProbSurface
from analytics.scenarios import run_swaption_scenarios, run_index_scenarios, run_portfolio_scenarios, run_tranche_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 run_scen(portf, tranche, spread_shock):
#Start with swaptions
index = portf.indices[0].index_type
series = portf.indices[0].series
trade_date=portf.indices[0].trade_date
earliest_expiry = min(portf.swaptions, key=lambda x: x.exercise_date).exercise_date
date_range = pd.bdate_range(trade_date, earliest_expiry - pd.offsets.BDay(), freq='5B')
vs = BlackSwaptionVolSurface(index,series, trade_date=trade_date)
ps = ProbSurface(index,series, trade_date=trade_date)
vol_surface = vs[vs.list(option_type='payer')[-1]]
df = run_portfolio_scenarios(portf, date_range, spread_shock, np.array([0]),
vol_surface, params=["pnl", "delta"])
df['frac_year'] = (df.index - pd.to_datetime(trade_date)).days/365
df['prob'] = df.apply(lambda df: ps.tail_prob(df.frac_year, df.spread, ps.list()[-1]), axis=1)
#now do the tranches
spread_range = (1+ spread_shock) * portf.indices[0].spread
results = run_tranche_scenarios(tranche, spread_range, date_range)
results.date = pd.to_datetime(results.date)
notional = 10000000
results['delta_tranche'] = -notional * (results['0-3_delta'] - 6* results['7-15_delta'])
results['pnl_tranche'] = notional * (results['0-3_pnl'] + results['0-3_carry'] -
6* (results['7-15_pnl'] + results['7-15_carry']))
results.index.name = 'spread'
#combine
df = df.reset_index().merge(results.reset_index(), on=['date', 'spread'])
df['final_pnl'] = df.pnl_tranche + df.pnl
df['final_delta'] = df.delta_tranche + df.delta
return df
def set_port():
#Construct Portfolio
option_delta = Index.from_name('IG', 30, '5yr')
option_delta.spread = 59
option1 = BlackSwaption(option_delta, date(2018, 6, 20), 80, option_type="payer")
option1.sigma = .621
option1.direction = 'Short'
option1.notional = 150_000_000
option_delta.notional = 1
portf = Portfolio([option1, option_delta])
portf.reset_pv()
trade_date = (pd.datetime.today() - pd.offsets.BDay(1)).normalize()
tranche = bkt.TrancheBasket('IG', 29, '5yr', trade_date=trade_date)
return portf, tranche
def set_df():
portf, tranche = set_port()
shock_min = -.3
shock_max = .8
spread_shock = np.arange(shock_min, shock_max, 0.05)
shock_range = (1+ spread_shock) * portf.indices[0].spread
results = run_scen(portf, tranche, spread_shock)
results = results.set_index('date')
return results, shock_range
def plot_scenarios():
df, shock_range = set_df()
plot_time_color_map(df, shock_range, attr="final_pnl")
plot_time_color_map(df, shock_range, attr="final_delta", color_map= 'rainbow', centered = False)
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