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, 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 earliest_expiry = min(portf.swaptions, key=lambda x: x.exercise_date).exercise_date date_range = pd.bdate_range(portf.indices[0].trade_date, earliest_expiry - pd.offsets.BDay(), freq='5B') vs = BlackSwaptionVolSurface(portf.indices[0].index_type, portf.indices[0].series, trade_date=portf.indices[0].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"]) #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)