diff options
Diffstat (limited to 'python/exploration/tranches.py')
| -rw-r--r-- | python/exploration/tranches.py | 76 |
1 files changed, 1 insertions, 75 deletions
diff --git a/python/exploration/tranches.py b/python/exploration/tranches.py index 430e8492..a8e8d05e 100644 --- a/python/exploration/tranches.py +++ b/python/exploration/tranches.py @@ -9,7 +9,6 @@ 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 @@ -77,7 +76,7 @@ def dispersion(): maturities = {} settings = Settings() for series in [24, 25, 26, 27, 28, 29]: - index_temp = idx_bkt.MarkitBasketIndex('IG', series, ["5yr",], trade_date=trade_date) + index_temp = idx_bkt.MarkitBasketIndex('IG', series, ["5yr",], value_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) @@ -95,76 +94,3 @@ def dispersion(): 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) |