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| -rw-r--r-- | python/exploration/swaption_calendar_spread.py | 48 |
1 files changed, 48 insertions, 0 deletions
diff --git a/python/exploration/swaption_calendar_spread.py b/python/exploration/swaption_calendar_spread.py new file mode 100644 index 00000000..1da708ec --- /dev/null +++ b/python/exploration/swaption_calendar_spread.py @@ -0,0 +1,48 @@ +import sys +#don't do this at home +sys.path.append("..") +from analytics import Swaption, Index, VolatilitySurface +from analytics.scenarios import run_swaption_scenarios +from pandas.tseries.offsets import BDay +import datetime +import numpy as np +import pandas as pd +from scipy.interpolate import SmoothBivariateSpline +from matplotlib import cm +from mpl_toolkits.mplot3d import Axes3D +import matplotlib.pyplot as plt + +def plot_df(df, spread_shock, vol_shock): + fig = plt.figure() + ax = fig.gca(projection='3d') + ## use smoothing spline on a finer grid + f = SmoothBivariateSpline(df.vol_shock.values, df.spread_shock.values, + df.pv.values) + xx, yy = np.meshgrid(vol_shock, spread_shock) + surf = ax.plot_surface(xx, yy, f(vol_shock, spread_shock).T, cmap=cm.viridis) + ax.set_xlabel("volatility shock") + ax.set_ylabel("spread shock") + ax.set_zlabel("PV") + +trade_date = datetime.date(2017, 2, 23) +ig27 = Index.from_name("IG", 27, '5yr', trade_date=trade_date) +ig27.ref = 62 +payer1 = Swaption(ig27, datetime.date(2017, 4, 19), 65) +payer2 = Swaption(ig27, datetime.date(2017, 5, 17), 72) +payer1.notional = 100e6 +payer2.notional = 100e6 +date_range = pd.bdate_range(trade_date, pd.Timestamp('2017-04-19') - BDay(), freq = '5B') +vol_shock = np.arange(-0.15, 0.3, 0.01) +spread_shock = np.arange(-0.2, 0.3, 0.01) +vs = VolatilitySurface("IG", 27, trade_date=trade_date) +vol_surface = vs[vs.list()[-1]] + +df1 = run_swaption_scenarios(payer1, date_range, spread_shock, vol_shock, vol_surface, + params=['pv_black', 'delta']) +df2 = run_swaption_scenarios(payer2, date_range, spread_shock, vol_shock, vol_surface, + params=['pv_black', 'delta']) +#plot it +df = df1 +df = df.assign(pv=df1.pv_black-df2.pv_black) +plot_df(df.loc[date_range[-1]], np.arange(-0.2, 0.3, 0.001), + np.arange(-0.15, 0.3, 0.001)) |