import sys #don't do this at home sys.path.append("..") from analytics import Swaption, BlackSwaption, Index, VolatilitySurface from analytics.scenarios import run_swaption_scenarios, run_index_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 import os import numpy as np import matplotlib import matplotlib.pyplot as plt from mpl_toolkits.axes_grid1 import AxesGrid def shiftedColorMap(cmap, start=0, midpoint=0.5, stop=1.0, name='shiftedcmap'): ''' Function to offset the "center" of a colormap. Useful for data with a negative min and positive max and you want the middle of the colormap's dynamic range to be at zero Input ----- cmap : The matplotlib colormap to be altered start : Offset from lowest point in the colormap's range. Defaults to 0.0 (no lower ofset). Should be between 0.0 and `midpoint`. midpoint : The new center of the colormap. Defaults to 0.5 (no shift). Should be between 0.0 and 1.0. In general, this should be 1 - vmax/(vmax + abs(vmin)) For example if your data range from -15.0 to +5.0 and you want the center of the colormap at 0.0, `midpoint` should be set to 1 - 5/(5 + 15)) or 0.75 stop : Offset from highets point in the colormap's range. Defaults to 1.0 (no upper ofset). Should be between `midpoint` and 1.0. ''' cdict = { 'red': [], 'green': [], 'blue': [], 'alpha': [] } # regular index to compute the colors reg_index = np.linspace(start, stop, 257) # shifted index to match the data shift_index = np.hstack([ np.linspace(0.0, midpoint, 128, endpoint=False), np.linspace(midpoint, 1.0, 129, endpoint=True) ]) for ri, si in zip(reg_index, shift_index): r, g, b, a = cmap(ri) cdict['red'].append((si, r, r)) cdict['green'].append((si, g, g)) cdict['blue'].append((si, b, b)) cdict['alpha'].append((si, a, a)) newcmap = matplotlib.colors.LinearSegmentedColormap(name, cdict) plt.register_cmap(cmap=newcmap) return newcmap def plot_df(df, spread_shock, vol_shock, attr="pv"): val_date = df.index[0].date() fig = plt.figure() ax = fig.gca(projection='3d') ## use smoothing spline on a finer grid series = df[attr] f = SmoothBivariateSpline(df.vol_shock.values, df.spread_shock.values, series.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") ax.set_zlabel("PnL") ax.set_title('{} of Trade on {}'.format(attr.title(), val_date)) def plot_color_map(df, spread_shock, vol_shock, attr="pv", path="."): val_date = df.index[0].date() #rows are spread, columns are volatility surface shift fig, ax = plt.subplots() series = df[attr] #Different ways to do a colormap: imshow and pcolormesh. using imshow here midpoint = 1 - series.max() / (series.max() + abs(series.min())) shifted_cmap = shiftedColorMap(cm.RdYlGn, midpoint=midpoint, name='shifted') chart = ax.imshow(series.values.reshape(spread_shock.size, vol_shock.size).T, extent=(spread_shock.min(), spread_shock.max(), vol_shock.min(), vol_shock.max()), aspect='auto', interpolation='bilinear', cmap=shifted_cmap) ax.set_xlabel('Spread') ax.set_ylabel('Volatility shock') ax.set_title('{} of Trade on {}'.format(attr.title(), val_date)) fig.colorbar(chart, shrink=.8) #fig.savefig(os.path.join(path, "payer_swap_", attr, "_{}.png".format(val_date))) def plot_time_color_map(df, spread_shock, attr="pv", path="."): val_date = df.index[0].date() dftemp = df.reset_index() dftemp['days'] = (dftemp['date'] - val_date).dt.days date_range = dftemp.days.unique() #rows are spread, columns are volatility surface shift fig, ax = plt.subplots() series = df[attr] #Different ways to do a colormap: imshow and pcolormesh. using imshow here midpoint = 1 - series.max() / (series.max() + abs(series.min())) #import pdb; pdb.set_trace() shifted_cmap = shiftedColorMap(cm.RdYlGn, midpoint=midpoint, name='shifted') chart = ax.imshow(series.values.reshape(date_range.size, spread_shock.size).T, extent=(date_range.min(), date_range.max(), spread_shock.min(), spread_shock.max()), aspect='auto', interpolation='bilinear', cmap=shifted_cmap) ax.set_xlabel('Days') ax.set_ylabel('Spread') ax.set_title('{} of Trade'.format(attr.title())) fig.colorbar(chart, shrink=.8) #fig.savefig(os.path.join(path, "payer_swap_", attr, "_{}.png".format(val_date))) def april_may_2017_trade(): option_delta = Index.from_tradeid(870) ref = option_delta.spread payer1 = BlackSwaption(option_delta, datetime.date(2017, 4, 19), 65) payer2 = BlackSwaption(option_delta, datetime.date(2017, 5, 17), 72) payer1.notional = 100_000_000 payer2.notional = 100_000_000 date_range = pd.bdate_range(option_delta.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=option_delta.trade_date) vol_surface = vs[vs.list()[-1]] # # df1 = run_swaption_scenarios(payer1, date_range, spread_shock, vol_shock, vol_surface) df2 = run_swaption_scenarios(payer2, date_range, spread_shock, vol_shock, vol_surface) df3 = run_index_scenarios(option_delta, date_range, spread_shock) # #plot it week = -1 df = df1.reset_index() df3 = df3.reset_index() df = df.merge(df3, on=['date','spread_shock']) df = df.set_index('date') df = df.assign(pv=df1.pv-df2.pv+df.pnl) spread_plot_range = ref * (1 + np.arange(-0.2, 0.3, 0.001)) vol_shock_range = np.arange(-0.15, 0.3, 0.001) plot_df(df.loc[date_range[week]], spread_plot_range, vol_shock_range) plot_color_map(df.loc[date_range[week]], ref * (1 + spread_shock), vol_shock) #def june_july_2017_trade(): option_delta = Index.from_name('ig', 28, '5yr') option_delta.spread = 67 payer1 = BlackSwaption(option_delta, datetime.date(2017, 7, 19), 80) payer2 = BlackSwaption(option_delta, datetime.date(2017, 5, 17), 80) payer1.sigma = .438 payer2.sigma = .479 payer1.notional = 100_000_000 payer2.notional = 100_000_000 ref = option_delta.spread option_delta.notional = payer1.notional * payer1.delta - payer2.notional * payer2.delta if option_delta.notional > 0: option_delta.direction = 'Seller' option_delta._original_clean_pv = option_delta._clean_pv option_delta._original_trade_date = option_delta.trade_date cost = payer1.pv - payer2.pv date_range = pd.bdate_range(option_delta.trade_date, pd.Timestamp('2017-05-17') - BDay(), freq = '3B') vol_shock = np.arange(-0.15, 0.3, 0.01) spread_shock = np.arange(-0.2, 0.3, 0.01) vs = VolatilitySurface("IG", 28, trade_date=option_delta.trade_date) vol_select = max([t for t in vs.list() if t[1] == 'BAML' and t[2] == 'payer' and t[3] == 'black']) vol_surface = vs[vol_select] # # df1 = run_swaption_scenarios(payer1, date_range, spread_shock, vol_shock, vol_surface, ['pv','delta']) df2 = run_swaption_scenarios(payer2, date_range, spread_shock, vol_shock, vol_surface, ['pv','delta']) df3 = run_index_scenarios(option_delta, date_range, spread_shock) # #plot it week = -2 df = df1.reset_index() df3 = df3.reset_index() df = df.merge(df3, on=['date','spread_shock']) df = df.set_index('date') df = df.assign(pv=df1.pv-df2.pv+df.pnl-cost) df = df.assign(delta=df1.delta*payer1.notional-df2.delta*payer2.notional+option_delta.notional) spread_plot_range = ref * (1 + np.arange(-0.2, 0.3, 0.001)) vol_shock_range = np.arange(-0.15, 0.3, 0.001) #plot_df(df.loc[date_range[week]], spread_plot_range, vol_shock_range) plot_color_map(df.loc[date_range[week]], ref * (1 + spread_shock), vol_shock) plot_time_color_map(df[round(df.vol_shock,2)==0], ref * (1 + spread_shock), 'delta') plot_time_color_map(df[round(df.vol_shock,2)==0], ref * (1 + spread_shock), 'pv')