import sys #don't do this at home sys.path.append("..") from analytics import Swaption, BlackSwaption, Index, VolatilitySurface, Portfolio from analytics.scenarios import run_swaption_scenarios, run_index_scenarios, run_portfolio_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 from operator import attrgetter import os import numpy as np import matplotlib import matplotlib.pyplot as plt from mpl_toolkits.axes_grid1 import AxesGrid import re from db import dbengine engine = dbengine('serenitasdb') 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="pnl"): 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="pnl", path=".", index ='IG'): val_date = df.index[0].date() #rows are spread, columns are volatility surface shift fig, ax = plt.subplots() #We are plotting an image, so we have to sort from high to low on the Y axis ascending = [False,False] if index == 'HY' else [True,False] df.sort_values(by=['spread','vol_shock'], ascending = ascending, inplace = True) series = df[attr] 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('Price') if index == 'HY' else 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, "vol_spread_color_map"+ attr+ "_{}.png".format(val_date))) def plot_time_color_map(df, spread_shock, attr="pnl", path=".", color_map = cm.RdYlGn, index ='IG'): val_date = df.index[0].date() df = df.reset_index() df['days'] = (df['date'] - val_date).dt.days ascending = [True,True] if index == 'HY' else [True,False] df.sort_values(by=['date','spread'], ascending = ascending, inplace = True) date_range = df.days.unique() #plt.style.use('seaborn-whitegrid') fig, ax = plt.subplots() series = df[attr] midpoint = 1 - series.max() / (series.max() + abs(series.min())) shifted_cmap = shiftedColorMap(color_map, 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) #chart = ax.contour(date_range, spread_shock, series.values.reshape(date_range.size, spread_shock.size).T) ax.set_xlabel('Days') ax.set_ylabel('Price') if index == 'HY' else ax.set_ylabel('Spread') ax.set_title('{} of Trade'.format(attr.title())) fig.colorbar(chart, shrink=.8) #fig.savefig(os.path.join(path, "spread_time_color_map_"+ attr+ "_{}.png".format(val_date))) def plot_trade_scenarios(portf, shock_min = -.15, shock_max = .2): portf.reset_pv() earliest_date = min(portf.swaptions,key=attrgetter('exercise_date')).exercise_date date_range = pd.bdate_range(portf.indices[0].trade_date, earliest_date - BDay(), freq = '5B') vol_shock = np.arange(-0.15, 0.3, 0.01) spread_shock = np.arange(shock_min, shock_max, 0.01) index = portf.indices[0].name.split()[1] series = portf.indices[0].name.split()[3][1:] vs = VolatilitySurface(index, series, trade_date=portf.indices[0].trade_date) vol_select = vs.list(option_type='payer', model='black')[-1] vol_surface = vs[vol_select] df = run_portfolio_scenarios(portf, date_range, spread_shock, vol_shock, vol_surface, params=["pnl","delta"], vol_time_roll=True) hy_plot_range = 100 + (500- portf.indices[0].spread * (1 + spread_shock))*abs(portf.indices[0].DV01)/portf.indices[0].notional*100 shock = hy_plot_range if index == 'HY' else portf.indices[0].spread * (1 + spread_shock) period = -4 plot_time_color_map(df[round(df.vol_shock,2)==0], shock, 'pnl', index=index) plot_time_color_map(df[round(df.vol_shock,2)==0], shock, 'delta', color_map = cm.coolwarm_r, index=index) plot_color_map(df.loc[date_range[period]], shock, vol_shock, 'pnl', index=index) #plot_df(df.loc[date_range[period]], shock, vol_shock) def exercise_probability(): from analytics import Swaption, BlackSwaption, Index, VolatilitySurface, Portfolio from analytics.scenarios import run_swaption_scenarios, run_index_scenarios, run_portfolio_scenarios import datetime from operator import attrgetter import exploration.swaption_calendar_spread as spread import sys #don't do this at home 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 from operator import attrgetter import os import numpy as np import matplotlib import matplotlib.pyplot as plt from mpl_toolkits.axes_grid1 import AxesGrid import re from db import dbengine engine = dbengine('serenitasdb') #import swaption_calendar_spread as spread #Ad hoc option_delta = Index.from_name('HY', 28, '5yr') option_delta.price = 107.625 option1 = BlackSwaption(option_delta, datetime.date(2017, 9, 20), 107, option_type="payer") option2 = BlackSwaption(option_delta, datetime.date(2017, 9, 20), 105, option_type="payer") option1.sigma = .270 option2.sigma = .3625 option1.notional = 20_000_000 option2.notional = 40_000_000 option1.direction = 'Long' option2.direction = 'Short' option_delta.notional = -2000000 #option_delta.notional = option_delta.notional - option_delta2.notional if option_delta.notional < 0: option_delta.direction = 'Seller' option_delta.notional = abs(option_delta.notional) portf = Portfolio([option1, option2, option_delta]) portf.reset_pv() earliest_date = min(portf.swaptions,key=attrgetter('exercise_date')).exercise_date date_range = pd.bdate_range(portf.indices[0].trade_date, earliest_date - BDay(), freq = '5B') vol_shock = np.arange(-0.15, 0.3, 0.01) spread_shock = np.arange(-0.15, 0.35, 0.01) index = portf.indices[0].name.split()[1] series = portf.indices[0].name.split()[3][1:] vs = VolatilitySurface(index, series, trade_date=portf.indices[0].trade_date) vol_select = vs.list(option_type='payer', model='black')[-1] vol_surface = vs[vol_select] prob = vs.prob_surf(vol_select) vs.prob_plot(vol_select)