path: root/python/exploration/swaption_calendar_spread.py

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

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="."):

    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
    df.sort_values(by=['spread','vol_shock'], ascending = [True,False], inplace = True)
    series = df[attr]

    #import pdb; pdb.set_trace()
    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, "vol_spread_color_map"+ attr+ "_{}.png".format(val_date)))

def plot_time_color_map(df, spread_shock, attr="pnl", path=".", color_map = cm.RdYlGn):

    val_date = df.index[0].date()
    df = df.reset_index()
    df['days'] = (df['date'] - val_date).dt.days
    df.sort_values(by=['date','spread'], ascending = [True,False], inplace = True)
    date_range = df.days.unique()

    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)

    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, "spread_time_color_map_"+ attr+ "_{}.png".format(val_date)))

def dec_jan_2017_trade():
    option_delta = Index.from_tradeid(864)
    option1 = BlackSwaption.from_tradeid(3, option_delta)
    option2 = BlackSwaption.from_tradeid(4, option_delta)

    portf = Portfolio([option1, option2, option_delta])
    date_range = pd.bdate_range(option_delta.trade_date, pd.Timestamp('2017-01-18') - BDay(), freq = '2B')
    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_select = vs.list('BAML', 'payer', '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=False)
    plot_time_color_map(df[round(df.vol_shock,2)==0], option_delta.spread * (1 + spread_shock), 'pnl')

def april_may_2017_trade(what='pnl'):
    option_delta = Index.from_tradeid(870)
    option1 = BlackSwaption.from_tradeid(5, option_delta)
    option2 = BlackSwaption.from_tradeid(6, option_delta)

    portf = Portfolio([option1, option2, option_delta])
    date_range = pd.bdate_range(option_delta.trade_date, pd.Timestamp('2017-04-19') - BDay(), freq = '2B')
    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_select = vs.list('BAML', 'payer', 'black')[-1]
    vol_surface = vs[vol_select]

    df = run_portfolio_scenarios(portf, date_range, spread_shock, vol_shock,
                                 vol_surface, params=[what], vol_time_roll=False)
    plot_time_color_map(df[abs(df.vol_shock)<1e-3], option_delta.spread * (1 + spread_shock), what)


def june_july_2017_trade():
    option_delta_pf = Index.from_tradeid(874)
    option_delta2_pf = Index.from_tradeid(879)

    option1_pf = BlackSwaption.from_tradeid(7, option_delta_pf)
    option2_pf = BlackSwaption.from_tradeid(9, option_delta_pf)
    #option_delta.notional = option_delta.notional - option_delta2.notional
    option_delta_pf.notional = 50_335_169

    portf = Portfolio([option1_pf, option2_pf, option_delta_pf])
    portf.trade_date = datetime.date(2017, 5, 17)
    portf.mark()
    portf.reset_pv()

    date_range = pd.bdate_range(option_delta_pf.trade_date, pd.Timestamp('2017-06-21') - BDay(), freq = '2B')
    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_pf.trade_date)
    vol_select = vs.list('BAML', 'payer', '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)

    #period = -4
    #plot_df(df.loc[date_range[period]], spread_plot_range, vol_shock_range)
    #plot_color_map(df.loc[date_range[period]], option_delta.spread * (1 + spread_shock), vol_shock, 'pnl')
    plot_time_color_map(df[round(df.vol_shock,2)==0], option_delta_pf.spread * (1 + spread_shock), 'pnl')
    #plot_time_color_map(df[round(df.vol_shock,2)==0], ref * (1 + spread_shock), 'delta', color_map = cm.coolwarm_r)
    return df

def hy_trade_scenario():

    #Manually Load trades
    option_delta = Index.from_name('hy', 28, '5yr')
    option_delta.price = 107.5
    option1 = BlackSwaption(option_delta, datetime.date(2017, 8, 16), 106, option_type="payer")
    option2 = BlackSwaption(option_delta, datetime.date(2017, 8, 16), 104, option_type="payer")
    option1.sigma = .331
    option2.sigma = .388
    option1.notional = 20_000_000
    option2.notional = 40_000_000
    option2.direction = 'Short'
    option_delta.notional = -(option1.delta * option1.notional + option2.delta*option2.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()
    date_range = pd.bdate_range(option_delta.trade_date, pd.Timestamp('2017-08-16') - BDay(), freq = '5B')
    vol_shock = np.arange(-0.15, 0.3, 0.01)
    spread_shock = np.arange(-0.1, 0.4, 0.01)
    vs = VolatilitySurface("HY", 28, trade_date=option_delta.trade_date)
    vol_select = vs.list('BAML', 'payer', '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)

    #period = -4
    #plot_df(df.loc[date_range[period]], spread_plot_range, vol_shock_range)
    #plot_color_map(df.loc[date_range[period]], option_delta.spread * (1 + spread_shock), vol_shock, 'pnl')
    #plot_time_color_map(df[round(df.vol_shock,2)==0], option_delta.spread * (1 + spread_shock), 'pnl')
    hy_plot_range = 100 + (500- option_delta.spread * (1 + spread_shock))*option_delta.DV01/option_delta.notional*100
    plot_time_color_map(df[round(df.vol_shock,2)==0], hy_plot_range, 'pnl')
    #Delta in protection terms: Blue = going short, red = going long
    #plot_time_color_map(df[round(df.vol_shock,2)==0], option_delta.spread * (1 + spread_shock), 'delta', color_map = cm.coolwarm_r)

    return df

def portfolio():
    option_delta = Index.from_tradeid(874)
    option1 = BlackSwaption.from_tradeid(7, option_delta)
    option2 = BlackSwaption.from_tradeid(8, option_delta)

    portf = Portfolio([option1, option2, option3, option_delta, option_delta1, option_delta2, option_delta3])
    date_range = pd.bdate_range(option_delta.trade_date, pd.Timestamp('2017-05-17') - BDay(), freq = '2B')
    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 = vs.list('BAML', 'payer', '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=False)

    #plot it
    period = -4
    #plot_df(df.loc[date_range[period]], spread_plot_range, vol_shock_range)
    plot_color_map(df.loc[date_range[period]], option_delta.spread * (1 + spread_shock), vol_shock, 'pnl')
    plot_time_color_map(df[round(df.vol_shock,2)==0], option_delta.spread * (1 + spread_shock), 'pnl')
    plot_time_color_map(df[round(df.vol_shock,2)==0], ref * (1 + spread_shock), 'delta', color_map = cm.coolwarm_r)

def probabilities():
    from scipy.stats import lognorm

    option_delta = Index.from_tradeid(874)
    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]
    t = .1
    mon = 1

    date_range = pd.bdate_range(option_delta.trade_date, pd.Timestamp('2017-05-17') - BDay(), freq = 'B')

    curr_vols = np.maximum(vol_surface.ev(t, mon), 0)
    dist = lognorm(curr_vols, scale)
    lognorm.ppf(.5, curr_vols, scale = np.exp(64))