#!/usr/bin/env python
# coding: utf-8

# # Expected Exposure simulation with QuantLib and Python

# *Copyright (c) 2015 Matthias Groncki*
# 
# 
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# 
#     - Redistributions of source code must retain the above copyright notice,
#     this list of conditions and the following disclaimer.
# 
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
# 
# This disclaimer is taken from the QuantLib license

# In[1]:


# import the used libraries
import numpy as np
import matplotlib.pyplot as plt
import QuantLib as ql
get_ipython().run_line_magic('matplotlib', 'inline')


# In[2]:


# Setting evaluation date
today = ql.Date(7,4,2015)
ql.Settings.instance().setEvaluationDate(today)


# In[3]:


# Setup Marketdata
rate = ql.SimpleQuote(0.03)
rate_handle = ql.QuoteHandle(rate)
dc = ql.Actual365Fixed()
yts = ql.FlatForward(today, rate_handle, dc)
yts.enableExtrapolation()
hyts = ql.RelinkableYieldTermStructureHandle(yts)
t0_curve = ql.YieldTermStructureHandle(yts)
euribor6m = ql.Euribor6M(hyts)


# In[4]:


# Setup a dummy portfolio with two Swaps
def makeSwap(start, maturity, nominal, fixedRate, index, typ=ql.VanillaSwap.Payer):
    """
    creates a plain vanilla swap with fixedLegTenor 1Y
    
    parameter:
        
        start (ql.Date) : Start Date
        
        maturity (ql.Period) : SwapTenor
        
        nominal (float) : Nominal
        
        fixedRate (float) : rate paid on fixed leg
        
        index (ql.IborIndex) : Index
        
    return: tuple(ql.Swap, list<Dates>) Swap and all fixing dates
    
        
    """
    end = ql.TARGET().advance(start, maturity)
    fixedLegTenor = ql.Period("1y")
    fixedLegBDC = ql.ModifiedFollowing
    fixedLegDC = ql.Thirty360(ql.Thirty360.BondBasis)
    spread = 0.0
    fixedSchedule = ql.Schedule(start,
                                end, 
                                fixedLegTenor, 
                                index.fixingCalendar(), 
                                fixedLegBDC,
                                fixedLegBDC, 
                                ql.DateGeneration.Backward,
                                False)
    floatSchedule = ql.Schedule(start,
                                end,
                                index.tenor(),
                                index.fixingCalendar(),
                                index.businessDayConvention(),
                                index.businessDayConvention(),
                                ql.DateGeneration.Backward,
                                False)
    swap = ql.VanillaSwap(typ, 
                          nominal,
                          fixedSchedule,
                          fixedRate,
                          fixedLegDC,
                          floatSchedule,
                          index,
                          spread,
                          index.dayCounter())
    return swap, [index.fixingDate(x) for x in floatSchedule][:-1]

portfolio = [makeSwap(today + ql.Period("2d"),
                      ql.Period("5Y"),
                      1e6,
                      0.03,
                      euribor6m),
             makeSwap(today + ql.Period("2d"),
                      ql.Period("4Y"),
                      5e5,
                      0.03,
                      euribor6m,
                      ql.VanillaSwap.Receiver),
            ]


# In[5]:


#%%timeit
# Setup pricing engine and calculate the npv
engine = ql.DiscountingSwapEngine(hyts)
for deal, fixingDates in portfolio:
    deal.setPricingEngine(engine)
    deal.NPV()
    #print(deal.NPV())


# In[6]:


# Stochastic Process 


# In[7]:


# Assume the model is already calibrated either historical or market implied
volas = [ql.QuoteHandle(ql.SimpleQuote(0.0075)),
         ql.QuoteHandle(ql.SimpleQuote(0.0075))]
meanRev = [ql.QuoteHandle(ql.SimpleQuote(0.02))]
model = ql.Gsr(t0_curve, [today+100], volas, meanRev, 16.)


# In[8]:


process = model.stateProcess()


# In[9]:


# Define evaluation grid
date_grid = [today + ql.Period(i,ql.Weeks) for i in range(0,52*6)]
for deal in portfolio:
    date_grid += deal[1]

date_grid = np.unique(np.sort(date_grid))
time_grid = np.vectorize(lambda x: ql.ActualActual().yearFraction(today, x))(date_grid)
dt = time_grid[1:] - time_grid[:-1]

print(len(time_grid)*1500*2*29e-6)


# In[10]:


# Random number generator
seed = 1
urng = ql.MersenneTwisterUniformRng(seed)
usrg = ql.MersenneTwisterUniformRsg(len(time_grid)-1,urng)
generator = ql.InvCumulativeMersenneTwisterGaussianRsg(usrg)


# In[11]:


#%%timeit
# Generate N paths
N = 1500
x = np.zeros((N, len(time_grid)))
y = np.zeros((N, len(time_grid)))
pillars = np.array([0.0, 0.5, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
zero_bonds = np.zeros((N, len(time_grid), 12))
for j in range(12):
    zero_bonds[:, 0, j] = model.zerobond(pillars[j],
                                         0,
                                         0)
for n in range(0,N):
    dWs = generator.nextSequence().value()
    for i in range(1, len(time_grid)):
        t0 = time_grid[i-1]
        t1 = time_grid[i]
        x[n,i] = process.expectation(t0, 
                                     x[n,i-1], 
                                     dt[i-1]) + dWs[i-1] * process.stdDeviation(t0,
                                              x[n,i-1],
                                              dt[i-1])
        y[n,i] = (x[n,i] - process.expectation(0,0,t1)) / process.stdDeviation(0,0,t1)
        for j in range(12):
            zero_bonds[n, i, j] = model.zerobond(t1+pillars[j],
                                                 t1,
                                                 y[n, i])
                                                 


# In[12]:


# plot the paths
for i in range(0,N):
    plt.plot(time_grid, x[i,:])


# In[13]:


#%%timeit
#Swap pricing under each scenario
npv_cube = np.zeros((N,len(date_grid), len(portfolio)))
for p in range(0,N):
    for t in range(0, len(date_grid)):
        date = date_grid[t]
        ql.Settings.instance().setEvaluationDate(date)
        ycDates = [date, 
                   date + ql.Period(6, ql.Months)] 
        ycDates += [date + ql.Period(i,ql.Years) for i in range(1,11)]
        yc = ql.DiscountCurve(ycDates, 
                              zero_bonds[p, t, :], 
                              ql.Actual365Fixed())
        yc.enableExtrapolation()
        hyts.linkTo(yc)
        if euribor6m.isValidFixingDate(date):
            fixing = euribor6m.fixing(date)
            euribor6m.addFixing(date, fixing)
        for i in range(len(portfolio)):
            npv_cube[p, t, i] = portfolio[i][0].NPV()
    ql.IndexManager.instance().clearHistories()
ql.Settings.instance().setEvaluationDate(today)
hyts.linkTo(yts)


# In[14]:


# Calculate the portfolio npv by netting all NPV
portfolio_npv = np.sum(npv_cube,axis=2)


# In[15]:


# Plot the first 30 NPV paths
plt.figure(figsize=(7,5), dpi=300)
for i in range(0,30):
    plt.plot(time_grid, portfolio_npv[i,:])
plt.xlabel("Time in years")
plt.ylabel("NPV")
plt.title("Simulated npv paths")


# In[16]:


# Calculate the exposure
E = portfolio_npv.copy()
E[E<0]=0
# Plot the first 30 exposure paths
plt.figure(figsize=(7,5), dpi=600)
for i in range(0,30):
    plt.plot(time_grid, E[i,:])
plt.xlabel("Time in years")
plt.ylabel("Exposure")
plt.ylim([-10000,70000])
plt.title("Simulated exposure paths")


# In[17]:


# Calculate the expected exposure
E = portfolio_npv.copy()
E[E<0]=0
EE = np.sum(E, axis=0)/N


# In[18]:


# plot the expected exposure path
plt.figure(figsize=(7,5), dpi=300)
plt.plot(time_grid, EE)
plt.xlabel("Time in years")
plt.ylabel("Expected Exposure")
plt.ylim([-2000,10000])
plt.title("Expected Exposure (netting set)")


# In[19]:


# Calculate the PFE curve (95% quantile)
PFE_curve = np.apply_along_axis(lambda x: np.sort(x)[0.95*N],0, E)

plt.figure(figsize=(7,5), dpi=300)
plt.plot(time_grid,PFE_curve)
plt.xlabel("Time in years")
plt.ylabel("PFE")
plt.ylim([-2000,35000])
plt.title("PFE (netting set)")


# In[20]:


# calculate the maximum pfe
MPFE = np.max(PFE_curve)
MPFE


# In[21]:


# alternative pfe 95% quantile of the maxima of each exposure paths
PFE = np.sort(np.max(E,axis=1))[0.95*N]
PFE


# In[ ]: