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# Copyright (C) 2016 EDF
# All Rights Reserved
# This code is published under the GNU Lesser General Public License (GNU LGPL)
import numpy as np
# Give an example for swing options of the optimization object describing a step in optimization and in simulation
# classical swing option with a given number of exercises
class OptimizeSwing:
# Constructor
# p_payoff pay off used
# p_nPointStock number of stock points
# p_actu actualization factor
def __init__(self, p_payoff, p_nPointStock, p_actu):
self.m_payoff = p_payoff
self.m_nPointStock = p_nPointStock
self.m_actu = p_actu
self.m_actuStep = 1.
# define the diffusion cone for parallelism
# p_regionByProcessor region (min max) treated by the processor for the differents regimes treated
# returns in each dimension the min max values in the stock that can be reached from the grid p_gridByProcessor for each regime
def getCone(self, p_regionByProcessor):
extrGrid = np.zeros(p_regionByProcessor)
# only a single exercise
extrGrid[0][1] += 1
return extrGrid
# permits to actuallize the time (needed fo simulation)
def incrementActuTimeStep(self):
self.m_actuStep *= self.m_actu
# defines a step in optimization
# Notice that this implementation is not optimal. In fact no interpolation is necessary for this asset.
# This implemnetation is for test and example purpose
# p_grid grid at arrival step after command
# p_stock coordinate of the stock point to treat
# p_condEsp continuation values for each regime
# p_phiIn for each regime gives the solution calculated at the previous step ( next time step by Dynamic Programming resolution)
# return a pair :
# - for each regimes (column) gives the solution for each particle (row)
# - for each control (column) gives the optimal control for each particule (rows)
def stepOptimize(self, p_grid, p_stock, p_condEsp, p_phiIn, p_simulator):
nbSimul = p_condEsp[0].getNbSimul()
solutionAndControl = []
solutionAndControl[0] = np.zeros((nbSimul, 1))
solutionAndControl[1] = np.zeros((nbSimul, 1))
payOffVal = self.m_payoff.applyVec(p_condEsp[0].getParticles())
# create interpolator at current stock point
interpolatorCurrentStock = p_grid.createInterpolator(p_stock)
# cash flow at current stock and previous step
cashSameStock = interpolatorCurrentStock.applyVec(p_phiIn[0])
# conditional expectation at current stock point
condExpSameStock = self.m_actu * p_condEsp[0].getAllSimulations(interpolatorCurrentStock)
if p_stock[0] < self.m_nPointStock:
# calculation detailed for clarity
# create interapolator at next stock point accessible
nextStock = np.zeros(p_stock)
nextStock[0] += 1
interpolatorNextStock = p_grid.createInterpolator(nextStock)
# cash flow at next stock previous step
cashNextStock = interpolatorNextStock.applyVec(p_phiIn[0])
# conditional espectation at next stock
condExpNextStock = self.m_actu * p_condEsp[0].getAllSimulations(interpolatorNextStock)
# arbitrage
solutionAndControl[0][:,0] = np.where(payOffVal + condExpNextStock > condExpSameStock, payOffVal + self.m_actu * cashNextStock, self.m_actu * cashSameStock)
solutionAndControl[1][:,0] = np.where(payOffVal + condExpNextStock > condExpSameStock, 1, 0.)
else :
solutionAndControl[0][:,0] = 0.
solutionAndControl[1][:,0] = 0.
return solutionAndControl
# defines a step in simulation
# Notice that this implementation is not optimal. In fact no interpolation is necessary for this asset.
# This implementation is for test and example purpose
# p_grid grid at arrival step after command
# p_continuation defines the continuation operator for each regime
# p_state defines the state value (modified)
# p_phiInOut defines the value function (modified): size number of functions to follow
def stepSimulate(self, p_grid, p_continuation, p_simulator, p_state, p_phiInOut):
# only if stock not maximal
if p_state.getPtStock()[0] < self.m_nPointStock:
payOffVal = self.m_payoff.apply(p_state.getStochasticRealization())
continuationValue = self.m_actu * p_continuation[0].getValue(p_state.getPtStock(), p_state.getStochasticRealization())
if p_state.getPtStock()[0] < self.m_nPointStock:
nextStock = np.zeros(p_state.getPtStock())
nextStock[0] += 1
continuationValueNext = self.m_actu * p_continuation[0].getValue(nextStock, p_state.getStochasticRealization())
if payOffVal + continuationValueNext > continuationValue:
p_state.setPtStock(nextStock)
p_phiInOut += payOffVal * self.m_actuStep
# get number of regimes
def getNbRegime(self):
return 1
# number of controls
def getNbControl(self):
return 1
# get actualization factor
def getActuStep(self):
return self.m_actuStep
# store the simulator
def setSimulator(self, p_simulator):
self.m_simulator = p_simulator
# get the simulator back
def getSimulator(self):
return self.m_simulator
# get size of the function to follow in simulation
def getSimuFuncSize(self):
return 1
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