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# Copyright (C) 2018 EDF
# All Rights Reserved
# This code is published under the GNU Lesser General Public License (GNU LGPL)
import numpy as np
import StOptReg as reg
import StOptGrids
import StOptGeners
from StOptGlobal import StateWithStocks
def SimulateRegressionControlUsingControl(p_grid, p_optimize, p_funcFinalValue, p_pointStock,
p_initialRegime, p_fileToDump, key="Control"):
"""
Simulate the optimal strategy.
But using the control
Parameters:
-----------
p_grid
Grid used for deterministic state (stocks for example).
p_optimize
Optimizer defining the optimization between two time steps.
p_funcFinalValue
Function defining the final value.
p_pointStock
Initial point stock.
p_initialRegime
Regime at initial date.
p_fileToDump
Name of the file used to dump continuation values in optimization.
"""
simulator = p_optimize.getSimulator()
nsteps = simulator.getNbStep()
nsims = simulator.getNbSimul()
dim = simulator.getDimension()
particle0 = simulator.getParticles()[:,0]
states = [StateWithStocks(p_initialRegime, p_pointStock,particle0 )
for _ in range(nsims)]
# Retrieve the file containing the continuation values:
ar = StOptGeners.BinaryFileArchive(p_fileToDump, "r")
# Cost function
costFunction = np.zeros((p_optimize.getSimuFuncSize(), nsims))
# Iterate on time steps.
for istep in range(nsteps) :
control = ar.readGridAndRegressedValue(istep, key)
grid = control[0].getGrid()
for i in range(nsims):
state = states[i]
p_optimize.stepSimulateControl(grid, control, state, costFunction[:, i])
particles = simulator.stepForwardAndGetParticles()
for i in range(nsims) :
states[i].setStochasticRealization(particles[:, i])
# Final step: accept to exercise if not already done entirely.
for i in range(simulator.getNbSimul()):
costFunction[0, i] += p_funcFinalValue.set(states[i].getRegime(),
states[i].getPtStock(),
states[i].getStochasticRealization())
# Average gain/cost.
return costFunction.mean()
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