def gopso(id, niter, popsize, nhood_size):
res = np.inf
if (id == 1):
print("parabloid")
numvar = 7
xmin = 0, 5
xmax = 0, 8
if (id == 2):
print("rasenbork")
numvar = 7
xmin = 0, 5
xmax = 0, 8
#gopso(idfunc,niter,popsize,nhood_size)
#run optimizzation algorithm
PSO = ParSwarm.ParSwarmOpt(xmin, xmax)
res = PSO.pso_solve(popsize, id, numvar, niter, nhood_size)
return res
print_figure(plt.gcf())
return yfore, horizon_data_length
if len(sys.argv) == 2:
forecast(sys.argv[1])
else:
for i in range(len(indices)):
f, horizon_data_length = forecast(indices[i])
result_forecasts.append(f)
portfolioInitialValue = 100000
numvar = 7
xmin = 0.05
xmax = 0.7
niter = 2
popsize = 50
nhood_size = 10
PSO = ParSwarm.ParSwarmOpt(xmin, xmax)
res = PSO.pso_solve(popsize, numvar, niter, nhood_size,
portfolioInitialValue, horizon_data_length,
result_forecasts)
print("Portfolio: ", end='')
for value in res.xsolbest:
print(value, end=' ')
print("")
print("Return: {}".format(res.return_valuebest))
print("Devst: {}".format(res.devstbest))
