def importanceSampling_parallel4(generateSample,args_generateSample,likelihoodRatio,args_likelihoodRatio, N):
# not working
temp = 3
from IPython.parallel import Client
p = Client()[:]
p.use_dill()
print('This is the parallel output')
p.map_sync(generateSample,np.arange(6))
#sample = p.map_sync(generateSample,np.array((10,)))
#print(sample)
return(1)
def main():
""" Set up the parallel engine and the data space. Call the parallel engine, then write out the results """
client = Client()[:]
client.use_dill()
client.execute("import furnace.performance, furnace.strategy, numpy, datetime")
stock_percents = numpy.linspace(0.0, 0.4, 10)
rebalancing_periods = numpy.arange(1, 40, 1)
days_in = numpy.arange(1, 250, 1)
begin = datetime.datetime(2003, 1, 2)
end = datetime.datetime(2011, 12, 31)
grid = list(itertools.product(stock_percents, rebalancing_periods, days_in))
builder = function_builder(begin, end)
results = client.map(builder, grid)
#TODO: does pandas have a plain 'save to csv' function?
with open('data.csv', 'wb') as csvfile:
writer = csv.writer(csvfile)
writer.writerow(['days_out', 'pct', 'ndays', 'r2r', 'cagr', 'volatility', 'ntrades'])
writer.writerows(results)
