def test_optimizer2():
optimizer = Optimizer()
run_id = 0
## set population size
optimizer.size = 40
optimizer.max_generations = 5
## if not given: outfile will default to using the strategy name
## optimizer.outfile ='retrace_optimized.xls'
## parameter space to search over
## momentum = entry momentum crossover
## average = moving average filter
## duration = trade holding period
param_list = [
dict(name='momentum', min_val=10, max_val=100, steps=32,
converter=int),
dict(name='average', min_val=20, max_val=200, steps=32, converter=int),
dict(name='duration', min_val=10, max_val=50, steps=16, converter=int)
]
optimizer.add_parameters(param_list)
while not optimizer.converged():
params_set = optimizer.generate_set()
for strategy_params in params_set:
log.debug('Testing: %s' % pprint.pformat(strategy_params))
sim_stats = test_retrace_strategy(strategy_params, run_id)
optimizer.score(sim_stats, run_id)
run_id += 1
optimizer.dump()
optimizer.write()
def test_optimizer3():
optimizer = Optimizer()
run_id = 0
## set population size
optimizer.size = 40
optimizer.max_generations = 5
optimizer.outfile = 'triple_optimized.xls'
## parameter space to search over
## mo1 = short term momentum crossover
## mo2 = medium term mo filter
## mo3 = long term mo filter
## duration = trade holding period
param_list = [
dict(name='mo1', min_val=5, max_val=50, steps=32, converter=int),
dict(name='mo2', min_val=60, max_val=100, steps=32, converter=int),
dict(name='mo3', min_val=110, max_val=200, steps=32, converter=int),
dict(name='duration', min_val=10, max_val=50, steps=16, converter=int)
]
optimizer.add_parameters(param_list)
while not optimizer.converged():
params_set = optimizer.generate_set()
for strategy_params in params_set:
log.debug('Testing: %s' % pprint.pformat(strategy_params))
sim_stats = test_triple_strategy(strategy_params, run_id)
optimizer.score(sim_stats, run_id)
run_id += 1
optimizer.dump()
optimizer.write()
def test_optimizer():
optimizer = Optimizer()
run_id = 0
optimizer.outfile = 'optimizer_out.xls'
## parameter space to search over
optimizer.add_parameter(
dict(name='length', min_val=100, max_val=200, steps=32, converter=int))
while not optimizer.converged():
params_set = optimizer.generate_set()
for strategy_params in params_set:
log.debug('Testing: %s' % pprint.pformat(strategy_params))
sim_stats = test_strategy(strategy_params, run_id)
optimizer.score(sim_stats, run_id)
run_id += 1
optimizer.dump()
optimizer.write()