def evaluate(self, results, risks):
'''
Define score from raw cost function results
'''
score = [risks['Returns'], risks['Sharpe.Ratio'], risks['Max.Drawdown'], risks['Volatility']]
if score[0]:
log.notice(risks)
log.notice([results['portfolio_value'][-1], results['pnl'].cumsum()[-1], (results['returns'] + 1).cumprod()[-1]])
return 1 - score[0]
def evaluate(self, risks):
'''
Define score from raw cost function results
'''
score = [risks['Returns'], risks['Sharpe.Ratio'], risks['Max.Drawdown'], risks['Volatility']]
# Debug purpose
if score[0]:
log.notice(risks)
# Compute score from cummulative returns,
return 1 - score[0]
def evaluate(self, risks):
'''
Define score from raw cost function results
'''
score = [risks['Returns'], risks['Sharpe.Ratio'], risks['Max.Drawdown'], risks['Volatility']]
# Debug purpose
if score[0]:
log.notice(risks)
# Compute score from cummulative returns,
return 1 - score[0]
default=0.5,
required=False,
help='Probability for a mutation to happen')
parser.add_argument('-n',
'--notify',
action='store_true',
help='Flag to send android notification')
parser.add_argument('-r',
'--remote',
action='store_true',
help='running mode, used for logging message endpoint')
args = parser.parse_args()
log_setup = (remote_setup if args.remote else color_setup)
with log_setup.applicationbound():
#TODO manage float parameters
#NOTE A dico might be more readable
#FIXME Step is the same whatever the parameter, scale issue
score, best_parameters = genetic_optimize([(100, 200), (3, 9),
(0, 20)],
Metric(),
popsize=args.popsize,
step=args.step,
elite=args.elitism,
maxiter=args.iteration,
mutprob=args.mutation,
notify_android=args.notify)
log.notice('Best parameters evolved: {} -> {}'.format(
best_parameters, score))
'''
Quick and dirty interface for running
genetic optimization process
'''
parser = argparse.ArgumentParser(description='Trading strategie optimization through genetic algorithm')
parser.add_argument('-v', '--version', action='version', version='%(prog)s v0.8.1 Licence rien du tout', help='Print program version')
parser.add_argument('-p', '--popsize', type=int, action='store', default=10, required=False, help='number of chromosomes in a population')
parser.add_argument('-e', '--elitism', type=float, action='store', default=0.2, required=False, help='% of best chromosomes kept as is for the next generation')
parser.add_argument('-s', '--step', type=float, action='store', default=1.0, required=False, help='Mutation leverage')
parser.add_argument('-i', '--iteration', type=int, action='store', default=20, required=False, help='Max number of evolution iteration')
parser.add_argument('-m', '--mutation', type=float, action='store', default=0.5, required=False, help='Probability for a mutation to happen')
parser.add_argument('-n', '--notify', action='store_true', help='Flag to send android notification')
parser.add_argument('-r', '--remote', action='store_true', help='running mode, used for logging message endpoint')
args = parser.parse_args()
log_setup = (remote_setup if args.remote else color_setup)
with log_setup.applicationbound():
#TODO manage float parameters
#NOTE A dico might be more readable
#FIXME Step is the same whatever the parameter, scale issue
score, best_parameters = genetic_optimize([(100, 200), (3, 9), (0, 20)],
Metric(),
popsize = args.popsize,
step = args.step,
elite = args.elitism,
maxiter = args.iteration,
mutprob = args.mutation,
notify_android=args.notify)
log.notice('Best parameters evolved: {} -> {}'.format(best_parameters, score))
