def gekko_search(**parameters):
parallel = settings['parallel']
num_rounds = settings['num_rounds']
# remake CS & HS variability;
candleSize = settings['candleSize']
historySize = settings['historySize']
if parallel:
p = Pool(mp.cpu_count())
param_list = list([
(Strategy, parameters),
] * num_rounds)
scores = p.starmap(evaluate_random, param_list)
p.close()
p.join()
else:
scores = [
evaluate_random(Strategy, parameters) for n in range(num_rounds)
]
print(scores)
series = pd.Series(scores)
mean = series.mean()
stats.append([series.count(
), mean, series.std(), series.min()] +
[series.quantile(x) for x in percentiles] + [series.max()])
all_val.append(mean)
write_evolution_logs(len(all_val), stats[-1])
return mean
def gekko_search(**parameters):
parallel = settings['parallel']
num_rounds = settings['num_rounds']
# remake CS & HS variability;
candleSize= settings['candleSize']
historySize= settings['historySize']
if parallel:
p = Pool(mp.cpu_count())
param_list = list([(Strategy, parameters),] * num_rounds)
scores = p.starmap(Evaluate, param_list)
p.close()
p.join()
else:
scores = [Evaluate(Strategy, parameters) for n in range(num_rounds)]
series = pd.Series(scores)
mean = series.mean()
stats.append([series.count(), mean, series.std(), series.min()] +
[series.quantile(x) for x in percentiles] + [series.max()])
all_val.append(mean)
write_evolution_logs(len(all_val), stats[-1])
return mean