def runSimpleParallel():
start = datetime.now()
print(
"run simple | start: %s -------------------------------------------" %
start)
setConfig()
optimize.runParallel() #并行优化，返回回测结果
report = optimize.runParallel()
report.sort_values(by='sharpeRatio', ascending=False, inplace=True)
# 将结果保存成csv
report.to_csv('opt_RB88.csv')
end = datetime.now()
print("run simple | end: %s | expire: %s -----------------------------" %
(end, end - start))
def runMemoryParallel(pardir=None, cache=False, test_run=None):
settings = import_setting(pardir)
start = datetime.now()
print("run memory | start: %s -------------------------------------------" % start)
pre_params = {}
folder_name = os.path.join(os.getcwd(),"opt_result",datetime.now().strftime('%y%m%d%H%M%S'))
if not os.path.isdir(folder_name):
os.makedirs(folder_name)
for idx, setting in enumerate(settings["OPT_TASK"]):
for method, params in setting.items():
params.update(pre_params)
root = f"opt_memory_{idx}" if cache else None
setConfig(params, root,settings["STRATEGYCLASS"],settings["ENGINESETTING"])
report = optimize.runParallel()
if settings["OPT_TARGET"] in report.columns:
report.sort_values(by = settings["OPT_TARGET"], ascending=False, inplace=True)
report.to_csv(f"{folder_name}/opt_{idx}_{method}.csv")
func = STR_FUNC_PAIR[method]
for param_name in params:
pick = func(report, param_name)
pre_params.update({param_name: [pick]})
end = datetime.now()
print("run memory | end: %s | expire: %s -----------------------------" % (end, end - start))
def runMemoryParallel(self, cpu_counts=None):
start = datetime.now()
print(
"run memory | start: %s -------------------------------------------"
% start)
pre_params = {}
for idx, setting in enumerate(self.optTask):
for method, params in setting.items():
params.update(pre_params)
# self.initOptimize(params, f"opt_memory_{idx}")
self.initOptimize(params)
report = optimize.runParallel(process=cpu_counts)
report.sort_values(by=self.optTarget,
ascending=False,
inplace=True)
# 保存路径设置
report = self.savePerformance(report, method, idx, start)
# 选取最优参数进行下一次优化
pickDict = self.optFunc(method, report, params)
pre_params.update(pickDict)
end = datetime.now()
print(
"run memory | end: %s | expire: %s -----------------------------" %
(end, end - start))
def runMemoryParallel():
start = datetime.now()
print("run memory | start: %s " % start)
setConfig("OptMemo_{0}".format(symbolName[:symbolName.find(':')]))
report = optimize.runParallel()
print(report)
end = datetime.now()
print("run memory | end: %s | expire: %s " % (end, end - start))
def runSimpleParallel():
start = datetime.now()
fprint("run simple | start: %s " % start)
setConfig()
report = optimize.runParallel()
print(report)
end = datetime.now()
fprint("run simple | end: %s | expire: %s " % (end, end - start))
def runMemoryParallel():
start = datetime.now()
fprint("run memory | start: %s " % start)
setConfig("test-memory-parallel")
report = optimize.runParallel()
print(report)
end = datetime.now()
fprint("run memory | end: %s | expire: %s " % (end, end - start))
def runSimpleParallel():
start = datetime.now()
print(
"run simple | start: %s -------------------------------------------" %
start)
setConfig()
# optimize.runParallel() 并行优化，返回回测结果
report = optimize.runParallel()
print(report)
report.sort_values(by='sharpeRatio', ascending=False, inplace=True)
# 将结果保存成csv
file_name = 'opt-' + start.strftime("%m-%d-%H-%M") + '.csv'
report.to_csv(file_name) # 记录保存在一个与开始时间关联的csv文件
# report.to_csv('opt_IF88.csv') # 这一行的问题是，csv文件如果没关闭，整个优化结果都会丢失
end = datetime.now()
print("run simple | end: %s | expire: %s -----------------------------" %
(end, end - start))
