def main():
parser = build_parser()
options = parser.parse_args()
if not os.path.exists('./train_package'):
os.makedirs('./train_package')
if not os.path.exists('./database'):
os.makedirs('./database')
if options.mode == 'train':
import pgportfolio.autotrain.training
if not options.algo:
pgportfolio.autotrain.training.train_all(int(options.processes), options.device)
else:
raise NotImplementedError()
elif options.mode == 'generate':
import pgportfolio.autotrain.generate as generate
logging.basicConfig(level=logging.INFO)
generate.add_packages(load_config(), int(options.repeat))
elif options.mode == 'download_data':
from pgportfolio.marketdata.datamatrices import DataMatrices
with open('./pgportfolio/net_config.json') as file:
config =json.load(file)
config = preprocess_config(file)
# コンフィグのインプットから開始日、終了日を取得
start = time.mktime(datetime.strptime(config["input"]["start_date"], "%Y/%m/%d").timetuple())
end = time.mktime(datetime.strptime(config["input"]["end_date"], "%Y/%m/%d").timetuple())
def main():
parser = build_parser()
options = parser.parse_args()
if not os.path.exists("./" + "train_package"):
os.makedirs("./" + "train_package")
if options.mode == "train":
import pgportfolio.autotrain.training
pgportfolio.autotrain.training.train_all(int(options.processes))
elif options.mode == "generate":
import pgportfolio.autotrain.generate as generate
logging.basicConfig(level=logging.INFO)
generate.add_packages(load_config(), int(options.repeat))
def main():
parser = build_parser()
options = parser.parse_args()
if not os.path.exists("./" + "train_package"):
os.makedirs("./" + "train_package")
if not os.path.exists("./" + "database"):
os.makedirs("./" + "database")
if options.mode == "train":
import pgportfolio.autotrain.training
pgportfolio.autotrain.training.train_all(int(options.processes), options.device, options.initial_asset)
elif options.mode == "generate":
import pgportfolio.autotrain.generate as generate
logging.basicConfig(level=logging.INFO)
generate.add_packages(load_config(), int(options.repeat))
elif options.mode == "backtest":
config = _config_by_algo(options.algo)
_set_logging_by_algo(logging.DEBUG, logging.DEBUG, options.algo, "backtestlog")
execute_backtest(options.algo, config)
elif options.mode == "save_test_data":
# This is used to export the test data
save_test_data(load_config(options.folder))
elif options.mode == "plot":
logging.basicConfig(level=logging.INFO)
algos = options.algos.split(",")
if options.labels:
labels = options.labels.replace("_"," ")
labels = labels.split(",")
else:
labels = algos
plot.plot_backtest(load_config(), algos, labels)
elif options.mode == "table":
algos = options.algos.split(",")
if options.labels:
labels = options.labels.replace("_"," ")
labels = labels.split(",")
else:
labels = algos
plot.table_backtest(load_config(), algos, labels, format=options.format)
def main():
parser = build_parser()
options = parser.parse_args()
if not os.path.exists("./" + "train_package"):
os.makedirs("./" + "train_package")
if not os.path.exists("./" + "database"):
os.makedirs("./" + "database")
if options.mode == "train":
import pgportfolio.autotrain.training
if not options.algo:
pgportfolio.autotrain.training.train_all(int(options.processes), options.device)
else:
for folder in options.train_floder:
raise NotImplementedError()
elif options.mode == "generate":
import pgportfolio.autotrain.generate as generate
logging.basicConfig(level=logging.INFO)
generate.add_packages(load_config(), int(options.repeat))
elif options.mode == "download_data":
from pgportfolio.marketdata.datamatrices import DataMatrices
with open("./pgportfolio/net_config.json") as file:
config = json.load(file)
config = preprocess_config(config)
start = time.mktime(datetime.strptime(config["input"]["start_date"], "%Y/%m/%d").timetuple())
end = time.mktime(datetime.strptime(config["input"]["end_date"], "%Y/%m/%d").timetuple())
DataMatrices(start=start,
end=end,
feature_number=config["input"]["feature_number"],
window_size=config["input"]["window_size"],
online=True,
period=config["input"]["global_period"],
volume_average_days=config["input"]["volume_average_days"],
coin_filter=config["input"]["coin_number"],
is_permed=config["input"]["is_permed"],
test_portion=config["input"]["test_portion"],
portion_reversed=config["input"]["portion_reversed"])
elif options.mode == "backtest":
config = _config_by_algo(options.algo)
_set_logging_by_algo(logging.DEBUG, logging.DEBUG, options.algo, "backtestlog")
execute_backtest(options.algo, config)
elif options.mode == "save_test_data":
# This is used to export the test data
save_test_data(load_config(options.folder))
elif options.mode == "plot":
logging.basicConfig(level=logging.INFO)
algos = options.algos.split(",")
if options.labels:
labels = options.labels.replace("_"," ")
labels = labels.split(",")
else:
labels = algos
plot.plot_backtest(load_config(), algos, labels)
elif options.mode == "table":
algos = options.algos.split(",")
if options.labels:
labels = options.labels.replace("_"," ")
labels = labels.split(",")
else:
labels = algos
plot.table_backtest(load_config(), algos, labels, format=options.format)
os.makedirs("./" + "train_package")
if not os.path.exists("./" + "database"):
os.makedirs("./" + "database")
if execute_mode == "train":
import pgportfolio.autotrain.training
if not options_algo:
pgportfolio.autotrain.training.train_all(int(options_processes),
options_device)
else:
for folder in options_folder:
raise NotImplementedError()
elif execute_mode == "generate":
import pgportfolio.autotrain.generate as generate
logging.basicConfig(level=logging.INFO)
generate.add_packages(load_config(), int(options_repeat))
elif execute_mode == "download_data":
from pgportfolio.marketdata.datamatrices import DataMatrices
with open("./pgportfolio/net_config.json") as file:
config = json.load(file)
config = preprocess_config(config)
start = time.mktime(
datetime.strptime(config["input"]["start_date"],
"%Y/%m/%d").timetuple())
end = time.mktime(
datetime.strptime(config["input"]["end_date"], "%Y/%m/%d").timetuple())
DataMatrices(start=start,
end=end,
feature_number=config["input"]["feature_number"],
window_size=config["input"]["window_size"],
online=True,
def generate_folders(repeat):
if not os.path.exists("./" + "train_package"):
os.makedirs("./" + "train_package")
logging.basicConfig(level=logging.INFO)
generate.add_packages(load_config(), repeat)
def main():
parser = build_parser()
options = parser.parse_args()
if not os.path.exists("./" + "train_package"):
os.makedirs("./" + "train_package")
if not os.path.exists("./" + "database"):
os.makedirs("./" + "database")
if options.mode == "train":
import pgportfolio.autotrain.training
if not options.algo:
pgportfolio.autotrain.training.train_all(int(options.processes), options.device)
else:
for folder in options.folder:
raise NotImplementedError()
elif options.mode == "generate":
import pgportfolio.autotrain.generate as generate
logging.basicConfig(level=logging.INFO)
generate.add_packages(load_config(), int(options.repeat))
elif options.mode == "download_data":
from pgportfolio.marketdata.datamatrices import DataMatrices
with open("./pgportfolio/net_config.json") as file:
config = json.load(file)
config = preprocess_config(config)
start = time.mktime(datetime.strptime(config["input"]["start_date"], "%Y/%m/%d").timetuple())
end = time.mktime(datetime.strptime(config["input"]["end_date"], "%Y/%m/%d").timetuple())
DataMatrices(start=start,
end=end,
feature_number=config["input"]["feature_number"],
window_size=config["input"]["window_size"],
online=True,
period=config["input"]["global_period"],
volume_average_days=config["input"]["volume_average_days"],
coin_filter=config["input"]["coin_number"],
is_permed=config["input"]["is_permed"],
test_portion=config["input"]["test_portion"],
portion_reversed=config["input"]["portion_reversed"])
elif options.mode == "backtest":
config = _config_by_algo(options.algo)
_set_logging_by_algo(logging.DEBUG, logging.DEBUG, options.algo, "backtestlog")
execute_backtest(options.algo, config)
elif options.mode == "save_test_data":
# This is used to export the test data
save_test_data(load_config(options.folder))
elif options.mode == "plot":
logging.basicConfig(level=logging.INFO)
algos = options.algos.split(",")
if options.labels:
labels = options.labels.replace("_"," ")
labels = labels.split(",")
else:
labels = algos
plot.plot_backtest(load_config(), algos, labels)
elif options.mode == "table":
algos = options.algos.split(",")
if options.labels:
labels = options.labels.replace("_"," ")
labels = labels.split(",")
else:
labels = algos
plot.table_backtest(load_config(), algos, labels, format=options.format)
def main(logPath, device):
parser = build_parser()
options = parser.parse_args()
'''
options.folder = oneKey
options.stockList = stockList
options.featureList = featureList
options.start_date = startDate
options.end_date = endDate
'''
if not os.path.exists("./" + "database"):
os.makedirs("./" + "database")
#options.repeat = 1
if options.mode == "train": #训练数据
if not options.algo:
save_path = logPath + str(options.folder) + "/netfile"
# 读取配置文件
with open(logPath + str(options.folder) +
"\\net_config.json") as file:
config_json = None
config_json = json.load(file)
config = preprocess_config(config_json)
log_file_dir = logPath + str(options.folder) + "/tensorboard"
# 定义错误等级
logfile_level = logging.DEBUG
console_level = logging.INFO
logging.basicConfig(filename=log_file_dir.replace(
"tensorboard", "programlog"),
level=logfile_level)
console = logging.StreamHandler()
console.setLevel(console_level)
logging.getLogger().addHandler(console)
trainer = TraderTrainer(config,
options.stockList,
options.featureList,
options.start_date,
options.end_date,
save_path=save_path,
device=device) #初始化训练器
trainer.train_net(log_file_dir=log_file_dir,
index=str(options.folder)) #训练网络
else:
for folder in options.folder:
raise NotImplementedError()
# 生成配置文件到路径中，要想修改配置，直接修改PGPortfolio\pgportfolio\net_config.json
elif options.mode == "generate":
import pgportfolio.autotrain.generate as generate
logging.basicConfig(level=logging.INFO)
config_ = load_config()
train_dir = logPath
generate.add_packages(train_dir, load_config(), int(options.repeat))
elif options.mode == "download_data":
from pgportfolio.marketdata.datamatrices import DataMatrices
with open("./pgportfolio/net_config.json") as file:
config = json.load(file)
config = preprocess_config(config)
start = time.mktime(
datetime.strptime(options.start_date, "%Y/%m/%d").timetuple())
end = time.mktime(
datetime.strptime(options.end_date, "%Y/%m/%d").timetuple())
DataMatrices(
start=start,
end=end,
feature_number=len(options.featureList),
window_size=config["input"]["window_size"],
online=True,
period=config["input"]["global_period"],
volume_average_days=config["input"]["volume_average_days"],
coin_filter=len(options["stockList"]),
is_permed=config["input"]["is_permed"],
test_portion=config["input"]["test_portion"],
portion_reversed=config["input"]["portion_reversed"])
elif options.mode == "backtest":
config = _config_by_algo(options.algo) #读取配置文件
_set_logging_by_algo(logging.DEBUG, logging.DEBUG, options.algo,
"backtestlog") #设置log的路径
values = execute_backtest(options.algo, config) #执行回测的步数为训练集的长度
elif options.mode == "save_test_data":
# This is used to export the test data
save_test_data(load_config(options.folder)) #保存测试集数据
elif options.mode == "plot":
logging.basicConfig(level=logging.INFO)
algos = options.algos.split(",")
if options.labels:
labels = options.labels.replace("_", " ")
labels = labels.split(",")
else:
labels = algos
plot.plot_backtest(load_config(), algos, labels)
elif options.mode == "table":
algos = options.algos.split(",")
if options.labels:
labels = options.labels.replace("_", " ")
labels = labels.split(",")
else:
labels = algos
plot.table_backtest(load_config(),
algos,
labels,
format=options.format)
def set_missing(config, name, value):
if name not in config:
config[name] = value
def parse_time(time_string):
return time.mktime(datetime.strptime(time_string, "%Y/%m/%d").timetuple())
if __name__ == "__main__":
initial_asset = int(sys.argv[1])
import pgportfolio.autotrain.generate as generate
logging.basicConfig(level=logging.INFO)
generate.add_packages(load_config(), 1)
import pgportfolio.autotrain.training
pgportfolio.autotrain.training.train_all(1, "cpu", initial_asset)
result = {}
# mdd, fapv, sharpe, weight_vector
import pgportfolio.resultprocess.plot
metrics = pgportfolio.resultprocess.plot.get_metrics(load_config(), ['1'],
['1'],
format=None)
result = metrics
result_file = "./train_package/train_summary.csv"
df = pd.read_csv(result_file)
weight_vector_str = df.iloc[0]['weight_vector']
weight_vector = weight_vector_str.split(',')
weight_vector.pop()
result['weight_vector'] = weight_vector
