def _config_by_algo(algo):
"""
:param algo: a string represent index or algo name
:return : a config dictionary
"""
if not algo:
raise ValueError("please input a specific algo")
elif algo.isdigit():
config = load_config(algo)
else:
config = load_config()
return config
def train_all(processes=1, device="cpu"):
"""
train all the agents in the train_package folders
:param processes: the number of the processes. If equal to 1, the logging level is debug
at file and info at console. If greater than 1, the logging level is
info at file and warming at console.
"""
if processes == 1:
console_level = logging.INFO
logfile_level = logging.DEBUG
else:
console_level = logging.WARNING
logfile_level = logging.INFO
train_dir = "train_package"
if not os.path.exists("./" + train_dir): #if the directory does not exist, creates one
os.makedirs("./" + train_dir)
all_subdir = os.listdir("./" + train_dir)
all_subdir.sort()
pool = []
for dir in all_subdir:
# train only if the log dir does not exist
if not str.isdigit(dir):
return
# NOTE: logfile is for compatibility reason
if not (os.path.isdir("./"+train_dir+"/"+dir+"/tensorboard") or os.path.isdir("./"+train_dir+"/"+dir+"/logfile")):
p = Process(
target=train_one,
args=(
"./" + train_dir + "/" + dir,
load_config(dir),
"./" + train_dir + "/" + dir + "/tensorboard",
dir, logfile_level, console_level, device
)
)
p.start()
pool.append(p)
else:
print("Already trained nnagents in "+train_dir+": generate new or reinitialize agents to start again")
continue
# suspend if the processes are too many
wait = True
while wait:
time.sleep(5)
for p in pool:
alive = p.is_alive()
if not alive:
pool.remove(p)
if len(pool)<processes:
wait = False
print("All the Tasks are Over")
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 axiom.autotrain.training
if not options.algo:
axiom.autotrain.training.train_all(int(options.processes),
options.device)
else:
for folder in options.folder:
raise NotImplementedError()
if options.mode == "act":
import axiom.autotrain.acting
if not options.algo:
axiom.autotrain.acting.act_all(int(options.processes),
options.device)
else:
for folder in options.folder:
raise NotImplementedError()
if options.mode == "serve":
import axiom.autotrain.serving
if not options.algo:
axiom.autotrain.serving.act_all(int(options.processes),
options.device,
options.serving_model)
else:
for folder in options.folder:
raise NotImplementedError()
elif options.mode == "generate":
import axiom.autotrain.generate as generate
print("Generating configuration...")
logging.basicConfig(level=logging.INFO)
generate.add_packages(load_config(), int(options.repeat))
elif options.mode == "download_data":
from axiom.marketdata.datamatrices import DataMatrices
with open("./axiom/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)