107 lines
4.7 KiB
Python
107 lines
4.7 KiB
Python
#####################################################
|
|
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.02 #
|
|
#####################################################
|
|
# python exps/trading/baselines.py --alg MLP #
|
|
# python exps/trading/baselines.py --alg GRU #
|
|
# python exps/trading/baselines.py --alg LSTM #
|
|
# python exps/trading/baselines.py --alg ALSTM #
|
|
# python exps/trading/baselines.py --alg NAIVE-V1 #
|
|
# python exps/trading/baselines.py --alg NAIVE-V2 #
|
|
# #
|
|
# python exps/trading/baselines.py --alg SFM #
|
|
# python exps/trading/baselines.py --alg XGBoost #
|
|
# python exps/trading/baselines.py --alg LightGBM #
|
|
# python exps/trading/baselines.py --alg DoubleE #
|
|
# python exps/trading/baselines.py --alg TabNet #
|
|
#####################################################
|
|
import sys
|
|
import argparse
|
|
from collections import OrderedDict
|
|
from pathlib import Path
|
|
from pprint import pprint
|
|
import ruamel.yaml as yaml
|
|
|
|
lib_dir = (Path(__file__).parent / ".." / ".." / "lib").resolve()
|
|
if str(lib_dir) not in sys.path:
|
|
sys.path.insert(0, str(lib_dir))
|
|
|
|
from procedures.q_exps import update_gpu
|
|
from procedures.q_exps import update_market
|
|
from procedures.q_exps import run_exp
|
|
|
|
import qlib
|
|
from qlib.utils import init_instance_by_config
|
|
from qlib.workflow import R
|
|
from qlib.utils import flatten_dict
|
|
|
|
|
|
def retrieve_configs():
|
|
# https://github.com/microsoft/qlib/blob/main/examples/benchmarks/
|
|
config_dir = (lib_dir / ".." / "configs" / "qlib").resolve()
|
|
# algorithm to file names
|
|
alg2names = OrderedDict()
|
|
alg2names["GRU"] = "workflow_config_gru_Alpha360.yaml"
|
|
alg2names["LSTM"] = "workflow_config_lstm_Alpha360.yaml"
|
|
alg2names["MLP"] = "workflow_config_mlp_Alpha360.yaml"
|
|
# A dual-stage attention-based recurrent neural network for time series prediction, IJCAI-2017
|
|
alg2names["ALSTM"] = "workflow_config_alstm_Alpha360.yaml"
|
|
# XGBoost: A Scalable Tree Boosting System, KDD-2016
|
|
alg2names["XGBoost"] = "workflow_config_xgboost_Alpha360.yaml"
|
|
# LightGBM: A Highly Efficient Gradient Boosting Decision Tree, NeurIPS-2017
|
|
alg2names["LightGBM"] = "workflow_config_lightgbm_Alpha360.yaml"
|
|
# State Frequency Memory (SFM): Stock Price Prediction via Discovering Multi-Frequency Trading Patterns, KDD-2017
|
|
alg2names["SFM"] = "workflow_config_sfm_Alpha360.yaml"
|
|
# DoubleEnsemble: A New Ensemble Method Based on Sample Reweighting and Feature Selection for Financial Data Analysis, https://arxiv.org/pdf/2010.01265.pdf
|
|
alg2names["DoubleE"] = "workflow_config_doubleensemble_Alpha360.yaml"
|
|
alg2names["TabNet"] = "workflow_config_TabNet_Alpha360.yaml"
|
|
alg2names["NAIVE-V1"] = "workflow_config_naive_v1_Alpha360.yaml"
|
|
alg2names["NAIVE-V2"] = "workflow_config_naive_v2_Alpha360.yaml"
|
|
alg2names["Transformer"] = "workflow_config_transformer_Alpha360.yaml"
|
|
|
|
# find the yaml paths
|
|
alg2paths = OrderedDict()
|
|
print("Start retrieving the algorithm configurations")
|
|
for idx, (alg, name) in enumerate(alg2names.items()):
|
|
path = config_dir / name
|
|
assert path.exists(), "{:} does not exist.".format(path)
|
|
alg2paths[alg] = str(path)
|
|
print("The {:02d}/{:02d}-th baseline algorithm is {:9s} ({:}).".format(idx, len(alg2names), alg, path))
|
|
return alg2paths
|
|
|
|
|
|
def main(xargs, exp_yaml):
|
|
assert Path(exp_yaml).exists(), "{:} does not exist.".format(exp_yaml)
|
|
|
|
pprint('Run {:}'.format(xargs.alg))
|
|
with open(exp_yaml) as fp:
|
|
config = yaml.safe_load(fp)
|
|
config = update_market(config, xargs.market)
|
|
config = update_gpu(config, xargs.gpu)
|
|
|
|
qlib.init(**config.get("qlib_init"))
|
|
dataset_config = config.get("task").get("dataset")
|
|
dataset = init_instance_by_config(dataset_config)
|
|
pprint("args: {:}".format(xargs))
|
|
pprint(dataset_config)
|
|
pprint(dataset)
|
|
|
|
for irun in range(xargs.times):
|
|
run_exp(
|
|
config.get("task"), dataset, xargs.alg, "recorder-{:02d}-{:02d}".format(irun, xargs.times), '{:}-{:}'.format(xargs.save_dir, xargs.market)
|
|
)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
|
|
alg2paths = retrieve_configs()
|
|
|
|
parser = argparse.ArgumentParser("Baselines")
|
|
parser.add_argument("--save_dir", type=str, default="./outputs/qlib-baselines", help="The checkpoint directory.")
|
|
parser.add_argument("--market", type=str, default="all", choices=["csi100", "csi300", "all"], help="The market indicator.")
|
|
parser.add_argument("--times", type=int, default=10, help="The repeated run times.")
|
|
parser.add_argument("--gpu", type=int, default=0, help="The GPU ID used for train / test.")
|
|
parser.add_argument("--alg", type=str, choices=list(alg2paths.keys()), required=True, help="The algorithm name.")
|
|
args = parser.parse_args()
|
|
|
|
main(args, alg2paths[args.alg])
|