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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.02 #
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# 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 #
# #############################
# python exps/trading/baselines.py --alg Transformer
# python exps/trading/baselines.py --alg TSF
# python exps/trading/baselines.py --alg TSF-2x24-drop0_0 --market csi300
# python exps/trading/baselines.py --alg TSF-6x32-drop0_0 --market csi300
#################################################################################
import sys
import copy
from datetime import datetime
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 config_utils import arg_str2bool
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 to_drop(config, pos_drop, other_drop):
config = copy.deepcopy(config)
net = config["task"]["model"]["kwargs"]["net_config"]
net["pos_drop"] = pos_drop
net["other_drop"] = other_drop
return config
def to_layer(config, embed_dim, depth):
config = copy.deepcopy(config)
net = config["task"]["model"]["kwargs"]["net_config"]
net["embed_dim"] = embed_dim
net["num_heads"] = [4] * depth
net["mlp_hidden_multipliers"] = [4] * depth
return config
def extend_transformer_settings(alg2configs, name):
config = copy.deepcopy(alg2configs[name])
for i in range(1, 9):
for j in (6, 12, 24, 32, 48, 64):
for k1 in (0, 0.05, 0.1, 0.2, 0.3):
for k2 in (0, 0.1):
alg2configs[
name + "-{:}x{:}-drop{:}_{:}".format(i, j, k1, k2)
] = to_layer(to_drop(config, k1, k2), j, i)
return alg2configs
def replace_start_time(config, start_time):
config = copy.deepcopy(config)
xtime = datetime.strptime(start_time, "%Y-%m-%d")
config["data_handler_config"]["start_time"] = xtime.date()
config["data_handler_config"]["fit_start_time"] = xtime.date()
config["task"]["dataset"]["kwargs"]["segments"]["train"][0] = xtime.date()
return config
def extend_train_data(alg2configs, name):
config = copy.deepcopy(alg2configs[name])
start_times = (
"2008-01-01",
"2008-07-01",
"2009-01-01",
"2009-07-01",
"2010-01-01",
"2011-01-01",
"2012-01-01",
"2013-01-01",
)
for start_time in start_times:
config = replace_start_time(config, start_time)
alg2configs[name + "s{:}".format(start_time)] = config
return alg2configs
def refresh_record(alg2configs):
alg2configs = copy.deepcopy(alg2configs)
for key, config in alg2configs.items():
xlist = config["task"]["record"]
new_list = []
for x in xlist:
# remove PortAnaRecord and SignalMseRecord
if x["class"] != "PortAnaRecord" and x["class"] != "SignalMseRecord":
new_list.append(x)
## add MultiSegRecord
new_list.append(
{
"class": "MultiSegRecord",
"module_path": "qlib.contrib.workflow",
"generate_kwargs": {
"segments": {"train": "train", "valid": "valid", "test": "test"},
"save": True,
},
}
)
config["task"]["record"] = new_list
return alg2configs
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"
alg2names["TSF"] = "workflow_config_transformer_basic_Alpha360.yaml"
# find the yaml paths
alg2configs = 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)
with open(path) as fp:
alg2configs[alg] = yaml.safe_load(fp)
print(
"The {:02d}/{:02d}-th baseline algorithm is {:9s} ({:}).".format(
idx, len(alg2configs), alg, path
)
)
alg2configs = extend_transformer_settings(alg2configs, "TSF")
alg2configs = refresh_record(alg2configs)
# extend the algorithms by different train-data
for name in ("TSF-2x24-drop0_0", "TSF-6x32-drop0_0"):
alg2configs = extend_train_data(alg2configs, name)
print(
"There are {:} algorithms : {:}".format(
len(alg2configs), list(alg2configs.keys())
)
)
return alg2configs
def main(alg_name, market, config, times, save_dir, gpu):
pprint("Run {:}".format(alg_name))
config = update_market(config, market)
config = update_gpu(config, gpu)
qlib.init(**config.get("qlib_init"))
dataset_config = config.get("task").get("dataset")
dataset = init_instance_by_config(dataset_config)
pprint(dataset_config)
pprint(dataset)
for irun in range(times):
run_exp(
config.get("task"),
dataset,
alg_name,
"recorder-{:02d}-{:02d}".format(irun, times),
"{:}-{:}".format(save_dir, market),
)
if __name__ == "__main__":
alg2configs = 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=5, help="The repeated run times.")
parser.add_argument(
"--shared_dataset",
type=arg_str2bool,
default=False,
help="Whether to share the dataset for all algorithms?",
)
parser.add_argument(
"--gpu", type=int, default=0, help="The GPU ID used for train / test."
)
parser.add_argument(
"--alg",
type=str,
choices=list(alg2configs.keys()),
nargs="+",
required=True,
help="The algorithm name(s).",
)
args = parser.parse_args()
if len(args.alg) == 1:
main(
args.alg[0],
args.market,
alg2configs[args.alg[0]],
args.times,
args.save_dir,
args.gpu,
)
elif len(args.alg) > 1:
assert args.shared_dataset, "Must allow share dataset"
pprint(args)
configs = [
update_gpu(update_market(alg2configs[name], args.market), args.gpu)
for name in args.alg
]
qlib.init(**configs[0].get("qlib_init"))
dataset_config = configs[0].get("task").get("dataset")
dataset = init_instance_by_config(dataset_config)
pprint(dataset_config)
pprint(dataset)
for alg_name, config in zip(args.alg, configs):
print("Run {:} over {:}".format(alg_name, args.alg))
for irun in range(args.times):
run_exp(
config.get("task"),
dataset,
alg_name,
"recorder-{:02d}-{:02d}".format(irun, args.times),
"{:}-{:}".format(args.save_dir, args.market),
)