262 lines
9.4 KiB
Python
262 lines
9.4 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 #
|
|
# #############################
|
|
# 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 pprint import pprint
|
|
import ruamel.yaml as yaml
|
|
|
|
from xautodl.config_utils import arg_str2bool
|
|
from xautodl.procedures.q_exps import update_gpu
|
|
from xautodl.procedures.q_exps import update_market
|
|
from xautodl.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),
|
|
)
|