This commit is contained in:
D-X-Y 2021-03-16 12:57:40 +00:00
parent 925da2d22b
commit 8b14b4c84a
4 changed files with 166 additions and 1 deletions

@ -1 +1 @@
Subproject commit d47e35d64e274524df3bbafa6b159714a699ccaa
Subproject commit d4aa6816520d306503a1f80c1834b37a9df83c3d

View File

@ -0,0 +1,64 @@
qlib_init:
provider_uri: "~/.qlib/qlib_data/cn_data"
region: cn
market: &market all
benchmark: &benchmark SH000300
data_handler_config: &data_handler_config
start_time: 2008-01-01
end_time: 2020-08-01
fit_start_time: 2008-01-01
fit_end_time: 2014-12-31
instruments: *market
infer_processors: []
learn_processors: []
label: ["Ref($close, -2) / Ref($close, -1) - 1"]
port_analysis_config: &port_analysis_config
strategy:
class: TopkDropoutStrategy
module_path: qlib.contrib.strategy.strategy
kwargs:
topk: 50
n_drop: 5
backtest:
verbose: False
limit_threshold: 0.095
account: 100000000
benchmark: *benchmark
deal_price: close
open_cost: 0.0005
close_cost: 0.0015
min_cost: 5
task:
model:
class: NAIVE
module_path: trade_models.naive_model
kwargs:
d_feat: 6
dataset:
class: DatasetH
module_path: qlib.data.dataset
kwargs:
handler:
class: Alpha360
module_path: qlib.contrib.data.handler
kwargs: *data_handler_config
segments:
train: [2008-01-01, 2014-12-31]
valid: [2015-01-01, 2016-12-31]
test: [2017-01-01, 2020-08-01]
record:
- class: SignalRecord
module_path: qlib.workflow.record_temp
kwargs: {}
- class: SignalMseRecord
module_path: qlib.contrib.workflow.record_temp
kwargs: {}
- class: SigAnaRecord
module_path: qlib.workflow.record_temp
kwargs:
ana_long_short: False
ann_scaler: 252
- class: PortAnaRecord
module_path: qlib.workflow.record_temp
kwargs:
config: *port_analysis_config

View File

@ -5,6 +5,7 @@
# 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 #
# #
# python exps/trading/baselines.py --alg SFM #
# python exps/trading/baselines.py --alg XGBoost #
@ -52,6 +53,7 @@ def retrieve_configs():
# 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"] = "workflow_config_naive_Alpha360.yaml"
# find the yaml paths
alg2paths = OrderedDict()

99
lib/trade_models/naive_model.py Executable file
View File

@ -0,0 +1,99 @@
##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021 #
##################################################
# A Simple Model that reused the prices of last day
##################################################
from __future__ import division
from __future__ import print_function
import random
import numpy as np
import pandas as pd
from qlib.log import get_module_logger
from qlib.model.base import Model
from qlib.data.dataset import DatasetH
from qlib.data.dataset.handler import DataHandlerLP
class NAIVE(Model):
"""NAIVE Quant Model"""
def __init__(self, d_feat=6, seed=None, **kwargs):
# Set logger.
self.logger = get_module_logger("NAIVE")
self.logger.info("NAIVE version...")
# set hyper-parameters.
self.d_feat = d_feat
self.seed = seed
self.logger.info(
"NAIVE parameters setting: d_feat={:}, seed={:}".format(self.d_feat, self.seed))
if self.seed is not None:
random.seed(self.seed)
np.random.seed(self.seed)
self.fitted = False
def process_data(self, features):
features = features.reshape(len(features), self.d_feat, -1)
features = features.transpose((0, 2, 1))
return features[:, :59, 0]
def mse(self, preds, labels):
masks = ~np.isnan(labels)
masked_preds = preds[masks]
masked_labels= labels[masks]
return np.square(masked_preds - masked_labels).mean()
def model(self, x):
x = 1 / x - 1
masks = ~np.isnan(x)
results = []
for rowd, rowm in zip(x, masks):
temp = rowd[rowm]
if rowm.any():
results.append(float(rowd[rowm][-1]))
else:
results.append(0)
return np.array(results, dtype=x.dtype)
def fit(
self,
dataset: DatasetH
):
def _prepare_dataset(df_data):
features = df_data["feature"].values
features = self.process_data(features)
labels = df_data["label"].values.squeeze()
return dict(features=features, labels=labels)
df_train, df_valid, df_test = dataset.prepare(
["train", "valid", "test"],
col_set=["feature", "label"],
data_key=DataHandlerLP.DK_L,
)
train_dataset, valid_dataset, test_dataset = (
_prepare_dataset(df_train),
_prepare_dataset(df_valid),
_prepare_dataset(df_test),
)
# df_train['feature']['CLOSE1'].values
# train_dataset['features'][:, -1]
train_mse_loss = self.mse(self.model(train_dataset['features']), train_dataset['labels'])
valid_mse_loss = self.mse(self.model(valid_dataset['features']), valid_dataset['labels'])
self.logger.info("Training MSE loss: {:}".format(train_mse_loss))
self.logger.info("Validation MSE loss: {:}".format(valid_mse_loss))
self.fitted = True
def predict(self, dataset):
if not self.fitted:
raise ValueError("The model is not fitted yet!")
x_test = dataset.prepare("test", col_set="feature")
index = x_test.index
preds = self.model(self.process_data(x_test.values))
return pd.Series(preds, index=index)