hats4

examples/benchmarks/HATS/README.md

@@ -0,0 +1,15 @@
+##Requirement
+
+* pandas==1.1.2
+* numpy==1.17.4
+* scikit_learn==0.23.2
+* torch==1.7.0
+
+##HATS
+
+* HATS is a a hierarchical attention network for stock prediction which uses relational data for stock market prediction. HATS selectively aggregates information
+on different relation types and adds the information to the representations of each company. HATS is used as a relational modeling module with initialized node representations.Furthermore, HATS
+can  predict not only individual stock prices but also market index movements, which is similar to the graph classification task.
+
+* HATS uses pretrained model of GRU and LSTM. The code of GRU and LSTM used in Qlib is a pyTorch implemention of GRU and LSTM.
+* Paper address:HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction https://arxiv.org/pdf/1908.07999.pdf

qlib/contrib/model/pytorch_hats.py

@@ -1,5 +1,14 @@
-# Copyright (c) Microsoft Corporation.
-# Licensed under the MIT License.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 
 
 from __future__ import division