Update all baseline models.

examples/benchmarks/HATS/README.md

@@ -1,15 +0,0 @@
-## 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

examples/benchmarks/HATS/requirements.txt

@@ -1,4 +0,0 @@
-pandas==1.1.2
-numpy==1.17.4
-scikit_learn==0.23.2
-torch==1.7.0

examples/benchmarks/HATS/worflow_config_hats.yaml

@@ -1,77 +0,0 @@
-provider_uri: "~/.qlib/qlib_data/cn_data"
-region: cn
-market: &market csi300
-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:
-        - class: RobustZScoreNorm
-          kwargs:
-              fields_group: feature
-              clip_outlier: true
-        - class: Fillna
-          kwargs:
-              fields_group: feature
-    learn_processors:
-        - class: DropnaLabel
-        - class: CSRankNorm
-          kwargs:
-              fields_group: label
-    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: HATS
-        module_path: qlib.contrib.model.pytorch_hats
-        kwargs:
-            d_feat: 6
-            hidden_size: 64
-            num_layers: 2
-            dropout: 0.6
-            n_epochs: 200
-            lr: 1e-3
-            early_stop: 20
-            metric: loss
-            loss: mse
-            base_model: GRU
-            seed: 0
-            GPU: 0
-    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: PortAnaRecord
-          module_path: qlib.workflow.record_temp
-          kwargs: 
-            config: *port_analysis_config