Merge branch 'main' of https://github.com/you-n-g/qlib into main

examples/benchmarks/ALSTM/requirements.txt

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

examples/benchmarks/ALSTM/workflow_config_alstm.yaml

@@ -0,0 +1,69 @@
+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
+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: ALSTM
+        module_path: qlib.contrib.model.pytorch_alstm
+        kwargs:
+            d_feat: 6
+            hidden_size: 64
+            num_layers: 2
+            dropout: 0.0
+            n_epochs: 200
+            lr: 1e-3
+            early_stop: 20
+            batch_size: 800
+            metric: IC
+            loss: mse
+            seed: 0
+            GPU: 0
+            rnn_type: GRU
+    dataset:
+        class: DatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: ALPHA360_Denoise
+                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: 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

examples/benchmarks/CatBoost/README.md

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+# CatBoost
+* Code: [https://github.com/catboost/catboost](https://github.com/catboost/catboost)
+* Paper: CatBoost: unbiased boosting with categorical features. [https://proceedings.neurips.cc/paper/2018/file/14491b756b3a51daac41c24863285549-Paper.pdf](https://proceedings.neurips.cc/paper/2018/file/14491b756b3a51daac41c24863285549-Paper.pdf).

examples/benchmarks/DNN/workflow_config_dnn.yaml

@@ -30,7 +30,7 @@ task:
         module_path: qlib.contrib.model.pytorch_nn
         kwargs:
             loss: mse
-            input_dim: 360
+            input_dim: 158
             output_dim: 1
             lr: 0.002
             lr_decay: 0.96

examples/benchmarks/GATs/workflow_config_gats.yaml

@@ -37,9 +37,10 @@ task:
             lr: 1e-3
             early_stop: 20
             batch_size: 800
-            metric: IC
+            metric: loss
             loss: mse
-            base_model: GRU
+            base_model: LSTM
+            with_pretrain: True
             seed: 0
             GPU: 0
     dataset:

examples/benchmarks/HATS/requirements.txt

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+pandas==1.1.2
+numpy==1.17.4
+scikit_learn==0.23.2
+torch==1.7.0

examples/benchmarks/HATS/worflow_config_hats.yaml

@@ -0,0 +1,64 @@
+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
+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_gats
+        kwargs:
+            d_feat: 6
+            hidden_size: 64
+            num_layers: 2
+            dropout: 0.6
+            n_epochs: 200
+            lr: 1e-3
+            early_stop: 20
+            batch_size: 800
+            metric: IC
+            loss: mse
+            base_model: GRU
+            seed: 0
+            GPU: 0
+    dataset:
+        class: DatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: ALPHA360_Denoise
+                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

examples/benchmarks/LightGBM/README.md

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+# LightGBM
+* Code: [https://github.com/microsoft/LightGBM](https://github.com/microsoft/LightGBM)
+* Paper: LightGBM: A Highly Efficient Gradient Boosting
+Decision Tree. [https://proceedings.neurips.cc/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf](https://proceedings.neurips.cc/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf).

examples/benchmarks/SFM/README.md

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+# State-Frequency-Memory
+- State Frequency Memory (SFM) is a novel recurrent network that uses Discrete Fourier Transform (DFT) to decompose the hidden states of memory cells and capture the multi-frequency trading patterns from past market data to make stock price predictions. 
+- The code used in Qlib is a pyTorch implementation of SFM (Zhang, L., Aggarwal, C., & Qi, G. J. (2017,)).
+- Paper: Stock Price Prediction via Discovering Multi-Frequency Trading Patterns. https://www.cs.ucf.edu/~gqi/publications/kdd2017_stock.pdf.

examples/benchmarks/TabNet/README.md

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+# TabNet
+* TabNet is a novel high-performance and interpretable canonical deep tabular data learning architectur. TabNet uses sequential attention to choose which features to reason from at each decision step, enabling interpretability and more effcient learning as the learning capacity is used for the most salient features.
+* The code used in Qlib is a pyTorch implementation of Tabnet (Arik, S. O., & Pfister, T. (2019). [https://github.com/dreamquark-ai/tabnet](https://github.com/dreamquark-ai/tabnet)
+* Paper: TabNet: Attentive Interpretable Tabular Learning. [https://arxiv.org/pdf/1908.07442.pdf](https://arxiv.org/pdf/1908.07442.pdf).

examples/benchmarks/XGBoost/README.md

@@ -0,0 +1,3 @@
+# XGBoost
+* Code: [https://github.com/dmlc/xgboost](https://github.com/dmlc/xgboost)
+* Paper: XGBoost: A Scalable Tree Boosting System. [https://dl.acm.org/doi/pdf/10.1145/2939672.2939785](https://dl.acm.org/doi/pdf/10.1145/2939672.2939785).