Add the MTMD model on Alpha360

README.md

@@ -13,6 +13,7 @@ Recent released features
 | --                      | ------    |
 | KRNN and Sandwich models | :chart_with_upwards_trend: [Released](https://github.com/microsoft/qlib/pull/1414/) on May 26, 2023 |
 | Release Qlib v0.9.0 | :octocat: [Released](https://github.com/microsoft/qlib/releases/tag/v0.9.0) on Dec 9, 2022 |
+| MTMD models | :chart_with_upwards_trend: [Released](https://github.com/microsoft/qlib/pull/1728) on Dec 7, 2022 |
 | RL Learning Framework | :hammer: :chart_with_upwards_trend: Released on Nov 10, 2022. [#1332](https://github.com/microsoft/qlib/pull/1332), [#1322](https://github.com/microsoft/qlib/pull/1322), [#1316](https://github.com/microsoft/qlib/pull/1316),[#1299](https://github.com/microsoft/qlib/pull/1299),[#1263](https://github.com/microsoft/qlib/pull/1263), [#1244](https://github.com/microsoft/qlib/pull/1244), [#1169](https://github.com/microsoft/qlib/pull/1169), [#1125](https://github.com/microsoft/qlib/pull/1125), [#1076](https://github.com/microsoft/qlib/pull/1076)|
 | HIST and IGMTF models | :chart_with_upwards_trend: [Released](https://github.com/microsoft/qlib/pull/1040) on Apr 10, 2022 |
 | Qlib [notebook tutorial](https://github.com/microsoft/qlib/tree/main/examples/tutorial) | 📖 [Released](https://github.com/microsoft/qlib/pull/1037) on Apr 7, 2022 | 
@@ -355,6 +356,7 @@ Here is a list of models built on `Qlib`.
 - [ADD based on pytorch (Hongshun Tang, et al.2020)](examples/benchmarks/ADD/)
 - [IGMTF based on pytorch (Wentao Xu, et al.2021)](examples/benchmarks/IGMTF/)
 - [HIST based on pytorch (Wentao Xu, et al.2021)](examples/benchmarks/HIST/)
+- [MTMD based on pytorch (Mingjie Wang, et al.2021)](examples/benchmarks/MTMD/)
 - [KRNN based on pytorch](examples/benchmarks/KRNN/)
 - [Sandwich based on pytorch](examples/benchmarks/Sandwich/)
 

examples/benchmarks/MTMD/README.md

@@ -0,0 +1,3 @@
+# MTMD
+* Code: [https://github.com/MingjieWang0606/MTMD-Public/tree/main](https://github.com/MingjieWang0606/MTMD-Public/tree/main)
+* Paper: "[MTMD: Multi-Scale Temporal Memory Learning and Efficient Debiasing Framework for Stock Trend Forecasting](https://arxiv.org/abs/2212.08656)".

examples/benchmarks/MTMD/requirements.txt

@@ -0,0 +1,6 @@
+numpy==1.22.0
+pandas==1.2.3
+scipy==1.6.1
+torch==1.8.0
+scikit_learn==0.24.1
+tqdm==4.61.1

examples/benchmarks/MTMD/workflow_config_mtmd_Alpha360.yaml

@@ -0,0 +1,90 @@
+qlib_init:
+    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
+        kwargs:
+            signal: <PRED>
+            topk: 50
+            n_drop: 5
+    backtest:
+        start_time: 2017-01-01
+        end_time: 2020-08-01
+        account: 100000000
+        benchmark: *benchmark
+        exchange_kwargs:
+            limit_threshold: 0.095
+            deal_price: close
+            open_cost: 0.0005
+            close_cost: 0.0015
+            min_cost: 5
+task:
+    model:
+        class: MTMD
+        module_path: qlib.contrib.model.pytorch_mtmd
+        kwargs:
+            d_feat: 6
+            hidden_size: 64
+            num_layers: 2
+            dropout: 0
+            n_epochs: 200
+            lr: 1e-4
+            early_stop: 20
+            metric: ic
+            loss: mse
+            base_model: LSTM #GRU
+            model_path: "benchmarks/LSTM/model_lstm_csi300.pkl" #"benchmarks/GRU/model_gru_csi300.pkl"
+            stock2concept: "benchmarks/MTMD/qlib_csi300_stock2concept.npy"
+            stock_index: "benchmarks/MTMD/qlib_csi300_stock_index.npy"
+            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: 
+            model: <MODEL>
+            dataset: <DATASET>
+        - 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/README.md

@@ -70,6 +70,7 @@ The numbers shown below demonstrate the performance of the entire `workflow` of
 | HIST(Wentao Xu, et al.)                   | Alpha360 | 0.0522±0.00 | 0.3530±0.01 | 0.0667±0.00 | 0.4576±0.01 | 0.0987±0.02       | 1.3726±0.27       | -0.0681±0.01 |
 | KRNN                                      | Alpha360 | 0.0173±0.01 | 0.1210±0.06 | 0.0270±0.01 | 0.2018±0.04 | -0.0465±0.05      | -0.5415±0.62      | -0.2919±0.13 |
 | Sandwich                                  | Alpha360 | 0.0258±0.00 | 0.1924±0.04 | 0.0337±0.00 | 0.2624±0.03 | 0.0005±0.03       | 0.0001±0.33       | -0.1752±0.05 |
+| MTMD(Mingjie Wang, et al.)                | Alpha360 | 0.0538±0.00 | 0.3849±0.01 | 0.0672±0.00 | 0.4656±0.01 | 0.1022±0.02       | 1.4031±0.26       | -0.0664±0.01 |
 
 
 - The selected 20 features are based on the feature importance of a lightgbm-based model.

qlib/contrib/model/__init__.py

@@ -34,8 +34,9 @@
     from .pytorch_sfm import SFM_Model
     from .pytorch_tcn import TCN
     from .pytorch_add import ADD
+    from .pytorch_mtmd import MTMD
 
-    pytorch_classes = (ALSTM, GATs, GRU, LSTM, DNNModelPytorch, TabnetModel, SFM_Model, TCN, ADD)
+    pytorch_classes = (ALSTM, GATs, GRU, LSTM, DNNModelPytorch, TabnetModel, SFM_Model, TCN, ADD, MTMD)
 except ModuleNotFoundError:
     pytorch_classes = ()
     print("ModuleNotFoundError.  PyTorch models are skipped (optional: maybe installing pytorch can fix it).")