update version for releasing

Qlib Highfreq Support & Highfreq DataHanlder/Operator/Processor Examples

.github/ISSUE_TEMPLATE/bug-report.md

@@ -28,7 +28,8 @@ Steps to reproduce the behavior:
 
 ## Environment
 
-**Note**: One could run `python scripts/collect_info.py` under the `qlib` directory to get the following information.
+**Note**: User could run `cd scripts && python collect_info.py all` under project directory to get system information
+and paste them here directly.
 
  - Qlib version:
  - Python version:
@@ -37,4 +38,4 @@ Steps to reproduce the behavior:
 
 ## Additional Notes
 
-<!-- Add any other information about the problem here. -->
+<!-- Add any other information about the problem here. -->

.github/stale.yml

@@ -0,0 +1,62 @@
+# Configuration for probot-stale - https://github.com/probot/stale
+
+# Number of days of inactivity before an Issue or Pull Request becomes stale
+daysUntilStale: 60
+
+# Number of days of inactivity before an Issue or Pull Request with the stale label is closed.
+# Set to false to disable. If disabled, issues still need to be closed manually, but will remain marked as stale.
+daysUntilClose: 7
+
+# Only issues or pull requests with all of these labels are check if stale. Defaults to `[]` (disabled)
+onlyLabels: []
+
+# Issues or Pull Requests with these labels will never be considered stale. Set to `[]` to disable
+exemptLabels:
+  - bug
+  - pinned
+  - security
+  - "[Status] Maybe Later"
+
+# Set to true to ignore issues in a project (defaults to false)
+exemptProjects: false
+
+# Set to true to ignore issues in a milestone (defaults to false)
+exemptMilestones: false
+
+# Set to true to ignore issues with an assignee (defaults to false)
+exemptAssignees: false
+
+# Label to use when marking as stale
+staleLabel: wontfix
+
+# Comment to post when marking as stale. Set to `false` to disable
+markComment: >
+  This issue has been automatically marked as stale because it has not had
+  recent activity. It will be closed if no further activity occurs. Thank you
+  for your contributions.
+
+# Comment to post when removing the stale label.
+# unmarkComment: >
+#   Your comment here.
+
+# Comment to post when closing a stale Issue or Pull Request.
+# closeComment: >
+#   Your comment here.
+
+# Limit the number of actions per hour, from 1-30. Default is 30
+limitPerRun: 30
+
+# Limit to only `issues` or `pulls`
+# only: issues
+
+# Optionally, specify configuration settings that are specific to just 'issues' or 'pulls':
+# pulls:
+#   daysUntilStale: 30
+#   markComment: >
+#     This pull request has been automatically marked as stale because it has not had
+#     recent activity. It will be closed if no further activity occurs. Thank you
+#     for your contributions.
+
+# issues:
+#   exemptLabels:
+#     - confirmed

.github/workflows/test.yml

@@ -12,8 +12,8 @@ jobs:
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        os: [windows-latest, ubuntu-16.04, ubuntu-18.04, macos-latest]
-        python-version: [3.6, 3.7, 3.8]
+        os: [windows-latest, ubuntu-16.04, ubuntu-18.04, ubuntu-20.04, macos-latest]
+        python-version: [3.6, 3.7, 3.8, 3.9]
 
     steps:
     - uses: actions/checkout@v2
@@ -23,37 +23,96 @@ jobs:
       with:
         python-version: ${{ matrix.python-version }}
 
-    - name: Install dependencies
+    - name: Lint with Black
       run: |
-        pip install --upgrade cython
-        pip install numpy jupyter jupyter_contrib_nbextensions
-        python setup.py install
+        cd ..
+        if [ "$RUNNER_OS" == "Windows" ]; then
+          $CONDA\\python.exe -m pip install black
+          $CONDA\\python.exe -m black qlib -l 120 --check --diff
+        else
+          sudo $CONDA/bin/python -m pip install black
+          $CONDA/bin/python -m black qlib -l 120 --check --diff
+        fi
+      shell: bash
 
+    # Test Qlib installed with pip
+    - name: Install Qlib with pip
+      run: |
+        if [ "$RUNNER_OS" == "Windows" ]; then
+          $CONDA\\python.exe -m pip install pyqlib --ignore-installed ruamel.yaml --user
+        else
+          sudo $CONDA/bin/python -m pip install pyqlib --ignore-installed ruamel.yaml
+        fi
+      shell: bash
+    
     - name: Install Lightgbm for MacOS
       if: runner.os == 'macOS'
       run: |
         /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Microsoft/qlib/main/.github/brew_install.sh)"
         HOMEBREW_NO_AUTO_UPDATE=1 brew install lightgbm
 
+    - name: Test data downloads
+      run: |
+        if [ "$RUNNER_OS" == "Windows" ]; then
+          $CONDA\\python.exe scripts/get_data.py qlib_data --target_dir ~/.qlib/qlib_data/cn_data --interval 1d --region cn
+        else
+          $CONDA/bin/python scripts/get_data.py qlib_data --target_dir ~/.qlib/qlib_data/cn_data --interval 1d --region cn
+        fi
+      shell: bash
+
+    - name: Test workflow by config (install from pip)
+      run: |
+        if [ "$RUNNER_OS" == "Windows" ]; then
+          $CONDA\\python.exe qlib\\workflow\\cli.py examples\\benchmarks\\LightGBM\\workflow_config_lightgbm_Alpha158.yaml 
+          $CONDA\\python.exe -m pip uninstall -y pyqlib
+        else
+          $CONDA/bin/python qlib/workflow/cli.py examples/benchmarks/LightGBM/workflow_config_lightgbm_Alpha158.yaml 
+          sudo $CONDA/bin/python -m pip uninstall -y pyqlib
+        fi
+      shell: bash
+      
+    # Test Qlib installed from source
+    - name: Install Qlib from source
+      run: |
+        if [ "$RUNNER_OS" == "Windows" ]; then
+          $CONDA\\python.exe -m pip install --upgrade cython
+          $CONDA\\python.exe -m pip install numpy jupyter jupyter_contrib_nbextensions
+          $CONDA\\python.exe -m pip install -U scipy scikit-learn # installing without this line will cause errors on GitHub Actions, while instsalling locally won't
+          $CONDA\\python.exe setup.py install
+        else
+          sudo $CONDA/bin/python -m pip install --upgrade cython
+          sudo $CONDA/bin/python -m pip install numpy jupyter jupyter_contrib_nbextensions
+          sudo $CONDA/bin/python -m pip install -U scipy scikit-learn # installing without this line will cause errors on GitHub Actions, while instsalling locally won't
+          sudo $CONDA/bin/python setup.py install
+        fi
+      shell: bash
+
     - name: Install test dependencies
       run: |
-        python -m pip install --upgrade pip
-        pip install black pytest
-
-    - name: Lint with Black
-      run: |
-        cd ..
-        python -m black qlib -l 120 --check --diff
+        if [ "$RUNNER_OS" == "Windows" ]; then
+          $CONDA\\python.exe -m pip install --upgrade pip
+          $CONDA\\python.exe -m pip install black pytest
+        else
+          sudo $CONDA/bin/python -m pip install --upgrade pip
+          sudo $CONDA/bin/python -m pip install black pytest
+        fi
+      shell: bash 
 
     - name: Unit tests with Pytest
       run: |
         cd tests
-        pytest . --durations=0
+        if [ "$RUNNER_OS" == "Windows" ]; then
+          $CONDA\\python.exe -m pytest . --durations=0
+        else
+          $CONDA/bin/python -m pytest . --durations=0
+        fi
+      shell: bash
 
-    - name: Test data downloads
+    - name: Test workflow by config (install from source)
       run: |
-        python scripts/get_data.py qlib_data --target_dir ~/.qlib/qlib_data/cn_data --interval 1d --region cn
-
-    - name: Test workflow by config
-      run: |
-        qrun examples/benchmarks/LightGBM/workflow_config_lightgbm.yaml 
+        if [ "$RUNNER_OS" == "Windows" ]; then
+          $CONDA\\python.exe qlib\\workflow\\cli.py examples\\benchmarks\\LightGBM\\workflow_config_lightgbm_Alpha158.yaml 
+        else
+          $CONDA/bin/python qlib/workflow/cli.py examples/benchmarks/LightGBM/workflow_config_lightgbm_Alpha158.yaml 
+        fi
+      shell: bash

.gitignore

@@ -2,6 +2,7 @@
 __pycache__/
 
 *.pyc
+*.pyd
 *.so
 *.ipynb
 .ipynb_checkpoints

.readthedocs.yml

@@ -0,0 +1,21 @@
+# .readthedocs.yml
+# Read the Docs configuration file
+# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
+
+# Required
+version: 2
+
+# Build documentation in the docs/ directory with Sphinx
+sphinx:
+  configuration: docs/conf.py
+
+# Build all formats
+formats: all
+
+# Optionally set the version of Python and requirements required to build your docs
+python:
+  version: 3.7
+  install:
+    - requirements: docs/requirements.txt
+    - method: setuptools
+      path: .

CHANGES.rst

@@ -114,7 +114,7 @@ Version 0.4.1
 Version 0.4.2
 --------------------
 - Refactor DataHandler
-- Add ``ALPHA360`` DataHandler
+- Add ``Alpha360`` DataHandler
 
 
 Version 0.4.3

README.md

@@ -34,6 +34,7 @@ For more details, please refer to our paper ["Qlib: An AI-oriented Quantitative
 - [More About Qlib](#more-about-qlib)
 - [Offline Mode and Online Mode](#offline-mode-and-online-mode)
   - [Performance of Qlib Data Server](#performance-of-qlib-data-server)
+- [Related Reports](#related-reports)
 - [Contributing](#contributing)
 
 
@@ -61,17 +62,36 @@ At the module level, Qlib is a platform that consists of the above components. T
 
 This quick start guide tries to demonstrate
 1. It's very easy to build a complete Quant research workflow and try your ideas with _Qlib_.
-1. Though with *public data* and *simple models*, machine learning technologies **work very well** in practical Quant investment.
+2. Though with *public data* and *simple models*, machine learning technologies **work very well** in practical Quant investment.
+
+Here is a quick **[demo](https://terminalizer.com/view/3f24561a4470)** shows how to install ``Qlib``, and run LightGBM with ``qrun``. **But**, please make sure you have already prepared the data following the [instruction](#data-preparation).
+
 
 ## Installation
 
-Users can easily install ``Qlib`` by pip according to the following command
+This table demonstrates the supported Python version of `Qlib`:
+|               | install with pip           | install from source  | plot |
+| ------------- |:---------------------:|:--------------------:|:----:|
+| Python 3.6    | :heavy_check_mark:    | :heavy_check_mark: (only with `Anaconda`)                  | :heavy_check_mark: |
+| Python 3.7    | :heavy_check_mark:    | :heavy_check_mark:   | :heavy_check_mark: |
+| Python 3.8    | :heavy_check_mark:    | :heavy_check_mark:   | :heavy_check_mark: |
+| Python 3.9    | :x:                   | :heavy_check_mark:   | :x: |
+
+**Note**: 
+1. Please pay attention that installing cython in Python 3.6 will raise some error when installing ``Qlib`` from source. If users use Python 3.6 on their machines, it is recommended to *upgrade* Python to version 3.7 or use `conda`'s Python to install ``Qlib`` from source.
+2. For Python 3.9, `Qlib` supports running workflows such as training models, doing backtest and plot most of the related figures (those included in [notebook](examples/workflow_by_code.ipynb)). However, plotting for the *model performance* is not supported for now and we will fix this when the dependent packages are upgraded in the future.
+
+### Install with pip
+Users can easily install ``Qlib`` by pip according to the following command.
 
 ```bash
   pip install pyqlib
 ```
 
-Also, users can install ``Qlib`` by the source code according to the following steps:
+**Note**: pip will install the latest stable qlib. However, the main branch of qlib is in active development. If you want to test the latest scripts or functions in the main branch. Please install qlib with the methods below.
+
+### Install from source
+Also, users can install the latest dev version ``Qlib`` by the source code according to the following steps:
 
 * Before installing ``Qlib`` from source, users need to install some dependencies:
 
@@ -80,13 +100,20 @@ Also, users can install ``Qlib`` by the source code according to the following s
   pip install --upgrade  cython
   ```
 
-* Clone the repository and install ``Qlib``:
-
-  ```bash
-  git clone https://github.com/microsoft/qlib.git && cd qlib
-  python setup.py install
-  ```
+* Clone the repository and install ``Qlib`` as follows.
+  * If you haven't installed qlib by the command ``pip install pyqlib`` before:
+    ```bash
+    git clone https://github.com/microsoft/qlib.git && cd qlib
+    python setup.py install
+    ```
+  * If you have already installed the stable version by the command ``pip install pyqlib``:
+    ```bash
+    git clone https://github.com/microsoft/qlib.git && cd qlib
+    pip install .
+    ```
+  **Note**: **Only** the command ``pip install .`` **can** overwrite the stable version installed by ``pip install pyqlib``, while the command ``python setup.py install`` **can't**.
 
+**Tips**: If you fail to install `Qlib` or run the examples in your environment,  comparing your steps and the [CI workflow](.github/workflows/test.yml) may help you find the problem.
 
 ## Data Preparation
 Load and prepare data by running the following code:
@@ -130,12 +157,16 @@ Users could create the same dataset with it.
 ## Auto Quant Research Workflow
 Qlib provides a tool named `qrun` to run the whole workflow automatically (including building dataset, training models, backtest and evaluation). You can start an auto quant research workflow and have a graphical reports analysis according to the following steps: 
 
-1. Quant Research Workflow: Run  `qrun` with lightgbm workflow config ([workflow_config_lightgbm.yaml](examples/benchmarks/LightGBM/workflow_config_lightgbm.yaml)) as following.
+1. Quant Research Workflow: Run  `qrun` with lightgbm workflow config ([workflow_config_lightgbm_Alpha158.yaml](examples/benchmarks/LightGBM/workflow_config_lightgbm_Alpha158.yaml) as following.
     ```bash
       cd examples  # Avoid running program under the directory contains `qlib`
-      qrun benchmarks/LightGBM/workflow_config_lightgbm.yaml
+      qrun benchmarks/LightGBM/workflow_config_lightgbm_Alpha158.yaml
     ```
-    The result of `qrun` is as follows, please refer to please refer to [Intraday Trading](https://qlib.readthedocs.io/en/latest/component/backtest.html) for more details about the result. 
+    If users want to use `qrun` under debug mode, please use the following command:
+    ```bash
+    python -m pdb qlib/workflow/cli.py examples/benchmarks/LightGBM/workflow_config_lightgbm_Alpha158.yaml
+    ```
+    The result of `qrun` is as follows, please refer to [Intraday Trading](https://qlib.readthedocs.io/en/latest/component/backtest.html) for more details about the result. 
 
     ```bash
 
@@ -153,9 +184,6 @@ Qlib provides a tool named `qrun` to run the whole workflow automatically (inclu
     annualized_return  0.128982
     information_ratio  1.444287
     max_drawdown      -0.091078
-
-
-
     ```
     Here are detailed documents for `qrun` and [workflow](https://qlib.readthedocs.io/en/latest/component/workflow.html).
 
@@ -193,19 +221,22 @@ The automatic workflow may not suite the research workflow of all Quant research
 # [Quant Model Zoo](examples/benchmarks)
 
 Here is a list of models built on `Qlib`.
-- [GBDT based on LightGBM](qlib/contrib/model/gbdt.py)
-- [GBDT based on Catboost](qlib/contrib/model/catboost_model.py)
-- [GBDT based on XGBoost](qlib/contrib/model/xgboost.py)
+- [GBDT based on XGBoost (Tianqi Chen, et al. 2016)](qlib/contrib/model/xgboost.py)
+- [GBDT based on LightGBM (Guolin Ke, et al. 2017)](qlib/contrib/model/gbdt.py)
+- [GBDT based on Catboost (Liudmila Prokhorenkova, et al. 2017)](qlib/contrib/model/catboost_model.py)
 - [MLP based on pytorch](qlib/contrib/model/pytorch_nn.py)
-- [GRU based on pytorch](qlib/contrib/model/pytorch_gru.py)
-- [LSTM based on pytorcn](qlib/contrib/model/pytorch_lstm.py)
-- [ALSTM based on pytorcn](qlib/contrib/model/pytorch_alstm.py)
-- [GATs based on pytorch](qlib/contrib/model/pytorch_gats.py)
-- [SFM based on pytorch](qlib/contrib/model/pytorch_sfm.py)
-<!-- - [TFT based on tensorflow](examples/benchmarks/TFT/tft.py) -->
+- [LSTM based on pytorch (Sepp Hochreiter, et al. 1997)](qlib/contrib/model/pytorch_lstm.py)
+- [GRU based on pytorch (Kyunghyun Cho, et al. 2014)](qlib/contrib/model/pytorch_gru.py)
+- [ALSTM based on pytorch (Yao Qin, et al. 2017)](qlib/contrib/model/pytorch_alstm.py)
+- [GATs based on pytorch (Petar Velickovic, et al. 2017)](qlib/contrib/model/pytorch_gats.py)
+- [SFM based on pytorch (Liheng Zhang, et al. 2017)](qlib/contrib/model/pytorch_sfm.py)
+- [TFT based on tensorflow (Bryan Lim, et al. 2019)](examples/benchmarks/TFT/tft.py)
+- [TabNet based on pytorch (Sercan O. Arik, et al. 2019)](qlib/contrib/model/pytorch_tabnet.py)
 
 Your PR of new Quant models is highly welcomed.
 
+The performance of each model on the `Alpha158` and `Alpha360` dataset can be found [here](examples/benchmarks/README.md).
+
 ## Run a single model
 All the models listed above are runnable with ``Qlib``. Users can find the config files we provide and some details about the model through the [benchmarks](examples/benchmarks) folder. More information can be retrieved at the model files listed above.
 
@@ -216,9 +247,9 @@ All the models listed above are runnable with ``Qlib``. Users can find the confi
 - User can use the script [`run_all_model.py`](examples/run_all_model.py) listed in the `examples` folder to run a model. Here is an example of the specific shell command to be used: `python run_all_model.py --models=lightgbm`, where the `--models` arguments can take any number of models listed above(the available models can be found  in [benchmarks](examples/benchmarks/)). For more use cases, please refer to the file's [docstrings](examples/run_all_model.py).
 
 ## Run multiple models
-`Qlib` also provides a script [`run_all_model.py`](examples/run_all_model.py) which can run multiple models for several iterations. (**Note**: the script only supprots *Linux* now. Other OS will be supported in the future.)
+`Qlib` also provides a script [`run_all_model.py`](examples/run_all_model.py) which can run multiple models for several iterations. (**Note**: the script only support *Linux* for now. Other OS will be supported in the future. Besides, it doesn't support parrallel running the same model for multiple times as well, and this will be fixed in the future development too.)
 
-The script will create a unique virtual environment for each model, and delete the environments after training. Thus, only experiment results such as `IC` and `backtest` results will be generated and stored. (**Note**: the script will erase your previous experiment records created by running itself.)
+The script will create a unique virtual environment for each model, and delete the environments after training. Thus, only experiment results such as `IC` and `backtest` results will be generated and stored.
 
 Here is an example of running all the models for 10 iterations:
 ```python
@@ -229,12 +260,12 @@ It also provides the API to run specific models at once. For more use cases, ple
 
 
 # Quant Dataset Zoo
-Dataset plays a very important role in Quant. Here is a list of the datasets built on `Qlib`.
+Dataset plays a very important role in Quant. Here is a list of the datasets built on `Qlib`:
 
 | Dataset                                    | US Market | China Market |
 | --                                         | --        | --           |
 | [Alpha360](./qlib/contrib/data/handler.py) |  √        |  √           |
-| [Alpha158](./qlib/contrib/data/handler.py) |  √        |  √           | 
+| [Alpha158](./qlib/contrib/data/handler.py) |  √        |  √           |
 
 [Here](https://qlib.readthedocs.io/en/latest/advanced/alpha.html) is a tutorial to build dataset with `Qlib`.
 Your PR to build new Quant dataset is highly welcomed.
@@ -281,7 +312,11 @@ Such overheads greatly slow down the data loading process.
 Qlib data are stored in a compact format, which is efficient to be combined into arrays for scientific computation.
 
 
-
+# Related Reports
+- [Guide To Qlib: Microsoft’s AI Investment Platform](https://analyticsindiamag.com/qlib/)
+- [【华泰金工林晓明团队】微软AI量化投资平台Qlib体验——华泰人工智能系列之四十](https://mp.weixin.qq.com/s/Brcd7im4NibJOJzZfMn6tQ)
+- [微软也搞AI量化平台？还是开源的！](https://mp.weixin.qq.com/s/47bP5YwxfTp2uTHjUBzJQQ)
+- [微矿Qlib：业内首个AI量化投资开源平台](https://mp.weixin.qq.com/s/vsJv7lsgjEi-ALYUz4CvtQ)
 
 
 # Contributing

docs/_static/demo.sh

@@ -0,0 +1,12 @@
+#!/bin/sh
+git clone https://github.com/microsoft/qlib.git
+cd qlib
+ls
+pip install pyqlib
+# or
+# pip install numpy
+# pip install --upgrade cython
+# python setup.py install
+cd examples
+ls
+qrun benchmarks/LightGBM/workflow_config_lightgbm_Alpha158.yaml

docs/advanced/alpha.rst

@@ -50,57 +50,37 @@ Users can use ``Data Handler`` to build formulaic alphas `MACD` in qlib:
 
 .. code-block:: python
 
-    >> from qlib.data.dataset.handler import QLibDataHandler
+    >> from qlib.data.dataset.loader import QlibDataLoader
     >> MACD_EXP = '(EMA($close, 12) - EMA($close, 26))/$close - EMA((EMA($close, 12) - EMA($close, 26))/$close, 9)/$close'
     >> fields = [MACD_EXP] # MACD
     >> names = ['MACD']
-    >> labels = ['$close'] # label
+    >> labels = ['Ref($close, -2)/Ref($close, -1) - 1'] # label
     >> label_names = ['LABEL']
-    >> data_handler = QLibDataHandler(start_date='2010-01-01', end_date='2017-12-31', fields=fields, names=names, labels=labels, label_names=label_names)
-    >> TRAINER_CONFIG = {
-    ..     "train_start_date": "2007-01-01",
-    ..     "train_end_date": "2014-12-31",
-    ..     "validate_start_date": "2015-01-01",
-    ..     "validate_end_date": "2016-12-31",
-    ..  "test_start_date": "2017-01-01",
-    ..  "test_end_date": "2020-08-01",
+    >> data_loader_config = {
+    ..     "feature": (fields, names),
+    ..     "label": (labels, label_names)
     .. }
-    >> feature_train, label_train, feature_validate, label_validate, feature_test, label_test = data_handler.get_split_data(**TRAINER_CONFIG)
-    >> print(feature_train, label_train)
-                            MACD
-    instrument  datetime            
-    SH600000    2010-01-04 -0.008625
-                2010-01-05 -0.007234
-                2010-01-06 -0.007693
-                2010-01-07 -0.009633
-                2010-01-08 -0.009891
-    ...                         ...
-    SZ300251    2014-12-25  0.043072
-                2014-12-26  0.041345
-                2014-12-29  0.042733
-                2014-12-30  0.042066
-                2014-12-31  0.036299
-
-    [322025 rows x 1 columns]    
-                            LABEL
-    instrument  datetime            
-    SH600000    2010-01-04  4.260015
-                2010-01-05  4.292182
-                2010-01-06  4.207747
-                2010-01-07  4.113258
-                2010-01-08  4.159496
-    ...                         ...
-    SZ300251    2014-12-25  4.343212
-                2014-12-26  4.470587
-                2014-12-29  4.762474
-                2014-12-30  4.369748
-                2014-12-31  4.182222
-
-    [322025 rows x 1 columns]
+    >> data_loader = QlibDataLoader(config=data_loader_config)
+    >> df = data_loader.load(instruments='csi300', start_time='2010-01-01', end_time='2017-12-31')
+    >> print(df)
+                            feature     label
+                               MACD     LABEL
+    datetime   instrument                    
+    2010-01-04 SH600000   -0.011547 -0.019672
+               SH600004    0.002745 -0.014721
+               SH600006    0.010133  0.002911
+               SH600008   -0.001113  0.009818
+               SH600009    0.025878 -0.017758
+    ...                         ...       ...
+    2017-12-29 SZ300124    0.007306 -0.005074
+               SZ300136   -0.013492  0.056352
+               SZ300144   -0.000966  0.011853
+               SZ300251    0.004383  0.021739
+               SZ300315   -0.030557  0.012455
 
 Reference
 ===========
 
-To learn more about ``Data Handler``, please refer to `Data Handler <../component/data.html>`_
+To learn more about ``Data Loader``, please refer to `Data Loader <../component/data.html#data-loader>`_
 
 To learn more about ``Data API``, please refer to `Data API <../component/data.html>`_

docs/component/data.rst

@@ -126,17 +126,17 @@ After conversion, users can find their Qlib format data in the directory `~/.qli
     The arguments of `--include_fields` should correspond with the column names of CSV files. The columns names of dataset provided by ``Qlib`` should include open, close, high, low, volume and factor at least.
     
     - `open`
-        The opening price
+        The adjusted opening price
     - `close`
-        The closing price
+        The adjusted closing price
     - `high`
-        The highest price
+        The adjusted highest price
     - `low`
-        The lowest price
+        The adjusted lowest price
     - `volume`
-        The trading volume
+        The adjusted trading volume
     - `factor`
-        The Restoration factor
+        The Restoration factor. Normally, ``factor = adjusted_price / original_price``, `adjusted price` reference: `split adjusted <https://www.investopedia.com/terms/s/splitadjusted.asp>`_
 
     In the convention of `Qlib` data processing, `open, close, high, low, volume, money and factor` will be set to NaN if the stock is suspended. 
 
@@ -195,6 +195,7 @@ Feature
 - `ExpressionOps`
     `ExpressionOps` will use operator for feature construction.
     To know more about  ``Operator``, please refer to `Operator API <../reference/api.html#module-qlib.data.ops>`_.
+    Also, ``Qlib`` supports users to define their own custom ``Operator``, an example has been given in ``tests/test_register_ops.py``.
 
 To know more about  ``Feature``, please refer to `Feature API <../reference/api.html#module-qlib.data.base>`_.
 
@@ -295,6 +296,7 @@ The ``Processor`` module in ``Qlib`` is designed to be learnable and it is respo
 - ``RobustZScoreNorm``: `processor` that applies robust z-score normalization.
 - ``CSZScoreNorm``: `processor` that applies cross sectional z-score normalization.
 - ``CSRankNorm``: `processor` that applies cross sectional rank normalization.
+- ``CSZFillna``: `processor` that fills N/A values in a cross sectional way by the mean of the column.
 
 Users can also create their own `processor` by inheriting the base class of ``Processor``. Please refer to the implementation of all the processors for more information (`Processor Link <https://github.com/microsoft/qlib/blob/main/qlib/data/dataset/processor.py>`_). 
 

docs/component/recorder.rst

@@ -34,8 +34,9 @@ Here is a general view of the structure of the system:
             - Recorder 2
             - ...
         - ...
-This experiment management system defines a set of interface and provided a concrete implementation based on the machine learning platform: ``MLFlow`` (`link <https://mlflow.org/>`_). 
+This experiment management system defines a set of interface and provided a concrete implementation ``MLflowExpManager``, which is based on the machine learning platform: ``MLFlow`` (`link <https://mlflow.org/>`_). 
 
+If users set the implementation of ``ExpManager`` to be ``MLflowExpManager``, they can use the command `mlflow ui` to visualize and check the experiment results. For more information, pleaes refer to the related documents `here <https://www.mlflow.org/docs/latest/cli.html#mlflow-ui>`_.
 
 Qlib Recorder
 ===================
@@ -91,7 +92,7 @@ Record Template
 
 The ``RecordTemp`` class is a class that enables generate experiment results such as IC and backtest in a certain format. We have provided three different `Record Template` class:
 
-- ``SignalRecord``: This class generates the `preidction` results of the model.
+- ``SignalRecord``: This class generates the `prediction` results of the model.
 - ``SigAnaRecord``: This class generates the `IC`, `ICIR`, `Rank IC` and `Rank ICIR` of the model.
 - ``PortAnaRecord``: This class generates the results of `backtest`. The detailed information about `backtest` as well as the available `strategy`, users can refer to `Strategy <../component/strategy.html>`_ and `Backtest <../component/backtest.html>`_.
 

docs/component/workflow.rst

@@ -103,6 +103,12 @@ After saving the config into `configuration.yaml`, users could start the workflo
 
     qrun configuration.yaml
 
+If users want to use ``qrun`` under debug mode, please use the following command:
+
+.. code-block:: bash
+
+    python -m pdb qlib/workflow/cli.py examples/benchmarks/LightGBM/workflow_config_lightgbm_Alpha158.yaml
+
 .. note:: 
 
     `qrun` will be placed in your $PATH directory when installing ``Qlib``.

docs/conf.py

@@ -226,3 +226,8 @@ epub_exclude_files = ["search.html"]
 
 autodoc_member_order = "bysource"
 autodoc_default_flags = ["members"]
+autodoc_default_options = {
+    "members": True,
+    "member-order": "bysource",
+    "special-members": "__init__",
+}

docs/requirements.txt

@@ -1 +1,5 @@
-Cython==0.29.21
+Cython
+cmake
+numpy
+scipy
+scikit-learn

docs/start/initialization.rst

@@ -63,6 +63,7 @@ Besides `provider_uri` and `region`, `qlib.init` has other parameters. The follo
         If Qlib fails to connect redis via `redis_host` and `redis_port`, cache mechanism will not be used! Please refer to `Cache <../component/data.html#cache>`_ for details.
 - `exp_manager`
     Type: dict, optional parameter, the setting of `experiment manager` to be used in qlib. Users can specify an experiment manager class, as well as the tracking URI for all the experiments. However, please be aware that we only support input of a dictionary in the following style for `exp_manager`. For more information about `exp_manager`, users can refer to `Recorder: Experiment Management <../component/recorder.html>`_.
+    
     .. code-block:: Python
 
         # For example, if you want to set your tracking_uri to a <specific folder>, you can initialize qlib below

examples/README.md

@@ -1,6 +1,6 @@
 # Requirements
 
-Here is the minimal hardware requirements to run the example.
+Here is the minimal hardware requirements to run the `workflow_by_code` example.
 - Memory: 16G
 - Free Disk: 5G
 

examples/benchmarks/ALSTM/workflow_config_alstm_Alpha158.yaml

@@ -0,0 +1,93 @@
+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: FilterCol
+          kwargs:
+              fields_group: feature
+              col_list: ["RESI5", "WVMA5", "RSQR5", "KLEN", "RSQR10", "CORR5", "CORD5", "CORR10", 
+                            "ROC60", "RESI10", "VSTD5", "RSQR60", "CORR60", "WVMA60", "STD5", 
+                            "RSQR20", "CORD60", "CORD10", "CORR20", "KLOW"
+                        ]
+        - 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: ALSTM
+        module_path: qlib.contrib.model.pytorch_alstm_ts
+        kwargs:
+            d_feat: 20
+            hidden_size: 64
+            num_layers: 2
+            dropout: 0.0
+            n_epochs: 200
+            lr: 1e-3
+            early_stop: 10
+            batch_size: 800
+            metric: loss
+            loss: mse
+            n_jobs: 20
+            GPU: 0
+            rnn_type: GRU
+    dataset:
+        class: TSDatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: Alpha158
+                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]
+            step_len: 20
+    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/ALSTM/workflow_config_alstm_Alpha360.yaml

@@ -54,7 +54,6 @@ task:
             batch_size: 800
             metric: loss
             loss: mse
-            seed: 0
             GPU: 0
             rnn_type: GRU
     dataset:
@@ -62,7 +61,7 @@ task:
         module_path: qlib.data.dataset
         kwargs:
             handler:
-                class: ALPHA360
+                class: Alpha360
                 module_path: qlib.contrib.data.handler
                 kwargs: *data_handler_config
             segments:

examples/benchmarks/CatBoost/workflow_config_catboost_Alpha360.yaml

@@ -0,0 +1,72 @@
+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: []
+    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: CatBoostModel
+        module_path: qlib.contrib.model.catboost_model
+        kwargs:
+            loss: RMSE
+            learning_rate: 0.0421
+            subsample: 0.8789
+            max_depth: 6
+            num_leaves: 100
+            thread_count: 20
+            grow_policy: Lossguide
+            bootstrap_type: Poisson
+    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: 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/GATs/workflow_config_gats_Alpha158.yaml

@@ -0,0 +1,92 @@
+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: FilterCol
+          kwargs:
+              fields_group: feature
+              col_list: ["RESI5", "WVMA5", "RSQR5", "KLEN", "RSQR10", "CORR5", "CORD5", "CORR10", 
+                            "ROC60", "RESI10", "VSTD5", "RSQR60", "CORR60", "WVMA60", "STD5", 
+                            "RSQR20", "CORD60", "CORD10", "CORR20", "KLOW"
+                        ]
+        - 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: GATs
+        module_path: qlib.contrib.model.pytorch_gats_ts
+        kwargs:
+            d_feat: 20
+            hidden_size: 64
+            num_layers: 2
+            dropout: 0.7
+            n_epochs: 200
+            lr: 1e-4
+            early_stop: 10
+            metric: loss
+            loss: mse
+            base_model: LSTM
+            with_pretrain: True
+            model_path: "benchmarks/LSTM/csi300_lstm_ts.pkl"
+            GPU: 0
+    dataset:
+        class: TSDatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: Alpha158
+                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]
+            step_len: 20
+    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/GATs/workflow_config_gats_Alpha360.yaml

@@ -56,14 +56,13 @@ task:
             base_model: LSTM
             with_pretrain: True
             model_path: "benchmarks/LSTM/model_lstm_csi300.pkl"
-            seed: 0
             GPU: 0
     dataset:
         class: DatasetH
         module_path: qlib.data.dataset
         kwargs:
             handler:
-                class: ALPHA360
+                class: Alpha360
                 module_path: qlib.contrib.data.handler
                 kwargs: *data_handler_config
             segments:
@@ -74,6 +73,11 @@ task:
         - 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: 

examples/benchmarks/GRU/workflow_config_gru_Alpha158.yaml

@@ -0,0 +1,92 @@
+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: FilterCol
+          kwargs:
+              fields_group: feature
+              col_list: ["RESI5", "WVMA5", "RSQR5", "KLEN", "RSQR10", "CORR5", "CORD5", "CORR10", 
+                            "ROC60", "RESI10", "VSTD5", "RSQR60", "CORR60", "WVMA60", "STD5", 
+                            "RSQR20", "CORD60", "CORD10", "CORR20", "KLOW"
+                        ]
+        - 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: GRU
+        module_path: qlib.contrib.model.pytorch_gru_ts
+        kwargs:
+            d_feat: 20
+            hidden_size: 64
+            num_layers: 2
+            dropout: 0.0
+            n_epochs: 200
+            lr: 2e-4
+            early_stop: 10
+            batch_size: 800
+            metric: loss
+            loss: mse
+            n_jobs: 20
+            GPU: 0
+    dataset:
+        class: TSDatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: Alpha158
+                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]
+            step_len: 20
+    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/GRU/workflow_config_gru_Alpha360.yaml

@@ -54,14 +54,13 @@ task:
             batch_size: 800
             metric: loss
             loss: mse
-            seed: 0
             GPU: 0
     dataset:
         class: DatasetH
         module_path: qlib.data.dataset
         kwargs:
             handler:
-                class: ALPHA360
+                class: Alpha360
                 module_path: qlib.contrib.data.handler
                 kwargs: *data_handler_config
             segments:

examples/benchmarks/LSTM/workflow_config_lstm_Alpha158.yaml

@@ -0,0 +1,92 @@
+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: FilterCol
+          kwargs:
+              fields_group: feature
+              col_list: ["RESI5", "WVMA5", "RSQR5", "KLEN", "RSQR10", "CORR5", "CORD5", "CORR10", 
+                            "ROC60", "RESI10", "VSTD5", "RSQR60", "CORR60", "WVMA60", "STD5", 
+                            "RSQR20", "CORD60", "CORD10", "CORR20", "KLOW"
+                        ]
+        - 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: LSTM
+        module_path: qlib.contrib.model.pytorch_lstm_ts
+        kwargs:
+            d_feat: 20
+            hidden_size: 64
+            num_layers: 2
+            dropout: 0.0
+            n_epochs: 200
+            lr: 1e-3
+            early_stop: 10
+            batch_size: 800
+            metric: loss
+            loss: mse
+            n_jobs: 20
+            GPU: 0
+    dataset:
+        class: TSDatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: Alpha158
+                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]
+            step_len: 20
+    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/LSTM/workflow_config_lstm_Alpha360.yaml

@@ -54,14 +54,13 @@ task:
             batch_size: 800
             metric: loss
             loss: mse
-            seed: 0
             GPU: 0
     dataset:
         class: DatasetH
         module_path: qlib.data.dataset
         kwargs:
             handler:
-                class: ALPHA360
+                class: Alpha360
                 module_path: qlib.contrib.data.handler
                 kwargs: *data_handler_config
             segments:

examples/benchmarks/LightGBM/workflow_config_lightgbm_Alpha158.yaml

@@ -32,7 +32,7 @@ task:
         kwargs:
             loss: mse
             colsample_bytree: 0.8879
-            learning_rate: 0.0421
+            learning_rate: 0.2
             subsample: 0.8789
             lambda_l1: 205.6999
             lambda_l2: 580.9768

examples/benchmarks/LightGBM/workflow_config_lightgbm_Alpha360.yaml

@@ -0,0 +1,73 @@
+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: []
+    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: LGBModel
+        module_path: qlib.contrib.model.gbdt
+        kwargs:
+            loss: mse
+            colsample_bytree: 0.8879
+            learning_rate: 0.0421
+            subsample: 0.8789
+            lambda_l1: 205.6999
+            lambda_l2: 580.9768
+            max_depth: 8
+            num_leaves: 210
+            num_threads: 20
+    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: 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/MLP/workflow_config_mlp_Alpha158.yaml

@@ -65,8 +65,9 @@ task:
             lr_decay_steps: 100
             optimizer: adam
             max_steps: 8000
-            batch_size: 4096
+            batch_size: 8192
             GPU: 0
+            weight_decay: 0.0002
     dataset:
         class: DatasetH
         module_path: qlib.data.dataset

examples/benchmarks/MLP/workflow_config_mlp_Alpha360.yaml

@@ -0,0 +1,82 @@
+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.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: DNNModelPytorch
+        module_path: qlib.contrib.model.pytorch_nn
+        kwargs:
+            loss: mse
+            input_dim: 360
+            output_dim: 1
+            lr: 0.002
+            lr_decay: 0.96
+            lr_decay_steps: 100
+            optimizer: adam
+            max_steps: 8000
+            batch_size: 4096
+            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: 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

@@ -0,0 +1,35 @@
+# Benchmarks Performance
+
+Here are the results of each benchmark model running on Qlib's `Alpha360` and `Alpha158` dataset with China's A shared-stock & CSI300 data respectively. The values of each metric are the mean and std calculated based on 20 runs.
+
+The numbers shown below demonstrate the performance of the entire `workflow` of each model. We will update the `workflow` as well as models in the near future for better results.
+
+## Alpha360 dataset
+| Model Name | Dataset | IC | ICIR | Rank IC | Rank ICIR | Annualized Return | Information Ratio | Max Drawdown |
+|---|---|---|---|---|---|---|---|---|
+| Linear | Alpha360 | 0.0150±0.00 | 0.1049±0.00| 0.0284±0.00 | 0.1970±0.00 | -0.0659±0.00 | -0.7072±0.00| -0.2955±0.00 |
+| CatBoost (Liudmila Prokhorenkova, et al.) | Alpha360 | 0.0397±0.00 | 0.2878±0.00| 0.0470±0.00 | 0.3703±0.00 | 0.0342±0.00 | 0.4092±0.00| -0.1057±0.00 |
+| XGBoost (Tianqi Chen, et al.) | Alpha360 | 0.0400±0.00 | 0.3031±0.00| 0.0461±0.00 | 0.3862±0.00 | 0.0528±0.00 | 0.6307±0.00| -0.1113±0.00 |
+| LightGBM (Guolin Ke, et al.) | Alpha360 | 0.0399±0.00 | 0.3075±0.00| 0.0492±0.00 | 0.4019±0.00 | 0.0323±0.00 | 0.4370±0.00| -0.0917±0.00 |
+| MLP | Alpha360 | 0.0285±0.00 | 0.1981±0.02| 0.0402±0.00 | 0.2993±0.02 | 0.0073±0.02 | 0.0880±0.22| -0.1446±0.03 |
+| GRU (Kyunghyun Cho, et al.) | Alpha360 | 0.0490±0.01 | 0.3787±0.05| 0.0581±0.00 | 0.4664±0.04 | 0.0726±0.02 | 0.9817±0.34| -0.0902±0.03 |
+| LSTM (Sepp Hochreiter, et al.) | Alpha360 | 0.0443±0.01 | 0.3401±0.05| 0.0536±0.01 | 0.4248±0.05 | 0.0627±0.03 | 0.8441±0.48| -0.0882±0.03 |
+| ALSTM (Yao Qin, et al.) | Alpha360 | 0.0493±0.01 | 0.3778±0.06| 0.0585±0.00 | 0.4606±0.04 | 0.0513±0.03 | 0.6727±0.38| -0.1085±0.02 |
+| GATs (Petar Velickovic, et al.) | Alpha360 | 0.0475±0.00 | 0.3515±0.02| 0.0592±0.00 | 0.4585±0.01 | 0.0876±0.02 | 1.1513±0.27| -0.0795±0.02 |
+
+## Alpha158 dataset
+| Model Name | Dataset | IC | ICIR | Rank IC | Rank ICIR | Annualized Return | Information Ratio | Max Drawdown |
+|---|---|---|---|---|---|---|---|---|
+| Linear | Alpha158 | 0.0393±0.00 | 0.2980±0.00| 0.0475±0.00 | 0.3546±0.00 | 0.0795±0.00 | 1.0712±0.00| -0.1449±0.00 |
+| CatBoost (Liudmila Prokhorenkova, et al.) | Alpha158 | 0.0503±0.00 | 0.3586±0.00| 0.0483±0.00 | 0.3667±0.00 | 0.1080±0.00 | 1.1561±0.00| -0.0787±0.00 |
+| XGBoost (Tianqi Chen, et al.) | Alpha158 | 0.0481±0.00 | 0.3659±0.00| 0.0495±0.00 | 0.4033±0.00 | 0.1111±0.00 | 1.2915±0.00| -0.0893±0.00 |
+| LightGBM (Guolin Ke, et al.) | Alpha158 | 0.0475±0.00 | 0.3979±0.00| 0.0485±0.00 | 0.4123±0.00 | 0.1143±0.00 | 1.2744±0.00| -0.0800±0.00 |
+| MLP | Alpha158 | 0.0358±0.00 | 0.2738±0.03| 0.0425±0.00 | 0.3221±0.01 | 0.0836±0.02 | 1.0323±0.25| -0.1127±0.02 |
+| TabNet with pretrain (Sercan O. Arikm et al) | Alpha158 | 0.0344±0.00|0.205±0.11|0.0398±0.00 |0.3479±0.01|0.0827±0.02|1.1141±0.32 |-0.0925±0.02  |
+| TFT (Bryan Lim, et al.) | Alpha158 (with selected 20 features) | 0.0343±0.00 | 0.2071±0.02| 0.0107±0.00 | 0.0660±0.02 | 0.0623±0.02 | 0.5818±0.20| -0.1762±0.01 |
+| GRU (Kyunghyun Cho, et al.) | Alpha158 (with selected 20 features) | 0.0311±0.00 | 0.2418±0.04| 0.0425±0.00 | 0.3434±0.02 | 0.0330±0.02 | 0.4805±0.30| -0.1021±0.02 |
+| LSTM (Sepp Hochreiter, et al.) | Alpha158 (with selected 20 features) | 0.0312±0.00 | 0.2394±0.04| 0.0418±0.00 | 0.3324±0.03 | 0.0298±0.02 | 0.4198±0.33| -0.1348±0.03 |
+| ALSTM (Yao Qin, et al.) | Alpha158 (with selected 20 features) | 0.0385±0.01 | 0.3022±0.06| 0.0478±0.00 | 0.3874±0.04 | 0.0486±0.03 | 0.7141±0.45| -0.1088±0.03 |
+| GATs (Petar Velickovic, et al.) | Alpha158 (with selected 20 features) | 0.0349±0.00 | 0.2511±0.01| 0.0457±0.00 | 0.3537±0.01 | 0.0578±0.02 | 0.8221±0.25| -0.0824±0.02 |
+
+- The selected 20 features are based on the feature importance of a lightgbm-based model.

examples/benchmarks/SFM/README.md

@@ -1,3 +1,3 @@
 # State-Frequency-Memory
 - State Frequency Memory (SFM) is a novel recurrent network that uses Discrete Fourier Transform to decompose the hidden states of memory cells and capture the multi-frequency trading patterns from past market data to make stock price predictions. 
-- Paper: Stock Price Prediction via Discovering Multi-Frequency Trading Patterns. [https://www.cs.ucf.edu/~gqi/publications/kdd2017_stock.pdf.](https://www.cs.ucf.edu/~gqi/publications/kdd2017_stock.pdf.)
+- Paper: Stock Price Prediction via Discovering Multi-Frequency Trading Patterns. [http://www.eecs.ucf.edu/~gqi/publications/kdd2017_stock.pdf.](http://www.eecs.ucf.edu/~gqi/publications/kdd2017_stock.pdf)

examples/benchmarks/SFM/workflow_config_sfm_Alpha360.yaml

@@ -57,14 +57,13 @@ task:
             eval_steps: 5
             loss: mse
             optimizer: adam
-            GPU: 1
-            seed: 710
+            GPU: 0
     dataset:
         class: DatasetH
         module_path: qlib.data.dataset
         kwargs:
             handler:
-                class: ALPHA360
+                class: Alpha360
                 module_path: qlib.contrib.data.handler
                 kwargs: *data_handler_config
             segments:

examples/benchmarks/TFT/data_formatters/qlib_Alpha158.py

@@ -1,219 +1,229 @@
-# coding=utf-8
-# Copyright 2020 The Google Research Authors.
-#
-# 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.
-
-# Lint as: python3
-"""Custom formatting functions for Alpha158 dataset.
-
-Defines dataset specific column definitions and data transformations.
-"""
-
-import data_formatters.base
-import libs.utils as utils
-import sklearn.preprocessing
-
-GenericDataFormatter = data_formatters.base.GenericDataFormatter
-DataTypes = data_formatters.base.DataTypes
-InputTypes = data_formatters.base.InputTypes
-
-
-class Alpha158Formatter(GenericDataFormatter):
-    """Defines and formats data for the Alpha158 dataset.
-
-    Attributes:
-      column_definition: Defines input and data type of column used in the
-        experiment.
-      identifiers: Entity identifiers used in experiments.
-    """
-
-    _column_definition = [
-        ("instrument", DataTypes.CATEGORICAL, InputTypes.ID),
-        ("LABEL0", DataTypes.REAL_VALUED, InputTypes.TARGET),
-        ("date", DataTypes.DATE, InputTypes.TIME),
-        ("month", DataTypes.CATEGORICAL, InputTypes.KNOWN_INPUT),
-        ("day_of_week", DataTypes.CATEGORICAL, InputTypes.KNOWN_INPUT),
-        # Selected 10 features
-        ("RESI5", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
-        ("WVMA5", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
-        ("RSQR5", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
-        ("KLEN", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
-        ("RSQR10", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
-        ("CORR5", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
-        ("CORD5", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
-        ("CORR10", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
-        ("ROC60", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
-        ("RESI10", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
-        ("const", DataTypes.CATEGORICAL, InputTypes.STATIC_INPUT),
-    ]
-
-    def __init__(self):
-        """Initialises formatter."""
-
-        self.identifiers = None
-        self._real_scalers = None
-        self._cat_scalers = None
-        self._target_scaler = None
-        self._num_classes_per_cat_input = None
-
-    def split_data(self, df, valid_boundary=2016, test_boundary=2018):
-        """Splits data frame into training-validation-test data frames.
-
-        This also calibrates scaling object, and transforms data for each split.
-
-        Args:
-          df: Source data frame to split.
-          valid_boundary: Starting year for validation data
-          test_boundary: Starting year for test data
-
-        Returns:
-          Tuple of transformed (train, valid, test) data.
-        """
-
-        print("Formatting train-valid-test splits.")
-
-        index = df["year"]
-        train = df.loc[index < valid_boundary]
-        valid = df.loc[(index >= valid_boundary) & (index < test_boundary)]
-        test = df.loc[index >= test_boundary]
-
-        self.set_scalers(train)
-
-        return (self.transform_inputs(data) for data in [train, valid, test])
-
-    def set_scalers(self, df):
-        """Calibrates scalers using the data supplied.
-
-        Args:
-          df: Data to use to calibrate scalers.
-        """
-        print("Setting scalers with training data...")
-
-        column_definitions = self.get_column_definition()
-        id_column = utils.get_single_col_by_input_type(InputTypes.ID, column_definitions)
-        target_column = utils.get_single_col_by_input_type(InputTypes.TARGET, column_definitions)
-
-        # Extract identifiers in case required
-        self.identifiers = list(df[id_column].unique())
-
-        # Format real scalers
-        real_inputs = utils.extract_cols_from_data_type(
-            DataTypes.REAL_VALUED, column_definitions, {InputTypes.ID, InputTypes.TIME}
-        )
-
-        data = df[real_inputs].values
-        self._real_scalers = sklearn.preprocessing.StandardScaler().fit(data)
-        self._target_scaler = sklearn.preprocessing.StandardScaler().fit(
-            df[[target_column]].values
-        )  # used for predictions
-
-        # Format categorical scalers
-        categorical_inputs = utils.extract_cols_from_data_type(
-            DataTypes.CATEGORICAL, column_definitions, {InputTypes.ID, InputTypes.TIME}
-        )
-
-        categorical_scalers = {}
-        num_classes = []
-        for col in categorical_inputs:
-            # Set all to str so that we don't have mixed integer/string columns
-            srs = df[col].apply(str)
-            categorical_scalers[col] = sklearn.preprocessing.LabelEncoder().fit(srs.values)
-            num_classes.append(srs.nunique())
-
-        # Set categorical scaler outputs
-        self._cat_scalers = categorical_scalers
-        self._num_classes_per_cat_input = num_classes
-
-    def transform_inputs(self, df):
-        """Performs feature transformations.
-
-        This includes both feature engineering, preprocessing and normalisation.
-
-        Args:
-          df: Data frame to transform.
-
-        Returns:
-          Transformed data frame.
-
-        """
-        output = df.copy()
-
-        if self._real_scalers is None and self._cat_scalers is None:
-            raise ValueError("Scalers have not been set!")
-
-        column_definitions = self.get_column_definition()
-
-        real_inputs = utils.extract_cols_from_data_type(
-            DataTypes.REAL_VALUED, column_definitions, {InputTypes.ID, InputTypes.TIME}
-        )
-        categorical_inputs = utils.extract_cols_from_data_type(
-            DataTypes.CATEGORICAL, column_definitions, {InputTypes.ID, InputTypes.TIME}
-        )
-
-        # Format real inputs
-        output[real_inputs] = self._real_scalers.transform(df[real_inputs].values)
-
-        # Format categorical inputs
-        for col in categorical_inputs:
-            string_df = df[col].apply(str)
-            output[col] = self._cat_scalers[col].transform(string_df)
-
-        return output
-
-    def format_predictions(self, predictions):
-        """Reverts any normalisation to give predictions in original scale.
-
-        Args:
-          predictions: Dataframe of model predictions.
-
-        Returns:
-          Data frame of unnormalised predictions.
-        """
-        output = predictions.copy()
-
-        column_names = predictions.columns
-
-        for col in column_names:
-            if col not in {"forecast_time", "identifier"}:
-                output[col] = self._target_scaler.inverse_transform(predictions[col])
-
-        return output
-
-    # Default params
-    def get_fixed_params(self):
-        """Returns fixed model parameters for experiments."""
-
-        fixed_params = {
-            "total_time_steps": 6 + 6,
-            "num_encoder_steps": 6,
-            "num_epochs": 100,
-            "early_stopping_patience": 10,
-            "multiprocessing_workers": 5,
-        }
-
-        return fixed_params
-
-    def get_default_model_params(self):
-        """Returns default optimised model parameters."""
-
-        model_params = {
-            "dropout_rate": 0.4,
-            "hidden_layer_size": 16,
-            "learning_rate": 0.0001,
-            "minibatch_size": 128,
-            "max_gradient_norm": 0.0135,
-            "num_heads": 1,
-            "stack_size": 1,
-        }
-
-        return model_params
+# coding=utf-8
+# Copyright 2020 The Google Research Authors.
+#
+# 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.
+
+# Lint as: python3
+"""Custom formatting functions for Alpha158 dataset.
+
+Defines dataset specific column definitions and data transformations.
+"""
+
+import data_formatters.base
+import libs.utils as utils
+import sklearn.preprocessing
+
+GenericDataFormatter = data_formatters.base.GenericDataFormatter
+DataTypes = data_formatters.base.DataTypes
+InputTypes = data_formatters.base.InputTypes
+
+
+class Alpha158Formatter(GenericDataFormatter):
+    """Defines and formats data for the Alpha158 dataset.
+
+    Attributes:
+      column_definition: Defines input and data type of column used in the
+        experiment.
+      identifiers: Entity identifiers used in experiments.
+    """
+
+    _column_definition = [
+        ("instrument", DataTypes.CATEGORICAL, InputTypes.ID),
+        ("LABEL0", DataTypes.REAL_VALUED, InputTypes.TARGET),
+        ("date", DataTypes.DATE, InputTypes.TIME),
+        ("month", DataTypes.CATEGORICAL, InputTypes.KNOWN_INPUT),
+        ("day_of_week", DataTypes.CATEGORICAL, InputTypes.KNOWN_INPUT),
+        # Selected features
+        ("RESI5", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
+        ("WVMA5", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
+        ("RSQR5", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
+        ("KLEN", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
+        ("RSQR10", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
+        ("CORR5", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
+        ("CORD5", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
+        ("CORR10", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
+        ("ROC60", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
+        ("RESI10", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
+        ("VSTD5", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
+        ("RSQR60", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
+        ("CORR60", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
+        ("WVMA60", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
+        ("STD5", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
+        ("RSQR20", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
+        ("CORD60", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
+        ("CORD10", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
+        ("CORR20", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
+        ("KLOW", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
+        ("const", DataTypes.CATEGORICAL, InputTypes.STATIC_INPUT),
+    ]
+
+    def __init__(self):
+        """Initialises formatter."""
+
+        self.identifiers = None
+        self._real_scalers = None
+        self._cat_scalers = None
+        self._target_scaler = None
+        self._num_classes_per_cat_input = None
+
+    def split_data(self, df, valid_boundary=2016, test_boundary=2018):
+        """Splits data frame into training-validation-test data frames.
+
+        This also calibrates scaling object, and transforms data for each split.
+
+        Args:
+          df: Source data frame to split.
+          valid_boundary: Starting year for validation data
+          test_boundary: Starting year for test data
+
+        Returns:
+          Tuple of transformed (train, valid, test) data.
+        """
+
+        print("Formatting train-valid-test splits.")
+
+        index = df["year"]
+        train = df.loc[index < valid_boundary]
+        valid = df.loc[(index >= valid_boundary) & (index < test_boundary)]
+        test = df.loc[index >= test_boundary]
+
+        self.set_scalers(train)
+
+        return (self.transform_inputs(data) for data in [train, valid, test])
+
+    def set_scalers(self, df):
+        """Calibrates scalers using the data supplied.
+
+        Args:
+          df: Data to use to calibrate scalers.
+        """
+        print("Setting scalers with training data...")
+
+        column_definitions = self.get_column_definition()
+        id_column = utils.get_single_col_by_input_type(InputTypes.ID, column_definitions)
+        target_column = utils.get_single_col_by_input_type(InputTypes.TARGET, column_definitions)
+
+        # Extract identifiers in case required
+        self.identifiers = list(df[id_column].unique())
+
+        # Format real scalers
+        real_inputs = utils.extract_cols_from_data_type(
+            DataTypes.REAL_VALUED, column_definitions, {InputTypes.ID, InputTypes.TIME}
+        )
+
+        data = df[real_inputs].values
+        self._real_scalers = sklearn.preprocessing.StandardScaler().fit(data)
+        self._target_scaler = sklearn.preprocessing.StandardScaler().fit(
+            df[[target_column]].values
+        )  # used for predictions
+
+        # Format categorical scalers
+        categorical_inputs = utils.extract_cols_from_data_type(
+            DataTypes.CATEGORICAL, column_definitions, {InputTypes.ID, InputTypes.TIME}
+        )
+
+        categorical_scalers = {}
+        num_classes = []
+        for col in categorical_inputs:
+            # Set all to str so that we don't have mixed integer/string columns
+            srs = df[col].apply(str)
+            categorical_scalers[col] = sklearn.preprocessing.LabelEncoder().fit(srs.values)
+            num_classes.append(srs.nunique())
+
+        # Set categorical scaler outputs
+        self._cat_scalers = categorical_scalers
+        self._num_classes_per_cat_input = num_classes
+
+    def transform_inputs(self, df):
+        """Performs feature transformations.
+
+        This includes both feature engineering, preprocessing and normalisation.
+
+        Args:
+          df: Data frame to transform.
+
+        Returns:
+          Transformed data frame.
+
+        """
+        output = df.copy()
+
+        if self._real_scalers is None and self._cat_scalers is None:
+            raise ValueError("Scalers have not been set!")
+
+        column_definitions = self.get_column_definition()
+
+        real_inputs = utils.extract_cols_from_data_type(
+            DataTypes.REAL_VALUED, column_definitions, {InputTypes.ID, InputTypes.TIME}
+        )
+        categorical_inputs = utils.extract_cols_from_data_type(
+            DataTypes.CATEGORICAL, column_definitions, {InputTypes.ID, InputTypes.TIME}
+        )
+
+        # Format real inputs
+        output[real_inputs] = self._real_scalers.transform(df[real_inputs].values)
+
+        # Format categorical inputs
+        for col in categorical_inputs:
+            string_df = df[col].apply(str)
+            output[col] = self._cat_scalers[col].transform(string_df)
+
+        return output
+
+    def format_predictions(self, predictions):
+        """Reverts any normalisation to give predictions in original scale.
+
+        Args:
+          predictions: Dataframe of model predictions.
+
+        Returns:
+          Data frame of unnormalised predictions.
+        """
+        output = predictions.copy()
+
+        column_names = predictions.columns
+
+        for col in column_names:
+            if col not in {"forecast_time", "identifier"}:
+                output[col] = self._target_scaler.inverse_transform(predictions[col])
+
+        return output
+
+    # Default params
+    def get_fixed_params(self):
+        """Returns fixed model parameters for experiments."""
+
+        fixed_params = {
+            "total_time_steps": 6 + 6,
+            "num_encoder_steps": 6,
+            "num_epochs": 100,
+            "early_stopping_patience": 10,
+            "multiprocessing_workers": 5,
+        }
+
+        return fixed_params
+
+    def get_default_model_params(self):
+        """Returns default optimised model parameters."""
+
+        model_params = {
+            "dropout_rate": 0.4,
+            "hidden_layer_size": 160,
+            "learning_rate": 0.0001,
+            "minibatch_size": 128,
+            "max_gradient_norm": 0.0135,
+            "num_heads": 1,
+            "stack_size": 1,
+        }
+
+        return model_params

examples/benchmarks/TFT/expt_settings/configs.py

@@ -25,7 +25,7 @@ import os
 import data_formatters.qlib_Alpha158
 
 
-class ExperimentConfig(object):
+class ExperimentConfig:
     """Defines experiment configs and paths to outputs.
 
     Attributes:

examples/benchmarks/TFT/libs/tft_model.py

@@ -320,7 +320,7 @@ class InterpretableMultiHeadAttention:
         return outputs, attn
 
 
-class TFTDataCache(object):
+class TFTDataCache:
     """Caches data for the TFT."""
 
     _data_cache = {}
@@ -348,7 +348,7 @@ class TFTDataCache(object):
 
 
 # TFT model definitions.
-class TemporalFusionTransformer(object):
+class TemporalFusionTransformer:
     """Defines Temporal Fusion Transformer.
 
     Attributes:
@@ -972,7 +972,7 @@ class TemporalFusionTransformer(object):
             valid_quantiles = self.quantiles
             output_size = self.output_size
 
-            class QuantileLossCalculator(object):
+            class QuantileLossCalculator:
                 """Computes the combined quantile loss for prespecified quantiles.
 
                 Attributes:

examples/benchmarks/TFT/tft.py

@@ -1,249 +1,291 @@
-# Copyright (c) Microsoft Corporation.
-# Licensed under the MIT License.
-
-import numpy as np
-import pandas as pd
-import tensorflow.compat.v1 as tf
-import data_formatters.base
-import expt_settings.configs
-import libs.hyperparam_opt
-import libs.tft_model
-import libs.utils as utils
-import os
-import datetime as dte
-
-
-from qlib.model.base import ModelFT
-from qlib.data.dataset import DatasetH
-from qlib.data.dataset.handler import DataHandlerLP
-
-
-# To register new datasets, please add them here.
-ALLOW_DATASET = ["Alpha158"]
-DATASET_SETTING = {
-    "Alpha158": {
-        "feature_col": ["RESI5", "WVMA5", "RSQR5", "KLEN", "RSQR10", "CORR5", "CORD5", "CORR10", "ROC60", "RESI10"],
-        "label_col": ["LABEL0"],
-    },
-}
-# To register new datasets, please add their configurations here.
-
-
-def get_shifted_label(data_df, shifts=5, col_shift="LABEL0"):
-    return data_df[[col_shift]].groupby("instrument").apply(lambda df: df.shift(shifts))
-
-
-def fill_test_na(test_df):
-    test_df_res = test_df.copy()
-    feature_cols = ~test_df_res.columns.str.contains("label", case=False)
-    test_feature_fna = test_df_res.loc[:, feature_cols].groupby("datetime").apply(lambda df: df.fillna(df.mean()))
-    test_df_res.loc[:, feature_cols] = test_feature_fna
-    return test_df_res
-
-
-def process_qlib_data(df, dataset, fillna=False):
-    """Prepare data to fit the TFT model.
-
-    Args:
-      df: Original DataFrame.
-      fillna: Whether to fill the data with the mean values.
-
-    Returns:
-      Transformed DataFrame.
-
-    """
-    # Several features selected manually
-    feature_col = DATASET_SETTING[dataset]["feature_col"]
-    label_col = DATASET_SETTING[dataset]["label_col"]
-    temp_df = df.loc[:, feature_col + label_col]
-    if fillna:
-        temp_df = fill_test_na(temp_df)
-    temp_df = temp_df.swaplevel()
-    temp_df = temp_df.sort_index()
-    temp_df = temp_df.reset_index(level=0)
-    dates = pd.to_datetime(temp_df.index)
-    temp_df["date"] = dates
-    temp_df["day_of_week"] = dates.dayofweek
-    temp_df["month"] = dates.month
-    temp_df["year"] = dates.year
-    temp_df["const"] = 1.0
-    return temp_df
-
-
-def process_predicted(df, col_name):
-    """Transform the TFT predicted data into Qlib format.
-
-    Args:
-      df: Original DataFrame.
-      fillna: New column name.
-
-    Returns:
-      Transformed DataFrame.
-
-    """
-    df_res = df.copy()
-    df_res = df_res.rename(columns={"forecast_time": "datetime", "identifier": "instrument", "t+4": col_name})
-    df_res = df_res.set_index(["datetime", "instrument"]).sort_index()
-    df_res = df_res[[col_name]]
-    return df_res
-
-
-def format_score(forecast_df, col_name="pred", label_shift=5):
-    pred = process_predicted(forecast_df, col_name=col_name)
-    pred = get_shifted_label(pred, shifts=-label_shift, col_shift=col_name)
-    pred = pred.dropna()[col_name]
-    return pred
-
-
-def transform_df(df, col_name="LABEL0"):
-    df_res = df["feature"]
-    df_res[col_name] = df["label"]
-    return df_res
-
-
-class TFTModel(ModelFT):
-    """TFT Model"""
-
-    def __init__(self, **kwargs):
-        self.model = None
-
-    def _prepare_data(self, dataset: DatasetH):
-        df_train, df_valid = dataset.prepare(
-            ["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L
-        )
-        return transform_df(df_train), transform_df(df_valid)
-
-    def fit(
-        self,
-        dataset: DatasetH,
-        DATASET="Alpha158",
-        MODEL_FOLDER="qlib_alpha158_model",
-        LABEL_COL="LABEL0",
-        LABEL_SHIFT=5,
-        USE_GPU_ID=0,
-        **kwargs
-    ):
-
-        if DATASET not in ALLOW_DATASET:
-            raise AssertionError("The dataset is not supported, please make a new formatter to fit this dataset")
-
-        dtrain, dvalid = self._prepare_data(dataset)
-        dtrain.loc[:, LABEL_COL] = get_shifted_label(dtrain, shifts=LABEL_SHIFT, col_shift=LABEL_COL)
-        dvalid.loc[:, LABEL_COL] = get_shifted_label(dvalid, shifts=LABEL_SHIFT, col_shift=LABEL_COL)
-
-        train = process_qlib_data(dtrain, DATASET, fillna=True).dropna()
-        valid = process_qlib_data(dvalid, DATASET, fillna=True).dropna()
-
-        ExperimentConfig = expt_settings.configs.ExperimentConfig
-        config = ExperimentConfig(DATASET)
-        self.data_formatter = config.make_data_formatter()
-        self.model_folder = MODEL_FOLDER
-        self.gpu_id = USE_GPU_ID
-        self.label_shift = LABEL_SHIFT
-        self.expt_name = DATASET
-        self.label_col = LABEL_COL
-
-        use_gpu = (True, self.gpu_id)
-        # ===========================Training Process===========================
-        ModelClass = libs.tft_model.TemporalFusionTransformer
-        if not isinstance(self.data_formatter, data_formatters.base.GenericDataFormatter):
-            raise ValueError(
-                "Data formatters should inherit from"
-                + "AbstractDataFormatter! Type={}".format(type(self.data_formatter))
-            )
-
-        default_keras_session = tf.keras.backend.get_session()
-
-        if use_gpu[0]:
-            self.tf_config = utils.get_default_tensorflow_config(tf_device="gpu", gpu_id=use_gpu[1])
-        else:
-            self.tf_config = utils.get_default_tensorflow_config(tf_device="cpu")
-
-        self.data_formatter.set_scalers(train)
-
-        # Sets up default params
-        fixed_params = self.data_formatter.get_experiment_params()
-        params = self.data_formatter.get_default_model_params()
-
-        # Wendi: 合并调优的参数和非调优的参数
-        params = {**params, **fixed_params}
-
-        if not os.path.exists(self.model_folder):
-            os.makedirs(self.model_folder)
-        params["model_folder"] = self.model_folder
-
-        print("*** Begin training ***")
-        best_loss = np.Inf
-
-        tf.reset_default_graph()
-
-        self.tf_graph = tf.Graph()
-        with self.tf_graph.as_default():
-            self.sess = tf.Session(config=self.tf_config)
-            tf.keras.backend.set_session(self.sess)
-            self.model = ModelClass(params, use_cudnn=use_gpu[0])
-            self.sess.run(tf.global_variables_initializer())
-            self.model.fit(train_df=train, valid_df=valid)
-            print("*** Finished training ***")
-            saved_model_dir = self.model_folder + "/" + "saved_model"
-            if not os.path.exists(saved_model_dir):
-                os.makedirs(saved_model_dir)
-            self.model.save(saved_model_dir)
-
-            def extract_numerical_data(data):
-                """Strips out forecast time and identifier columns."""
-                return data[[col for col in data.columns if col not in {"forecast_time", "identifier"}]]
-
-            # p50_loss = utils.numpy_normalised_quantile_loss(
-            #    extract_numerical_data(targets), extract_numerical_data(p50_forecast),
-            #    0.5)
-            # p90_loss = utils.numpy_normalised_quantile_loss(
-            #    extract_numerical_data(targets), extract_numerical_data(p90_forecast),
-            #    0.9)
-            tf.keras.backend.set_session(default_keras_session)
-        print("Training completed.".format(dte.datetime.now()))
-        # ===========================Training Process===========================
-
-    def predict(self, dataset):
-        if self.model is None:
-            raise ValueError("model is not fitted yet!")
-        d_test = dataset.prepare("test", col_set=["feature", "label"])
-        d_test = transform_df(d_test)
-        d_test.loc[:, self.label_col] = get_shifted_label(d_test, shifts=self.label_shift, col_shift=self.label_col)
-        test = process_qlib_data(d_test, self.expt_name, fillna=True).dropna()
-
-        use_gpu = (True, self.gpu_id)
-        # ===========================Predicting Process===========================
-        default_keras_session = tf.keras.backend.get_session()
-
-        # Sets up default params
-        fixed_params = self.data_formatter.get_experiment_params()
-        params = self.data_formatter.get_default_model_params()
-        params = {**params, **fixed_params}
-
-        print("*** Begin predicting ***")
-        tf.reset_default_graph()
-
-        with self.tf_graph.as_default():
-            tf.keras.backend.set_session(self.sess)
-            output_map = self.model.predict(test, return_targets=True)
-            targets = self.data_formatter.format_predictions(output_map["targets"])
-            p50_forecast = self.data_formatter.format_predictions(output_map["p50"])
-            p90_forecast = self.data_formatter.format_predictions(output_map["p90"])
-            tf.keras.backend.set_session(default_keras_session)
-
-        predict50 = format_score(p50_forecast, "pred", 1)
-        predict90 = format_score(p90_forecast, "pred", 1)
-        predict = (predict50 + predict90) / 2  # self.label_shift
-        # ===========================Predicting Process===========================
-        return predict
-
-    def finetune(self, dataset: DatasetH):
-        """
-        finetune model
-        Parameters
-        ----------
-        dataset : DatasetH
-            dataset for finetuning
-        """
-        pass
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+import numpy as np
+import pandas as pd
+import tensorflow.compat.v1 as tf
+import data_formatters.base
+import expt_settings.configs
+import libs.hyperparam_opt
+import libs.tft_model
+import libs.utils as utils
+import os
+import datetime as dte
+
+
+from qlib.model.base import ModelFT
+from qlib.data.dataset import DatasetH
+from qlib.data.dataset.handler import DataHandlerLP
+
+
+# To register new datasets, please add them here.
+ALLOW_DATASET = ["Alpha158", "Alpha360"]
+# To register new datasets, please add their configurations here.
+DATASET_SETTING = {
+    "Alpha158": {
+        "feature_col": [
+            "RESI5",
+            "WVMA5",
+            "RSQR5",
+            "KLEN",
+            "RSQR10",
+            "CORR5",
+            "CORD5",
+            "CORR10",
+            "ROC60",
+            "RESI10",
+            "VSTD5",
+            "RSQR60",
+            "CORR60",
+            "WVMA60",
+            "STD5",
+            "RSQR20",
+            "CORD60",
+            "CORD10",
+            "CORR20",
+            "KLOW",
+        ],
+        "label_col": "LABEL0",
+    },
+    "Alpha360": {
+        "feature_col": [
+            "HIGH0",
+            "LOW0",
+            "OPEN0",
+            "CLOSE1",
+            "HIGH1",
+            "VOLUME1",
+            "LOW1",
+            "VOLUME3",
+            "OPEN1",
+            "VOLUME4",
+            "CLOSE2",
+            "CLOSE4",
+            "VOLUME5",
+            "LOW2",
+            "CLOSE3",
+            "VOLUME2",
+            "HIGH2",
+            "LOW4",
+            "VOLUME8",
+            "VOLUME11",
+        ],
+        "label_col": "LABEL0",
+    },
+}
+
+
+def get_shifted_label(data_df, shifts=5, col_shift="LABEL0"):
+    return data_df[[col_shift]].groupby("instrument").apply(lambda df: df.shift(shifts))
+
+
+def fill_test_na(test_df):
+    test_df_res = test_df.copy()
+    feature_cols = ~test_df_res.columns.str.contains("label", case=False)
+    test_feature_fna = test_df_res.loc[:, feature_cols].groupby("datetime").apply(lambda df: df.fillna(df.mean()))
+    test_df_res.loc[:, feature_cols] = test_feature_fna
+    return test_df_res
+
+
+def process_qlib_data(df, dataset, fillna=False):
+    """Prepare data to fit the TFT model.
+
+    Args:
+      df: Original DataFrame.
+      fillna: Whether to fill the data with the mean values.
+
+    Returns:
+      Transformed DataFrame.
+
+    """
+    # Several features selected manually
+    feature_col = DATASET_SETTING[dataset]["feature_col"]
+    label_col = [DATASET_SETTING[dataset]["label_col"]]
+    temp_df = df.loc[:, feature_col + label_col]
+    if fillna:
+        temp_df = fill_test_na(temp_df)
+    temp_df = temp_df.swaplevel()
+    temp_df = temp_df.sort_index()
+    temp_df = temp_df.reset_index(level=0)
+    dates = pd.to_datetime(temp_df.index)
+    temp_df["date"] = dates
+    temp_df["day_of_week"] = dates.dayofweek
+    temp_df["month"] = dates.month
+    temp_df["year"] = dates.year
+    temp_df["const"] = 1.0
+    return temp_df
+
+
+def process_predicted(df, col_name):
+    """Transform the TFT predicted data into Qlib format.
+
+    Args:
+      df: Original DataFrame.
+      fillna: New column name.
+
+    Returns:
+      Transformed DataFrame.
+
+    """
+    df_res = df.copy()
+    df_res = df_res.rename(columns={"forecast_time": "datetime", "identifier": "instrument", "t+4": col_name})
+    df_res = df_res.set_index(["datetime", "instrument"]).sort_index()
+    df_res = df_res[[col_name]]
+    return df_res
+
+
+def format_score(forecast_df, col_name="pred", label_shift=5):
+    pred = process_predicted(forecast_df, col_name=col_name)
+    pred = get_shifted_label(pred, shifts=-label_shift, col_shift=col_name)
+    pred = pred.dropna()[col_name]
+    return pred
+
+
+def transform_df(df, col_name="LABEL0"):
+    df_res = df["feature"]
+    df_res[col_name] = df["label"]
+    return df_res
+
+
+class TFTModel(ModelFT):
+    """TFT Model"""
+
+    def __init__(self, **kwargs):
+        self.model = None
+        self.params = {"DATASET": "Alpha158", "label_shift": 5}
+        self.params.update(kwargs)
+
+    def _prepare_data(self, dataset: DatasetH):
+        df_train, df_valid = dataset.prepare(
+            ["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L
+        )
+        return transform_df(df_train), transform_df(df_valid)
+
+    def fit(self, dataset: DatasetH, MODEL_FOLDER="qlib_tft_model", USE_GPU_ID=0, **kwargs):
+        DATASET = self.params["DATASET"]
+        LABEL_SHIFT = self.params["label_shift"]
+        LABEL_COL = DATASET_SETTING[DATASET]["label_col"]
+
+        if DATASET not in ALLOW_DATASET:
+            raise AssertionError("The dataset is not supported, please make a new formatter to fit this dataset")
+
+        dtrain, dvalid = self._prepare_data(dataset)
+        dtrain.loc[:, LABEL_COL] = get_shifted_label(dtrain, shifts=LABEL_SHIFT, col_shift=LABEL_COL)
+        dvalid.loc[:, LABEL_COL] = get_shifted_label(dvalid, shifts=LABEL_SHIFT, col_shift=LABEL_COL)
+
+        train = process_qlib_data(dtrain, DATASET, fillna=True).dropna()
+        valid = process_qlib_data(dvalid, DATASET, fillna=True).dropna()
+
+        ExperimentConfig = expt_settings.configs.ExperimentConfig
+        config = ExperimentConfig(DATASET)
+        self.data_formatter = config.make_data_formatter()
+        self.model_folder = MODEL_FOLDER
+        self.gpu_id = USE_GPU_ID
+        self.label_shift = LABEL_SHIFT
+        self.expt_name = DATASET
+        self.label_col = LABEL_COL
+
+        use_gpu = (True, self.gpu_id)
+        # ===========================Training Process===========================
+        ModelClass = libs.tft_model.TemporalFusionTransformer
+        if not isinstance(self.data_formatter, data_formatters.base.GenericDataFormatter):
+            raise ValueError(
+                "Data formatters should inherit from"
+                + "AbstractDataFormatter! Type={}".format(type(self.data_formatter))
+            )
+
+        default_keras_session = tf.keras.backend.get_session()
+
+        if use_gpu[0]:
+            self.tf_config = utils.get_default_tensorflow_config(tf_device="gpu", gpu_id=use_gpu[1])
+        else:
+            self.tf_config = utils.get_default_tensorflow_config(tf_device="cpu")
+
+        self.data_formatter.set_scalers(train)
+
+        # Sets up default params
+        fixed_params = self.data_formatter.get_experiment_params()
+        params = self.data_formatter.get_default_model_params()
+
+        # Wendi: 合并调优的参数和非调优的参数
+        params = {**params, **fixed_params}
+
+        if not os.path.exists(self.model_folder):
+            os.makedirs(self.model_folder)
+        params["model_folder"] = self.model_folder
+
+        print("*** Begin training ***")
+        best_loss = np.Inf
+
+        tf.reset_default_graph()
+
+        self.tf_graph = tf.Graph()
+        with self.tf_graph.as_default():
+            self.sess = tf.Session(config=self.tf_config)
+            tf.keras.backend.set_session(self.sess)
+            self.model = ModelClass(params, use_cudnn=use_gpu[0])
+            self.sess.run(tf.global_variables_initializer())
+            self.model.fit(train_df=train, valid_df=valid)
+            print("*** Finished training ***")
+            saved_model_dir = self.model_folder + "/" + "saved_model"
+            if not os.path.exists(saved_model_dir):
+                os.makedirs(saved_model_dir)
+            self.model.save(saved_model_dir)
+
+            def extract_numerical_data(data):
+                """Strips out forecast time and identifier columns."""
+                return data[[col for col in data.columns if col not in {"forecast_time", "identifier"}]]
+
+            # p50_loss = utils.numpy_normalised_quantile_loss(
+            #    extract_numerical_data(targets), extract_numerical_data(p50_forecast),
+            #    0.5)
+            # p90_loss = utils.numpy_normalised_quantile_loss(
+            #    extract_numerical_data(targets), extract_numerical_data(p90_forecast),
+            #    0.9)
+            tf.keras.backend.set_session(default_keras_session)
+        print("Training completed.".format(dte.datetime.now()))
+        # ===========================Training Process===========================
+
+    def predict(self, dataset):
+        if self.model is None:
+            raise ValueError("model is not fitted yet!")
+        d_test = dataset.prepare("test", col_set=["feature", "label"])
+        d_test = transform_df(d_test)
+        d_test.loc[:, self.label_col] = get_shifted_label(d_test, shifts=self.label_shift, col_shift=self.label_col)
+        test = process_qlib_data(d_test, self.expt_name, fillna=True).dropna()
+
+        use_gpu = (True, self.gpu_id)
+        # ===========================Predicting Process===========================
+        default_keras_session = tf.keras.backend.get_session()
+
+        # Sets up default params
+        fixed_params = self.data_formatter.get_experiment_params()
+        params = self.data_formatter.get_default_model_params()
+        params = {**params, **fixed_params}
+
+        print("*** Begin predicting ***")
+        tf.reset_default_graph()
+
+        with self.tf_graph.as_default():
+            tf.keras.backend.set_session(self.sess)
+            output_map = self.model.predict(test, return_targets=True)
+            targets = self.data_formatter.format_predictions(output_map["targets"])
+            p50_forecast = self.data_formatter.format_predictions(output_map["p50"])
+            p90_forecast = self.data_formatter.format_predictions(output_map["p90"])
+            tf.keras.backend.set_session(default_keras_session)
+
+        predict50 = format_score(p50_forecast, "pred", 1)
+        predict90 = format_score(p90_forecast, "pred", 1)
+        predict = (predict50 + predict90) / 2  # self.label_shift
+        # ===========================Predicting Process===========================
+        return predict
+
+    def finetune(self, dataset: DatasetH):
+        """
+        finetune model
+        Parameters
+        ----------
+        dataset : DatasetH
+            dataset for finetuning
+        """
+        pass

examples/benchmarks/TabNet/requirements.txt

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

examples/benchmarks/TabNet/workflow_config_TabNet_Alpha158.yaml

@@ -0,0 +1,74 @@
+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.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: TabnetModel
+        module_path: qlib.contrib.model.pytorch_tabnet
+        kwargs:
+            pretrain: True
+    dataset:
+        class: DatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: Alpha158
+                module_path: qlib.contrib.data.handler
+                kwargs: *data_handler_config
+            segments:
+                pretrain: [2008-01-01, 2014-12-31]
+                pretrain_validation: [2015-01-01, 2020-08-01]
+                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/XGBoost/workflow_config_xgboost_Alpha360.yaml

@@ -0,0 +1,71 @@
+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: []
+    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: XGBModel
+        module_path: qlib.contrib.model.xgboost
+        kwargs:
+            eval_metric: rmse
+            colsample_bytree: 0.8879
+            eta: 0.0421
+            max_depth: 8
+            n_estimators: 647
+            subsample: 0.8789
+            nthread: 20
+    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: 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/highfreq/highfreq_handler.py

@@ -0,0 +1,172 @@
+from qlib.data.dataset.handler import DataHandler, DataHandlerLP
+from qlib.data.dataset.processor import Processor
+from qlib.utils import get_cls_kwargs
+from qlib.log import TimeInspector
+
+
+class HighFreqHandler(DataHandlerLP):
+    def __init__(
+        self,
+        instruments="csi300",
+        start_time=None,
+        end_time=None,
+        infer_processors=[],
+        learn_processors=[],
+        fit_start_time=None,
+        fit_end_time=None,
+        drop_raw=True,
+    ):
+        def check_transform_proc(proc_l):
+            new_l = []
+            for p in proc_l:
+                p["kwargs"].update(
+                    {
+                        "fit_start_time": fit_start_time,
+                        "fit_end_time": fit_end_time,
+                    }
+                )
+                new_l.append(p)
+            return new_l
+
+        infer_processors = check_transform_proc(infer_processors)
+        learn_processors = check_transform_proc(learn_processors)
+
+        data_loader = {
+            "class": "QlibDataLoader",
+            "kwargs": {
+                "config": self.get_feature_config(),
+                "swap_level": False,
+                "freq": "1min",
+            },
+        }
+        super().__init__(
+            instruments=instruments,
+            start_time=start_time,
+            end_time=end_time,
+            data_loader=data_loader,
+            infer_processors=infer_processors,
+            learn_processors=learn_processors,
+            drop_raw=drop_raw,
+        )
+
+    def get_feature_config(self):
+        fields = []
+        names = []
+
+        template_if = "If(IsNull({1}), {0}, {1})"
+        template_paused = "Select(Or(IsNull($paused), Eq($paused, 0.0)), {0})"
+        template_fillnan = "BFillNan(FFillNan({0}))"
+        # Because there is no vwap field in the yahoo data, a method similar to Simpson integration is used to approximate vwap
+        simpson_vwap = "($open + 2*$high + 2*$low + $close)/6"
+
+        def get_normalized_price_feature(price_field, shift=0):
+            """Get normalized price feature ops"""
+            if shift == 0:
+                template_norm = "{0}/Ref(DayLast({1}), 240)"
+            else:
+                template_norm = "Ref({0}, " + str(shift) + ")/Ref(DayLast({1}), 240)"
+
+            feature_ops = template_norm.format(
+                template_if.format(
+                    template_fillnan.format(template_paused.format("$close")),
+                    template_paused.format(price_field),
+                ),
+                template_fillnan.format(template_paused.format("$close")),
+            )
+            return feature_ops
+
+        fields += [get_normalized_price_feature("$open", 0)]
+        fields += [get_normalized_price_feature("$high", 0)]
+        fields += [get_normalized_price_feature("$low", 0)]
+        fields += [get_normalized_price_feature("$close", 0)]
+        fields += [get_normalized_price_feature(simpson_vwap, 0)]
+        names += ["$open", "$high", "$low", "$close", "$vwap"]
+
+        fields += [get_normalized_price_feature("$open", 240)]
+        fields += [get_normalized_price_feature("$high", 240)]
+        fields += [get_normalized_price_feature("$low", 240)]
+        fields += [get_normalized_price_feature("$close", 240)]
+        fields += [get_normalized_price_feature(simpson_vwap, 240)]
+        names += ["$open_1", "$high_1", "$low_1", "$close_1", "$vwap_1"]
+
+        fields += [
+            "{0}/Ref(DayLast(Mean({0}, 7200)), 240)".format(
+                "If(IsNull({0}), 0, If(Or(Gt({1}, Mul(1.001, {3})), Lt({1}, Mul(0.999, {2}))), 0, {0}))".format(
+                    template_paused.format("$volume"),
+                    template_paused.format(simpson_vwap),
+                    template_paused.format("$low"),
+                    template_paused.format("$high"),
+                )
+            )
+        ]
+        names += ["$volume"]
+        fields += [
+            "Ref({0}, 240)/Ref(DayLast(Mean({0}, 7200)), 240)".format(
+                "If(IsNull({0}), 0, If(Or(Gt({1}, Mul(1.001, {3})), Lt({1}, Mul(0.999, {2}))), 0, {0}))".format(
+                    template_paused.format("$volume"),
+                    template_paused.format(simpson_vwap),
+                    template_paused.format("$low"),
+                    template_paused.format("$high"),
+                )
+            )
+        ]
+        names += ["$volume_1"]
+
+        fields += [template_paused.format("Date($close)")]
+        names += ["date"]
+        return fields, names
+
+
+class HighFreqBacktestHandler(DataHandler):
+    def __init__(
+        self,
+        instruments="csi300",
+        start_time=None,
+        end_time=None,
+    ):
+        data_loader = {
+            "class": "QlibDataLoader",
+            "kwargs": {
+                "config": self.get_feature_config(),
+                "swap_level": False,
+                "freq": "1min",
+            },
+        }
+        super().__init__(
+            instruments=instruments,
+            start_time=start_time,
+            end_time=end_time,
+            data_loader=data_loader,
+        )
+
+    def get_feature_config(self):
+        fields = []
+        names = []
+
+        template_if = "If(IsNull({1}), {0}, {1})"
+        template_paused = "Select(Or(IsNull($paused), Eq($paused, 0.0)), {0})"
+        template_fillnan = "BFillNan(FFillNan({0}))"
+        # Because there is no vwap field in the yahoo data, a method similar to Simpson integration is used to approximate vwap
+        simpson_vwap = "($open + 2*$high + 2*$low + $close)/6"
+        fields += [
+            template_fillnan.format(template_paused.format("$close")),
+        ]
+        names += ["$close0"]
+        fields += [
+            template_if.format(
+                template_fillnan.format(template_paused.format("$close")),
+                template_paused.format(simpson_vwap),
+            )
+        ]
+        names += ["$vwap0"]
+        fields += [
+            "If(IsNull({0}), 0, If(Or(Gt({1}, Mul(1.001, {3})), Lt({1}, Mul(0.999, {2}))), 0, {0}))".format(
+                template_paused.format("$volume"),
+                template_paused.format(simpson_vwap),
+                template_paused.format("$low"),
+                template_paused.format("$high"),
+            )
+        ]
+        names += ["$volume0"]
+
+        return fields, names

examples/highfreq/highfreq_ops.py

@@ -0,0 +1,56 @@
+import numpy as np
+import pandas as pd
+import importlib
+from qlib.data.ops import ElemOperator, PairOperator
+from qlib.config import C
+from qlib.data.cache import H
+from qlib.data.data import Cal
+
+
+def get_calendar_day(freq="day", future=False):
+    flag = f"{freq}_future_{future}_day"
+    if flag in H["c"]:
+        _calendar = H["c"][flag]
+    else:
+        _calendar = np.array(list(map(lambda x: x.date(), Cal.load_calendar(freq, future))))
+        H["c"][flag] = _calendar
+    return _calendar
+
+
+class DayLast(ElemOperator):
+    def _load_internal(self, instrument, start_index, end_index, freq):
+        _calendar = get_calendar_day(freq=freq)
+        series = self.feature.load(instrument, start_index, end_index, freq)
+        return series.groupby(_calendar[series.index]).transform("last")
+
+
+class FFillNan(ElemOperator):
+    def _load_internal(self, instrument, start_index, end_index, freq):
+        series = self.feature.load(instrument, start_index, end_index, freq)
+        return series.fillna(method="ffill")
+
+
+class BFillNan(ElemOperator):
+    def _load_internal(self, instrument, start_index, end_index, freq):
+        series = self.feature.load(instrument, start_index, end_index, freq)
+        return series.fillna(method="bfill")
+
+
+class Date(ElemOperator):
+    def _load_internal(self, instrument, start_index, end_index, freq):
+        _calendar = get_calendar_day(freq=freq)
+        series = self.feature.load(instrument, start_index, end_index, freq)
+        return pd.Series(_calendar[series.index], index=series.index)
+
+
+class Select(PairOperator):
+    def _load_internal(self, instrument, start_index, end_index, freq):
+        series_condition = self.feature_left.load(instrument, start_index, end_index, freq)
+        series_feature = self.feature_right.load(instrument, start_index, end_index, freq)
+        return series_feature.loc[series_condition]
+
+
+class IsNull(ElemOperator):
+    def _load_internal(self, instrument, start_index, end_index, freq):
+        series = self.feature.load(instrument, start_index, end_index, freq)
+        return series.isnull()

examples/highfreq/highfreq_processor.py

@@ -0,0 +1,72 @@
+import numpy as np
+import pandas as pd
+from qlib.data.dataset.processor import Processor
+from qlib.data.dataset.utils import fetch_df_by_index
+
+
+class HighFreqNorm(Processor):
+    def __init__(self, fit_start_time, fit_end_time):
+        self.fit_start_time = fit_start_time
+        self.fit_end_time = fit_end_time
+
+    def fit(self, df_features):
+        fetch_df = fetch_df_by_index(df_features, slice(self.fit_start_time, self.fit_end_time), level="datetime")
+        del df_features
+        df_values = fetch_df.values
+        names = {
+            "price": slice(0, 10),
+            "volume": slice(10, 12),
+        }
+        self.feature_med = {}
+        self.feature_std = {}
+        self.feature_vmax = {}
+        self.feature_vmin = {}
+        for name, name_val in names.items():
+            part_values = df_values[:, name_val].astype(np.float32)
+            if name == "volume":
+                part_values = np.log1p(part_values)
+            self.feature_med[name] = np.nanmedian(part_values)
+            part_values = part_values - self.feature_med[name]
+            self.feature_std[name] = np.nanmedian(np.absolute(part_values)) * 1.4826 + 1e-12
+            part_values = part_values / self.feature_std[name]
+            self.feature_vmax[name] = np.nanmax(part_values)
+            self.feature_vmin[name] = np.nanmin(part_values)
+
+    def __call__(self, df_features):
+        df_features.set_index("date", append=True, drop=True, inplace=True)
+        df_values = df_features.values
+        names = {
+            "price": slice(0, 10),
+            "volume": slice(10, 12),
+        }
+
+        for name, name_val in names.items():
+            if name == "volume":
+                df_values[:, name_val] = np.log1p(df_values[:, name_val])
+            df_values[:, name_val] -= self.feature_med[name]
+            df_values[:, name_val] /= self.feature_std[name]
+            slice0 = df_values[:, name_val] > 3.0
+            slice1 = df_values[:, name_val] > 3.5
+            slice2 = df_values[:, name_val] < -3.0
+            slice3 = df_values[:, name_val] < -3.5
+
+            df_values[:, name_val][slice0] = (
+                3.0 + (df_values[:, name_val][slice0] - 3.0) / (self.feature_vmax[name] - 3) * 0.5
+            )
+            df_values[:, name_val][slice1] = 3.5
+            df_values[:, name_val][slice2] = (
+                -3.0 - (df_values[:, name_val][slice2] + 3.0) / (self.feature_vmin[name] + 3) * 0.5
+            )
+            df_values[:, name_val][slice3] = -3.5
+        idx = df_features.index.droplevel("datetime").drop_duplicates()
+        idx.set_names(["instrument", "datetime"], inplace=True)
+
+        # Reshape is specifically for adapting to RL high-freq executor
+        feat = df_values[:, [0, 1, 2, 3, 4, 10]].reshape(-1, 6 * 240)
+        feat_1 = df_values[:, [5, 6, 7, 8, 9, 11]].reshape(-1, 6 * 240)
+        df_new_features = pd.DataFrame(
+            data=np.concatenate((feat, feat_1), axis=1),
+            index=idx,
+            columns=["FEATURE_%d" % i for i in range(12 * 240)],
+        ).sort_index()
+        return df_new_features

examples/highfreq/workflow.py

@@ -0,0 +1,166 @@
+#  Copyright (c) Microsoft Corporation.
+#  Licensed under the MIT License.
+
+import sys
+import fire
+from pathlib import Path
+
+import qlib
+import pickle
+import numpy as np
+import pandas as pd
+from qlib.config import HIGH_FREQ_CONFIG
+from qlib.contrib.model.gbdt import LGBModel
+from qlib.contrib.data.handler import Alpha158
+from qlib.contrib.strategy.strategy import TopkDropoutStrategy
+from qlib.contrib.evaluate import (
+    backtest as normal_backtest,
+    risk_analysis,
+)
+
+from qlib.utils import init_instance_by_config, exists_qlib_data
+from qlib.data.dataset.handler import DataHandlerLP
+from qlib.data.ops import Operators
+from qlib.data.data import Cal
+from qlib.tests.data import GetData
+
+from highfreq_ops import get_calendar_day, DayLast, FFillNan, BFillNan, Date, Select, IsNull
+
+
+class HighfreqWorkflow(object):
+
+    SPEC_CONF = {"custom_ops": [DayLast, FFillNan, BFillNan, Date, Select, IsNull], "expression_cache": None}
+
+    MARKET = "all"
+    BENCHMARK = "SH000300"
+
+    start_time = "2020-09-14 00:00:00"
+    end_time = "2021-01-18 16:00:00"
+    train_end_time = "2020-11-30 16:00:00"
+    test_start_time = "2020-12-01 00:00:00"
+
+    DATA_HANDLER_CONFIG0 = {
+        "start_time": start_time,
+        "end_time": end_time,
+        "freq": "1min",
+        "fit_start_time": start_time,
+        "fit_end_time": train_end_time,
+        "instruments": MARKET,
+        "infer_processors": [{"class": "HighFreqNorm", "module_path": "highfreq_processor", "kwargs": {}}],
+    }
+    DATA_HANDLER_CONFIG1 = {
+        "start_time": start_time,
+        "end_time": end_time,
+        "freq": "1min",
+        "instruments": MARKET,
+    }
+
+    task = {
+        "dataset": {
+            "class": "DatasetH",
+            "module_path": "qlib.data.dataset",
+            "kwargs": {
+                "handler": {
+                    "class": "HighFreqHandler",
+                    "module_path": "highfreq_handler",
+                    "kwargs": DATA_HANDLER_CONFIG0,
+                },
+                "segments": {
+                    "train": (start_time, train_end_time),
+                    "test": (
+                        test_start_time,
+                        end_time,
+                    ),
+                },
+            },
+        },
+        "dataset_backtest": {
+            "class": "DatasetH",
+            "module_path": "qlib.data.dataset",
+            "kwargs": {
+                "handler": {
+                    "class": "HighFreqBacktestHandler",
+                    "module_path": "highfreq_handler",
+                    "kwargs": DATA_HANDLER_CONFIG1,
+                },
+                "segments": {
+                    "train": (start_time, train_end_time),
+                    "test": (
+                        test_start_time,
+                        end_time,
+                    ),
+                },
+            },
+        },
+    }
+
+    def _init_qlib(self):
+        """initialize qlib"""
+        # use yahoo_cn_1min data
+        QLIB_INIT_CONFIG = {**HIGH_FREQ_CONFIG, **self.SPEC_CONF}
+        provider_uri = QLIB_INIT_CONFIG.get("provider_uri")
+        if not exists_qlib_data(provider_uri):
+            print(f"Qlib data is not found in {provider_uri}")
+            GetData().qlib_data(target_dir=provider_uri, interval="1min", region=REG_CN)
+        qlib.init(**QLIB_INIT_CONFIG)
+
+    def _prepare_calender_cache(self):
+        """preload the calendar for cache"""
+
+        # This code used the copy-on-write feature of Linux to avoid calculating the calendar multiple times in the subprocess
+        # This code may accelerate, but may be not useful on Windows and Mac Os
+        Cal.calendar(freq="1min")
+        get_calendar_day(freq="1min")
+
+    def get_data(self):
+        """use dataset to get highreq data"""
+        self._init_qlib()
+        self._prepare_calender_cache()
+
+        dataset = init_instance_by_config(self.task["dataset"])
+        xtrain, xtest = dataset.prepare(["train", "test"])
+        print(xtrain, xtest)
+
+        dataset_backtest = init_instance_by_config(self.task["dataset_backtest"])
+        backtest_train, backtest_test = dataset_backtest.prepare(["train", "test"])
+        print(backtest_train, backtest_test)
+
+        del xtrain, xtest
+        del backtest_train, backtest_test
+
+    def dump_and_load_dataset(self):
+        """dump and load dataset state on disk"""
+        self._init_qlib()
+        self._prepare_calender_cache()
+        dataset = init_instance_by_config(self.task["dataset"])
+        dataset_backtest = init_instance_by_config(self.task["dataset_backtest"])
+
+        ##=============dump dataset=============
+        dataset.to_pickle(path="dataset.pkl")
+        dataset_backtest.to_pickle(path="dataset_backtest.pkl")
+
+        del dataset, dataset_backtest
+        ##=============reload dataset=============
+        with open("dataset.pkl", "rb") as file_dataset:
+            dataset = pickle.load(file_dataset)
+
+        with open("dataset_backtest.pkl", "rb") as file_dataset_backtest:
+            dataset_backtest = pickle.load(file_dataset_backtest)
+
+        self._prepare_calender_cache()
+        ##=============reload_dataset=============
+        dataset.init(init_type=DataHandlerLP.IT_LS)
+        dataset_backtest.init()
+
+        ##=============get data=============
+        xtrain, xtest = dataset.prepare(["train", "test"])
+        backtest_train, backtest_test = dataset_backtest.prepare(["train", "test"])
+
+        print(xtrain, xtest)
+        print(backtest_train, backtest_test)
+        del xtrain, xtest
+        del backtest_train, backtest_test
+
+
+if __name__ == "__main__":
+    fire.Fire(HighfreqWorkflow)

examples/run_all_model.py

@@ -15,6 +15,7 @@ import traceback
 import functools
 import statistics
 import subprocess
+from datetime import datetime
 from pathlib import Path
 from operator import xor
 from pprint import pprint
@@ -45,8 +46,6 @@ if not exists_qlib_data(provider_uri):
 
     GetData().qlib_data(target_dir=provider_uri, region=REG_CN)
 qlib.init(provider_uri=provider_uri, region=REG_CN, exp_manager=exp_manager)
-if os.path.isdir(exp_path):
-    shutil.rmtree(exp_path)
 
 # decorator to check the arguments
 def only_allow_defined_args(function_to_decorate):
@@ -70,9 +69,9 @@ def handler(signum, frame):
     os.system("kill -9 %d" % os.getpid())
 
 
-signal.signal(signal.SIGTSTP, handler)
 signal.signal(signal.SIGINT, handler)
 
+
 # function to calculate the mean and std of a list in the results dictionary
 def cal_mean_std(results) -> dict:
     mean_std = dict()
@@ -136,9 +135,9 @@ def get_all_folders(models, exclude) -> dict:
 
 
 # function to get all the files under the model folder
-def get_all_files(folder_path) -> (str, str):
-    yaml_path = str(Path(f"{folder_path}") / "*.yaml")
-    req_path = str(Path(f"{folder_path}") / "*.txt")
+def get_all_files(folder_path, dataset) -> (str, str):
+    yaml_path = str(Path(f"{folder_path}") / f"*{dataset}*.yaml")
+    req_path = str(Path(f"{folder_path}") / f"*.txt")
     return glob.glob(yaml_path)[0], glob.glob(req_path)[0]
 
 
@@ -152,6 +151,10 @@ def get_all_results(folders) -> dict:
         result["annualized_return_with_cost"] = list()
         result["information_ratio_with_cost"] = list()
         result["max_drawdown_with_cost"] = list()
+        result["ic"] = list()
+        result["icir"] = list()
+        result["rank_ic"] = list()
+        result["rank_icir"] = list()
         for recorder_id in recorders:
             if recorders[recorder_id].status == "FINISHED":
                 recorder = R.get_recorder(recorder_id=recorder_id, experiment_name=fn)
@@ -159,19 +162,27 @@ def get_all_results(folders) -> dict:
                 result["annualized_return_with_cost"].append(metrics["excess_return_with_cost.annualized_return"])
                 result["information_ratio_with_cost"].append(metrics["excess_return_with_cost.information_ratio"])
                 result["max_drawdown_with_cost"].append(metrics["excess_return_with_cost.max_drawdown"])
+                result["ic"].append(metrics["IC"])
+                result["icir"].append(metrics["ICIR"])
+                result["rank_ic"].append(metrics["Rank IC"])
+                result["rank_icir"].append(metrics["Rank ICIR"])
         results[fn] = result
     return results
 
 
 # function to generate and save markdown table
-def gen_and_save_md_table(metrics):
-    table = "| Model Name | Annualized Return | Information Ratio | Max Drawdown |\n"
-    table += "|---|---|---|---|\n"
+def gen_and_save_md_table(metrics, dataset):
+    table = "| Model Name | Dataset | IC | ICIR | Rank IC | Rank ICIR | Annualized Return | Information Ratio | Max Drawdown |\n"
+    table += "|---|---|---|---|---|---|---|---|---|\n"
     for fn in metrics:
+        ic = metrics[fn]["ic"]
+        icir = metrics[fn]["icir"]
+        ric = metrics[fn]["rank_ic"]
+        ricir = metrics[fn]["rank_icir"]
         ar = metrics[fn]["annualized_return_with_cost"]
         ir = metrics[fn]["information_ratio_with_cost"]
         md = metrics[fn]["max_drawdown_with_cost"]
-        table += f"| {fn} | {ar[0]:9.4f}±{ar[1]:9.2f} | {ir[0]:9.4f}±{ir[1]:9.2f}| {md[0]:9.4f}±{md[1]:9.2f} |\n"
+        table += f"| {fn} | {dataset} | {ic[0]:5.4f}±{ic[1]:2.2f} | {icir[0]:5.4f}±{icir[1]:2.2f}| {ric[0]:5.4f}±{ric[1]:2.2f} | {ricir[0]:5.4f}±{ricir[1]:2.2f} | {ar[0]:5.4f}±{ar[1]:2.2f} | {ir[0]:5.4f}±{ir[1]:2.2f}| {md[0]:5.4f}±{md[1]:2.2f} |\n"
     pprint(table)
     with open("table.md", "w") as f:
         f.write(table)
@@ -180,10 +191,11 @@ def gen_and_save_md_table(metrics):
 
 # function to run the all the models
 @only_allow_defined_args
-def run(times=1, models=None, exclude=False):
+def run(times=1, models=None, dataset="Alpha360", exclude=False):
     """
     Please be aware that this function can only work under Linux. MacOS and Windows will be supported in the future.
-    Any PR to enhance this method is highly welcomed.
+    Any PR to enhance this method is highly welcomed. Besides, this script doesn't support parrallel running the same model
+    for multiple times, and this will be fixed in the future development.
 
     Parameters:
     -----------
@@ -193,6 +205,8 @@ def run(times=1, models=None, exclude=False):
         determines the specific model or list of models to run or exclude.
     exclude : boolean
         determines whether the model being used is excluded or included.
+    dataset : str
+        determines the dataset to be used for each model.
 
     Usage:
     -------
@@ -206,13 +220,16 @@ def run(times=1, models=None, exclude=False):
         # Case 2 - run specific models multiple times
         python run_all_model.py 3 mlp
 
-        # Case 3 - run other models except those are given as arguments for multiple times
-        python run_all_model.py 3 [mlp,tft,lstm] True
+        # Case 3 - run specific models multiple times with specific dataset
+        python run_all_model.py 3 mlp Alpha158
 
-        # Case 4 - run specific models for one time
+        # Case 4 - run other models except those are given as arguments for multiple times
+        python run_all_model.py 3 [mlp,tft,lstm] --exclude=True
+
+        # Case 5 - run specific models for one time
         python run_all_model.py --models=[mlp,lightgbm]
 
-        # Case 5 - run other models except those are given as aruments for one time
+        # Case 6 - run other models except those are given as aruments for one time
         python run_all_model.py --models=[mlp,tft,sfm] --exclude=True
 
     """
@@ -226,7 +243,7 @@ def run(times=1, models=None, exclude=False):
         env_path, python_path, conda_activate = create_env()
         # get all files
         sys.stderr.write("Retrieving files...\n")
-        yaml_path, req_path = get_all_files(folders[fn])
+        yaml_path, req_path = get_all_files(folders[fn], dataset)
         sys.stderr.write("\n")
         # install requirements.txt
         sys.stderr.write("Installing requirements.txt...\n")
@@ -240,6 +257,7 @@ def run(times=1, models=None, exclude=False):
             sys.stderr.write("\n")
         # install qlib
         sys.stderr.write("Installing qlib...\n")
+        execute(f"{python_path} -m pip install --upgrade pip")  # TODO: FIX ME!
         execute(f"{python_path} -m pip install --upgrade cython")  # TODO: FIX ME!
         if fn == "TFT":
             execute(
@@ -272,12 +290,15 @@ def run(times=1, models=None, exclude=False):
     results = cal_mean_std(results)
     # generating md table
     sys.stderr.write(f"Generating markdown table...\n")
-    gen_and_save_md_table(results)
+    gen_and_save_md_table(results, dataset)
     sys.stderr.write("\n")
     # print erros
     sys.stderr.write(f"Here are some of the errors of the models...\n")
     pprint(errors)
     sys.stderr.write("\n")
+    # move results folder
+    shutil.move(exp_path, exp_path + f"_{dataset}_{datetime.now().strftime('%Y-%m-%d_%H:%M:%S')}")
+    shutil.move("table.md", f"table_{dataset}_{datetime.now().strftime('%Y-%m-%d_%H:%M:%S')}.md")
 
 
 if __name__ == "__main__":

examples/workflow_by_code.py

@@ -17,7 +17,7 @@ from qlib.contrib.evaluate import (
 from qlib.utils import exists_qlib_data, init_instance_by_config, flatten_dict
 from qlib.workflow import R
 from qlib.workflow.record_temp import SignalRecord, PortAnaRecord
-
+from qlib.tests.data import GetData
 
 if __name__ == "__main__":
 
@@ -25,9 +25,6 @@ if __name__ == "__main__":
     provider_uri = "~/.qlib/qlib_data/cn_data"  # target_dir
     if not exists_qlib_data(provider_uri):
         print(f"Qlib data is not found in {provider_uri}")
-        sys.path.append(str(Path(__file__).resolve().parent.parent.joinpath("scripts")))
-        from get_data import GetData
-
         GetData().qlib_data(target_dir=provider_uri, region=REG_CN)
 
     qlib.init(provider_uri=provider_uri, region=REG_CN)
@@ -98,6 +95,7 @@ if __name__ == "__main__":
             "open_cost": 0.0005,
             "close_cost": 0.0015,
             "min_cost": 5,
+            "return_order": True,
         },
     }
 
@@ -105,6 +103,11 @@ if __name__ == "__main__":
     model = init_instance_by_config(task["model"])
     dataset = init_instance_by_config(task["dataset"])
 
+    # NOTE: This line is optional
+    # It demonstrates that the dataset can be used standalone.
+    example_df = dataset.prepare("train")
+    print(example_df.head())
+
     # start exp
     with R.start(experiment_name="workflow"):
         R.log_params(**flatten_dict(task))

qlib/__init__.py

@@ -2,91 +2,49 @@
 # Licensed under the MIT License.
 
 
-__version__ = "0.6.0"
+__version__ = "0.6.2"
+
 
 import os
-import re
-import sys
-import copy
 import yaml
 import logging
 import platform
 import subprocess
-from pathlib import Path
 
-from .utils import can_use_cache, init_instance_by_config, get_module_by_module_path
-from .workflow.utils import experiment_exit_handler
 
 # init qlib
 def init(default_conf="client", **kwargs):
-    from .config import C, REG_CN, REG_US, QlibConfig
-    from .data.data import register_all_wrappers
-    from .log import get_module_logger, set_log_with_config
+    from .config import C
+    from .log import get_module_logger
     from .data.cache import H
-    from .workflow import R, QlibRecorder
 
-    C.reset()
     H.clear()
 
-    _logging_config = C.logging_config
-    if "logging_config" in kwargs:
-        _logging_config = kwargs["logging_config"]
-
-    # set global config
-    if _logging_config:
-        set_log_with_config(_logging_config)
-
     # FIXME: this logger ignored the level in config
-    LOG = get_module_logger("Initialization", level=logging.INFO)
-    LOG.info(f"default_conf: {default_conf}.")
+    logger = get_module_logger("Initialization", level=logging.INFO)
 
-    C.set_mode(default_conf)
-    C.set_region(kwargs.get("region", C["region"] if "region" in C else REG_CN))
-
-    for k, v in kwargs.items():
-        C[k] = v
-        if k not in C:
-            LOG.warning("Unrecognized config %s" % k)
-
-    C.resolve_path()
-
-    if not (C["expression_cache"] is None and C["dataset_cache"] is None):
-        # check redis
-        if not can_use_cache():
-            LOG.warning(
-                f"redis connection failed(host={C['redis_host']} port={C['redis_port']}), cache will not be used!"
-            )
-            C["expression_cache"] = None
-            C["dataset_cache"] = None
+    C.set(default_conf, **kwargs)
 
     # check path if server/local
-    if C.get_uri_type() == QlibConfig.LOCAL_URI:
+    if C.get_uri_type() == C.LOCAL_URI:
         if not os.path.exists(C["provider_uri"]):
             if C["auto_mount"]:
-                LOG.error(
+                logger.error(
                     f"Invalid provider uri: {C['provider_uri']}, please check if a valid provider uri has been set. This path does not exist."
                 )
             else:
-                LOG.warning(f"auto_path is False, please make sure {C['mount_path']} is mounted")
-    elif C.get_uri_type() == QlibConfig.NFS_URI:
+                logger.warning(f"auto_path is False, please make sure {C['mount_path']} is mounted")
+    elif C.get_uri_type() == C.NFS_URI:
         _mount_nfs_uri(C)
     else:
         raise NotImplementedError(f"This type of URI is not supported")
 
-    LOG.info("qlib successfully initialized based on %s settings." % default_conf)
-    register_all_wrappers()
-
-    LOG.info(f"data_path={C.get_data_path()}")
+    C.register()
 
     if "flask_server" in C:
-        LOG.info(f"flask_server={C['flask_server']}, flask_port={C['flask_port']}")
-
-    # set up QlibRecorder
-    exp_manager = init_instance_by_config(C["exp_manager"])
-    qr = QlibRecorder(exp_manager)
-    R.register(qr)
-    # clean up experiment when python program ends
-    experiment_exit_handler()
+        logger.info(f"flask_server={C['flask_server']}, flask_port={C['flask_port']}")
+    logger.info("qlib successfully initialized based on %s settings." % default_conf)
+    logger.info(f"data_path={C.get_data_path()}")
 
 
 def _mount_nfs_uri(C):

qlib/config.py

@@ -11,26 +11,27 @@ Two modes are supported
 
 """
 
-import copy
-from pathlib import Path
-import re
 import os
+import re
+import copy
+import logging
 import multiprocessing
+from pathlib import Path
 
 
 class Config:
     def __init__(self, default_conf):
-        self.__dict__["_default_config"] = default_conf  # avoiding conflictions with __getattr__
+        self.__dict__["_default_config"] = copy.deepcopy(default_conf)  # avoiding conflictions with __getattr__
         self.reset()
 
     def __getitem__(self, key):
         return self.__dict__["_config"][key]
 
     def __getattr__(self, attr):
-        try:
+        if attr in self.__dict__["_config"]:
             return self.__dict__["_config"][attr]
-        except KeyError:
-            return AttributeError(f"No such {attr} in self._config")
+
+        raise AttributeError(f"No such {attr} in self._config")
 
     def __setitem__(self, key, value):
         self.__dict__["_config"][key] = value
@@ -59,6 +60,9 @@ class Config:
     def update(self, *args, **kwargs):
         self.__dict__["_config"].update(*args, **kwargs)
 
+    def set_conf_from_C(self, config_c):
+        self.update(**config_c.__dict__["_config"])
+
 
 # REGION CONST
 REG_CN = "cn"
@@ -86,7 +90,6 @@ _default_config = {
     # How many tasks belong to one process. Recommend 1 for high-frequency data and None for daily data.
     "maxtasksperchild": None,
     "default_disk_cache": 1,  # 0:skip/1:use
-    "disable_disk_cache": False,  # disable disk cache; if High-frequency data generally disable_disk_cache=True
     "mem_cache_size_limit": 500,
     # memory cache expire second, only in used 'DatasetURICache' and 'client D.calendar'
     # default 1 hour
@@ -184,9 +187,17 @@ MODE_CONF = {
         "timeout": 100,
         "logging_level": "INFO",
         "region": REG_CN,
+        ## Custom Operator
+        "custom_ops": [],
     },
 }
 
+HIGH_FREQ_CONFIG = {
+    "provider_uri": "~/.qlib/qlib_data/yahoo_cn_1min",
+    "dataset_cache": None,
+    "expression_cache": "DiskExpressionCache",
+    "region": REG_CN,
+}
 
 _default_region_config = {
     REG_CN: {
@@ -207,6 +218,10 @@ class QlibConfig(Config):
     LOCAL_URI = "local"
     NFS_URI = "nfs"
 
+    def __init__(self, default_conf):
+        super().__init__(default_conf)
+        self._registered = False
+
     def set_mode(self, mode):
         # raise KeyError
         self.update(MODE_CONF[mode])
@@ -243,6 +258,64 @@ class QlibConfig(Config):
         else:
             raise NotImplementedError(f"This type of uri is not supported")
 
+    def set(self, default_conf="client", **kwargs):
+        from .utils import set_log_with_config, get_module_logger, can_use_cache
+
+        self.reset()
+
+        _logging_config = self.logging_config
+        if "logging_config" in kwargs:
+            _logging_config = kwargs["logging_config"]
+
+        # set global config
+        if _logging_config:
+            set_log_with_config(_logging_config)
+
+        # FIXME: this logger ignored the level in config
+        logger = get_module_logger("Initialization", level=logging.INFO)
+        logger.info(f"default_conf: {default_conf}.")
+
+        self.set_mode(default_conf)
+        self.set_region(kwargs.get("region", self["region"] if "region" in self else REG_CN))
+
+        for k, v in kwargs.items():
+            if k not in self:
+                logger.warning("Unrecognized config %s" % k)
+            self[k] = v
+
+        self.resolve_path()
+
+        if not (self["expression_cache"] is None and self["dataset_cache"] is None):
+            # check redis
+            if not can_use_cache():
+                logger.warning(
+                    f"redis connection failed(host={self['redis_host']} port={self['redis_port']}), cache will not be used!"
+                )
+                self["expression_cache"] = None
+                self["dataset_cache"] = None
+
+    def register(self):
+        from .utils import init_instance_by_config
+        from .data.ops import register_all_ops
+        from .data.data import register_all_wrappers
+        from .workflow import R, QlibRecorder
+        from .workflow.utils import experiment_exit_handler
+
+        register_all_ops(self)
+        register_all_wrappers(self)
+        # set up QlibRecorder
+        exp_manager = init_instance_by_config(self["exp_manager"])
+        qr = QlibRecorder(exp_manager)
+        R.register(qr)
+        # clean up experiment when python program ends
+        experiment_exit_handler()
+
+        self._registered = True
+
+    @property
+    def registered(self):
+        return self._registered
+
 
 # global config
 C = QlibConfig(_default_config)

qlib/contrib/backtest/__init__.py

@@ -1,9 +1,324 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
 
-# -*- coding: utf-8 -*-
 from .order import Order
 from .account import Account
 from .position import Position
 from .exchange import Exchange
 from .report import Report
+from .backtest import backtest as backtest_func, get_date_range
+
+import numpy as np
+import inspect
+from ...utils import init_instance_by_config
+from ...log import get_module_logger
+from ...config import C
+
+logger = get_module_logger("backtest caller")
+
+
+def get_strategy(
+    strategy=None,
+    topk=50,
+    margin=0.5,
+    n_drop=5,
+    risk_degree=0.95,
+    str_type="dropout",
+    adjust_dates=None,
+):
+    """get_strategy
+
+    There will be 3 ways to return a stratgy. Please follow the code.
+
+
+    Parameters
+    ----------
+
+    strategy : Strategy()
+        strategy used in backtest.
+    topk : int (Default value: 50)
+        top-N stocks to buy.
+    margin : int or float(Default value: 0.5)
+        - if isinstance(margin, int):
+
+            sell_limit = margin
+
+        - else:
+
+            sell_limit = pred_in_a_day.count() * margin
+
+        buffer margin, in single score_mode, continue holding stock if it is in nlargest(sell_limit).
+        sell_limit should be no less than topk.
+    n_drop : int
+        number of stocks to be replaced in each trading date.
+    risk_degree: float
+        0-1, 0.95 for example, use 95% money to trade.
+    str_type: 'amount', 'weight' or 'dropout'
+        strategy type: TopkAmountStrategy ,TopkWeightStrategy or TopkDropoutStrategy.
+
+    Returns
+    -------
+    :class: Strategy
+    an initialized strategy object
+    """
+
+    # There  will be 3 ways to return a strategy.
+    if strategy is None:
+        # 1) create strategy with param `strategy`
+        str_cls_dict = {
+            "amount": "TopkAmountStrategy",
+            "weight": "TopkWeightStrategy",
+            "dropout": "TopkDropoutStrategy",
+        }
+        logger.info("Create new strategy ")
+        from .. import strategy as strategy_pool
+
+        str_cls = getattr(strategy_pool, str_cls_dict.get(str_type))
+        strategy = str_cls(
+            topk=topk,
+            buffer_margin=margin,
+            n_drop=n_drop,
+            risk_degree=risk_degree,
+            adjust_dates=adjust_dates,
+        )
+    elif isinstance(strategy, (dict, str)):
+        # 2) create strategy with init_instance_by_config
+        logger.info("Create new strategy ")
+        strategy = init_instance_by_config(strategy)
+
+    from ..strategy.strategy import BaseStrategy
+
+    # else: nothing happens. 3) Use the strategy directly
+    if not isinstance(strategy, BaseStrategy):
+        raise TypeError("Strategy not supported")
+    return strategy
+
+
+def get_exchange(
+    pred,
+    exchange=None,
+    subscribe_fields=[],
+    open_cost=0.0015,
+    close_cost=0.0025,
+    min_cost=5.0,
+    trade_unit=None,
+    limit_threshold=None,
+    deal_price=None,
+    extract_codes=False,
+    shift=1,
+):
+    """get_exchange
+
+    Parameters
+    ----------
+
+    # exchange related arguments
+    exchange: Exchange().
+    subscribe_fields: list
+        subscribe fields.
+    open_cost : float
+        open transaction cost.
+    close_cost : float
+        close transaction cost.
+    min_cost : float
+        min transaction cost.
+    trade_unit : int
+        100 for China A.
+    deal_price: str
+        dealing price type: 'close', 'open', 'vwap'.
+    limit_threshold : float
+        limit move 0.1 (10%) for example, long and short with same limit.
+    extract_codes: bool
+        will we pass the codes extracted from the pred to the exchange.
+        NOTE: This will be faster with offline qlib.
+
+    Returns
+    -------
+    :class: Exchange
+    an initialized Exchange object
+    """
+
+    if trade_unit is None:
+        trade_unit = C.trade_unit
+    if limit_threshold is None:
+        limit_threshold = C.limit_threshold
+    if deal_price is None:
+        deal_price = C.deal_price
+    if exchange is None:
+        logger.info("Create new exchange")
+        # handle exception for deal_price
+        if deal_price[0] != "$":
+            deal_price = "$" + deal_price
+        if extract_codes:
+            codes = sorted(pred.index.get_level_values("instrument").unique())
+        else:
+            codes = "all"  # TODO: We must ensure that 'all.txt' includes all the stocks
+
+        dates = sorted(pred.index.get_level_values("datetime").unique())
+        dates = np.append(dates, get_date_range(dates[-1], left_shift=1, right_shift=shift))
+
+        exchange = Exchange(
+            trade_dates=dates,
+            codes=codes,
+            deal_price=deal_price,
+            subscribe_fields=subscribe_fields,
+            limit_threshold=limit_threshold,
+            open_cost=open_cost,
+            close_cost=close_cost,
+            min_cost=min_cost,
+            trade_unit=trade_unit,
+        )
+    return exchange
+
+
+def get_executor(
+    executor=None,
+    trade_exchange=None,
+    verbose=True,
+):
+    """get_executor
+
+    There will be 3 ways to return a executor. Please follow the code.
+
+    Parameters
+    ----------
+
+    executor : BaseExecutor
+        executor used in backtest.
+    trade_exchange : Exchange
+        exchange used in executor
+    verbose : bool
+        whether to print log.
+
+    Returns
+    -------
+    :class: BaseExecutor
+    an initialized BaseExecutor object
+    """
+
+    # There  will be 3 ways to return a executor.
+    if executor is None:
+        # 1) create executor with param `executor`
+        logger.info("Create new executor ")
+        from ..online.executor import SimulatorExecutor
+
+        executor = SimulatorExecutor(trade_exchange=trade_exchange, verbose=verbose)
+    elif isinstance(executor, (dict, str)):
+        # 2) create executor with config
+        logger.info("Create new executor ")
+        executor = init_instance_by_config(executor)
+
+    from ..online.executor import BaseExecutor
+
+    # 3) Use the executor directly
+    if not isinstance(executor, BaseExecutor):
+        raise TypeError("Executor not supported")
+    return executor
+
+
+# This is the API for compatibility for legacy code
+def backtest(pred, account=1e9, shift=1, benchmark="SH000905", verbose=True, return_order=False, **kwargs):
+    """This function will help you set a reasonable Exchange and provide default value for strategy
+    Parameters
+    ----------
+
+    - **backtest workflow related or commmon arguments**
+
+    pred : pandas.DataFrame
+        predict should has <datetime, instrument> index and one `score` column.
+    account : float
+        init account value.
+    shift : int
+        whether to shift prediction by one day.
+    benchmark : str
+        benchmark code, default is SH000905 CSI 500.
+    verbose : bool
+        whether to print log.
+    return_order : bool
+        whether to return order list
+
+    - **strategy related arguments**
+
+    strategy : Strategy()
+        strategy used in backtest.
+    topk : int (Default value: 50)
+        top-N stocks to buy.
+    margin : int or float(Default value: 0.5)
+        - if isinstance(margin, int):
+
+            sell_limit = margin
+
+        - else:
+
+            sell_limit = pred_in_a_day.count() * margin
+
+        buffer margin, in single score_mode, continue holding stock if it is in nlargest(sell_limit).
+        sell_limit should be no less than topk.
+    n_drop : int
+        number of stocks to be replaced in each trading date.
+    risk_degree: float
+        0-1, 0.95 for example, use 95% money to trade.
+    str_type: 'amount', 'weight' or 'dropout'
+        strategy type: TopkAmountStrategy ,TopkWeightStrategy or TopkDropoutStrategy.
+
+    - **exchange related arguments**
+
+    exchange: Exchange()
+        pass the exchange for speeding up.
+    subscribe_fields: list
+        subscribe fields.
+    open_cost : float
+        open transaction cost. The default value is 0.002(0.2%).
+    close_cost : float
+        close transaction cost. The default value is 0.002(0.2%).
+    min_cost : float
+        min transaction cost.
+    trade_unit : int
+        100 for China A.
+    deal_price: str
+        dealing price type: 'close', 'open', 'vwap'.
+    limit_threshold : float
+        limit move 0.1 (10%) for example, long and short with same limit.
+    extract_codes: bool
+        will we pass the codes extracted from the pred to the exchange.
+
+        .. note:: This will be faster with offline qlib.
+
+    - **executor related arguments**
+
+    executor : BaseExecutor()
+        executor used in backtest.
+    verbose : bool
+        whether to print log.
+
+    """
+    # check strategy:
+    spec = inspect.getfullargspec(get_strategy)
+    str_args = {k: v for k, v in kwargs.items() if k in spec.args}
+    strategy = get_strategy(**str_args)
+
+    # init exchange:
+    spec = inspect.getfullargspec(get_exchange)
+    ex_args = {k: v for k, v in kwargs.items() if k in spec.args}
+    trade_exchange = get_exchange(pred, **ex_args)
+
+    # init executor:
+    executor = get_executor(executor=kwargs.get("executor"), trade_exchange=trade_exchange, verbose=verbose)
+
+    # run backtest
+    report_dict = backtest_func(
+        pred=pred,
+        strategy=strategy,
+        executor=executor,
+        trade_exchange=trade_exchange,
+        shift=shift,
+        verbose=verbose,
+        account=account,
+        benchmark=benchmark,
+        return_order=return_order,
+    )
+    # for  compatibility of the old API. return the dict positions
+
+    positions = report_dict.get("positions")
+    report_dict.update({"positions": {k: p.position for k, p in positions.items()}})
+    return report_dict

qlib/contrib/backtest/backtest.py

@@ -5,7 +5,6 @@
 import numpy as np
 import pandas as pd
 from ...utils import get_date_by_shift, get_date_range
-from ..online.executor import SimulatorExecutor
 from ...data import D
 from .account import Account
 from ...config import C
@@ -15,7 +14,7 @@ from ...data.dataset.utils import get_level_index
 LOG = get_module_logger("backtest")
 
 
-def backtest(pred, strategy, trade_exchange, shift, verbose, account, benchmark):
+def backtest(pred, strategy, executor, trade_exchange, shift, verbose, account, benchmark, return_order):
     """Parameters
     ----------
     pred : pandas.DataFrame
@@ -69,9 +68,9 @@ def backtest(pred, strategy, trade_exchange, shift, verbose, account, benchmark)
             raise ValueError(f"The benchmark {_codes} does not exist. Please provide the right benchmark")
         bench = _temp_result.groupby(level="datetime")[_temp_result.columns.tolist()[0]].mean()
 
-    trade_dates = np.append(predict_dates[shift:], get_date_range(predict_dates[-1], shift=shift))
-    executor = SimulatorExecutor(trade_exchange, verbose=verbose)
-
+    trade_dates = np.append(predict_dates[shift:], get_date_range(predict_dates[-1], left_shift=1, right_shift=shift))
+    if return_order:
+        multi_order_list = []
     # trading apart
     for pred_date, trade_date in zip(predict_dates, trade_dates):
         # for loop predict date and trading date
@@ -103,6 +102,8 @@ def backtest(pred, strategy, trade_exchange, shift, verbose, account, benchmark)
             )
         else:
             order_list = []
+        if return_order:
+            multi_order_list.append((trade_account, order_list, trade_date))
         # 4. Get result after executing order list
         # NOTE: The following operation will modify order.amount.
         # NOTE: If it is buy and the cash is insufficient, the tradable amount will be recalculated
@@ -115,7 +116,11 @@ def backtest(pred, strategy, trade_exchange, shift, verbose, account, benchmark)
     report_df = trade_account.report.generate_report_dataframe()
     report_df["bench"] = bench
     positions = trade_account.get_positions()
-    return report_df, positions
+
+    report_dict = {"report_df": report_df, "positions": positions}
+    if return_order:
+        report_dict.update({"order_list": multi_order_list})
+    return report_dict
 
 
 def update_account(trade_account, trade_info, trade_exchange, trade_date):

qlib/contrib/data/handler.py

@@ -43,16 +43,18 @@ _DEFAULT_INFER_PROCESSORS = [
 ]
 
 
-class ALPHA360(DataHandlerLP):
+class Alpha360(DataHandlerLP):
     def __init__(
         self,
         instruments="csi500",
         start_time=None,
         end_time=None,
+        freq="day",
         infer_processors=_DEFAULT_INFER_PROCESSORS,
         learn_processors=_DEFAULT_LEARN_PROCESSORS,
         fit_start_time=None,
         fit_end_time=None,
+        filter_pipe=None,
         **kwargs,
     ):
         infer_processors = check_transform_proc(infer_processors, fit_start_time, fit_end_time)
@@ -65,13 +67,15 @@ class ALPHA360(DataHandlerLP):
                     "feature": self.get_feature_config(),
                     "label": kwargs.get("label", self.get_label_config()),
                 },
+                "filter_pipe": filter_pipe,
+                "freq": freq,
             },
         }
 
         super().__init__(
-            instruments,
-            start_time,
-            end_time,
+            instruments=instruments,
+            start_time=start_time,
+            end_time=end_time,
             data_loader=data_loader,
             learn_processors=learn_processors,
             infer_processors=infer_processors,
@@ -119,7 +123,7 @@ class ALPHA360(DataHandlerLP):
         return fields, names
 
 
-class ALPHA360vwap(ALPHA360):
+class Alpha360vwap(Alpha360):
     def get_label_config(self):
         return (["Ref($vwap, -2)/Ref($vwap, -1) - 1"], ["LABEL0"])
 
@@ -130,11 +134,13 @@ class Alpha158(DataHandlerLP):
         instruments="csi500",
         start_time=None,
         end_time=None,
+        freq="day",
         infer_processors=[],
         learn_processors=_DEFAULT_LEARN_PROCESSORS,
         fit_start_time=None,
         fit_end_time=None,
         process_type=DataHandlerLP.PTYPE_A,
+        filter_pipe=None,
         **kwargs,
     ):
         infer_processors = check_transform_proc(infer_processors, fit_start_time, fit_end_time)
@@ -143,13 +149,18 @@ class Alpha158(DataHandlerLP):
         data_loader = {
             "class": "QlibDataLoader",
             "kwargs": {
-                "config": {"feature": self.get_feature_config(), "label": kwargs.get("label", self.get_label_config())},
+                "config": {
+                    "feature": self.get_feature_config(),
+                    "label": kwargs.get("label", self.get_label_config()),
+                },
+                "filter_pipe": filter_pipe,
+                "freq": freq,
             },
         }
         super().__init__(
-            instruments,
-            start_time,
-            end_time,
+            instruments=instruments,
+            start_time=start_time,
+            end_time=end_time,
             data_loader=data_loader,
             infer_processors=infer_processors,
             learn_processors=learn_processors,

qlib/contrib/evaluate.py

@@ -6,17 +6,16 @@ from __future__ import print_function
 
 import numpy as np
 import pandas as pd
-import inspect
+import warnings
 from ..log import get_module_logger
-from . import strategy as strategy_pool
-from .strategy.strategy import BaseStrategy
-from .backtest.exchange import Exchange
-from .backtest.backtest import backtest as backtest_func, get_date_range
+from .backtest import get_exchange, backtest as backtest_func
+from .backtest.backtest import get_date_range
 
 from ..data import D
 from ..config import C
 from ..data.dataset.utils import get_level_index
 
+
 logger = get_module_logger("Evaluate")
 
 
@@ -46,144 +45,6 @@ def risk_analysis(r, N=252):
     return res
 
 
-def get_strategy(
-    strategy=None,
-    topk=50,
-    margin=0.5,
-    n_drop=5,
-    risk_degree=0.95,
-    str_type="amount",
-    adjust_dates=None,
-):
-    """get_strategy
-
-    Parameters
-    ----------
-
-    strategy : Strategy()
-        strategy used in backtest.
-    topk : int (Default value: 50)
-        top-N stocks to buy.
-    margin : int or float(Default value: 0.5)
-        - if isinstance(margin, int):
-
-            sell_limit = margin
-
-        - else:
-
-            sell_limit = pred_in_a_day.count() * margin
-
-        buffer margin, in single score_mode, continue holding stock if it is in nlargest(sell_limit).
-        sell_limit should be no less than topk.
-    n_drop : int
-        number of stocks to be replaced in each trading date.
-    risk_degree: float
-        0-1, 0.95 for example, use 95% money to trade.
-    str_type: 'amount', 'weight' or 'dropout'
-        strategy type: TopkAmountStrategy ,TopkWeightStrategy or TopkDropoutStrategy.
-
-    Returns
-    -------
-    :class: Strategy
-    an initialized strategy object
-    """
-    if strategy is None:
-        str_cls_dict = {
-            "amount": "TopkAmountStrategy",
-            "weight": "TopkWeightStrategy",
-            "dropout": "TopkDropoutStrategy",
-        }
-        logger.info("Create new streategy ")
-        str_cls = getattr(strategy_pool, str_cls_dict.get(str_type))
-        strategy = str_cls(
-            topk=topk,
-            buffer_margin=margin,
-            n_drop=n_drop,
-            risk_degree=risk_degree,
-            adjust_dates=adjust_dates,
-        )
-    if not isinstance(strategy, BaseStrategy):
-        raise TypeError("Strategy not supported")
-    return strategy
-
-
-def get_exchange(
-    pred,
-    exchange=None,
-    subscribe_fields=[],
-    open_cost=0.0015,
-    close_cost=0.0025,
-    min_cost=5.0,
-    trade_unit=None,
-    limit_threshold=None,
-    deal_price=None,
-    extract_codes=False,
-    shift=1,
-):
-    """get_exchange
-
-    Parameters
-    ----------
-
-    # exchange related arguments
-    exchange: Exchange().
-    subscribe_fields: list
-        subscribe fields.
-    open_cost : float
-        open transaction cost.
-    close_cost : float
-        close transaction cost.
-    min_cost : float
-        min transaction cost.
-    trade_unit : int
-        100 for China A.
-    deal_price: str
-        dealing price type: 'close', 'open', 'vwap'.
-    limit_threshold : float
-        limit move 0.1 (10%) for example, long and short with same limit.
-    extract_codes: bool
-        will we pass the codes extracted from the pred to the exchange.
-        NOTE: This will be faster with offline qlib.
-
-    Returns
-    -------
-    :class: Exchange
-    an initialized Exchange object
-    """
-
-    if trade_unit is None:
-        trade_unit = C.trade_unit
-    if limit_threshold is None:
-        limit_threshold = C.limit_threshold
-    if deal_price is None:
-        deal_price = C.deal_price
-    if exchange is None:
-        logger.info("Create new exchange")
-        # handle exception for deal_price
-        if deal_price[0] != "$":
-            deal_price = "$" + deal_price
-        if extract_codes:
-            codes = sorted(pred.index.get_level_values("instrument").unique())
-        else:
-            codes = "all"  # TODO: We must ensure that 'all.txt' includes all the stocks
-
-        dates = sorted(pred.index.get_level_values("datetime").unique())
-        dates = np.append(dates, get_date_range(dates[-1], shift=shift))
-
-        exchange = Exchange(
-            trade_dates=dates,
-            codes=codes,
-            deal_price=deal_price,
-            subscribe_fields=subscribe_fields,
-            limit_threshold=limit_threshold,
-            open_cost=open_cost,
-            close_cost=close_cost,
-            min_cost=min_cost,
-            trade_unit=trade_unit,
-        )
-    return exchange
-
-
 # This is the API for compatibility for legacy code
 def backtest(pred, account=1e9, shift=1, benchmark="SH000905", verbose=True, **kwargs):
     """This function will help you set a reasonable Exchange and provide default value for strategy
@@ -249,30 +110,22 @@ def backtest(pred, account=1e9, shift=1, benchmark="SH000905", verbose=True, **k
         will we pass the codes extracted from the pred to the exchange.
 
         .. note:: This will be faster with offline qlib.
+
+    - **executor related arguments**
+
+    executor : BaseExecutor()
+        executor used in backtest.
+    verbose : bool
+        whether to print log.
+
     """
-    # check strategy:
-    spec = inspect.getfullargspec(get_strategy)
-    str_args = {k: v for k, v in kwargs.items() if k in spec.args}
-    strategy = get_strategy(**str_args)
-
-    # init exchange:
-    spec = inspect.getfullargspec(get_exchange)
-    ex_args = {k: v for k, v in kwargs.items() if k in spec.args}
-    trade_exchange = get_exchange(pred, **ex_args)
-
-    # run backtest
-    report_df, positions = backtest_func(
-        pred=pred,
-        strategy=strategy,
-        trade_exchange=trade_exchange,
-        shift=shift,
-        verbose=verbose,
-        account=account,
-        benchmark=benchmark,
+    warnings.warn(
+        "this function is deprecated, please use backtest function in qlib.contrib.backtest", DeprecationWarning
     )
-    # for  compatibility of the old API. return the dict positions
-    positions = {k: p.position for k, p in positions.items()}
-    return report_df, positions
+    report_dict = backtest_func(
+        pred=pred, account=account, shift=shift, benchmark=benchmark, verbose=verbose, return_order=False, **kwargs
+    )
+    return report_dict.get("report_df"), report_dict.get("positions")
 
 
 def long_short_backtest(
@@ -340,7 +193,7 @@ def long_short_backtest(
 
     _pred_dates = pred.index.get_level_values(level="datetime")
     predict_dates = D.calendar(start_time=_pred_dates.min(), end_time=_pred_dates.max())
-    trade_dates = np.append(predict_dates[shift:], get_date_range(predict_dates[-1], shift=shift))
+    trade_dates = np.append(predict_dates[shift:], get_date_range(predict_dates[-1], left_shift=1, right_shift=shift))
 
     long_returns = {}
     short_returns = {}

qlib/contrib/model/pytorch_alstm.py

@@ -56,8 +56,8 @@ class ALSTM(Model):
         early_stop=20,
         loss="mse",
         optimizer="adam",
-        GPU="0",
-        seed=0,
+        GPU=0,
+        seed=None,
         **kwargs
     ):
         # Set logger.
@@ -76,7 +76,7 @@ class ALSTM(Model):
         self.early_stop = early_stop
         self.optimizer = optimizer.lower()
         self.loss = loss
-        self.visible_GPU = GPU
+        self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() else "cpu")
         self.use_gpu = torch.cuda.is_available()
         self.seed = seed
 
@@ -113,8 +113,9 @@ class ALSTM(Model):
             )
         )
 
-        np.random.seed(self.seed)
-        torch.manual_seed(self.seed)
+        if self.seed is not None:
+            np.random.seed(self.seed)
+            torch.manual_seed(self.seed)
 
         self.ALSTM_model = ALSTMModel(
             d_feat=self.d_feat,
@@ -130,11 +131,7 @@ class ALSTM(Model):
             raise NotImplementedError("optimizer {} is not supported!".format(optimizer))
 
         self._fitted = False
-        if self.use_gpu:
-            self.ALSTM_model.cuda()
-            # set the visible GPU
-            if self.visible_GPU:
-                os.environ["CUDA_VISIBLE_DEVICES"] = self.visible_GPU
+        self.ALSTM_model.to(self.device)
 
     def mse(self, pred, label):
         loss = (pred - label) ** 2
@@ -172,12 +169,8 @@ class ALSTM(Model):
             if len(indices) - i < self.batch_size:
                 break
 
-            feature = torch.from_numpy(x_train_values[indices[i : i + self.batch_size]]).float()
-            label = torch.from_numpy(y_train_values[indices[i : i + self.batch_size]]).float()
-
-            if self.use_gpu:
-                feature = feature.cuda()
-                label = label.cuda()
+            feature = torch.from_numpy(x_train_values[indices[i : i + self.batch_size]]).float().to(self.device)
+            label = torch.from_numpy(y_train_values[indices[i : i + self.batch_size]]).float().to(self.device)
 
             pred = self.ALSTM_model(feature)
             loss = self.loss_fn(pred, label)
@@ -205,12 +198,8 @@ class ALSTM(Model):
             if len(indices) - i < self.batch_size:
                 break
 
-            feature = torch.from_numpy(x_values[indices[i : i + self.batch_size]]).float()
-            label = torch.from_numpy(y_values[indices[i : i + self.batch_size]]).float()
-
-            if self.use_gpu:
-                feature = feature.cuda()
-                label = label.cuda()
+            feature = torch.from_numpy(x_values[indices[i : i + self.batch_size]]).float().to(self.device)
+            label = torch.from_numpy(y_values[indices[i : i + self.batch_size]]).float().to(self.device)
 
             pred = self.ALSTM_model(feature)
             loss = self.loss_fn(pred, label)
@@ -298,10 +287,7 @@ class ALSTM(Model):
             else:
                 end = begin + self.batch_size
 
-            x_batch = torch.from_numpy(x_values[begin:end]).float()
-
-            if self.use_gpu:
-                x_batch = x_batch.cuda()
+            x_batch = torch.from_numpy(x_values[begin:end]).float().to(self.device)
 
             with torch.no_grad():
                 if self.use_gpu:

qlib/contrib/model/pytorch_alstm_ts.py

@@ -0,0 +1,331 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+
+from __future__ import division
+from __future__ import print_function
+
+import os
+import numpy as np
+import pandas as pd
+import copy
+from sklearn.metrics import roc_auc_score, mean_squared_error
+import logging
+from ...utils import (
+    unpack_archive_with_buffer,
+    save_multiple_parts_file,
+    create_save_path,
+    drop_nan_by_y_index,
+)
+from ...log import get_module_logger, TimeInspector
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.utils.data import DataLoader
+
+from ...model.base import Model
+from ...data.dataset import DatasetH, TSDatasetH
+from ...data.dataset.handler import DataHandlerLP
+
+
+class ALSTM(Model):
+    """ALSTM Model
+
+    Parameters
+    ----------
+    d_feat : int
+        input dimension for each time step
+    metric: str
+        the evaluate metric used in early stop
+    optimizer : str
+        optimizer name
+    GPU : str
+        the GPU ID(s) used for training
+    """
+
+    def __init__(
+        self,
+        d_feat=6,
+        hidden_size=64,
+        num_layers=2,
+        dropout=0.0,
+        n_epochs=200,
+        lr=0.001,
+        metric="",
+        batch_size=2000,
+        early_stop=20,
+        loss="mse",
+        optimizer="adam",
+        n_jobs=10,
+        GPU=0,
+        seed=None,
+        **kwargs
+    ):
+        # Set logger.
+        self.logger = get_module_logger("ALSTM")
+        self.logger.info("ALSTM pytorch version...")
+
+        # set hyper-parameters.
+        self.d_feat = d_feat
+        self.hidden_size = hidden_size
+        self.num_layers = num_layers
+        self.dropout = dropout
+        self.n_epochs = n_epochs
+        self.lr = lr
+        self.metric = metric
+        self.batch_size = batch_size
+        self.early_stop = early_stop
+        self.optimizer = optimizer.lower()
+        self.loss = loss
+        self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() else "cpu")
+        self.n_jobs = n_jobs
+        self.use_gpu = torch.cuda.is_available()
+        self.seed = seed
+
+        self.logger.info(
+            "ALSTM parameters setting:"
+            "\nd_feat : {}"
+            "\nhidden_size : {}"
+            "\nnum_layers : {}"
+            "\ndropout : {}"
+            "\nn_epochs : {}"
+            "\nlr : {}"
+            "\nmetric : {}"
+            "\nbatch_size : {}"
+            "\nearly_stop : {}"
+            "\noptimizer : {}"
+            "\nloss_type : {}"
+            "\nvisible_GPU : {}"
+            "\nn_jobs : {}"
+            "\nuse_GPU : {}"
+            "\nseed : {}".format(
+                d_feat,
+                hidden_size,
+                num_layers,
+                dropout,
+                n_epochs,
+                lr,
+                metric,
+                batch_size,
+                early_stop,
+                optimizer.lower(),
+                loss,
+                GPU,
+                n_jobs,
+                self.use_gpu,
+                seed,
+            )
+        )
+
+        if self.seed is not None:
+            np.random.seed(self.seed)
+            torch.manual_seed(self.seed)
+
+        self.ALSTM_model = ALSTMModel(
+            d_feat=self.d_feat,
+            hidden_size=self.hidden_size,
+            num_layers=self.num_layers,
+            dropout=self.dropout,
+        ).to(self.device)
+        if optimizer.lower() == "adam":
+            self.train_optimizer = optim.Adam(self.ALSTM_model.parameters(), lr=self.lr)
+        elif optimizer.lower() == "gd":
+            self.train_optimizer = optim.SGD(self.ALSTM_model.parameters(), lr=self.lr)
+        else:
+            raise NotImplementedError("optimizer {} is not supported!".format(optimizer))
+
+        self._fitted = False
+        self.ALSTM_model.to(self.device)
+
+    def mse(self, pred, label):
+        loss = (pred - label) ** 2
+        return torch.mean(loss)
+
+    def loss_fn(self, pred, label):
+        mask = ~torch.isnan(label)
+
+        if self.loss == "mse":
+            return self.mse(pred[mask], label[mask])
+
+        raise ValueError("unknown loss `%s`" % self.loss)
+
+    def metric_fn(self, pred, label):
+
+        mask = torch.isfinite(label)
+
+        if self.metric == "" or self.metric == "loss":
+            return -self.loss_fn(pred[mask], label[mask])
+
+        raise ValueError("unknown metric `%s`" % self.metric)
+
+    def train_epoch(self, data_loader):
+
+        self.ALSTM_model.train()
+
+        for data in data_loader:
+            feature = data[:, :, 0:-1].to(self.device)
+            label = data[:, -1, -1].to(self.device)
+
+            pred = self.ALSTM_model(feature.float())
+            loss = self.loss_fn(pred, label)
+
+            self.train_optimizer.zero_grad()
+            loss.backward()
+            torch.nn.utils.clip_grad_value_(self.ALSTM_model.parameters(), 3.0)
+            self.train_optimizer.step()
+
+    def test_epoch(self, data_loader):
+
+        self.ALSTM_model.eval()
+
+        scores = []
+        losses = []
+
+        for data in data_loader:
+
+            feature = data[:, :, 0:-1].to(self.device)
+            # feature[torch.isnan(feature)] = 0
+            label = data[:, -1, -1].to(self.device)
+
+            pred = self.ALSTM_model(feature.float())
+            loss = self.loss_fn(pred, label)
+            losses.append(loss.item())
+
+            score = self.metric_fn(pred, label)
+            scores.append(score.item())
+
+        return np.mean(losses), np.mean(scores)
+
+    def fit(
+        self,
+        dataset,
+        evals_result=dict(),
+        verbose=True,
+        save_path=None,
+    ):
+        dl_train = dataset.prepare("train", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L)
+        dl_valid = dataset.prepare("valid", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L)
+
+        dl_train.config(fillna_type="ffill+bfill")  # process nan brought by dataloader
+        dl_valid.config(fillna_type="ffill+bfill")  # process nan brought by dataloader
+
+        train_loader = DataLoader(dl_train, batch_size=self.batch_size, shuffle=True, num_workers=self.n_jobs)
+        valid_loader = DataLoader(dl_valid, batch_size=self.batch_size, shuffle=False, num_workers=self.n_jobs)
+
+        if save_path == None:
+            save_path = create_save_path(save_path)
+
+        stop_steps = 0
+        train_loss = 0
+        best_score = -np.inf
+        best_epoch = 0
+        evals_result["train"] = []
+        evals_result["valid"] = []
+
+        # train
+        self.logger.info("training...")
+        self._fitted = True
+
+        for step in range(self.n_epochs):
+            self.logger.info("Epoch%d:", step)
+            self.logger.info("training...")
+            self.train_epoch(train_loader)
+            self.logger.info("evaluating...")
+            train_loss, train_score = self.test_epoch(train_loader)
+            val_loss, val_score = self.test_epoch(valid_loader)
+            self.logger.info("train %.6f, valid %.6f" % (train_score, val_score))
+            evals_result["train"].append(train_score)
+            evals_result["valid"].append(val_score)
+
+            if val_score > best_score:
+                best_score = val_score
+                stop_steps = 0
+                best_epoch = step
+                best_param = copy.deepcopy(self.ALSTM_model.state_dict())
+            else:
+                stop_steps += 1
+                if stop_steps >= self.early_stop:
+                    self.logger.info("early stop")
+                    break
+
+        self.logger.info("best score: %.6lf @ %d" % (best_score, best_epoch))
+        self.ALSTM_model.load_state_dict(best_param)
+        torch.save(best_param, save_path)
+
+        if self.use_gpu:
+            torch.cuda.empty_cache()
+
+    def predict(self, dataset):
+        if not self._fitted:
+            raise ValueError("model is not fitted yet!")
+
+        dl_test = dataset.prepare("test", col_set=["feature", "label"], data_key=DataHandlerLP.DK_I)
+        dl_test.config(fillna_type="ffill+bfill")
+        test_loader = DataLoader(dl_test, batch_size=self.batch_size, num_workers=self.n_jobs)
+        self.ALSTM_model.eval()
+        preds = []
+
+        for data in test_loader:
+
+            feature = data[:, :, 0:-1].to(self.device)
+
+            with torch.no_grad():
+                if self.use_gpu:
+                    pred = self.ALSTM_model(feature.float()).detach().cpu().numpy()
+                else:
+                    pred = self.ALSTM_model(feature.float()).detach().numpy()
+
+            preds.append(pred)
+
+        return pd.Series(np.concatenate(preds), index=dl_test.get_index())
+
+
+class ALSTMModel(nn.Module):
+    def __init__(self, d_feat=6, hidden_size=64, num_layers=2, dropout=0.0, rnn_type="GRU"):
+        super().__init__()
+        self.hid_size = hidden_size
+        self.input_size = d_feat
+        self.dropout = dropout
+        self.rnn_type = rnn_type
+        self.rnn_layer = num_layers
+        self._build_model()
+
+    def _build_model(self):
+        try:
+            klass = getattr(nn, self.rnn_type.upper())
+        except:
+            raise ValueError("unknown rnn_type `%s`" % self.rnn_type)
+        self.net = nn.Sequential()
+        self.net.add_module("fc_in", nn.Linear(in_features=self.input_size, out_features=self.hid_size))
+        self.net.add_module("act", nn.Tanh())
+        self.rnn = klass(
+            input_size=self.hid_size,
+            hidden_size=self.hid_size,
+            num_layers=self.rnn_layer,
+            batch_first=True,
+            dropout=self.dropout,
+        )
+        self.fc_out = nn.Linear(in_features=self.hid_size * 2, out_features=1)
+        self.att_net = nn.Sequential()
+        self.att_net.add_module(
+            "att_fc_in",
+            nn.Linear(in_features=self.hid_size, out_features=int(self.hid_size / 2)),
+        )
+        self.att_net.add_module("att_dropout", torch.nn.Dropout(self.dropout))
+        self.att_net.add_module("att_act", nn.Tanh())
+        self.att_net.add_module(
+            "att_fc_out",
+            nn.Linear(in_features=int(self.hid_size / 2), out_features=1, bias=False),
+        )
+        self.att_net.add_module("att_softmax", nn.Softmax(dim=1))
+
+    def forward(self, inputs):
+        rnn_out, _ = self.rnn(self.net(inputs))  # [batch, seq_len, num_directions * hidden_size]
+        attention_score = self.att_net(rnn_out)  # [batch, seq_len, 1]
+        out_att = torch.mul(rnn_out, attention_score)
+        out_att = torch.sum(out_att, dim=1)
+        out = self.fc_out(
+            torch.cat((rnn_out[:, -1, :], out_att), dim=1)
+        )  # [batch, seq_len, num_directions * hidden_size] -> [batch, 1]
+        return out[..., 0]

qlib/contrib/model/pytorch_gats.py

@@ -61,8 +61,8 @@ class GATs(Model):
         with_pretrain=True,
         model_path=None,
         optimizer="adam",
-        GPU="0",
-        seed=0,
+        GPU=0,
+        seed=None,
         **kwargs
     ):
         # Set logger.
@@ -83,7 +83,7 @@ class GATs(Model):
         self.base_model = base_model
         self.with_pretrain = with_pretrain
         self.model_path = model_path
-        self.visible_GPU = GPU
+        self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() else "cpu")
         self.use_gpu = torch.cuda.is_available()
         self.seed = seed
 
@@ -123,8 +123,11 @@ class GATs(Model):
                 seed,
             )
         )
-        np.random.seed(self.seed)
-        torch.manual_seed(self.seed)
+
+        if self.seed is not None:
+            np.random.seed(self.seed)
+            torch.manual_seed(self.seed)
+
         self.GAT_model = GATModel(
             d_feat=self.d_feat,
             hidden_size=self.hidden_size,
@@ -140,11 +143,7 @@ class GATs(Model):
             raise NotImplementedError("optimizer {} is not supported!".format(optimizer))
 
         self._fitted = False
-        if self.use_gpu:
-            self.GAT_model.cuda()
-            # set the visible GPU
-            if self.visible_GPU:
-                os.environ["CUDA_VISIBLE_DEVICES"] = self.visible_GPU
+        self.GAT_model.to(self.device)
 
     def mse(self, pred, label):
         loss = (pred - label) ** 2
@@ -190,12 +189,8 @@ class GATs(Model):
 
         for idx, count in zip(daily_index, daily_count):
             batch = slice(idx, idx + count)
-            feature = torch.from_numpy(x_train_values[batch]).float()
-            label = torch.from_numpy(y_train_values[batch]).float()
-
-            if self.use_gpu:
-                feature = feature.cuda()
-                label = label.cuda()
+            feature = torch.from_numpy(x_train_values[batch]).float().to(self.device)
+            label = torch.from_numpy(y_train_values[batch]).float().to(self.device)
 
             pred = self.GAT_model(feature)
             loss = self.loss_fn(pred, label)
@@ -221,12 +216,8 @@ class GATs(Model):
 
         for idx, count in zip(daily_index, daily_count):
             batch = slice(idx, idx + count)
-            feature = torch.from_numpy(x_values[batch]).float()
-            label = torch.from_numpy(y_values[batch]).float()
-
-            if self.use_gpu:
-                feature = feature.cuda()
-                label = label.cuda()
+            feature = torch.from_numpy(x_values[batch]).float().to(self.device)
+            label = torch.from_numpy(y_values[batch]).float().to(self.device)
 
             pred = self.GAT_model(feature)
             loss = self.loss_fn(pred, label)
@@ -330,10 +321,7 @@ class GATs(Model):
 
         for idx, count in zip(daily_index, daily_count):
             batch = slice(idx, idx + count)
-            x_batch = torch.from_numpy(x_values[batch]).float()
-
-            if self.use_gpu:
-                x_batch = x_batch.cuda()
+            x_batch = torch.from_numpy(x_values[batch]).float().to(self.device)
 
             with torch.no_grad():
                 if self.use_gpu:

qlib/contrib/model/pytorch_gats_ts.py

@@ -0,0 +1,413 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+
+from __future__ import division
+from __future__ import print_function
+
+import os
+import numpy as np
+import pandas as pd
+import copy
+from sklearn.metrics import roc_auc_score, mean_squared_error
+import logging
+from ...utils import (
+    unpack_archive_with_buffer,
+    save_multiple_parts_file,
+    create_save_path,
+    drop_nan_by_y_index,
+)
+from ...log import get_module_logger, TimeInspector
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.utils.data import DataLoader
+from torch.utils.data import Sampler
+
+from ...model.base import Model
+from ...data.dataset import DatasetH
+from ...data.dataset.handler import DataHandlerLP
+from ...contrib.model.pytorch_lstm import LSTMModel
+from ...contrib.model.pytorch_gru import GRUModel
+
+
+class DailyBatchSampler(Sampler):
+    def __init__(self, data_source):
+
+        self.data_source = data_source
+        self.data = self.data_source.data.loc[self.data_source.get_index()]
+        self.daily_count = self.data.groupby(level=0).size().values  # calculate number of samples in each batch
+        self.daily_index = np.roll(np.cumsum(self.daily_count), 1)  # calculate begin index of each batch
+        self.daily_index[0] = 0
+
+    def __iter__(self):
+
+        for idx, count in zip(self.daily_index, self.daily_count):
+            yield np.arange(idx, idx + count)
+
+    def __len__(self):
+        return len(self.data_source)
+
+
+class GATs(Model):
+    """GATs Model
+
+    Parameters
+    ----------
+    lr : float
+        learning rate
+    d_feat : int
+        input dimensions for each time step
+    metric : str
+        the evaluate metric used in early stop
+    optimizer : str
+        optimizer name
+    GPU : str
+        the GPU ID(s) used for training
+    """
+
+    def __init__(
+        self,
+        d_feat=20,
+        hidden_size=64,
+        num_layers=2,
+        dropout=0.0,
+        n_epochs=200,
+        lr=0.001,
+        metric="",
+        early_stop=20,
+        loss="mse",
+        base_model="GRU",
+        with_pretrain=True,
+        model_path=None,
+        optimizer="adam",
+        GPU="0",
+        n_jobs=10,
+        seed=None,
+        **kwargs
+    ):
+        # Set logger.
+        self.logger = get_module_logger("GATs")
+        self.logger.info("GATs pytorch version...")
+
+        # set hyper-parameters.
+        self.d_feat = d_feat
+        self.hidden_size = hidden_size
+        self.num_layers = num_layers
+        self.dropout = dropout
+        self.n_epochs = n_epochs
+        self.lr = lr
+        self.metric = metric
+        self.early_stop = early_stop
+        self.optimizer = optimizer.lower()
+        self.loss = loss
+        self.base_model = base_model
+        self.with_pretrain = with_pretrain
+        self.model_path = model_path
+        self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() else "cpu")
+        self.n_jobs = n_jobs
+        self.use_gpu = torch.cuda.is_available()
+        self.seed = seed
+
+        self.logger.info(
+            "GATs parameters setting:"
+            "\nd_feat : {}"
+            "\nhidden_size : {}"
+            "\nnum_layers : {}"
+            "\ndropout : {}"
+            "\nn_epochs : {}"
+            "\nlr : {}"
+            "\nmetric : {}"
+            "\nearly_stop : {}"
+            "\noptimizer : {}"
+            "\nloss_type : {}"
+            "\nbase_model : {}"
+            "\nwith_pretrain : {}"
+            "\nmodel_path : {}"
+            "\nvisible_GPU : {}"
+            "\nuse_GPU : {}"
+            "\nseed : {}".format(
+                d_feat,
+                hidden_size,
+                num_layers,
+                dropout,
+                n_epochs,
+                lr,
+                metric,
+                early_stop,
+                optimizer.lower(),
+                loss,
+                base_model,
+                with_pretrain,
+                model_path,
+                GPU,
+                self.use_gpu,
+                seed,
+            )
+        )
+
+        if self.seed is not None:
+            np.random.seed(self.seed)
+            torch.manual_seed(self.seed)
+
+        self.GAT_model = GATModel(
+            d_feat=self.d_feat,
+            hidden_size=self.hidden_size,
+            num_layers=self.num_layers,
+            dropout=self.dropout,
+            base_model=self.base_model,
+        )
+        if optimizer.lower() == "adam":
+            self.train_optimizer = optim.Adam(self.GAT_model.parameters(), lr=self.lr)
+        elif optimizer.lower() == "gd":
+            self.train_optimizer = optim.SGD(self.GAT_model.parameters(), lr=self.lr)
+        else:
+            raise NotImplementedError("optimizer {} is not supported!".format(optimizer))
+
+        self._fitted = False
+        self.GAT_model.to(self.device)
+
+    def mse(self, pred, label):
+        loss = (pred - label) ** 2
+        return torch.mean(loss)
+
+    def loss_fn(self, pred, label):
+        mask = ~torch.isnan(label)
+
+        if self.loss == "mse":
+            return self.mse(pred[mask], label[mask])
+
+        raise ValueError("unknown loss `%s`" % self.loss)
+
+    def metric_fn(self, pred, label):
+
+        mask = torch.isfinite(label)
+
+        if self.metric == "" or self.metric == "loss":
+            return -self.loss_fn(pred[mask], label[mask])
+
+        raise ValueError("unknown metric `%s`" % self.metric)
+
+    def get_daily_inter(self, df, shuffle=False):
+        # organize the train data into daily batches
+        daily_count = df.groupby(level=0).size().values
+        daily_index = np.roll(np.cumsum(daily_count), 1)
+        daily_index[0] = 0
+        if shuffle:
+            # shuffle data
+            daily_shuffle = list(zip(daily_index, daily_count))
+            np.random.shuffle(daily_shuffle)
+            daily_index, daily_count = zip(*daily_shuffle)
+        return daily_index, daily_count
+
+    def train_epoch(self, data_loader):
+
+        self.GAT_model.train()
+
+        for data in data_loader:
+
+            data = data.squeeze()
+            feature = data[:, :, 0:-1].to(self.device)
+            label = data[:, -1, -1].to(self.device)
+
+            pred = self.GAT_model(feature.float())
+            loss = self.loss_fn(pred, label)
+
+            self.train_optimizer.zero_grad()
+            loss.backward()
+            torch.nn.utils.clip_grad_value_(self.GAT_model.parameters(), 3.0)
+            self.train_optimizer.step()
+
+    def test_epoch(self, data_loader):
+
+        self.GAT_model.eval()
+
+        scores = []
+        losses = []
+
+        for data in data_loader:
+
+            data = data.squeeze()
+            feature = data[:, :, 0:-1].to(self.device)
+            # feature[torch.isnan(feature)] = 0
+            label = data[:, -1, -1].to(self.device)
+
+            pred = self.GAT_model(feature.float())
+            loss = self.loss_fn(pred, label)
+            losses.append(loss.item())
+
+            score = self.metric_fn(pred, label)
+            scores.append(score.item())
+
+        return np.mean(losses), np.mean(scores)
+
+    def fit(
+        self,
+        dataset,
+        evals_result=dict(),
+        verbose=True,
+        save_path=None,
+    ):
+
+        dl_train = dataset.prepare("train", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L)
+        dl_valid = dataset.prepare("valid", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L)
+
+        dl_train.config(fillna_type="ffill+bfill")  # process nan brought by dataloader
+        dl_valid.config(fillna_type="ffill+bfill")  # process nan brought by dataloader
+
+        sampler_train = DailyBatchSampler(dl_train)
+        sampler_valid = DailyBatchSampler(dl_valid)
+
+        train_loader = DataLoader(dl_train, sampler=sampler_train, num_workers=self.n_jobs)
+        valid_loader = DataLoader(dl_valid, sampler=sampler_valid, num_workers=self.n_jobs)
+
+        if save_path == None:
+            save_path = create_save_path(save_path)
+
+        stop_steps = 0
+        train_loss = 0
+        best_score = -np.inf
+        best_epoch = 0
+        evals_result["train"] = []
+        evals_result["valid"] = []
+
+        # load pretrained base_model
+        if self.with_pretrain:
+            if self.model_path == None:
+                raise ValueError("the path of the pretrained model should be given first!")
+            self.logger.info("Loading pretrained model...")
+            if self.base_model == "LSTM":
+                pretrained_model = LSTMModel(
+                    d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers
+                )
+                pretrained_model.load_state_dict(torch.load(self.model_path))
+            elif self.base_model == "GRU":
+                pretrained_model = GRUModel(
+                    d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers
+                )
+                pretrained_model.load_state_dict(torch.load(self.model_path))
+            else:
+                raise ValueError("unknown base model name `%s`" % self.base_model)
+
+            model_dict = self.GAT_model.state_dict()
+            pretrained_dict = {k: v for k, v in pretrained_model.state_dict().items() if k in model_dict}
+            model_dict.update(pretrained_dict)
+            self.GAT_model.load_state_dict(model_dict)
+            self.logger.info("Loading pretrained model Done...")
+
+        # train
+        self.logger.info("training...")
+        self._fitted = True
+
+        for step in range(self.n_epochs):
+            self.logger.info("Epoch%d:", step)
+            self.logger.info("training...")
+            self.train_epoch(train_loader)
+            self.logger.info("evaluating...")
+            train_loss, train_score = self.test_epoch(train_loader)
+            val_loss, val_score = self.test_epoch(valid_loader)
+            self.logger.info("train %.6f, valid %.6f" % (train_score, val_score))
+            evals_result["train"].append(train_score)
+            evals_result["valid"].append(val_score)
+
+            if val_score > best_score:
+                best_score = val_score
+                stop_steps = 0
+                best_epoch = step
+                best_param = copy.deepcopy(self.GAT_model.state_dict())
+            else:
+                stop_steps += 1
+                if stop_steps >= self.early_stop:
+                    self.logger.info("early stop")
+                    break
+
+        self.logger.info("best score: %.6lf @ %d" % (best_score, best_epoch))
+        self.GAT_model.load_state_dict(best_param)
+        torch.save(best_param, save_path)
+
+        if self.use_gpu:
+            torch.cuda.empty_cache()
+
+    def predict(self, dataset):
+        if not self._fitted:
+            raise ValueError("model is not fitted yet!")
+
+        dl_test = dataset.prepare("test", col_set=["feature", "label"], data_key=DataHandlerLP.DK_I)
+        dl_test.config(fillna_type="ffill+bfill")
+        sampler_test = DailyBatchSampler(dl_test)
+        test_loader = DataLoader(dl_test, sampler=sampler_test, num_workers=self.n_jobs)
+        self.GAT_model.eval()
+        preds = []
+
+        for data in test_loader:
+
+            data = data.squeeze()
+            feature = data[:, :, 0:-1].to(self.device)
+
+            with torch.no_grad():
+                if self.use_gpu:
+                    pred = self.GAT_model(feature.float()).detach().cpu().numpy()
+                else:
+                    pred = self.GAT_model(feature.float()).detach().numpy()
+
+            preds.append(pred)
+
+        return pd.Series(np.concatenate(preds), index=dl_test.get_index())
+
+
+class GATModel(nn.Module):
+    def __init__(self, d_feat=6, hidden_size=64, num_layers=2, dropout=0.0, base_model="GRU"):
+        super().__init__()
+
+        if base_model == "GRU":
+            self.rnn = nn.GRU(
+                input_size=d_feat,
+                hidden_size=hidden_size,
+                num_layers=num_layers,
+                batch_first=True,
+                dropout=dropout,
+            )
+        elif base_model == "LSTM":
+            self.rnn = nn.LSTM(
+                input_size=d_feat,
+                hidden_size=hidden_size,
+                num_layers=num_layers,
+                batch_first=True,
+                dropout=dropout,
+            )
+        else:
+            raise ValueError("unknown base model name `%s`" % base_model)
+
+        self.hidden_size = hidden_size
+        self.d_feat = d_feat
+        self.transformation = nn.Linear(self.hidden_size, self.hidden_size)
+        self.a = nn.Parameter(torch.randn(self.hidden_size * 2, 1))
+        self.a.requires_grad = True
+        self.fc = nn.Linear(self.hidden_size, self.hidden_size)
+        self.fc_out = nn.Linear(hidden_size, 1)
+        self.leaky_relu = nn.LeakyReLU()
+        self.softmax = nn.Softmax(dim=1)
+
+    def cal_attention(self, x, y):
+        x = self.transformation(x)
+        y = self.transformation(y)
+
+        sample_num = x.shape[0]
+        dim = x.shape[1]
+        e_x = x.expand(sample_num, sample_num, dim)
+        e_y = torch.transpose(e_x, 0, 1)
+        attention_in = torch.cat((e_x, e_y), 2).view(-1, dim * 2)
+        self.a_t = torch.t(self.a)
+        attention_out = self.a_t.mm(torch.t(attention_in)).view(sample_num, sample_num)
+        attention_out = self.leaky_relu(attention_out)
+        att_weight = self.softmax(attention_out)
+        return att_weight
+
+    def forward(self, x):
+        out, _ = self.rnn(x)
+        hidden = out[:, -1, :]
+        att_weight = self.cal_attention(hidden, hidden)
+        hidden = att_weight.mm(hidden) + hidden
+        hidden = self.fc(hidden)
+        hidden = self.leaky_relu(hidden)
+        return self.fc_out(hidden).squeeze()

qlib/contrib/model/pytorch_gru.py

@@ -56,8 +56,8 @@ class GRU(Model):
         early_stop=20,
         loss="mse",
         optimizer="adam",
-        GPU="0",
-        seed=0,
+        GPU=0,
+        seed=None,
         **kwargs
     ):
         # Set logger.
@@ -76,7 +76,7 @@ class GRU(Model):
         self.early_stop = early_stop
         self.optimizer = optimizer.lower()
         self.loss = loss
-        self.visible_GPU = GPU
+        self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() else "cpu")
         self.use_gpu = torch.cuda.is_available()
         self.seed = seed
 
@@ -113,8 +113,9 @@ class GRU(Model):
             )
         )
 
-        np.random.seed(self.seed)
-        torch.manual_seed(self.seed)
+        if self.seed is not None:
+            np.random.seed(self.seed)
+            torch.manual_seed(self.seed)
 
         self.gru_model = GRUModel(
             d_feat=self.d_feat,
@@ -130,11 +131,7 @@ class GRU(Model):
             raise NotImplementedError("optimizer {} is not supported!".format(optimizer))
 
         self._fitted = False
-        if self.use_gpu:
-            self.gru_model.cuda()
-            # set the visible GPU
-            if self.visible_GPU:
-                os.environ["CUDA_VISIBLE_DEVICES"] = self.visible_GPU
+        self.gru_model.to(self.device)
 
     def mse(self, pred, label):
         loss = (pred - label) ** 2
@@ -172,12 +169,8 @@ class GRU(Model):
             if len(indices) - i < self.batch_size:
                 break
 
-            feature = torch.from_numpy(x_train_values[indices[i : i + self.batch_size]]).float()
-            label = torch.from_numpy(y_train_values[indices[i : i + self.batch_size]]).float()
-
-            if self.use_gpu:
-                feature = feature.cuda()
-                label = label.cuda()
+            feature = torch.from_numpy(x_train_values[indices[i : i + self.batch_size]]).float().to(self.device)
+            label = torch.from_numpy(y_train_values[indices[i : i + self.batch_size]]).float().to(self.device)
 
             pred = self.gru_model(feature)
             loss = self.loss_fn(pred, label)
@@ -205,12 +198,8 @@ class GRU(Model):
             if len(indices) - i < self.batch_size:
                 break
 
-            feature = torch.from_numpy(x_values[indices[i : i + self.batch_size]]).float()
-            label = torch.from_numpy(y_values[indices[i : i + self.batch_size]]).float()
-
-            if self.use_gpu:
-                feature = feature.cuda()
-                label = label.cuda()
+            feature = torch.from_numpy(x_values[indices[i : i + self.batch_size]]).float().to(self.device)
+            label = torch.from_numpy(y_values[indices[i : i + self.batch_size]]).float().to(self.device)
 
             pred = self.gru_model(feature)
             loss = self.loss_fn(pred, label)
@@ -298,10 +287,7 @@ class GRU(Model):
             else:
                 end = begin + self.batch_size
 
-            x_batch = torch.from_numpy(x_values[begin:end]).float()
-
-            if self.use_gpu:
-                x_batch = x_batch.cuda()
+            x_batch = torch.from_numpy(x_values[begin:end]).float().to(self.device)
 
             with torch.no_grad():
                 if self.use_gpu:

qlib/contrib/model/pytorch_gru_ts.py

@@ -0,0 +1,301 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+
+from __future__ import division
+from __future__ import print_function
+
+import os
+import numpy as np
+import pandas as pd
+import copy
+from sklearn.metrics import roc_auc_score, mean_squared_error
+import logging
+from ...utils import (
+    unpack_archive_with_buffer,
+    save_multiple_parts_file,
+    create_save_path,
+    drop_nan_by_y_index,
+)
+from ...log import get_module_logger, TimeInspector
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.utils.data import DataLoader
+
+from ...model.base import Model
+from ...data.dataset import DatasetH, TSDatasetH
+from ...data.dataset.handler import DataHandlerLP
+
+
+class GRU(Model):
+    """GRU Model
+
+    Parameters
+    ----------
+    d_feat : int
+        input dimension for each time step
+    metric: str
+        the evaluate metric used in early stop
+    optimizer : str
+        optimizer name
+    GPU : str
+        the GPU ID(s) used for training
+    """
+
+    def __init__(
+        self,
+        d_feat=6,
+        hidden_size=64,
+        num_layers=2,
+        dropout=0.0,
+        n_epochs=200,
+        lr=0.001,
+        metric="",
+        batch_size=2000,
+        early_stop=20,
+        loss="mse",
+        optimizer="adam",
+        n_jobs=10,
+        GPU=0,
+        seed=None,
+        **kwargs
+    ):
+        # Set logger.
+        self.logger = get_module_logger("GRU")
+        self.logger.info("GRU pytorch version...")
+
+        # set hyper-parameters.
+        self.d_feat = d_feat
+        self.hidden_size = hidden_size
+        self.num_layers = num_layers
+        self.dropout = dropout
+        self.n_epochs = n_epochs
+        self.lr = lr
+        self.metric = metric
+        self.batch_size = batch_size
+        self.early_stop = early_stop
+        self.optimizer = optimizer.lower()
+        self.loss = loss
+        self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() else "cpu")
+        self.n_jobs = n_jobs
+        self.use_gpu = torch.cuda.is_available()
+        self.seed = seed
+
+        self.logger.info(
+            "GRU parameters setting:"
+            "\nd_feat : {}"
+            "\nhidden_size : {}"
+            "\nnum_layers : {}"
+            "\ndropout : {}"
+            "\nn_epochs : {}"
+            "\nlr : {}"
+            "\nmetric : {}"
+            "\nbatch_size : {}"
+            "\nearly_stop : {}"
+            "\noptimizer : {}"
+            "\nloss_type : {}"
+            "\nvisible_GPU : {}"
+            "\nn_jobs : {}"
+            "\nuse_GPU : {}"
+            "\nseed : {}".format(
+                d_feat,
+                hidden_size,
+                num_layers,
+                dropout,
+                n_epochs,
+                lr,
+                metric,
+                batch_size,
+                early_stop,
+                optimizer.lower(),
+                loss,
+                GPU,
+                n_jobs,
+                self.use_gpu,
+                seed,
+            )
+        )
+
+        if self.seed is not None:
+            np.random.seed(self.seed)
+            torch.manual_seed(self.seed)
+
+        self.GRU_model = GRUModel(
+            d_feat=self.d_feat,
+            hidden_size=self.hidden_size,
+            num_layers=self.num_layers,
+            dropout=self.dropout,
+        ).to(self.device)
+        if optimizer.lower() == "adam":
+            self.train_optimizer = optim.Adam(self.GRU_model.parameters(), lr=self.lr)
+        elif optimizer.lower() == "gd":
+            self.train_optimizer = optim.SGD(self.GRU_model.parameters(), lr=self.lr)
+        else:
+            raise NotImplementedError("optimizer {} is not supported!".format(optimizer))
+
+        self._fitted = False
+        self.GRU_model.to(self.device)
+
+    def mse(self, pred, label):
+        loss = (pred - label) ** 2
+        return torch.mean(loss)
+
+    def loss_fn(self, pred, label):
+        mask = ~torch.isnan(label)
+
+        if self.loss == "mse":
+            return self.mse(pred[mask], label[mask])
+
+        raise ValueError("unknown loss `%s`" % self.loss)
+
+    def metric_fn(self, pred, label):
+
+        mask = torch.isfinite(label)
+
+        if self.metric == "" or self.metric == "loss":
+            return -self.loss_fn(pred[mask], label[mask])
+
+        raise ValueError("unknown metric `%s`" % self.metric)
+
+    def train_epoch(self, data_loader):
+
+        self.GRU_model.train()
+
+        for data in data_loader:
+            feature = data[:, :, 0:-1].to(self.device)
+            label = data[:, -1, -1].to(self.device)
+
+            pred = self.GRU_model(feature.float())
+            loss = self.loss_fn(pred, label)
+
+            self.train_optimizer.zero_grad()
+            loss.backward()
+            torch.nn.utils.clip_grad_value_(self.GRU_model.parameters(), 3.0)
+            self.train_optimizer.step()
+
+    def test_epoch(self, data_loader):
+
+        self.GRU_model.eval()
+
+        scores = []
+        losses = []
+
+        for data in data_loader:
+
+            feature = data[:, :, 0:-1].to(self.device)
+            # feature[torch.isnan(feature)] = 0
+            label = data[:, -1, -1].to(self.device)
+
+            pred = self.GRU_model(feature.float())
+            loss = self.loss_fn(pred, label)
+            losses.append(loss.item())
+
+            score = self.metric_fn(pred, label)
+            scores.append(score.item())
+
+        return np.mean(losses), np.mean(scores)
+
+    def fit(
+        self,
+        dataset,
+        evals_result=dict(),
+        verbose=True,
+        save_path=None,
+    ):
+        dl_train = dataset.prepare("train", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L)
+        dl_valid = dataset.prepare("valid", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L)
+
+        dl_train.config(fillna_type="ffill+bfill")  # process nan brought by dataloader
+        dl_valid.config(fillna_type="ffill+bfill")  # process nan brought by dataloader
+
+        train_loader = DataLoader(dl_train, batch_size=self.batch_size, shuffle=True, num_workers=self.n_jobs)
+        valid_loader = DataLoader(dl_valid, batch_size=self.batch_size, shuffle=False, num_workers=self.n_jobs)
+
+        if save_path == None:
+            save_path = create_save_path(save_path)
+
+        stop_steps = 0
+        train_loss = 0
+        best_score = -np.inf
+        best_epoch = 0
+        evals_result["train"] = []
+        evals_result["valid"] = []
+
+        # train
+        self.logger.info("training...")
+        self._fitted = True
+
+        for step in range(self.n_epochs):
+            self.logger.info("Epoch%d:", step)
+            self.logger.info("training...")
+            self.train_epoch(train_loader)
+            self.logger.info("evaluating...")
+            train_loss, train_score = self.test_epoch(train_loader)
+            val_loss, val_score = self.test_epoch(valid_loader)
+            self.logger.info("train %.6f, valid %.6f" % (train_score, val_score))
+            evals_result["train"].append(train_score)
+            evals_result["valid"].append(val_score)
+
+            if val_score > best_score:
+                best_score = val_score
+                stop_steps = 0
+                best_epoch = step
+                best_param = copy.deepcopy(self.GRU_model.state_dict())
+            else:
+                stop_steps += 1
+                if stop_steps >= self.early_stop:
+                    self.logger.info("early stop")
+                    break
+
+        self.logger.info("best score: %.6lf @ %d" % (best_score, best_epoch))
+        self.GRU_model.load_state_dict(best_param)
+        torch.save(best_param, save_path)
+
+        if self.use_gpu:
+            torch.cuda.empty_cache()
+
+    def predict(self, dataset):
+        if not self._fitted:
+            raise ValueError("model is not fitted yet!")
+
+        dl_test = dataset.prepare("test", col_set=["feature", "label"], data_key=DataHandlerLP.DK_I)
+        dl_test.config(fillna_type="ffill+bfill")
+        test_loader = DataLoader(dl_test, batch_size=self.batch_size, num_workers=self.n_jobs)
+        self.GRU_model.eval()
+        preds = []
+
+        for data in test_loader:
+
+            feature = data[:, :, 0:-1].to(self.device)
+
+            with torch.no_grad():
+                if self.use_gpu:
+                    pred = self.GRU_model(feature.float()).detach().cpu().numpy()
+                else:
+                    pred = self.GRU_model(feature.float()).detach().numpy()
+
+            preds.append(pred)
+
+        return pd.Series(np.concatenate(preds), index=dl_test.get_index())
+
+
+class GRUModel(nn.Module):
+    def __init__(self, d_feat=6, hidden_size=64, num_layers=2, dropout=0.0):
+        super().__init__()
+
+        self.rnn = nn.GRU(
+            input_size=d_feat,
+            hidden_size=hidden_size,
+            num_layers=num_layers,
+            batch_first=True,
+            dropout=dropout,
+        )
+        self.fc_out = nn.Linear(hidden_size, 1)
+
+        self.d_feat = d_feat
+
+    def forward(self, x):
+        out, _ = self.rnn(x)
+        return self.fc_out(out[:, -1, :]).squeeze()

qlib/contrib/model/pytorch_lstm.py

@@ -56,8 +56,8 @@ class LSTM(Model):
         early_stop=20,
         loss="mse",
         optimizer="adam",
-        GPU="0",
-        seed=0,
+        GPU=0,
+        seed=None,
         **kwargs
     ):
         # Set logger.
@@ -76,7 +76,7 @@ class LSTM(Model):
         self.early_stop = early_stop
         self.optimizer = optimizer.lower()
         self.loss = loss
-        self.visible_GPU = GPU
+        self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() else "cpu")
         self.use_gpu = torch.cuda.is_available()
         self.seed = seed
 
@@ -113,8 +113,9 @@ class LSTM(Model):
             )
         )
 
-        np.random.seed(self.seed)
-        torch.manual_seed(self.seed)
+        if self.seed is not None:
+            np.random.seed(self.seed)
+            torch.manual_seed(self.seed)
 
         self.lstm_model = LSTMModel(
             d_feat=self.d_feat,
@@ -130,11 +131,7 @@ class LSTM(Model):
             raise NotImplementedError("optimizer {} is not supported!".format(optimizer))
 
         self._fitted = False
-        if self.use_gpu:
-            self.lstm_model.cuda()
-            # set the visible GPU
-            if self.visible_GPU:
-                os.environ["CUDA_VISIBLE_DEVICES"] = self.visible_GPU
+        self.lstm_model.to(self.device)
 
     def mse(self, pred, label):
         loss = (pred - label) ** 2
@@ -172,12 +169,8 @@ class LSTM(Model):
             if len(indices) - i < self.batch_size:
                 break
 
-            feature = torch.from_numpy(x_train_values[indices[i : i + self.batch_size]]).float()
-            label = torch.from_numpy(y_train_values[indices[i : i + self.batch_size]]).float()
-
-            if self.use_gpu:
-                feature = feature.cuda()
-                label = label.cuda()
+            feature = torch.from_numpy(x_train_values[indices[i : i + self.batch_size]]).float().to(self.device)
+            label = torch.from_numpy(y_train_values[indices[i : i + self.batch_size]]).float().to(self.device)
 
             pred = self.lstm_model(feature)
             loss = self.loss_fn(pred, label)
@@ -205,12 +198,8 @@ class LSTM(Model):
             if len(indices) - i < self.batch_size:
                 break
 
-            feature = torch.from_numpy(x_values[indices[i : i + self.batch_size]]).float()
-            label = torch.from_numpy(y_values[indices[i : i + self.batch_size]]).float()
-
-            if self.use_gpu:
-                feature = feature.cuda()
-                label = label.cuda()
+            feature = torch.from_numpy(x_values[indices[i : i + self.batch_size]]).float().to(self.device)
+            label = torch.from_numpy(y_values[indices[i : i + self.batch_size]]).float().to(self.device)
 
             pred = self.lstm_model(feature)
             loss = self.loss_fn(pred, label)
@@ -298,10 +287,7 @@ class LSTM(Model):
             else:
                 end = begin + self.batch_size
 
-            x_batch = torch.from_numpy(x_values[begin:end]).float()
-
-            if self.use_gpu:
-                x_batch = x_batch.cuda()
+            x_batch = torch.from_numpy(x_values[begin:end]).float().to(self.device)
 
             with torch.no_grad():
                 if self.use_gpu:

qlib/contrib/model/pytorch_lstm_ts.py

@@ -0,0 +1,301 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+
+from __future__ import division
+from __future__ import print_function
+
+import os
+import numpy as np
+import pandas as pd
+import copy
+from sklearn.metrics import roc_auc_score, mean_squared_error
+import logging
+from ...utils import (
+    unpack_archive_with_buffer,
+    save_multiple_parts_file,
+    create_save_path,
+    drop_nan_by_y_index,
+)
+from ...log import get_module_logger, TimeInspector
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.utils.data import DataLoader
+
+from ...model.base import Model
+from ...data.dataset import DatasetH, TSDatasetH
+from ...data.dataset.handler import DataHandlerLP
+
+
+class LSTM(Model):
+    """LSTM Model
+
+    Parameters
+    ----------
+    d_feat : int
+        input dimension for each time step
+    metric: str
+        the evaluate metric used in early stop
+    optimizer : str
+        optimizer name
+    GPU : str
+        the GPU ID(s) used for training
+    """
+
+    def __init__(
+        self,
+        d_feat=6,
+        hidden_size=64,
+        num_layers=2,
+        dropout=0.0,
+        n_epochs=200,
+        lr=0.001,
+        metric="",
+        batch_size=2000,
+        early_stop=20,
+        loss="mse",
+        optimizer="adam",
+        n_jobs=10,
+        GPU=0,
+        seed=None,
+        **kwargs
+    ):
+        # Set logger.
+        self.logger = get_module_logger("LSTM")
+        self.logger.info("LSTM pytorch version...")
+
+        # set hyper-parameters.
+        self.d_feat = d_feat
+        self.hidden_size = hidden_size
+        self.num_layers = num_layers
+        self.dropout = dropout
+        self.n_epochs = n_epochs
+        self.lr = lr
+        self.metric = metric
+        self.batch_size = batch_size
+        self.early_stop = early_stop
+        self.optimizer = optimizer.lower()
+        self.loss = loss
+        self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() else "cpu")
+        self.n_jobs = n_jobs
+        self.use_gpu = torch.cuda.is_available()
+        self.seed = seed
+
+        self.logger.info(
+            "LSTM parameters setting:"
+            "\nd_feat : {}"
+            "\nhidden_size : {}"
+            "\nnum_layers : {}"
+            "\ndropout : {}"
+            "\nn_epochs : {}"
+            "\nlr : {}"
+            "\nmetric : {}"
+            "\nbatch_size : {}"
+            "\nearly_stop : {}"
+            "\noptimizer : {}"
+            "\nloss_type : {}"
+            "\nvisible_GPU : {}"
+            "\nn_jobs : {}"
+            "\nuse_GPU : {}"
+            "\nseed : {}".format(
+                d_feat,
+                hidden_size,
+                num_layers,
+                dropout,
+                n_epochs,
+                lr,
+                metric,
+                batch_size,
+                early_stop,
+                optimizer.lower(),
+                loss,
+                GPU,
+                n_jobs,
+                self.use_gpu,
+                seed,
+            )
+        )
+
+        if self.seed is not None:
+            np.random.seed(self.seed)
+            torch.manual_seed(self.seed)
+
+        self.LSTM_model = LSTMModel(
+            d_feat=self.d_feat,
+            hidden_size=self.hidden_size,
+            num_layers=self.num_layers,
+            dropout=self.dropout,
+        ).to(self.device)
+        if optimizer.lower() == "adam":
+            self.train_optimizer = optim.Adam(self.LSTM_model.parameters(), lr=self.lr)
+        elif optimizer.lower() == "gd":
+            self.train_optimizer = optim.SGD(self.LSTM_model.parameters(), lr=self.lr)
+        else:
+            raise NotImplementedError("optimizer {} is not supported!".format(optimizer))
+
+        self._fitted = False
+        self.LSTM_model.to(self.device)
+
+    def mse(self, pred, label):
+        loss = (pred - label) ** 2
+        return torch.mean(loss)
+
+    def loss_fn(self, pred, label):
+        mask = ~torch.isnan(label)
+
+        if self.loss == "mse":
+            return self.mse(pred[mask], label[mask])
+
+        raise ValueError("unknown loss `%s`" % self.loss)
+
+    def metric_fn(self, pred, label):
+
+        mask = torch.isfinite(label)
+
+        if self.metric == "" or self.metric == "loss":
+            return -self.loss_fn(pred[mask], label[mask])
+
+        raise ValueError("unknown metric `%s`" % self.metric)
+
+    def train_epoch(self, data_loader):
+
+        self.LSTM_model.train()
+
+        for data in data_loader:
+            feature = data[:, :, 0:-1].to(self.device)
+            label = data[:, -1, -1].to(self.device)
+
+            pred = self.LSTM_model(feature.float())
+            loss = self.loss_fn(pred, label)
+
+            self.train_optimizer.zero_grad()
+            loss.backward()
+            torch.nn.utils.clip_grad_value_(self.LSTM_model.parameters(), 3.0)
+            self.train_optimizer.step()
+
+    def test_epoch(self, data_loader):
+
+        self.LSTM_model.eval()
+
+        scores = []
+        losses = []
+
+        for data in data_loader:
+
+            feature = data[:, :, 0:-1].to(self.device)
+            # feature[torch.isnan(feature)] = 0
+            label = data[:, -1, -1].to(self.device)
+
+            pred = self.LSTM_model(feature.float())
+            loss = self.loss_fn(pred, label)
+            losses.append(loss.item())
+
+            score = self.metric_fn(pred, label)
+            scores.append(score.item())
+
+        return np.mean(losses), np.mean(scores)
+
+    def fit(
+        self,
+        dataset,
+        evals_result=dict(),
+        verbose=True,
+        save_path=None,
+    ):
+        dl_train = dataset.prepare("train", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L)
+        dl_valid = dataset.prepare("valid", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L)
+
+        dl_train.config(fillna_type="ffill+bfill")  # process nan brought by dataloader
+        dl_valid.config(fillna_type="ffill+bfill")  # process nan brought by dataloader
+
+        train_loader = DataLoader(dl_train, batch_size=self.batch_size, shuffle=True, num_workers=self.n_jobs)
+        valid_loader = DataLoader(dl_valid, batch_size=self.batch_size, shuffle=False, num_workers=self.n_jobs)
+
+        if save_path == None:
+            save_path = create_save_path(save_path)
+
+        stop_steps = 0
+        train_loss = 0
+        best_score = -np.inf
+        best_epoch = 0
+        evals_result["train"] = []
+        evals_result["valid"] = []
+
+        # train
+        self.logger.info("training...")
+        self._fitted = True
+
+        for step in range(self.n_epochs):
+            self.logger.info("Epoch%d:", step)
+            self.logger.info("training...")
+            self.train_epoch(train_loader)
+            self.logger.info("evaluating...")
+            train_loss, train_score = self.test_epoch(train_loader)
+            val_loss, val_score = self.test_epoch(valid_loader)
+            self.logger.info("train %.6f, valid %.6f" % (train_score, val_score))
+            evals_result["train"].append(train_score)
+            evals_result["valid"].append(val_score)
+
+            if val_score > best_score:
+                best_score = val_score
+                stop_steps = 0
+                best_epoch = step
+                best_param = copy.deepcopy(self.LSTM_model.state_dict())
+            else:
+                stop_steps += 1
+                if stop_steps >= self.early_stop:
+                    self.logger.info("early stop")
+                    break
+
+        self.logger.info("best score: %.6lf @ %d" % (best_score, best_epoch))
+        self.LSTM_model.load_state_dict(best_param)
+        torch.save(best_param, save_path)
+
+        if self.use_gpu:
+            torch.cuda.empty_cache()
+
+    def predict(self, dataset):
+        if not self._fitted:
+            raise ValueError("model is not fitted yet!")
+
+        dl_test = dataset.prepare("test", col_set=["feature", "label"], data_key=DataHandlerLP.DK_I)
+        dl_test.config(fillna_type="ffill+bfill")
+        test_loader = DataLoader(dl_test, batch_size=self.batch_size, num_workers=self.n_jobs)
+        self.LSTM_model.eval()
+        preds = []
+
+        for data in test_loader:
+
+            feature = data[:, :, 0:-1].to(self.device)
+
+            with torch.no_grad():
+                if self.use_gpu:
+                    pred = self.LSTM_model(feature.float()).detach().cpu().numpy()
+                else:
+                    pred = self.LSTM_model(feature.float()).detach().numpy()
+
+            preds.append(pred)
+
+        return pd.Series(np.concatenate(preds), index=dl_test.get_index())
+
+
+class LSTMModel(nn.Module):
+    def __init__(self, d_feat=6, hidden_size=64, num_layers=2, dropout=0.0):
+        super().__init__()
+
+        self.rnn = nn.LSTM(
+            input_size=d_feat,
+            hidden_size=hidden_size,
+            num_layers=num_layers,
+            batch_first=True,
+            dropout=dropout,
+        )
+        self.fc_out = nn.Linear(hidden_size, 1)
+
+        self.d_feat = d_feat
+
+    def forward(self, x):
+        out, _ = self.rnn(x)
+        return self.fc_out(out[:, -1, :]).squeeze()

qlib/contrib/model/pytorch_nn.py

@@ -50,7 +50,7 @@ class DNNModelPytorch(Model):
         self,
         input_dim,
         output_dim,
-        layers=(256, 512, 768, 512, 256, 128, 64),
+        layers=(256,),
         lr=0.001,
         max_steps=300,
         batch_size=2000,
@@ -60,8 +60,9 @@ class DNNModelPytorch(Model):
         lr_decay_steps=100,
         optimizer="gd",
         loss="mse",
-        GPU="0",
-        seed=0,
+        GPU=0,
+        seed=None,
+        weight_decay=0.0,
         **kwargs
     ):
         # Set logger.
@@ -79,9 +80,10 @@ class DNNModelPytorch(Model):
         self.lr_decay_steps = lr_decay_steps
         self.optimizer = optimizer.lower()
         self.loss_type = loss
-        self.visible_GPU = GPU
+        self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() else "cpu")
         self.use_GPU = torch.cuda.is_available()
         self.seed = seed
+        self.weight_decay = weight_decay
 
         self.logger.info(
             "DNN parameters setting:"
@@ -98,7 +100,8 @@ class DNNModelPytorch(Model):
             "\neval_steps : {}"
             "\nseed : {}"
             "\nvisible_GPU : {}"
-            "\nuse_GPU : {}".format(
+            "\nuse_GPU : {}"
+            "\nweight_decay : {}".format(
                 layers,
                 lr,
                 max_steps,
@@ -113,11 +116,13 @@ class DNNModelPytorch(Model):
                 seed,
                 GPU,
                 self.use_GPU,
+                weight_decay,
             )
         )
 
-        np.random.seed(self.seed)
-        torch.manual_seed(self.seed)
+        if self.seed is not None:
+            np.random.seed(self.seed)
+            torch.manual_seed(self.seed)
 
         if loss not in {"mse", "binary"}:
             raise NotImplementedError("loss {} is not supported!".format(loss))
@@ -125,9 +130,9 @@ class DNNModelPytorch(Model):
 
         self.dnn_model = Net(input_dim, output_dim, layers, loss=self.loss_type)
         if optimizer.lower() == "adam":
-            self.train_optimizer = optim.Adam(self.dnn_model.parameters(), lr=self.lr)
+            self.train_optimizer = optim.Adam(self.dnn_model.parameters(), lr=self.lr, weight_decay=self.weight_decay)
         elif optimizer.lower() == "gd":
-            self.train_optimizer = optim.SGD(self.dnn_model.parameters(), lr=self.lr)
+            self.train_optimizer = optim.SGD(self.dnn_model.parameters(), lr=self.lr, weight_decay=self.weight_decay)
         else:
             raise NotImplementedError("optimizer {} is not supported!".format(optimizer))
 
@@ -146,11 +151,7 @@ class DNNModelPytorch(Model):
         )
 
         self._fitted = False
-        if self.use_GPU:
-            self.dnn_model.cuda()
-            # set the visible GPU
-            if self.visible_GPU:
-                os.environ["CUDA_VISIBLE_DEVICES"] = self.visible_GPU
+        self.dnn_model.to(self.device)
 
     def fit(
         self,
@@ -187,13 +188,9 @@ class DNNModelPytorch(Model):
         w_train_values = torch.from_numpy(w_train.values).float()
         train_num = y_train_values.shape[0]
         # prepare validation data
-        x_val_auto = torch.from_numpy(x_valid.values).float()
-        y_val_auto = torch.from_numpy(y_valid.values).float()
-        w_val_auto = torch.from_numpy(w_valid.values).float()
-        if self.use_GPU:
-            x_val_auto = x_val_auto.cuda()
-            y_val_auto = y_val_auto.cuda()
-            w_val_auto = w_val_auto.cuda()
+        x_val_auto = torch.from_numpy(x_valid.values).float().to(self.device)
+        y_val_auto = torch.from_numpy(y_valid.values).float().to(self.device)
+        w_val_auto = torch.from_numpy(w_valid.values).float().to(self.device)
 
         for step in range(self.max_steps):
             if stop_steps >= self.early_stop_rounds:
@@ -204,14 +201,9 @@ class DNNModelPytorch(Model):
             self.dnn_model.train()
             self.train_optimizer.zero_grad()
             choice = np.random.choice(train_num, self.batch_size)
-            x_batch_auto = x_train_values[choice]
-            y_batch_auto = y_train_values[choice]
-            w_batch_auto = w_train_values[choice]
-
-            if self.use_GPU:
-                x_batch_auto = x_batch_auto.cuda()
-                y_batch_auto = y_batch_auto.cuda()
-                w_batch_auto = w_batch_auto.cuda()
+            x_batch_auto = x_train_values[choice].to(self.device)
+            y_batch_auto = y_train_values[choice].to(self.device)
+            w_batch_auto = w_train_values[choice].to(self.device)
 
             # forward
             preds = self.dnn_model(x_batch_auto)
@@ -267,7 +259,7 @@ class DNNModelPytorch(Model):
             loss = torch.mul(sqr_loss, w).mean()
             return loss
         elif loss_type == "binary":
-            loss = nn.BCELoss()
+            loss = nn.BCELoss(weight=w)
             return loss(pred, target)
         else:
             raise NotImplementedError("loss {} is not supported!".format(loss_type))
@@ -276,9 +268,7 @@ class DNNModelPytorch(Model):
         if not self._fitted:
             raise ValueError("model is not fitted yet!")
         x_test_pd = dataset.prepare("test", col_set="feature")
-        x_test = torch.from_numpy(x_test_pd.values).float()
-        if self.use_GPU:
-            x_test = x_test.cuda()
+        x_test = torch.from_numpy(x_test_pd.values).float().to(self.device)
         self.dnn_model.eval()
 
         with torch.no_grad():
@@ -306,7 +296,7 @@ class DNNModelPytorch(Model):
         self._fitted = True
 
 
-class AverageMeter(object):
+class AverageMeter:
     """Computes and stores the average and current value"""
 
     def __init__(self):

qlib/contrib/model/pytorch_sfm.py

@@ -217,7 +217,7 @@ class SFM(Model):
         loss="mse",
         optimizer="gd",
         GPU="0",
-        seed=0,
+        seed=None,
         **kwargs
     ):
         # Set logger.
@@ -239,7 +239,7 @@ class SFM(Model):
         self.eval_steps = eval_steps
         self.optimizer = optimizer.lower()
         self.loss = loss
-        self.device = "cuda:%d" % (GPU) if torch.cuda.is_available() else "cpu"
+        self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() else "cpu")
         self.use_gpu = torch.cuda.is_available()
         self.seed = seed
 
@@ -282,8 +282,9 @@ class SFM(Model):
             )
         )
 
-        np.random.seed(self.seed)
-        torch.manual_seed(self.seed)
+        if self.seed is not None:
+            np.random.seed(self.seed)
+            torch.manual_seed(self.seed)
 
         self.sfm_model = SFM_Model(
             d_feat=self.d_feat,
@@ -463,7 +464,7 @@ class SFM(Model):
         return pd.Series(np.concatenate(preds), index=index)
 
 
-class AverageMeter(object):
+class AverageMeter:
     """Computes and stores the average and current value"""
 
     def __init__(self):

qlib/contrib/model/pytorch_tabnet.py

@@ -0,0 +1,642 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+from __future__ import division
+from __future__ import print_function
+
+import os
+import numpy as np
+import pandas as pd
+import copy
+from sklearn.metrics import roc_auc_score, mean_squared_error
+import logging
+from ...utils import (
+    unpack_archive_with_buffer,
+    save_multiple_parts_file,
+    create_save_path,
+    drop_nan_by_y_index,
+)
+from ...log import get_module_logger, TimeInspector
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torch.nn.functional as F
+from torch.autograd import Function
+
+from ...model.base import Model
+from ...data.dataset import DatasetH
+from ...data.dataset.handler import DataHandlerLP
+
+
+class TabnetModel(Model):
+    def __init__(
+        self,
+        d_feat=158,
+        out_dim=64,
+        final_out_dim=1,
+        batch_size=4096,
+        n_d=64,
+        n_a=64,
+        n_shared=2,
+        n_ind=2,
+        n_steps=5,
+        n_epochs=100,
+        pretrain_n_epochs=50,
+        relax=1.3,
+        vbs=2048,
+        seed=993,
+        optimizer="adam",
+        loss="mse",
+        metric="",
+        early_stop=20,
+        GPU="1",
+        pretrain_loss="custom",
+        ps=0.3,
+        lr=0.01,
+        pretrain=True,
+        pretrain_file="./pretrain/best.model",
+    ):
+        """
+        TabNet model for Qlib
+
+        Args：
+        ps: probability to generate the bernoulli mask
+        """
+        # set hyper-parameters.
+        self.d_feat = d_feat
+        self.out_dim = out_dim
+        self.final_out_dim = final_out_dim
+        self.lr = lr
+        self.batch_size = batch_size
+        self.optimizer = optimizer.lower()
+        self.pretrain_loss = pretrain_loss
+        self.seed = seed
+        self.ps = ps
+        self.n_epochs = n_epochs
+        self.logger = get_module_logger("TabNet")
+        self.pretrain_n_epochs = pretrain_n_epochs
+        self.device = "cuda:%s" % (GPU) if torch.cuda.is_available() else "cpu"
+        self.loss = loss
+        self.metric = metric
+        self.early_stop = early_stop
+        self.pretrain = pretrain
+        self.pretrain_file = pretrain_file
+        self.logger.info(
+            "TabNet:"
+            "\nbatch_size : {}"
+            "\nvirtual bs : {}"
+            "\nGPU : {}"
+            "\npretrain: {}".format(self.batch_size, vbs, GPU, pretrain)
+        )
+        np.random.seed(self.seed)
+        torch.manual_seed(self.seed)
+
+        self.tabnet_model = TabNet(
+            inp_dim=self.d_feat, out_dim=self.out_dim, vbs=vbs, relax=relax, device=self.device
+        ).to(self.device)
+        self.tabnet_decoder = TabNet_Decoder(self.out_dim, self.d_feat, n_shared, n_ind, vbs, n_steps, self.device).to(
+            self.device
+        )
+
+        if optimizer.lower() == "adam":
+            self.pretrain_optimizer = optim.Adam(
+                list(self.tabnet_model.parameters()) + list(self.tabnet_decoder.parameters()), lr=self.lr
+            )
+            self.train_optimizer = optim.Adam(self.tabnet_model.parameters(), lr=self.lr)
+
+        elif optimizer.lower() == "gd":
+            self.pretrain_optimizer = optim.SGD(
+                list(self.tabnet_model.parameters()) + list(self.tabnet_decoder.parameters()), lr=self.lr
+            )
+            self.train_optimizer = optim.SGD(self.tabnet_model.parameters(), lr=self.lr)
+        else:
+            raise NotImplementedError("optimizer {} is not supported!".format(optimizer))
+
+    def pretrain_fn(self, dataset=DatasetH, pretrain_file="./pretrain/best.model"):
+        # make a directory if pretrian director does not exist
+        if pretrain_file.startswith("./pretrain") and not os.path.exists("pretrain"):
+            self.logger.info("make folder to store model...")
+            os.makedirs("pretrain")
+
+        [df_train, df_valid] = dataset.prepare(
+            ["pretrain", "pretrain_validation"],
+            col_set=["feature", "label"],
+            data_key=DataHandlerLP.DK_L,
+        )
+
+        df_train.fillna(df_train.mean(), inplace=True)
+        df_valid.fillna(df_valid.mean(), inplace=True)
+
+        x_train = df_train["feature"]
+        x_valid = df_valid["feature"]
+
+        # Early stop setup
+        stop_steps = 0
+        train_loss = 0
+        best_loss = np.inf
+
+        for epoch_idx in range(self.pretrain_n_epochs):
+            self.logger.info("epoch: %s" % (epoch_idx))
+            self.logger.info("pre-training...")
+            self.pretrain_epoch(x_train)
+            self.logger.info("evaluating...")
+            train_loss = self.pretrain_test_epoch(x_train)
+            valid_loss = self.pretrain_test_epoch(x_valid)
+            self.logger.info("train %.6f, valid %.6f" % (train_loss, valid_loss))
+
+            if valid_loss < best_loss:
+                self.logger.info("Save Model...")
+                torch.save(self.tabnet_model.state_dict(), pretrain_file)
+                best_loss = valid_loss
+            else:
+                stop_steps += 1
+                if stop_steps >= self.early_stop:
+                    self.logger.info("early stop")
+                    break
+
+    def fit(
+        self,
+        dataset: DatasetH,
+        evals_result=dict(),
+        verbose=True,
+        save_path=None,
+    ):
+        if self.pretrain:
+            # there is a  pretrained model, load the model
+            self.logger.info("Pretrain...")
+            self.pretrain_fn(dataset, self.pretrain_file)
+            self.logger.info("Load Pretrain model")
+            self.tabnet_model.load_state_dict(torch.load(self.pretrain_file))
+
+        # adding one more linear layer to fit the final output dimension
+        self.tabnet_model = FinetuneModel(self.out_dim, self.final_out_dim, self.tabnet_model).to(self.device)
+        df_train, df_valid = dataset.prepare(
+            ["train", "valid"],
+            col_set=["feature", "label"],
+            data_key=DataHandlerLP.DK_L,
+        )
+        df_train.fillna(df_train.mean(), inplace=True)
+        x_train, y_train = df_train["feature"], df_train["label"]
+        x_valid, y_valid = df_valid["feature"], df_valid["label"]
+
+        stop_steps = 0
+        train_loss = 0
+        best_score = np.inf
+        best_epoch = 0
+        evals_result["train"] = []
+        evals_result["valid"] = []
+
+        self.logger.info("training...")
+        self._fitted = True
+
+        for epoch_idx in range(self.n_epochs):
+            self.logger.info("epoch: %s" % (epoch_idx))
+            self.logger.info("training...")
+            self.train_epoch(x_train, y_train)
+            self.logger.info("evaluating...")
+            train_loss, train_score = self.test_epoch(x_train, y_train)
+            valid_loss, val_score = self.test_epoch(x_valid, y_valid)
+            self.logger.info("train %.6f, valid %.6f" % (train_score, val_score))
+            evals_result["train"].append(train_score)
+            evals_result["valid"].append(val_score)
+
+            if val_score < best_score:
+                best_score = val_score
+                stop_steps = 0
+                best_epoch = epoch_idx
+            else:
+                stop_steps += 1
+                if stop_steps >= self.early_stop:
+                    self.logger.info("early stop")
+                    break
+        self.logger.info("best score: %.6lf @ %d" % (best_score, best_epoch))
+
+    def predict(self, dataset):
+        if not self._fitted:
+            raise ValueError("model is not fitted yet!")
+
+        x_test = dataset.prepare("test", col_set="feature", data_key=DataHandlerLP.DK_I)
+        index = x_test.index
+        self.tabnet_model.eval()
+        x_values = torch.from_numpy(x_test.values)
+        x_values[torch.isnan(x_values)] = 0
+        sample_num = x_values.shape[0]
+        preds = []
+
+        for begin in range(sample_num)[:: self.batch_size]:
+            if sample_num - begin < self.batch_size:
+                end = sample_num
+            else:
+                end = begin + self.batch_size
+
+            x_batch = x_values[begin:end].float().to(self.device)
+            priors = torch.ones(end - begin, self.d_feat).to(self.device)
+
+            with torch.no_grad():
+                pred = self.tabnet_model(x_batch, priors).detach().cpu().numpy()
+
+            preds.append(pred)
+
+        return pd.Series(np.concatenate(preds), index=index)
+
+    def test_epoch(self, data_x, data_y):
+        # prepare training data
+        x_values = torch.from_numpy(data_x.values)
+        y_values = torch.from_numpy(np.squeeze(data_y.values))
+        x_values[torch.isnan(x_values)] = 0
+        y_values[torch.isnan(y_values)] = 0
+        self.tabnet_model.eval()
+
+        scores = []
+        losses = []
+
+        indices = np.arange(len(x_values))
+
+        for i in range(len(indices))[:: self.batch_size]:
+
+            if len(indices) - i < self.batch_size:
+                break
+            feature = x_values[indices[i : i + self.batch_size]].float().to(self.device)
+            label = y_values[indices[i : i + self.batch_size]].float().to(self.device)
+            priors = torch.ones(self.batch_size, self.d_feat).to(self.device)
+            pred = self.tabnet_model(feature, priors)
+            loss = self.loss_fn(pred, label)
+            losses.append(loss.item())
+
+            score = self.metric_fn(pred, label)
+            scores.append(score.item())
+
+        return np.mean(losses), np.mean(scores)
+
+    def train_epoch(self, x_train, y_train):
+        x_train_values = torch.from_numpy(x_train.values)
+        y_train_values = torch.from_numpy(np.squeeze(y_train.values))
+        x_train_values[torch.isnan(x_train_values)] = 0
+        y_train_values[torch.isnan(y_train_values)] = 0
+        self.tabnet_model.train()
+
+        indices = np.arange(len(x_train_values))
+        np.random.shuffle(indices)
+
+        for i in range(len(indices))[:: self.batch_size]:
+
+            if len(indices) - i < self.batch_size:
+                break
+
+            feature = x_train_values[indices[i : i + self.batch_size]].float().to(self.device)
+            label = y_train_values[indices[i : i + self.batch_size]].float().to(self.device)
+            priors = torch.ones(self.batch_size, self.d_feat).to(self.device)
+            pred = self.tabnet_model(feature, priors)
+            loss = self.loss_fn(pred, label)
+
+            self.train_optimizer.zero_grad()
+            loss.backward()
+            torch.nn.utils.clip_grad_value_(self.tabnet_model.parameters(), 3.0)
+            self.train_optimizer.step()
+
+    def pretrain_epoch(self, x_train):
+        train_set = torch.from_numpy(x_train.values)
+        train_set[torch.isnan(train_set)] = 0
+        indices = np.arange(len(train_set))
+        np.random.shuffle(indices)
+
+        self.tabnet_model.train()
+        self.tabnet_decoder.train()
+
+        for i in range(len(indices))[:: self.batch_size]:
+
+            if len(indices) - i < self.batch_size:
+                break
+
+            S_mask = torch.bernoulli(torch.empty(self.batch_size, self.d_feat).fill_(self.ps))
+            x_train_values = train_set[indices[i : i + self.batch_size]] * (1 - S_mask)
+            y_train_values = train_set[indices[i : i + self.batch_size]] * (S_mask)
+
+            S_mask = S_mask.to(self.device)
+            feature = x_train_values.float().to(self.device)
+            label = y_train_values.float().to(self.device)
+            priors = 1 - S_mask
+            (vec, sparse_loss) = self.tabnet_model(feature, priors)
+            f = self.tabnet_decoder(vec)
+            loss = self.pretrain_loss_fn(label, f, S_mask)
+
+            self.pretrain_optimizer.zero_grad()
+            loss.backward()
+            self.pretrain_optimizer.step()
+
+    def pretrain_test_epoch(self, x_train):
+        train_set = torch.from_numpy(x_train.values)
+        train_set[torch.isnan(train_set)] = 0
+        indices = np.arange(len(train_set))
+
+        self.tabnet_model.eval()
+        self.tabnet_decoder.eval()
+
+        losses = []
+
+        for i in range(len(indices))[:: self.batch_size]:
+
+            if len(indices) - i < self.batch_size:
+                break
+
+            S_mask = torch.bernoulli(torch.empty(self.batch_size, self.d_feat).fill_(self.ps))
+            x_train_values = train_set[indices[i : i + self.batch_size]] * (1 - S_mask)
+            y_train_values = train_set[indices[i : i + self.batch_size]] * (S_mask)
+
+            feature = x_train_values.float().to(self.device)
+            label = y_train_values.float().to(self.device)
+            S_mask = S_mask.to(self.device)
+            priors = 1 - S_mask
+            (vec, sparse_loss) = self.tabnet_model(feature, priors)
+            f = self.tabnet_decoder(vec)
+
+            loss = self.pretrain_loss_fn(label, f, S_mask)
+            losses.append(loss.item())
+
+        return np.mean(losses)
+
+    def pretrain_loss_fn(self, f_hat, f, S):
+        """
+        Pretrain loss function defined in the original paper, read "Tabular self-supervised learning" in https://arxiv.org/pdf/1908.07442.pdf
+        """
+        down_mean = torch.mean(f, dim=0)
+        down = torch.sqrt(torch.sum(torch.square(f - down_mean), dim=0))
+        up = (f_hat - f) * S
+        return torch.sum(torch.square(up / down))
+
+    def loss_fn(self, pred, label):
+        mask = ~torch.isnan(label)
+        if self.loss == "mse":
+            return self.mse(pred[mask], label[mask])
+        raise ValueError("unknown loss `%s`" % self.loss)
+
+    def metric_fn(self, pred, label):
+        mask = torch.isfinite(label)
+        if self.metric == "" or self.metric == "loss":
+            return -self.loss_fn(pred[mask], label[mask])
+        raise ValueError("unknown metric `%s`" % self.metric)
+
+    def mse(self, pred, label):
+        loss = (pred - label) ** 2
+        return torch.mean(loss)
+
+
+class FinetuneModel(nn.Module):
+    """
+    FinuetuneModel for adding a layer by the end
+    """
+
+    def __init__(self, input_dim, output_dim, trained_model):
+        super().__init__()
+        self.model = trained_model
+        self.fc = nn.Linear(input_dim, output_dim)
+
+    def forward(self, x, priors):
+        return self.fc(self.model(x, priors)[0]).squeeze()  # take the vec out
+
+
+class DecoderStep(nn.Module):
+    def __init__(self, inp_dim, out_dim, shared, n_ind, vbs, device):
+        super().__init__()
+        self.fea_tran = FeatureTransformer(inp_dim, out_dim, shared, n_ind, vbs, device)
+        self.fc = nn.Linear(out_dim, out_dim)
+
+    def forward(self, x):
+        x = self.fea_tran(x)
+        return self.fc(x)
+
+
+class TabNet_Decoder(nn.Module):
+    def __init__(self, inp_dim, out_dim, n_shared, n_ind, vbs, n_steps, device):
+        """
+        TabNet decoder that is used in pre-training
+        """
+        self.out_dim = out_dim
+
+        super().__init__()
+        if n_shared > 0:
+            self.shared = nn.ModuleList()
+            self.shared.append(nn.Linear(inp_dim, 2 * out_dim))
+            for x in range(n_shared - 1):
+                self.shared.append(nn.Linear(out_dim, 2 * out_dim))  # preset the linear function we will use
+        else:
+            self.shared = None
+        self.n_steps = n_steps
+        self.steps = nn.ModuleList()
+        for x in range(n_steps):
+            self.steps.append(DecoderStep(inp_dim, out_dim, self.shared, n_ind, vbs, device))
+
+    def forward(self, x):
+        out = torch.zeros(x.size(0), self.out_dim).to(x.device)
+        for step in self.steps:
+            out += step(x)
+        return out
+
+
+class TabNet(nn.Module):
+    def __init__(
+        self, inp_dim=6, out_dim=6, n_d=64, n_a=64, n_shared=2, n_ind=2, n_steps=5, relax=1.2, vbs=1024, device="cpu"
+    ):
+        """
+        TabNet AKA the original encoder
+
+        Args:
+            n_d: dimension of the features used to calculate the final results
+            n_a: dimension of the features input to the attention transformer of the next step
+            n_shared: numbr of shared steps in feature transfomer(optional)
+            n_ind: number of independent steps in feature transformer
+            n_steps: number of steps of pass through tabbet
+            relax coefficient:
+            virtual batch size:
+        """
+        super().__init__()
+
+        # set the number of shared step in feature transformer
+        if n_shared > 0:
+            self.shared = nn.ModuleList()
+            self.shared.append(nn.Linear(inp_dim, 2 * (n_d + n_a)))
+            for x in range(n_shared - 1):
+                self.shared.append(nn.Linear(n_d + n_a, 2 * (n_d + n_a)))  # preset the linear function we will use
+        else:
+            self.shared = None
+
+        self.first_step = FeatureTransformer(inp_dim, n_d + n_a, self.shared, n_ind, vbs, device)
+        self.steps = nn.ModuleList()
+        for x in range(n_steps - 1):
+            self.steps.append(DecisionStep(inp_dim, n_d, n_a, self.shared, n_ind, relax, vbs, device))
+        self.fc = nn.Linear(n_d, out_dim)
+        self.bn = nn.BatchNorm1d(inp_dim, momentum=0.01)
+        self.n_d = n_d
+
+    def forward(self, x, priors):
+        assert not torch.isnan(x).any()
+        x = self.bn(x)
+        x_a = self.first_step(x)[:, self.n_d :]
+        sparse_loss = torch.zeros(1).to(x.device)
+        out = torch.zeros(x.size(0), self.n_d).to(x.device)
+        for step in self.steps:
+            x_te, l = step(x, x_a, priors)
+            out += F.relu(x_te[:, : self.n_d])  # split the feautre from feat_transformer
+            x_a = x_te[:, self.n_d :]
+            sparse_loss += l
+        return self.fc(out), sparse_loss
+
+
+class GBN(nn.Module):
+    """
+    Ghost Batch Normalization
+    an efficient way of doing batch normalization
+
+    Args:
+        vbs: virtual batch size
+    """
+
+    def __init__(self, inp, vbs=1024, momentum=0.01):
+        super().__init__()
+        self.bn = nn.BatchNorm1d(inp, momentum=momentum)
+        self.vbs = vbs
+
+    def forward(self, x):
+        chunk = torch.chunk(x, x.size(0) // self.vbs, 0)
+        res = [self.bn(y) for y in chunk]
+        return torch.cat(res, 0)
+
+
+class GLU(nn.Module):
+    """
+    GLU block that extracts only the most essential information
+
+    Args:
+        vbs: virtual batch size
+    """
+
+    def __init__(self, inp_dim, out_dim, fc=None, vbs=1024):
+        super().__init__()
+        if fc:
+            self.fc = fc
+        else:
+            self.fc = nn.Linear(inp_dim, out_dim * 2)
+        self.bn = GBN(out_dim * 2, vbs=vbs)
+        self.od = out_dim
+
+    def forward(self, x):
+        x = self.bn(self.fc(x))
+        return torch.mul(x[:, : self.od], torch.sigmoid(x[:, self.od :]))
+
+
+class AttentionTransformer(nn.Module):
+    """
+    Args:
+        relax: relax coefficient. The greater it is, we can
+        use the same features more. When it is set to 1
+        we can use every feature only once
+    """
+
+    def __init__(self, d_a, inp_dim, relax, vbs=1024):
+        super().__init__()
+        self.fc = nn.Linear(d_a, inp_dim)
+        self.bn = GBN(inp_dim, vbs=vbs)
+        self.r = relax
+
+    # a:feature from previous decision step
+    def forward(self, a, priors):
+        a = self.bn(self.fc(a))
+        mask = SparsemaxFunction.apply(a * priors)
+        priors = priors * (self.r - mask)  # updating the prior
+        return mask
+
+
+class FeatureTransformer(nn.Module):
+    def __init__(self, inp_dim, out_dim, shared, n_ind, vbs, device):
+        super().__init__()
+        first = True
+        self.shared = nn.ModuleList()
+        if shared:
+            self.shared.append(GLU(inp_dim, out_dim, shared[0], vbs=vbs))
+            first = False
+            for fc in shared[1:]:
+                self.shared.append(GLU(out_dim, out_dim, fc, vbs=vbs))
+        else:
+            self.shared = None
+        self.independ = nn.ModuleList()
+        if first:
+            self.independ.append(GLU(inp, out_dim, vbs=vbs))
+        for x in range(first, n_ind):
+            self.independ.append(GLU(out_dim, out_dim, vbs=vbs))
+        self.scale = torch.sqrt(torch.tensor([0.5], device=device))
+
+    def forward(self, x):
+        if self.shared:
+            x = self.shared[0](x)
+            for glu in self.shared[1:]:
+                x = torch.add(x, glu(x))
+                x = x * self.scale
+        for glu in self.independ:
+            x = torch.add(x, glu(x))
+            x = x * self.scale
+        return x
+
+
+class DecisionStep(nn.Module):
+    """
+    One step for the TabNet
+    """
+
+    def __init__(self, inp_dim, n_d, n_a, shared, n_ind, relax, vbs, device):
+        super().__init__()
+        self.atten_tran = AttentionTransformer(n_a, inp_dim, relax, vbs)
+        self.fea_tran = FeatureTransformer(inp_dim, n_d + n_a, shared, n_ind, vbs, device)
+
+    def forward(self, x, a, priors):
+        mask = self.atten_tran(a, priors)
+        sparse_loss = ((-1) * mask * torch.log(mask + 1e-10)).mean()
+        x = self.fea_tran(x * mask)
+        return x, sparse_loss
+
+
+def make_ix_like(input, dim=0):
+    d = input.size(dim)
+    rho = torch.arange(1, d + 1, device=input.device, dtype=input.dtype)
+    view = [1] * input.dim()
+    view[0] = -1
+    return rho.view(view).transpose(0, dim)
+
+
+class SparsemaxFunction(Function):
+    """
+    SparseMax function for replacing reLU
+    """
+
+    @staticmethod
+    def forward(ctx, input, dim=-1):
+        ctx.dim = dim
+        max_val, _ = input.max(dim=dim, keepdim=True)
+        input -= max_val  # same numerical stability trick as for softmax
+        tau, supp_size = SparsemaxFunction.threshold_and_support(input, dim=dim)
+        output = torch.clamp(input - tau, min=0)
+        ctx.save_for_backward(supp_size, output)
+        return output
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        supp_size, output = ctx.saved_tensors
+        dim = ctx.dim
+        grad_input = grad_output.clone()
+        grad_input[output == 0] = 0
+
+        v_hat = grad_input.sum(dim=dim) / supp_size.to(output.dtype).squeeze()
+        v_hat = v_hat.unsqueeze(dim)
+        grad_input = torch.where(output != 0, grad_input - v_hat, grad_input)
+        return grad_input, None
+
+    @staticmethod
+    def threshold_and_support(input, dim=-1):
+        input_srt, _ = torch.sort(input, descending=True, dim=dim)
+        input_cumsum = input_srt.cumsum(dim) - 1
+        rhos = make_ix_like(input, dim)
+        support = rhos * input_srt > input_cumsum
+
+        support_size = support.sum(dim=dim).unsqueeze(dim)
+        tau = input_cumsum.gather(dim, support_size - 1)
+        tau /= support_size.to(input.dtype)
+        return tau, support_size

qlib/contrib/online/operator.py

@@ -21,7 +21,7 @@ from .executor import SimulatorExecutor
 from .executor import save_score_series, load_score_series
 
 
-class Operator(object):
+class Operator:
     def __init__(self, client: str):
         """
         Parameters

qlib/contrib/report/analysis_position/report.py

@@ -38,7 +38,7 @@ def _calculate_report_data(df: pd.DataFrame) -> pd.DataFrame:
     :param df:
     :return:
     """
-
+    index_names = df.index.names
     df.index = df.index.strftime("%Y-%m-%d")
 
     report_df = pd.DataFrame()
@@ -58,6 +58,8 @@ def _calculate_report_data(df: pd.DataFrame) -> pd.DataFrame:
 
     report_df["turnover"] = df["turnover"]
     report_df.sort_index(ascending=True, inplace=True)
+
+    report_df.index.names = index_names
     return report_df
 
 

qlib/contrib/report/graph.py

@@ -17,7 +17,7 @@ from plotly.figure_factory import create_distplot
 from ...utils import get_module_by_module_path
 
 
-class BaseGraph(object):
+class BaseGraph:
     """"""
 
     _name = None
@@ -204,7 +204,7 @@ class HistogramGraph(BaseGraph):
         return _data
 
 
-class SubplotsGraph(object):
+class SubplotsGraph:
     """Create subplots same as df.plot(subplots=True)
 
     Simple package for `plotly.tools.subplots`

qlib/contrib/strategy/strategy.py

@@ -30,7 +30,7 @@ class BaseStrategy:
 
         Parameters
         -----------
-        score_series : pd.Seires
+        score_series : pd.Series
             stock_id , score.
         current : Position()
             current state of position.

qlib/contrib/tuner/config.py

@@ -6,7 +6,7 @@ import copy
 import os
 
 
-class TunerConfigManager(object):
+class TunerConfigManager:
     def __init__(self, config_path):
 
         if not config_path:
@@ -27,7 +27,7 @@ class TunerConfigManager(object):
         self.qlib_client_config = config.get("qlib_client", dict())
 
 
-class PipelineExperimentConfig(object):
+class PipelineExperimentConfig:
     def __init__(self, config, TUNER_CONFIG_MANAGER):
         """
         :param config:  The config dict for tuner experiment
@@ -53,7 +53,7 @@ class PipelineExperimentConfig(object):
             yaml.dump(TUNER_CONFIG_MANAGER.config, fp)
 
 
-class OptimizationConfig(object):
+class OptimizationConfig:
     def __init__(self, config, TUNER_CONFIG_MANAGER):
 
         self.report_type = config.get("report_type", "pred_long")

qlib/contrib/tuner/pipeline.py

@@ -11,7 +11,7 @@ from ...log import get_module_logger, TimeInspector
 from ...utils import get_module_by_module_path
 
 
-class Pipeline(object):
+class Pipeline:
 
     GLOBAL_BEST_PARAMS_NAME = "global_best_params.json"
 

qlib/contrib/tuner/tuner.py

@@ -19,7 +19,7 @@ from hyperopt import fmin, tpe
 from hyperopt import STATUS_OK, STATUS_FAIL
 
 
-class Tuner(object):
+class Tuner:
     def __init__(self, tuner_config, optim_config):
 
         self.logger = get_module_logger("Tuner", sh_level=logging.INFO)

qlib/data/_libs/expanding.pyx

@@ -8,7 +8,7 @@ from libc.math cimport sqrt, isnan, NAN
 from libcpp.vector cimport vector
 
 
-cdef class Expanding(object):
+cdef class Expanding:
     """1-D array expanding"""
     cdef vector[double] barv
     cdef int na_count

qlib/data/_libs/rolling.pyx

@@ -8,7 +8,7 @@ from libc.math cimport sqrt, isnan, NAN
 from libcpp.deque cimport deque
 
 
-cdef class Rolling(object):
+cdef class Rolling:
     """1-D array rolling"""
     cdef int window
     cdef deque[double] barv

qlib/data/base.py

@@ -157,7 +157,7 @@ class Expression(abc.ABC):
 
     @abc.abstractmethod
     def _load_internal(self, instrument, start_index, end_index, freq):
-        pass
+        raise NotImplementedError("This function must be implemented in your newly defined feature")
 
     @abc.abstractmethod
     def get_longest_back_rolling(self):

qlib/data/cache.py

@@ -13,6 +13,7 @@ import pickle
 import traceback
 import redis_lock
 import contextlib
+import abc
 from pathlib import Path
 import numpy as np
 import pandas as pd
@@ -32,43 +33,107 @@ from ..utils import (
 from ..log import get_module_logger
 from .base import Feature
 
-from .ops import *
+from .ops import Operators
 
 
 class QlibCacheException(RuntimeError):
     pass
 
 
-class MemCacheUnit(OrderedDict):
+class MemCacheUnit(abc.ABC):
     """Memory Cache Unit."""
 
-    # TODO: use min_heap to replace ordereddict for better performance
-
     def __init__(self, *args, **kwargs):
-        self.size_limit = kwargs.pop("size_limit", None)
-        # limit_type: check size_limit type, length(call fun: len) or size(call fun: sys.getsizeof)
-        self.limit_type = kwargs.pop("limit_type", "length")
-        super(MemCacheUnit, self).__init__(*args, **kwargs)
-        self._check_size_limit()
+        self.size_limit = kwargs.pop("size_limit", 0)
+        self._size = 0
+        self.od = OrderedDict()
 
     def __setitem__(self, key, value):
-        super(MemCacheUnit, self).__setitem__(key, value)
-        self._check_size_limit()
+        # TODO: thread safe?__setitem__ failure might cause inconsistent size?
 
-    def __getitem__(self, key):
-        value = super(MemCacheUnit, self).__getitem__(key)
-        super(MemCacheUnit, self).__delitem__(key)
-        super(MemCacheUnit, self).__setitem__(key, value)
-        return value
+        # precalculate the size after od.__setitem__
+        self._adjust_size(key, value)
 
-    def _check_size_limit(self):
-        if self.size_limit is not None:
-            get_cur_size = lambda x: len(x) if self.limit_type == "length" else sum(map(sys.getsizeof, x.values()))
-            while get_cur_size(self) > self.size_limit:
+        self.od.__setitem__(key, value)
+
+        # move the key to end,make it latest
+        self.od.move_to_end(key)
+
+        if self.limited:
+            # pop the oldest items beyond size limit
+            while self._size > self.size_limit:
                 self.popitem(last=False)
 
+    def __getitem__(self, key):
+        v = self.od.__getitem__(key)
+        self.od.move_to_end(key)
+        return v
 
-class MemCache(object):
+    def __contains__(self, key):
+        return key in self.od
+
+    def __len__(self):
+        return self.od.__len__()
+
+    def __repr__(self):
+        return f"{self.__class__.__name__}<size_limit:{self.size_limit if self.limited else 'no limit'} total_size:{self._size}>\n{self.od.__repr__()}"
+
+    def set_limit_size(self, limit):
+        self.size_limit = limit
+
+    @property
+    def limited(self):
+        """whether memory cache is limited"""
+        return self.size_limit > 0
+
+    @property
+    def total_size(self):
+        return self._size
+
+    def clear(self):
+        self._size = 0
+        self.od.clear()
+
+    def popitem(self, last=True):
+        k, v = self.od.popitem(last=last)
+        self._size -= self._get_value_size(v)
+
+        return k, v
+
+    def pop(self, key):
+        v = self.od.pop(key)
+        self._size -= self._get_value_size(v)
+
+        return v
+
+    def _adjust_size(self, key, value):
+        if key in self.od:
+            self._size -= self._get_value_size(self.od[key])
+
+        self._size += self._get_value_size(value)
+
+    @abc.abstractmethod
+    def _get_value_size(self, value):
+        raise NotImplementedError
+
+
+class MemCacheLengthUnit(MemCacheUnit):
+    def __init__(self, size_limit=0):
+        super().__init__(size_limit=size_limit)
+
+    def _get_value_size(self, value):
+        return 1
+
+
+class MemCacheSizeofUnit(MemCacheUnit):
+    def __init__(self, size_limit=0):
+        super().__init__(size_limit=size_limit)
+
+    def _get_value_size(self, value):
+        return sys.getsizeof(value)
+
+
+class MemCache:
     """Memory cache."""
 
     def __init__(self, mem_cache_size_limit=None, limit_type="length"):
@@ -79,21 +144,19 @@ class MemCache(object):
         mem_cache_size_limit: cache max size.
         limit_type: length or sizeof; length(call fun: len), size(call fun: sys.getsizeof).
         """
-        if limit_type not in ["length", "sizeof"]:
+
+        size_limit = C.mem_cache_size_limit if mem_cache_size_limit is None else mem_cache_size_limit
+
+        if limit_type == "length":
+            klass = MemCacheLengthUnit
+        elif limit_type == "sizeof":
+            klass = MemCacheSizeofUnit
+        else:
             raise ValueError(f"limit_type must be length or sizeof, your limit_type is {limit_type}")
 
-        self.__calendar_mem_cache = MemCacheUnit(
-            size_limit=C.mem_cache_size_limit if mem_cache_size_limit is None else mem_cache_size_limit,
-            limit_type=limit_type,
-        )
-        self.__instrument_mem_cache = MemCacheUnit(
-            size_limit=C.mem_cache_size_limit if mem_cache_size_limit is None else mem_cache_size_limit,
-            limit_type=limit_type,
-        )
-        self.__feature_mem_cache = MemCacheUnit(
-            size_limit=C.mem_cache_size_limit if mem_cache_size_limit is None else mem_cache_size_limit,
-            limit_type=limit_type,
-        )
+        self.__calendar_mem_cache = klass(size_limit)
+        self.__instrument_mem_cache = klass(size_limit)
+        self.__feature_mem_cache = klass(size_limit)
 
     def __getitem__(self, key):
         if key == "c":
@@ -140,7 +203,7 @@ class MemCacheExpire:
         return value, expire
 
 
-class CacheUtils(object):
+class CacheUtils:
     LOCK_ID = "QLIB"
 
     @staticmethod
@@ -224,7 +287,7 @@ class CacheUtils(object):
             current_cache_wlock.release()
 
 
-class BaseProviderCache(object):
+class BaseProviderCache:
     """Provider cache base class"""
 
     def __init__(self, provider):
@@ -762,8 +825,8 @@ class DiskDatasetCache(DatasetCache):
 
             .. note:: The start is closed. The end is open!!!!!
 
-            - Each line contains two element <timestamp, end_index>
-            - It indicates the `end_index` of the data for `timestamp`
+            - Each line contains two element <start_index, end_index> with a timestamp as its index.
+            - It indicates the `start_index`(included) and `end_index`(excluded) of the data for `timestamp`
 
         - meta data: cache/d41366901e25de3ec47297f12e2ba11d.meta
 

qlib/data/client.py

@@ -12,7 +12,7 @@ from ..log import get_module_logger
 import pickle
 
 
-class Client(object):
+class Client:
     """A client class
 
     Provide the connection tool functions for ClientProvider.

qlib/data/data.py

@@ -15,14 +15,13 @@ import importlib
 import traceback
 import numpy as np
 import pandas as pd
-from pathlib import Path
 from multiprocessing import Pool
 
 from .cache import H
 from ..config import C
-from .ops import *
+from .ops import Operators
 from ..log import get_module_logger
-from ..utils import parse_field, read_bin, hash_args, normalize_cache_fields
+from ..utils import parse_field, read_bin, hash_args, normalize_cache_fields, code_to_fname
 from .base import Feature
 from .cache import DiskDatasetCache, DiskExpressionCache
 from ..utils import Wrapper, init_instance_by_config, register_wrapper, get_module_by_module_path
@@ -118,7 +117,7 @@ class CalendarProvider(abc.ABC):
         if flag in H["c"]:
             _calendar, _calendar_index = H["c"][flag]
         else:
-            _calendar = np.array(self._load_calendar(freq, future))
+            _calendar = np.array(self.load_calendar(freq, future))
             _calendar_index = {x: i for i, x in enumerate(_calendar)}  # for fast search
             H["c"][flag] = _calendar, _calendar_index
         return _calendar, _calendar_index
@@ -215,20 +214,6 @@ class InstrumentProvider(abc.ABC):
             return cls.LIST
         raise ValueError(f"Unknown instrument type {inst}")
 
-    def convert_instruments(self, instrument):
-        _instruments_map = getattr(self, "_instruments_map", None)
-        if _instruments_map is None:
-            _df_list = []
-            # FIXME: each process will read these files
-            for _path in Path(C.get_data_path()).joinpath("instruments").glob("*.txt"):
-                _df = pd.read_csv(_path, sep="\t", names=["inst", "start_datetime", "end_datetime", "save_inst"])
-                _df_list.append(_df.iloc[:, [0, -1]])
-            df = pd.concat(_df_list, sort=False).sort_values("save_inst")
-            df = df.drop_duplicates(subset=["save_inst"], keep="first").fillna(axis=1, method="ffill")
-            _instruments_map = df.set_index("inst").iloc[:, 0].to_dict()
-            setattr(self, "_instruments_map", _instruments_map)
-        return _instruments_map.get(instrument, instrument)
-
 
 class FeatureProvider(abc.ABC):
     """Feature provider class
@@ -481,11 +466,10 @@ class DatasetProvider(abc.ABC):
 
         """
         # FIXME: Windows OS or MacOS using spawn: https://docs.python.org/3.8/library/multiprocessing.html?highlight=spawn#contexts-and-start-methods
-        global C
-        C = g_config
         # NOTE: This place is compatible with windows, windows multi-process is spawn
-        if getattr(ExpressionD, "_provider", None) is None:
-            register_all_wrappers()
+        if not C.registered:
+            C.set_conf_from_C(g_config)
+            C.register()
 
         obj = dict()
         for field in column_names:
@@ -520,7 +504,7 @@ class LocalCalendarProvider(CalendarProvider):
         """Calendar file uri."""
         return os.path.join(C.get_data_path(), "calendars", "{}.txt")
 
-    def _load_calendar(self, freq, future):
+    def load_calendar(self, freq, future):
         """Load original calendar timestamp from file.
 
         Parameters
@@ -587,10 +571,16 @@ class LocalInstrumentProvider(InstrumentProvider):
         fname = self._uri_inst.format(market)
         if not os.path.exists(fname):
             raise ValueError("instruments not exists for market " + market)
+
         _instruments = dict()
-        df = pd.read_csv(fname, sep="\t", names=["inst", "start_datetime", "end_datetime", "save_inst"])
-        df["start_datetime"] = pd.to_datetime(df["start_datetime"])
-        df["end_datetime"] = pd.to_datetime(df["end_datetime"])
+        df = pd.read_csv(
+            fname,
+            sep="\t",
+            usecols=[0, 1, 2],
+            names=["inst", "start_datetime", "end_datetime"],
+            dtype={"inst": str},
+            parse_dates=["start_datetime", "end_datetime"],
+        )
         for row in df.itertuples(index=False):
             _instruments.setdefault(row[0], []).append((row[1], row[2]))
         return _instruments
@@ -647,7 +637,7 @@ class LocalFeatureProvider(FeatureProvider):
     def feature(self, instrument, field, start_index, end_index, freq):
         # validate
         field = str(field).lower()[1:]
-        instrument = Inst.convert_instruments(instrument)
+        instrument = code_to_fname(instrument)
         uri_data = self._uri_data.format(instrument.lower(), field, freq)
         if not os.path.exists(uri_data):
             get_module_logger("data").warning("WARN: data not found for %s.%s" % (instrument, field))
@@ -682,6 +672,8 @@ class LocalExpressionProvider(ExpressionProvider):
             series = series.astype(np.float32)
         except ValueError:
             pass
+        except TypeError:
+            pass
         if not series.empty:
             series = series.loc[start_index:end_index]
         return series
@@ -969,8 +961,7 @@ class BaseProvider:
         is a provider class.
         """
         disk_cache = C.default_disk_cache if disk_cache is None else disk_cache
-        if C.disable_disk_cache:
-            disk_cache = False
+        fields = list(fields)  # In case of tuple.
         try:
             return DatasetD.dataset(instruments, fields, start_time, end_time, freq, disk_cache)
         except TypeError:
@@ -1035,15 +1026,34 @@ class ClientProvider(BaseProvider):
             DatasetD.set_conn(self.client)
 
 
-Cal = Wrapper()
-Inst = Wrapper()
-FeatureD = Wrapper()
-ExpressionD = Wrapper()
-DatasetD = Wrapper()
-D = Wrapper()
+import sys
+
+if sys.version_info >= (3, 9):
+    from typing import Annotated
+
+    CalendarProviderWrapper = Annotated[CalendarProvider, Wrapper]
+    InstrumentProviderWrapper = Annotated[InstrumentProvider, Wrapper]
+    FeatureProviderWrapper = Annotated[FeatureProvider, Wrapper]
+    ExpressionProviderWrapper = Annotated[ExpressionProvider, Wrapper]
+    DatasetProviderWrapper = Annotated[DatasetProvider, Wrapper]
+    BaseProviderWrapper = Annotated[BaseProvider, Wrapper]
+else:
+    CalendarProviderWrapper = CalendarProvider
+    InstrumentProviderWrapper = InstrumentProvider
+    FeatureProviderWrapper = FeatureProvider
+    ExpressionProviderWrapper = ExpressionProvider
+    DatasetProviderWrapper = DatasetProvider
+    BaseProviderWrapper = BaseProvider
+
+Cal: CalendarProviderWrapper = Wrapper()
+Inst: InstrumentProviderWrapper = Wrapper()
+FeatureD: FeatureProviderWrapper = Wrapper()
+ExpressionD: ExpressionProviderWrapper = Wrapper()
+DatasetD: DatasetProviderWrapper = Wrapper()
+D: BaseProviderWrapper = Wrapper()
 
 
-def register_all_wrappers():
+def register_all_wrappers(C):
     """register_all_wrappers"""
     logger = get_module_logger("data")
     module = get_module_by_module_path("qlib.data")
@@ -1052,7 +1062,7 @@ def register_all_wrappers():
     if getattr(C, "calendar_cache", None) is not None:
         _calendar_provider = init_instance_by_config(C.calendar_cache, module, provide=_calendar_provider)
     register_wrapper(Cal, _calendar_provider, "qlib.data")
-    logger.debug(f"registering Cal {C.calendar_provider}-{C.calenar_cache}")
+    logger.debug(f"registering Cal {C.calendar_provider}-{C.calendar_cache}")
 
     register_wrapper(Inst, C.instrument_provider, "qlib.data")
     logger.debug(f"registering Inst {C.instrument_provider}")

qlib/data/dataset/__init__.py

@@ -1,10 +1,14 @@
 from ...utils.serial import Serializable
 from typing import Union, List, Tuple
-from ...utils import init_instance_by_config
+from ...utils import init_instance_by_config, np_ffill
 from ...log import get_module_logger
 from .handler import DataHandler, DataHandlerLP
 from inspect import getfullargspec
 import pandas as pd
+import numpy as np
+import bisect
+from ...utils import lazy_sort_index
+from .utils import get_level_index
 
 
 class Dataset(Serializable):
@@ -72,18 +76,22 @@ class DatasetH(Dataset):
     - The processing is related to data split.
     """
 
-    def __init__(self, handler: Union[dict, DataHandler], segments: list):
+    def __init__(self, handler: Union[dict, DataHandler], segments: dict):
         """
         Parameters
         ----------
         handler : Union[dict, DataHandler]
             handler will be passed into setup_data.
-        segments : list
+        segments : dict
             handler will be passed into setup_data.
         """
         super().__init__(handler, segments)
 
-    def setup_data(self, handler: Union[dict, DataHandler], segments: list):
+    def init(self, **kwargs):
+        """Initialize the DatasetH, Only parameters belonging to handler.init will be passed in"""
+        self.handler.init(**kwargs)
+
+    def setup_data(self, handler: Union[dict, DataHandler], segments: dict):
         """
         Setup the underlying data.
 
@@ -96,7 +104,7 @@ class DatasetH(Dataset):
 
             - config of `DataHandler`.  Please refer to `DataHandler`
 
-        segments : list
+        segments : dict
             Describe the options to segment the data.
             Here are some examples:
 
@@ -112,8 +120,18 @@ class DatasetH(Dataset):
                         'outsample': ("2017-01-01", "2020-08-01",),
                     }
         """
-        self._handler = init_instance_by_config(handler, accept_types=DataHandler)
-        self._segments = segments.copy()
+        self.handler = init_instance_by_config(handler, accept_types=DataHandler)
+        self.segments = segments.copy()
+
+    def _prepare_seg(self, slc: slice, **kwargs):
+        """
+        Give a slice, retrieve the according data
+
+        Parameters
+        ----------
+        slc : slice
+        """
+        return self.handler.fetch(slc, **kwargs)
 
     def prepare(
         self,
@@ -136,7 +154,7 @@ class DatasetH(Dataset):
             - ['train', 'valid']
 
         col_set : str
-            The col_set will be passed to self._handler when fetching data.
+            The col_set will be passed to self.handler when fetching data.
         data_key : str
             The data to fetch:  DK_*
             Default is DK_I, which indicate fetching data for **inference**.
@@ -152,14 +170,263 @@ class DatasetH(Dataset):
         logger = get_module_logger("DatasetH")
         fetch_kwargs = {"col_set": col_set}
         fetch_kwargs.update(kwargs)
-        if "data_key" in getfullargspec(self._handler.fetch).args:
+        if "data_key" in getfullargspec(self.handler.fetch).args:
             fetch_kwargs["data_key"] = data_key
         else:
             logger.info(f"data_key[{data_key}] is ignored.")
 
+        # Handle all kinds of segments format
         if isinstance(segments, (list, tuple)):
-            return [self._handler.fetch(slice(*self._segments[seg]), **fetch_kwargs) for seg in segments]
+            return [self._prepare_seg(slice(*self.segments[seg]), **fetch_kwargs) for seg in segments]
         elif isinstance(segments, str):
-            return self._handler.fetch(slice(*self._segments[segments]), **fetch_kwargs)
+            return self._prepare_seg(slice(*self.segments[segments]), **fetch_kwargs)
+        elif isinstance(segments, slice):
+            return self._prepare_seg(segments, **fetch_kwargs)
         else:
             raise NotImplementedError(f"This type of input is not supported")
+
+
+class TSDataSampler:
+    """
+    (T)ime-(S)eries DataSampler
+    This is the result of TSDatasetH
+
+    It works like `torch.data.utils.Dataset`, it provides a very convient interface for constructing time-series
+    dataset based on tabular data.
+
+    If user have further requirements for processing data, user could process them based on `TSDataSampler` or create
+    more powerful subclasses.
+
+    Known Issues:
+    - For performance issues, this Sampler will convert dataframe into arrays for better performance. This could result
+      in a different data type
+
+    """
+
+    def __init__(self, data: pd.DataFrame, start, end, step_len: int, fillna_type: str = "none"):
+        """
+        Build a dataset which looks like torch.data.utils.Dataset.
+
+        Parameters
+        ----------
+        data : pd.DataFrame
+            The raw tabular data
+        start :
+            The indexable start time
+        end :
+            The indexable end time
+        step_len : int
+            The length of the time-series step
+        fillna_type : int
+            How will qlib handle the sample if there is on sample in a specific date.
+            none:
+                fill with np.nan
+            ffill:
+                ffill with previous sample
+            ffill+bfill:
+                ffill with previous samples first and fill with later samples second
+        """
+        self.start = start
+        self.end = end
+        self.step_len = step_len
+        self.fillna_type = fillna_type
+        assert get_level_index(data, "datetime") == 0
+        self.data = lazy_sort_index(data)
+        self.data_arr = np.array(self.data)  # Get index from numpy.array will much faster than DataFrame.values! But
+        # NOTE: append last line with full NaN for better performance in `__getitem__`
+        self.data_arr = np.append(self.data_arr, np.full((1, self.data_arr.shape[1]), np.nan), axis=0)
+        self.nan_idx = -1  # The last line is all NaN
+
+        # the data type will be changed
+        # The index of usable data is between start_idx and end_idx
+        self.start_idx, self.end_idx = self.data.index.slice_locs(start=pd.Timestamp(start), end=pd.Timestamp(end))
+        # self.index_link = self.build_link(self.data)
+        self.idx_df, self.idx_map = self.build_index(self.data)
+        self.idx_arr = np.array(self.idx_df.values, dtype=np.float64)  # for better performance
+
+    def get_index(self):
+        """
+        Get the pandas index of the data, it will be useful in following scenarios
+        - Special sampler will be used (e.g. user want to sample day by day)
+        """
+        return self.data.index[self.start_idx : self.end_idx]
+
+    def config(self, **kwargs):
+        # Config the attributes
+        for k, v in kwargs.items():
+            setattr(self, k, v)
+
+    @staticmethod
+    def build_index(data: pd.DataFrame) -> dict:
+        """
+        The relation of the data
+
+        Parameters
+        ----------
+        data : pd.DataFrame
+            The dataframe with <datetime, DataFrame>
+
+        Returns
+        -------
+        dict:
+            {<index>: <prev_index or None>}
+            # get the previous index of a line given index
+        """
+        # object incase of pandas converting int to flaot
+        idx_df = pd.Series(range(data.shape[0]), index=data.index, dtype=np.object)
+        idx_df = lazy_sort_index(idx_df.unstack())
+        # NOTE: the correctness of `__getitem__` depends on columns sorted here
+        idx_df = lazy_sort_index(idx_df, axis=1)
+
+        idx_map = {}
+        for i, (_, row) in enumerate(idx_df.iterrows()):
+            for j, real_idx in enumerate(row):
+                if not np.isnan(real_idx):
+                    idx_map[real_idx] = (i, j)
+        return idx_df, idx_map
+
+    def _get_indices(self, row: int, col: int) -> np.array:
+        """
+        get series indices of self.data_arr from the row, col indices of self.idx_df
+
+        Parameters
+        ----------
+        row : int
+            the row in self.idx_df
+        col : int
+            the col in self.idx_df
+
+        Returns
+        -------
+        np.array:
+            The indices of data of the data
+        """
+        indices = self.idx_arr[max(row - self.step_len + 1, 0) : row + 1, col]
+
+        if len(indices) < self.step_len:
+            indices = np.concatenate([np.full((self.step_len - len(indices),), np.nan), indices])
+
+        if self.fillna_type == "ffill":
+            indices = np_ffill(indices)
+        elif self.fillna_type == "ffill+bfill":
+            indices = np_ffill(np_ffill(indices)[::-1])[::-1]
+        else:
+            assert self.fillna_type == "none"
+        return indices
+
+    def _get_row_col(self, idx) -> Tuple[int]:
+        """
+        get the col index and row index of a given sample index in self.idx_df
+
+        Parameters
+        ----------
+        idx :
+            the input of  `__getitem__`
+
+        Returns
+        -------
+        Tuple[int]:
+            the row and col index
+        """
+        # The the right row number `i` and col number `j` in idx_df
+        if isinstance(idx, (int, np.integer)):
+            real_idx = self.start_idx + idx
+            if self.start_idx <= real_idx < self.end_idx:
+                i, j = self.idx_map[real_idx]  # TODO: The performance of this line is not good
+            else:
+                raise KeyError(f"{real_idx} is out of [{self.start_idx}, {self.end_idx})")
+        elif isinstance(idx, tuple):
+            # <TSDataSampler object>["datetime", "instruments"]
+            date, inst = idx
+            date = pd.Timestamp(date)
+            i = bisect.bisect_right(self.idx_df.index, date) - 1
+            # NOTE: This relies on the idx_df columns sorted in `__init__`
+            j = bisect.bisect_left(self.idx_df.columns, inst)
+        else:
+            raise NotImplementedError(f"This type of input is not supported")
+        return i, j
+
+    def __getitem__(self, idx: Union[int, Tuple[object, str], List[int]]):
+        """
+        # We have two method to get the time-series of a sample
+        tsds is a instance of TSDataSampler
+
+        # 1) sample by int index directly
+        tsds[len(tsds) - 1]
+
+        # 2) sample by <datetime,instrument> index
+        tsds['2016-12-31', "SZ300315"]
+
+        # The return value will be similar to the data retrieved by following code
+        df.loc(axis=0)['2015-01-01':'2016-12-31', "SZ300315"].iloc[-30:]
+
+        Parameters
+        ----------
+        idx : Union[int, Tuple[object, str]]
+        """
+        # Multi-index type
+        mtit = (list, np.ndarray)
+        if isinstance(idx, mtit):
+            indices = [self._get_indices(*self._get_row_col(i)) for i in idx]
+            indices = np.concatenate(indices)
+        else:
+            indices = self._get_indices(*self._get_row_col(idx))
+
+        # 1) for better performance, use the last nan line for padding the lost date
+        # 2) In case of precision problems. We use np.float64. # TODO: I'm not sure if whether np.float64 will result in
+        # precision problems. It will not cause any problems in my tests at least
+        indices = np.nan_to_num(indices.astype(np.float64), nan=self.nan_idx).astype(np.int)
+
+        data = self.data_arr[indices]
+        if isinstance(idx, mtit):
+            # if we get multiple indexes, addition dimension should be added.
+            # <sample_idx, step_idx, feature_idx>
+            data = data.reshape(-1, self.step_len, *data.shape[1:])
+        return data
+
+    def __len__(self):
+        return self.end_idx - self.start_idx
+
+
+class TSDatasetH(DatasetH):
+    """
+    (T)ime-(S)eries Dataset (H)andler
+
+
+    Covnert the tabular data to Time-Series data
+
+    Requirements analysis
+
+    The typical workflow of a user to get time-series data for an sample
+    - process features
+    - slice proper data from data handler:  dimension of sample <feature, >
+    - Build relation of samples by <time, instrument> index
+        - Be able to sample times series of data <timestep, feature>
+        - It will be better if the interface is like "torch.utils.data.Dataset"
+    - User could build customized batch based on the data
+        - The dimension of a batch of data <batch_idx, feature, timestep>
+    """
+
+    def __init__(self, step_len=30, *args, **kwargs):
+        self.step_len = step_len
+        super().__init__(*args, **kwargs)
+
+    def setup_data(self, *args, **kwargs):
+        super().setup_data(*args, **kwargs)
+        cal = self.handler.fetch(col_set=self.handler.CS_RAW).index.get_level_values("datetime").unique()
+        cal = sorted(cal)
+        # Get the datatime index for building timestamp
+        self.cal = cal
+
+    def _prepare_seg(self, slc: slice, **kwargs) -> TSDataSampler:
+        # Dataset decide how to slice data(Get more data for timeseries).
+        start, end = slc.start, slc.stop
+        start_idx = bisect.bisect_left(self.cal, pd.Timestamp(start))
+        pad_start_idx = max(0, start_idx - self.step_len)
+        pad_start = self.cal[pad_start_idx]
+
+        # TSDatasetH will retrieve more data for complete
+        data = super()._prepare_seg(slice(pad_start, end), **kwargs)
+
+        tsds = TSDataSampler(data=data, start=start, end=end, step_len=self.step_len)
+        return tsds

qlib/data/dataset/handler.py

@@ -83,22 +83,42 @@ class DataHandler(Serializable):
         # Setup data loader
         assert data_loader is not None  # to make start_time end_time could have None default value
 
+        # what data source to load data
         self.data_loader = init_instance_by_config(
             data_loader,
             None if (isinstance(data_loader, dict) and "module_path" in data_loader) else data_loader_module,
             accept_types=DataLoader,
         )
 
+        # what data to be loaded from data source
+        # For IDE auto-completion.
         self.instruments = instruments
         self.start_time = start_time
         self.end_time = end_time
+
         self.fetch_orig = fetch_orig
         if init_data:
             with TimeInspector.logt("Init data"):
                 self.init()
         super().__init__()
 
-    def init(self, enable_cache: bool = True):
+    def conf_data(self, **kwargs):
+        """
+        configuration of data.
+        # what data to be loaded from data source
+
+        This method will be used when loading pickled handler from dataset.
+        The data will be initialized with different time range.
+
+        """
+        attr_list = {"instruments", "start_time", "end_time"}
+        for k, v in kwargs.items():
+            if k in attr_list:
+                setattr(self, k, v)
+            else:
+                raise KeyError("Such config is not supported.")
+
+    def init(self, enable_cache: bool = False):
         """
         initialize the data.
         In case of running intialization for multiple time, it will do nothing for the second time.
@@ -155,6 +175,9 @@ class DataHandler(Serializable):
 
                 select a set of meaningful columns.(e.g. features, columns)
 
+                if cal_set == CS_RAW:
+                    the raw dataset will be returned.
+
             - if isinstance(col_set, List[str]):
 
                 select several sets of meaningful columns, the returned data has multiple levels
@@ -259,6 +282,7 @@ class DataHandlerLP(DataHandler):
         infer_processors=[],
         learn_processors=[],
         process_type=PTYPE_A,
+        drop_raw=False,
         **kwargs,
     ):
         """
@@ -300,6 +324,8 @@ class DataHandlerLP(DataHandler):
             - self._learn will be processed by infer_processors + learn_processors
 
               - (e.g. self._infer processed by learn_processors )
+        drop_raw: bool
+            Whether to drop the raw data
         """
 
         # Setup preprocessor
@@ -316,6 +342,7 @@ class DataHandlerLP(DataHandler):
                 )
 
         self.process_type = process_type
+        self.drop_raw = drop_raw
         super().__init__(instruments, start_time, end_time, data_loader, **kwargs)
 
     def get_all_processors(self):
@@ -345,7 +372,7 @@ class DataHandlerLP(DataHandler):
         """
         # data for inference
         _infer_df = self._data
-        if len(self.infer_processors) > 0:  # avoid modifying the original  data
+        if len(self.infer_processors) > 0 and not self.drop_raw:  # avoid modifying the original  data
             _infer_df = _infer_df.copy()
 
         for proc in self.infer_processors:
@@ -375,6 +402,9 @@ class DataHandlerLP(DataHandler):
                 _learn_df = proc(_learn_df)
         self._learn = _learn_df
 
+        if self.drop_raw:
+            del self._data
+
     # init type
     IT_FIT_SEQ = "fit_seq"  # the input of `fit` will be the output of the previous processor
     IT_FIT_IND = "fit_ind"  # the input of `fit` will be the original df
@@ -413,6 +443,10 @@ class DataHandlerLP(DataHandler):
         # TODO: Be able to cache handler data. Save the memory for data processing
 
     def _get_df_by_key(self, data_key: str = DK_I) -> pd.DataFrame:
+        if data_key == self.DK_R and self.drop_raw:
+            raise AttributeError(
+                "DataHandlerLP has not attribute _data, please set drop_raw = False if you want to use raw data"
+            )
         df = getattr(self, {self.DK_R: "_data", self.DK_I: "_infer", self.DK_L: "_learn"}[data_key])
         return df
 

qlib/data/dataset/loader.py

@@ -10,7 +10,9 @@ import pandas as pd
 from typing import Tuple, Union
 
 from qlib.data import D
-from qlib.utils import load_dataset
+from qlib.data import filter as filter_module
+from qlib.data.filter import BaseDFilter
+from qlib.utils import load_dataset, init_instance_by_config
 
 
 class DataLoader(abc.ABC):
@@ -76,6 +78,7 @@ class DLWParser(DataLoader):
                 <config> := <fields_info>
 
                 <fields_info> := ["expr", ...] | (["expr", ...], ["col_name", ...])
+                # NOTE: list or tuple will be treated as the things when parsing
         """
         self.is_group = isinstance(config, dict)
 
@@ -85,9 +88,15 @@ class DLWParser(DataLoader):
             self.fields = self._parse_fields_info(config)
 
     def _parse_fields_info(self, fields_info: Tuple[list, tuple]) -> Tuple[list, list]:
-        if isinstance(fields_info, list):
+        if len(fields_info) == 0:
+            raise ValueError("The size of fields must be greater than 0")
+
+        if not isinstance(fields_info, (list, tuple)):
+            raise TypeError("Unsupported type")
+
+        if isinstance(fields_info[0], str):
             exprs = names = fields_info
-        elif isinstance(fields_info, tuple):
+        elif isinstance(fields_info[0], (list, tuple)):
             exprs, names = fields_info
         else:
             raise NotImplementedError(f"This type of input is not supported")
@@ -132,7 +141,7 @@ class DLWParser(DataLoader):
 class QlibDataLoader(DLWParser):
     """Same as QlibDataLoader. The fields can be define by config"""
 
-    def __init__(self, config: Tuple[list, tuple, dict], filter_pipe=None):
+    def __init__(self, config: Tuple[list, tuple, dict], filter_pipe=None, swap_level=True, freq="day"):
         """
         Parameters
         ----------
@@ -140,8 +149,19 @@ class QlibDataLoader(DLWParser):
             Please refer to the doc of DLWParser
         filter_pipe :
             Filter pipe for the instruments
+        swap_level :
+            Whether to swap level of MultiIndex
         """
+        if filter_pipe is not None:
+            assert isinstance(filter_pipe, list), "The type of `filter_pipe` must be list."
+            filter_pipe = [
+                init_instance_by_config(fp, None if "module_path" in fp else filter_module, accept_types=BaseDFilter)
+                for fp in filter_pipe
+            ]
+
         self.filter_pipe = filter_pipe
+        self.swap_level = swap_level
+        self.freq = freq
         super().__init__(config)
 
     def load_group_df(self, instruments, exprs: list, names: list, start_time=None, end_time=None) -> pd.DataFrame:
@@ -153,9 +173,10 @@ class QlibDataLoader(DLWParser):
         elif self.filter_pipe is not None:
             warnings.warn("`filter_pipe` is not None, but it will not be used with `instruments` as list")
 
-        df = D.features(instruments, exprs, start_time, end_time)
+        df = D.features(instruments, exprs, start_time, end_time, self.freq)
         df.columns = names
-        df = df.swaplevel().sort_index()  # NOTE: always return <datetime, instrument>
+        if self.swap_level:
+            df = df.swaplevel().sort_index()  # NOTE: if swaplevel, return <datetime, instrument>
         return df
 
 

qlib/data/dataset/processor.py

@@ -102,6 +102,21 @@ class DropCol(Processor):
         return df.loc[:, ~mask]
 
 
+class FilterCol(Processor):
+    def __init__(self, fields_group="feature", col_list=[]):
+        self.fields_group = fields_group
+        self.col_list = col_list
+
+    def __call__(self, df):
+
+        cols = get_group_columns(df, self.fields_group)
+        all_cols = df.columns
+        diff_cols = np.setdiff1d(all_cols.get_level_values(-1), cols.get_level_values(-1))
+        self.col_list = np.union1d(diff_cols, self.col_list)
+        mask = df.columns.get_level_values(-1).isin(self.col_list)
+        return df.loc[:, mask]
+
+
 class TanhProcess(Processor):
     """ Use tanh to process noise data"""
 

qlib/data/ops.py

@@ -6,6 +6,7 @@ from __future__ import division
 from __future__ import print_function
 
 import sys
+import abc
 import numpy as np
 import pandas as pd
 
@@ -17,57 +18,12 @@ from ..log import get_module_logger
 try:
     from ._libs.rolling import rolling_slope, rolling_rsquare, rolling_resi
     from ._libs.expanding import expanding_slope, expanding_rsquare, expanding_resi
-except ImportError as err:
-    print("Do not import qlib package in the repository directory!")
+except ImportError:
+    print(
+        "#### Do not import qlib package in the repository directory in case of importing qlib from . without compiling #####"
+    )
     raise
 
-__all__ = (
-    "Ref",
-    "Max",
-    "Min",
-    "Sum",
-    "Mean",
-    "Std",
-    "Var",
-    "Skew",
-    "Kurt",
-    "Med",
-    "Mad",
-    "Slope",
-    "Rsquare",
-    "Resi",
-    "Rank",
-    "Quantile",
-    "Count",
-    "EMA",
-    "WMA",
-    "Corr",
-    "Cov",
-    "Delta",
-    "Abs",
-    "Sign",
-    "Log",
-    "Power",
-    "Add",
-    "Sub",
-    "Mul",
-    "Div",
-    "Greater",
-    "Less",
-    "And",
-    "Or",
-    "Not",
-    "Gt",
-    "Ge",
-    "Lt",
-    "Le",
-    "Eq",
-    "Ne",
-    "Mask",
-    "IdxMax",
-    "IdxMin",
-    "If",
-)
 
 np.seterr(invalid="ignore")
 
@@ -77,12 +33,39 @@ np.seterr(invalid="ignore")
 class ElemOperator(ExpressionOps):
     """Element-wise Operator
 
+    Parameters
+    ----------
+    feature : Expression
+        feature instance
+
+    Returns
+    ----------
+    Expression
+        feature operation output
+    """
+
+    def __init__(self, feature):
+        self.feature = feature
+
+    def __str__(self):
+        return "{}({})".format(type(self).__name__, self.feature)
+
+    def get_longest_back_rolling(self):
+        return self.feature.get_longest_back_rolling()
+
+    def get_extended_window_size(self):
+        return self.feature.get_extended_window_size()
+
+
+class NpElemOperator(ElemOperator):
+    """Numpy Element-wise Operator
+
     Parameters
     ----------
     feature : Expression
         feature instance
     func : str
-        feature operation method
+        numpy feature operation method
 
     Returns
     ----------
@@ -93,22 +76,14 @@ class ElemOperator(ExpressionOps):
     def __init__(self, feature, func):
         self.feature = feature
         self.func = func
-
-    def __str__(self):
-        return "{}({})".format(type(self).__name__, self.feature)
+        super(NpElemOperator, self).__init__(feature)
 
     def _load_internal(self, instrument, start_index, end_index, freq):
         series = self.feature.load(instrument, start_index, end_index, freq)
         return getattr(np, self.func)(series)
 
-    def get_longest_back_rolling(self):
-        return self.feature.get_longest_back_rolling()
 
-    def get_extended_window_size(self):
-        return self.feature.get_extended_window_size()
-
-
-class Abs(ElemOperator):
+class Abs(NpElemOperator):
     """Feature Absolute Value
 
     Parameters
@@ -126,7 +101,7 @@ class Abs(ElemOperator):
         super(Abs, self).__init__(feature, "abs")
 
 
-class Sign(ElemOperator):
+class Sign(NpElemOperator):
     """Feature Sign
 
     Parameters
@@ -143,8 +118,17 @@ class Sign(ElemOperator):
     def __init__(self, feature):
         super(Sign, self).__init__(feature, "sign")
 
+    def _load_internal(self, instrument, start_index, end_index, freq):
+        """
+        To avoid error raised by bool type input, we transform the data into float32.
+        """
+        series = self.feature.load(instrument, start_index, end_index, freq)
+        # TODO:  More precision types should be configurable
+        series = series.astype(np.float32)
+        return getattr(np, self.func)(series)
 
-class Log(ElemOperator):
+
+class Log(NpElemOperator):
     """Feature Log
 
     Parameters
@@ -162,7 +146,7 @@ class Log(ElemOperator):
         super(Log, self).__init__(feature, "log")
 
 
-class Power(ElemOperator):
+class Power(NpElemOperator):
     """Feature Power
 
     Parameters
@@ -188,7 +172,7 @@ class Power(ElemOperator):
         return getattr(np, self.func)(series, self.exponent)
 
 
-class Mask(ElemOperator):
+class Mask(NpElemOperator):
     """Feature Mask
 
     Parameters
@@ -215,7 +199,7 @@ class Mask(ElemOperator):
         return self.feature.load(self.instrument, start_index, end_index, freq)
 
 
-class Not(ElemOperator):
+class Not(NpElemOperator):
     """Not Operator
 
     Parameters
@@ -254,28 +238,13 @@ class PairOperator(ExpressionOps):
         two features' operation output
     """
 
-    def __init__(self, feature_left, feature_right, func):
+    def __init__(self, feature_left, feature_right):
         self.feature_left = feature_left
         self.feature_right = feature_right
-        self.func = func
 
     def __str__(self):
         return "{}({},{})".format(type(self).__name__, self.feature_left, self.feature_right)
 
-    def _load_internal(self, instrument, start_index, end_index, freq):
-        assert any(
-            [isinstance(self.feature_left, Expression), self.feature_right, Expression]
-        ), "at least one of two inputs is Expression instance"
-        if isinstance(self.feature_left, Expression):
-            series_left = self.feature_left.load(instrument, start_index, end_index, freq)
-        else:
-            series_left = self.feature_left  # numeric value
-        if isinstance(self.feature_right, Expression):
-            series_right = self.feature_right.load(instrument, start_index, end_index, freq)
-        else:
-            series_right = self.feature_right
-        return getattr(np, self.func)(series_left, series_right)
-
     def get_longest_back_rolling(self):
         if isinstance(self.feature_left, Expression):
             left_br = self.feature_left.get_longest_back_rolling()
@@ -301,7 +270,46 @@ class PairOperator(ExpressionOps):
         return max(ll, rl), max(lr, rr)
 
 
-class Add(PairOperator):
+class NpPairOperator(PairOperator):
+    """Numpy Pair-wise operator
+
+    Parameters
+    ----------
+    feature_left : Expression
+        feature instance or numeric value
+    feature_right : Expression
+        feature instance or numeric value
+    func : str
+        operator function
+
+    Returns
+    ----------
+    Feature:
+        two features' operation output
+    """
+
+    def __init__(self, feature_left, feature_right, func):
+        self.feature_left = feature_left
+        self.feature_right = feature_right
+        self.func = func
+        super(NpPairOperator, self).__init__(feature_left, feature_right)
+
+    def _load_internal(self, instrument, start_index, end_index, freq):
+        assert any(
+            [isinstance(self.feature_left, Expression), self.feature_right, Expression]
+        ), "at least one of two inputs is Expression instance"
+        if isinstance(self.feature_left, Expression):
+            series_left = self.feature_left.load(instrument, start_index, end_index, freq)
+        else:
+            series_left = self.feature_left  # numeric value
+        if isinstance(self.feature_right, Expression):
+            series_right = self.feature_right.load(instrument, start_index, end_index, freq)
+        else:
+            series_right = self.feature_right
+        return getattr(np, self.func)(series_left, series_right)
+
+
+class Add(NpPairOperator):
     """Add Operator
 
     Parameters
@@ -321,7 +329,7 @@ class Add(PairOperator):
         super(Add, self).__init__(feature_left, feature_right, "add")
 
 
-class Sub(PairOperator):
+class Sub(NpPairOperator):
     """Subtract Operator
 
     Parameters
@@ -341,7 +349,7 @@ class Sub(PairOperator):
         super(Sub, self).__init__(feature_left, feature_right, "subtract")
 
 
-class Mul(PairOperator):
+class Mul(NpPairOperator):
     """Multiply Operator
 
     Parameters
@@ -361,7 +369,7 @@ class Mul(PairOperator):
         super(Mul, self).__init__(feature_left, feature_right, "multiply")
 
 
-class Div(PairOperator):
+class Div(NpPairOperator):
     """Division Operator
 
     Parameters
@@ -381,7 +389,7 @@ class Div(PairOperator):
         super(Div, self).__init__(feature_left, feature_right, "divide")
 
 
-class Greater(PairOperator):
+class Greater(NpPairOperator):
     """Greater Operator
 
     Parameters
@@ -401,7 +409,7 @@ class Greater(PairOperator):
         super(Greater, self).__init__(feature_left, feature_right, "maximum")
 
 
-class Less(PairOperator):
+class Less(NpPairOperator):
     """Less Operator
 
     Parameters
@@ -421,7 +429,7 @@ class Less(PairOperator):
         super(Less, self).__init__(feature_left, feature_right, "minimum")
 
 
-class Gt(PairOperator):
+class Gt(NpPairOperator):
     """Greater Than Operator
 
     Parameters
@@ -441,7 +449,7 @@ class Gt(PairOperator):
         super(Gt, self).__init__(feature_left, feature_right, "greater")
 
 
-class Ge(PairOperator):
+class Ge(NpPairOperator):
     """Greater Equal Than Operator
 
     Parameters
@@ -461,7 +469,7 @@ class Ge(PairOperator):
         super(Ge, self).__init__(feature_left, feature_right, "greater_equal")
 
 
-class Lt(PairOperator):
+class Lt(NpPairOperator):
     """Less Than Operator
 
     Parameters
@@ -481,7 +489,7 @@ class Lt(PairOperator):
         super(Lt, self).__init__(feature_left, feature_right, "less")
 
 
-class Le(PairOperator):
+class Le(NpPairOperator):
     """Less Equal Than Operator
 
     Parameters
@@ -501,7 +509,7 @@ class Le(PairOperator):
         super(Le, self).__init__(feature_left, feature_right, "less_equal")
 
 
-class Eq(PairOperator):
+class Eq(NpPairOperator):
     """Equal Operator
 
     Parameters
@@ -521,7 +529,7 @@ class Eq(PairOperator):
         super(Eq, self).__init__(feature_left, feature_right, "equal")
 
 
-class Ne(PairOperator):
+class Ne(NpPairOperator):
     """Not Equal Operator
 
     Parameters
@@ -541,7 +549,7 @@ class Ne(PairOperator):
         super(Ne, self).__init__(feature_left, feature_right, "not_equal")
 
 
-class And(PairOperator):
+class And(NpPairOperator):
     """And Operator
 
     Parameters
@@ -561,7 +569,7 @@ class And(PairOperator):
         super(And, self).__init__(feature_left, feature_right, "bitwise_and")
 
 
-class Or(PairOperator):
+class Or(NpPairOperator):
     """Or Operator
 
     Parameters
@@ -1430,3 +1438,93 @@ class Cov(PairRolling):
 
     def __init__(self, feature_left, feature_right, N):
         super(Cov, self).__init__(feature_left, feature_right, N, "cov")
+
+
+OpsList = [
+    Ref,
+    Max,
+    Min,
+    Sum,
+    Mean,
+    Std,
+    Var,
+    Skew,
+    Kurt,
+    Med,
+    Mad,
+    Slope,
+    Rsquare,
+    Resi,
+    Rank,
+    Quantile,
+    Count,
+    EMA,
+    WMA,
+    Corr,
+    Cov,
+    Delta,
+    Abs,
+    Sign,
+    Log,
+    Power,
+    Add,
+    Sub,
+    Mul,
+    Div,
+    Greater,
+    Less,
+    And,
+    Or,
+    Not,
+    Gt,
+    Ge,
+    Lt,
+    Le,
+    Eq,
+    Ne,
+    Mask,
+    IdxMax,
+    IdxMin,
+    If,
+]
+
+
+class OpsWrapper(object):
+    """Ops Wrapper"""
+
+    def __init__(self):
+        self._ops = {}
+
+    def reset(self):
+        self._ops = {}
+
+    def register(self, ops_list):
+        for operator in ops_list:
+            if not issubclass(operator, ExpressionOps):
+                raise TypeError("operator must be subclass of ExpressionOps, not {}".format(operator))
+
+            if operator.__name__ in self._ops:
+                get_module_logger(self.__class__.__name__).warning(
+                    "The custom operator [{}] will override the qlib default definition".format(operator.__name__)
+                )
+            self._ops[operator.__name__] = operator
+
+    def __getattr__(self, key):
+        if key not in self._ops:
+            raise AttributeError("The operator [{0}] is not registered".format(key))
+        return self._ops[key]
+
+
+Operators = OpsWrapper()
+
+
+def register_all_ops(C):
+    """register all operator"""
+    logger = get_module_logger("ops")
+
+    Operators.reset()
+    Operators.register(OpsList)
+
+    if getattr(C, "custom_ops", None) is not None:
+        Operators.register(C.custom_ops)
+        logger.debug("register custom operator {}".format(C.custom_ops))

qlib/log.py

@@ -36,7 +36,7 @@ def get_module_logger(module_name, level=None):
     return module_logger
 
 
-class TimeInspector(object):
+class TimeInspector:
 
     timer_logger = get_module_logger("timer", level=logging.WARNING)
 

qlib/model/base.py

@@ -30,11 +30,6 @@ class Model(BaseModel):
             The attribute names of learned model should `not` start with '_'. So that the model could be
             dumped to disk.
 
-        Parameters
-        ----------
-        dataset : Dataset
-            dataset will generate the processed data from model training.
-
         The following code example shows how to retrieve `x_train`, `y_train` and `w_train` from the `dataset`:
 
             .. code-block:: Python
@@ -53,6 +48,12 @@ class Model(BaseModel):
                 except KeyError as e:
                     w_train = pd.DataFrame(np.ones_like(y_train.values), index=y_train.index)
                     w_valid = pd.DataFrame(np.ones_like(y_valid.values), index=y_valid.index)
+
+        Parameters
+        ----------
+        dataset : Dataset
+            dataset will generate the processed data from model training.
+
         """
         raise NotImplementedError()
 

qlib/portfolio/optimizer.py

@@ -9,7 +9,7 @@ import scipy.optimize as so
 from typing import Optional, Union, Callable, List
 
 
-class PortfolioOptimizer(object):
+class PortfolioOptimizer:
     """Portfolio Optimizer
 
     The following optimization algorithms are supported:

qlib/tests/__init__.py

@@ -0,0 +1,27 @@
+import sys
+import unittest
+from ..utils import exists_qlib_data
+from .data import GetData
+from .. import init
+from ..config import REG_CN
+
+
+class TestAutoData(unittest.TestCase):
+
+    _setup_kwargs = {}
+
+    @classmethod
+    def setUpClass(cls) -> None:
+        # use default data
+        provider_uri = "~/.qlib/qlib_data/cn_data_simple"  # target_dir
+        if not exists_qlib_data(provider_uri):
+            print(f"Qlib data is not found in {provider_uri}")
+
+            GetData().qlib_data(
+                name="qlib_data_simple",
+                region="cn",
+                interval="1d",
+                target_dir=provider_uri,
+                delete_old=False,
+            )
+        init(provider_uri=provider_uri, region=REG_CN, **cls._setup_kwargs)

qlib/tests/data.py

@@ -0,0 +1,167 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+import re
+import qlib
+import shutil
+import zipfile
+import requests
+import datetime
+from tqdm import tqdm
+from pathlib import Path
+from loguru import logger
+
+
+class GetData:
+    DATASET_VERSION = "v1"
+    REMOTE_URL = "http://fintech.msra.cn/stock_data/downloads"
+    QLIB_DATA_NAME = "{dataset_name}_{region}_{interval}_{qlib_version}.zip"
+
+    def __init__(self, delete_zip_file=False):
+        """
+
+        Parameters
+        ----------
+        delete_zip_file : bool, optional
+            Whether to delete the zip file, value from True or False, by default False
+        """
+        self.delete_zip_file = delete_zip_file
+
+    def normalize_dataset_version(self, dataset_version: str = None):
+        if dataset_version is None:
+            dataset_version = self.DATASET_VERSION
+        return dataset_version
+
+    def merge_remote_url(self, file_name: str, dataset_version: str = None):
+        return f"{self.REMOTE_URL}/{self.normalize_dataset_version(dataset_version)}/{file_name}"
+
+    def _download_data(
+        self, file_name: str, target_dir: [Path, str], delete_old: bool = True, dataset_version: str = None
+    ):
+        target_dir = Path(target_dir).expanduser()
+        target_dir.mkdir(exist_ok=True, parents=True)
+        # saved file name
+        _target_file_name = datetime.datetime.now().strftime("%Y%m%d%H%M%S") + "_" + file_name
+        target_path = target_dir.joinpath(_target_file_name)
+
+        url = self.merge_remote_url(file_name, dataset_version)
+        resp = requests.get(url, stream=True)
+        if resp.status_code != 200:
+            raise requests.exceptions.HTTPError()
+
+        chunk_size = 1024
+        logger.warning(
+            f"The data for the example is collected from Yahoo Finance. Please be aware that the quality of the data might not be perfect. (You can refer to the original data source: https://finance.yahoo.com/lookup.)"
+        )
+        logger.info(f"{file_name} downloading......")
+        with tqdm(total=int(resp.headers.get("Content-Length", 0))) as p_bar:
+            with target_path.open("wb") as fp:
+                for chunk in resp.iter_content(chunk_size=chunk_size):
+                    fp.write(chunk)
+                    p_bar.update(chunk_size)
+
+        self._unzip(target_path, target_dir, delete_old)
+        if self.delete_zip_file:
+            target_path.unlink()
+
+    def check_dataset(self, file_name: str, dataset_version: str = None):
+        url = self.merge_remote_url(file_name, dataset_version)
+        resp = requests.get(url, stream=True)
+        status = True
+        if resp.status_code == 404:
+            status = False
+        return status
+
+    @staticmethod
+    def _unzip(file_path: Path, target_dir: Path, delete_old: bool = True):
+        if delete_old:
+            logger.warning(
+                f"will delete the old qlib data directory(features, instruments, calendars, features_cache, dataset_cache): {target_dir}"
+            )
+            GetData._delete_qlib_data(target_dir)
+        logger.info(f"{file_path} unzipping......")
+        with zipfile.ZipFile(str(file_path.resolve()), "r") as zp:
+            for _file in tqdm(zp.namelist()):
+                zp.extract(_file, str(target_dir.resolve()))
+
+    @staticmethod
+    def _delete_qlib_data(file_dir: Path):
+        logger.info(f"delete {file_dir}")
+        rm_dirs = []
+        for _name in ["features", "calendars", "instruments", "features_cache", "dataset_cache"]:
+            _p = file_dir.joinpath(_name)
+            if _p.exists():
+                rm_dirs.append(str(_p.resolve()))
+        if rm_dirs:
+            flag = input(
+                f"Will be deleted: "
+                f"\n\t{rm_dirs}"
+                f"\nIf you do not need to delete {file_dir}, please change the <--target_dir>"
+                f"\nAre you sure you want to delete, yes(Y/y), no (N/n):"
+            )
+            if str(flag) not in ["Y", "y"]:
+                exit()
+            for _p in rm_dirs:
+                logger.warning(f"delete: {_p}")
+                shutil.rmtree(_p)
+
+    def qlib_data(
+        self,
+        name="qlib_data",
+        target_dir="~/.qlib/qlib_data/cn_data",
+        version=None,
+        interval="1d",
+        region="cn",
+        delete_old=True,
+    ):
+        """download cn qlib data from remote
+
+        Parameters
+        ----------
+        target_dir: str
+            data save directory
+        name: str
+            dataset name, value from [qlib_data, qlib_data_simple], by default qlib_data
+        version: str
+            data version, value from [v1, ...], by default None(use script to specify version)
+        interval: str
+            data freq, value from [1d], by default 1d
+        region: str
+            data region, value from [cn, us], by default cn
+        delete_old: bool
+            delete an existing directory, by default True
+
+        Examples
+        ---------
+        python get_data.py qlib_data --name qlib_data --target_dir ~/.qlib/qlib_data/cn_data --interval 1d --region cn
+        -------
+
+        """
+        qlib_version = ".".join(re.findall(r"(\d+)\.+", qlib.__version__))
+
+        def _get_file_name(v):
+            return self.QLIB_DATA_NAME.format(
+                dataset_name=name, region=region.lower(), interval=interval.lower(), qlib_version=v
+            )
+
+        file_name = _get_file_name(qlib_version)
+        if not self.check_dataset(file_name, version):
+            file_name = _get_file_name("latest")
+        self._download_data(file_name.lower(), target_dir, delete_old, dataset_version=version)
+
+    def csv_data_cn(self, target_dir="~/.qlib/csv_data/cn_data"):
+        """download cn csv data from remote
+
+        Parameters
+        ----------
+        target_dir: str
+            data save directory
+
+        Examples
+        ---------
+        python get_data.py csv_data_cn --target_dir ~/.qlib/csv_data/cn_data
+        -------
+
+        """
+        file_name = "csv_data_cn.zip"
+        self._download_data(file_name, target_dir)

qlib/utils/__init__.py

@@ -27,7 +27,7 @@ from pathlib import Path
 from typing import Union, Tuple
 
 from ..config import C
-from ..log import get_module_logger
+from ..log import get_module_logger, set_log_with_config
 
 log = get_module_logger("utils")
 
@@ -55,6 +55,22 @@ def read_bin(file_path, start_index, end_index):
     return series
 
 
+def np_ffill(arr: np.array):
+    """
+    forward fill a 1D numpy array
+
+    Parameters
+    ----------
+    arr : np.array
+        Input numpy 1D array
+    """
+    mask = np.isnan(arr.astype(np.float))  # np.isnan only works on np.float
+    # get fill index
+    idx = np.where(~mask, np.arange(mask.shape[0]), 0)
+    np.maximum.accumulate(idx, out=idx)
+    return arr[idx]
+
+
 #################### Search ####################
 def lower_bound(data, val, level=0):
     """multi fields list lower bound.
@@ -146,7 +162,7 @@ def parse_field(field):
     # - $open+$close -> Feature("open")+Feature("close")
     if not isinstance(field, str):
         field = str(field)
-    return re.sub(r"\$(\w+)", r'Feature("\1")', field)
+    return re.sub(r"\$(\w+)", r'Feature("\1")', re.sub(r"(\w+\s*)\(", r"Operators.\1(", field))
 
 
 def get_module_by_module_path(module_path):
@@ -263,8 +279,10 @@ def compare_dict_value(src_data: dict, dst_data: dict):
 def create_save_path(save_path=None):
     """Create save path
 
-    :param save_path:
-    :return:
+    Parameters
+    ----------
+    save_path: str
+
     """
     if save_path:
         if not os.path.exists(save_path):
@@ -455,30 +473,28 @@ def is_tradable_date(cur_date):
     return str(cur_date.date()) == str(D.calendar(start_time=cur_date, future=True)[0].date())
 
 
-def get_date_range(trading_date, shift, future=False):
+def get_date_range(trading_date, left_shift=0, right_shift=0, future=False):
     """get trading date range by shift
 
-    :param trading_date:
-    :param shift: int
-    :param future: bool
-    :return:
+    Parameters
+    ----------
+    trading_date: pd.Timestamp
+    left_shift: int
+    right_shift: int
+    future: bool
+
     """
+
     from ..data import D
 
-    calendar = D.calendar(future=future)
-    if pd.to_datetime(trading_date) not in list(calendar):
-        raise ValueError("{} is not trading day!".format(str(trading_date)))
-    day_index = bisect.bisect_left(calendar, trading_date)
-    if 0 <= (day_index + shift) < len(calendar):
-        if shift > 0:
-            return calendar[day_index + 1 : day_index + 1 + shift]
-        else:
-            return calendar[day_index + shift : day_index]
-    else:
-        return calendar
+    start = get_date_by_shift(trading_date, left_shift, future=future)
+    end = get_date_by_shift(trading_date, right_shift, future=future)
+
+    calendar = D.calendar(start, end, future=future)
+    return calendar
 
 
-def get_date_by_shift(trading_date, shift, future=False):
+def get_date_by_shift(trading_date, shift, future=False, clip_shift=True):
     """get trading date with shift bias wil cur_date
         e.g. : shift == 1,  return next trading date
                shift == -1, return previous trading date
@@ -486,8 +502,22 @@ def get_date_by_shift(trading_date, shift, future=False):
     trading_date : pandas.Timestamp
         current date
     shift : int
+    clip_shift: bool
+
     """
-    return get_date_range(trading_date, shift, future)[0 if shift < 0 else -1] if shift != 0 else trading_date
+    from qlib.data import D
+
+    cal = D.calendar(future=future)
+    if pd.to_datetime(trading_date) not in list(cal):
+        raise ValueError("{} is not trading day!".format(str(trading_date)))
+    _index = bisect.bisect_left(cal, trading_date)
+    shift_index = _index + shift
+    if shift_index < 0 or shift_index >= len(cal):
+        if clip_shift:
+            shift_index = np.clip(shift_index, 0, len(cal) - 1)
+        else:
+            raise IndexError(f"The shift_index({shift_index}) of the trading day ({trading_date}) is out of range")
+    return cal[shift_index]
 
 
 def get_next_trading_date(trading_date, future=False):
@@ -613,18 +643,31 @@ def exists_qlib_data(qlib_dir):
     # check instruments
     code_names = set(map(lambda x: x.name.lower(), features_dir.iterdir()))
     _instrument = instruments_dir.joinpath("all.txt")
-    df = pd.read_csv(_instrument, sep="\t", names=["inst", "start_datetime", "end_datetime", "save_inst"])
-    df = df.iloc[:, [0, -1]].fillna(axis=1, method="ffill")
-    miss_code = set(df.iloc[:, -1].apply(str.lower)) - set(code_names)
+    miss_code = set(pd.read_csv(_instrument, sep="\t", header=None).loc[:, 0].apply(str.lower)) - set(code_names)
     if miss_code and any(map(lambda x: "sht" not in x, miss_code)):
         return False
 
     return True
 
 
-def lexsort_index(df: pd.DataFrame) -> pd.DataFrame:
+def check_qlib_data(qlib_config):
+    inst_dir = Path(qlib_config["provider_uri"]).joinpath("instruments")
+    for _p in inst_dir.glob("*.txt"):
+        try:
+            assert len(pd.read_csv(_p, sep="\t", nrows=0, header=None).columns) == 3, (
+                f"\nThe {str(_p.resolve())} of qlib data is not equal to 3 columns:"
+                f"\n\tIf you are using the data provided by qlib: "
+                f"https://qlib.readthedocs.io/en/latest/component/data.html#qlib-format-dataset"
+                f"\n\tIf you are using your own data, please dump the data again: "
+                f"https://qlib.readthedocs.io/en/latest/component/data.html#converting-csv-format-into-qlib-format"
+            )
+        except AssertionError:
+            raise
+
+
+def lazy_sort_index(df: pd.DataFrame, axis=0) -> pd.DataFrame:
     """
-    make the df index lexsorted
+    make the df index sorted
 
     df.sort_index() will take a lot of time even when `df.is_lexsorted() == True`
     This function could avoid such case
@@ -638,10 +681,11 @@ def lexsort_index(df: pd.DataFrame) -> pd.DataFrame:
     pd.DataFrame:
         sorted dataframe
     """
-    if df.index.is_lexsorted():
+    idx = df.index if axis == 0 else df.columns
+    if idx.is_monotonic_increasing:
         return df
     else:
-        return df.sort_index()
+        return df.sort_index(axis=axis)
 
 
 def flatten_dict(d, parent_key="", sep="."):
@@ -669,7 +713,7 @@ def flatten_dict(d, parent_key="", sep="."):
 
 
 #################### Wrapper #####################
-class Wrapper(object):
+class Wrapper:
     """Wrapper class for anything that needs to set up during qlib.init"""
 
     def __init__(self):
@@ -711,3 +755,36 @@ def load_dataset(path_or_obj):
     elif extension == ".csv":
         return pd.read_csv(path_or_obj, parse_dates=True, index_col=[0, 1])
     raise ValueError(f"unsupported file type `{extension}`")
+
+
+def code_to_fname(code: str):
+    """stock code to file name
+
+    Parameters
+    ----------
+    code: str
+    """
+    # NOTE: In windows, the following name is I/O device, and the file with the corresponding name cannot be created
+    # reference: https://superuser.com/questions/86999/why-cant-i-name-a-folder-or-file-con-in-windows
+    replace_names = ["CON", "PRN", "AUX", "NUL"]
+    replace_names += [f"COM{i}" for i in range(10)]
+    replace_names += [f"LPT{i}" for i in range(10)]
+
+    prefix = "_qlib_"
+    if str(code).upper() in replace_names:
+        code = prefix + str(code)
+
+    return code
+
+
+def fname_to_code(fname: str):
+    """file name to stock code
+
+    Parameters
+    ----------
+    fname: str
+    """
+    prefix = "_qlib_"
+    if fname.startswith(prefix):
+        fname = fname.lstrip(prefix)
+    return fname

qlib/utils/serial.py

@@ -27,11 +27,6 @@ class Serializable:
     def dump_all(self):
         """
         will the object dump all object
-
-        Parameters
-        ----------
-        self : [TODO:type]
-            [TODO:description]
         """
         return getattr(self, "_dump_all", False)
 
@@ -39,11 +34,6 @@ class Serializable:
     def exclude(self):
         """
         What attribute will be dumped
-
-        Parameters
-        ----------
-        self : [TODO:type]
-            [TODO:description]
         """
         return getattr(self, "_exclude", [])
 

qlib/workflow/__init__.py

@@ -3,6 +3,7 @@
 
 from contextlib import contextmanager
 from .expm import MLflowExpManager
+from .exp import Experiment
 from .recorder import Recorder
 from ..utils import Wrapper
 
@@ -165,7 +166,7 @@ class QlibRecorder:
         """
         return self.get_exp(experiment_id, experiment_name).list_recorders()
 
-    def get_exp(self, experiment_id=None, experiment_name=None, create: bool = True):
+    def get_exp(self, experiment_id=None, experiment_name=None, create: bool = True) -> Experiment:
         """
         Method for retrieving an experiment with given id or name. Once the `create` argument is set to
         True, if no valid experiment is found, this method will create one for you. Otherwise, it will
@@ -461,5 +462,14 @@ class QlibRecorder:
         self.get_exp().get_recorder().set_tags(**kwargs)
 
 
+import sys
+
+if sys.version_info >= (3, 9):
+    from typing import Annotated
+
+    QlibRecorderWrapper = Annotated[QlibRecorder, Wrapper]
+else:
+    QlibRecorderWrapper = QlibRecorder
+
 # global record
-R = Wrapper()
+R: QlibRecorderWrapper = Wrapper()

qlib/workflow/cli.py

@@ -44,7 +44,7 @@ def sys_config(config, config_path):
 # worflow handler function
 def workflow(config_path, experiment_name="workflow", uri_folder="mlruns"):
     with open(config_path) as fp:
-        config = yaml.load(fp, Loader=yaml.Loader)
+        config = yaml.load(fp, Loader=yaml.SafeLoader)
 
     # config the `sys` section
     sys_config(config, config_path)

qlib/workflow/exp.py

@@ -65,13 +65,13 @@ class Experiment:
         """
         raise NotImplementedError(f"Please implement the `end` method.")
 
-    def create_recorder(self, name=None):
+    def create_recorder(self, recorder_name=None):
         """
         Create a recorder for each experiment.
 
         Parameters
         ----------
-        name : str
+        recorder_name : str
             the name of the recorder to be created.
 
         Returns

qlib/workflow/record_temp.py

@@ -5,10 +5,9 @@ import re
 import pandas as pd
 from pathlib import Path
 from pprint import pprint
-from ..contrib.evaluate import (
-    backtest as normal_backtest,
-    risk_analysis,
-)
+from ..contrib.evaluate import risk_analysis
+from ..contrib.backtest import backtest as normal_backtest
+
 from ..data.dataset import DatasetH
 from ..data.dataset.handler import DataHandlerLP
 from ..utils import init_instance_by_config, get_module_by_module_path
@@ -130,18 +129,25 @@ class SignalRecord(RecordTemp):
         pprint(f"The following are prediction results of the {type(self.model).__name__} model.")
         pprint(pred.head(5))
 
-        # save according label
         if isinstance(self.dataset, DatasetH):
-            params = dict(self=self.dataset, segments="test", col_set="label", data_key=DataHandlerLP.DK_R)
+            # NOTE:
+            # Python doesn't provide the downcasting mechanism.
+            # We use the trick here to downcast the class
+            orig_cls = self.dataset.__class__
+            self.dataset.__class__ = DatasetH
+
+            params = dict(segments="test", col_set="label", data_key=DataHandlerLP.DK_R)
             try:
                 # Assume the backend handler is DataHandlerLP
-                raw_label = DatasetH.prepare(**params)
+                raw_label = self.dataset.prepare(**params)
             except TypeError:
                 # The argument number is not right
                 del params["data_key"]
                 # The backend handler should be DataHandler
-                raw_label = DatasetH.prepare(**params)
+                raw_label = self.dataset.prepare(**params)
+
             self.recorder.save_objects(**{"label.pkl": raw_label})
+            self.dataset.__class__ = orig_cls
 
     def list(self):
         return ["pred.pkl", "label.pkl"]
@@ -206,6 +212,11 @@ class SigAnaRecord(SignalRecord):
 class PortAnaRecord(SignalRecord):
     """
     This is the Portfolio Analysis Record class that generates the analysis results such as those of backtest. This class inherits the ``RecordTemp`` class.
+
+    The following files will be stored in recorder
+    - report_normal.pkl & positions_normal.pkl:
+        - The return report and detailed positions of the backtest, returned by `qlib/contrib/evaluate.py:backtest`
+    - port_analysis.pkl : The risk analysis of your portfolio, returned by `qlib/contrib/evaluate.py:risk_analysis`
     """
 
     artifact_path = "portfolio_analysis"
@@ -229,9 +240,14 @@ class PortAnaRecord(SignalRecord):
 
         # custom strategy and get backtest
         pred_score = super().load()
-        report_normal, positions_normal = normal_backtest(pred_score, strategy=self.strategy, **self.backtest_config)
+        report_dict = normal_backtest(pred_score, strategy=self.strategy, **self.backtest_config)
+        report_normal = report_dict.get("report_df")
+        positions_normal = report_dict.get("positions")
         self.recorder.save_objects(**{"report_normal.pkl": report_normal}, artifact_path=PortAnaRecord.get_path())
         self.recorder.save_objects(**{"positions_normal.pkl": positions_normal}, artifact_path=PortAnaRecord.get_path())
+        order_normal = report_dict.get("order_list")
+        if order_normal:
+            self.recorder.save_objects(**{"order_normal.pkl": order_normal}, artifact_path=PortAnaRecord.get_path())
 
         # analysis
         analysis = dict()

qlib/workflow/recorder.py

@@ -2,7 +2,7 @@
 # Licensed under the MIT License.
 
 import mlflow
-import shutil, os, pickle, tempfile, codecs
+import shutil, os, pickle, tempfile, codecs, pickle
 from pathlib import Path
 from datetime import datetime
 from ..utils.objm import FileManager
@@ -202,9 +202,6 @@ class MLflowRecorder(Recorder):
         super(MLflowRecorder, self).__init__(experiment_id, name)
         self._uri = uri
         self.artifact_uri = None
-        # set up file manager for saving objects
-        self.temp_dir = tempfile.mkdtemp()
-        self.fm = FileManager(Path(self.temp_dir).absolute())
         self.client = mlflow.tracking.MlflowClient(tracking_uri=self._uri)
         # construct from mlflow run
         if mlflow_run is not None:
@@ -248,16 +245,18 @@ class MLflowRecorder(Recorder):
         self.end_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
         if self.status != Recorder.STATUS_S:
             self.status = status
-        shutil.rmtree(self.temp_dir)
 
     def save_objects(self, local_path=None, artifact_path=None, **kwargs):
         assert self._uri is not None, "Please start the experiment and recorder first before using recorder directly."
         if local_path is not None:
             self.client.log_artifacts(self.id, local_path, artifact_path)
         else:
+            temp_dir = Path(tempfile.mkdtemp()).resolve()
             for name, data in kwargs.items():
-                self.fm.save_obj(data, name)
-                self.client.log_artifact(self.id, self.fm.path / name, artifact_path)
+                with (temp_dir / name).open("wb") as f:
+                    pickle.dump(data, f)
+                self.client.log_artifact(self.id, temp_dir / name, artifact_path)
+            shutil.rmtree(temp_dir)
 
     def load_object(self, name):
         assert self._uri is not None, "Please start the experiment and recorder first before using recorder directly."