Add analyser example and finetune example

.github/workflows/python-publish.yml

@@ -12,9 +12,8 @@ jobs:
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        os: [windows-latest, macos-latest, macos-11]
-        # not supporting 3.6 due to annotations is not supported https://stackoverflow.com/a/52890129
-        python-version: [3.7, 3.8]
+        os: [windows-latest, macos-latest]
+        python-version: [3.6, 3.7, 3.8]
 
     steps:
     - uses: actions/checkout@v2
@@ -45,8 +44,7 @@ jobs:
     - name: Build wheel on Linux
       uses: RalfG/python-wheels-manylinux-build@v0.3.1-manylinux2010_x86_64
       with:
-        # not supporting 3.6 due to annotations is not supported https://stackoverflow.com/a/52890129
-        python-versions: 'cp37-cp37m cp38-cp38'
+        python-versions: 'cp36-cp36m cp37-cp37m cp38-cp38'
         build-requirements: 'numpy cython'
     - name: Set up Python
       uses: actions/setup-python@v2

.github/workflows/test.yml

@@ -1,4 +1,4 @@
-name: Test
+name: Test 
 
 on:
   push:
@@ -12,9 +12,8 @@ jobs:
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        os: [windows-latest, ubuntu-18.04, ubuntu-20.04]
-        # not supporting 3.6 due to annotations is not supported https://stackoverflow.com/a/52890129
-        python-version: [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
@@ -26,41 +25,96 @@ jobs:
 
     - name: Lint with Black
       run: |
-        pip install --upgrade pip
-        pip install black wheel
-        black qlib -l 120 --check --diff
+        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: |
-        pip install numpy==1.19.5 ruamel.yaml
-        pip install pyqlib --ignore-installed 
+        if [ "$RUNNER_OS" == "Windows" ]; then
+          $CONDA\\python.exe -m pip install numpy==1.19.5
+          $CONDA\\python.exe -m pip install pyqlib --ignore-installed ruamel.yaml numpy --user
+        else
+          sudo $CONDA/bin/python -m pip install numpy==1.19.5
+          sudo $CONDA/bin/python -m pip install pyqlib --ignore-installed ruamel.yaml numpy
+        fi
+      shell: bash
+    
+    - name: Install Lightgbm for MacOS
+      if: runner.os == 'macOS'
+      run: |
+        /bin/bash -c "$(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: |
-        python scripts/get_data.py qlib_data --target_dir ~/.qlib/qlib_data/cn_data --interval 1d --region cn
+        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: |
-        python qlib/workflow/cli.py examples/benchmarks/LightGBM/workflow_config_lightgbm_Alpha158.yaml
-        python -m pip uninstall -y pyqlib
-
-     # Test Qlib installed from source
+        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 $CONDA/bin/python -m pip uninstall -y pyqlib
+        fi
+      shell: bash
+      
+    # Test Qlib installed from source
     - name: Install Qlib from source
       run: |
-        pip install --upgrade cython jupyter jupyter_contrib_nbextensions numpy scipy scikit-learn # installing without this line will cause errors on GitHub Actions, while instsalling locally won't
-        pip install -e .
+        if [ "$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: |
-        pip install --upgrade pip
-        pip install black pytest
+        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
-        python -m pytest . --durations=10
+        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 workflow by config (install from source)
       run: |
-        python qlib/workflow/cli.py examples/benchmarks/LightGBM/workflow_config_lightgbm_Alpha158.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

.github/workflows/test_macos.yml

@@ -1,75 +0,0 @@
-# There are some issues (in the downloading data phase) on MacOS when running with other tests. So we split it into an individual config.
-name: Test MacOS
-
-on:
-  push:
-    branches: [ main ]
-  pull_request:
-    branches: [ main ]
-
-jobs:
-  build:
-
-    runs-on: ${{ matrix.os }}
-    strategy:
-      matrix:
-        os: [macos-11, macos-latest]
-        # not supporting 3.6 due to annotations is not supported https://stackoverflow.com/a/52890129
-        python-version: [3.7, 3.8]
-
-    steps:
-    - uses: actions/checkout@v2
-
-    - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v2
-      with:
-        python-version: ${{ matrix.python-version }}
-
-    - name: Lint with Black
-      run: |
-        cd ..
-        python -m pip install pip --upgrade
-        python -m pip install wheel --upgrade
-        python -m pip install black
-        python -m black qlib -l 120 --check --diff
-    # Test Qlib installed with pip
-
-    - name: Install Qlib with pip
-      run: |
-          python -m pip install numpy==1.19.5
-          python -m pip install pyqlib --ignore-installed ruamel.yaml numpy
-    - name: Install Lightgbm for MacOS
-      run: |
-        /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Microsoft/qlib/main/.github/brew_install.sh)"
-        HOMEBREW_NO_AUTO_UPDATE=1 brew install lightgbm
-        # FIX MacOS error: Segmentation fault
-        # reference: https://github.com/microsoft/LightGBM/issues/4229
-        wget https://raw.githubusercontent.com/Homebrew/homebrew-core/fb8323f2b170bd4ae97e1bac9bf3e2983af3fdb0/Formula/libomp.rb
-        brew unlink libomp
-        brew install libomp.rb
-    - name: Test data downloads
-      run: |
-        python scripts/get_data.py qlib_data --target_dir ~/.qlib/qlib_data/cn_data --interval 1d --region cn
-    - name: Test workflow by config (install from pip)
-      run: |
-        python qlib/workflow/cli.py examples/benchmarks/LightGBM/workflow_config_lightgbm_Alpha158.yaml
-        python -m pip uninstall -y pyqlib
-    # Test Qlib installed from source
-    - name: Install Qlib from source
-      run: |
-        python -m pip install --upgrade cython
-        python -m pip install numpy jupyter jupyter_contrib_nbextensions
-        python -m pip install -U scipy scikit-learn # installing without this line will cause errors on GitHub Actions, while instsalling locally won't
-        python setup.py install
-    - name: Install test dependencies
-      run: |
-        python -m pip install --upgrade pip
-        python -m pip install -U pyopenssl idna
-        python -m pip install black pytest
-    - name: Unit tests with Pytest
-      run: |
-        cd tests
-        python -m pytest . --durations=0
-    - name: Test workflow by config (install from source)
-      run: |
-          python qlib/workflow/cli.py examples/benchmarks/LightGBM/workflow_config_lightgbm_Alpha158.yaml

.gitignore

@@ -20,7 +20,6 @@ dist/
 .nvimrc
 .vscode
 
-qlib/VERSION.txt
 qlib/data/_libs/expanding.cpp
 qlib/data/_libs/rolling.cpp
 examples/estimator/estimator_example/

CHANGES.rst

@@ -159,21 +159,6 @@ Version 0.5.0
     - Add baselines
     - public data crawler
 
-
-Version 0.8.0
---------------------
-- The backtest is greatly refactored.
-    - Nested decision execution framework is supported
-    - There are lots of changes for daily trading, it is hard to list all of them. But a few important changes could be noticed
-        - The trading limitation is more accurate;
-            - In `previous version <https://github.com/microsoft/qlib/blob/v0.7.2/qlib/contrib/backtest/exchange.py#L160>`_, longing and shorting actions share the same action.
-            - In `current verison <https://github.com/microsoft/qlib/blob/7c31012b507a3823117bddcc693fc64899460b2a/qlib/backtest/exchange.py#L304>`_, the trading limitation is different between loging and shorting action.
-        - The constant is different when calculating annualized metrics.
-            - `Current version <https://github.com/microsoft/qlib/blob/7c31012b507a3823117bddcc693fc64899460b2a/qlib/contrib/evaluate.py#L42>`_ uses more accurate constant than `previous version <https://github.com/microsoft/qlib/blob/v0.7.2/qlib/contrib/evaluate.py#L22>`_
-        - `A new version <https://github.com/microsoft/qlib/blob/7c31012b507a3823117bddcc693fc64899460b2a/qlib/tests/data.py#L17>`_ of data is released. Due to the unstability of Yahoo data source, the data may be different after downloading data again.
-        - Users could chec kout the backtesting results between  `Current version <https://github.com/microsoft/qlib/tree/7c31012b507a3823117bddcc693fc64899460b2a/examples/benchmarks>`_ and `previous version <https://github.com/microsoft/qlib/tree/v0.7.2/examples/benchmarks>`_
-
-
-Other Versions
+Version greater than Version 0.5.0
 ----------------------------------
 Please refer to `Github release Notes <https://github.com/microsoft/qlib/releases>`_

MANIFEST.in

@@ -1 +0,0 @@
-include qlib/VERSION.txt

README.md

@@ -11,15 +11,6 @@
 Recent released features
 | Feature | Status |
 | --                      | ------    |
-| Release Qlib v0.8.0 | [Released](https://github.com/microsoft/qlib/releases/tag/v0.8.0) on Dec 8, 2021 |
-| ADD model | [Released](https://github.com/microsoft/qlib/pull/704) on Nov 22, 2021 |
-| ADARNN  model | [Released](https://github.com/microsoft/qlib/pull/689) on Nov 14, 2021 |
-| TCN  model | [Released](https://github.com/microsoft/qlib/pull/668) on Nov 4, 2021 |
-| Nested Decision Framework | [Released](https://github.com/microsoft/qlib/pull/438) on Oct 1, 2021. [Example](https://github.com/microsoft/qlib/blob/main/examples/nested_decision_execution/workflow.py) and [Doc](https://qlib.readthedocs.io/en/latest/component/highfreq.html) |
-|Temporal Routing Adaptor (TRA) | [Released](https://github.com/microsoft/qlib/pull/531) on July 30, 2021 |
-| Transformer & Localformer | [Released](https://github.com/microsoft/qlib/pull/508) on July 22, 2021 |
-| Release Qlib v0.7.0 | [Released](https://github.com/microsoft/qlib/releases/tag/v0.7.0) on July 12, 2021 |
-| TCTS Model | [Released](https://github.com/microsoft/qlib/pull/491) on July 1, 2021 |
 | Online serving and automatic model rolling | :star: [Released](https://github.com/microsoft/qlib/pull/290) on May 17, 2021 | 
 | DoubleEnsemble Model | [Released](https://github.com/microsoft/qlib/pull/286) on Mar 2, 2021 | 
 | High-frequency data processing example | [Released](https://github.com/microsoft/qlib/pull/257) on Feb 5, 2021  |
@@ -29,8 +20,10 @@ Recent released features
 
 Features released before 2021 are not listed here.
 
+
+
 <p align="center">
-  <img src="http://fintech.msra.cn/images_v070/logo/1.png" />
+  <img src="http://fintech.msra.cn/images_v060/logo/1.png" />
 </p>
 
 
@@ -49,7 +42,7 @@ For more details, please refer to our paper ["Qlib: An AI-oriented Quantitative
   - [Data Preparation](#data-preparation)
   - [Auto Quant Research Workflow](#auto-quant-research-workflow)
   - [Building Customized Quant Research Workflow by Code](#building-customized-quant-research-workflow-by-code)
-- [**Quant Model(Paper) Zoo**](#quant-model-paper-zoo)
+- [**Quant Model Zoo**](#quant-model-zoo)
   - [Run a single model](#run-a-single-model)
   - [Run multiple models](#run-multiple-models)
 - [**Quant Dataset Zoo**](#quant-dataset-zoo)
@@ -69,12 +62,13 @@ Your feedbacks about the features are very important.
 | Planning-based portfolio optimization | Under review:  https://github.com/microsoft/qlib/pull/280 | 
 | Fund data supporting and analysis  |  Under review: https://github.com/microsoft/qlib/pull/292 |
 | Point-in-Time database | Under review: https://github.com/microsoft/qlib/pull/343 |
+| High-frequency trading | Under review: https://github.com/microsoft/qlib/pull/408 | 
 | Meta-Learning-based data selection | Initial opensource version under development |
 
 # Framework of Qlib
 
 <div style="align: center">
-<img src="docs/_static/img/framework.svg" />
+<img src="http://fintech.msra.cn/images_v060/framework.png?v=0.1" />
 </div>
 
 
@@ -83,7 +77,7 @@ At the module level, Qlib is a platform that consists of the above components. T
 | Name                   | Description                                                                                                                                                                                                                                                                                                                                                             |
 | ------                 | -----                                                                                                                                                                                                                                                                                                                                                                   |
 | `Infrastructure` layer | `Infrastructure` layer provides underlying support for Quant research. `DataServer` provides a high-performance infrastructure for users to manage and retrieve raw data. `Trainer` provides a flexible interface to control the training process of models, which enable algorithms to control the training process.                                                       |
-| `Workflow` layer       | `Workflow` layer covers the whole workflow of quantitative investment. `Information Extractor` extracts data for models. `Forecast Model` focuses on producing all kinds of forecast signals (e.g. _alpha_, risk) for other modules. With these signals `Decision Generator` will generate the target trading decisions(i.e. portfolio, orders)  to be executed by `Execution Env` (i.e. the trading market).  There may be multiple levels of `Trading Agent` and `Execution Env` (e.g. an _order executor trading agent and intraday order execution environment_ could behave like an interday trading environment and nested in  _daily portfolio management trading agent and interday trading environment_  ) |
+| `Workflow` layer       | `Workflow` layer covers the whole workflow of quantitative investment. `Information Extractor` extracts data for models. `Forecast Model` focuses on producing all kinds of forecast signals (e.g. _alpha_, risk) for other modules. With these signals `Portfolio Generator` will generate the target portfolio and produce orders to be executed by `Order Executor`. |
 | `Interface` layer      | `Interface` layer tries to present a user-friendly interface for the underlying system. `Analyser` module will provide users detailed analysis reports of forecasting signals, portfolios and execution results                                                                                                                                                                 |
 
 * The modules with hand-drawn style are under development and will be released in the future.
@@ -104,14 +98,14 @@ Here is a quick **[demo](https://terminalizer.com/view/3f24561a4470)** shows how
 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. **Conda** is suggested for managing your Python environment.
 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.
-1. 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.
+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.
@@ -160,35 +154,11 @@ Load and prepare data by running the following code:
 
 This dataset is created by public data collected by [crawler scripts](scripts/data_collector/), which have been released in
 the same repository.
-Users could create the same dataset with it. [Description of dataset](https://github.com/microsoft/qlib/tree/main/scripts/data_collector#description-of-dataset)
+Users could create the same dataset with it. 
 
 *Please pay **ATTENTION** that the data is collected from [Yahoo Finance](https://finance.yahoo.com/lookup), and the data might not be perfect.
 We recommend users to prepare their own data if they have a high-quality dataset. For more information, users can refer to the [related document](https://qlib.readthedocs.io/en/latest/component/data.html#converting-csv-format-into-qlib-format)*.
 
-### Automatic update of daily frequency data (from yahoo finance)
-  > This step is *Optional* if users only want to try their models and strategies on history data.
-  > 
-  > It is recommended that users update the data manually once (--trading_date 2021-05-25) and then set it to update automatically.
-  >
-  > For more information, please refer to: [yahoo collector](https://github.com/microsoft/qlib/tree/main/scripts/data_collector/yahoo#automatic-update-of-daily-frequency-datafrom-yahoo-finance)
-
-  * Automatic update of data to the "qlib" directory each trading day(Linux)
-      * use *crontab*: `crontab -e`
-      * set up timed tasks:
-
-        ```
-        * * * * 1-5 python <script path> update_data_to_bin --qlib_data_1d_dir <user data dir>
-        ```
-        * **script path**: *scripts/data_collector/yahoo/collector.py*
-
-  * Manual update of data
-      ```
-      python scripts/data_collector/yahoo/collector.py update_data_to_bin --qlib_data_1d_dir <user data dir> --trading_date <start date> --end_date <end date>
-      ```
-      * *trading_date*: start of trading day
-      * *end_date*: end of trading day(not included)
-
-
 <!-- 
 - Run the initialization code and get stock data:
 
@@ -252,19 +222,19 @@ Qlib provides a tool named `qrun` to run the whole workflow automatically (inclu
 2. Graphical Reports Analysis: Run `examples/workflow_by_code.ipynb` with `jupyter notebook` to get graphical reports
     - Forecasting signal (model prediction) analysis
       - Cumulative Return of groups
-      ![Cumulative Return](http://fintech.msra.cn/images_v070/analysis/analysis_model_cumulative_return.png?v=0.1)
+      ![Cumulative Return](http://fintech.msra.cn/images_v060/analysis/analysis_model_cumulative_return.png?v=0.1)
       - Return distribution
-      ![long_short](http://fintech.msra.cn/images_v070/analysis/analysis_model_long_short.png?v=0.1)
+      ![long_short](http://fintech.msra.cn/images_v060/analysis/analysis_model_long_short.png?v=0.1)
       - Information Coefficient (IC)
-      ![Information Coefficient](http://fintech.msra.cn/images_v070/analysis/analysis_model_IC.png?v=0.1)
-      ![Monthly IC](http://fintech.msra.cn/images_v070/analysis/analysis_model_monthly_IC.png?v=0.1)
-      ![IC](http://fintech.msra.cn/images_v070/analysis/analysis_model_NDQ.png?v=0.1)
+      ![Information Coefficient](http://fintech.msra.cn/images_v060/analysis/analysis_model_IC.png?v=0.1)        
+      ![Monthly IC](http://fintech.msra.cn/images_v060/analysis/analysis_model_monthly_IC.png?v=0.1)
+      ![IC](http://fintech.msra.cn/images_v060/analysis/analysis_model_NDQ.png?v=0.1)
       - Auto Correlation of forecasting signal (model prediction)
-      ![Auto Correlation](http://fintech.msra.cn/images_v070/analysis/analysis_model_auto_correlation.png?v=0.1)
+      ![Auto Correlation](http://fintech.msra.cn/images_v060/analysis/analysis_model_auto_correlation.png?v=0.1)
 
     - Portfolio analysis
       - Backtest return
-      ![Report](http://fintech.msra.cn/images_v070/analysis/report.png?v=0.1)
+      ![Report](http://fintech.msra.cn/images_v060/analysis/report.png?v=0.1)
       <!-- 
       - Score IC
       ![Score IC](docs/_static/img/score_ic.png)
@@ -281,28 +251,21 @@ Qlib provides a tool named `qrun` to run the whole workflow automatically (inclu
 The automatic workflow may not suit the research workflow of all Quant researchers. To support a flexible Quant research workflow, Qlib also provides a modularized interface to allow researchers to build their own workflow by code. [Here](examples/workflow_by_code.ipynb) is a demo for customized Quant research workflow by code.
 
 
-# [Quant Model (Paper) Zoo](examples/benchmarks)
+# [Quant Model Zoo](examples/benchmarks)
 
 Here is a list of models built on `Qlib`.
-- [GBDT based on XGBoost (Tianqi Chen, et al. KDD 2016)](examples/benchmarks/XGBoost/)
-- [GBDT based on LightGBM (Guolin Ke, et al. NIPS 2017)](examples/benchmarks/LightGBM/)
-- [GBDT based on Catboost (Liudmila Prokhorenkova, et al. NIPS 2018)](examples/benchmarks/CatBoost/)
-- [MLP based on pytorch](examples/benchmarks/MLP/)
-- [LSTM based on pytorch (Sepp Hochreiter, et al. Neural computation 1997)](examples/benchmarks/LSTM/)
-- [GRU based on pytorch (Kyunghyun Cho, et al. 2014)](examples/benchmarks/GRU/)
-- [ALSTM based on pytorch (Yao Qin, et al. IJCAI 2017)](examples/benchmarks/ALSTM)
-- [GATs based on pytorch (Petar Velickovic, et al. 2017)](examples/benchmarks/GATs/)
-- [SFM based on pytorch (Liheng Zhang, et al. KDD 2017)](examples/benchmarks/SFM/)
-- [TFT based on tensorflow (Bryan Lim, et al. International Journal of Forecasting 2019)](examples/benchmarks/TFT/)
-- [TabNet based on pytorch (Sercan O. Arik, et al. AAAI 2019)](examples/benchmarks/TabNet/)
-- [DoubleEnsemble based on LightGBM (Chuheng Zhang, et al. ICDM 2020)](examples/benchmarks/DoubleEnsemble/)
-- [TCTS based on pytorch (Xueqing Wu, et al. ICML 2021)](examples/benchmarks/TCTS/)
-- [Transformer based on pytorch (Ashish Vaswani, et al. NeurIPS 2017)](examples/benchmarks/Transformer/)
-- [Localformer based on pytorch (Juyong Jiang, et al.)](examples/benchmarks/Localformer/)
-- [TRA based on pytorch (Hengxu, Dong, et al. KDD 2021)](examples/benchmarks/TRA/)
-- [TCN based on pytorch (Shaojie Bai, et al. 2018)](examples/benchmarks/TCN/)
-- [ADARNN based on pytorch (YunTao Du, et al. 2021)](examples/benchmarks/ADARNN/)
-- [ADD based on pytorch (Hongshun Tang, et al.2020)](examples/benchmarks/ADD/)
+- [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)
+- [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)
+- [DoubleEnsemble based on LightGBM (Chuheng Zhang, et al. 2020)](qlib/contrib/model/double_ensemble.py)
 
 Your PR of new Quant models is highly welcomed.
 
@@ -315,17 +278,16 @@ All the models listed above are runnable with ``Qlib``. Users can find the confi
 - Users can use the tool `qrun` mentioned above to run a model's workflow based from a config file.
 - Users can create a `workflow_by_code` python script based on the [one](examples/workflow_by_code.py) listed in the `examples` folder.
 
-- Users 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 run --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).
-    - **NOTE**: Each baseline has different environment dependencies, please make sure that your python version aligns with the requirements(e.g. TFT only supports Python 3.6~3.7 due to the limitation of `tensorflow==1.15.0`)
+- Users 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 support *Linux* for now. Other OS will be supported in the future. Besides, it doesn't support parallel running the same model for multiple times as well, and this will be fixed in the future development too.)
+`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.
 
 Here is an example of running all the models for 10 iterations:
 ```python
-python run_all_model.py run 10
+python run_all_model.py 10
 ```
 
 It also provides the API to run specific models at once. For more use cases, please refer to the file's [docstrings](examples/run_all_model.py). 
@@ -384,7 +346,9 @@ 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
+- [【华泰金工林晓明团队】图神经网络选股与Qlib实践——华泰人工智能系列之四十二](https://mp.weixin.qq.com/s/w5fDB6oAv9dO6vlhf1kmhA)
 - [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)
 
@@ -397,30 +361,11 @@ Qlib data are stored in a compact format, which is efficient to be combined into
 Join IM discussion groups:
 |[Gitter](https://gitter.im/Microsoft/qlib)|
 |----|
-|![image](http://fintech.msra.cn/images_v070/qrcode/gitter_qr.png)|
+|![image](http://fintech.msra.cn/images_v060/qrcode/gitter_qr.png)|
 
 # Contributing
-We appreciate all contributions and thank all the contributors!
-<a href="https://github.com/microsoft/qlib/graphs/contributors"><img src="https://contrib.rocks/image?repo=microsoft/qlib" /></a>
 
-Before we released Qlib as an open-source project on Github in Sep 2020, Qlib is an internal project in our group. Unfortunately, the internal commit history is not kept. A lot of members in our group have also contributed a lot to Qlib, which includes Ruihua Wang, Yinda Zhang, Haisu Yu, Shuyu Wang, Bochen Pang, and [Dong Zhou](https://github.com/evanzd/evanzd). Especially thanks to [Dong Zhou](https://github.com/evanzd/evanzd) due to his initial version of Qlib.
-
-## Guidance
-
-This project welcomes contributions and suggestions.  
-**Here are some 
-[code standards](docs/developer/code_standard.rst) for submiting a pull request.**
-
-Making contributions is not a hard thing. Solving an issue(maybe just answering a question raised in [issues list](https://github.com/microsoft/qlib/issues) or [gitter](https://gitter.im/Microsoft/qlib)), fixing/issuing a bug, improving the documents and even fixing a typo are important contributions to Qlib.
-
-For example, if you want to contribute to Qlib's document/code, you can follow the steps in the figure below.
-<p align="center">
-  <img src="https://github.com/demon143/qlib/blob/main/docs/_static/img/change%20doc.gif" />
-</p>
-
-
-## Licence
-Most contributions require you to agree to a
+This project welcomes contributions and suggestions.  Most contributions require you to agree to a
 Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
 the right to use your contribution. For details, visit https://cla.opensource.microsoft.com.
 

VERSION.txt

@@ -1 +0,0 @@
-0.8.0.99

docs/FAQ/FAQ.rst

@@ -97,57 +97,4 @@ Also, feel free to post a new issue in our GitHub repository. We always check ea
 
         python setup.py build_ext --inplace
 
-- If the error occurs when importing ``qlib`` package with command ``python`` , users need to change the running directory to ensure that the script does not run in the project directory.
-
-
-4. BadNamespaceError: / is not a connected namespace
-------------------------------------------------------------------------------------------------------------------------------------
-
-.. code-block:: python
-
-      File "qlib_online.py", line 35, in <module>
-        cal = D.calendar()
-      File "e:\code\python\microsoft\qlib_latest\qlib\qlib\data\data.py", line 973, in calendar
-        return Cal.calendar(start_time, end_time, freq, future=future)
-      File "e:\code\python\microsoft\qlib_latest\qlib\qlib\data\data.py", line 798, in calendar
-        self.conn.send_request(
-      File "e:\code\python\microsoft\qlib_latest\qlib\qlib\data\client.py", line 101, in send_request
-        self.sio.emit(request_type + "_request", request_content)
-      File "G:\apps\miniconda\envs\qlib\lib\site-packages\python_socketio-5.3.0-py3.8.egg\socketio\client.py", line 369, in emit
-        raise exceptions.BadNamespaceError(
-      BadNamespaceError: / is not a connected namespace.
-
-- The version of ``python-socketio`` in qlib needs to be the same as the version of ``python-socketio`` in qlib-server:
-
-    .. code-block:: bash
-
-        pip install -U python-socketio==<qlib-server python-socketio version>
-
-
-5. TypeError: send() got an unexpected keyword argument 'binary'
-------------------------------------------------------------------------------------------------------------------------------------
-
-.. code-block:: python
-
-      File "qlib_online.py", line 35, in <module>
-        cal = D.calendar()
-      File "e:\code\python\microsoft\qlib_latest\qlib\qlib\data\data.py", line 973, in calendar
-        return Cal.calendar(start_time, end_time, freq, future=future)
-      File "e:\code\python\microsoft\qlib_latest\qlib\qlib\data\data.py", line 798, in calendar
-        self.conn.send_request(
-      File "e:\code\python\microsoft\qlib_latest\qlib\qlib\data\client.py", line 101, in send_request
-        self.sio.emit(request_type + "_request", request_content)
-      File "G:\apps\miniconda\envs\qlib\lib\site-packages\socketio\client.py", line 263, in emit
-        self._send_packet(packet.Packet(packet.EVENT, namespace=namespace,
-      File "G:\apps\miniconda\envs\qlib\lib\site-packages\socketio\client.py", line 339, in _send_packet
-        self.eio.send(ep, binary=binary)
-      TypeError: send() got an unexpected keyword argument 'binary'
-
-
-- The ``python-engineio`` version needs to be compatible with the ``python-socketio`` version, reference: https://github.com/miguelgrinberg/python-socketio#version-compatibility
-
-    .. code-block:: bash
-
-        pip install -U python-engineio==<compatible python-socketio version>
-        # or
-        pip install -U python-socketio==3.1.2 python-engineio==3.13.2
+- If the error occurs when importing ``qlib`` package with command ``python`` , users need to change the running directory to ensure that the script does not run in the project directory.

docs/advanced/task_management.rst

@@ -11,10 +11,7 @@ Introduction
 
 The `Workflow <../component/introduction.html>`_ part introduces how to run research workflow in a loosely-coupled way. But it can only execute one ``task`` when you use ``qrun``.
 To automatically generate and execute different tasks, ``Task Management`` provides a whole process including `Task Generating`_, `Task Storing`_, `Task Training`_ and `Task Collecting`_. 
-With this module, users can run their ``task`` automatically at different periods, in different losses, or even by different models.The processes of task generation, model training and combine and collect data are shown in the following figure.
-
-.. image:: ../_static/img/Task-Gen-Recorder-Collector.svg
-    :align: center
+With this module, users can run their ``task`` automatically at different periods, in different losses, or even by different models.
 
 This whole process can be used in `Online Serving <../component/online.html>`_.
 
@@ -77,8 +74,6 @@ If you do not want to use ``Task Manager`` to manage tasks, then use TrainerR to
 
 Task Collecting
 ===============
-Before collecting model training results, you need to use the ``qlib.init`` to specify the path of mlruns.
-
 To collect the results of ``task`` after training, ``Qlib`` provides `Collector <../reference/api.html#Collector>`_, `Group <../reference/api.html#Group>`_ and `Ensemble <../reference/api.html#Ensemble>`_ to collect the results in a readable, expandable and loosely-coupled way.
 
 `Collector <../reference/api.html#Collector>`_ can collect objects from everywhere and process them such as merging, grouping, averaging and so on. It has 2 step action including ``collect`` (collect anything in a dict) and ``process_collect`` (process collected dict).
@@ -87,10 +82,8 @@ To collect the results of ``task`` after training, ``Qlib`` provides `Collector
 For example: {(A,B,C1): object, (A,B,C2): object} ---``group``---> {(A,B): {C1: object, C2: object}} ---``reduce``---> {(A,B): object}
 
 `Ensemble <../reference/api.html#Ensemble>`_ can merge the objects in an ensemble. 
-For example: {C1: object, C2: object} ---``Ensemble``---> object.
-You can set the ensembles you want in the ``Collector``'s process_list.
-Common ensembles include ``AverageEnsemble`` and ``RollingEnsemble``. Average ensemble is used to ensemble the results of different models in the same time period. Rollingensemble is used to ensemble the results of different models in the same time period
+For example: {C1: object, C2: object} ---``Ensemble``---> object
 
 So the hierarchy is ``Collector``'s second step corresponds to ``Group``. And ``Group``'s second step correspond to ``Ensemble``.
 
-For more information, please see `Collector <../reference/api.html#Collector>`_, `Group <../reference/api.html#Group>`_ and `Ensemble <../reference/api.html#Ensemble>`_, or the `example <https://github.com/microsoft/qlib/tree/main/examples/model_rolling/task_manager_rolling.py>`_.
+For more information, please see `Collector <../reference/api.html#Collector>`_, `Group <../reference/api.html#Group>`_ and `Ensemble <../reference/api.html#Ensemble>`_, or the `example <https://github.com/microsoft/qlib/tree/main/examples/model_rolling/task_manager_rolling.py>`_.

docs/component/backtest.rst

@@ -0,0 +1,114 @@
+.. _backtest:
+
+============================================
+Intraday Trading: Model&Strategy Testing
+============================================
+.. currentmodule:: qlib
+
+Introduction
+===================
+
+``Intraday Trading`` is designed to test models and strategies, which help users to check the performance of a custom model/strategy.
+
+
+.. note::
+
+    ``Intraday Trading`` uses ``Order Executor`` to trade and execute orders output by ``Portfolio Strategy``. ``Order Executor`` is a component in `Qlib Framework <../introduction/introduction.html#framework>`_, which can execute orders. ``VWAP Executor`` and ``Close Executor`` is supported by ``Qlib`` now. In the future, ``Qlib`` will support ``HighFreq Executor`` also. 
+
+
+
+Example
+===========================
+
+Users need to generate a `prediction score`(a pandas DataFrame) with MultiIndex<instrument, datetime> and a `score` column. And users need to assign a strategy used in backtest, if strategy is not assigned,
+a `TopkDropoutStrategy` strategy with `(topk=50, n_drop=5, risk_degree=0.95, limit_threshold=0.0095)` will be used.
+If ``Strategy`` module is not users' interested part, `TopkDropoutStrategy` is enough. 
+
+The simple example of the default strategy is as follows.
+
+.. code-block:: python
+
+    from qlib.contrib.evaluate import backtest
+    # pred_score is the prediction score
+    report, positions = backtest(pred_score, topk=50, n_drop=0.5, verbose=False, limit_threshold=0.0095)
+
+To know more about backtesting with a specific ``Strategy``, please refer to `Portfolio Strategy <strategy.html>`_.
+
+To know more about the prediction score `pred_score` output by ``Forecast Model``, please refer to `Forecast Model: Model Training & Prediction <model.html>`_.
+
+Prediction Score
+-----------------
+
+The `prediction score` is a pandas DataFrame. Its index is <datetime(pd.Timestamp), instrument(str)> and it must
+contains a `score` column.
+
+A prediction sample is shown as follows.
+
+.. code-block:: python
+
+      datetime instrument     score
+    2019-01-04   SH600000 -0.505488
+    2019-01-04   SZ002531 -0.320391
+    2019-01-04   SZ000999  0.583808
+    2019-01-04   SZ300569  0.819628
+    2019-01-04   SZ001696 -0.137140
+                 ...            ...
+    2019-04-30   SZ000996 -1.027618
+    2019-04-30   SH603127  0.225677
+    2019-04-30   SH603126  0.462443
+    2019-04-30   SH603133 -0.302460
+    2019-04-30   SZ300760 -0.126383
+
+``Forecast Model`` module can make predictions, please refer to `Forecast Model: Model Training & Prediction <model.html>`_.
+
+Backtest Result
+------------------
+
+The backtest results are in the following form:
+
+.. code-block:: python
+
+                                                      risk
+    excess_return_without_cost mean               0.000605
+                               std                0.005481
+                               annualized_return  0.152373
+                               information_ratio  1.751319
+                               max_drawdown      -0.059055
+    excess_return_with_cost    mean               0.000410
+                               std                0.005478
+                               annualized_return  0.103265
+                               information_ratio  1.187411
+                               max_drawdown      -0.075024
+
+
+
+- `excess_return_without_cost`
+    - `mean`
+        Mean value of the `CAR` (cumulative abnormal return) without cost
+    - `std`
+        The `Standard Deviation` of `CAR` (cumulative abnormal return) without cost.
+    - `annualized_return`
+        The `Annualized Rate` of `CAR` (cumulative abnormal return) without cost.
+    - `information_ratio`
+        The `Information Ratio` without cost. please refer to `Information Ratio – IR <https://www.investopedia.com/terms/i/informationratio.asp>`_.
+    - `max_drawdown`
+        The `Maximum Drawdown` of `CAR` (cumulative abnormal return) without cost, please refer to `Maximum Drawdown (MDD) <https://www.investopedia.com/terms/m/maximum-drawdown-mdd.asp>`_.
+
+- `excess_return_with_cost`
+    - `mean`
+        Mean value of the `CAR` (cumulative abnormal return) series with cost
+    - `std`
+        The `Standard Deviation` of `CAR` (cumulative abnormal return) series with cost.
+    - `annualized_return`
+        The `Annualized Rate` of `CAR` (cumulative abnormal return) with cost.
+    - `information_ratio`
+        The `Information Ratio` with cost. please refer to `Information Ratio – IR <https://www.investopedia.com/terms/i/informationratio.asp>`_.
+    - `max_drawdown`
+        The `Maximum Drawdown` of `CAR` (cumulative abnormal return) with cost, please refer to `Maximum Drawdown (MDD) <https://www.investopedia.com/terms/m/maximum-drawdown-mdd.asp>`_.
+
+
+
+Reference
+==============
+
+To know more about ``Intraday Trading``, please refer to `Intraday Trading <../reference/api.html#module-qlib.contrib.evaluate>`_.

docs/component/data.rst

@@ -67,34 +67,6 @@ After running the above command, users can find china-stock and us-stock data in
 
 When ``Qlib`` is initialized with this dataset, users could build and evaluate their own models with it.  Please refer to `Initialization <../start/initialization.html>`_ for more details.
 
-Automatic update of daily frequency data
-----------------------------------------
-
-  **It is recommended that users update the data manually once (\-\-trading_date 2021-05-25) and then set it to update automatically.**
-
-  For more information refer to: `yahoo collector <https://github.com/microsoft/qlib/tree/main/scripts/data_collector/yahoo#Automatic-update-of-daily-frequency-data>`_
-
-  - Automatic update of data to the "qlib" directory each trading day(Linux)
-      - use *crontab*: `crontab -e`
-      - set up timed tasks:
-
-        .. code-block:: bash
-
-            * * * * 1-5 python <script path> update_data_to_bin --qlib_data_1d_dir <user data dir>
-
-        - **script path**: *scripts/data_collector/yahoo/collector.py*
-
-  - Manual update of data
-
-      .. code-block:: bash
-
-        python scripts/data_collector/yahoo/collector.py update_data_to_bin --qlib_data_1d_dir <user data dir> --trading_date <start date> --end_date <end date>
-
-      - *trading_date*: start of trading day
-      - *end_date*: end of trading day(not included)
-
-
-
 Converting CSV Format into Qlib Format
 -------------------------------------------
 
@@ -179,7 +151,6 @@ After conversion, users can find their Qlib format data in the directory `~/.qli
         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. 
-    If you want to use your own alpha-factor which can't be calculate by OCHLV, like PE, EPS and so on, you could add it to the CSV files with OHCLV together and then dump it to the Qlib format data.
 
 Stock Pool (Market)
 --------------------------------
@@ -338,7 +309,7 @@ DataHandlerLP
 
 In addition to use ``Data Handler`` in an automatic workflow with ``qrun``, ``Data Handler`` can be used as an independent module, by which users can easily preprocess data (standardization, remove NaN, etc.) and build datasets. 
 
-In order to achieve so, ``Qlib`` provides a base class `qlib.data.dataset.DataHandlerLP <../reference/api.html#qlib.data.dataset.handler.DataHandlerLP>`_. The core idea of this class is that: we will have some learnable ``Processors`` which can learn the parameters of data processing(e.g., parameters for zscore normalization). When new data comes in, these `trained` ``Processors`` can then process the new data and thus processing real-time data in an efficient way becomes possible. More information about ``Processors`` will be listed in the next subsection.
+In order to achieve so, ``Qlib`` provides a base class `qlib.data.dataset.DataHandlerLP <../reference/api.html#qlib.data.dataset.handler.DataHandlerLP>`_. The core idea of this class is that: we will have some leanable ``Processors`` which can learn the parameters of data processing(e.g., parameters for zscore normalization). When new data comes in, these `trained` ``Processors`` can then process the new data and thus processing real-time data in an efficient way becomes possible. More information about ``Processors`` will be listed in the next subsection.
 
 
 Interface

docs/component/highfreq.rst

@@ -1,31 +0,0 @@
-.. _highfreq:
-
-============================================
-Design of Nested Decision Execution Framework for High-Frequency Trading
-============================================
-.. currentmodule:: qlib
-
-Introduction
-===================
-
-Daily trading (e.g. portfolio management) and intraday trading (e.g. orders execution) are two hot topics in Quant investment and usually studied separately.
-
-To get the join trading performance of daily and intraday trading, they must interact with each other and run backtest jointly.
-In order to support the joint backtest strategies in multiple levels, a corresponding framework is required. None of the publicly available high-frequency trading frameworks considers multi-level joint trading, which make the backtesting aforementioned inaccurate.
-
-Besides backtesting, the optimization of strategies from different levels is not standalone and can be affected by each other.
-For example, the best portfolio management strategy may change with the performance of order executions(e.g. a portfolio with higher turnover may becomes a better choice when we imporve the order execution strategies).
-To achieve the overall good performance , it is necessary to consider the interaction of strategies in different level. 
-
-Therefore, building a new framework for trading in multiple levels becomes necessary to solve the various problems mentioned above, for which we designed a nested decision execution framework that consider the interaction of strategies.
-
-.. image:: ../_static/img/framework.svg
-
-The design of the framework is shown in the yellow part in the middle of the figure above. Each level consists of ``Trading Agent`` and ``Execution Env``. ``Trading Agent`` has its own data processing module (``Information Extractor``), forecasting module (``Forecast Model``) and decision generator (``Decision Generator``). The trading algorithm generates the decisions by the ``Decision Generator`` based on the forecast signals output by the ``Forecast Module``, and the decisions generated by the trading algorithm are passed to the ``Execution Env``, which returns the execution results. 
-
-The frequency of trading algorithm, decision content and execution environment can be customized by users (e.g. intraday trading, daily-frequency trading, weekly-frequency trading), and the execution environment can be nested with finer-grained trading algorithm and execution environment inside (i.e. sub-workflow in the figure, e.g. daily-frequency orders can be turned into finer-grained decisions by splitting orders within the day). The flexibility of nested decision execution framework makes it easy for users to explore the effects of combining different levels of trading strategies and break down the optimization barriers between different levels of trading algorithm.
-
-Example
-===========================
-
-An example of nested decision execution framework for high-frequency can be found `here <https://github.com/microsoft/qlib/blob/main/examples/nested_decision_execution/workflow.py>`_.

docs/component/online.rst

@@ -21,8 +21,6 @@ which including `Online Manager <#Online Manager>`_, `Online Strategy <#Online S
 If you have many models or `task` needs to be managed, please consider `Task Management <../advanced/task_management.html>`_.
 The `examples <https://github.com/microsoft/qlib/tree/main/examples/online_srv>`_ are based on some components in `Task Management <../advanced/task_management.html>`_ such as ``TrainerRM`` or ``Collector``.
 
-**NOTE**: User should keep his data source updated to support online serving. For example, Qlib provides `a batch of scripts <https://github.com/microsoft/qlib/blob/main/scripts/data_collector/yahoo/README.md#automatic-update-of-daily-frequency-datafrom-yahoo-finance>`_ to help users update Yahoo daily data.
-
 Online Manager
 =============
 
@@ -45,4 +43,4 @@ Updater
 =============
 
 .. automodule:: qlib.workflow.online.update
-    :members:
+    :members:

docs/component/recorder.rst

@@ -123,6 +123,7 @@ Here is a simple exampke of what is done in ``PortAnaRecord``, which users can r
         "n_drop": 5,
     }
     BACKTEST_CONFIG = {
+        "verbose": False,
         "limit_threshold": 0.095,
         "account": 100000000,
         "benchmark": BENCHMARK,

docs/component/strategy.rst

@@ -12,9 +12,7 @@ Introduction
 
 Because the components in ``Qlib`` are designed in a loosely-coupled way, ``Portfolio Strategy`` can be used as an independent module also.
 
-``Qlib`` provides several implemented portfolio strategies. Also, ``Qlib`` supports custom strategy, users can customize strategies according to their own requirements.
-
-After users specifying the models(forecasting signals) and strategies, running backtest will help users to check the performance of a custom model(forecasting signals)/strategy.
+``Qlib`` provides several implemented portfolio strategies. Also, ``Qlib`` supports custom strategy, users can customize strategies according to their own needs.
 
 Base Class & Interface
 ======================
@@ -84,206 +82,39 @@ TopkDropoutStrategy
 
 Usage & Example
 ====================
-
-First, user can create a model to get trading signals(the variable name is ``pred_score`` in following cases).
-
-Prediction Score
------------------
-
-The `prediction score` is a pandas DataFrame. Its index is <datetime(pd.Timestamp), instrument(str)> and it must
-contains a `score` column.
-
-A prediction sample is shown as follows.
+``Portfolio Strategy`` can be specified in the ``Intraday Trading(Backtest)``, the example is as follows.
 
 .. code-block:: python
 
-      datetime instrument     score
-    2019-01-04   SH600000 -0.505488
-    2019-01-04   SZ002531 -0.320391
-    2019-01-04   SZ000999  0.583808
-    2019-01-04   SZ300569  0.819628
-    2019-01-04   SZ001696 -0.137140
-                 ...            ...
-    2019-04-30   SZ000996 -1.027618
-    2019-04-30   SH603127  0.225677
-    2019-04-30   SH603126  0.462443
-    2019-04-30   SH603133 -0.302460
-    2019-04-30   SZ300760 -0.126383
+    from qlib.contrib.strategy.strategy import TopkDropoutStrategy
+    from qlib.contrib.evaluate import backtest
+    STRATEGY_CONFIG = {
+        "topk": 50,
+        "n_drop": 5,
+    }
+    BACKTEST_CONFIG = {
+        "verbose": False,
+        "limit_threshold": 0.095,
+        "account": 100000000,
+        "benchmark": BENCHMARK,
+        "deal_price": "close",
+        "open_cost": 0.0005,
+        "close_cost": 0.0015,
+        "min_cost": 5,
+        
+    }
+    # use default strategy
+    strategy = TopkDropoutStrategy(**STRATEGY_CONFIG)
 
-``Forecast Model`` module can make predictions, please refer to `Forecast Model: Model Training & Prediction <model.html>`_.
+    # pred_score is the `prediction score` output by Model
+    report_normal, positions_normal = backtest(
+        pred_score, strategy=strategy, **BACKTEST_CONFIG
+    )
 
-Normally, the prediction score is the output of the models. But some models are learned from a label with a different scale. So the scale of the prediction score may be different from your expectation(e.g. the return of instruments).
-
-Qlib didn't add a step to scale the prediction score to a unified scale. Because not every trading strategy cares about the scale(e.g. TopkDropoutStrategy only cares about the order).  So the strategy is responsible for rescaling the prediction score(e.g. some portfolio-optimization-based strategies may require a meaningful scale).
-
-Running backtest
------------------
-
-- In most cases, users could backtest their portfolio management strategy  with ``backtest_daily``.
-
-    .. code-block:: python
-
-        from pprint import pprint
-
-        import qlib
-        import pandas as pd
-        from qlib.utils.time import Freq
-        from qlib.utils import flatten_dict
-        from qlib.contrib.evaluate import backtest_daily
-        from qlib.contrib.evaluate import risk_analysis
-        from qlib.contrib.strategy import TopkDropoutStrategy
-
-        # init qlib
-        qlib.init(provider_uri=<qlib data dir>)
-
-        CSI300_BENCH = "SH000300"
-        STRATEGY_CONFIG = {
-            "topk": 50,
-            "n_drop": 5,
-            # pred_score, pd.Series
-            "signal": pred_score,
-        }
-
-
-        strategy_obj = TopkDropoutStrategy(**STRATEGY_CONFIG)
-        report_normal, positions_normal = backtest_daily(
-            start_time="2017-01-01", end_time="2020-08-01", strategy=strategy_obj
-        )
-        analysis = dict()
-        analysis["excess_return_without_cost"] = risk_analysis(
-            report_normal["return"] - report_normal["bench"], freq=analysis_freq
-        )
-        analysis["excess_return_with_cost"] = risk_analysis(
-            report_normal["return"] - report_normal["bench"] - report_normal["cost"], freq=analysis_freq
-        )
-
-        analysis_df = pd.concat(analysis)  # type: pd.DataFrame
-        pprint(analysis_df)
-
-
-
-- If users would like to control their strategies in a more detailed(e.g. users have a more advanced version of executor), user could follow this example.
-
-    .. code-block:: python
-
-        from pprint import pprint
-
-        import qlib
-        import pandas as pd
-        from qlib.utils.time import Freq
-        from qlib.utils import flatten_dict
-        from qlib.backtest import backtest, executor
-        from qlib.contrib.evaluate import risk_analysis
-        from qlib.contrib.strategy import TopkDropoutStrategy
-
-        # init qlib
-        qlib.init(provider_uri=<qlib data dir>)
-
-        CSI300_BENCH = "SH000300"
-        FREQ = "day"
-        STRATEGY_CONFIG = {
-            "topk": 50,
-            "n_drop": 5,
-            # pred_score, pd.Series
-            "signal": pred_score,
-        }
-
-        EXECUTOR_CONFIG = {
-            "time_per_step": "day",
-            "generate_portfolio_metrics": True,
-        }
-
-        backtest_config = {
-            "start_time": "2017-01-01",
-            "end_time": "2020-08-01",
-            "account": 100000000,
-            "benchmark": CSI300_BENCH,
-            "exchange_kwargs": {
-                "freq": FREQ,
-                "limit_threshold": 0.095,
-                "deal_price": "close",
-                "open_cost": 0.0005,
-                "close_cost": 0.0015,
-                "min_cost": 5,
-            },
-        }
-
-        # strategy object
-        strategy_obj = TopkDropoutStrategy(**STRATEGY_CONFIG)
-        # executor object
-        executor_obj = executor.SimulatorExecutor(**EXECUTOR_CONFIG)
-        # backtest
-        portfolio_metric_dict, indicator_dict = backtest(executor=executor_obj, strategy=strategy_obj, **backtest_config)
-        analysis_freq = "{0}{1}".format(*Freq.parse(FREQ))
-        # backtest info
-        report_normal, positions_normal = portfolio_metric_dict.get(analysis_freq)
-
-        # analysis
-        analysis = dict()
-        analysis["excess_return_without_cost"] = risk_analysis(
-            report_normal["return"] - report_normal["bench"], freq=analysis_freq
-        )
-        analysis["excess_return_with_cost"] = risk_analysis(
-            report_normal["return"] - report_normal["bench"] - report_normal["cost"], freq=analysis_freq
-        )
-
-        analysis_df = pd.concat(analysis)  # type: pd.DataFrame
-        # log metrics
-        analysis_dict = flatten_dict(analysis_df["risk"].unstack().T.to_dict())
-        # print out results
-        pprint(f"The following are analysis results of benchmark return({analysis_freq}).")
-        pprint(risk_analysis(report_normal["bench"], freq=analysis_freq))
-        pprint(f"The following are analysis results of the excess return without cost({analysis_freq}).")
-        pprint(analysis["excess_return_without_cost"])
-        pprint(f"The following are analysis results of the excess return with cost({analysis_freq}).")
-        pprint(analysis["excess_return_with_cost"])
-
-
-Result
-------------------
-
-The backtest results are in the following form:
-
-.. code-block:: python
-
-                                                      risk
-    excess_return_without_cost mean               0.000605
-                               std                0.005481
-                               annualized_return  0.152373
-                               information_ratio  1.751319
-                               max_drawdown      -0.059055
-    excess_return_with_cost    mean               0.000410
-                               std                0.005478
-                               annualized_return  0.103265
-                               information_ratio  1.187411
-                               max_drawdown      -0.075024
-
-
-- `excess_return_without_cost`
-    - `mean`
-        Mean value of the `CAR` (cumulative abnormal return) without cost
-    - `std`
-        The `Standard Deviation` of `CAR` (cumulative abnormal return) without cost.
-    - `annualized_return`
-        The `Annualized Rate` of `CAR` (cumulative abnormal return) without cost.
-    - `information_ratio`
-        The `Information Ratio` without cost. please refer to `Information Ratio – IR <https://www.investopedia.com/terms/i/informationratio.asp>`_.
-    - `max_drawdown`
-        The `Maximum Drawdown` of `CAR` (cumulative abnormal return) without cost, please refer to `Maximum Drawdown (MDD) <https://www.investopedia.com/terms/m/maximum-drawdown-mdd.asp>`_.
-
-- `excess_return_with_cost`
-    - `mean`
-        Mean value of the `CAR` (cumulative abnormal return) series with cost
-    - `std`
-        The `Standard Deviation` of `CAR` (cumulative abnormal return) series with cost.
-    - `annualized_return`
-        The `Annualized Rate` of `CAR` (cumulative abnormal return) with cost.
-    - `information_ratio`
-        The `Information Ratio` with cost. please refer to `Information Ratio – IR <https://www.investopedia.com/terms/i/informationratio.asp>`_.
-    - `max_drawdown`
-        The `Maximum Drawdown` of `CAR` (cumulative abnormal return) with cost, please refer to `Maximum Drawdown (MDD) <https://www.investopedia.com/terms/m/maximum-drawdown-mdd.asp>`_.
+To know more about the `prediction score` `pred_score` output by ``Forecast Model``, please refer to `Forecast Model: Model Training & Prediction <model.html>`_.
 
+To know more about ``Intraday Trading``, please refer to `Intraday Trading: Model&Strategy Testing <backtest.html>`_.
 
 Reference
 ===================
-To know more about the `prediction score` `pred_score` output by ``Forecast Model``, please refer to `Forecast Model: Model Training & Prediction <model.html>`_.
+To know more about ``Portfolio Strategy``, please refer to `Strategy API <../reference/api.html#module-qlib.contrib.strategy.strategy>`_.

docs/component/workflow.rst

@@ -53,10 +53,8 @@ Below is a typical config file of ``qrun``.
             kwargs:
                 topk: 50
                 n_drop: 5
-                signal:
-                    - <MODEL> 
-                    - <DATASET>
         backtest:
+            verbose: False
             limit_threshold: 0.095
             account: 100000000
             benchmark: *benchmark
@@ -92,12 +90,12 @@ Below is a typical config file of ``qrun``.
                     test: [2017-01-01, 2020-08-01]
         record: 
             - class: SignalRecord
-              module_path: qlib.workflow.record_temp
-              kwargs: {}
+                module_path: qlib.workflow.record_temp
+                kwargs: {}
             - class: PortAnaRecord
-              module_path: qlib.workflow.record_temp
-              kwargs: 
-                  config: *port_analysis_config
+                module_path: qlib.workflow.record_temp
+                kwargs: 
+                    config: *port_analysis_config
 
 After saving the config into `configuration.yaml`, users could start the workflow and test their ideas with a single command below.
 
@@ -144,7 +142,7 @@ The meaning of each field is as follows:
 
 - `region`
     - If `region` == "us", ``Qlib`` will be initialized in US-stock mode. 
-    - If `region` == "cn", ``Qlib`` will be initialized in China-stock mode.
+    - If `region` == "cn", ``Qlib`` will be initialized in china-stock mode.
 
     .. note:: 
         
@@ -243,10 +241,8 @@ The following script is the configuration of `backtest` and the `strategy` used
             kwargs:
                 topk: 50
                 n_drop: 5
-                signal:
-                    - <MODEL> 
-                    - <DATASET>
         backtest:
+            verbose: False
             limit_threshold: 0.095
             account: 100000000
             benchmark: *benchmark

docs/developer/code_standard.rst

@@ -1,22 +0,0 @@
-.. _code_standard:
-
-=================================
-Code Standard
-=================================
-
-Docstring
-=================================
-Please use the `Numpydoc Style <https://stackoverflow.com/a/24385103>`_.
-
-Continuous Integration
-=================================
-Continuous Integration (CI) tools help you stick to the quality standards by running tests every time you push a new commit and reporting the results to a pull request. 
-
-When you submit a PR request, you can check whether your code passes the CI tests in the "check" section at the bottom of the web page.
-
-A common error is the mixed use of space and tab. You can fix the bug by inputing the following code in the command line.
-
-.. code-block:: python
-
-    pip install black
-    python -m black . -l 120

docs/hidden/tuner.rst

@@ -93,6 +93,7 @@ We write a simple configuration example as following,
                   fend_time: 2018-12-11
     backtest:
         normal_backtest_args:
+            verbose: False
             limit_threshold: 0.095
             account: 500000
             benchmark: SH000905
@@ -305,6 +306,7 @@ About the data and backtest
                   fend_time: 2018-12-11
     backtest:
         normal_backtest_args:
+            verbose: False
             limit_threshold: 0.095
             account: 500000
             benchmark: SH000905

docs/index.rst

@@ -38,8 +38,8 @@ Document Structure
    Workflow: Workflow Management <component/workflow.rst>
    Data Layer: Data Framework&Usage <component/data.rst>
    Forecast Model: Model Training & Prediction <component/model.rst>
-   Portfolio Management and Backtest <component/strategy.rst>
-   Nested Decision Execution: High-Frequency Trading <component/highfreq.rst>
+   Strategy: Portfolio Management <component/strategy.rst>
+   Intraday Trading: Model&Strategy Testing <component/backtest.rst>
    Qlib Recorder: Experiment Management <component/recorder.rst>
    Analysis: Evaluation & Results Analysis <component/report.rst>
    Online Serving: Online Management & Strategy & Tool <component/online.rst>

docs/introduction/introduction.rst

@@ -15,7 +15,7 @@ With ``Qlib``, users can easily try their ideas to create better Quant investmen
 Framework
 ===================
    
-.. image:: ../_static/img/framework.svg
+.. image:: ../_static/img/framework.png
     :align: center
 
 
@@ -34,14 +34,9 @@ Name                      Description
 
 `Workflow` layer          `Workflow` layer covers the whole workflow of quantitative investment.
                           `Information Extractor` extracts data for models. `Forecast Model` focuses
-                          on producing all kinds of forecast signals (e.g. *alpha*, risk) for other
-                          modules.  With these signals `Decision Generator` will generate the target 
-                          trading decisions(i.e. portfolio, orders)  to be executed by `Execution Env`
-                          (i.e. the trading market).  There may be multiple levels of `Trading Agent`
-                          and `Execution Env` (e.g. an *order executor trading agent and intraday
-                          order execution environment* could behave like an interday trading
-                          environment and nested in  *daily portfolio management trading agent and
-                          interday trading environment*  ) 
+                          on producing all kinds of forecast signals (e.g. _alpha_, risk) for other
+                          modules. With these signals `Portfolio Generator` will generate the target
+                          portfolio and produce orders to be executed by `Order Executor`.
 
 `Interface` layer         `Interface` layer tries to present a user-friendly interface for the underlying
                           system. `Analyser` module will provide users detailed analysis reports of

docs/reference/api.rst

@@ -241,7 +241,6 @@ Online Tool
 .. automodule:: qlib.workflow.online.utils
     :members:
 
-
 RecordUpdater
 --------------------
 .. automodule:: qlib.workflow.online.update
@@ -258,4 +257,4 @@ Serializable
     :members:
 
 
-    
+    

docs/start/initialization.rst

@@ -48,7 +48,6 @@ Besides `provider_uri` and `region`, `qlib.init` has other parameters. The follo
         - ``qlib.config.REG_CN``: China stock market.
 
         Different modes will result in different trading limitations and costs.
-        The region is just `shortcuts for defining a batch of configurations <https://github.com/microsoft/qlib/blob/main/qlib/config.py#L239>`_. Users can set the key configurations manually if the existing region setting can't meet their requirements.
 - `redis_host`
     Type: str, optional parameter(default: "127.0.0.1"), host of `redis`
         The lock and cache mechanism relies on redis.
@@ -78,8 +77,7 @@ Besides `provider_uri` and `region`, `qlib.init` has other parameters. The follo
         })
 - `mongo`
     Type: dict, optional parameter, the setting of `MongoDB <https://www.mongodb.com/>`_ which will be used in some features such as `Task Management <../advanced/task_management.html>`_, with high performance and clustered processing. 
-    Users need to follow the steps in  `installation <https://www.mongodb.com/try/download/community>`_  to install MongoDB firstly and then access it via a URI.
-    Users can access mongodb with credential by setting "task_url"  to a string like `"mongodb://%s:%s@%s" % (user, pwd, host + ":" + port)`.
+    Users need finished `installation <https://www.mongodb.com/try/download/community>`_ firstly, and run it in a fixed URL.
 
     .. code-block:: Python
 

examples/benchmarks/ADARNN/README.md

@@ -1,4 +0,0 @@
-# AdaRNN
-* Code: [https://github.com/jindongwang/transferlearning/tree/master/code/deep/adarnn](https://github.com/jindongwang/transferlearning/tree/master/code/deep/adarnn)
-* Paper: [AdaRNN: Adaptive Learning and Forecasting for Time Series](https://arxiv.org/pdf/2108.04443.pdf).
-

examples/benchmarks/ADARNN/requirements.txt

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

examples/benchmarks/ADARNN/workflow_config_adarnn_Alpha360.yaml

@@ -1,88 +0,0 @@
-qlib_init:
-    provider_uri: "~/.qlib/qlib_data/cn_data"
-    region: cn
-market: &market csi300
-benchmark: &benchmark SH000300
-data_handler_config: &data_handler_config
-    start_time: 2008-01-01
-    end_time: 2020-08-01
-    fit_start_time: 2008-01-01
-    fit_end_time: 2014-12-31
-    instruments: *market
-    infer_processors:
-        - class: RobustZScoreNorm
-          kwargs:
-              fields_group: feature
-              clip_outlier: true
-        - class: Fillna
-          kwargs:
-              fields_group: feature
-    learn_processors:
-        - class: DropnaLabel
-        - class: CSRankNorm
-          kwargs:
-              fields_group: label
-    label: ["Ref($close, -2) / Ref($close, -1) - 1"]
-port_analysis_config: &port_analysis_config
-    strategy:
-        class: TopkDropoutStrategy
-        module_path: qlib.contrib.strategy
-        kwargs:
-            model: <MODEL>
-            dataset: <DATASET>
-            topk: 50
-            n_drop: 5
-    backtest:
-        start_time: 2017-01-01
-        end_time: 2020-08-01
-        account: 100000000
-        benchmark: *benchmark
-        exchange_kwargs:
-            limit_threshold: 0.095
-            deal_price: close
-            open_cost: 0.0005
-            close_cost: 0.0015
-            min_cost: 5
-task:
-    model:
-        class: ADARNN
-        module_path: qlib.contrib.model.pytorch_adarnn
-        kwargs:
-            d_feat: 6
-            hidden_size: 64
-            num_layers: 2
-            dropout: 0.0
-            n_epochs: 200
-            lr: 1e-3
-            early_stop: 20
-            batch_size: 800
-            metric: loss
-            loss: mse
-            GPU: 0
-    dataset:
-        class: DatasetH
-        module_path: qlib.data.dataset
-        kwargs:
-            handler:
-                class: Alpha360
-                module_path: qlib.contrib.data.handler
-                kwargs: *data_handler_config
-            segments:
-                train: [2008-01-01, 2014-12-31]
-                valid: [2015-01-01, 2016-12-31]
-                test: [2017-01-01, 2020-08-01]
-    record: 
-        - class: SignalRecord
-          module_path: qlib.workflow.record_temp
-          kwargs: 
-            model: <MODEL>
-            dataset: <DATASET>
-        - class: SigAnaRecord
-          module_path: qlib.workflow.record_temp
-          kwargs: 
-            ana_long_short: False
-            ann_scaler: 252
-        - class: PortAnaRecord
-          module_path: qlib.workflow.record_temp
-          kwargs: 
-            config: *port_analysis_config

examples/benchmarks/ADD/README.md

@@ -1,3 +0,0 @@
-# ADD
-* Paper: [ADD: Augmented Disentanglement Distillation Framework for Improving Stock Trend Forecasting](https://arxiv.org/abs/2012.06289).
-

examples/benchmarks/ADD/requirements.txt

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

examples/benchmarks/ADD/workflow_config_add_Alpha360.yaml

@@ -1,94 +0,0 @@
-qlib_init:
-    provider_uri: "~/.qlib/qlib_data/cn_data"
-    region: cn
-market: &market csi300
-benchmark: &benchmark SH000300
-data_handler_config: &data_handler_config
-    start_time: 2008-01-01
-    end_time: 2020-08-01
-    fit_start_time: 2008-01-01
-    fit_end_time: 2014-12-31
-    instruments: *market
-    infer_processors:
-        - class: RobustZScoreNorm
-          kwargs:
-              fields_group: feature
-              clip_outlier: true
-        - class: Fillna
-          kwargs:
-              fields_group: feature
-    learn_processors:
-        - class: DropnaLabel
-        - class: CSRankNorm
-          kwargs:
-              fields_group: label
-    label: ["Ref($close, -2) / Ref($close, -1) - 1"]
-port_analysis_config: &port_analysis_config
-    strategy:
-        class: TopkDropoutStrategy
-        module_path: qlib.contrib.strategy
-        kwargs:
-            signal:
-                - <MODEL>
-                - <DATASET>
-            topk: 50
-            n_drop: 5
-    backtest:
-        start_time: 2017-01-01
-        end_time: 2020-08-01
-        account: 100000000
-        benchmark: *benchmark
-        exchange_kwargs:
-            limit_threshold: 0.095
-            deal_price: close
-            open_cost: 0.0005
-            close_cost: 0.0015
-            min_cost: 5
-task:
-    model:
-        class: ADD
-        module_path: qlib.contrib.model.pytorch_add
-        kwargs:
-            d_feat: 6
-            hidden_size: 64
-            num_layers: 2
-            dropout: 0.1
-            dec_dropout: 0.0
-            n_epochs: 200
-            lr: 1e-3
-            early_stop: 20
-            batch_size: 5000
-            metric: ic
-            base_model: GRU
-            gamma: 0.1
-            gamma_clip: 0.2
-            optimizer: adam
-            mu: 0.2
-            GPU: 0
-    dataset:
-        class: DatasetH
-        module_path: qlib.data.dataset
-        kwargs:
-            handler:
-                class: Alpha360
-                module_path: qlib.contrib.data.handler
-                kwargs: *data_handler_config
-            segments:
-                train: [2008-01-01, 2014-12-31]
-                valid: [2015-01-01, 2016-12-31]
-                test: [2017-01-01, 2020-08-01]
-    record:
-        - class: SignalRecord
-          module_path: qlib.workflow.record_temp
-          kwargs:
-            model: <MODEL>
-            dataset: <DATASET>
-        - class: SigAnaRecord
-          module_path: qlib.workflow.record_temp
-          kwargs:
-            ana_long_short: False
-            ann_scaler: 252
-        - class: PortAnaRecord
-          module_path: qlib.workflow.record_temp
-          kwargs:
-            config: *port_analysis_config

examples/benchmarks/ALSTM/workflow_config_alstm_Alpha158.yaml

@@ -34,24 +34,19 @@ data_handler_config: &data_handler_config
 port_analysis_config: &port_analysis_config
     strategy:
         class: TopkDropoutStrategy
-        module_path: qlib.contrib.strategy
+        module_path: qlib.contrib.strategy.strategy
         kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
             topk: 50
             n_drop: 5
     backtest:
-        start_time: 2017-01-01
-        end_time: 2020-08-01
+        verbose: False
+        limit_threshold: 0.095
         account: 100000000
         benchmark: *benchmark
-        exchange_kwargs:
-            limit_threshold: 0.095
-            deal_price: close
-            open_cost: 0.0005
-            close_cost: 0.0015
-            min_cost: 5
+        deal_price: close
+        open_cost: 0.0005
+        close_cost: 0.0015
+        min_cost: 5
 task:
     model:
         class: ALSTM
@@ -86,9 +81,7 @@ task:
     record: 
         - class: SignalRecord
           module_path: qlib.workflow.record_temp
-          kwargs: 
-            model: <MODEL>
-            dataset: <DATASET>
+          kwargs: {}
         - class: SigAnaRecord
           module_path: qlib.workflow.record_temp
           kwargs: 

examples/benchmarks/ALSTM/workflow_config_alstm_Alpha360.yaml

@@ -26,24 +26,19 @@ data_handler_config: &data_handler_config
 port_analysis_config: &port_analysis_config
     strategy:
         class: TopkDropoutStrategy
-        module_path: qlib.contrib.strategy
+        module_path: qlib.contrib.strategy.strategy
         kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
             topk: 50
             n_drop: 5
     backtest:
-        start_time: 2017-01-01
-        end_time: 2020-08-01
+        verbose: False
+        limit_threshold: 0.095
         account: 100000000
         benchmark: *benchmark
-        exchange_kwargs:
-            limit_threshold: 0.095
-            deal_price: close
-            open_cost: 0.0005
-            close_cost: 0.0015
-            min_cost: 5
+        deal_price: close
+        open_cost: 0.0005
+        close_cost: 0.0015
+        min_cost: 5
 task:
     model:
         class: ALSTM
@@ -76,9 +71,7 @@ task:
     record: 
         - class: SignalRecord
           module_path: qlib.workflow.record_temp
-          kwargs: 
-            model: <MODEL>
-            dataset: <DATASET>
+          kwargs: {}
         - class: SigAnaRecord
           module_path: qlib.workflow.record_temp
           kwargs: 
@@ -87,4 +80,4 @@ task:
         - class: PortAnaRecord
           module_path: qlib.workflow.record_temp
           kwargs: 
-            config: *port_analysis_config
+            config: *port_analysis_config

examples/benchmarks/CatBoost/workflow_config_catboost_Alpha158.yaml

@@ -12,24 +12,19 @@ data_handler_config: &data_handler_config
 port_analysis_config: &port_analysis_config
     strategy:
         class: TopkDropoutStrategy
-        module_path: qlib.contrib.strategy
+        module_path: qlib.contrib.strategy.strategy
         kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
             topk: 50
             n_drop: 5
     backtest:
-        start_time: 2017-01-01
-        end_time: 2020-08-01
+        verbose: False
+        limit_threshold: 0.095
         account: 100000000
         benchmark: *benchmark
-        exchange_kwargs:
-            limit_threshold: 0.095
-            deal_price: close
-            open_cost: 0.0005
-            close_cost: 0.0015
-            min_cost: 5
+        deal_price: close
+        open_cost: 0.0005
+        close_cost: 0.0015
+        min_cost: 5
 task:
     model:
         class: CatBoostModel
@@ -58,9 +53,7 @@ task:
     record: 
         - class: SignalRecord
           module_path: qlib.workflow.record_temp
-          kwargs: 
-            model: <MODEL>
-            dataset: <DATASET>
+          kwargs: {}
         - class: SigAnaRecord
           module_path: qlib.workflow.record_temp
           kwargs: 

examples/benchmarks/CatBoost/workflow_config_catboost_Alpha360.yaml

@@ -19,24 +19,19 @@ data_handler_config: &data_handler_config
 port_analysis_config: &port_analysis_config
     strategy:
         class: TopkDropoutStrategy
-        module_path: qlib.contrib.strategy
+        module_path: qlib.contrib.strategy.strategy
         kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
             topk: 50
             n_drop: 5
     backtest:
-        start_time: 2017-01-01
-        end_time: 2020-08-01
+        verbose: False
+        limit_threshold: 0.095
         account: 100000000
         benchmark: *benchmark
-        exchange_kwargs:
-            limit_threshold: 0.095
-            deal_price: close
-            open_cost: 0.0005
-            close_cost: 0.0015
-            min_cost: 5
+        deal_price: close
+        open_cost: 0.0005
+        close_cost: 0.0015
+        min_cost: 5
 task:
     model:
         class: CatBoostModel
@@ -65,9 +60,7 @@ task:
     record: 
         - class: SignalRecord
           module_path: qlib.workflow.record_temp
-          kwargs: 
-            model: <MODEL>
-            dataset: <DATASET>
+          kwargs: {}
         - class: SigAnaRecord
           module_path: qlib.workflow.record_temp
           kwargs: 

examples/benchmarks/DoubleEnsemble/workflow_config_doubleensemble_Alpha158.yaml

@@ -12,24 +12,19 @@ data_handler_config: &data_handler_config
 port_analysis_config: &port_analysis_config
     strategy:
         class: TopkDropoutStrategy
-        module_path: qlib.contrib.strategy
+        module_path: qlib.contrib.strategy.strategy
         kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
             topk: 50
             n_drop: 5
     backtest:
-        start_time: 2017-01-01
-        end_time: 2020-08-01
+        verbose: False
+        limit_threshold: 0.095
         account: 100000000
         benchmark: *benchmark
-        exchange_kwargs:
-            limit_threshold: 0.095
-            deal_price: close
-            open_cost: 0.0005
-            close_cost: 0.0015
-            min_cost: 5
+        deal_price: close
+        open_cost: 0.0005
+        close_cost: 0.0015
+        min_cost: 5
 task:
     model:
         class: DEnsembleModel
@@ -80,18 +75,16 @@ task:
                 train: [2008-01-01, 2014-12-31]
                 valid: [2015-01-01, 2016-12-31]
                 test: [2017-01-01, 2020-08-01]
-    record: 
+    record:
         - class: SignalRecord
           module_path: qlib.workflow.record_temp
-          kwargs: 
-            model: <MODEL>
-            dataset: <DATASET>
+          kwargs: {}
         - class: SigAnaRecord
           module_path: qlib.workflow.record_temp
-          kwargs: 
+          kwargs:
             ana_long_short: False
             ann_scaler: 252
         - class: PortAnaRecord
           module_path: qlib.workflow.record_temp
-          kwargs: 
+          kwargs:
             config: *port_analysis_config

examples/benchmarks/DoubleEnsemble/workflow_config_doubleensemble_Alpha360.yaml

@@ -19,24 +19,19 @@ data_handler_config: &data_handler_config
 port_analysis_config: &port_analysis_config
     strategy:
         class: TopkDropoutStrategy
-        module_path: qlib.contrib.strategy
+        module_path: qlib.contrib.strategy.strategy
         kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
             topk: 50
             n_drop: 5
     backtest:
-        start_time: 2017-01-01
-        end_time: 2020-08-01
+        verbose: False
+        limit_threshold: 0.095
         account: 100000000
         benchmark: *benchmark
-        exchange_kwargs:
-            limit_threshold: 0.095
-            deal_price: close
-            open_cost: 0.0005
-            close_cost: 0.0015
-            min_cost: 5
+        deal_price: close
+        open_cost: 0.0005
+        close_cost: 0.0015
+        min_cost: 5
 task:
     model:
         class: DEnsembleModel
@@ -87,12 +82,10 @@ task:
                 train: [2008-01-01, 2014-12-31]
                 valid: [2015-01-01, 2016-12-31]
                 test: [2017-01-01, 2020-08-01]
-    record: 
+    record:
         - class: SignalRecord
           module_path: qlib.workflow.record_temp
-          kwargs: 
-            model: <MODEL>
-            dataset: <DATASET>
+          kwargs: {}
         - class: SigAnaRecord
           module_path: qlib.workflow.record_temp
           kwargs:
@@ -100,5 +93,5 @@ task:
             ann_scaler: 252
         - class: PortAnaRecord
           module_path: qlib.workflow.record_temp
-          kwargs: 
-            config: *port_analysis_config
+          kwargs:
+            config: *port_analysis_config

examples/benchmarks/GATs/workflow_config_gats_Alpha158.yaml

@@ -33,24 +33,19 @@ data_handler_config: &data_handler_config
 port_analysis_config: &port_analysis_config
     strategy:
         class: TopkDropoutStrategy
-        module_path: qlib.contrib.strategy
+        module_path: qlib.contrib.strategy.strategy
         kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
             topk: 50
             n_drop: 5
     backtest:
-        start_time: 2017-01-01
-        end_time: 2020-08-01
+        verbose: False
+        limit_threshold: 0.095
         account: 100000000
         benchmark: *benchmark
-        exchange_kwargs:
-            limit_threshold: 0.095
-            deal_price: close
-            open_cost: 0.0005
-            close_cost: 0.0015
-            min_cost: 5
+        deal_price: close
+        open_cost: 0.0005
+        close_cost: 0.0015
+        min_cost: 5
 task:
     model:
         class: GATs
@@ -66,6 +61,7 @@ task:
             metric: loss
             loss: mse
             base_model: LSTM
+            with_pretrain: True
             model_path: "benchmarks/LSTM/csi300_lstm_ts.pkl"
             GPU: 0
     dataset:
@@ -84,9 +80,7 @@ task:
     record: 
         - class: SignalRecord
           module_path: qlib.workflow.record_temp
-          kwargs: 
-            model: <MODEL>
-            dataset: <DATASET>
+          kwargs: {}
         - class: SigAnaRecord
           module_path: qlib.workflow.record_temp
           kwargs: 
@@ -95,4 +89,4 @@ task:
         - class: PortAnaRecord
           module_path: qlib.workflow.record_temp
           kwargs: 
-            config: *port_analysis_config
+            config: *port_analysis_config

examples/benchmarks/GATs/workflow_config_gats_Alpha360.yaml

@@ -26,24 +26,19 @@ data_handler_config: &data_handler_config
 port_analysis_config: &port_analysis_config
     strategy:
         class: TopkDropoutStrategy
-        module_path: qlib.contrib.strategy
+        module_path: qlib.contrib.strategy.strategy
         kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
             topk: 50
             n_drop: 5
     backtest:
-        start_time: 2017-01-01
-        end_time: 2020-08-01
+        verbose: False
+        limit_threshold: 0.095
         account: 100000000
         benchmark: *benchmark
-        exchange_kwargs:
-            limit_threshold: 0.095
-            deal_price: close
-            open_cost: 0.0005
-            close_cost: 0.0015
-            min_cost: 5
+        deal_price: close
+        open_cost: 0.0005
+        close_cost: 0.0015
+        min_cost: 5
 task:
     model:
         class: GATs
@@ -59,6 +54,7 @@ task:
             metric: loss
             loss: mse
             base_model: LSTM
+            with_pretrain: True
             model_path: "benchmarks/LSTM/model_lstm_csi300.pkl"
             GPU: 0
     dataset:
@@ -76,9 +72,7 @@ task:
     record: 
         - class: SignalRecord
           module_path: qlib.workflow.record_temp
-          kwargs: 
-            model: <MODEL>
-            dataset: <DATASET>
+          kwargs: {}
         - class: SigAnaRecord
           module_path: qlib.workflow.record_temp
           kwargs: 
@@ -87,4 +81,4 @@ task:
         - class: PortAnaRecord
           module_path: qlib.workflow.record_temp
           kwargs: 
-            config: *port_analysis_config
+            config: *port_analysis_config

examples/benchmarks/GRU/README.md

@@ -1,2 +0,0 @@
-# Gated Recurrent Unit (GRU)
-* Paper: [Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation](https://aclanthology.org/D14-1179.pdf).

examples/benchmarks/GRU/workflow_config_gru_Alpha158.yaml

@@ -34,24 +34,19 @@ data_handler_config: &data_handler_config
 port_analysis_config: &port_analysis_config
     strategy:
         class: TopkDropoutStrategy
-        module_path: qlib.contrib.strategy
+        module_path: qlib.contrib.strategy.strategy
         kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
             topk: 50
             n_drop: 5
     backtest:
-        start_time: 2017-01-01
-        end_time: 2020-08-01
+        verbose: False
+        limit_threshold: 0.095
         account: 100000000
         benchmark: *benchmark
-        exchange_kwargs:
-            limit_threshold: 0.095
-            deal_price: close
-            open_cost: 0.0005
-            close_cost: 0.0015
-            min_cost: 5
+        deal_price: close
+        open_cost: 0.0005
+        close_cost: 0.0015
+        min_cost: 5
 task:
     model:
         class: GRU
@@ -85,9 +80,7 @@ task:
     record: 
         - class: SignalRecord
           module_path: qlib.workflow.record_temp
-          kwargs: 
-            model: <MODEL>
-            dataset: <DATASET>
+          kwargs: {}
         - class: SigAnaRecord
           module_path: qlib.workflow.record_temp
           kwargs: 

examples/benchmarks/GRU/workflow_config_gru_Alpha360.yaml

@@ -26,24 +26,19 @@ data_handler_config: &data_handler_config
 port_analysis_config: &port_analysis_config
     strategy:
         class: TopkDropoutStrategy
-        module_path: qlib.contrib.strategy
+        module_path: qlib.contrib.strategy.strategy
         kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
             topk: 50
             n_drop: 5
     backtest:
-        start_time: 2017-01-01
-        end_time: 2020-08-01
+        verbose: False
+        limit_threshold: 0.095
         account: 100000000
         benchmark: *benchmark
-        exchange_kwargs:
-            limit_threshold: 0.095
-            deal_price: close
-            open_cost: 0.0005
-            close_cost: 0.0015
-            min_cost: 5
+        deal_price: close
+        open_cost: 0.0005
+        close_cost: 0.0015
+        min_cost: 5
 task:
     model:
         class: GRU
@@ -75,9 +70,7 @@ task:
     record: 
         - class: SignalRecord
           module_path: qlib.workflow.record_temp
-          kwargs: 
-            model: <MODEL>
-            dataset: <DATASET>
+          kwargs: {}
         - class: SigAnaRecord
           module_path: qlib.workflow.record_temp
           kwargs: 
@@ -86,4 +79,4 @@ task:
         - class: PortAnaRecord
           module_path: qlib.workflow.record_temp
           kwargs: 
-            config: *port_analysis_config
+            config: *port_analysis_config

examples/benchmarks/LSTM/README.md

@@ -1,2 +0,0 @@
-# Long Short-Term Memory (LSTM)
-* Paper: [Long Short-Term Memory](https://direct.mit.edu/neco/article-abstract/9/8/1735/6109/Long-Short-Term-Memory?redirectedFrom=fulltext).

examples/benchmarks/LSTM/workflow_config_lstm_Alpha158.yaml

@@ -34,24 +34,19 @@ data_handler_config: &data_handler_config
 port_analysis_config: &port_analysis_config
     strategy:
         class: TopkDropoutStrategy
-        module_path: qlib.contrib.strategy
+        module_path: qlib.contrib.strategy.strategy
         kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
             topk: 50
             n_drop: 5
     backtest:
-        start_time: 2017-01-01
-        end_time: 2020-08-01
+        verbose: False
+        limit_threshold: 0.095
         account: 100000000
         benchmark: *benchmark
-        exchange_kwargs:
-            limit_threshold: 0.095
-            deal_price: close
-            open_cost: 0.0005
-            close_cost: 0.0015
-            min_cost: 5
+        deal_price: close
+        open_cost: 0.0005
+        close_cost: 0.0015
+        min_cost: 5
 task:
     model:
         class: LSTM
@@ -85,9 +80,7 @@ task:
     record: 
         - class: SignalRecord
           module_path: qlib.workflow.record_temp
-          kwargs: 
-            model: <MODEL>
-            dataset: <DATASET>
+          kwargs: {}
         - class: SigAnaRecord
           module_path: qlib.workflow.record_temp
           kwargs: 

examples/benchmarks/LSTM/workflow_config_lstm_Alpha360.yaml

@@ -26,24 +26,19 @@ data_handler_config: &data_handler_config
 port_analysis_config: &port_analysis_config
     strategy:
         class: TopkDropoutStrategy
-        module_path: qlib.contrib.strategy
+        module_path: qlib.contrib.strategy.strategy
         kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
             topk: 50
             n_drop: 5
     backtest:
-        start_time: 2017-01-01
-        end_time: 2020-08-01
+        verbose: False
+        limit_threshold: 0.095
         account: 100000000
         benchmark: *benchmark
-        exchange_kwargs:
-            limit_threshold: 0.095
-            deal_price: close
-            open_cost: 0.0005
-            close_cost: 0.0015
-            min_cost: 5
+        deal_price: close
+        open_cost: 0.0005
+        close_cost: 0.0015
+        min_cost: 5
 task:
     model:
         class: LSTM
@@ -75,9 +70,7 @@ task:
     record: 
         - class: SignalRecord
           module_path: qlib.workflow.record_temp
-          kwargs: 
-            model: <MODEL>
-            dataset: <DATASET>
+          kwargs: {}
         - class: SigAnaRecord
           module_path: qlib.workflow.record_temp
           kwargs: 
@@ -86,4 +79,4 @@ task:
         - class: PortAnaRecord
           module_path: qlib.workflow.record_temp
           kwargs: 
-            config: *port_analysis_config
+            config: *port_analysis_config

examples/benchmarks/LightGBM/features_resample_N.py

@@ -1,18 +0,0 @@
-#  Copyright (c) Microsoft Corporation.
-#  Licensed under the MIT License.
-
-import pandas as pd
-
-from qlib.data.inst_processor import InstProcessor
-from qlib.utils.resam import resam_calendar
-
-
-class ResampleNProcessor(InstProcessor):
-    def __init__(self, target_frq: str, **kwargs):
-        self.target_frq = target_frq
-
-    def __call__(self, df: pd.DataFrame, *args, **kwargs):
-        df.index = pd.to_datetime(df.index)
-        res_index = resam_calendar(df.index, "1min", self.target_frq)
-        df = df.resample(self.target_frq).last().reindex(res_index)
-        return df

examples/benchmarks/LightGBM/features_sample.py

@@ -1,16 +0,0 @@
-import datetime
-import pandas as pd
-
-from qlib.data.inst_processor import InstProcessor
-
-
-class Resample1minProcessor(InstProcessor):
-    def __init__(self, hour: int, minute: int, **kwargs):
-        self.hour = hour
-        self.minute = minute
-
-    def __call__(self, df: pd.DataFrame, *args, **kwargs):
-        df.index = pd.to_datetime(df.index)
-        df = df.loc[df.index.time == datetime.time(self.hour, self.minute)]
-        df.index = df.index.normalize()
-        return df

examples/benchmarks/LightGBM/multi_freq_handler.py

@@ -1,135 +0,0 @@
-#  Copyright (c) Microsoft Corporation.
-#  Licensed under the MIT License.
-
-import pandas as pd
-
-from qlib.data.dataset.loader import QlibDataLoader
-from qlib.contrib.data.handler import DataHandlerLP, _DEFAULT_LEARN_PROCESSORS, check_transform_proc
-
-
-class Avg15minLoader(QlibDataLoader):
-    def load(self, instruments=None, start_time=None, end_time=None) -> pd.DataFrame:
-        df = super(Avg15minLoader, self).load(instruments, start_time, end_time)
-        if self.is_group:
-            # feature_day(day freq) and feature_15min(1min freq, Average every 15 minutes) renamed feature
-            df.columns = df.columns.map(lambda x: ("feature", x[1]) if x[0].startswith("feature") else x)
-        return df
-
-
-class Avg15minHandler(DataHandlerLP):
-    def __init__(
-        self,
-        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,
-        inst_processor=None,
-        **kwargs,
-    ):
-        infer_processors = check_transform_proc(infer_processors, fit_start_time, fit_end_time)
-        learn_processors = check_transform_proc(learn_processors, fit_start_time, fit_end_time)
-        data_loader = Avg15minLoader(
-            config=self.loader_config(), filter_pipe=filter_pipe, freq=freq, inst_processor=inst_processor
-        )
-        super().__init__(
-            instruments=instruments,
-            start_time=start_time,
-            end_time=end_time,
-            data_loader=data_loader,
-            infer_processors=infer_processors,
-            learn_processors=learn_processors,
-            process_type=process_type,
-        )
-
-    def loader_config(self):
-
-        # Results for dataset: df: pd.DataFrame
-        #   len(df.columns) == 6 + 6 * 16, len(df.index.get_level_values(level="datetime").unique()) == T
-        #   df.columns: close0, close1, ..., close16, open0, ..., open16, ..., vwap16
-        #       freq == day:
-        #           close0, open0, low0, high0, volume0, vwap0
-        #       freq == 1min:
-        #           close1, ..., close16, ..., vwap1, ..., vwap16
-        #   df.index.name == ["datetime", "instrument"]: pd.MultiIndex
-        # Example:
-        #                          feature                        ...                  label
-        #                           close0      open0       low0  ... vwap1 vwap16    LABEL0
-        # datetime   instrument                                   ...
-        # 2020-10-09 SH600000    11.794546  11.819587  11.769505  ...   NaN    NaN -0.005214
-        # 2020-10-15 SH600000    12.044961  11.944795  11.932274  ...   NaN    NaN -0.007202
-        # ...                          ...        ...        ...  ...   ...    ...       ...
-        # 2021-05-28 SZ300676     6.369684   6.495406   6.306568  ...   NaN    NaN -0.001321
-        # 2021-05-31 SZ300676     6.601626   6.465643   6.465130  ...   NaN    NaN -0.023428
-
-        # features day: len(columns) == 6, freq = day
-        # $close is the closing price of the current trading day:
-        #   if the user needs to get the `close` before the last T days, use Ref($close, T-1), for example:
-        #                                    $close  Ref($close, 1)  Ref($close, 2)  Ref($close, 3)  Ref($close, 4)
-        #         instrument datetime
-        #         SH600519   2021-06-01  244.271530
-        #                    2021-06-02  242.205917      244.271530
-        #                    2021-06-03  242.229889      242.205917      244.271530
-        #                    2021-06-04  245.421524      242.229889      242.205917      244.271530
-        #                    2021-06-07  247.547089      245.421524      242.229889      242.205917      244.271530
-
-        # WARNING: Ref($close, N), if N == 0, Ref($close, N) ==> $close
-
-        fields = ["$close", "$open", "$low", "$high", "$volume", "$vwap"]
-        # names: close0, open0, ..., vwap0
-        names = list(map(lambda x: x.strip("$") + "0", fields))
-
-        config = {"feature_day": (fields, names)}
-
-        # features 15min: len(columns) == 6 * 16, freq = 1min
-        #   $close is the closing price of the current trading day:
-        #       if the user gets 'close' for the i-th 15min of the last T days, use `Ref(Mean($close, 15), (T-1) * 240 + i * 15)`, for example:
-        #                                    Ref(Mean($close, 15), 225)  Ref(Mean($close, 15), 465)  Ref(Mean($close, 15), 705)
-        #             instrument datetime
-        #             SH600519   2021-05-31                  241.769897                  243.077942                  244.712997
-        #                        2021-06-01                  244.271530                  241.769897                  243.077942
-        #                        2021-06-02                  242.205917                  244.271530                  241.769897
-
-        # WARNING: Ref(Mean($close, 15), N), if N == 0, Ref(Mean($close, 15), N) ==> Mean($close, 15)
-
-        # Results of the current script:
-        #   time:   09:00 --> 09:14,            ..., 14:45 --> 14:59
-        #   fields: Ref(Mean($close, 15), 225), ..., Mean($close, 15)
-        #   name:   close1,                     ..., close16
-        #
-
-        # Expression description: take close as an example
-        #   Mean($close, 15) ==> df["$close"].rolling(15, min_periods=1).mean()
-        #   Ref(Mean($close, 15), 15) ==> df["$close"].rolling(15, min_periods=1).mean().shift(15)
-
-        #   NOTE: The last data of each trading day, which is the average of the i-th 15 minutes
-
-        # Average:
-        #   Average of the i-th 15-minute period of each trading day: 1 <= i <= 250 // 16
-        #       Avg(15minutes): Ref(Mean($close, 15), 240 - i * 15)
-        #
-        #   Average of the first 15 minutes of each trading day; i = 1
-        #       Avg(09:00 --> 09:14), df.index.loc["09:14"]: Ref(Mean($close, 15), 240- 1 * 15) ==> Ref(Mean($close, 15), 225)
-        #   Average of the last 15 minutes of each trading day; i = 16
-        #       Avg(14:45 --> 14:59), df.index.loc["14:59"]: Ref(Mean($close, 15), 240 - 16 * 15) ==> Ref(Mean($close, 15), 0) ==> Mean($close, 15)
-
-        # 15min resample to day
-        #   df.resample("1d").last()
-        tmp_fields = []
-        tmp_names = []
-        for i, _f in enumerate(fields):
-            _fields = [f"Ref(Mean({_f}, 15), {j * 15})" for j in range(1, 240 // 15)]
-            _names = [f"{names[i][:-1]}{int(names[i][-1])+j}" for j in range(240 // 15 - 1, 0, -1)]
-            _fields.append(f"Mean({_f}, 15)")
-            _names.append(f"{names[i][:-1]}{int(names[i][-1])+240 // 15}")
-            tmp_fields += _fields
-            tmp_names += _names
-        config["feature_15min"] = (tmp_fields, tmp_names)
-        # label
-        config["label"] = (["Ref($close, -2)/Ref($close, -1) - 1"], ["LABEL0"])
-        return config

examples/benchmarks/LightGBM/workflow_config_lightgbm_Alpha158.yaml

@@ -12,23 +12,19 @@ data_handler_config: &data_handler_config
 port_analysis_config: &port_analysis_config
     strategy:
         class: TopkDropoutStrategy
-        module_path: qlib.contrib.strategy
+        module_path: qlib.contrib.strategy.strategy
         kwargs:
-            model: <MODEL> 
-            dataset: <DATASET>
             topk: 50
             n_drop: 5
     backtest:
-        start_time: 2017-01-01
-        end_time: 2020-08-01
+        verbose: False
+        limit_threshold: 0.095
         account: 100000000
         benchmark: *benchmark
-        exchange_kwargs:
-            limit_threshold: 0.095
-            deal_price: close
-            open_cost: 0.0005
-            close_cost: 0.0015
-            min_cost: 5
+        deal_price: close
+        open_cost: 0.0005
+        close_cost: 0.0015
+        min_cost: 5
 task:
     model:
         class: LGBModel
@@ -58,9 +54,7 @@ task:
     record: 
         - class: SignalRecord
           module_path: qlib.workflow.record_temp
-          kwargs: 
-            model: <MODEL>
-            dataset: <DATASET>
+          kwargs: {}
         - class: SigAnaRecord
           module_path: qlib.workflow.record_temp
           kwargs: 
@@ -69,4 +63,4 @@ task:
         - class: PortAnaRecord
           module_path: qlib.workflow.record_temp
           kwargs: 
-            config: *port_analysis_config
+            config: *port_analysis_config

examples/benchmarks/LightGBM/workflow_config_lightgbm_Alpha158_multi_freq.yaml

@@ -1,86 +0,0 @@
-qlib_init:
-    provider_uri:
-        day: "~/.qlib/qlib_data/cn_data"
-        1min: "~/.qlib/qlib_data/cn_data_1min"
-    region: cn
-    dataset_cache: null
-    maxtasksperchild: 1
-market: &market csi300
-benchmark: &benchmark SH000300
-data_handler_config: &data_handler_config
-    start_time: 2008-01-01
-    # 1min closing time is 15:00:00
-    end_time: "2020-08-01 15:00:00"
-    fit_start_time: 2008-01-01
-    fit_end_time: 2014-12-31
-    instruments: *market
-    freq:
-        label: day
-        feature: 1min
-    # with label as reference
-    inst_processor:
-        feature:
-            - class: Resample1minProcessor
-              module_path: features_sample.py
-              kwargs:
-                  hour: 14
-                  minute: 56
-
-port_analysis_config: &port_analysis_config
-    strategy:
-        class: TopkDropoutStrategy
-        module_path: qlib.contrib.strategy.strategy
-        kwargs:
-            topk: 50
-            n_drop: 5
-            signal:
-                - <MODEL> 
-                - <DATASET>
-    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: 0.2
-            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: 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]
-    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/LightGBM/workflow_config_lightgbm_Alpha360.yaml

@@ -19,24 +19,19 @@ data_handler_config: &data_handler_config
 port_analysis_config: &port_analysis_config
     strategy:
         class: TopkDropoutStrategy
-        module_path: qlib.contrib.strategy
+        module_path: qlib.contrib.strategy.strategy
         kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
             topk: 50
             n_drop: 5
     backtest:
-        start_time: 2017-01-01
-        end_time: 2020-08-01
+        verbose: False
+        limit_threshold: 0.095
         account: 100000000
         benchmark: *benchmark
-        exchange_kwargs:
-            limit_threshold: 0.095
-            deal_price: close
-            open_cost: 0.0005
-            close_cost: 0.0015
-            min_cost: 5
+        deal_price: close
+        open_cost: 0.0005
+        close_cost: 0.0015
+        min_cost: 5
 task:
     model:
         class: LGBModel
@@ -66,9 +61,7 @@ task:
     record: 
         - class: SignalRecord
           module_path: qlib.workflow.record_temp
-          kwargs: 
-            model: <MODEL>
-            dataset: <DATASET>
+          kwargs: {}
         - class: SigAnaRecord
           module_path: qlib.workflow.record_temp
           kwargs: 
@@ -77,4 +70,4 @@ task:
         - class: PortAnaRecord
           module_path: qlib.workflow.record_temp
           kwargs: 
-            config: *port_analysis_config
+            config: *port_analysis_config

examples/benchmarks/LightGBM/workflow_config_lightgbm_configurable_dataset.yaml

@@ -27,24 +27,19 @@ data_handler_config: &data_handler_config
 port_analysis_config: &port_analysis_config
     strategy:
         class: TopkDropoutStrategy
-        module_path: qlib.contrib.strategy
+        module_path: qlib.contrib.strategy.strategy
         kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
             topk: 50
             n_drop: 5
     backtest:
-        start_time: 2017-01-01
-        end_time: 2020-08-01
+        verbose: False
+        limit_threshold: 0.095
         account: 100000000
         benchmark: *benchmark
-        exchange_kwargs:
-            limit_threshold: 0.095
-            deal_price: close
-            open_cost: 0.0005
-            close_cost: 0.0015
-            min_cost: 5
+        deal_price: close
+        open_cost: 0.0005
+        close_cost: 0.0015
+        min_cost: 5
 task:
     model:
         class: LGBModel
@@ -74,9 +69,7 @@ task:
     record: 
         - class: SignalRecord
           module_path: qlib.workflow.record_temp
-          kwargs: 
-            model: <MODEL>
-            dataset: <DATASET>
+          kwargs: {}
         - class: SigAnaRecord
           module_path: qlib.workflow.record_temp
           kwargs: 
@@ -85,4 +78,4 @@ task:
         - class: PortAnaRecord
           module_path: qlib.workflow.record_temp
           kwargs: 
-            config: *port_analysis_config
+            config: *port_analysis_config

examples/benchmarks/LightGBM/workflow_config_lightgbm_multi_freq.yaml

@@ -1,90 +0,0 @@
-qlib_init:
-    provider_uri:
-        day: "~/.qlib/qlib_data/cn_data"
-        1min: "~/.qlib/qlib_data/cn_data_1min"
-    region: cn
-    dataset_cache: null
-    maxtasksperchild: null
-market: &market csi300
-benchmark: &benchmark SH000300
-data_handler_config: &data_handler_config
-    start_time: 2008-01-01
-    # 1min closing time is 15:00:00
-    end_time: "2020-08-01 15:00:00"
-    fit_start_time: 2008-01-01
-    fit_end_time: 2014-12-31
-    instruments: *market
-    freq:
-        label: day
-        feature_15min: 1min
-        feature_day: day
-    # with label as reference
-    inst_processor:
-        feature_15min:
-            - class: ResampleNProcessor
-              module_path: features_resample_N.py
-              kwargs:
-                  target_frq: 1d
-
-port_analysis_config: &port_analysis_config
-    strategy:
-        class: TopkDropoutStrategy
-        module_path: qlib.contrib.strategy
-        kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
-            topk: 50
-            n_drop: 5
-    backtest:
-        start_time: 2017-01-01
-        end_time: 2020-08-01
-        account: 100000000
-        benchmark: *benchmark
-        exchange_kwargs:
-            limit_threshold: 0.095
-            deal_price: close
-            open_cost: 0.0005
-            close_cost: 0.0015
-            min_cost: 5
-task:
-    model:
-        class: LGBModel
-        module_path: qlib.contrib.model.gbdt
-        kwargs:
-            loss: mse
-            colsample_bytree: 0.8879
-            learning_rate: 0.2
-            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: Avg15minHandler
-                module_path: multi_freq_handler.py
-                kwargs: *data_handler_config
-            segments:
-                train: [2008-01-01, 2014-12-31]
-                valid: [2015-01-01, 2016-12-31]
-                test: [2017-01-01, 2020-08-01]
-    record:
-        - class: SignalRecord
-          module_path: qlib.workflow.record_temp
-          kwargs:
-            model: <MODEL>
-            dataset: <DATASET>
-        - class: SigAnaRecord
-          module_path: qlib.workflow.record_temp
-          kwargs:
-            ana_long_short: False
-            ann_scaler: 252
-        - class: PortAnaRecord
-          module_path: qlib.workflow.record_temp
-          kwargs:
-            config: *port_analysis_config

examples/benchmarks/Linear/workflow_config_linear_Alpha158.yaml

@@ -26,24 +26,19 @@ data_handler_config: &data_handler_config
 port_analysis_config: &port_analysis_config
     strategy:
         class: TopkDropoutStrategy
-        module_path: qlib.contrib.strategy
+        module_path: qlib.contrib.strategy.strategy
         kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
             topk: 50
             n_drop: 5
     backtest:
-        start_time: 2017-01-01
-        end_time: 2020-08-01
+        verbose: False
+        limit_threshold: 0.095
         account: 100000000
         benchmark: *benchmark
-        exchange_kwargs:
-            limit_threshold: 0.095
-            deal_price: close
-            open_cost: 0.0005
-            close_cost: 0.0015
-            min_cost: 5
+        deal_price: close
+        open_cost: 0.0005
+        close_cost: 0.0015
+        min_cost: 5
 task:
     model:
         class: LinearModel
@@ -62,18 +57,16 @@ task:
                 train: [2008-01-01, 2014-12-31]
                 valid: [2015-01-01, 2016-12-31]
                 test: [2017-01-01, 2020-08-01]
-    record: 
+    record:
         - class: SignalRecord
           module_path: qlib.workflow.record_temp
-          kwargs: 
-            model: <MODEL>
-            dataset: <DATASET>
+          kwargs: {}
         - class: SigAnaRecord
           module_path: qlib.workflow.record_temp
-          kwargs: 
+          kwargs:
             ana_long_short: True
             ann_scaler: 252
         - class: PortAnaRecord
           module_path: qlib.workflow.record_temp
-          kwargs: 
+          kwargs:
             config: *port_analysis_config

examples/benchmarks/Localformer/README.md

@@ -1 +0,0 @@
-# Localformer

examples/benchmarks/Localformer/requirements.txt

@@ -1,3 +0,0 @@
-numpy==1.17.4
-pandas==1.1.2
-torch==1.2.0

examples/benchmarks/Localformer/workflow_config_localformer_Alpha158.yaml

@@ -1,89 +0,0 @@
-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
-        kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
-            topk: 50
-            n_drop: 5
-    backtest:
-        start_time: 2017-01-01
-        end_time: 2020-08-01
-        account: 100000000
-        benchmark: *benchmark
-        exchange_kwargs:
-            limit_threshold: 0.095
-            deal_price: close
-            open_cost: 0.0005
-            close_cost: 0.0015
-            min_cost: 5
-task:
-    model:
-        class: LocalformerModel
-        module_path: qlib.contrib.model.pytorch_localformer_ts
-        kwargs:
-            seed: 0
-            n_jobs: 20
-    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: 
-              model: <MODEL>
-              dataset: <DATASET>
-        - class: SigAnaRecord
-          module_path: qlib.workflow.record_temp
-          kwargs: 
-              ana_long_short: False
-              ann_scaler: 252
-        - class: PortAnaRecord
-          module_path: qlib.workflow.record_temp
-          kwargs: 
-              config: *port_analysis_config

examples/benchmarks/Localformer/workflow_config_localformer_Alpha360.yaml

@@ -1,80 +0,0 @@
-qlib_init:
-    provider_uri: "~/.qlib/qlib_data/cn_data"
-    region: cn
-market: &market csi300
-benchmark: &benchmark SH000300
-data_handler_config: &data_handler_config
-    start_time: 2008-01-01
-    end_time: 2020-08-01
-    fit_start_time: 2008-01-01
-    fit_end_time: 2014-12-31
-    instruments: *market
-    infer_processors:
-        - class: RobustZScoreNorm
-          kwargs:
-              fields_group: feature
-              clip_outlier: true
-        - class: Fillna
-          kwargs:
-              fields_group: feature
-    learn_processors:
-        - class: DropnaLabel
-        - class: CSRankNorm
-          kwargs:
-              fields_group: label
-    label: ["Ref($close, -2) / Ref($close, -1) - 1"]
-port_analysis_config: &port_analysis_config
-    strategy:
-        class: TopkDropoutStrategy
-        module_path: qlib.contrib.strategy
-        kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
-            topk: 50
-            n_drop: 5
-    backtest:
-        start_time: 2017-01-01
-        end_time: 2020-08-01
-        account: 100000000
-        benchmark: *benchmark
-        exchange_kwargs:
-            limit_threshold: 0.095
-            deal_price: close
-            open_cost: 0.0005
-            close_cost: 0.0015
-            min_cost: 5
-task:
-    model:
-        class: LocalformerModel
-        module_path: qlib.contrib.model.pytorch_localformer
-        kwargs:
-            d_feat: 6
-            seed: 0
-    dataset:
-        class: DatasetH
-        module_path: qlib.data.dataset
-        kwargs:
-            handler:
-                class: Alpha360
-                module_path: qlib.contrib.data.handler
-                kwargs: *data_handler_config
-            segments:
-                train: [2008-01-01, 2014-12-31]
-                valid: [2015-01-01, 2016-12-31]
-                test: [2017-01-01, 2020-08-01]
-    record: 
-    - class: SignalRecord
-      module_path: qlib.workflow.record_temp
-      kwargs: 
-        model: <MODEL>
-        dataset: <DATASET>
-    - class: SigAnaRecord
-      module_path: qlib.workflow.record_temp
-      kwargs: 
-        ana_long_short: False
-        ann_scaler: 252
-    - class: PortAnaRecord
-      module_path: qlib.workflow.record_temp
-      kwargs: 
-        config: *port_analysis_config

examples/benchmarks/MLP/README.md

@@ -1 +0,0 @@
-# Multi-Layer Perceptron (MLP)

examples/benchmarks/MLP/workflow_config_mlp_Alpha158.yaml

@@ -39,24 +39,19 @@ data_handler_config: &data_handler_config
 port_analysis_config: &port_analysis_config
     strategy:
         class: TopkDropoutStrategy
-        module_path: qlib.contrib.strategy
+        module_path: qlib.contrib.strategy.strategy
         kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
             topk: 50
             n_drop: 5
     backtest:
-        start_time: 2017-01-01
-        end_time: 2020-08-01
+        verbose: False
+        limit_threshold: 0.095
         account: 100000000
         benchmark: *benchmark
-        exchange_kwargs:
-            limit_threshold: 0.095
-            deal_price: close
-            open_cost: 0.0005
-            close_cost: 0.0015
-            min_cost: 5
+        deal_price: close
+        open_cost: 0.0005
+        close_cost: 0.0015
+        min_cost: 5
 task:
     model:
         class: DNNModelPytorch
@@ -88,9 +83,7 @@ task:
     record: 
         - class: SignalRecord
           module_path: qlib.workflow.record_temp
-          kwargs: 
-            model: <MODEL>
-            dataset: <DATASET>
+          kwargs: {}
         - class: SigAnaRecord
           module_path: qlib.workflow.record_temp
           kwargs: 
@@ -99,4 +92,4 @@ task:
         - class: PortAnaRecord
           module_path: qlib.workflow.record_temp
           kwargs: 
-            config: *port_analysis_config
+            config: *port_analysis_config

examples/benchmarks/MLP/workflow_config_mlp_Alpha360.yaml

@@ -27,24 +27,19 @@ data_handler_config: &data_handler_config
 port_analysis_config: &port_analysis_config
     strategy:
         class: TopkDropoutStrategy
-        module_path: qlib.contrib.strategy
+        module_path: qlib.contrib.strategy.strategy
         kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
             topk: 50
             n_drop: 5
     backtest:
-        start_time: 2017-01-01
-        end_time: 2020-08-01
+        verbose: False
+        limit_threshold: 0.095
         account: 100000000
         benchmark: *benchmark
-        exchange_kwargs:
-            limit_threshold: 0.095
-            deal_price: close
-            open_cost: 0.0005
-            close_cost: 0.0015
-            min_cost: 5
+        deal_price: close
+        open_cost: 0.0005
+        close_cost: 0.0015
+        min_cost: 5
 task:
     model:
         class: DNNModelPytorch
@@ -75,9 +70,7 @@ task:
     record: 
         - class: SignalRecord
           module_path: qlib.workflow.record_temp
-          kwargs: 
-            model: <MODEL>
-            dataset: <DATASET>
+          kwargs: {}
         - class: SigAnaRecord
           module_path: qlib.workflow.record_temp
           kwargs: 
@@ -86,4 +79,4 @@ task:
         - class: PortAnaRecord
           module_path: qlib.workflow.record_temp
           kwargs: 
-            config: *port_analysis_config
+            config: *port_analysis_config

examples/benchmarks/README.md

@@ -1,68 +1,39 @@
 # Benchmarks Performance
-This page lists a batch of methods designed for alpha seeking. Each method tries to give scores/predictions for all stocks each day(e.g. forecasting the future excess return of stocks). The scores/predictions of the models will be used as the mined alpha. Investing in stocks with higher scores is expected to yield more profit.  
 
-The alpha is evaluated in two ways.
-1. The correlation between the alpha and future return.
-1. Constructing portfolio based on the alpha and evaluating the final total return.
-
-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 with different random seeds.
+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.
-<!-- 
-> If you need to reproduce the results below, please use the **v1** dataset: `python scripts/get_data.py qlib_data --target_dir ~/.qlib/qlib_data/qlib_cn_1d --region cn --version v1`
->
-> In the new version of qlib, the default dataset is **v2**. Since the data is collected from the YahooFinance API (which is not very stable), the results of *v2* and *v1* may differ -->
-
-> NOTE:
-> The backtest start from 0.8.0 is quite different from previous version. Please check out the changelog for the difference.
-
-
-## Alpha158 dataset
-
-| Model Name                               | Dataset                             | IC          | ICIR        | Rank IC     | Rank ICIR   | Annualized Return | Information Ratio | Max Drawdown |
-|------------------------------------------|-------------------------------------|-------------|-------------|-------------|-------------|-------------------|-------------------|--------------|
-| TCN(Shaojie Bai, et al.)                 | Alpha158                            | 0.0275±0.00 | 0.2157±0.01 | 0.0411±0.00 | 0.3379±0.01 | 0.0190±0.02       | 0.2887±0.27       | -0.1202±0.03 |
-| TabNet(Sercan O. Arik, et al.)           | Alpha158                            | 0.0204±0.01 | 0.1554±0.07 | 0.0333±0.00 | 0.2552±0.05 | 0.0227±0.04       | 0.3676±0.54       | -0.1089±0.08 |
-| Transformer(Ashish Vaswani, et al.)      | Alpha158                            | 0.0264±0.00 | 0.2053±0.02 | 0.0407±0.00 | 0.3273±0.02 | 0.0273±0.02       | 0.3970±0.26       | -0.1101±0.02 |
-| GRU(Kyunghyun Cho, et al.)               | Alpha158(with selected 20 features) | 0.0315±0.00 | 0.2450±0.04 | 0.0428±0.00 | 0.3440±0.03 | 0.0344±0.02       | 0.5160±0.25       | -0.1017±0.02 |
-| LSTM(Sepp Hochreiter, et al.)            | Alpha158(with selected 20 features) | 0.0318±0.00 | 0.2367±0.04 | 0.0435±0.00 | 0.3389±0.03 | 0.0381±0.03       | 0.5561±0.46       | -0.1207±0.04 |
-| Localformer(Juyong Jiang, et al.)        | Alpha158                            | 0.0356±0.00 | 0.2756±0.03 | 0.0468±0.00 | 0.3784±0.03 | 0.0438±0.02       | 0.6600±0.33       | -0.0952±0.02 |
-| SFM(Liheng Zhang, et al.)                | Alpha158                            | 0.0379±0.00 | 0.2959±0.04 | 0.0464±0.00 | 0.3825±0.04 | 0.0465±0.02       | 0.5672±0.29       | -0.1282±0.03 |
-| ALSTM (Yao Qin, et al.)                  | Alpha158(with selected 20 features) | 0.0362±0.01 | 0.2789±0.06 | 0.0463±0.01 | 0.3661±0.05 | 0.0470±0.03       | 0.6992±0.47       | -0.1072±0.03 |
-| GATs (Petar Velickovic, et al.)          | Alpha158(with selected 20 features) | 0.0349±0.00 | 0.2511±0.01 | 0.0462±0.00 | 0.3564±0.01 | 0.0497±0.01       | 0.7338±0.19       | -0.0777±0.02 |
-| TRA(Hengxu Lin, et al.)                  | Alpha158(with selected 20 features) | 0.0404±0.00 | 0.3197±0.05 | 0.0490±0.00 | 0.4047±0.04 | 0.0649±0.02       | 1.0091±0.30       | -0.0860±0.02 |
-| Linear                                   | Alpha158                            | 0.0397±0.00 | 0.3000±0.00 | 0.0472±0.00 | 0.3531±0.00 | 0.0692±0.00       | 0.9209±0.00       | -0.1509±0.00 |
-| TRA(Hengxu Lin, et al.)                  | Alpha158                            | 0.0440±0.00 | 0.3535±0.05 | 0.0540±0.00 | 0.4451±0.03 | 0.0718±0.02       | 1.0835±0.35       | -0.0760±0.02 |
-| CatBoost(Liudmila Prokhorenkova, et al.) | Alpha158                            | 0.0481±0.00 | 0.3366±0.00 | 0.0454±0.00 | 0.3311±0.00 | 0.0765±0.00       | 0.8032±0.01       | -0.1092±0.00 |
-| XGBoost(Tianqi Chen, et al.)             | Alpha158                            | 0.0498±0.00 | 0.3779±0.00 | 0.0505±0.00 | 0.4131±0.00 | 0.0780±0.00       | 0.9070±0.00       | -0.1168±0.00 |
-| TFT (Bryan Lim, et al.)                  | Alpha158(with selected 20 features) | 0.0358±0.00 | 0.2160±0.03 | 0.0116±0.01 | 0.0720±0.03 | 0.0847±0.02       | 0.8131±0.19       | -0.1824±0.03 |
-| MLP                                      | Alpha158                            | 0.0376±0.00 | 0.2846±0.02 | 0.0429±0.00 | 0.3220±0.01 | 0.0895±0.02       | 1.1408±0.23       | -0.1103±0.02 |
-| LightGBM(Guolin Ke, et al.)              | Alpha158                            | 0.0448±0.00 | 0.3660±0.00 | 0.0469±0.00 | 0.3877±0.00 | 0.0901±0.00       | 1.0164±0.00       | -0.1038±0.00 |
-| DoubleEnsemble(Chuheng Zhang, et al.)    | Alpha158                            | 0.0544±0.00 | 0.4340±0.00 | 0.0523±0.00 | 0.4284±0.01 | 0.1168±0.01       | 1.3384±0.12       | -0.1036±0.01 |
-
 
 ## 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 |
+| DoubleEnsemble (Chuheng Zhang, et al.) | Alpha360 | 0.0407±0.00| 0.3053±0.00 | 0.0490±0.00 | 0.3840±0.00 | 0.0380±0.02 | 0.5000±0.21 | -0.0984±0.02 |
+| TabNet (Sercan O. Arik, et al.)| Alpha360 | 0.0192±0.00 | 0.1401±0.00| 0.0291±0.00 | 0.2163±0.00 | -0.0258±0.00 | -0.2961±0.00| -0.1429±0.00 |
 
-| Model Name                                | Dataset  | IC          | ICIR        | Rank IC     | Rank ICIR   | Annualized Return | Information Ratio | Max Drawdown |
-|-------------------------------------------|----------|-------------|-------------|-------------|-------------|-------------------|-------------------|--------------|
-| Transformer(Ashish Vaswani, et al.)       | Alpha360 | 0.0114±0.00 | 0.0716±0.03 | 0.0327±0.00 | 0.2248±0.02 | -0.0270±0.03      | -0.3378±0.37      | -0.1653±0.05 |
-| TabNet(Sercan O. Arik, et al.)            | Alpha360 | 0.0099±0.00 | 0.0593±0.00 | 0.0290±0.00 | 0.1887±0.00 | -0.0369±0.00      | -0.3892±0.00      | -0.2145±0.00 |
-| MLP                                       | Alpha360 | 0.0273±0.00 | 0.1870±0.02 | 0.0396±0.00 | 0.2910±0.02 | 0.0029±0.02       | 0.0274±0.23       | -0.1385±0.03 |
-| Localformer(Juyong Jiang, et al.)         | Alpha360 | 0.0404±0.00 | 0.2932±0.04 | 0.0542±0.00 | 0.4110±0.03 | 0.0246±0.02       | 0.3211±0.21       | -0.1095±0.02 |
-| CatBoost((Liudmila Prokhorenkova, et al.) | Alpha360 | 0.0378±0.00 | 0.2714±0.00 | 0.0467±0.00 | 0.3659±0.00 | 0.0292±0.00       | 0.3781±0.00       | -0.0862±0.00 |
-| XGBoost(Tianqi Chen, et al.)              | Alpha360 | 0.0394±0.00 | 0.2909±0.00 | 0.0448±0.00 | 0.3679±0.00 | 0.0344±0.00       | 0.4527±0.02       | -0.1004±0.00 |
-| DoubleEnsemble(Chuheng Zhang, et al.)     | Alpha360 | 0.0404±0.00 | 0.3023±0.00 | 0.0495±0.00 | 0.3898±0.00 | 0.0468±0.01       | 0.6302±0.20       | -0.0860±0.01 |
-| LightGBM(Guolin Ke, et al.)               | Alpha360 | 0.0400±0.00 | 0.3037±0.00 | 0.0499±0.00 | 0.4042±0.00 | 0.0558±0.00       | 0.7632±0.00       | -0.0659±0.00 |
-| TCN(Shaojie Bai, et al.)                  | Alpha360 | 0.0441±0.00 | 0.3301±0.02 | 0.0519±0.00 | 0.4130±0.01 | 0.0604±0.02       | 0.8295±0.34       | -0.1018±0.03 |
-| ALSTM (Yao Qin, et al.)                   | Alpha360 | 0.0497±0.00 | 0.3829±0.04 | 0.0599±0.00 | 0.4736±0.03 | 0.0626±0.02       | 0.8651±0.31       | -0.0994±0.03 |
-| LSTM(Sepp Hochreiter, et al.)             | Alpha360 | 0.0448±0.00 | 0.3474±0.04 | 0.0549±0.00 | 0.4366±0.03 | 0.0647±0.03       | 0.8963±0.39       | -0.0875±0.02 |
-| ADD                                       | Alpha360 | 0.0430±0.00 | 0.3188±0.04 | 0.0559±0.00 | 0.4301±0.03 | 0.0667±0.02       | 0.8992±0.34       | -0.0855±0.02 |
-| GRU(Kyunghyun Cho, et al.)                | Alpha360 | 0.0493±0.00 | 0.3772±0.04 | 0.0584±0.00 | 0.4638±0.03 | 0.0720±0.02       | 0.9730±0.33       | -0.0821±0.02 |
-| AdaRNN(Yuntao Du, et al.)                 | Alpha360 | 0.0464±0.01 | 0.3619±0.08 | 0.0539±0.01 | 0.4287±0.06 | 0.0753±0.03       | 1.0200±0.40       | -0.0936±0.03 |
-| GATs (Petar Velickovic, et al.)           | Alpha360 | 0.0476±0.00 | 0.3508±0.02 | 0.0598±0.00 | 0.4604±0.01 | 0.0824±0.02       | 1.1079±0.26       | -0.0894±0.03 |
-| TCTS(Xueqing Wu, et al.)                  | Alpha360 | 0.0508±0.00 | 0.3931±0.04 | 0.0599±0.00 | 0.4756±0.03 | 0.0893±0.03       | 1.2256±0.36       | -0.0857±0.02 |
-| TRA(Hengxu Lin, et al.)                   | Alpha360 | 0.0485±0.00 | 0.3787±0.03 | 0.0587±0.00 | 0.4756±0.03 | 0.0920±0.03       | 1.2789±0.42       | -0.0834±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 |
+| 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 |
+| DoubleEnsemble (Chuheng Zhang, et al.) | Alpha158 | 0.0544±0.00 | 0.4338±0.01 | 0.0523±0.00 | 0.4257±0.01 | 0.1253±0.01 | 1.4105±0.14 | -0.0902±0.01 |
+| TabNet (Sercan O. Arik, et al.)| Alpha158 | 0.0383±0.00 | 0.3414±0.00| 0.0388±0.00 | 0.3460±0.00 | 0.0226±0.00 | 0.2652±0.00| -0.1072±0.00 |
 
 - The selected 20 features are based on the feature importance of a lightgbm-based model.
 - The base model of DoubleEnsemble is LGBM.
-- The base model of TCTS is GRU.

examples/benchmarks/SFM/requirements.txt

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

examples/benchmarks/SFM/workflow_config_sfm_Alpha360.yaml

@@ -26,24 +26,19 @@ data_handler_config: &data_handler_config
 port_analysis_config: &port_analysis_config
     strategy:
         class: TopkDropoutStrategy
-        module_path: qlib.contrib.strategy
+        module_path: qlib.contrib.strategy.strategy
         kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
             topk: 50
             n_drop: 5
     backtest:
-        start_time: 2017-01-01
-        end_time: 2020-08-01
+        verbose: False
+        limit_threshold: 0.095
         account: 100000000
         benchmark: *benchmark
-        exchange_kwargs:
-            limit_threshold: 0.095
-            deal_price: close
-            open_cost: 0.0005
-            close_cost: 0.0015
-            min_cost: 5
+        deal_price: close
+        open_cost: 0.0005
+        close_cost: 0.0015
+        min_cost: 5
 task:
     model:
         class: SFM
@@ -78,9 +73,7 @@ task:
     record: 
         - class: SignalRecord
           module_path: qlib.workflow.record_temp
-          kwargs: 
-            model: <MODEL>
-            dataset: <DATASET>
+          kwargs: {}
         - class: SigAnaRecord
           module_path: qlib.workflow.record_temp
           kwargs: 

examples/benchmarks/TCN/README.md

@@ -1,4 +0,0 @@
-# TCN
-* Code: [https://github.com/locuslab/TCN](https://github.com/locuslab/TCN)
-* Paper: [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://arxiv.org/abs/1803.01271).
-

examples/benchmarks/TCN/requirements.txt

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

examples/benchmarks/TCN/workflow_config_tcn_Alpha158.yaml

@@ -1,100 +0,0 @@
-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
-        kwargs:
-            model: <MODEL>
-            dataset: <DATASET>
-            topk: 50
-            n_drop: 5
-    backtest:
-        start_time: 2017-01-01
-        end_time: 2020-08-01
-        account: 100000000
-        benchmark: *benchmark
-        exchange_kwargs:
-            limit_threshold: 0.095
-            deal_price: close
-            open_cost: 0.0005
-            close_cost: 0.0015
-            min_cost: 5
-task:
-    model:
-        class: TCN
-        module_path: qlib.contrib.model.pytorch_tcn_ts
-        kwargs:
-            d_feat: 20
-            num_layers: 5
-            n_chans: 32
-            kernel_size: 7
-            dropout: 0.5
-            n_epochs: 200
-            lr: 1e-4
-            early_stop: 20
-            batch_size: 2000
-            metric: loss
-            loss: mse
-            optimizer: adam
-            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: 
-            model: <MODEL>
-            dataset: <DATASET>
-        - class: SigAnaRecord
-          module_path: qlib.workflow.record_temp
-          kwargs: 
-            ana_long_short: False
-            ann_scaler: 252
-        - class: PortAnaRecord
-          module_path: qlib.workflow.record_temp
-          kwargs: 
-            config: *port_analysis_config

examples/benchmarks/TCN/workflow_config_tcn_Alpha360.yaml

@@ -1,90 +0,0 @@
-qlib_init:
-    provider_uri: "~/.qlib/qlib_data/cn_data"
-    region: cn
-market: &market csi300
-benchmark: &benchmark SH000300
-data_handler_config: &data_handler_config
-    start_time: 2008-01-01
-    end_time: 2020-08-01
-    fit_start_time: 2008-01-01
-    fit_end_time: 2014-12-31
-    instruments: *market
-    infer_processors:
-        - class: RobustZScoreNorm
-          kwargs:
-              fields_group: feature
-              clip_outlier: true
-        - class: Fillna
-          kwargs:
-              fields_group: feature
-    learn_processors:
-        - class: DropnaLabel
-        - class: CSRankNorm
-          kwargs:
-              fields_group: label
-    label: ["Ref($close, -2) / Ref($close, -1) - 1"]
-port_analysis_config: &port_analysis_config
-    strategy:
-        class: TopkDropoutStrategy
-        module_path: qlib.contrib.strategy
-        kwargs:
-            model: <MODEL>
-            dataset: <DATASET>
-            topk: 50
-            n_drop: 5
-    backtest:
-        start_time: 2017-01-01
-        end_time: 2020-08-01
-        account: 100000000
-        benchmark: *benchmark
-        exchange_kwargs:
-            limit_threshold: 0.095
-            deal_price: close
-            open_cost: 0.0005
-            close_cost: 0.0015
-            min_cost: 5
-task:
-    model:
-        class: TCN
-        module_path: qlib.contrib.model.pytorch_tcn
-        kwargs:
-            d_feat: 6
-            num_layers: 5
-            n_chans: 128
-            kernel_size: 3
-            dropout: 0.5
-            n_epochs: 200
-            lr: 1e-3
-            early_stop: 20
-            batch_size: 2000
-            metric: loss
-            loss: mse
-            optimizer: adam
-            GPU: 0
-    dataset:
-        class: DatasetH
-        module_path: qlib.data.dataset
-        kwargs:
-            handler:
-                class: Alpha360
-                module_path: qlib.contrib.data.handler
-                kwargs: *data_handler_config
-            segments:
-                train: [2008-01-01, 2014-12-31]
-                valid: [2015-01-01, 2016-12-31]
-                test: [2017-01-01, 2020-08-01]
-    record: 
-        - class: SignalRecord
-          module_path: qlib.workflow.record_temp
-          kwargs: 
-            model: <MODEL>
-            dataset: <DATASET>
-        - class: SigAnaRecord
-          module_path: qlib.workflow.record_temp
-          kwargs: 
-            ana_long_short: False
-            ann_scaler: 252
-        - class: PortAnaRecord
-          module_path: qlib.workflow.record_temp
-          kwargs: 
-            config: *port_analysis_config

examples/benchmarks/TCTS/README.md

@@ -1,38 +0,0 @@
-# Temporally Correlated Task Scheduling for Sequence Learning
-### Background
-Sequence learning has attracted much research attention from the machine learning community in recent years. In many applications, a sequence learning task is usually associated with multiple temporally correlated auxiliary tasks, which are different in terms of how much input information to use or which future step to predict. In stock trend forecasting, as demonstrated in Figure1, one can predict the price of a stock in different future days (e.g., tomorrow, the day after tomorrow). In this paper, we propose a framework to make use of those temporally correlated tasks to help each other. 
-
-### Method
-Given that there are usually multiple temporally correlated tasks, the key challenge lies in which tasks to use and when to use them in the training process. This work introduces a learnable task scheduler for sequence learning, which adaptively selects temporally correlated tasks during the training process. The scheduler accesses the model status and the current training data (e.g., in the current minibatch) and selects the best auxiliary task to help the training of the main task. The scheduler and the model for the main task are jointly trained through bi-level optimization: the scheduler is trained to maximize the validation performance of the model, and the model is trained to minimize the training loss guided by the scheduler. The process is demonstrated in Figure2.
-
-<p align="center"> 
-<img src="workflow.png"/>
-</p>
-
-At step <img src="https://latex.codecogs.com/png.latex?s" title="s" />, with training data <img src="https://latex.codecogs.com/png.latex?x_s,y_s" title="x_s,y_s" />, the scheduler <img src="https://latex.codecogs.com/png.latex?\varphi" title="\varphi" /> chooses a suitable task <img src="https://latex.codecogs.com/png.latex?T_{i_s}" title="T_{i_s}" /> (green solid lines) to update the model <img src="https://latex.codecogs.com/png.latex?f" title="f" /> (blue solid lines). After <img src="https://latex.codecogs.com/png.latex?S" title="S" /> steps, we evaluate the model <img src="https://latex.codecogs.com/png.latex?f" title="f" /> on the validation set and update the scheduler <img src="https://latex.codecogs.com/png.latex?\varphi" title="\varphi" /> (green dashed lines).
-
-### Experiments
-Due to different data versions and different Qlib versions, the original data and data preprocessing methods of the experimental settings in the paper are different from those experimental settings in the existing Qlib version. Therefore, we provide two versions of the code according to the two kinds of settings, 1) the [code](https://github.com/lwwang1995/tcts) that can be used to reproduce the experimental results and 2) the [code](https://github.com/microsoft/qlib/blob/main/qlib/contrib/model/pytorch_tcts.py) in the current Qlib baseline.
-
-#### Setting1
-* Dataset: We use the historical transaction data for 300 stocks on [CSI300](http://www.csindex.com.cn/en/indices/index-detail/000300) from 01/01/2008 to 08/01/2020. We split the data into training (01/01/2008-12/31/2013), validation (01/01/2014-12/31/2015), and test sets (01/01/2016-08/01/2020) based on the transaction time. 
-
-* The main tasks <img src="https://latex.codecogs.com/png.latex?T_k" title="T_k" /> refers to forecasting return of stock <img src="https://latex.codecogs.com/png.latex?i" title="i" /> as following,
-<div align=center>
-<img src="https://latex.codecogs.com/png.image?\dpi{110}&space;r_{i}^{t,k}&space;=&space;\frac{price_i^{t&plus;k}}{price_i^{t&plus;k-1}}-1" title="r_{i}^{t,k} = \frac{price_i^{t+k}}{price_i^{t+k-1}}-1" />
-</div>
-
-* Temporally correlated task sets <img src="https://latex.codecogs.com/png.latex?\mathcal{T}_k&space;=&space;\{T_1,&space;T_2,&space;...&space;,&space;T_k\}" title="\mathcal{T}_k = \{T_1, T_2, ... , T_k\}" />, in this paper, <img src="https://latex.codecogs.com/png.latex?\mathcal{T}_3" title="\mathcal{T}_3" />, <img src="https://latex.codecogs.com/png.latex?\mathcal{T}_5" title="\mathcal{T}_5" /> and <img src="https://latex.codecogs.com/png.latex?\mathcal{T}_{10}" title="\mathcal{T}_{10}" /> are used in <img src="https://latex.codecogs.com/png.latex?T_1" title="T_1" />, <img src="https://latex.codecogs.com/png.latex?T_2" title="T_2" />, and <img src="https://latex.codecogs.com/png.latex?T_3" title="T_3" />.
-
-#### Setting2
-* Dataset: We use the historical transaction data for 300 stocks on [CSI300](http://www.csindex.com.cn/en/indices/index-detail/000300) from 01/01/2008 to 08/01/2020. We split the data into training (01/01/2008-12/31/2014), validation (01/01/2015-12/31/2016), and test sets (01/01/2017-08/01/2020) based on the transaction time. 
-
-* The main tasks <img src="https://latex.codecogs.com/png.latex?T_k" title="T_k" /> refers to forecasting return of stock <img src="https://latex.codecogs.com/png.latex?i" title="i" /> as following,
-<div align=center>
-<img src="https://latex.codecogs.com/png.image?\dpi{110}&space;r_{i}^{t,k}&space;=&space;\frac{price_i^{t&plus;1&plus;k}}{price_i^{t&plus;1}}-1" title="r_{i}^{t,k} = \frac{price_i^{t+1+k}}{price_i^{t+1}}-1" />
-</div>
-
-* In Qlib baseline, <img src="https://latex.codecogs.com/png.latex?\mathcal{T}_3" title="\mathcal{T}_3" />, is used in  <img src="https://latex.codecogs.com/png.latex?T_1" title="T_1" />.
-
-### Experimental Result
-You can find the experimental result of setting1 in the [paper](http://proceedings.mlr.press/v139/wu21e/wu21e.pdf) and the experimental result of setting2 in this [page](https://github.com/microsoft/qlib/tree/main/examples/benchmarks).

examples/benchmarks/TCTS/requirements.txt

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

examples/benchmarks/TCTS/workflow_config_tcts_Alpha360.yaml

@@ -1,98 +0,0 @@
-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", 
-            "Ref($close, -3) / Ref($close, -1) - 1", 
-            "Ref($close, -4) / Ref($close, -1) - 1"]
-port_analysis_config: &port_analysis_config
-    strategy:
-        class: TopkDropoutStrategy
-        module_path: qlib.contrib.strategy
-        kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
-            topk: 50
-            n_drop: 5
-    backtest:
-        start_time: 2017-01-01
-        end_time: 2020-08-01
-        account: 100000000
-        benchmark: *benchmark
-        exchange_kwargs:
-            limit_threshold: 0.095
-            deal_price: close
-            open_cost: 0.0005
-            close_cost: 0.0015
-            min_cost: 5
-task:
-    model:
-        class: TCTS
-        module_path: qlib.contrib.model.pytorch_tcts
-        kwargs:
-            d_feat: 6
-            hidden_size: 64
-            num_layers: 2
-            dropout: 0.3
-            n_epochs: 200
-            early_stop: 20
-            batch_size: 800
-            metric: loss
-            loss: mse
-            GPU: 0
-            fore_optimizer: adam
-            weight_optimizer: adam
-            output_dim: 3
-            fore_lr: 2e-3
-            weight_lr: 2e-3
-            steps: 3
-            target_label: 0
-            lowest_valid_performance: 0.993
-    dataset:
-        class: DatasetH
-        module_path: qlib.data.dataset
-        kwargs:
-            handler:
-                class: Alpha360
-                module_path: qlib.contrib.data.handler
-                kwargs: *data_handler_config
-            segments:
-                train: [2008-01-01, 2014-12-31]
-                valid: [2015-01-01, 2016-12-31]
-                test: [2017-01-01, 2020-08-01]
-    record: 
-        - class: SignalRecord
-          module_path: qlib.workflow.record_temp
-          kwargs: 
-            model: <MODEL>
-            dataset: <DATASET>
-        - class: SigAnaRecord
-          module_path: qlib.workflow.record_temp
-          kwargs: 
-            ana_long_short: False
-            ann_scaler: 252
-        - class: PortAnaRecord
-          module_path: qlib.workflow.record_temp
-          kwargs: 
-            config: *port_analysis_config

examples/benchmarks/TFT/README.md

@@ -8,7 +8,7 @@
 Users can follow the ``workflow_by_code_tft.py`` to run the benchmark. 
 
 ### Notes
-1. Please be **aware** that this script can only support `Python 3.6 - 3.7`.
+1. Please be **aware** that this script can only support `Python 3.5 - 3.8`.
 2. If the CUDA version on your machine is not 10.0, please remember to run the following commands `conda install anaconda cudatoolkit=10.0` and `conda install cudnn` on your machine.
 3. The model must run in GPU, or an error will be raised.
 4. New datasets should be registered in ``data_formatters``, for detail please visit the source.

examples/benchmarks/TFT/data_formatters/qlib_Alpha158.py

@@ -195,8 +195,7 @@ class Alpha158Formatter(GenericDataFormatter):
 
         for col in column_names:
             if col not in {"forecast_time", "identifier"}:
-                # Using [col] is for aligning with the format when fitting
-                output[col] = self._target_scaler.inverse_transform(predictions[[col]])
+                output[col] = self._target_scaler.inverse_transform(predictions[col])
 
         return output
 

examples/benchmarks/TFT/requirements.txt

@@ -1,2 +1,3 @@
 tensorflow-gpu==1.15.0
-pandas==1.1.0
+numpy == 1.19.4
+pandas==1.1.0

examples/benchmarks/TFT/tft.py

@@ -1,8 +1,6 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
 
-from pathlib import Path
-from typing import Union
 import numpy as np
 import pandas as pd
 import tensorflow.compat.v1 as tf
@@ -245,7 +243,7 @@ class TFTModel(ModelFT):
             #    extract_numerical_data(targets), extract_numerical_data(p90_forecast),
             #    0.9)
             tf.keras.backend.set_session(default_keras_session)
-        print("Training completed at {}.".format(dte.datetime.now()))
+        print("Training completed.".format(dte.datetime.now()))
         # ===========================Training Process===========================
 
     def predict(self, dataset):
@@ -291,31 +289,3 @@ class TFTModel(ModelFT):
             dataset for finetuning
         """
         pass
-
-    def to_pickle(self, path: Union[Path, str]):
-        """
-        Tensorflow model can't be dumped directly.
-        So the data should be save seperatedly
-
-        **TODO**: Please implement the function to load the files
-
-        Parameters
-        ----------
-        path : Union[Path, str]
-            the target path to be dumped
-        """
-        # FIXME: implementing saving tensorflow models
-        # save tensorflow model
-        # path = Path(path)
-        # path.mkdir(parents=True)
-        # self.model.save(path)
-
-        # save qlib model wrapper
-        drop_attrs = ["model", "tf_graph", "sess", "data_formatter"]
-        orig_attr = {}
-        for attr in drop_attrs:
-            orig_attr[attr] = getattr(self, attr)
-            setattr(self, attr, None)
-        super(TFTModel, self).to_pickle(path)
-        for attr in drop_attrs:
-            setattr(self, attr, orig_attr[attr])

examples/benchmarks/TFT/workflow_config_tft_Alpha158.yaml

@@ -14,24 +14,19 @@ data_handler_config: &data_handler_config
 port_analysis_config: &port_analysis_config
     strategy:
         class: TopkDropoutStrategy
-        module_path: qlib.contrib.strategy
+        module_path: qlib.contrib.strategy.strategy
         kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
             topk: 50
             n_drop: 5
     backtest:
-        start_time: 2017-01-01
-        end_time: 2020-08-01
+        verbose: False
+        limit_threshold: 0.095
         account: 100000000
         benchmark: *benchmark
-        exchange_kwargs:
-            limit_threshold: 0.095
-            deal_price: close
-            open_cost: 0.0005
-            close_cost: 0.0015
-            min_cost: 5
+        deal_price: close
+        open_cost: 0.0005
+        close_cost: 0.0015
+        min_cost: 5
 task:
     model:
         class: TFTModel
@@ -51,9 +46,7 @@ task:
     record: 
         - class: SignalRecord
           module_path: qlib.workflow.record_temp
-          kwargs: 
-            model: <MODEL>
-            dataset: <DATASET>
+          kwargs: {}
         - class: SigAnaRecord
           module_path: qlib.workflow.record_temp
           kwargs: 

examples/benchmarks/TRA/README.md

@@ -1,93 +0,0 @@
-# Learning Multiple Stock Trading Patterns with Temporal Routing Adaptor and Optimal Transport
-
-Temporal Routing Adaptor (TRA) is designed to capture multiple trading patterns in the stock market data. Please refer to [our paper](http://arxiv.org/abs/2106.12950) for more details.
-
-If you find our work useful in your research, please cite:
-```
-@inproceedings{HengxuKDD2021,
- author = {Hengxu Lin and Dong Zhou and Weiqing Liu and Jiang Bian},
- title = {Learning Multiple Stock Trading Patterns with Temporal Routing Adaptor and Optimal Transport},
- booktitle = {Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery \& Data Mining},
- series = {KDD '21},
- year = {2021},
- publisher = {ACM},
-}
-
-@article{yang2020qlib,
-  title={Qlib: An AI-oriented Quantitative Investment Platform},
-  author={Yang, Xiao and Liu, Weiqing and Zhou, Dong and Bian, Jiang and Liu, Tie-Yan},
-  journal={arXiv preprint arXiv:2009.11189},
-  year={2020}
-}
-```
-
-## Usage (Recommended)
-
-**Update**: `TRA` has been moved to `qlib.contrib.model.pytorch_tra` to support other `Qlib` components like  `qlib.workflow` and `Alpha158/Alpha360` dataset.
-
-Please follow the official [doc](https://qlib.readthedocs.io/en/latest/component/workflow.html) to use `TRA` with `workflow`. Here we also provide several example config files:
-
-- `workflow_config_tra_Alpha360.yaml`: running `TRA` with `Alpha360` dataset
-- `workflow_config_tra_Alpha158.yaml`: running `TRA` with `Alpha158` dataset (with feature subsampling)
-- `workflow_config_tra_Alpha158_full.yaml`: running `TRA` with `Alpha158` dataset (without feature subsampling)
-
-The performances of `TRA` are reported in [Benchmarks](https://github.com/microsoft/qlib/tree/main/examples/benchmarks).
-
-## Usage (Not Maintained)
-
-This section is used to reproduce the results in the paper.
-
-### Running
-
-We attach our running scripts for the paper in `run.sh`.
-
-And here are two ways to run the model:
-
-* Running from scripts with default parameters
-
-  You can directly run from Qlib command `qrun`:
-  ```
-  qrun configs/config_alstm.yaml
-  ```
-
-* Running from code with self-defined parameters
-
-  Setting different parameters is also allowed. See codes in `example.py`:
-  ```
-  python example.py --config_file configs/config_alstm.yaml
-  ```
-
-Here we trained TRA on a pretrained backbone model. Therefore we run `*_init.yaml` before TRA's scipts.
-
-### Results
-
-After running the scripts, you can find result files in path `./output`:
-
-* `info.json` - config settings and result metrics.
-* `log.csv` - running logs.
-* `model.bin` - the model parameter dictionary.
-* `pred.pkl` - the prediction scores and output for inference.
-
-Evaluation metrics reported in the paper:
-This result is generated by qlib==0.7.1.
-
-| Methods | MSE| MAE| IC | ICIR | AR | AV | SR | MDD |
-|-------|-------|------|-----|-----|-----|-----|-----|-----|
-|Linear|0.163|0.327|0.020|0.132|-3.2%|16.8%|-0.191|32.1%|
-|LightGBM|0.160(0.000)|0.323(0.000)|0.041|0.292|7.8%|15.5%|0.503|25.7%|
-|MLP|0.160(0.002)|0.323(0.003)|0.037|0.273|3.7%|15.3%|0.264|26.2%|
-|SFM|0.159(0.001)	|0.321(0.001)	|0.047	|0.381	|7.1%	|14.3%	|0.497	|22.9%|
-|ALSTM|0.158(0.001)	|0.320(0.001)	|0.053	|0.419	|12.3%	|13.7%	|0.897	|20.2%|
-|Trans.|0.158(0.001)	|0.322(0.001)	|0.051	|0.400	|14.5%	|14.2%	|1.028	|22.5%|
-|ALSTM+TS|0.160(0.002)	|0.321(0.002)	|0.039	|0.291	|6.7%	|14.6%	|0.480|22.3%|
-|Trans.+TS|0.160(0.004)	|0.324(0.005)	|0.037	|0.278	|10.4%	|14.7%	|0.722	|23.7%|
-|ALSTM+TRA(Ours)|0.157(0.000)	|0.318(0.000)	|0.059	|0.460	|12.4%	|14.0%	|0.885	|20.4%|
-|Trans.+TRA(Ours)|0.157(0.000)	|0.320(0.000)	|0.056	|0.442	|16.1%	|14.2%	|1.133	|23.1%|
-
-A more detailed demo for our experiment results in the paper can be found in `Report.ipynb`.
-
-## Common Issues
-
-For help or issues using TRA, please submit a GitHub issue.
-
-Sometimes we might encounter situation where the loss is `NaN`, please check the `epsilon` parameter in the sinkhorn algorithm, adjusting the `epsilon` according to input's scale is important.

examples/benchmarks/TRA/configs/config_alstm.yaml

@@ -1,63 +0,0 @@
-qlib_init:
-  provider_uri: "~/.qlib/qlib_data/cn_data"
-  region: cn
-
-data_loader_config: &data_loader_config
-  class: StaticDataLoader
-  module_path: qlib.data.dataset.loader
-  kwargs:
-    config:
-      feature: data/feature.pkl
-      label: data/label.pkl
-
-model_config: &model_config
-  input_size: 16
-  hidden_size: 256
-  num_layers: 2
-  num_heads: 2
-  use_attn: True
-  dropout: 0.1
-
-num_states: &num_states 1
-
-tra_config: &tra_config
-  num_states: *num_states
-  hidden_size: 16
-  tau: 1.0
-  src_info: LR_TPE
-
-task:
-  model:
-    class: TRAModel
-    module_path: src/model.py
-    kwargs:
-      lr: 0.0002
-      n_epochs: 500
-      max_steps_per_epoch: 100
-      early_stop: 20
-      seed: 1000
-      logdir: output/test/alstm
-      model_type: LSTM
-      model_config: *model_config
-      tra_config: *tra_config
-      lamb: 1.0
-      rho: 0.99
-      freeze_model: False
-      model_init_state: 
-  dataset:
-    class: MTSDatasetH
-    module_path: src/dataset.py
-    kwargs:
-      handler:
-        class: DataHandler
-        module_path: qlib.data.dataset.handler
-        kwargs:
-          data_loader: *data_loader_config
-      segments:
-        train: [2007-10-30, 2016-05-27]
-        valid: [2016-09-26, 2018-05-29]
-        test: [2018-09-21, 2020-06-30]
-      seq_len: 60
-      horizon: 21
-      num_states: *num_states
-      batch_size: 1024

examples/benchmarks/TRA/configs/config_alstm_tra.yaml

@@ -1,63 +0,0 @@
-qlib_init:
-  provider_uri: "~/.qlib/qlib_data/cn_data"
-  region: cn
-
-data_loader_config: &data_loader_config
-  class: StaticDataLoader
-  module_path: qlib.data.dataset.loader
-  kwargs:
-    config:
-      feature: data/feature.pkl
-      label: data/label.pkl
-
-model_config: &model_config
-  input_size: 16
-  hidden_size: 256
-  num_layers: 2
-  num_heads: 2
-  use_attn: True
-  dropout: 0.1
-
-num_states: &num_states 10
-
-tra_config: &tra_config
-  num_states: *num_states
-  hidden_size: 16
-  tau: 1.0
-  src_info: LR_TPE
-
-task:
-  model:
-    class: TRAModel
-    module_path: src/model.py
-    kwargs:
-      lr: 0.0001
-      n_epochs: 500
-      max_steps_per_epoch: 100
-      early_stop: 20
-      seed: 1000
-      logdir: output/test/alstm_tra
-      model_type: LSTM
-      model_config: *model_config
-      tra_config: *tra_config
-      lamb: 2.0
-      rho: 0.99
-      freeze_model: True
-      model_init_state: output/test/alstm_tra_init/model.bin
-  dataset:
-    class: MTSDatasetH
-    module_path: src/dataset.py
-    kwargs:
-      handler:
-        class: DataHandler
-        module_path: qlib.data.dataset.handler
-        kwargs:
-          data_loader: *data_loader_config
-      segments:
-        train: [2007-10-30, 2016-05-27]
-        valid: [2016-09-26, 2018-05-29]
-        test: [2018-09-21, 2020-06-30]
-      seq_len: 60
-      horizon: 21
-      num_states: *num_states
-      batch_size: 1024

examples/benchmarks/TRA/configs/config_alstm_tra_init.yaml

@@ -1,63 +0,0 @@
-qlib_init:
-  provider_uri: "~/.qlib/qlib_data/cn_data"
-  region: cn
-
-data_loader_config: &data_loader_config
-  class: StaticDataLoader
-  module_path: qlib.data.dataset.loader
-  kwargs:
-    config:
-      feature: data/feature.pkl
-      label: data/label.pkl
-
-model_config: &model_config
-  input_size: 16
-  hidden_size: 256
-  num_layers: 2
-  num_heads: 2
-  use_attn: True
-  dropout: 0.1
-
-num_states: &num_states 3
-
-tra_config: &tra_config
-  num_states: *num_states
-  hidden_size: 16
-  tau: 1.0
-  src_info: LR_TPE
-
-task:
-  model:
-    class: TRAModel
-    module_path: src/model.py
-    kwargs:
-      lr: 0.0002
-      n_epochs: 500
-      max_steps_per_epoch: 100
-      early_stop: 20
-      seed: 1000
-      logdir: output/test/alstm_tra_init
-      model_type: LSTM
-      model_config: *model_config
-      tra_config: *tra_config
-      lamb: 1.0
-      rho: 0.99
-      freeze_model: False
-      model_init_state: 
-  dataset:
-    class: MTSDatasetH
-    module_path: src/dataset.py
-    kwargs:
-      handler:
-        class: DataHandler
-        module_path: qlib.data.dataset.handler
-        kwargs:
-          data_loader: *data_loader_config
-      segments:
-        train: [2007-10-30, 2016-05-27]
-        valid: [2016-09-26, 2018-05-29]
-        test: [2018-09-21, 2020-06-30]
-      seq_len: 60
-      horizon: 21
-      num_states: *num_states
-      batch_size: 512

examples/benchmarks/TRA/configs/config_transformer.yaml

@@ -1,63 +0,0 @@
-qlib_init:
-  provider_uri: "~/.qlib/qlib_data/cn_data"
-  region: cn
-
-data_loader_config: &data_loader_config
-  class: StaticDataLoader
-  module_path: qlib.data.dataset.loader
-  kwargs:
-    config:
-      feature: data/feature.pkl
-      label: data/label.pkl
-
-model_config: &model_config
-  input_size: 16
-  hidden_size: 64
-  num_layers: 2
-  num_heads: 4
-  use_attn: False
-  dropout: 0.1
-
-num_states: &num_states 1
-
-tra_config: &tra_config
-  num_states: *num_states
-  hidden_size: 16
-  tau: 1.0
-  src_info: LR_TPE
-
-task:
-  model:
-    class: TRAModel
-    module_path: src/model.py
-    kwargs:
-      lr: 0.0002
-      n_epochs: 500
-      max_steps_per_epoch: 100
-      early_stop: 20
-      seed: 1000
-      logdir: output/test/transformer
-      model_type: Transformer
-      model_config: *model_config
-      tra_config: *tra_config
-      lamb: 1.0
-      rho: 0.99
-      freeze_model: False
-      model_init_state: 
-  dataset:
-    class: MTSDatasetH
-    module_path: src/dataset.py
-    kwargs:
-      handler:
-        class: DataHandler
-        module_path: qlib.data.dataset.handler
-        kwargs:
-          data_loader: *data_loader_config
-      segments:
-        train: [2007-10-30, 2016-05-27]
-        valid: [2016-09-26, 2018-05-29]
-        test: [2018-09-21, 2020-06-30]
-      seq_len: 60
-      horizon: 21
-      num_states: *num_states
-      batch_size: 1024

examples/benchmarks/TRA/configs/config_transformer_tra.yaml

@@ -1,63 +0,0 @@
-qlib_init:
-  provider_uri: "~/.qlib/qlib_data/cn_data"
-  region: cn
-
-data_loader_config: &data_loader_config
-  class: StaticDataLoader
-  module_path: qlib.data.dataset.loader
-  kwargs:
-    config:
-      feature: data/feature.pkl
-      label: data/label.pkl
-
-model_config: &model_config
-  input_size: 16
-  hidden_size: 64
-  num_layers: 2
-  num_heads: 4
-  use_attn: False
-  dropout: 0.1
-
-num_states: &num_states 3
-
-tra_config: &tra_config
-  num_states: *num_states
-  hidden_size: 16
-  tau: 1.0
-  src_info: LR_TPE
-
-task:
-  model:
-    class: TRAModel
-    module_path: src/model.py
-    kwargs:
-      lr: 0.0005
-      n_epochs: 500
-      max_steps_per_epoch: 100
-      early_stop: 20
-      seed: 1000
-      logdir: output/test/transformer_tra
-      model_type: Transformer
-      model_config: *model_config
-      tra_config: *tra_config
-      lamb: 1.0
-      rho: 0.99
-      freeze_model: True
-      model_init_state: output/test/transformer_tra_init/model.bin
-  dataset:
-    class: MTSDatasetH
-    module_path: src/dataset.py
-    kwargs:
-      handler:
-        class: DataHandler
-        module_path: qlib.data.dataset.handler
-        kwargs:
-          data_loader: *data_loader_config
-      segments:
-        train: [2007-10-30, 2016-05-27]
-        valid: [2016-09-26, 2018-05-29]
-        test: [2018-09-21, 2020-06-30]
-      seq_len: 60
-      horizon: 21
-      num_states: *num_states
-      batch_size: 512

examples/benchmarks/TRA/configs/config_transformer_tra_init.yaml

@@ -1,63 +0,0 @@
-qlib_init:
-  provider_uri: "~/.qlib/qlib_data/cn_data"
-  region: cn
-
-data_loader_config: &data_loader_config
-  class: StaticDataLoader
-  module_path: qlib.data.dataset.loader
-  kwargs:
-    config:
-      feature: data/feature.pkl
-      label: data/label.pkl
-
-model_config: &model_config
-  input_size: 16
-  hidden_size: 64
-  num_layers: 2
-  num_heads: 4
-  use_attn: False
-  dropout: 0.1
-
-num_states: &num_states 3
-
-tra_config: &tra_config
-  num_states: *num_states
-  hidden_size: 16
-  tau: 1.0
-  src_info: LR_TPE
-
-task:
-  model:
-    class: TRAModel
-    module_path: src/model.py
-    kwargs:
-      lr: 0.0002
-      n_epochs: 500
-      max_steps_per_epoch: 100
-      early_stop: 20
-      seed: 1000
-      logdir: output/test/transformer_tra_init
-      model_type: Transformer
-      model_config: *model_config
-      tra_config: *tra_config
-      lamb: 1.0
-      rho: 0.99
-      freeze_model: False
-      model_init_state: 
-  dataset:
-    class: MTSDatasetH
-    module_path: src/dataset.py
-    kwargs:
-      handler:
-        class: DataHandler
-        module_path: qlib.data.dataset.handler
-        kwargs:
-          data_loader: *data_loader_config
-      segments:
-        train: [2007-10-30, 2016-05-27]
-        valid: [2016-09-26, 2018-05-29]
-        test: [2018-09-21, 2020-06-30]
-      seq_len: 60
-      horizon: 21
-      num_states: *num_states
-      batch_size: 512