test build package with macos-15

test pip install rebuild package
2026-06-06 14:01:28 +08:00 · 2024-12-20 11:55:29 +08:00 · 2024-12-20 11:43:36 +08:00 · 2024-12-20 11:40:43 +08:00 · 2024-12-20 11:40:27 +08:00 · 2024-12-20 11:19:36 +08:00
244 changed files with 4615 additions and 2404 deletions
--- a/.dockerignore
+++ b/.dockerignore
@@ -0,0 +1,8 @@
+__pycache__
+*.pyc
+*.pyo
+*.pyd
+.Python
+.env
+.git
+
--- a/.github/workflows/python-publish.yml
+++ b/.github/workflows/python-publish.yml
@@ -3,62 +3,73 @@

 name: Upload Python Package

+# on:
+#   release:
+#     types: [published]
+
 on:
-  release:
-    types: [published]
+  push:
+    branches: [ main ]
+  pull_request:
+    branches: [ main ]

 jobs:
  deploy_with_bdist_wheel:
    runs-on: ${{ matrix.os }}
    strategy:
      matrix:
-        os: [windows-latest, macos-11]
-        # FIXME:  macos-latest will raise error now.
-        # 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-13, macos-latest, macos-15]
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
+        exclude:
+          - os: macos-13
+            python-version: "3.11"
+          - os: macos-13
+            python-version: "3.12"

    steps:
-    - uses: actions/checkout@v2
-    - name: Set up Python
-      uses: actions/setup-python@v2
+    - uses: actions/checkout@v3
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v4
      with:
        python-version: ${{ matrix.python-version }}
    - name: Install dependencies
      run: |
-        python -m pip install --upgrade pip
-        pip install setuptools wheel twine
-    - name: Build wheel on Windows
+        make dev
+    - name: Build wheel on ${{ matrix.os }}
      run: |
-        pip install numpy
-        pip install cython
-        python setup.py bdist_wheel
+        make build
    - name: Build and publish
      env:
-        TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }}
-        TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }}
+        TWINE_USERNAME: __token__
+        TWINE_PASSWORD: ${{ secrets.TESTPYPI_TOKEN }}
      run: |
-        twine upload dist/*
-        
+        ls dist
+        twine check dist/*.whl
+
  deploy_with_manylinux:
    runs-on: ubuntu-latest
    steps:
-    - uses: actions/checkout@v2
+    - uses: actions/checkout@v3
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v4
+      with:
+        python-version: ${{ matrix.python-version }}
    - name: Build wheel on Linux
-      uses: RalfG/python-wheels-manylinux-build@v0.3.1-manylinux2010_x86_64
+      uses: RalfG/python-wheels-manylinux-build@v0.7.1-manylinux2014_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: 'cp38-cp38 cp39-cp39 cp310-cp310 cp311-cp311 cp312-cp312'
        build-requirements: 'numpy cython'
-    - name: Set up Python
-      uses: actions/setup-python@v2
-      with:
-        python-version: 3.7
    - name: Install dependencies
      run: |
-        pip install twine  
+        python -m pip install twine
+        python -m pip list
    - name: Build and publish
      env:
-        TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }}
-        TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }}
+        TWINE_USERNAME: __token__
+        TWINE_PASSWORD: ${{ secrets.TESTPYPI_TOKEN }}
      run: |
-        twine upload dist/pyqlib-*-manylinux*.whl
+        ls dist
+        twine check dist/*.whl
+        
+
--- a/.github/workflows/release-drafter.yml
+++ b/.github/workflows/release-drafter.yml
@@ -6,8 +6,14 @@ on:
    branches:
      - main

+permissions:
+  contents: read
+
 jobs:
  update_release_draft:
+    permissions:
+      contents: write
+      pull-requests: read
    runs-on: ubuntu-latest
    steps:
      # Drafts your next Release notes as Pull Requests are merged into "master"
--- a/.github/workflows/stale.yml
+++ b/.github/workflows/stale.yml
@@ -18,7 +18,8 @@ jobs:
        stale-issue-label: 'stale'
        stale-pr-label: 'stale'
        days-before-stale: 90
+        days-before-pr-stale: 365
        days-before-close: 5
        operations-per-run: 100
        exempt-issue-labels: 'bug,enhancement'
-        remove-stale-when-updated: true
+        remove-stale-when-updated: true
--- a/.github/workflows/test_qlib_from_pip.yml
+++ b/.github/workflows/test_qlib_from_pip.yml
@@ -13,16 +13,21 @@ jobs:
    runs-on: ${{ matrix.os }}
    strategy:
      matrix:
-        os: [windows-latest, ubuntu-20.04, ubuntu-22.04, macos-11, macos-latest]
+        # Since macos-latest changed from 12.7.4 to 14.4.1,
+        # the minimum python version that matches a 14.4.1 version of macos is 3.10,
+        # If you want to use python 3.7 in github action, then the latest macos system version is macos-13,
+        # after macos-13 python 3.7 is no longer supported.
+        # so we limit the macos version to macos-13.
+        os: [windows-latest, ubuntu-20.04, ubuntu-22.04, macos-13, macos-14, macos-15]
        # not supporting 3.6 due to annotations is not supported https://stackoverflow.com/a/52890129
-        python-version: [3.7, 3.8]
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]

    steps:
    - name: Test qlib from pip
-      uses: actions/checkout@v2
+      uses: actions/checkout@v3

    - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v2
+      uses: actions/setup-python@v4
      with:
        python-version: ${{ matrix.python-version }}

@@ -32,13 +37,10 @@ jobs:

    - name: Qlib installation test
      run: |
-        python -m pip install pyqlib
-        # Specify the numpy version because the numpy upgrade caused the CI test to fail, 
-        # and this line of code will be removed when the next version of qlib is released.
-        python -m pip install "numpy<1.23"
+        python -m pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/ pyqlib==0.9.5.80

    - name: Install Lightgbm for MacOS
-      if: ${{ matrix.os == 'macos-11' || matrix.os == 'macos-latest' }}
+      if: ${{ matrix.os == 'macos-13' || matrix.os == 'macos-14' || matrix.os == 'macos-15' }}
      run: |
        /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Microsoft/qlib/main/.github/brew_install.sh)"
        HOMEBREW_NO_AUTO_UPDATE=1 brew install lightgbm
@@ -50,7 +52,9 @@ jobs:

    - name: Downloads dependencies data
      run: |
-        python scripts/get_data.py qlib_data --name qlib_data_simple --target_dir ~/.qlib/qlib_data/cn_data --interval 1d --region cn
+        cd ..
+        python -m qlib.run.get_data qlib_data --target_dir ~/.qlib/qlib_data/cn_data --region cn
+        cd qlib

    - name: Test workflow by config
      run: |
--- a/.github/workflows/test_qlib_from_source.yml
+++ b/.github/workflows/test_qlib_from_source.yml
@@ -1,173 +0,0 @@
-name: Test qlib from source
-
-on:
-  push:
-    branches: [ main ]
-  pull_request:
-    branches: [ main ]
-
-jobs:
-  build:
-    timeout-minutes: 180
-    # we may retry for 3 times for `Unit tests with Pytest`
-
-    runs-on: ${{ matrix.os }}
-    strategy:
-      matrix:
-        os: [windows-latest, ubuntu-20.04, ubuntu-22.04, 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:
-    - name: Test qlib from source
-      uses: actions/checkout@v3
-
-    # Since version 3.7 of python for MacOS is installed in CI, version 3.7.17, this version causes "_bz not found error".
-    # So we make the version number of python 3.7 for MacOS more specific.
-    # refs: https://github.com/actions/setup-python/issues/682
-    - name: Set up Python ${{ matrix.python-version }}
-      if: (matrix.os == 'macos-latest' && matrix.python-version == '3.7') || (matrix.os == 'macos-11' && matrix.python-version == '3.7')
-      uses: actions/setup-python@v4
-      with:
-        python-version: "3.7.16"
-
-    - name: Set up Python ${{ matrix.python-version }}
-      if: (matrix.os != 'macos-latest' || matrix.python-version != '3.7') && (matrix.os != 'macos-11' || matrix.python-version != '3.7')
-      uses: actions/setup-python@v4
-      with:
-        python-version: ${{ matrix.python-version }}
-
-    - name: Update pip to the latest version
-      # pip release version 23.1 on Apr.15 2023, CI failed to run, Please refer to #1495 ofr detailed logs.
-      # The pip version has been temporarily fixed to 23.0
-      run: |
-        python -m pip install pip==23.0
-
-    - name: Installing pytorch for macos
-      if: ${{ matrix.os == 'macos-11' || matrix.os == 'macos-latest' }}
-      run: |
-        python -m pip install torch torchvision torchaudio
-
-    - name: Installing pytorch for ubuntu
-      if: ${{ matrix.os == 'ubuntu-20.04' || matrix.os == 'ubuntu-22.04' }}
-      run: |
-        python -m pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cpu
-
-    - name: Installing pytorch for windows
-      if: ${{ matrix.os == 'windows-latest' }}
-      run: |
-        python -m pip install torch torchvision torchaudio
-
-    - name: Set up Python tools
-      run: |
-        python -m pip install --upgrade cython
-        python -m pip install -e .[dev]
-
-    - name: Lint with Black
-      run: |
-        black . -l 120 --check --diff
-
-    - name: Make html with sphinx
-      run: |
-        cd docs 
-        sphinx-build -W --keep-going -b html . _build
-        cd ..
-
-    # Check Qlib with pylint
-    # TODO: These problems we will solve in the future. Important among them are: W0221, W0223, W0237, E1102
-      # C0103: invalid-name
-      # C0209: consider-using-f-string
-      # R0402: consider-using-from-import
-      # R1705: no-else-return
-      # R1710: inconsistent-return-statements
-      # R1725: super-with-arguments
-      # R1735: use-dict-literal
-      # W0102: dangerous-default-value
-      # W0212: protected-access
-      # W0221: arguments-differ
-      # W0223: abstract-method
-      # W0231: super-init-not-called
-      # W0237: arguments-renamed
-      # W0612: unused-variable
-      # W0621: redefined-outer-name
-      # W0622: redefined-builtin
-      # FIXME: specify exception type
-      # W0703: broad-except
-      # W1309: f-string-without-interpolation
-      # E1102: not-callable
-      # E1136: unsubscriptable-object
-    # References for parameters: https://github.com/PyCQA/pylint/issues/4577#issuecomment-1000245962
-    # We use sys.setrecursionlimit(2000) to make the recursion depth larger to ensure that pylint works properly (the default recursion depth is 1000).
-    - name: Check Qlib with pylint
-      run: |
-        pylint --disable=C0104,C0114,C0115,C0116,C0301,C0302,C0411,C0413,C1802,R0401,R0801,R0902,R0903,R0911,R0912,R0913,R0914,R0915,R1720,W0105,W0123,W0201,W0511,W0613,W1113,W1514,E0401,E1121,C0103,C0209,R0402,R1705,R1710,R1725,R1735,W0102,W0212,W0221,W0223,W0231,W0237,W0612,W0621,W0622,W0703,W1309,E1102,E1136 --const-rgx='[a-z_][a-z0-9_]{2,30}$' qlib --init-hook "import astroid; astroid.context.InferenceContext.max_inferred = 500; import sys; sys.setrecursionlimit(2000)"
-
-    # The following flake8 error codes were ignored:
-      # E501 line too long
-        # Description: We have used black to limit the length of each line to 120.
-      # F541 f-string is missing placeholders
-        # Description: The same thing is done when using pylint for detection.
-      # E266 too many leading '#' for block comment
-        # Description: To make the code more readable, a lot of "#" is used.
-        # This error code appears centrally in:
-          # qlib/backtest/executor.py
-          # qlib/data/ops.py
-          # qlib/utils/__init__.py
-      # E402 module level import not at top of file
-        # Description: There are times when module level import is not available at the top of the file.
-      # W503 line break before binary operator
-        # Description: Since black formats the length of each line of code, it has to perform a line break when a line of arithmetic is too long.
-      # E731 do not assign a lambda expression, use a def
-        # Description: Restricts the use of lambda expressions, but at some point lambda expressions are required.
-      # E203 whitespace before ':'
-        # Description: If there is whitespace before ":", it cannot pass the black check.
-    - name: Check Qlib with flake8
-      run: |
-        flake8 --ignore=E501,F541,E266,E402,W503,E731,E203 --per-file-ignores="__init__.py:F401,F403" qlib
-
-    # https://github.com/python/mypy/issues/10600
-    - name: Check Qlib with mypy
-      run: |
-        mypy qlib --install-types --non-interactive || true
-        mypy qlib --verbose
-    
-    - name: Check Qlib ipynb with nbqa
-      run: |
-        nbqa black . -l 120 --check --diff
-        nbqa pylint . --disable=C0104,C0114,C0115,C0116,C0301,C0302,C0411,C0413,C1802,R0401,R0801,R0902,R0903,R0911,R0912,R0913,R0914,R0915,R1720,W0105,W0123,W0201,W0511,W0613,W1113,W1514,E0401,E1121,C0103,C0209,R0402,R1705,R1710,R1725,R1735,W0102,W0212,W0221,W0223,W0231,W0237,W0612,W0621,W0622,W0703,W1309,E1102,E1136,W0719,W0104,W0404,C0412,W0611,C0410 --const-rgx='[a-z_][a-z0-9_]{2,30}$'
-
-    - name: Test data downloads
-      run: |
-        python scripts/get_data.py qlib_data --name qlib_data_simple --target_dir ~/.qlib/qlib_data/cn_data --interval 1d --region cn
-        python scripts/get_data.py download_data --file_name rl_data.zip --target_dir tests/.data/rl
-
-    - name: Install Lightgbm for MacOS
-      if: ${{ matrix.os == 'macos-11' || matrix.os == 'macos-latest' }}
-      run: |
-        /bin/bash -c "$(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
-
-    # Run after data downloads
-    - name: Check Qlib ipynb with nbconvert
-      run: |
-        # add more ipynb files in future
-        jupyter nbconvert --to notebook --execute examples/workflow_by_code.ipynb
-
-    - name: Test workflow by config (install from source)
-      run: |
-        python -m pip install numba
-        python qlib/workflow/cli.py examples/benchmarks/LightGBM/workflow_config_lightgbm_Alpha158.yaml
-
-    - name: Unit tests with Pytest
-      uses: nick-fields/retry@v2
-      with:
-        timeout_minutes: 60
-        max_attempts: 3
-        command: |
-          cd tests
-          python -m pytest . -m "not slow" --durations=0
--- a/.github/workflows/test_qlib_from_source_slow.yml
+++ b/.github/workflows/test_qlib_from_source_slow.yml
@@ -1,70 +0,0 @@
-name: Test qlib from source slow
-
-on:
-  push:
-    branches: [ main ]
-  pull_request:
-    branches: [ main ]
-
-jobs:
-  build:
-    timeout-minutes: 720
-    # we may retry for 3 times for `Unit tests with Pytest`
-
-    runs-on: ${{ matrix.os }}
-    strategy:
-      matrix:
-        os: [windows-latest, ubuntu-20.04, ubuntu-22.04, 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:
-    - name: Test qlib from source slow
-      uses: actions/checkout@v3
-
-    # Since version 3.7 of python for MacOS is installed in CI, version 3.7.17, this version causes "_bz not found error".
-    # So we make the version number of python 3.7 for MacOS more specific.
-    # refs: https://github.com/actions/setup-python/issues/682
-    - name: Set up Python ${{ matrix.python-version }}
-      if: (matrix.os == 'macos-latest' && matrix.python-version == '3.7') || (matrix.os == 'macos-11' && matrix.python-version == '3.7')
-      uses: actions/setup-python@v4
-      with:
-        python-version: "3.7.16"
-
-    - name: Set up Python ${{ matrix.python-version }}
-      if: (matrix.os != 'macos-latest' || matrix.python-version != '3.7') && (matrix.os != 'macos-11' || matrix.python-version != '3.7')
-      uses: actions/setup-python@v4
-      with:
-        python-version: ${{ matrix.python-version }}
-
-    - name: Set up Python tools
-      # pip release version 23.1 on Apr.15 2023, CI failed to run, Please refer to #1495 ofr detailed logs.
-      # The pip version has been temporarily fixed to 23.0
-      run: |
-        python -m pip install pip==23.0
-        pip install --upgrade cython numpy
-        pip install -e .[dev]
-
-    - name: Downloads dependencies data
-      run: |
-        python scripts/get_data.py qlib_data --name qlib_data_simple --target_dir ~/.qlib/qlib_data/cn_data --interval 1d --region cn
-
-    - name: Install Lightgbm for MacOS
-      if: ${{ matrix.os == 'macos-11' || matrix.os == 'macos-latest' }}
-      run: |
-        /bin/bash -c "$(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: Unit tests with Pytest
-      uses: nick-fields/retry@v2
-      with:
-        timeout_minutes: 240
-        max_attempts: 3
-        command: |
-          cd tests
-          python -m pytest . -m "slow" --durations=0
--- a/.gitignore
+++ b/.gitignore
@@ -49,3 +49,4 @@ tags

 ./pretrain
 .idea/
+.aider*
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -1,6 +1,6 @@
 repos:
 -   repo: https://github.com/psf/black
-    rev: 22.6.0
+    rev: 23.7.0
    hooks:
    -   id: black
        args: ["qlib", "-l 120"]
@@ -9,4 +9,4 @@ repos:
    rev: 4.0.1
    hooks:
        - id: flake8
-          args: ["--ignore=E501,F541,E266,E402,W503,E731,E203"]
+          args: ["--ignore=E501,F541,E266,E402,W503,E731,E203"]
--- a/.readthedocs.yaml
+++ b/.readthedocs.yaml
@@ -5,6 +5,12 @@
 # Required
 version: 2

+# Set the version of Python and other tools you might need
+build:
+  os: ubuntu-22.04
+  tools:
+    python: "3.8"
+
 # Build documentation in the docs/ directory with Sphinx
 sphinx:
  configuration: docs/conf.py
@@ -14,7 +20,6 @@ formats: all

 # Optionally set the version of Python and requirements required to build your docs
 python:
-  version: 3.7
  install:
    - requirements: docs/requirements.txt
    - method: pip
--- a/31
+++ b/31
@@ -0,0 +1,31 @@
+FROM continuumio/miniconda3:latest
+
+WORKDIR /qlib
+
+COPY . .
+
+RUN apt-get update && \
+    apt-get install -y build-essential
+
+RUN conda create --name qlib_env python=3.8 -y
+RUN echo "conda activate qlib_env" >> ~/.bashrc
+ENV PATH /opt/conda/envs/qlib_env/bin:$PATH
+
+RUN python -m pip install --upgrade pip
+
+RUN python -m pip install numpy==1.23.5
+RUN python -m pip install pandas==1.5.3
+RUN python -m pip install importlib-metadata==5.2.0
+RUN python -m pip install "cloudpickle<3"
+RUN python -m pip install scikit-learn==1.3.2
+
+RUN python -m pip install cython packaging tables matplotlib statsmodels
+RUN python -m pip install pybind11 cvxpy
+
+ARG IS_STABLE="yes"
+
+RUN if [ "$IS_STABLE" = "yes" ]; then \
+        python -m pip install pyqlib; \
+    else \
+        python setup.py install; \
+    fi
--- a/MANIFEST.in
+++ b/MANIFEST.in
@@ -1 +1,6 @@
-include qlib/VERSION.txt
+exclude tests/*
+include qlib/*
+include qlib/*/*
+include qlib/*/*/*
+include qlib/*/*/*/*
+include qlib/*/*/*/*/*
--- a/195
+++ b/195
@@ -0,0 +1,195 @@
+.PHONY: clean deepclean prerequisite dependencies lightgbm rl develop lint docs package test analysis all install dev black pylint flake8 mypy nbqa nbconvert lint build upload docs-gen
+#You can modify it according to your terminal
+SHELL := /bin/bash
+
+########################################################################################
+# Variables
+########################################################################################
+
+# Documentation target directory, will be adapted to specific folder for readthedocs.
+PUBLIC_DIR := $(shell [ "$$READTHEDOCS" = "True" ] && echo "$$READTHEDOCS_OUTPUT/html" || echo "public")
+
+SO_DIR := qlib/data/_libs
+SO_FILES := $(wildcard $(SO_DIR)/*.so)
+
+########################################################################################
+# Development Environment Management
+########################################################################################
+# Remove common intermediate files.
+clean:
+	-rm -rf \
+		$(PUBLIC_DIR) \
+		qlib/data/_libs/*.cpp \
+		qlib/data/_libs/*.so \
+		mlruns \
+		public \
+		build \
+		.coverage \
+		.mypy_cache \
+		.pytest_cache \
+		.ruff_cache \
+		Pipfile* \
+		coverage.xml \
+		dist \
+		release-notes.md
+
+	find . -name '*.egg-info' -print0 | xargs -0 rm -rf
+	find . -name '*.pyc' -print0 | xargs -0 rm -f
+	find . -name '*.swp' -print0 | xargs -0 rm -f
+	find . -name '.DS_Store' -print0 | xargs -0 rm -f
+	find . -name '__pycache__' -print0 | xargs -0 rm -rf
+
+# Remove pre-commit hook, virtual environment alongside itermediate files.
+deepclean: clean
+	if command -v pre-commit > /dev/null 2>&1; then pre-commit uninstall --hook-type pre-push; fi
+	if command -v pipenv >/dev/null 2>&1 && pipenv --venv >/dev/null 2>&1; then pipenv --rm; fi
+
+# Prerequisite section
+# What this code does is compile two Cython modules, rolling and expanding, using setuptools and Cython,
+# and builds them as binary expansion modules that can be imported directly into Python.
+# Since pyproject.toml can't do that, we compile it here.
+prerequisite:
+	@if [ -n "$(SO_FILES)" ]; then \
+		echo "Shared library files exist, skipping build."; \
+	else \
+		echo "No shared library files found, building..."; \
+		pip install --upgrade setuptools wheel; \
+		python -m pip install cython numpy; \
+		python -c "from setuptools import setup, Extension; from Cython.Build import cythonize; import numpy; extensions = [Extension('qlib.data._libs.rolling', ['qlib/data/_libs/rolling.pyx'], language='c++', include_dirs=[numpy.get_include()]), Extension('qlib.data._libs.expanding', ['qlib/data/_libs/expanding.pyx'], language='c++', include_dirs=[numpy.get_include()])]; setup(ext_modules=cythonize(extensions, language_level='3'), script_args=['build_ext', '--inplace'])"; \
+	fi
+
+# Install the package in editable mode.
+dependencies:
+	python -m pip install -e .
+
+lightgbm:
+	python -m pip install lightgbm --prefer-binary
+
+rl:
+	python -m pip install -e .[rl]
+
+develop:
+	python -m pip install -e .[dev]
+
+lint:
+	python -m pip install -e .[lint]
+
+docs:
+	python -m pip install -e .[docs]
+
+package:
+	python -m pip install -e .[package]
+
+test:
+	python -m pip install -e .[test]
+
+analysis:
+	python -m pip install -e .[analysis]
+
+all:
+	python -m pip install -e .[dev,lint,docs,package,test,analysis,rl]
+
+install: prerequisite dependencies
+
+dev: prerequisite all
+
+########################################################################################
+# Lint and pre-commit
+########################################################################################
+
+# Check lint with black.
+black:
+	black . -l 120 --check --diff
+
+# Check code folder with pylint.
+# TODO: These problems we will solve in the future. Important among them are: W0221, W0223, W0237, E1102
+# 	C0103: invalid-name
+# 	C0209: consider-using-f-string
+# 	R0402: consider-using-from-import
+# 	R1705: no-else-return
+# 	R1710: inconsistent-return-statements
+# 	R1725: super-with-arguments
+# 	R1735: use-dict-literal
+# 	W0102: dangerous-default-value
+# 	W0212: protected-access
+# 	W0221: arguments-differ
+# 	W0223: abstract-method
+# 	W0231: super-init-not-called
+# 	W0237: arguments-renamed
+# 	W0612: unused-variable
+# 	W0621: redefined-outer-name
+# 	W0622: redefined-builtin
+# 	FIXME: specify exception type
+# 	W0703: broad-except
+# 	W1309: f-string-without-interpolation
+# 	E1102: not-callable
+# 	E1136: unsubscriptable-object
+# 	W4904: deprecated-class
+# 	R0917: too-many-positional-arguments
+# 	E1123: unexpected-keyword-arg
+# References for disable error: https://pylint.pycqa.org/en/latest/user_guide/messages/messages_overview.html
+# We use sys.setrecursionlimit(2000) to make the recursion depth larger to ensure that pylint works properly (the default recursion depth is 1000).
+# References for parameters: https://github.com/PyCQA/pylint/issues/4577#issuecomment-1000245962
+pylint:
+	pylint --disable=C0104,C0114,C0115,C0116,C0301,C0302,C0411,C0413,C1802,R0401,R0801,R0902,R0903,R0911,R0912,R0913,R0914,R0915,R0917,R1720,W0105,W0123,W0201,W0511,W0613,W1113,W1514,W4904,E0401,E1121,C0103,C0209,R0402,R1705,R1710,R1725,R1730,R1735,W0102,W0212,W0221,W0223,W0231,W0237,W0612,W0621,W0622,W0703,W1309,E1102,E1136 --const-rgx='[a-z_][a-z0-9_]{2,30}' qlib --init-hook="import astroid; astroid.context.InferenceContext.max_inferred = 500; import sys; sys.setrecursionlimit(2000)"
+	pylint --disable=C0104,C0114,C0115,C0116,C0301,C0302,C0411,C0413,C1802,R0401,R0801,R0902,R0903,R0911,R0912,R0913,R0914,R0915,R0917,R1720,W0105,W0123,W0201,W0511,W0613,W1113,W1514,E0401,E1121,E1123,C0103,C0209,R0402,R1705,R1710,R1725,R1735,W0102,W0212,W0221,W0223,W0231,W0237,W0246,W0612,W0621,W0622,W0703,W1309,E1102,E1136 --const-rgx='[a-z_][a-z0-9_]{2,30}' scripts --init-hook="import astroid; astroid.context.InferenceContext.max_inferred = 500; import sys; sys.setrecursionlimit(2000)"
+
+# Check code with flake8.
+# The following flake8 error codes were ignored:
+# E501 line too long
+# 	Description: We have used black to limit the length of each line to 120.
+# F541 f-string is missing placeholders
+# 	Description: The same thing is done when using pylint for detection.
+# E266 too many leading '#' for block comment
+# 	Description: To make the code more readable, a lot of "#" is used.
+#         This error code appears centrally in:
+# 			qlib/backtest/executor.py
+# 			qlib/data/ops.py
+# 			qlib/utils/__init__.py
+# E402 module level import not at top of file
+# 	Description: There are times when module level import is not available at the top of the file.
+# W503 line break before binary operator
+# 	Description: Since black formats the length of each line of code, it has to perform a line break when a line of arithmetic is too long.
+# E731 do not assign a lambda expression, use a def
+# 	Description: Restricts the use of lambda expressions, but at some point lambda expressions are required.
+# E203 whitespace before ':'
+# 	Description: If there is whitespace before ":", it cannot pass the black check.
+flake8:
+	flake8 --ignore=E501,F541,E266,E402,W503,E731,E203 --per-file-ignores="__init__.py:F401,F403" qlib
+
+# Check code with mypy.
+# https://github.com/python/mypy/issues/10600
+mypy:
+	mypy qlib --install-types --non-interactive
+	mypy qlib --verbose
+
+# Check ipynb with nbqa.
+nbqa:
+	nbqa black . -l 120 --check --diff
+	nbqa pylint . --disable=C0104,C0114,C0115,C0116,C0301,C0302,C0411,C0413,C1802,R0401,R0801,R0902,R0903,R0911,R0912,R0913,R0914,R0915,R1720,W0105,W0123,W0201,W0511,W0613,W1113,W1514,E0401,E1121,C0103,C0209,R0402,R1705,R1710,R1725,R1735,W0102,W0212,W0221,W0223,W0231,W0237,W0612,W0621,W0622,W0703,W1309,E1102,E1136,W0719,W0104,W0404,C0412,W0611,C0410 --const-rgx='[a-z_][a-z0-9_]{2,30}'
+
+# Check ipynb with nbconvert.(Run after data downloads)
+# TODO: Add more ipynb files in future
+nbconvert:
+	jupyter nbconvert --to notebook --execute examples/workflow_by_code.ipynb
+
+lint: black pylint flake8 mypy nbqa
+
+########################################################################################
+# Package
+########################################################################################
+
+# Build the package.
+build:
+	python -m build --wheel
+
+# Upload the package.
+upload:
+	python -m twine upload dist/*
+
+########################################################################################
+# Documentation
+########################################################################################
+
+docs-gen:
+	python -m sphinx.cmd.build -W docs $(PUBLIC_DIR)
--- a/README.md
+++ b/README.md
@@ -8,9 +8,30 @@
 [![Join the chat at https://gitter.im/Microsoft/qlib](https://badges.gitter.im/Microsoft/qlib.svg)](https://gitter.im/Microsoft/qlib?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)

 ## :newspaper: **What's NEW!** &nbsp;   :sparkling_heart: 
+
 Recent released features
+
+### Introducing <a href="https://github.com/microsoft/RD-Agent"><img src="docs/_static/img/rdagent_logo.png" alt="RD_Agent" style="height: 2em"></a>: LLM-Based Autonomous Evolving Agents for Industrial Data-Driven R&D
+
+We are excited to announce the release of **RD-Agent**📢, a powerful tool that supports automated factor mining and model optimization in quant investment R&D.
+
+RD-Agent is now available on [GitHub](https://github.com/microsoft/RD-Agent), and we welcome your star🌟!
+
+To learn more, please visit our [♾️Demo page](https://rdagent.azurewebsites.net/). Here, you will find demo videos in both English and Chinese to help you better understand the scenario and usage of RD-Agent.
+
+We have prepared several demo videos for you:
+| Scenario | Demo video (English) | Demo video (中文) |
+| --                      | ------    | ------    |
+| Quant Factor Mining | [Link](https://rdagent.azurewebsites.net/factor_loop?lang=en) | [Link](https://rdagent.azurewebsites.net/factor_loop?lang=zh) |
+| Quant Factor Mining from reports | [Link](https://rdagent.azurewebsites.net/report_factor?lang=en) | [Link](https://rdagent.azurewebsites.net/report_factor?lang=zh) |
+| Quant Model Optimization | [Link](https://rdagent.azurewebsites.net/model_loop?lang=en) | [Link](https://rdagent.azurewebsites.net/model_loop?lang=zh) |
+
+***
+
 | Feature | Status |
 | --                      | ------    |
+| BPQP for End-to-end learning | 📈Coming soon!([Under review](https://github.com/microsoft/qlib/pull/1863)) |
+| 🔥LLM-driven Auto Quant Factory🔥 | 🚀 Released in [♾️RD-Agent](https://github.com/microsoft/RD-Agent) on Aug 8, 2024 |
 | KRNN and Sandwich models | :chart_with_upwards_trend: [Released](https://github.com/microsoft/qlib/pull/1414/) on May 26, 2023 |
 | Release Qlib v0.9.0 | :octocat: [Released](https://github.com/microsoft/qlib/releases/tag/v0.9.0) on Dec 9, 2022 |
 | RL Learning Framework | :hammer: :chart_with_upwards_trend: Released on Nov 10, 2022. [#1332](https://github.com/microsoft/qlib/pull/1332), [#1322](https://github.com/microsoft/qlib/pull/1322), [#1316](https://github.com/microsoft/qlib/pull/1316),[#1299](https://github.com/microsoft/qlib/pull/1299),[#1263](https://github.com/microsoft/qlib/pull/1263), [#1244](https://github.com/microsoft/qlib/pull/1244), [#1169](https://github.com/microsoft/qlib/pull/1169), [#1125](https://github.com/microsoft/qlib/pull/1125), [#1076](https://github.com/microsoft/qlib/pull/1076)|
@@ -40,7 +61,7 @@ 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="docs/_static/img/logo/1.png" />
 </p>

 Qlib is an open-source, AI-oriented quantitative investment platform that aims to realize the potential, empower research, and create value using AI technologies in quantitative investment, from exploring ideas to implementing productions. Qlib supports diverse machine learning modeling paradigms, including supervised learning, market dynamics modeling, and reinforcement learning.
@@ -91,6 +112,7 @@ For more details, please refer to our paper ["Qlib: An AI-oriented Quantitative
              </ul>
            </li>
          <li type="circle"><a href="#adapting-to-market-dynamics">Adapting to Market Dynamics</a></li>
+          <li type="circle"><a href="#reinforcement-learning-modeling-continuous-decisions">Reinforcement Learning: modeling continuous decisions</a></li>
          </ul>
        </li>
      </td>
@@ -131,14 +153,14 @@ Here is a quick **[demo](https://terminalizer.com/view/3f24561a4470)** shows how
 ## Installation

 This table demonstrates the supported Python version of `Qlib`:
-|               | install with pip           | install from source  | plot |
-| ------------- |:---------------------:|:--------------------:|:----:|
+|               | install with pip      | install from source  |        plot        |
+| ------------- |:---------------------:|:--------------------:|:------------------:|
 | 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: |
+| Python 3.9    | :x:                   | :heavy_check_mark:   | :x:                |

 **Note**: 
-1. **Conda** is suggested for managing your Python environment.
+1. **Conda** is suggested for managing your Python environment. In some cases, using Python outside of a `conda` environment may result in missing header files, causing the installation failure of certain packages.
 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.
 1. `Qlib`Requires `tables` package, `hdf5` in tables does not support python3.9. 
@@ -165,13 +187,29 @@ Also, users can install the latest dev version ``Qlib`` by the source code accor
 * Clone the repository and install ``Qlib`` as follows.
    ```bash
    git clone https://github.com/microsoft/qlib.git && cd qlib
-    pip install .
+    pip install .  # `pip install -e .[dev]` is recommended for development. check details in docs/developer/code_standard_and_dev_guide.rst
    ```
  **Note**:  You can install Qlib with `python setup.py install` as well. But it is not the recommended approach. It will skip `pip` and cause obscure problems. For example, **only** the command ``pip install .`` **can** overwrite the stable version installed by ``pip install pyqlib``, while the command ``python setup.py install`` **can't**.

 **Tips**: If you fail to install `Qlib` or run the examples in your environment,  comparing your steps and the [CI workflow](.github/workflows/test_qlib_from_source.yml) may help you find the problem.

+**Tips for Mac**: If you are using Mac with M1, you might encounter issues in building the wheel for LightGBM, which is due to missing dependencies from OpenMP. To solve the problem, install openmp first with ``brew install libomp`` and then run ``pip install .`` to build it successfully. 
+
 ## Data Preparation
+❗ Due to more restrict data security policy. The offical dataset is disabled temporarily. You can try [this data source](https://github.com/chenditc/investment_data/releases) contributed by the community.
+Here is an example to download the data updated on 20240809.
+```bash
+wget https://github.com/chenditc/investment_data/releases/download/2024-08-09/qlib_bin.tar.gz
+mkdir -p ~/.qlib/qlib_data/cn_data
+tar -zxvf qlib_bin.tar.gz -C ~/.qlib/qlib_data/cn_data --strip-components=1
+rm -f qlib_bin.tar.gz
+```
+
+The official dataset below will resume in short future.
+
+
+----
+
 Load and prepare data by running the following code:

 ### Get with module
@@ -255,6 +293,38 @@ We recommend users to prepare their own data if they have a high-quality dataset
  ```
 -->

+## Docker images
+1. Pulling a docker image from a docker hub repository
+    ```bash
+    docker pull pyqlib/qlib_image_stable:stable
+    ```
+2. Start a new Docker container
+    ```bash
+    docker run -it --name <container name> -v <Mounted local directory>:/app qlib_image_stable
+    ```
+3. At this point you are in the docker environment and can run the qlib scripts. An example:
+    ```bash
+    >>> python scripts/get_data.py qlib_data --name qlib_data_simple --target_dir ~/.qlib/qlib_data/cn_data --interval 1d --region cn
+    >>> python qlib/workflow/cli.py examples/benchmarks/LightGBM/workflow_config_lightgbm_Alpha158.yaml
+    ```
+4. Exit the container
+    ```bash
+    >>> exit
+    ```
+5. Restart the container
+    ```bash
+    docker start -i -a <container name>
+    ```
+6. Stop the container
+    ```bash
+    docker stop <container name>
+    ```
+7. Delete the container
+    ```bash
+    docker rm <container name>
+    ```
+8. If you want to know more information, please refer to the [documentation](https://qlib.readthedocs.io/en/latest/developer/how_to_build_image.html).
+
 ## Auto Quant Research Workflow
 Qlib provides a tool named `qrun` to run the whole workflow automatically (including building dataset, training models, backtest and evaluation). You can start an auto quant research workflow and have a graphical reports analysis according to the following steps: 

@@ -288,22 +358,22 @@ Qlib provides a tool named `qrun` to run the whole workflow automatically (inclu
    ```
    Here are detailed documents for `qrun` and [workflow](https://qlib.readthedocs.io/en/latest/component/workflow.html).

-2. Graphical Reports Analysis: Run `examples/workflow_by_code.ipynb` with `jupyter notebook` to get graphical reports
+2. Graphical Reports Analysis: First, run `python -m pip install .[analysis]` to install the required dependencies. Then 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](https://github.com/microsoft/qlib/blob/main/docs/_static/img/analysis/analysis_model_cumulative_return.png)
      - Return distribution
-      ![long_short](http://fintech.msra.cn/images_v070/analysis/analysis_model_long_short.png?v=0.1)
+      ![long_short](https://github.com/microsoft/qlib/blob/main/docs/_static/img/analysis/analysis_model_long_short.png)
      - 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](https://github.com/microsoft/qlib/blob/main/docs/_static/img/analysis/analysis_model_IC.png)
+      ![Monthly IC](https://github.com/microsoft/qlib/blob/main/docs/_static/img/analysis/analysis_model_monthly_IC.png)
+      ![IC](https://github.com/microsoft/qlib/blob/main/docs/_static/img/analysis/analysis_model_NDQ.png)
      - 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](https://github.com/microsoft/qlib/blob/main/docs/_static/img/analysis/analysis_model_auto_correlation.png)

    - Portfolio analysis
      - Backtest return
-      ![Report](http://fintech.msra.cn/images_v070/analysis/report.png?v=0.1)
+      ![Report](https://github.com/microsoft/qlib/blob/main/docs/_static/img/analysis/report.png)
      <!-- 
      - Score IC
      ![Score IC](docs/_static/img/score_ic.png)
@@ -320,7 +390,7 @@ 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.

 # Main Challenges & Solutions in Quant Research
-Quant investment is an very unique scenario with lots of key challenges to be solved.
+Quant investment is a very unique scenario with lots of key challenges to be solved.
 Currently, Qlib provides some solutions for several of them.

 ## Forecasting: Finding Valuable Signals/Patterns
@@ -359,7 +429,7 @@ Here is a list of models built on `Qlib`.

 Your PR of new Quant models is highly welcomed.

-The performance of each model on the `Alpha158` and `Alpha360` dataset can be found [here](examples/benchmarks/README.md).
+The performance of each model on the `Alpha158` and `Alpha360` datasets can be found [here](examples/benchmarks/README.md).

 ### Run a single model
 All the models listed above are runnable with ``Qlib``. Users can find the config files we provide and some details about the model through the [benchmarks](examples/benchmarks) folder. More information can be retrieved at the model files listed above.
@@ -392,6 +462,17 @@ Here is a list of solutions built on `Qlib`.
 - [Rolling Retraining](examples/benchmarks_dynamic/baseline/)
 - [DDG-DA on pytorch (Wendi, et al. AAAI 2022)](examples/benchmarks_dynamic/DDG-DA/)

+##  Reinforcement Learning: modeling continuous decisions
+Qlib now supports reinforcement learning, a feature designed to model continuous investment decisions. This functionality assists investors in optimizing their trading strategies by learning from interactions with the environment to maximize some notion of cumulative reward.
+
+Here is a list of solutions built on `Qlib` categorized by scenarios.
+
+### [RL for order execution](examples/rl_order_execution)
+[Here](https://qlib.readthedocs.io/en/latest/component/rl/overall.html#order-execution) is the introduction of this scenario.  All the methods below are compared [here](examples/rl_order_execution).
+- [TWAP](examples/rl_order_execution/exp_configs/backtest_twap.yml)
+- [PPO: "An End-to-End Optimal Trade Execution Framework based on Proximal Policy Optimization", IJCAL 2020](examples/rl_order_execution/exp_configs/backtest_ppo.yml)
+- [OPDS: "Universal Trading for Order Execution with Oracle Policy Distillation", AAAI 2021](examples/rl_order_execution/exp_configs/backtest_opds.yml)
+
 # Quant Dataset Zoo
 Dataset plays a very important role in Quant. Here is a list of the datasets built on `Qlib`:

@@ -471,7 +552,7 @@ 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](https://github.com/microsoft/qlib/blob/main/docs/_static/img/qrcode/gitter_qr.png)|

 # Contributing
 We appreciate all contributions and thank all the contributors!
--- a/build_docker_image.sh
+++ b/build_docker_image.sh
@@ -0,0 +1,31 @@
+#!/bin/bash
+
+docker_user="your_dockerhub_username"
+
+read -p "Do you want to build the nightly version of the qlib image? (default is stable) (yes/no): " answer;
+answer=$(echo "$answer" | tr '[:upper:]' '[:lower:]')
+
+if [ "$answer" = "yes" ]; then
+    # Build the nightly version of the qlib image
+    docker build --build-arg IS_STABLE=no -t qlib_image -f ./Dockerfile .
+    image_tag="nightly"
+else
+    # Build the stable version of the qlib image
+    docker build -t qlib_image -f ./Dockerfile .
+    image_tag="stable"
+fi
+
+read -p "Is it uploaded to docker hub? (default is no) (yes/no): " answer;
+answer=$(echo "$answer" | tr '[:upper:]' '[:lower:]')
+
+if [ "$answer" = "yes" ]; then
+    # Log in to Docker Hub
+    # If you are a new docker hub user, please verify your email address before proceeding with this step.
+    docker login
+    # Tag the Docker image
+    docker tag qlib_image "$docker_user/qlib_image:$image_tag"
+    # Push the Docker image to Docker Hub
+    docker push "$docker_user/qlib_image:$image_tag"
+else
+    echo "Not uploaded to docker hub."
+fi
--- a/docs/_static/img/rdagent_logo.png
+++ b/docs/_static/img/rdagent_logo.png
--- a/docs/component/data.rst
+++ b/docs/component/data.rst
@@ -52,7 +52,7 @@ Also, ``Qlib`` provides a high-frequency dataset. Users can run a high-frequency
 Qlib Format Dataset
 -------------------
 ``Qlib`` has provided an off-the-shelf dataset in `.bin` format, users could use the script ``scripts/get_data.py`` to download the China-Stock dataset as follows. User can also use numpy to load `.bin` file to validate data.
-The price volume data look different from the actual dealling price because of they are **adjusted** (`adjusted price <https://www.investopedia.com/terms/a/adjusted_closing_price.asp>`_).  And then you may find that the adjusted price may be different from different data sources. This is because different data sources may vary in the way of adjusting prices. Qlib normalize the price on first trading day of each stock to 1 when adjusting them.
+The price volume data look different from the actual dealing price because of they are **adjusted** (`adjusted price <https://www.investopedia.com/terms/a/adjusted_closing_price.asp>`_).  And then you may find that the adjusted price may be different from different data sources. This is because different data sources may vary in the way of adjusting prices. Qlib normalize the price on first trading day of each stock to 1 when adjusting them.
 Users can leverage `$factor` to get the original trading price (e.g. `$close / $factor` to get the original close price).

 Here are some discussions about the price adjusting of Qlib. 
@@ -140,12 +140,13 @@ Users can also provide their own data in CSV format. However, the CSV data **mus

        where the data are in the following format:

-        .. code-block::
+            +-----------+-------+
+            | symbol    | close |
+            +===========+=======+
+            | SH600000  | 120   |
+            +-----------+-------+

-            symbol,close
-            SH600000,120
-
- CSV file **must** includes a column for the date, and when dumping the data, user must specify the date column name. Here is an example:
+- CSV file **must** include a column for the date, and when dumping the data, user must specify the date column name. Here is an example:

    .. code-block:: bash

@@ -153,11 +154,13 @@ Users can also provide their own data in CSV format. However, the CSV data **mus

    where the data are in the following format:

-    .. code-block::
-
-        symbol,date,close,open,volume
-        SH600000,2020-11-01,120,121,12300000
-        SH600000,2020-11-02,123,120,12300000
+        +---------+------------+-------+------+----------+
+        | symbol  | date       | close | open | volume   |
+        +=========+============+=======+======+==========+
+        | SH600000| 2020-11-01 | 120   | 121  | 12300000 |
+        +---------+------------+-------+------+----------+
+        | SH600000| 2020-11-02 | 123   | 120  | 12300000 |
+        +---------+------------+-------+------+----------+


 Supposed that users prepare their CSV format data in the directory ``~/.qlib/csv_data/my_data``, they can run the following command to start the conversion.
--- a/docs/component/model.rst
+++ b/docs/component/model.rst
@@ -86,7 +86,7 @@ Example
            },
        }

-        # model initiaiton
+        # model initialization
        model = init_instance_by_config(task["model"])
        dataset = init_instance_by_config(task["dataset"])

--- a/docs/component/rl/guidance.rst
+++ b/docs/component/rl/guidance.rst
@@ -0,0 +1,32 @@
+
+========
+Guidance
+========
+.. currentmodule:: qlib
+
+QlibRL can help users quickly get started and conveniently implement quantitative strategies based on reinforcement learning(RL) algorithms. For different user groups, we recommend the following guidance to use QlibRL.
+
+Beginners to Reinforcement Learning Algorithms
+==============================================
+Whether you are a quantitative researcher who wants to understand what RL can do in trading or a learner who wants to get started with RL algorithms in trading scenarios, if you have limited knowledge of RL and want to shield various detailed settings to quickly get started with RL algorithms, we recommend the following sequence to learn qlibrl:
+ - Learn the fundamentals of RL in `part1 <https://qlib.readthedocs.io/en/latest/component/rl/overall.html#reinforcement-learning>`_.
+ - Understand the trading scenarios where RL methods can be applied in `part2 <https://qlib.readthedocs.io/en/latest/component/rl/overall.html#potential-application-scenarios-in-quantitative-trading>`_.
+ - Run the examples in `part3 <https://qlib.readthedocs.io/en/latest/component/rl/quickstart.html>`_ to solve trading problems using RL.
+ - If you want to further explore QlibRL and make some customizations, you need to first understand the framework of QlibRL in `part4 <https://qlib.readthedocs.io/en/latest/component/rl/framework.html>`_ and rewrite specific components according to your needs.
+
+Reinforcement Learning Algorithm Researcher
+==============================================
+If you are already familiar with existing RL algorithms and dedicated to researching RL algorithms but lack domain knowledge in the financial field, and you want to validate the effectiveness of your algorithms in financial trading scenarios, we recommend the following steps to get started with QlibRL:
+ - Understand the trading scenarios where RL methods can be applied in `part2 <https://qlib.readthedocs.io/en/latest/component/rl/overall.html#potential-application-scenarios-in-quantitative-trading>`_.
+ - Choose an RL application scenario (currently, QlibRL has implemented two scenario examples: order execution and algorithmic trading). Run the example in `part3 <https://qlib.readthedocs.io/en/latest/component/rl/quickstart.html>`_ to get it working.
+ - Modify the `policy <https://github.com/microsoft/qlib/blob/main/qlib/rl/order_execution/policy.py>`_ part to incorporate your own RL algorithm.
+
+Quantitative Researcher
+=======================
+If you have a certain level of financial domain knowledge and coding skills, and you want to explore the application of RL algorithms in the investment field, we recommend the following steps to explore QlibRL:
+ - Learn the fundamentals of RL in `part1 <https://qlib.readthedocs.io/en/latest/component/rl/overall.html#reinforcement-learning>`_.
+ - Understand the trading scenarios where RL methods can be applied in `part2 <https://qlib.readthedocs.io/en/latest/component/rl/overall.html#potential-application-scenarios-in-quantitative-trading>`_.
+ - Run the examples in `part3 <https://qlib.readthedocs.io/en/latest/component/rl/quickstart.html>`_ to solve trading problems using RL.
+ - Understand the framework of QlibRL in `part4 <https://qlib.readthedocs.io/en/latest/component/rl/framework.html>`_.
+ - Choose a suitable RL algorithm based on the characteristics of the problem you want to solve (currently, QlibRL supports PPO and DQN algorithms based on tianshou).
+ - Design the MDP (Markov Decision Process) process based on market trading rules and the problem you want to solve. Refer to the example in order execution and make corresponding modifications to the following modules: `State <https://github.com/microsoft/qlib/blob/main/qlib/rl/order_execution/state.py#L70>`_, `Metrics <https://github.com/microsoft/qlib/blob/main/qlib/rl/order_execution/state.py#L18>`_, `ActionInterpreter <https://github.com/microsoft/qlib/blob/main/qlib/rl/order_execution/interpreter.py#L199>`_, `StateInterpreter <https://github.com/microsoft/qlib/blob/main/qlib/rl/order_execution/interpreter.py#L68>`_, `Reward <https://github.com/microsoft/qlib/blob/main/qlib/rl/order_execution/reward.py>`_, `Observation <https://github.com/microsoft/qlib/blob/main/qlib/rl/order_execution/interpreter.py#L44>`_, `Simulator <https://github.com/microsoft/qlib/blob/main/qlib/rl/order_execution/simulator_simple.py>`_.
--- a/docs/component/rl/overall.rst
+++ b/docs/component/rl/overall.rst
@@ -4,7 +4,7 @@ Reinforcement Learning in Quantitative Trading

 Reinforcement Learning
 ======================
-Different from supervised learning tasks such as classification tasks and regression tasks. Another important paradigm in machine learning is Reinforcement Learning, 
+Different from supervised learning tasks such as classification tasks and regression tasks. Another important paradigm in machine learning is Reinforcement Learning(RL), 
 which attempts to optimize an accumulative numerical reward signal by directly interacting with the environment under a few assumptions such as Markov Decision Process(MDP).

 As demonstrated in the following figure, an RL system consists of four elements, 1)the agent 2) the environment the agent interacts with 3) the policy that the agent follows to take actions on the environment and 4)the reward signal from the environment to the agent. 
@@ -25,26 +25,46 @@ The Qlib Reinforcement Learning toolkit (QlibRL) is an RL platform for quantitat

 Potential Application Scenarios in Quantitative Trading
 =======================================================
-RL methods have already achieved outstanding achievement in many applications, such as game playing, resource allocating, recommendation, marketing and advertising, etc.
-Investment is always a continuous process, taking the stock market as an example, investors need to control their positions and stock holdings by one or more buying and selling behaviors, to maximize the investment returns.
-Besides, each buy and sell decision is made by investors after fully considering the overall market information and stock information. 
-From the view of an investor, the process could be described as a continuous decision-making process generated according to interaction with the market, such problems could be solved by the RL algorithms. 
-Following are some scenarios where RL can potentially be used in quantitative investment.
-
-Portfolio Construction
----------------------
-Portfolio construction is a process of selecting securities optimally by taking a minimum risk to achieve maximum returns. With an RL-based solution, an agent allocates stocks at every time step by obtaining information for each stock and the market. The key is to develop of policy for building a portfolio and make the policy able to pick the optimal portfolio. 
+RL methods have demonstrated remarkable achievements in various applications, including game playing, resource allocation, recommendation systems, marketing, and advertising.
+In the context of investment, which involves continuous decision-making, let's consider the example of the stock market. Investors strive to optimize their investment returns by effectively managing their positions and stock holdings through various buying and selling behaviors.
+Furthermore, investors carefully evaluate market conditions and stock-specific information before making each buying or selling decision. From an investor's perspective, this process can be viewed as a continuous decision-making process driven by interactions with the market. RL algorithms offer a promising approach to tackle such challenges.
+Here are several scenarios where RL holds potential for application in quantitative investment.

 Order Execution
 ---------------
-As a fundamental problem in algorithmic trading, order execution aims at fulfilling a specific trading order, either liquidation or acquirement, for a given instrument. Essentially, the goal of order execution is twofold: it not only requires to fulfill the whole order but also targets a more economical execution with maximizing profit gain (or minimizing capital loss). The order execution with only one order of liquidation or acquirement is called single-asset order execution.
+The order execution task is to execute orders efficiently while considering multiple factors, including optimal prices, minimizing trading costs, reducing market impact, maximizing order fullfill rates, and achieving execution within a specified time frame. RL can be applied to such tasks by incorporating these objectives into the reward function and action selection process. Specifically, the RL agent interacts with the market environment, observes the state from market information, and makes decisions on next step execution. The RL algorithm learns an optimal execution strategy through trial and error, aiming to maximize the expected cumulative reward, which incorporates the desired objectives.

-Considering stock investment always aim to pursue long-term maximized profits, it usually manifests as a sequential process of continuously adjusting the asset portfolios, execution for multiple orders, including order of liquidation and acquirement, brings more constraints and makes the sequence of execution for different orders should be considered, e.g. before executing an order to buy some stocks, we have to sell at least one stock. The order execution with multiple assets is called multi-asset order execution. 
+ - General Setting
+    - Environment: The environment represents the financial market where order execution takes place. It encompasses variables such as the order book dynamics, liquidity, price movements, and market conditions.

-According to the order execution’s trait of sequential decision-making, an RL-based solution could be applied to solve the order execution. With an RL-based solution, an agent optimizes execution strategy by interacting with the market environment. 
+    - State: The state refers to the information available to the RL agent at a given time step. It typically includes features such as the current order book state (bid-ask spread, order depth), historical price data, historical trading volume, market volatility, and any other relevant information that can aid in decision-making.

-With QlibRL, the RL algorithm in the above scenarios can be easily implemented.
+    - Action: The action is the decision made by the RL agent based on the observed state. In order execution, actions can include selecting the order size, price, and timing of execution.

-Nested Portfolio Construction and Order Executor
------------------------------------------------
-QlibRL makes it possible to jointly optimize different levels of strategies/models/agents. Take `Nested Decision Execution Framework <https://github.com/microsoft/qlib/blob/main/examples/nested_decision_execution>`_ as an example, the optimization of order execution strategy and portfolio management strategies can interact with each other to maximize returns.
+    - Reward: The reward is a scalar signal that indicates the performance of the RL agent's action in the environment. The reward function is designed to encourage actions that lead to efficient and cost-effective order execution. It typically considers multiple objectives, such as maximizing price advantages, minimizing trading costs (including transaction fees and slippage), reducing market impact (the effect of the order on the market price) and maximizing order fullfill rates. 
+
+ - Scenarios
+    - Single-asset order execution: Single-asset order execution focuses on the task of executing a single order for a specific asset, such as a stock or a cryptocurrency. The primary objective is to execute the order efficiently while considering factors such as maximizing price advantages, minimizing trading costs, reducing market impact, and achieving a high fullfill rate. The RL agent interacts with the market environment and makes decisions on order size, price, and timing of execution for that particular asset. The goal is to learn an optimal execution strategy for the single asset, maximizing the expected cumulative reward while considering the specific dynamics and characteristics of that asset.
+
+    - Multi-asset order execution: Multi-asset order execution expands the order execution task to involve multiple assets or securities. It typically involves executing a portfolio of orders across different assets simultaneously or sequentially. Unlike single-asset order execution, the focus is not only on the execution of individual orders but also on managing the interactions and dependencies between different assets within the portfolio. The RL agent needs to make decisions on the order sizes, prices, and timings for each asset in the portfolio, considering their interdependencies, cash constraints, market conditions, and transaction costs. The goal is to learn an optimal execution strategy that balances the execution efficiency for each asset while considering the overall performance and objectives of the portfolio as a whole.
+   
+The choice of settings and RL algorithm depends on the specific requirements of the task, available data, and desired performance objectives. 
+
+Portfolio Construction
+----------------------
+Portfolio construction is a process of selecting and allocating assets in an investment portfolio. RL provides a framework to optimize portfolio management decisions by learning from interactions with the market environment and maximizing long-term returns while considering risk management.
+ - General Setting
+    - State: The state represents the current information about the market and the portfolio. It typically includes historical prices and volumes, technical indicators, and other relevant data.
+
+    - Action: The action corresponds to the decision of allocating capital to different assets in the portfolio. It determines the weights or proportions of investments in each asset.
+
+    - Reward: The reward is a metric that evaluates the performance of the portfolio. It can be defined in various ways, such as total return, risk-adjusted return, or other objectives like maximizing Sharpe ratio or minimizing drawdown.
+
+ - Scenarios
+    - Stock market: RL can be used to construct portfolios of stocks, where the agent learns to allocate capital among different stocks.
+
+    - Cryptocurrency market: RL can be applied to construct portfolios of cryptocurrencies, where the agent learns to make allocation decisions.
+
+    - Foreign exchange (Forex) market: RL can be used to construct portfolios of currency pairs, where the agent learns to allocate capital across different currencies based on exchange rate data, economic indicators, and other factors.
+
+Similarly, the choice of basic setting and algorithm depends on the specific requirements of the problem and the characteristics of the market.
--- a/docs/component/rl/toctree.rst
+++ b/docs/component/rl/toctree.rst
@@ -5,6 +5,7 @@ Reinforcement Learning in Quantitative Trading
 ========================================================================

 .. toctree::
+    Guidance <guidance>
    Overall <overall>
    Quick Start <quickstart>
    Framework <framework>
--- a/docs/component/workflow.rst
+++ b/docs/component/workflow.rst
@@ -53,9 +53,7 @@ Below is a typical config file of ``qrun``.
            kwargs:
                topk: 50
                n_drop: 5
-                signal:
-                    - <MODEL>
-                    - <DATASET>
+                signal: <PRED>
        backtest:
            limit_threshold: 0.095
            account: 100000000
@@ -281,9 +279,7 @@ The following script is the configuration of `backtest` and the `strategy` used
            kwargs:
                topk: 50
                n_drop: 5
-                signal:
-                    - <MODEL>
-                    - <DATASET>
+                signal: <PRED>
        backtest:
            limit_threshold: 0.095
            account: 100000000
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -123,7 +123,6 @@ html_logo = "_static/img/logo/1.png"
 html_theme_options = {
    "logo_only": True,
    "collapse_navigation": False,
-    "display_version": False,
    "navigation_depth": 4,
 }

--- a/docs/developer/code_standard_and_dev_guide.rst
+++ b/docs/developer/code_standard_and_dev_guide.rst
@@ -60,4 +60,4 @@ The `[dev]` option will help you to install some related packages when developin

 .. code-block:: bash

-    pip install -e .[dev]
+    pip install -e ".[dev]"
--- a/docs/developer/how_to_build_image.rst
+++ b/docs/developer/how_to_build_image.rst
@@ -0,0 +1,81 @@
+.. _docker_image:
+
+==================
+Build Docker Image
+==================
+
+Dockerfile
+==========
+
+There is a **Dockerfile** file in the root directory of the project from which you can build the docker image. There are two build methods in Dockerfile to choose from.
+When executing the build command, use the ``--build-arg`` parameter to control the image version. The ``--build-arg`` parameter defaults to ``yes``, which builds the ``stable`` version of the qlib image.
+
+1.For the ``stable`` version, use ``pip install pyqlib`` to build the qlib image.
+
+.. code-block:: bash
+
+    docker build --build-arg IS_STABLE=yes -t <image name> -f ./Dockerfile .
+
+.. code-block:: bash
+
+    docker build -t <image name> -f ./Dockerfile .
+
+2. For the ``nightly`` version, use current source code to build the qlib image.
+
+.. code-block:: bash
+
+    docker build --build-arg IS_STABLE=no -t <image name> -f ./Dockerfile .
+
+Auto build of qlib images
+=========================
+
+1. There is a **build_docker_image.sh** file in the root directory of your project, which can be used to automatically build docker images and upload them to your docker hub repository(Optional, configuration required).
+
+.. code-block:: bash
+
+    sh build_docker_image.sh
+    >>> Do you want to build the nightly version of the qlib image? (default is stable) (yes/no):
+    >>> Is it uploaded to docker hub? (default is no) (yes/no):
+
+2. If you want to upload the built image to your docker hub repository, you need to edit your **build_docker_image.sh** file first, fill in ``docker_user`` in the file, and then execute this file.
+
+How to use qlib images
+======================
+1. Start a new Docker container
+
+.. code-block:: bash
+
+    docker run -it --name <container name> -v <Mounted local directory>:/app <image name>
+
+2. At this point you are in the docker environment and can run the qlib scripts. An example:
+
+.. code-block:: bash
+
+    >>> python scripts/get_data.py qlib_data --name qlib_data_simple --target_dir ~/.qlib/qlib_data/cn_data --interval 1d --region cn
+    >>> python qlib/workflow/cli.py examples/benchmarks/LightGBM/workflow_config_lightgbm_Alpha158.yaml
+
+3. Exit the container
+
+.. code-block:: bash
+
+    >>> exit
+
+4. Restart the container
+
+.. code-block:: bash
+
+    docker start -i -a <container name>
+
+5. Stop the container
+
+.. code-block:: bash
+
+    docker stop -i -a <container name>
+
+6. Delete the container
+
+.. code-block:: bash
+
+    docker rm <container name>
+
+7. For more information on using docker see the `docker documentation <https://docs.docker.com/reference/cli/docker/>`_.
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -61,6 +61,7 @@ Document Structure
   :caption: FOR DEVELOPERS:

   Code Standard & Development Guidance <developer/code_standard_and_dev_guide.rst>
+   How to build image <developer/how_to_build_image.rst>

 .. toctree::
   :maxdepth: 3
--- a/docs/introduction/introduction.rst
+++ b/docs/introduction/introduction.rst
@@ -36,7 +36,7 @@ Name                         Description
                             the training process of models which enable algorithms controlling the
                             training process.

-`Learning Framework` layer   The `Forecast Model` and `Trading Agent` are learnable. They are learned
+`Learning Framework` layer   The `Forecast Model` and `Trading Agent` are trainable. They are trained
                             based on the `Learning Framework` layer and then applied to multiple scenarios
                             in `Workflow` layer. The supported learning paradigms can be categorized into
                             reinforcement learning and supervised learning.  The learning framework
@@ -51,7 +51,7 @@ Name                         Description
                             modules.  With these signals `Decision Generator` will generate the target
                             trading decisions(i.e. portfolio, orders)
                             If RL-based Strategies are adopted, the `Policy` is learned in a end-to-end way,
-                             the trading deicsions are generated directly.
+                             the trading decisions are generated directly.
                             Decisions will be executed by `Execution Env`
                             (i.e. the trading market).  There may be multiple levels of `Strategy`
                             and `Executor` (e.g. an *order executor trading strategy and intraday order executor*
--- a/docs/introduction/quick.rst
+++ b/docs/introduction/quick.rst
@@ -16,7 +16,7 @@ This ``Quick Start`` guide tries to demonstrate
 Installation
 ============

-Users can easily intsall ``Qlib`` according to the following steps:
+Users can easily install ``Qlib`` according to the following steps:

 - Before installing ``Qlib`` from source, users need to install some dependencies:

--- a/docs/requirements.txt
+++ b/docs/requirements.txt
@@ -5,3 +5,4 @@ scipy
 scikit-learn
 pandas
 tianshou
+sphinx_rtd_theme
--- a/examples/benchmarks/ADARNN/workflow_config_adarnn_Alpha360.yaml
+++ b/examples/benchmarks/ADARNN/workflow_config_adarnn_Alpha360.yaml
@@ -28,8 +28,7 @@ port_analysis_config: &port_analysis_config
        class: TopkDropoutStrategy
        module_path: qlib.contrib.strategy
        kwargs:
-            model: <MODEL>
-            dataset: <DATASET>
+            signal: <PRED>
            topk: 50
            n_drop: 5
    backtest:
--- a/examples/benchmarks/ADD/workflow_config_add_Alpha360.yaml
+++ b/examples/benchmarks/ADD/workflow_config_add_Alpha360.yaml
@@ -28,9 +28,7 @@ port_analysis_config: &port_analysis_config
        class: TopkDropoutStrategy
        module_path: qlib.contrib.strategy
        kwargs:
-            signal:
-                - <MODEL>
-                - <DATASET>
+            signal: <PRED>
            topk: 50
            n_drop: 5
    backtest:
--- a/examples/benchmarks/ALSTM/workflow_config_alstm_Alpha158.yaml
+++ b/examples/benchmarks/ALSTM/workflow_config_alstm_Alpha158.yaml
@@ -36,9 +36,7 @@ port_analysis_config: &port_analysis_config
        class: TopkDropoutStrategy
        module_path: qlib.contrib.strategy
        kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
+            signal: <PRED>
            topk: 50
            n_drop: 5
    backtest:
--- a/examples/benchmarks/ALSTM/workflow_config_alstm_Alpha360.yaml
+++ b/examples/benchmarks/ALSTM/workflow_config_alstm_Alpha360.yaml
@@ -28,9 +28,7 @@ port_analysis_config: &port_analysis_config
        class: TopkDropoutStrategy
        module_path: qlib.contrib.strategy
        kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
+            signal: <PRED>
            topk: 50
            n_drop: 5
    backtest:
--- a/examples/benchmarks/CatBoost/workflow_config_catboost_Alpha158.yaml
+++ b/examples/benchmarks/CatBoost/workflow_config_catboost_Alpha158.yaml
@@ -14,9 +14,7 @@ port_analysis_config: &port_analysis_config
        class: TopkDropoutStrategy
        module_path: qlib.contrib.strategy
        kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
+            signal: <PRED>
            topk: 50
            n_drop: 5
    backtest:
--- a/examples/benchmarks/CatBoost/workflow_config_catboost_Alpha158_csi500.yaml
+++ b/examples/benchmarks/CatBoost/workflow_config_catboost_Alpha158_csi500.yaml
@@ -14,9 +14,7 @@ port_analysis_config: &port_analysis_config
        class: TopkDropoutStrategy
        module_path: qlib.contrib.strategy
        kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
+            signal: <PRED>
            topk: 50
            n_drop: 5
    backtest:
--- a/examples/benchmarks/CatBoost/workflow_config_catboost_Alpha360.yaml
+++ b/examples/benchmarks/CatBoost/workflow_config_catboost_Alpha360.yaml
@@ -21,9 +21,7 @@ port_analysis_config: &port_analysis_config
        class: TopkDropoutStrategy
        module_path: qlib.contrib.strategy
        kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
+            signal: <PRED>
            topk: 50
            n_drop: 5
    backtest:
--- a/examples/benchmarks/CatBoost/workflow_config_catboost_Alpha360_csi500.yaml
+++ b/examples/benchmarks/CatBoost/workflow_config_catboost_Alpha360_csi500.yaml
@@ -21,9 +21,7 @@ port_analysis_config: &port_analysis_config
        class: TopkDropoutStrategy
        module_path: qlib.contrib.strategy
        kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
+            signal: <PRED>
            topk: 50
            n_drop: 5
    backtest:
--- a/examples/benchmarks/DoubleEnsemble/workflow_config_doubleensemble_Alpha158.yaml
+++ b/examples/benchmarks/DoubleEnsemble/workflow_config_doubleensemble_Alpha158.yaml
@@ -14,9 +14,7 @@ port_analysis_config: &port_analysis_config
        class: TopkDropoutStrategy
        module_path: qlib.contrib.strategy
        kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
+            signal: <PRED>
            topk: 50
            n_drop: 5
    backtest:
--- a/examples/benchmarks/DoubleEnsemble/workflow_config_doubleensemble_Alpha158_csi500.yaml
+++ b/examples/benchmarks/DoubleEnsemble/workflow_config_doubleensemble_Alpha158_csi500.yaml
@@ -14,9 +14,7 @@ port_analysis_config: &port_analysis_config
        class: TopkDropoutStrategy
        module_path: qlib.contrib.strategy
        kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
+            signal: <PRED>
            topk: 50
            n_drop: 5
    backtest:
--- a/examples/benchmarks/DoubleEnsemble/workflow_config_doubleensemble_Alpha360.yaml
+++ b/examples/benchmarks/DoubleEnsemble/workflow_config_doubleensemble_Alpha360.yaml
@@ -21,9 +21,7 @@ port_analysis_config: &port_analysis_config
        class: TopkDropoutStrategy
        module_path: qlib.contrib.strategy
        kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
+            signal: <PRED>
            topk: 50
            n_drop: 5
    backtest:
--- a/examples/benchmarks/DoubleEnsemble/workflow_config_doubleensemble_Alpha360_csi500.yaml
+++ b/examples/benchmarks/DoubleEnsemble/workflow_config_doubleensemble_Alpha360_csi500.yaml
@@ -21,9 +21,7 @@ port_analysis_config: &port_analysis_config
        class: TopkDropoutStrategy
        module_path: qlib.contrib.strategy
        kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
+            signal: <PRED>
            topk: 50
            n_drop: 5
    backtest:
--- a/examples/benchmarks/DoubleEnsemble/workflow_config_doubleensemble_early_stop_Alpha158.yaml
+++ b/examples/benchmarks/DoubleEnsemble/workflow_config_doubleensemble_early_stop_Alpha158.yaml
@@ -14,9 +14,7 @@ port_analysis_config: &port_analysis_config
        class: TopkDropoutStrategy
        module_path: qlib.contrib.strategy
        kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
+            signal: <PRED>
            topk: 50
            n_drop: 5
    backtest:
--- a/examples/benchmarks/GATs/workflow_config_gats_Alpha158.yaml
+++ b/examples/benchmarks/GATs/workflow_config_gats_Alpha158.yaml
@@ -35,9 +35,7 @@ port_analysis_config: &port_analysis_config
        class: TopkDropoutStrategy
        module_path: qlib.contrib.strategy
        kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
+            signal: <PRED>
            topk: 50
            n_drop: 5
    backtest:
--- a/examples/benchmarks/GATs/workflow_config_gats_Alpha360.yaml
+++ b/examples/benchmarks/GATs/workflow_config_gats_Alpha360.yaml
@@ -28,9 +28,7 @@ port_analysis_config: &port_analysis_config
        class: TopkDropoutStrategy
        module_path: qlib.contrib.strategy
        kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
+            signal: <PRED>
            topk: 50
            n_drop: 5
    backtest:
--- a/examples/benchmarks/GRU/workflow_config_gru_Alpha158.yaml
+++ b/examples/benchmarks/GRU/workflow_config_gru_Alpha158.yaml
@@ -36,9 +36,7 @@ port_analysis_config: &port_analysis_config
        class: TopkDropoutStrategy
        module_path: qlib.contrib.strategy
        kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
+            signal: <PRED>
            topk: 50
            n_drop: 5
    backtest:
--- a/examples/benchmarks/GRU/workflow_config_gru_Alpha360.yaml
+++ b/examples/benchmarks/GRU/workflow_config_gru_Alpha360.yaml
@@ -28,9 +28,7 @@ port_analysis_config: &port_analysis_config
        class: TopkDropoutStrategy
        module_path: qlib.contrib.strategy
        kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
+            signal: <PRED>
            topk: 50
            n_drop: 5
    backtest:
--- a/examples/benchmarks/GeneralPtNN/README.md
+++ b/examples/benchmarks/GeneralPtNN/README.md
@@ -0,0 +1,19 @@
+
+
+# Introduction
+
+What is GeneralPtNN
+- Fix previous design that fail to support both Time-series and tabular data
+- Now you can just replace the Pytorch model structure to run a NN model.
+
+We provide an example to demonstrate the effectiveness of the current design.
+- `workflow_config_gru.yaml` align with previous results [GRU(Kyunghyun Cho, et al.)](../README.md#Alpha158-dataset)
+  - `workflow_config_gru2mlp.yaml` to demonstrate we can convert config from time-series to tabular data with minimal changes
+    - You only have to change the net & dataset class to make the conversion.
+- `workflow_config_mlp.yaml` achieved similar functionality with [MLP](../README.md#Alpha158-dataset)
+
+# TODO
+
+- We will align existing models to current design.
+
+- The result of `workflow_config_mlp.yaml` is different with the result of [MLP](../README.md#Alpha158-dataset) since GeneralPtNN has a different stopping method compared to previous implementations. Specificly, GeneralPtNN controls training according to epoches, whereas previous methods controlled by max_steps. 
--- a/examples/benchmarks/GeneralPtNN/workflow_config_gru.yaml
+++ b/examples/benchmarks/GeneralPtNN/workflow_config_gru.yaml
@@ -0,0 +1,100 @@
+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: <PRED>
+            topk: 50
+            n_drop: 5
+    backtest:
+        start_time: 2017-01-01
+        end_time: 2020-08-01
+        account: 100000000
+        benchmark: *benchmark
+        exchange_kwargs:
+            limit_threshold: 0.095
+            deal_price: close
+            open_cost: 0.0005
+            close_cost: 0.0015
+            min_cost: 5
+task:
+    model:
+        class: GeneralPTNN
+        module_path: qlib.contrib.model.pytorch_general_nn
+        kwargs:
+            n_epochs: 200
+            lr: 2e-4
+            early_stop: 10
+            batch_size: 800
+            metric: loss
+            loss: mse
+            n_jobs: 20
+            GPU: 0
+            pt_model_uri: "qlib.contrib.model.pytorch_gru_ts.GRUModel"
+            pt_model_kwargs: {
+                "d_feat": 20,
+                "hidden_size": 64,
+                "num_layers": 2,
+                "dropout": 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
--- a/examples/benchmarks/GeneralPtNN/workflow_config_gru2mlp.yaml
+++ b/examples/benchmarks/GeneralPtNN/workflow_config_gru2mlp.yaml
@@ -0,0 +1,93 @@
+qlib_init:
+    provider_uri: "~/.qlib/qlib_data/cn_data"
+    region: cn
+market: &market csi300
+benchmark: &benchmark SH000300
+data_handler_config: &data_handler_config
+    start_time: 2008-01-01
+    end_time: 2020-08-01
+    fit_start_time: 2008-01-01
+    fit_end_time: 2014-12-31
+    instruments: *market
+    infer_processors:
+        - class: FilterCol
+          kwargs:
+              fields_group: feature
+              col_list: ["RESI5", "WVMA5", "RSQR5", "KLEN", "RSQR10", "CORR5", "CORD5", "CORR10", 
+                            "ROC60", "RESI10", "VSTD5", "RSQR60", "CORR60", "WVMA60", "STD5", 
+                            "RSQR20", "CORD60", "CORD10", "CORR20", "KLOW"
+                        ]
+        - class: RobustZScoreNorm
+          kwargs:
+              fields_group: feature
+              clip_outlier: true
+        - class: Fillna
+          kwargs:
+              fields_group: feature
+    learn_processors:
+        - class: DropnaLabel
+        - class: CSRankNorm
+          kwargs:
+              fields_group: label
+    label: ["Ref($close, -2) / Ref($close, -1) - 1"] 
+
+port_analysis_config: &port_analysis_config
+    strategy:
+        class: TopkDropoutStrategy
+        module_path: qlib.contrib.strategy
+        kwargs:
+            signal: <PRED>
+            topk: 50
+            n_drop: 5
+    backtest:
+        start_time: 2017-01-01
+        end_time: 2020-08-01
+        account: 100000000
+        benchmark: *benchmark
+        exchange_kwargs:
+            limit_threshold: 0.095
+            deal_price: close
+            open_cost: 0.0005
+            close_cost: 0.0015
+            min_cost: 5
+task:
+    model:
+        class: GeneralPTNN
+        module_path: qlib.contrib.model.pytorch_general_nn
+        kwargs:
+            lr: 1e-3
+            n_epochs: 1
+            batch_size: 800
+            loss: mse
+            optimizer: adam
+            pt_model_uri: "qlib.contrib.model.pytorch_nn.Net"
+            pt_model_kwargs: 
+                input_dim: 20
+                layers: [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: 
+            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
--- a/examples/benchmarks/GeneralPtNN/workflow_config_mlp.yaml
+++ b/examples/benchmarks/GeneralPtNN/workflow_config_mlp.yaml
@@ -0,0 +1,98 @@
+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" : "DropCol", 
+            "kwargs":{"col_list": ["VWAP0"]}
+        },
+        {
+             "class" : "CSZFillna", 
+             "kwargs":{"fields_group": "feature"}
+        }
+    ]
+    learn_processors: [
+        {
+            "class" : "DropCol", 
+            "kwargs":{"col_list": ["VWAP0"]}
+        },
+        {
+            "class" : "DropnaProcessor", 
+            "kwargs":{"fields_group": "feature"}
+        },
+        "DropnaLabel",
+        {
+            "class": "CSZScoreNorm", 
+            "kwargs": {"fields_group": "label"}
+        }
+    ]
+    process_type: "independent"
+
+port_analysis_config: &port_analysis_config
+    strategy:
+        class: TopkDropoutStrategy
+        module_path: qlib.contrib.strategy
+        kwargs:
+            signal: <PRED>
+            topk: 50
+            n_drop: 5
+    backtest:
+        start_time: 2017-01-01
+        end_time: 2020-08-01
+        account: 100000000
+        benchmark: *benchmark
+        exchange_kwargs:
+            limit_threshold: 0.095
+            deal_price: close
+            open_cost: 0.0005
+            close_cost: 0.0015
+            min_cost: 5
+task:
+    model:
+        class: GeneralPTNN
+        module_path: qlib.contrib.model.pytorch_general_nn
+        kwargs:
+            # FIXME: wrong parameters.
+            lr: 2e-3
+            batch_size: 8192
+            loss: mse
+            weight_decay: 0.0002
+            optimizer: adam
+            pt_model_uri: "qlib.contrib.model.pytorch_nn.Net"
+            pt_model_kwargs: 
+                input_dim: 157
+    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: 
+            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
--- a/examples/benchmarks/HIST/workflow_config_hist_Alpha360.yaml
+++ b/examples/benchmarks/HIST/workflow_config_hist_Alpha360.yaml
@@ -28,9 +28,7 @@ port_analysis_config: &port_analysis_config
        class: TopkDropoutStrategy
        module_path: qlib.contrib.strategy
        kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
+            signal: <PRED>
            topk: 50
            n_drop: 5
    backtest:
@@ -89,4 +87,4 @@ task:
        - class: PortAnaRecord
          module_path: qlib.workflow.record_temp
          kwargs: 
-            config: *port_analysis_config
+            config: *port_analysis_config
--- a/examples/benchmarks/IGMTF/workflow_config_igmtf_Alpha360.yaml
+++ b/examples/benchmarks/IGMTF/workflow_config_igmtf_Alpha360.yaml
@@ -28,8 +28,7 @@ port_analysis_config: &port_analysis_config
        class: TopkDropoutStrategy
        module_path: qlib.contrib.strategy
        kwargs:
-            model: <MODEL>
-            dataset: <DATASET>
+            signal: <PRED>
            topk: 50
            n_drop: 5
    backtest:
--- a/examples/benchmarks/KRNN/workflow_config_krnn_Alpha360.yaml
+++ b/examples/benchmarks/KRNN/workflow_config_krnn_Alpha360.yaml
@@ -28,9 +28,7 @@ port_analysis_config: &port_analysis_config
        class: TopkDropoutStrategy
        module_path: qlib.contrib.strategy
        kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
+            signal: <PRED>
            topk: 50
            n_drop: 5
    backtest:
--- a/examples/benchmarks/LSTM/workflow_config_lstm_Alpha158.yaml
+++ b/examples/benchmarks/LSTM/workflow_config_lstm_Alpha158.yaml
@@ -36,9 +36,7 @@ port_analysis_config: &port_analysis_config
        class: TopkDropoutStrategy
        module_path: qlib.contrib.strategy
        kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
+            signal: <PRED>
            topk: 50
            n_drop: 5
    backtest:
--- a/examples/benchmarks/LSTM/workflow_config_lstm_Alpha360.yaml
+++ b/examples/benchmarks/LSTM/workflow_config_lstm_Alpha360.yaml
@@ -28,9 +28,7 @@ port_analysis_config: &port_analysis_config
        class: TopkDropoutStrategy
        module_path: qlib.contrib.strategy
        kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
+            signal: <PRED>
            topk: 50
            n_drop: 5
    backtest:
--- a/examples/benchmarks/LightGBM/multi_freq_handler.py
+++ b/examples/benchmarks/LightGBM/multi_freq_handler.py
@@ -48,7 +48,6 @@ class Avg15minHandler(DataHandlerLP):
        )

    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
--- a/examples/benchmarks/LightGBM/workflow_config_lightgbm_Alpha158.yaml
+++ b/examples/benchmarks/LightGBM/workflow_config_lightgbm_Alpha158.yaml
@@ -14,8 +14,7 @@ port_analysis_config: &port_analysis_config
        class: TopkDropoutStrategy
        module_path: qlib.contrib.strategy
        kwargs:
-            model: <MODEL> 
-            dataset: <DATASET>
+            signal: <PRED>
            topk: 50
            n_drop: 5
    backtest:
--- a/examples/benchmarks/LightGBM/workflow_config_lightgbm_Alpha158_csi500.yaml
+++ b/examples/benchmarks/LightGBM/workflow_config_lightgbm_Alpha158_csi500.yaml
@@ -14,8 +14,7 @@ port_analysis_config: &port_analysis_config
        class: TopkDropoutStrategy
        module_path: qlib.contrib.strategy
        kwargs:
-            model: <MODEL> 
-            dataset: <DATASET>
+            signal: <PRED>
            topk: 50
            n_drop: 5
    backtest:
--- a/examples/benchmarks/LightGBM/workflow_config_lightgbm_Alpha158_multi_freq.yaml
+++ b/examples/benchmarks/LightGBM/workflow_config_lightgbm_Alpha158_multi_freq.yaml
@@ -33,9 +33,7 @@ port_analysis_config: &port_analysis_config
        kwargs:
            topk: 50
            n_drop: 5
-            signal:
-                - <MODEL> 
-                - <DATASET>
+            signal: <PRED>
    backtest:
        verbose: False
        limit_threshold: 0.095
--- a/examples/benchmarks/LightGBM/workflow_config_lightgbm_Alpha360.yaml
+++ b/examples/benchmarks/LightGBM/workflow_config_lightgbm_Alpha360.yaml
@@ -21,9 +21,7 @@ port_analysis_config: &port_analysis_config
        class: TopkDropoutStrategy
        module_path: qlib.contrib.strategy
        kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
+            signal: <PRED>
            topk: 50
            n_drop: 5
    backtest:
--- a/examples/benchmarks/LightGBM/workflow_config_lightgbm_Alpha360_csi500.yaml
+++ b/examples/benchmarks/LightGBM/workflow_config_lightgbm_Alpha360_csi500.yaml
@@ -21,9 +21,7 @@ port_analysis_config: &port_analysis_config
        class: TopkDropoutStrategy
        module_path: qlib.contrib.strategy
        kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
+            signal: <PRED>
            topk: 50
            n_drop: 5
    backtest:
--- a/examples/benchmarks/LightGBM/workflow_config_lightgbm_configurable_dataset.yaml
+++ b/examples/benchmarks/LightGBM/workflow_config_lightgbm_configurable_dataset.yaml
@@ -29,9 +29,7 @@ port_analysis_config: &port_analysis_config
        class: TopkDropoutStrategy
        module_path: qlib.contrib.strategy
        kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
+            signal: <PRED>
            topk: 50
            n_drop: 5
    backtest:
--- a/examples/benchmarks/LightGBM/workflow_config_lightgbm_multi_freq.yaml
+++ b/examples/benchmarks/LightGBM/workflow_config_lightgbm_multi_freq.yaml
@@ -31,9 +31,7 @@ port_analysis_config: &port_analysis_config
        class: TopkDropoutStrategy
        module_path: qlib.contrib.strategy
        kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
+            signal: <PRED>
            topk: 50
            n_drop: 5
    backtest:
--- a/examples/benchmarks/Linear/workflow_config_linear_Alpha158.yaml
+++ b/examples/benchmarks/Linear/workflow_config_linear_Alpha158.yaml
@@ -27,9 +27,7 @@ port_analysis_config: &port_analysis_config
        class: TopkDropoutStrategy
        module_path: qlib.contrib.strategy
        kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
+            signal: <PRED>
            topk: 50
            n_drop: 5
    backtest:
--- a/examples/benchmarks/Linear/workflow_config_linear_Alpha158_csi500.yaml
+++ b/examples/benchmarks/Linear/workflow_config_linear_Alpha158_csi500.yaml
@@ -27,9 +27,7 @@ port_analysis_config: &port_analysis_config
        class: TopkDropoutStrategy
        module_path: qlib.contrib.strategy
        kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
+            signal: <PRED>
            topk: 50
            n_drop: 5
    backtest:
--- a/examples/benchmarks/Linear/workflow_config_linear_Alpha158_multi_pass_bt.yaml
+++ b/examples/benchmarks/Linear/workflow_config_linear_Alpha158_multi_pass_bt.yaml
@@ -0,0 +1,78 @@
+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
+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: LinearModel
+        module_path: qlib.contrib.model.linear
+        kwargs:
+            estimator: ols
+    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: 
+            model: <MODEL>
+            dataset: <DATASET>
+        - class: SigAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: 
+            ana_long_short: True
+            ann_scaler: 252
+        - class: MultiPassPortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: 
+            config: *port_analysis_config
--- a/examples/benchmarks/Localformer/workflow_config_localformer_Alpha158.yaml
+++ b/examples/benchmarks/Localformer/workflow_config_localformer_Alpha158.yaml
@@ -36,9 +36,7 @@ port_analysis_config: &port_analysis_config
        class: TopkDropoutStrategy
        module_path: qlib.contrib.strategy
        kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
+            signal: <PRED>
            topk: 50
            n_drop: 5
    backtest:
--- a/examples/benchmarks/Localformer/workflow_config_localformer_Alpha360.yaml
+++ b/examples/benchmarks/Localformer/workflow_config_localformer_Alpha360.yaml
@@ -28,9 +28,7 @@ port_analysis_config: &port_analysis_config
        class: TopkDropoutStrategy
        module_path: qlib.contrib.strategy
        kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
+            signal: <PRED>
            topk: 50
            n_drop: 5
    backtest:
--- a/examples/benchmarks/MLP/workflow_config_mlp_Alpha158.yaml
+++ b/examples/benchmarks/MLP/workflow_config_mlp_Alpha158.yaml
@@ -41,9 +41,7 @@ port_analysis_config: &port_analysis_config
        class: TopkDropoutStrategy
        module_path: qlib.contrib.strategy
        kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
+            signal: <PRED>
            topk: 50
            n_drop: 5
    backtest:
--- a/examples/benchmarks/MLP/workflow_config_mlp_Alpha158_csi500.yaml
+++ b/examples/benchmarks/MLP/workflow_config_mlp_Alpha158_csi500.yaml
@@ -41,9 +41,7 @@ port_analysis_config: &port_analysis_config
        class: TopkDropoutStrategy
        module_path: qlib.contrib.strategy
        kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
+            signal: <PRED>
            topk: 50
            n_drop: 5
    backtest:
--- a/examples/benchmarks/MLP/workflow_config_mlp_Alpha360.yaml
+++ b/examples/benchmarks/MLP/workflow_config_mlp_Alpha360.yaml
@@ -29,9 +29,7 @@ port_analysis_config: &port_analysis_config
        class: TopkDropoutStrategy
        module_path: qlib.contrib.strategy
        kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
+            signal: <PRED>
            topk: 50
            n_drop: 5
    backtest:
--- a/examples/benchmarks/MLP/workflow_config_mlp_Alpha360_csi500.yaml
+++ b/examples/benchmarks/MLP/workflow_config_mlp_Alpha360_csi500.yaml
@@ -29,9 +29,7 @@ port_analysis_config: &port_analysis_config
        class: TopkDropoutStrategy
        module_path: qlib.contrib.strategy
        kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
+            signal: <PRED>
            topk: 50
            n_drop: 5
    backtest:
--- a/examples/benchmarks/README.md
+++ b/examples/benchmarks/README.md
@@ -136,7 +136,7 @@ If you want to contribute your new models, you can follow the steps below.
    - `README.md`: a brief introduction to your models
    - `workflow_config_<model name>_<dataset>.yaml`: a configuration which can read by `qrun`. You are encouraged to run your model in all datasets.
 3. You can integrate your model as a module [in this folder](https://github.com/microsoft/qlib/tree/main/qlib/contrib/model).
-4. Please update your results in the above **Benchmark Tables**, e.g. [Alpha360](#alpha158-dataset), [Alpha158](#alpha158-dataset)(the values of each metric are the mean and std calculated based on **20 Runs** with different random seeds. You can accomplish the above operations through the automated [script](https://github.com/microsoft/qlib/blob/main/examples/run_all_model.py#LL286C22-L286C22) provided by Qlib, and get the final result in the .md file. if you don't have enough computational resource, you can ask for help in the PR).
+4. Please update your results in the above **Benchmark Tables**, e.g. [Alpha360](#alpha158-dataset), [Alpha158](#alpha158-dataset)(the values of each metric are the mean and std calculated based on **20 Runs** with different random seeds. You can accomplish the above operations through the automated [script](https://github.com/microsoft/qlib/blob/main/examples/run_all_model.py) provided by Qlib, and get the final result in the .md file. if you don't have enough computational resource, you can ask for help in the PR).
 5. Update the info in the index page in the [news list](https://github.com/microsoft/qlib#newspaper-whats-new----sparkling_heart) and [model list](https://github.com/microsoft/qlib#quant-model-paper-zoo).

 Finally, you can send PR for review. ([here is an example](https://github.com/microsoft/qlib/pull/1040))
--- a/examples/benchmarks/SFM/workflow_config_sfm_Alpha360.yaml
+++ b/examples/benchmarks/SFM/workflow_config_sfm_Alpha360.yaml
@@ -28,9 +28,7 @@ port_analysis_config: &port_analysis_config
        class: TopkDropoutStrategy
        module_path: qlib.contrib.strategy
        kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
+            signal: <PRED>
            topk: 50
            n_drop: 5
    backtest:
--- a/examples/benchmarks/Sandwich/workflow_config_sandwich_Alpha360.yaml
+++ b/examples/benchmarks/Sandwich/workflow_config_sandwich_Alpha360.yaml
@@ -28,9 +28,7 @@ port_analysis_config: &port_analysis_config
        class: TopkDropoutStrategy
        module_path: qlib.contrib.strategy
        kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
+            signal: <PRED>
            topk: 50
            n_drop: 5
    backtest:
--- a/examples/benchmarks/TCN/workflow_config_tcn_Alpha158.yaml
+++ b/examples/benchmarks/TCN/workflow_config_tcn_Alpha158.yaml
@@ -36,8 +36,7 @@ port_analysis_config: &port_analysis_config
        class: TopkDropoutStrategy
        module_path: qlib.contrib.strategy
        kwargs:
-            model: <MODEL>
-            dataset: <DATASET>
+            signal: <PRED>
            topk: 50
            n_drop: 5
    backtest:
--- a/examples/benchmarks/TCN/workflow_config_tcn_Alpha360.yaml
+++ b/examples/benchmarks/TCN/workflow_config_tcn_Alpha360.yaml
@@ -28,8 +28,7 @@ port_analysis_config: &port_analysis_config
        class: TopkDropoutStrategy
        module_path: qlib.contrib.strategy
        kwargs:
-            model: <MODEL>
-            dataset: <DATASET>
+            signal: <PRED>
            topk: 50
            n_drop: 5
    backtest:
--- a/examples/benchmarks/TCTS/workflow_config_tcts_Alpha360.yaml
+++ b/examples/benchmarks/TCTS/workflow_config_tcts_Alpha360.yaml
@@ -30,9 +30,7 @@ port_analysis_config: &port_analysis_config
        class: TopkDropoutStrategy
        module_path: qlib.contrib.strategy
        kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
+            signal: <PRED>
            topk: 50
            n_drop: 5
    backtest:
@@ -95,4 +93,4 @@ task:
        - class: PortAnaRecord
          module_path: qlib.workflow.record_temp
          kwargs: 
-            config: *port_analysis_config
+            config: *port_analysis_config
--- a/examples/benchmarks/TFT/data_formatters/base.py
+++ b/examples/benchmarks/TFT/data_formatters/base.py
@@ -139,7 +139,6 @@ class GenericDataFormatter(abc.ABC):
        # Sanity checks first.
        # Ensure only one ID and time column exist
        def _check_single_column(input_type):
-
            length = len([tup for tup in column_definition if tup[2] == input_type])

            if length != 1:
--- a/examples/benchmarks/TFT/expt_settings/configs.py
+++ b/examples/benchmarks/TFT/expt_settings/configs.py
@@ -78,7 +78,6 @@ class ExperimentConfig:

    @property
    def hyperparam_iterations(self):
-
        return 240 if self.experiment == "volatility" else 60

    def make_data_formatter(self):
--- a/examples/benchmarks/TFT/libs/hyperparam_opt.py
+++ b/examples/benchmarks/TFT/libs/hyperparam_opt.py
@@ -88,7 +88,6 @@ class HyperparamOptManager:
        params_file = os.path.join(self.hyperparam_folder, "params.csv")

        if os.path.exists(results_file) and os.path.exists(params_file):
-
            self.results = pd.read_csv(results_file, index_col=0)
            self.saved_params = pd.read_csv(params_file, index_col=0)

@@ -178,7 +177,6 @@ class HyperparamOptManager:
            return parameters

        for _ in range(self._max_tries):
-
            parameters = _get_next()
            name = self._get_name(parameters)

--- a/examples/benchmarks/TFT/libs/tft_model.py
+++ b/examples/benchmarks/TFT/libs/tft_model.py
@@ -475,7 +475,6 @@ class TemporalFusionTransformer:

        embeddings = []
        for i in range(num_categorical_variables):
-
            embedding = tf.keras.Sequential(
                [
                    tf.keras.layers.InputLayer([time_steps]),
@@ -680,7 +679,6 @@ class TemporalFusionTransformer:

        data_map = {}
        for _, sliced in data.groupby(id_col):
-
            col_mappings = {"identifier": [id_col], "time": [time_col], "outputs": [target_col], "inputs": input_cols}

            for k in col_mappings:
@@ -954,7 +952,6 @@ class TemporalFusionTransformer:
        """

        with tf.variable_scope(self.name):
-
            transformer_layer, all_inputs, attention_components = self._build_base_graph()

            outputs = tf.keras.layers.TimeDistributed(tf.keras.layers.Dense(self.output_size * len(self.quantiles)))(
--- a/examples/benchmarks/TFT/workflow_config_tft_Alpha158.yaml
+++ b/examples/benchmarks/TFT/workflow_config_tft_Alpha158.yaml
@@ -16,9 +16,7 @@ port_analysis_config: &port_analysis_config
        class: TopkDropoutStrategy
        module_path: qlib.contrib.strategy
        kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
+            signal: <PRED>
            topk: 50
            n_drop: 5
    backtest:
--- a/examples/benchmarks/TRA/example.py
+++ b/examples/benchmarks/TRA/example.py
@@ -1,15 +1,15 @@
 import argparse

 import qlib
-import ruamel.yaml as yaml
+from ruamel.yaml import YAML
 from qlib.utils import init_instance_by_config


 def main(seed, config_file="configs/config_alstm.yaml"):
-
    # set random seed
    with open(config_file) as f:
-        config = yaml.safe_load(f)
+        yaml = YAML(typ="safe", pure=True)
+        config = yaml.load(f)

    # seed_suffix = "/seed1000" if "init" in config_file else f"/seed{seed}"
    seed_suffix = ""
@@ -30,7 +30,6 @@ def main(seed, config_file="configs/config_alstm.yaml"):


 if __name__ == "__main__":
-
    # set params from cmd
    parser = argparse.ArgumentParser(allow_abbrev=False)
    parser.add_argument("--seed", type=int, default=1000, help="random seed")
--- a/examples/benchmarks/TRA/src/dataset.py
+++ b/examples/benchmarks/TRA/src/dataset.py
@@ -96,7 +96,6 @@ class MTSDatasetH(DatasetH):
        drop_last=False,
        **kwargs,
    ):
-
        assert horizon > 0, "please specify `horizon` to avoid data leakage"

        self.seq_len = seq_len
@@ -111,7 +110,6 @@ class MTSDatasetH(DatasetH):
        super().__init__(handler, segments, **kwargs)

    def setup_data(self, handler_kwargs: dict = None, **kwargs):
-
        super().setup_data()

        # change index to <code, date>
--- a/examples/benchmarks/TRA/src/model.py
+++ b/examples/benchmarks/TRA/src/model.py
@@ -45,7 +45,6 @@ class TRAModel(Model):
        avg_params=True,
        **kwargs,
    ):
-
        np.random.seed(seed)
        torch.manual_seed(seed)

@@ -93,7 +92,6 @@ class TRAModel(Model):
        self.global_step = -1

    def train_epoch(self, data_set):
-
        self.model.train()
        self.tra.train()

@@ -146,7 +144,6 @@ class TRAModel(Model):
        return total_loss

    def test_epoch(self, data_set, return_pred=False):
-
        self.model.eval()
        self.tra.eval()
        data_set.eval()
@@ -204,7 +201,6 @@ class TRAModel(Model):
        return metrics, preds

    def fit(self, dataset, evals_result=dict()):
-
        train_set, valid_set, test_set = dataset.prepare(["train", "valid", "test"])

        best_score = -1
@@ -328,7 +324,6 @@ class TRAModel(Model):


 class LSTM(nn.Module):
-
    """LSTM Model

    Args:
@@ -380,7 +375,6 @@ class LSTM(nn.Module):
            self.output_size = hidden_size

    def forward(self, x):
-
        x = self.input_drop(x)

        if self.training and self.noise_level > 0:
@@ -419,7 +413,6 @@ class PositionalEncoding(nn.Module):


 class Transformer(nn.Module):
-
    """Transformer Model

    Args:
@@ -464,7 +457,6 @@ class Transformer(nn.Module):
        self.output_size = hidden_size

    def forward(self, x):
-
        x = self.input_drop(x)

        if self.training and self.noise_level > 0:
@@ -481,7 +473,6 @@ class Transformer(nn.Module):


 class TRA(nn.Module):
-
    """Temporal Routing Adaptor (TRA)

    TRA takes historical prediction errors & latent representation as inputs,
@@ -514,7 +505,6 @@ class TRA(nn.Module):
        self.predictors = nn.Linear(input_size, num_states)

    def forward(self, hidden, hist_loss):
-
        preds = self.predictors(hidden)

        if self.num_states == 1:
--- a/examples/benchmarks/TRA/workflow_config_tra_Alpha158.yaml
+++ b/examples/benchmarks/TRA/workflow_config_tra_Alpha158.yaml
@@ -57,9 +57,7 @@ port_analysis_config: &port_analysis_config
        class: TopkDropoutStrategy
        module_path: qlib.contrib.strategy
        kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
+            signal: <PRED>
            topk: 50
            n_drop: 5
    backtest:
--- a/examples/benchmarks/TRA/workflow_config_tra_Alpha158_full.yaml
+++ b/examples/benchmarks/TRA/workflow_config_tra_Alpha158_full.yaml
@@ -51,9 +51,7 @@ port_analysis_config: &port_analysis_config
        class: TopkDropoutStrategy
        module_path: qlib.contrib.strategy
        kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
+            signal: <PRED>
            topk: 50
            n_drop: 5
    backtest:
--- a/examples/benchmarks/TRA/workflow_config_tra_Alpha360.yaml
+++ b/examples/benchmarks/TRA/workflow_config_tra_Alpha360.yaml
@@ -51,9 +51,7 @@ port_analysis_config: &port_analysis_config
        class: TopkDropoutStrategy
        module_path: qlib.contrib.strategy
        kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
+            signal: <PRED>
            topk: 50
            n_drop: 5
    backtest:
--- a/examples/benchmarks/TabNet/workflow_config_TabNet_Alpha158.yaml
+++ b/examples/benchmarks/TabNet/workflow_config_TabNet_Alpha158.yaml
@@ -28,9 +28,7 @@ port_analysis_config: &port_analysis_config
        class: TopkDropoutStrategy
        module_path: qlib.contrib.strategy
        kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
+            signal: <PRED>
            topk: 50
            n_drop: 5
    backtest:
--- a/examples/benchmarks/TabNet/workflow_config_TabNet_Alpha360.yaml
+++ b/examples/benchmarks/TabNet/workflow_config_TabNet_Alpha360.yaml
@@ -28,9 +28,7 @@ port_analysis_config: &port_analysis_config
        class: TopkDropoutStrategy
        module_path: qlib.contrib.strategy
        kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
+            signal: <PRED>
            topk: 50
            n_drop: 5
    backtest:
--- a/examples/benchmarks/Transformer/workflow_config_transformer_Alpha158.yaml
+++ b/examples/benchmarks/Transformer/workflow_config_transformer_Alpha158.yaml
@@ -36,9 +36,7 @@ port_analysis_config: &port_analysis_config
        class: TopkDropoutStrategy
        module_path: qlib.contrib.strategy
        kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
+            signal: <PRED>
            topk: 50
            n_drop: 5
    backtest:
--- a/examples/benchmarks/Transformer/workflow_config_transformer_Alpha360.yaml
+++ b/examples/benchmarks/Transformer/workflow_config_transformer_Alpha360.yaml
@@ -28,9 +28,7 @@ port_analysis_config: &port_analysis_config
        class: TopkDropoutStrategy
        module_path: qlib.contrib.strategy
        kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
+            signal: <PRED>
            topk: 50
            n_drop: 5
    backtest:
--- a/examples/benchmarks/XGBoost/workflow_config_xgboost_Alpha158.yaml
+++ b/examples/benchmarks/XGBoost/workflow_config_xgboost_Alpha158.yaml
@@ -14,9 +14,7 @@ port_analysis_config: &port_analysis_config
        class: TopkDropoutStrategy
        module_path: qlib.contrib.strategy
        kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
+            signal: <PRED>
            topk: 50
            n_drop: 5
    backtest:
--- a/examples/benchmarks/XGBoost/workflow_config_xgboost_Alpha360.yaml
+++ b/examples/benchmarks/XGBoost/workflow_config_xgboost_Alpha360.yaml
@@ -21,9 +21,7 @@ port_analysis_config: &port_analysis_config
        class: TopkDropoutStrategy
        module_path: qlib.contrib.strategy
        kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
+            signal: <PRED>
            topk: 50
            n_drop: 5
    backtest:
--- a/examples/benchmarks_dynamic/DDG-DA/README.md
+++ b/examples/benchmarks_dynamic/DDG-DA/README.md
@@ -16,12 +16,12 @@ Though the dataset is different, the conclusion remains the same. By applying `D
 # Run the Code
 Users can try `DDG-DA` by running the following command:
 ```bash
-    python workflow.py run_all
+    python workflow.py run
 ```

 The default forecasting models are `Linear`. Users can choose other forecasting models by changing the `forecast_model` parameter when `DDG-DA` initializes. For example, users can try `LightGBM` forecasting models by running the following command:
 ```bash
-    python workflow.py --forecast_model="gbdt" run_all
+    python workflow.py --conf_path=../workflow_config_lightgbm_Alpha158.yaml run
 ```

 # Results
--- a/examples/benchmarks_dynamic/DDG-DA/workflow.py
+++ b/examples/benchmarks_dynamic/DDG-DA/workflow.py
@@ -1,305 +1,45 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
+import os
 from pathlib import Path
-from qlib.model.meta.task import MetaTask
-from qlib.contrib.meta.data_selection.model import MetaModelDS
-from qlib.contrib.meta.data_selection.dataset import InternalData, MetaDatasetDS
-from qlib.data.dataset.handler import DataHandlerLP
+from typing import Union

-import pandas as pd
 import fire
-import sys
-import pickle
-from typing import Optional
+
 from qlib import auto_init
-from qlib.model.trainer import TrainerR
-from qlib.typehint import Literal
-from qlib.utils import init_instance_by_config
-from qlib.workflow import R
+from qlib.contrib.rolling.ddgda import DDGDA
 from qlib.tests.data import GetData

 DIRNAME = Path(__file__).absolute().resolve().parent
-sys.path.append(str(DIRNAME.parent / "baseline"))
-from rolling_benchmark import RollingBenchmark  # NOTE: sys.path is changed for import RollingBenchmark
+BENCH_DIR = DIRNAME.parent / "baseline"


-class DDGDA:
-    """
-    please run `python workflow.py run_all` to run the full workflow of the experiment
+class DDGDABench(DDGDA):
+    # The config in the README.md
+    CONF_LIST = [
+        BENCH_DIR / "workflow_config_linear_Alpha158.yaml",
+        BENCH_DIR / "workflow_config_lightgbm_Alpha158.yaml",
+    ]

-    **NOTE**
-    before running the example, please clean your previous results with following command
-    - `rm -r mlruns`
-    """
+    DEFAULT_CONF = CONF_LIST[0]  # Linear by default due to efficiency

-    def __init__(
-        self,
-        sim_task_model: Literal["linear", "gbdt"] = "gbdt",
-        forecast_model: Literal["linear", "gbdt"] = "linear",
-        h_path: Optional[str] = None,
-        test_end: Optional[str] = None,
-        train_start: Optional[str] = None,
-        meta_1st_train_end: Optional[str] = None,
-        task_ext_conf: Optional[dict] = None,
-        alpha: float = 0.01,
-        proxy_hd: str = "handler_proxy.pkl",
-    ):
-        """
+    def __init__(self, conf_path: Union[str, Path] = DEFAULT_CONF, horizon=20, **kwargs) -> None:
+        # This code is for being compatible with the previous old code
+        conf_path = Path(conf_path)
+        super().__init__(conf_path=conf_path, horizon=horizon, working_dir=DIRNAME, **kwargs)

-        Parameters
-        ----------
-
-        train_start: Optional[str]
-            the start datetime for data.  It is used in training start time (for both tasks & meta learing)
-        test_end: Optional[str]
-            the end datetime for data. It is used in test end time
-        meta_1st_train_end: Optional[str]
-            the datetime of training end of the first meta_task
-        alpha: float
-            Setting the L2 regularization for ridge
-            The `alpha` is only passed to MetaModelDS (it is not passed to sim_task_model currently..)
-        """
-        self.step = 20
-        # NOTE:
-        # the horizon must match the meaning in the base task template
-        self.horizon = 20
-        self.meta_exp_name = "DDG-DA"
-        self.sim_task_model = sim_task_model  # The model to capture the distribution of data.
-        self.forecast_model = forecast_model  # downstream forecasting models' type
-        self.rb_kwargs = {
-            "h_path": h_path,
-            "test_end": test_end,
-            "train_start": train_start,
-            "task_ext_conf": task_ext_conf,
-        }
-        self.alpha = alpha
-        self.meta_1st_train_end = meta_1st_train_end
-        self.proxy_hd = proxy_hd
-
-    def get_feature_importance(self):
-        # this must be lightGBM, because it needs to get the feature importance
-        rb = RollingBenchmark(model_type="gbdt", **self.rb_kwargs)
-        task = rb.basic_task()
-
-        with R.start(experiment_name="feature_importance"):
-            model = init_instance_by_config(task["model"])
-            dataset = init_instance_by_config(task["dataset"])
-            model.fit(dataset)
-
-        fi = model.get_feature_importance()
-
-        # Because the model use numpy instead of dataframe for training lightgbm
-        # So the we must use following extra steps to get the right feature importance
-        df = dataset.prepare(segments=slice(None), col_set="feature", data_key=DataHandlerLP.DK_R)
-        cols = df.columns
-        fi_named = {cols[int(k.split("_")[1])]: imp for k, imp in fi.to_dict().items()}
-
-        return pd.Series(fi_named)
-
-    def dump_data_for_proxy_model(self):
-        """
-        Dump data for training meta model.
-        The meta model will be trained upon the proxy forecasting model.
-        This dataset is for the proxy forecasting model.
-        """
-        topk = 30
-        fi = self.get_feature_importance()
-        col_selected = fi.nlargest(topk)
-
-        rb = RollingBenchmark(model_type=self.sim_task_model, **self.rb_kwargs)
-        task = rb.basic_task()
-        dataset = init_instance_by_config(task["dataset"])
-        prep_ds = dataset.prepare(slice(None), col_set=["feature", "label"], data_key=DataHandlerLP.DK_L)
-
-        feature_df = prep_ds["feature"]
-        label_df = prep_ds["label"]
-
-        feature_selected = feature_df.loc[:, col_selected.index]
-
-        feature_selected = feature_selected.groupby("datetime", group_keys=False).apply(
-            lambda df: (df - df.mean()).div(df.std())
-        )
-        feature_selected = feature_selected.fillna(0.0)
-
-        df_all = {
-            "label": label_df.reindex(feature_selected.index),
-            "feature": feature_selected,
-        }
-        df_all = pd.concat(df_all, axis=1)
-        df_all.to_pickle(DIRNAME / "fea_label_df.pkl")
-
-        # dump data in handler format for aligning the interface
-        handler = DataHandlerLP(
-            data_loader={
-                "class": "qlib.data.dataset.loader.StaticDataLoader",
-                "kwargs": {"config": DIRNAME / "fea_label_df.pkl"},
-            }
-        )
-        handler.to_pickle(DIRNAME / self.proxy_hd, dump_all=True)
-
-    @property
-    def _internal_data_path(self):
-        return DIRNAME / f"internal_data_s{self.step}.pkl"
-
-    def dump_meta_ipt(self):
-        """
-        Dump data for training meta model.
-        This function will dump the input data for meta model
-        """
-        # According to the experiments, the choice of the model type is very important for achieving good results
-        rb = RollingBenchmark(model_type=self.sim_task_model, **self.rb_kwargs)
-        sim_task = rb.basic_task()
-
-        if self.sim_task_model == "gbdt":
-            sim_task["model"].setdefault("kwargs", {}).update({"early_stopping_rounds": None, "num_boost_round": 150})
-
-        exp_name_sim = f"data_sim_s{self.step}"
-
-        internal_data = InternalData(sim_task, self.step, exp_name=exp_name_sim)
-        internal_data.setup(trainer=TrainerR)
-
-        with self._internal_data_path.open("wb") as f:
-            pickle.dump(internal_data, f)
-
-    def train_meta_model(self, fill_method="max"):
-        """
-        training a meta model based on a simplified linear proxy model;
-        """
-
-        # 1) leverage the simplified proxy forecasting model to train meta model.
-        # - Only the dataset part is important, in current version of meta model will integrate the
-        rb = RollingBenchmark(model_type=self.sim_task_model, **self.rb_kwargs)
-        sim_task = rb.basic_task()
-        # the train_start for training meta model does not necessarily align with final rolling
-        train_start = "2008-01-01" if self.rb_kwargs.get("train_start") is None else self.rb_kwargs.get("train_start")
-        train_end = "2010-12-31" if self.meta_1st_train_end is None else self.meta_1st_train_end
-        test_start = (pd.Timestamp(train_end) + pd.Timedelta(days=1)).strftime("%Y-%m-%d")
-        proxy_forecast_model_task = {
-            # "model": "qlib.contrib.model.linear.LinearModel",
-            "dataset": {
-                "class": "qlib.data.dataset.DatasetH",
-                "kwargs": {
-                    "handler": f"file://{(DIRNAME / self.proxy_hd).absolute()}",
-                    "segments": {
-                        "train": (train_start, train_end),
-                        "test": (test_start, sim_task["dataset"]["kwargs"]["segments"]["test"][1]),
-                    },
-                },
-            },
-            # "record": ["qlib.workflow.record_temp.SignalRecord"]
-        }
-        # the proxy_forecast_model_task will be used to create meta tasks.
-        # The test date of first task will be 2011-01-01. Each test segment will be about 20days
-        # The tasks include all training tasks and test tasks.
-
-        # 2) preparing meta dataset
-        kwargs = dict(
-            task_tpl=proxy_forecast_model_task,
-            step=self.step,
-            segments=0.62,  # keep test period consistent with the dataset yaml
-            trunc_days=1 + self.horizon,
-            hist_step_n=30,
-            fill_method=fill_method,
-            rolling_ext_days=0,
-        )
-        # NOTE:
-        # the input of meta model (internal data) are shared between proxy model and final forecasting model
-        # but their task test segment are not aligned! It worked in my previous experiment.
-        # So the misalignment will not affect the effectiveness of the method.
-        with self._internal_data_path.open("rb") as f:
-            internal_data = pickle.load(f)
-
-        md = MetaDatasetDS(exp_name=internal_data, **kwargs)
-
-        # 3) train and logging meta model
-        with R.start(experiment_name=self.meta_exp_name):
-            R.log_params(**kwargs)
-            mm = MetaModelDS(
-                step=self.step, hist_step_n=kwargs["hist_step_n"], lr=0.001, max_epoch=30, seed=43, alpha=self.alpha
-            )
-            mm.fit(md)
-            R.save_objects(model=mm)
-
-    @property
-    def _task_path(self):
-        return DIRNAME / f"tasks_s{self.step}.pkl"
-
-    def meta_inference(self):
-        """
-        Leverage meta-model for inference:
-        - Given
-            - baseline tasks
-            - input for meta model(internal data)
-            - meta model (its learnt knowledge on proxy forecasting model is expected to transfer to normal forecasting model)
-        """
-        # 1) get meta model
-        exp = R.get_exp(experiment_name=self.meta_exp_name)
-        rec = exp.list_recorders(rtype=exp.RT_L)[0]
-        meta_model: MetaModelDS = rec.load_object("model")
-
-        # 2)
-        # we are transfer to knowledge of meta model to final forecasting tasks.
-        # Create MetaTaskDataset for the final forecasting tasks
-        # Aligning the setting of it to the MetaTaskDataset when training Meta model is necessary
-
-        # 2.1) get previous config
-        param = rec.list_params()
-        trunc_days = int(param["trunc_days"])
-        step = int(param["step"])
-        hist_step_n = int(param["hist_step_n"])
-        fill_method = param.get("fill_method", "max")
-
-        rb = RollingBenchmark(model_type=self.forecast_model, **self.rb_kwargs)
-        task_l = rb.create_rolling_tasks()
-
-        # 2.2) create meta dataset for final dataset
-        kwargs = dict(
-            task_tpl=task_l,
-            step=step,
-            segments=0.0,  # all the tasks are for testing
-            trunc_days=trunc_days,
-            hist_step_n=hist_step_n,
-            fill_method=fill_method,
-            task_mode=MetaTask.PROC_MODE_TRANSFER,
-        )
-
-        with self._internal_data_path.open("rb") as f:
-            internal_data = pickle.load(f)
-        mds = MetaDatasetDS(exp_name=internal_data, **kwargs)
-
-        # 3) meta model make inference and get new qlib task
-        new_tasks = meta_model.inference(mds)
-        with self._task_path.open("wb") as f:
-            pickle.dump(new_tasks, f)
-
-    def train_and_eval_tasks(self):
-        """
-        Training the tasks generated by meta model
-        Then evaluate it
-        """
-        with self._task_path.open("rb") as f:
-            tasks = pickle.load(f)
-        rb = RollingBenchmark(rolling_exp="rolling_ds", model_type=self.forecast_model, **self.rb_kwargs)
-        rb.train_rolling_tasks(tasks)
-        rb.ens_rolling()
-        rb.update_rolling_rec()
-
-    def run_all(self):
-        # 1) file: handler_proxy.pkl (self.proxy_hd)
-        self.dump_data_for_proxy_model()
-        # 2)
-        # file: internal_data_s20.pkl
-        # mlflow: data_sim_s20, models for calculating meta_ipt
-        self.dump_meta_ipt()
-        # 3) meta model will be stored in `DDG-DA`
-        self.train_meta_model()
-        # 4) new_tasks are saved in "tasks_s20.pkl" (reweighter is added)
-        self.meta_inference()
-        # 5) load the saved tasks and train model
-        self.train_and_eval_tasks()
+        for f in self.CONF_LIST:
+            if conf_path.samefile(f):
+                break
+        else:
+            self.logger.warning("Model type is not in the benchmark!")


 if __name__ == "__main__":
-    GetData().qlib_data(exists_skip=True)
-    auto_init()
-    fire.Fire(DDGDA)
+    kwargs = {}
+    if os.environ.get("PROVIDER_URI", "") == "":
+        GetData().qlib_data(exists_skip=True)
+    else:
+        kwargs["provider_uri"] = os.environ["PROVIDER_URI"]
+    auto_init(**kwargs)
+    fire.Fire(DDGDABench)
--- a/examples/benchmarks_dynamic/baseline/README.md
+++ b/examples/benchmarks_dynamic/baseline/README.md
@@ -5,11 +5,12 @@ This is the framework of periodically Rolling Retrain (RR) forecasting models. R
 ## Run the Code
 Users can try RR by running the following command:
 ```bash
-    python rolling_benchmark.py run_all
+    python rolling_benchmark.py run
 ```

 The default forecasting models are `Linear`. Users can choose other forecasting models by changing the `model_type` parameter.
 For example, users can try `LightGBM` forecasting models by running the following command:
 ```bash
-    python rolling_benchmark.py --model_type="gbdt" run_all
-```
+    python rolling_benchmark.py --conf_path=workflow_config_lightgbm_Alpha158.yaml run
+
+```
--- a/examples/benchmarks_dynamic/baseline/rolling_benchmark.py
+++ b/examples/benchmarks_dynamic/baseline/rolling_benchmark.py
@@ -1,164 +1,41 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
-from typing import Optional
-from qlib.model.ens.ensemble import RollingEnsemble
-from qlib.utils import init_instance_by_config
-import fire
-import yaml
-import pandas as pd
-from qlib import auto_init
+import os
 from pathlib import Path
-from tqdm.auto import tqdm
-from qlib.model.trainer import TrainerR
-from qlib.log import get_module_logger
-from qlib.utils.data import update_config
-from qlib.workflow import R
+from typing import Union
+
+import fire
+
+from qlib import auto_init
+from qlib.contrib.rolling.base import Rolling
 from qlib.tests.data import GetData

 DIRNAME = Path(__file__).absolute().resolve().parent
-from qlib.workflow.task.gen import task_generator, RollingGen
-from qlib.workflow.task.collect import RecorderCollector
-from qlib.workflow.record_temp import PortAnaRecord, SigAnaRecord


-class RollingBenchmark:
-    """
-    **NOTE**
-    before running the example, please clean your previous results with following command
-    - `rm -r mlruns`
+class RollingBenchmark(Rolling):
+    # The config in the README.md
+    CONF_LIST = [DIRNAME / "workflow_config_linear_Alpha158.yaml", DIRNAME / "workflow_config_lightgbm_Alpha158.yaml"]

-    """
+    DEFAULT_CONF = CONF_LIST[0]

-    def __init__(
-        self,
-        rolling_exp: str = "rolling_models",
-        model_type: str = "linear",
-        h_path: Optional[str] = None,
-        train_start: Optional[str] = None,
-        test_end: Optional[str] = None,
-        task_ext_conf: Optional[dict] = None,
-    ) -> None:
-        """
-        Parameters
-        ----------
-        rolling_exp : str
-            The name for the experiments for rolling
-        model_type : str
-            The model to be boosted.
-        h_path : Optional[str]
-            the dumped data handler;
-        test_end : Optional[str]
-            the test end for the data. It is typically used together with the handler
-        train_start : Optional[str]
-            the train start for the data.  It is typically used together with the handler.
-        task_ext_conf : Optional[dict]
-            some option to update the
-        """
-        self.step = 20
-        self.horizon = 20
-        self.rolling_exp = rolling_exp
-        self.model_type = model_type
-        self.h_path = h_path
-        self.train_start = train_start
-        self.test_end = test_end
-        self.logger = get_module_logger("RollingBenchmark")
-        self.task_ext_conf = task_ext_conf
+    def __init__(self, conf_path: Union[str, Path] = DEFAULT_CONF, horizon=20, **kwargs) -> None:
+        # This code is for being compatible with the previous old code
+        conf_path = Path(conf_path)
+        super().__init__(conf_path=conf_path, horizon=horizon, **kwargs)

-    def basic_task(self):
-        """For fast training rolling"""
-        if self.model_type == "gbdt":
-            conf_path = DIRNAME / "workflow_config_lightgbm_Alpha158.yaml"
-            # dump the processed data on to disk for later loading to speed up the processing
-            h_path = DIRNAME / "lightgbm_alpha158_handler_horizon{}.pkl".format(self.horizon)
-        elif self.model_type == "linear":
-            # We use ridge regression to stabilize the performance
-            conf_path = DIRNAME / "workflow_config_linear_Alpha158.yaml"
-            h_path = DIRNAME / "linear_alpha158_handler_horizon{}.pkl".format(self.horizon)
+        for f in self.CONF_LIST:
+            if conf_path.samefile(f):
+                break
        else:
-            raise AssertionError("Model type is not supported!")
-
-        if self.h_path is not None:
-            h_path = Path(self.h_path)
-
-        with conf_path.open("r") as f:
-            conf = yaml.safe_load(f)
-
-        # modify dataset horizon
-        conf["task"]["dataset"]["kwargs"]["handler"]["kwargs"]["label"] = [
-            "Ref($close, -{}) / Ref($close, -1) - 1".format(self.horizon + 1)
-        ]
-
-        task = conf["task"]
-
-        if self.task_ext_conf is not None:
-            task = update_config(task, self.task_ext_conf)
-
-        if not h_path.exists():
-            h_conf = task["dataset"]["kwargs"]["handler"]
-            h = init_instance_by_config(h_conf)
-            h.to_pickle(h_path, dump_all=True)
-
-        task["dataset"]["kwargs"]["handler"] = f"file://{h_path}"
-        task["record"] = ["qlib.workflow.record_temp.SignalRecord"]
-
-        if self.train_start is not None:
-            seg = task["dataset"]["kwargs"]["segments"]["train"]
-            task["dataset"]["kwargs"]["segments"]["train"] = pd.Timestamp(self.train_start), seg[1]
-
-        if self.test_end is not None:
-            seg = task["dataset"]["kwargs"]["segments"]["test"]
-            task["dataset"]["kwargs"]["segments"]["test"] = seg[0], pd.Timestamp(self.test_end)
-        self.logger.info(task)
-        return task
-
-    def create_rolling_tasks(self):
-        task = self.basic_task()
-        task_l = task_generator(
-            task, RollingGen(step=self.step, trunc_days=self.horizon + 1)
-        )  # the last two days should be truncated to avoid information leakage
-        return task_l
-
-    def train_rolling_tasks(self, task_l=None):
-        if task_l is None:
-            task_l = self.create_rolling_tasks()
-        trainer = TrainerR(experiment_name=self.rolling_exp)
-        trainer(task_l)
-
-    COMB_EXP = "rolling"
-
-    def ens_rolling(self):
-        rc = RecorderCollector(
-            experiment=self.rolling_exp,
-            artifacts_key=["pred", "label"],
-            process_list=[RollingEnsemble()],
-            # rec_key_func=lambda rec: (self.COMB_EXP, rec.info["id"]),
-            artifacts_path={"pred": "pred.pkl", "label": "label.pkl"},
-        )
-        res = rc()
-        with R.start(experiment_name=self.COMB_EXP):
-            R.log_params(exp_name=self.rolling_exp)
-            R.save_objects(**{"pred.pkl": res["pred"], "label.pkl": res["label"]})
-
-    def update_rolling_rec(self):
-        """
-        Evaluate the combined rolling results
-        """
-        for _, rec in R.list_recorders(experiment_name=self.COMB_EXP).items():
-            for rt_cls in SigAnaRecord, PortAnaRecord:
-                rt = rt_cls(recorder=rec, skip_existing=True)
-                rt.generate()
-        print(f"Your evaluation results can be found in the experiment named `{self.COMB_EXP}`.")
-
-    def run_all(self):
-        # the results will be  save in mlruns.
-        # 1) each rolling task is saved in rolling_models
-        self.train_rolling_tasks()
-        # 2) combined rolling tasks and evaluation results are saved in rolling
-        self.ens_rolling()
-        self.update_rolling_rec()
+            self.logger.warning("Model type is not in the benchmark!")


 if __name__ == "__main__":
-    GetData().qlib_data(exists_skip=True)
-    auto_init()
+    kwargs = {}
+    if os.environ.get("PROVIDER_URI", "") == "":
+        GetData().qlib_data(exists_skip=True)
+    else:
+        kwargs["provider_uri"] = os.environ["PROVIDER_URI"]
+    auto_init(**kwargs)
    fire.Fire(RollingBenchmark)
--- a/examples/benchmarks_dynamic/baseline/workflow_config_lightgbm_Alpha158.yaml
+++ b/examples/benchmarks_dynamic/baseline/workflow_config_lightgbm_Alpha158.yaml
@@ -14,8 +14,7 @@ port_analysis_config: &port_analysis_config
        class: TopkDropoutStrategy
        module_path: qlib.contrib.strategy
        kwargs:
-            model: <MODEL> 
-            dataset: <DATASET>
+            signal: <PRED>
            topk: 50
            n_drop: 5
    backtest:
--- a/Show More
+++ b/Show More