Update version

* update publish

* reformat with black

.dockerignore

@@ -0,0 +1,8 @@
+__pycache__
+*.pyc
+*.pyo
+*.pyd
+.Python
+.env
+.git
+

.github/workflows/python-publish.yml

@@ -12,70 +12,54 @@ jobs:
     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]
+        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
-    #  This is because on macos systems you can install pyqlib using
-    # `pip install pyqlib` installs, it does not recognize the
-    # `pyqlib-<version>-cp38-cp38-macosx_11_0_x86_64.whl` and `pyqlib-<veresion>-cp38-cp37m-macosx_11_0_x86_64.whl`.
-    # So we limit the version of python, in order to generate a version of qlib that is usable for macos: `pyqlib-<veresion>-cp38-cp37m 
-    # `pyqlib-<version>-cp38-cp38-macosx_10_15_x86_64.whl` and `pyqlib-<veresion>-cp38-cp37m-macosx_10_15_x86_64.whl`.
-    # Python 3.7.16, 3.8.16  can build macosx_10_15.  But  Python 3.7.17, 3.8.17  can build macosx_11_0
+    - uses: actions/checkout@v3
     - name: Set up Python ${{ matrix.python-version }}
-      if: matrix.os == 'macos-11' && matrix.python-version == '3.7'
-      uses: actions/setup-python@v2
-      with:
-        python-version: "3.7.16"
-    - name: Set up Python ${{ matrix.python-version }}
-      if: matrix.os == 'macos-11' && matrix.python-version == '3.8'
-      uses: actions/setup-python@v2
-      with:
-        python-version: "3.8.16"
-    - name: Set up Python ${{ matrix.python-version }}
-      if: matrix.os != 'macos-11'
-      uses: actions/setup-python@v2
+      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
+        make dev
     - name: Build wheel on ${{ matrix.os }}
       run: |
-        pip install numpy
-        pip install cython
-        python setup.py bdist_wheel
-    - name: Build and publish
+        make build
+    - name: Upload to PyPi
       env:
         TWINE_USERNAME: __token__
         TWINE_PASSWORD: ${{ secrets.PYPI_TOKEN }}
       run: |
-        twine upload dist/*
+        twine check dist/*.whl
+        twine upload dist/*.whl --verbose
 
   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
-    - name: Build and publish
+        python -m pip install twine
+    - name: Upload to PyPi
       env:
         TWINE_USERNAME: __token__
         TWINE_PASSWORD: ${{ secrets.PYPI_TOKEN }}
       run: |
-        twine upload dist/pyqlib-*-manylinux*.whl
+        twine check dist/pyqlib-*-manylinux*.whl
+        twine upload dist/pyqlib-*-manylinux*.whl --verbose

.github/workflows/test_qlib_from_pip.yml

@@ -13,28 +13,17 @@ jobs:
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        # 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,
-        # so we limit the macos version to macos-12.
-        os: [windows-latest, ubuntu-20.04, ubuntu-22.04, macos-11, macos-12]
-        # not supporting 3.6 due to annotations is not supported https://stackoverflow.com/a/52890129
-        python-version: [3.7, 3.8]
+        os: [windows-latest, ubuntu-20.04, ubuntu-22.04, macos-13, macos-14, macos-15]
+        # In github action, using python 3.7, pip install will not match the latest version of the package.
+        # Also, python 3.7 is no longer supported from macos-14, and will be phased out from macos-13 in the near future.
+        # All things considered, we have removed python 3.7.
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
 
     steps:
     - name: Test qlib from pip
       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 }}
@@ -45,13 +34,10 @@ jobs:
 
     - name: Qlib installation test
       run: |
-        # 2024-05-30 scs has released a new version: 3.2.4.post2,
-        # This will cause the CI to fail, so we have limited the version of scs for now.
-        python -m pip install "scs<=3.2.4"
-        python -m pip install pyqlib
+        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
@@ -68,8 +54,5 @@ jobs:
         cd qlib
 
     - name: Test workflow by config
-      # On macos-11 system, it will lead to "Segmentation fault: 11" error,
-      # which may be caused by the excessive memory overhead of macos-11 system, so we disable macos-11 temporarily here.
-      if: ${{ matrix.os != 'macos-11' }}
       run: |
         qrun examples/benchmarks/LightGBM/workflow_config_lightgbm_Alpha158.yaml

.github/workflows/test_qlib_from_source.yml

@@ -14,28 +14,17 @@ jobs:
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        # 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,
-        # so we limit the macos version to macos-12.
-        os: [windows-latest, ubuntu-20.04, ubuntu-22.04, macos-11, macos-12]
-        # not supporting 3.6 due to annotations is not supported https://stackoverflow.com/a/52890129
-        python-version: [3.7, 3.8]
+        os: [windows-latest, ubuntu-20.04, ubuntu-22.04, macos-13, macos-14, macos-15]
+        # In github action, using python 3.7, pip install will not match the latest version of the package.
+        # Also, python 3.7 is no longer supported from macos-14, and will be phased out from macos-13 in the near future.
+        # All things considered, we have removed python 3.7.
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
 
     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 }}
@@ -45,7 +34,7 @@ jobs:
         python -m pip install --upgrade pip
 
     - name: Installing pytorch 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: |
         python -m pip install torch torchvision torchaudio
 
@@ -61,87 +50,33 @@ jobs:
 
     - name: Set up Python tools
       run: |
-        python -m pip install --upgrade cython
-        python -m pip install -e .[dev]
+        make dev
 
     - name: Lint with Black
-      # Python 3.7 will use a black with low level. So we use python with higher version for black check
-      if: (matrix.python-version != '3.7')
       run: |
-        pip install -U black  # follow the latest version of black, previous Qlib dependency will downgrade black
-        black . -l 120 --check --diff
+        make black
 
     - name: Make html with sphinx
       # Since read the docs builds on ubuntu 22.04, we only need to test that the build passes on ubuntu 22.04.
       if: ${{ matrix.os == 'ubuntu-22.04' }}
       run: |
-        cd docs
-        sphinx-build -W --keep-going -b html . _build
-        cd ..
+        make docs-gen
 
-    # 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}$' qlib --init-hook "import astroid; astroid.context.InferenceContext.max_inferred = 500; import sys; sys.setrecursionlimit(2000)"
-        pylint --disable=C0104,C0114,C0115,C0116,C0301,C0302,C0411,C0413,C1802,R0401,R0801,R0902,R0903,R0911,R0912,R0913,R0914,R0915,R1720,W0105,W0123,W0201,W0511,W0613,W1113,W1514,E0401,E1121,C0103,C0209,R0402,R1705,R1710,R1725,R1735,W0102,W0212,W0221,W0223,W0231,W0237,W0246,W0612,W0621,W0622,W0703,W1309,E1102,E1136 --const-rgx='[a-z_][a-z0-9_]{2,30}$' scripts --init-hook "import astroid; astroid.context.InferenceContext.max_inferred = 500; import sys; sys.setrecursionlimit(2000)"
+        make pylint
 
-    # 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
+        make flake8
 
-    # https://github.com/python/mypy/issues/10600
     - name: Check Qlib with mypy
       run: |
-        mypy qlib --install-types --non-interactive || true
-        mypy qlib --verbose
+        make mypy
     
     - 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}$'
+        make nbqa
 
     - name: Test data downloads
       run: |
@@ -149,7 +84,7 @@ jobs:
         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' }}
+      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
@@ -159,18 +94,11 @@ jobs:
         brew unlink libomp
         brew install libomp.rb
 
-    # Run after data downloads
     - name: Check Qlib ipynb with nbconvert
-      # Running the nbconvert check on a macos-11 system results in a "Kernel died" error, so we've temporarily disabled macos-11 here.
-      if: ${{ matrix.os != 'macos-11' }}
       run: |
-        # add more ipynb files in future
-        jupyter nbconvert --to notebook --execute examples/workflow_by_code.ipynb
+        make nbconvert
 
     - name: Test workflow by config (install from source)
-      # On macos-11 system, it will lead to "Segmentation fault: 11" error,
-      # which may be caused by the excessive memory overhead of macos-11 system, so we disable macos-11 temporarily here.
-      if: ${{ matrix.os != 'macos-11' }}
       run: |
         python -m pip install numba
         python qlib/workflow/cli.py examples/benchmarks/LightGBM/workflow_config_lightgbm_Alpha158.yaml

.github/workflows/test_qlib_from_source_slow.yml

@@ -14,44 +14,31 @@ jobs:
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        # 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,
-        # so we limit the macos version to macos-12.
-        os: [windows-latest, ubuntu-20.04, ubuntu-22.04, macos-11, macos-12]
-        # not supporting 3.6 due to annotations is not supported https://stackoverflow.com/a/52890129
-        python-version: [3.7, 3.8]
+        os: [windows-latest, ubuntu-20.04, ubuntu-22.04, macos-13, macos-14, macos-15]
+        # In github action, using python 3.7, pip install will not match the latest version of the package.
+        # Also, python 3.7 is no longer supported from macos-14, and will be phased out from macos-13 in the near future.
+        # All things considered, we have removed python 3.7.
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
 
     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
       run: |
-        python -m pip install --upgrade pip
-        pip install --upgrade cython numpy
-        pip install -e .[dev]
+        make 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' }}
+      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

.gitignore

@@ -48,4 +48,5 @@ tags
 *.swp
 
 ./pretrain
-.idea/
+.idea/
+.aider*

.readthedocs.yaml

@@ -9,7 +9,7 @@ version: 2
 build:
   os: ubuntu-22.04
   tools:
-    python: "3.7"
+    python: "3.8"
 
 # Build documentation in the docs/ directory with Sphinx
 sphinx:

Dockerfile

@@ -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

MANIFEST.in

@@ -1 +1,6 @@
-include qlib/VERSION.txt
+exclude tests/*
+include qlib/*
+include qlib/*/*
+include qlib/*/*/*
+include qlib/*/*/*/*
+include qlib/*/*/*/*/*

Makefile

@@ -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)

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!**     :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)|
@@ -132,11 +153,11 @@ 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. In some cases, using Python outside of a `conda` environment may result in missing header files, causing the installation failure of certain packages.
@@ -176,11 +197,11 @@ Also, users can install the latest dev version ``Qlib`` by the source code accor
 
 ## 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 20220720.
+Here is an example to download the data updated on 20240809.
 ```bash
-wget https://github.com/chenditc/investment_data/releases/download/20220720/qlib_bin.tar.gz
+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=2
+tar -zxvf qlib_bin.tar.gz -C ~/.qlib/qlib_data/cn_data --strip-components=1
 rm -f qlib_bin.tar.gz
 ```
 
@@ -272,6 +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: 
 
@@ -305,22 +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)
@@ -499,7 +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!

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

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,
 }
 

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]"

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/>`_.

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

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. 

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

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

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

examples/benchmarks/TRA/example.py

@@ -1,14 +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 = ""

examples/benchmarks_dynamic/DDG-DA/workflow.py

@@ -1,5 +1,6 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
+import os
 from pathlib import Path
 from typing import Union
 
@@ -35,6 +36,10 @@ class DDGDABench(DDGDA):
 
 
 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(DDGDABench)

examples/benchmarks_dynamic/baseline/rolling_benchmark.py

@@ -1,5 +1,6 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
+import os
 from pathlib import Path
 from typing import Union
 
@@ -31,6 +32,10 @@ class RollingBenchmark(Rolling):
 
 
 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)

examples/data_demo/data_cache_demo.py

@@ -9,8 +9,8 @@ from copy import deepcopy
 from pathlib import Path
 import pickle
 from pprint import pprint
+from ruamel.yaml import YAML
 import subprocess
-import yaml
 from qlib.log import TimeInspector
 
 from qlib import init
@@ -30,7 +30,8 @@ if __name__ == "__main__":
         subprocess.run(f"qrun {config_path}", shell=True)
 
     # 2) dump handler
-    task_config = yaml.safe_load(config_path.open())
+    yaml = YAML(typ="safe", pure=True)
+    task_config = yaml.load(config_path.open())
     hd_conf = task_config["task"]["dataset"]["kwargs"]["handler"]
     pprint(hd_conf)
     hd: DataHandlerLP = init_instance_by_config(hd_conf)

examples/data_demo/data_mem_resuse_demo.py

@@ -9,10 +9,9 @@ from copy import deepcopy
 from pathlib import Path
 import pickle
 from pprint import pprint
+from ruamel.yaml import YAML
 import subprocess
 
-import yaml
-
 from qlib import init
 from qlib.data.dataset.handler import DataHandlerLP
 from qlib.log import TimeInspector
@@ -29,7 +28,8 @@ if __name__ == "__main__":
     exp_name = "data_mem_reuse_demo"
 
     config_path = DIRNAME.parent / "benchmarks/LightGBM/workflow_config_lightgbm_Alpha158.yaml"
-    task_config = yaml.safe_load(config_path.open())
+    yaml = YAML(typ="safe", pure=True)
+    task_config = yaml.load(config_path.open())
 
     # 1) without using processed data in memory
     with TimeInspector.logt("The original time without reusing processed data in memory:"):

examples/portfolio/README.md

@@ -20,7 +20,7 @@ We use China stock market data for our example.
 1. Prepare CSI300 weight:
 
    ```bash
-   wget http://fintech.msra.cn/stock_data/downloads/csi300_weight.zip
+   wget https://github.com/SunsetWolf/qlib_dataset/releases/download/v0/csi300_weight.zip
    unzip -d ~/.qlib/qlib_data/cn_data csi300_weight.zip
    rm -f csi300_weight.zip
    ```

examples/run_all_model.py

@@ -6,7 +6,6 @@ import sys
 import fire
 import time
 import glob
-import yaml
 import shutil
 import signal
 import inspect
@@ -15,6 +14,7 @@ import functools
 import statistics
 import subprocess
 from datetime import datetime
+from ruamel.yaml import YAML
 from pathlib import Path
 from operator import xor
 from pprint import pprint
@@ -188,7 +188,8 @@ def gen_and_save_md_table(metrics, dataset):
 # read yaml, remove seed kwargs of model, and then save file in the temp_dir
 def gen_yaml_file_without_seed_kwargs(yaml_path, temp_dir):
     with open(yaml_path, "r") as fp:
-        config = yaml.safe_load(fp)
+        yaml = YAML(typ="safe", pure=True)
+        config = yaml.load(fp)
     try:
         del config["task"]["model"]["kwargs"]["seed"]
     except KeyError:

pyproject.toml

@@ -1,2 +1,93 @@
 [build-system]
-requires = ["setuptools", "numpy", "Cython"]
+requires = ["setuptools", "cython", "numpy>=1.24.0"]
+build-backend = "setuptools.build_meta"
+
+[project]
+classifiers = [
+  "Operating System :: POSIX :: Linux",
+  "Operating System :: Microsoft :: Windows",
+  "Operating System :: MacOS",
+  "License :: OSI Approved :: MIT License",
+  "Development Status :: 3 - Alpha",
+  "Programming Language :: Python",
+  "Programming Language :: Python :: 3",
+  "Programming Language :: Python :: 3.8",
+  "Programming Language :: Python :: 3.9",
+  "Programming Language :: Python :: 3.10",
+  "Programming Language :: Python :: 3.11",
+  "Programming Language :: Python :: 3.12",
+]
+name = "pyqlib"
+dynamic = ["version"]
+description = "A Quantitative-research Platform"
+requires-python = ">=3.8.0"
+readme = {file = "README.md", content-type = "text/markdown"}
+
+dependencies = [
+  "pyyaml",
+  "numpy",
+  "pandas",
+  "mlflow",
+  "filelock>=3.16.0",
+  "redis",
+  "dill",
+  "fire",
+  "ruamel.yaml>=0.17.38",
+  "python-redis-lock",
+  "tqdm",
+  "pymongo",
+  "loguru",
+  "lightgbm",
+  "gym",
+  "cvxpy",
+  "joblib",
+  "matplotlib",
+  "jupyter",
+  "nbconvert",
+]
+
+[project.optional-dependencies]
+dev = [
+  "pytest",
+  "statsmodels",
+]
+# On macos-13 system, when using python version greater than or equal to 3.10,
+# pytorch can't fully support Numpy version above 2.0, so, when you want to install torch,
+# it will limit the version of Numpy less than 2.0.
+rl = [
+  "tianshou<=0.4.10",
+  "torch",
+  "numpy<2.0.0",
+]
+lint = [
+  "black",
+  "pylint",
+  "mypy<1.5.0",
+  "flake8",
+  "nbqa",
+]
+docs = [
+  "sphinx",
+  "sphinx_rtd_theme",
+  "readthedocs_sphinx_ext",
+]
+package = [
+  "twine",
+  "build",
+]
+# test_pit dependency packages
+test = [
+  "yahooquery",
+  "baostock",
+]
+analysis = [
+  "plotly",
+]
+
+[tool.setuptools]
+packages = [
+  "qlib",
+]
+
+[project.scripts]
+qrun = "qlib.workflow.cli:run"

qlib/__init__.py

@@ -2,11 +2,11 @@
 # Licensed under the MIT License.
 from pathlib import Path
 
-__version__ = "0.9.5.99"
+__version__ = "0.9.6"
 __version__bak = __version__  # This version is backup for QlibConfig.reset_qlib_version
 import os
 from typing import Union
-import yaml
+from ruamel.yaml import YAML
 import logging
 import platform
 import subprocess
@@ -176,7 +176,8 @@ def init_from_yaml_conf(conf_path, **kwargs):
         config = {}
     else:
         with open(conf_path) as f:
-            config = yaml.safe_load(f)
+            yaml = YAML(typ="safe", pure=True)
+            config = yaml.load(f)
     config.update(kwargs)
     default_conf = config.pop("default_conf", "client")
     init(default_conf, **config)
@@ -272,7 +273,8 @@ def auto_init(**kwargs):
         logger = get_module_logger("Initialization")
         conf_pp = pp / "config.yaml"
         with conf_pp.open() as f:
-            conf = yaml.safe_load(f)
+            yaml = YAML(typ="safe", pure=True)
+            conf = yaml.load(f)
 
         conf_type = conf.get("conf_type", "origin")
         if conf_type == "origin":

qlib/backtest/high_performance_ds.py

@@ -278,7 +278,7 @@ class BaseSingleMetric:
         raise NotImplementedError(f"Please implement the `empty` method")
 
     def add(self, other: BaseSingleMetric, fill_value: float = None) -> BaseSingleMetric:
-        """Replace np.NaN with fill_value in two metrics and add them."""
+        """Replace np.nan with fill_value in two metrics and add them."""
 
         raise NotImplementedError(f"Please implement the `add` method")
 
@@ -412,7 +412,7 @@ class BaseOrderIndicator:
         metrics : Union[str, List[str]]
             all metrics needs to be sumed.
         fill_value : float, optional
-            fill np.NaN with value. By default None.
+            fill np.nan with value. By default None.
         """
 
         raise NotImplementedError(f"Please implement the 'sum_all_indicators' method")

qlib/backtest/report.py

@@ -325,9 +325,9 @@ class Indicator:
 
     def _update_order_fulfill_rate(self) -> None:
         def func(deal_amount, amount):
-            # deal_amount is np.NaN or None when there is no inner decision. So full fill rate is 0.
+            # deal_amount is np.nan or None when there is no inner decision. So full fill rate is 0.
             tmp_deal_amount = deal_amount.reindex(amount.index, 0)
-            tmp_deal_amount = tmp_deal_amount.replace({np.NaN: 0})
+            tmp_deal_amount = tmp_deal_amount.replace({np.nan: 0})
             return tmp_deal_amount / amount
 
         self.order_indicator.transfer(func, "ffr")
@@ -354,8 +354,8 @@ class Indicator:
         )
 
         def func(trade_price, deal_amount):
-            # trade_price is np.NaN instead of inf when deal_amount is zero.
-            tmp_deal_amount = deal_amount.replace({0: np.NaN})
+            # trade_price is np.nan instead of inf when deal_amount is zero.
+            tmp_deal_amount = deal_amount.replace({0: np.nan})
             return trade_price / tmp_deal_amount
 
         self.order_indicator.transfer(func, "trade_price")
@@ -425,7 +425,7 @@ class Indicator:
         assert isinstance(price_s, idd.SingleData)
         price_s = price_s.loc[(price_s > 1e-08).data.astype(bool)]
         # NOTE ~(price_s < 1e-08) is different from price_s >= 1e-8
-        #   ~(np.NaN < 1e-8) -> ~(False)  -> True
+        #   ~(np.nan < 1e-8) -> ~(False)  -> True
 
         assert isinstance(price_s, idd.SingleData)
         if agg == "vwap":

qlib/config.py

@@ -173,7 +173,11 @@ _default_config = {
                 "filters": ["field_not_found"],
             }
         },
-        "loggers": {"qlib": {"level": logging.DEBUG, "handlers": ["console"]}},
+        # Normally this should be set to `False` to avoid duplicated logging [1].
+        # However, due to bug in pytest, it requires log message to propagate to root logger to be captured by `caplog` [2].
+        # [1] https://github.com/microsoft/qlib/pull/1661
+        # [2] https://github.com/pytest-dev/pytest/issues/3697
+        "loggers": {"qlib": {"level": logging.DEBUG, "handlers": ["console"], "propagate": False}},
         # To let qlib work with other packages, we shouldn't disable existing loggers.
         # Note that this param is default to True according to the documentation of logging.
         "disable_existing_loggers": False,

qlib/contrib/data/handler.py

@@ -1,6 +1,7 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
 
+from qlib.contrib.data.loader import Alpha158DL, Alpha360DL
 from ...data.dataset.handler import DataHandlerLP
 from ...data.dataset.processor import Processor
 from ...utils import get_callable_kwargs
@@ -57,7 +58,7 @@ class Alpha360(DataHandlerLP):
         fit_end_time=None,
         filter_pipe=None,
         inst_processors=None,
-        **kwargs
+        **kwargs,
     ):
         infer_processors = check_transform_proc(infer_processors, fit_start_time, fit_end_time)
         learn_processors = check_transform_proc(learn_processors, fit_start_time, fit_end_time)
@@ -66,7 +67,7 @@ class Alpha360(DataHandlerLP):
             "class": "QlibDataLoader",
             "kwargs": {
                 "config": {
-                    "feature": self.get_feature_config(),
+                    "feature": Alpha360DL.get_feature_config(),
                     "label": kwargs.pop("label", self.get_label_config()),
                 },
                 "filter_pipe": filter_pipe,
@@ -82,57 +83,12 @@ class Alpha360(DataHandlerLP):
             data_loader=data_loader,
             learn_processors=learn_processors,
             infer_processors=infer_processors,
-            **kwargs
+            **kwargs,
         )
 
     def get_label_config(self):
         return ["Ref($close, -2)/Ref($close, -1) - 1"], ["LABEL0"]
 
-    @staticmethod
-    def get_feature_config():
-        # NOTE:
-        # Alpha360 tries to provide a dataset with original price data
-        # the original price data includes the prices and volume in the last 60 days.
-        # To make it easier to learn models from this dataset, all the prices and volume
-        # are normalized by the latest price and volume data ( dividing by $close, $volume)
-        # So the latest normalized $close will be 1 (with name CLOSE0), the latest normalized $volume will be 1 (with name VOLUME0)
-        # If further normalization are executed (e.g. centralization),  CLOSE0 and VOLUME0 will be 0.
-        fields = []
-        names = []
-
-        for i in range(59, 0, -1):
-            fields += ["Ref($close, %d)/$close" % i]
-            names += ["CLOSE%d" % i]
-        fields += ["$close/$close"]
-        names += ["CLOSE0"]
-        for i in range(59, 0, -1):
-            fields += ["Ref($open, %d)/$close" % i]
-            names += ["OPEN%d" % i]
-        fields += ["$open/$close"]
-        names += ["OPEN0"]
-        for i in range(59, 0, -1):
-            fields += ["Ref($high, %d)/$close" % i]
-            names += ["HIGH%d" % i]
-        fields += ["$high/$close"]
-        names += ["HIGH0"]
-        for i in range(59, 0, -1):
-            fields += ["Ref($low, %d)/$close" % i]
-            names += ["LOW%d" % i]
-        fields += ["$low/$close"]
-        names += ["LOW0"]
-        for i in range(59, 0, -1):
-            fields += ["Ref($vwap, %d)/$close" % i]
-            names += ["VWAP%d" % i]
-        fields += ["$vwap/$close"]
-        names += ["VWAP0"]
-        for i in range(59, 0, -1):
-            fields += ["Ref($volume, %d)/($volume+1e-12)" % i]
-            names += ["VOLUME%d" % i]
-        fields += ["$volume/($volume+1e-12)"]
-        names += ["VOLUME0"]
-
-        return fields, names
-
 
 class Alpha360vwap(Alpha360):
     def get_label_config(self):
@@ -153,7 +109,7 @@ class Alpha158(DataHandlerLP):
         process_type=DataHandlerLP.PTYPE_A,
         filter_pipe=None,
         inst_processors=None,
-        **kwargs
+        **kwargs,
     ):
         infer_processors = check_transform_proc(infer_processors, fit_start_time, fit_end_time)
         learn_processors = check_transform_proc(learn_processors, fit_start_time, fit_end_time)
@@ -178,7 +134,7 @@ class Alpha158(DataHandlerLP):
             infer_processors=infer_processors,
             learn_processors=learn_processors,
             process_type=process_type,
-            **kwargs
+            **kwargs,
         )
 
     def get_feature_config(self):
@@ -190,242 +146,11 @@ class Alpha158(DataHandlerLP):
             },
             "rolling": {},
         }
-        return self.parse_config_to_fields(conf)
+        return Alpha158DL.get_feature_config(conf)
 
     def get_label_config(self):
         return ["Ref($close, -2)/Ref($close, -1) - 1"], ["LABEL0"]
 
-    @staticmethod
-    def parse_config_to_fields(config):
-        """create factors from config
-
-        config = {
-            'kbar': {}, # whether to use some hard-code kbar features
-            'price': { # whether to use raw price features
-                'windows': [0, 1, 2, 3, 4], # use price at n days ago
-                'feature': ['OPEN', 'HIGH', 'LOW'] # which price field to use
-            },
-            'volume': { # whether to use raw volume features
-                'windows': [0, 1, 2, 3, 4], # use volume at n days ago
-            },
-            'rolling': { # whether to use rolling operator based features
-                'windows': [5, 10, 20, 30, 60], # rolling windows size
-                'include': ['ROC', 'MA', 'STD'], # rolling operator to use
-                #if include is None we will use default operators
-                'exclude': ['RANK'], # rolling operator not to use
-            }
-        }
-        """
-        fields = []
-        names = []
-        if "kbar" in config:
-            fields += [
-                "($close-$open)/$open",
-                "($high-$low)/$open",
-                "($close-$open)/($high-$low+1e-12)",
-                "($high-Greater($open, $close))/$open",
-                "($high-Greater($open, $close))/($high-$low+1e-12)",
-                "(Less($open, $close)-$low)/$open",
-                "(Less($open, $close)-$low)/($high-$low+1e-12)",
-                "(2*$close-$high-$low)/$open",
-                "(2*$close-$high-$low)/($high-$low+1e-12)",
-            ]
-            names += [
-                "KMID",
-                "KLEN",
-                "KMID2",
-                "KUP",
-                "KUP2",
-                "KLOW",
-                "KLOW2",
-                "KSFT",
-                "KSFT2",
-            ]
-        if "price" in config:
-            windows = config["price"].get("windows", range(5))
-            feature = config["price"].get("feature", ["OPEN", "HIGH", "LOW", "CLOSE", "VWAP"])
-            for field in feature:
-                field = field.lower()
-                fields += ["Ref($%s, %d)/$close" % (field, d) if d != 0 else "$%s/$close" % field for d in windows]
-                names += [field.upper() + str(d) for d in windows]
-        if "volume" in config:
-            windows = config["volume"].get("windows", range(5))
-            fields += ["Ref($volume, %d)/($volume+1e-12)" % d if d != 0 else "$volume/($volume+1e-12)" for d in windows]
-            names += ["VOLUME" + str(d) for d in windows]
-        if "rolling" in config:
-            windows = config["rolling"].get("windows", [5, 10, 20, 30, 60])
-            include = config["rolling"].get("include", None)
-            exclude = config["rolling"].get("exclude", [])
-            # `exclude` in dataset config unnecessary filed
-            # `include` in dataset config necessary field
-
-            def use(x):
-                return x not in exclude and (include is None or x in include)
-
-            # Some factor ref: https://guorn.com/static/upload/file/3/134065454575605.pdf
-            if use("ROC"):
-                # https://www.investopedia.com/terms/r/rateofchange.asp
-                # Rate of change, the price change in the past d days, divided by latest close price to remove unit
-                fields += ["Ref($close, %d)/$close" % d for d in windows]
-                names += ["ROC%d" % d for d in windows]
-            if use("MA"):
-                # https://www.investopedia.com/ask/answers/071414/whats-difference-between-moving-average-and-weighted-moving-average.asp
-                # Simple Moving Average, the simple moving average in the past d days, divided by latest close price to remove unit
-                fields += ["Mean($close, %d)/$close" % d for d in windows]
-                names += ["MA%d" % d for d in windows]
-            if use("STD"):
-                # The standard diviation of close price for the past d days, divided by latest close price to remove unit
-                fields += ["Std($close, %d)/$close" % d for d in windows]
-                names += ["STD%d" % d for d in windows]
-            if use("BETA"):
-                # The rate of close price change in the past d days, divided by latest close price to remove unit
-                # For example, price increase 10 dollar per day in the past d days, then Slope will be 10.
-                fields += ["Slope($close, %d)/$close" % d for d in windows]
-                names += ["BETA%d" % d for d in windows]
-            if use("RSQR"):
-                # The R-sqaure value of linear regression for the past d days, represent the trend linear
-                fields += ["Rsquare($close, %d)" % d for d in windows]
-                names += ["RSQR%d" % d for d in windows]
-            if use("RESI"):
-                # The redisdual for linear regression for the past d days, represent the trend linearity for past d days.
-                fields += ["Resi($close, %d)/$close" % d for d in windows]
-                names += ["RESI%d" % d for d in windows]
-            if use("MAX"):
-                # The max price for past d days, divided by latest close price to remove unit
-                fields += ["Max($high, %d)/$close" % d for d in windows]
-                names += ["MAX%d" % d for d in windows]
-            if use("LOW"):
-                # The low price for past d days, divided by latest close price to remove unit
-                fields += ["Min($low, %d)/$close" % d for d in windows]
-                names += ["MIN%d" % d for d in windows]
-            if use("QTLU"):
-                # The 80% quantile of past d day's close price, divided by latest close price to remove unit
-                # Used with MIN and MAX
-                fields += ["Quantile($close, %d, 0.8)/$close" % d for d in windows]
-                names += ["QTLU%d" % d for d in windows]
-            if use("QTLD"):
-                # The 20% quantile of past d day's close price, divided by latest close price to remove unit
-                fields += ["Quantile($close, %d, 0.2)/$close" % d for d in windows]
-                names += ["QTLD%d" % d for d in windows]
-            if use("RANK"):
-                # Get the percentile of current close price in past d day's close price.
-                # Represent the current price level comparing to past N days, add additional information to moving average.
-                fields += ["Rank($close, %d)" % d for d in windows]
-                names += ["RANK%d" % d for d in windows]
-            if use("RSV"):
-                # Represent the price position between upper and lower resistent price for past d days.
-                fields += ["($close-Min($low, %d))/(Max($high, %d)-Min($low, %d)+1e-12)" % (d, d, d) for d in windows]
-                names += ["RSV%d" % d for d in windows]
-            if use("IMAX"):
-                # The number of days between current date and previous highest price date.
-                # Part of Aroon Indicator https://www.investopedia.com/terms/a/aroon.asp
-                # The indicator measures the time between highs and the time between lows over a time period.
-                # The idea is that strong uptrends will regularly see new highs, and strong downtrends will regularly see new lows.
-                fields += ["IdxMax($high, %d)/%d" % (d, d) for d in windows]
-                names += ["IMAX%d" % d for d in windows]
-            if use("IMIN"):
-                # The number of days between current date and previous lowest price date.
-                # Part of Aroon Indicator https://www.investopedia.com/terms/a/aroon.asp
-                # The indicator measures the time between highs and the time between lows over a time period.
-                # The idea is that strong uptrends will regularly see new highs, and strong downtrends will regularly see new lows.
-                fields += ["IdxMin($low, %d)/%d" % (d, d) for d in windows]
-                names += ["IMIN%d" % d for d in windows]
-            if use("IMXD"):
-                # The time period between previous lowest-price date occur after highest price date.
-                # Large value suggest downward momemtum.
-                fields += ["(IdxMax($high, %d)-IdxMin($low, %d))/%d" % (d, d, d) for d in windows]
-                names += ["IMXD%d" % d for d in windows]
-            if use("CORR"):
-                # The correlation between absolute close price and log scaled trading volume
-                fields += ["Corr($close, Log($volume+1), %d)" % d for d in windows]
-                names += ["CORR%d" % d for d in windows]
-            if use("CORD"):
-                # The correlation between price change ratio and volume change ratio
-                fields += ["Corr($close/Ref($close,1), Log($volume/Ref($volume, 1)+1), %d)" % d for d in windows]
-                names += ["CORD%d" % d for d in windows]
-            if use("CNTP"):
-                # The percentage of days in past d days that price go up.
-                fields += ["Mean($close>Ref($close, 1), %d)" % d for d in windows]
-                names += ["CNTP%d" % d for d in windows]
-            if use("CNTN"):
-                # The percentage of days in past d days that price go down.
-                fields += ["Mean($close<Ref($close, 1), %d)" % d for d in windows]
-                names += ["CNTN%d" % d for d in windows]
-            if use("CNTD"):
-                # The diff between past up day and past down day
-                fields += ["Mean($close>Ref($close, 1), %d)-Mean($close<Ref($close, 1), %d)" % (d, d) for d in windows]
-                names += ["CNTD%d" % d for d in windows]
-            if use("SUMP"):
-                # The total gain / the absolute total price changed
-                # Similar to RSI indicator. https://www.investopedia.com/terms/r/rsi.asp
-                fields += [
-                    "Sum(Greater($close-Ref($close, 1), 0), %d)/(Sum(Abs($close-Ref($close, 1)), %d)+1e-12)" % (d, d)
-                    for d in windows
-                ]
-                names += ["SUMP%d" % d for d in windows]
-            if use("SUMN"):
-                # The total lose / the absolute total price changed
-                # Can be derived from SUMP by SUMN = 1 - SUMP
-                # Similar to RSI indicator. https://www.investopedia.com/terms/r/rsi.asp
-                fields += [
-                    "Sum(Greater(Ref($close, 1)-$close, 0), %d)/(Sum(Abs($close-Ref($close, 1)), %d)+1e-12)" % (d, d)
-                    for d in windows
-                ]
-                names += ["SUMN%d" % d for d in windows]
-            if use("SUMD"):
-                # The diff ratio between total gain and total lose
-                # Similar to RSI indicator. https://www.investopedia.com/terms/r/rsi.asp
-                fields += [
-                    "(Sum(Greater($close-Ref($close, 1), 0), %d)-Sum(Greater(Ref($close, 1)-$close, 0), %d))"
-                    "/(Sum(Abs($close-Ref($close, 1)), %d)+1e-12)" % (d, d, d)
-                    for d in windows
-                ]
-                names += ["SUMD%d" % d for d in windows]
-            if use("VMA"):
-                # Simple Volume Moving average: https://www.barchart.com/education/technical-indicators/volume_moving_average
-                fields += ["Mean($volume, %d)/($volume+1e-12)" % d for d in windows]
-                names += ["VMA%d" % d for d in windows]
-            if use("VSTD"):
-                # The standard deviation for volume in past d days.
-                fields += ["Std($volume, %d)/($volume+1e-12)" % d for d in windows]
-                names += ["VSTD%d" % d for d in windows]
-            if use("WVMA"):
-                # The volume weighted price change volatility
-                fields += [
-                    "Std(Abs($close/Ref($close, 1)-1)*$volume, %d)/(Mean(Abs($close/Ref($close, 1)-1)*$volume, %d)+1e-12)"
-                    % (d, d)
-                    for d in windows
-                ]
-                names += ["WVMA%d" % d for d in windows]
-            if use("VSUMP"):
-                # The total volume increase / the absolute total volume changed
-                fields += [
-                    "Sum(Greater($volume-Ref($volume, 1), 0), %d)/(Sum(Abs($volume-Ref($volume, 1)), %d)+1e-12)"
-                    % (d, d)
-                    for d in windows
-                ]
-                names += ["VSUMP%d" % d for d in windows]
-            if use("VSUMN"):
-                # The total volume increase / the absolute total volume changed
-                # Can be derived from VSUMP by VSUMN = 1 - VSUMP
-                fields += [
-                    "Sum(Greater(Ref($volume, 1)-$volume, 0), %d)/(Sum(Abs($volume-Ref($volume, 1)), %d)+1e-12)"
-                    % (d, d)
-                    for d in windows
-                ]
-                names += ["VSUMN%d" % d for d in windows]
-            if use("VSUMD"):
-                # The diff ratio between total volume increase and total volume decrease
-                # RSI indicator for volume
-                fields += [
-                    "(Sum(Greater($volume-Ref($volume, 1), 0), %d)-Sum(Greater(Ref($volume, 1)-$volume, 0), %d))"
-                    "/(Sum(Abs($volume-Ref($volume, 1)), %d)+1e-12)" % (d, d, d)
-                    for d in windows
-                ]
-                names += ["VSUMD%d" % d for d in windows]
-
-        return fields, names
-
 
 class Alpha158vwap(Alpha158):
     def get_label_config(self):

qlib/contrib/data/loader.py

@@ -0,0 +1,310 @@
+from qlib.data.dataset.loader import QlibDataLoader
+
+
+class Alpha360DL(QlibDataLoader):
+    """Dataloader to get Alpha360"""
+
+    def __init__(self, config=None, **kwargs):
+        _config = {
+            "feature": self.get_feature_config(),
+        }
+        if config is not None:
+            _config.update(config)
+        super().__init__(config=_config, **kwargs)
+
+    @staticmethod
+    def get_feature_config():
+        # NOTE:
+        # Alpha360 tries to provide a dataset with original price data
+        # the original price data includes the prices and volume in the last 60 days.
+        # To make it easier to learn models from this dataset, all the prices and volume
+        # are normalized by the latest price and volume data ( dividing by $close, $volume)
+        # So the latest normalized $close will be 1 (with name CLOSE0), the latest normalized $volume will be 1 (with name VOLUME0)
+        # If further normalization are executed (e.g. centralization),  CLOSE0 and VOLUME0 will be 0.
+        fields = []
+        names = []
+
+        for i in range(59, 0, -1):
+            fields += ["Ref($close, %d)/$close" % i]
+            names += ["CLOSE%d" % i]
+        fields += ["$close/$close"]
+        names += ["CLOSE0"]
+        for i in range(59, 0, -1):
+            fields += ["Ref($open, %d)/$close" % i]
+            names += ["OPEN%d" % i]
+        fields += ["$open/$close"]
+        names += ["OPEN0"]
+        for i in range(59, 0, -1):
+            fields += ["Ref($high, %d)/$close" % i]
+            names += ["HIGH%d" % i]
+        fields += ["$high/$close"]
+        names += ["HIGH0"]
+        for i in range(59, 0, -1):
+            fields += ["Ref($low, %d)/$close" % i]
+            names += ["LOW%d" % i]
+        fields += ["$low/$close"]
+        names += ["LOW0"]
+        for i in range(59, 0, -1):
+            fields += ["Ref($vwap, %d)/$close" % i]
+            names += ["VWAP%d" % i]
+        fields += ["$vwap/$close"]
+        names += ["VWAP0"]
+        for i in range(59, 0, -1):
+            fields += ["Ref($volume, %d)/($volume+1e-12)" % i]
+            names += ["VOLUME%d" % i]
+        fields += ["$volume/($volume+1e-12)"]
+        names += ["VOLUME0"]
+
+        return fields, names
+
+
+class Alpha158DL(QlibDataLoader):
+    """Dataloader to get Alpha158"""
+
+    def __init__(self, config=None, **kwargs):
+        _config = {
+            "feature": self.get_feature_config(),
+        }
+        if config is not None:
+            _config.update(config)
+        super().__init__(config=_config, **kwargs)
+
+    @staticmethod
+    def get_feature_config(
+        config={
+            "kbar": {},
+            "price": {
+                "windows": [0],
+                "feature": ["OPEN", "HIGH", "LOW", "VWAP"],
+            },
+            "rolling": {},
+        }
+    ):
+        """create factors from config
+
+        config = {
+            'kbar': {}, # whether to use some hard-code kbar features
+            'price': { # whether to use raw price features
+                'windows': [0, 1, 2, 3, 4], # use price at n days ago
+                'feature': ['OPEN', 'HIGH', 'LOW'] # which price field to use
+            },
+            'volume': { # whether to use raw volume features
+                'windows': [0, 1, 2, 3, 4], # use volume at n days ago
+            },
+            'rolling': { # whether to use rolling operator based features
+                'windows': [5, 10, 20, 30, 60], # rolling windows size
+                'include': ['ROC', 'MA', 'STD'], # rolling operator to use
+                #if include is None we will use default operators
+                'exclude': ['RANK'], # rolling operator not to use
+            }
+        }
+        """
+        fields = []
+        names = []
+        if "kbar" in config:
+            fields += [
+                "($close-$open)/$open",
+                "($high-$low)/$open",
+                "($close-$open)/($high-$low+1e-12)",
+                "($high-Greater($open, $close))/$open",
+                "($high-Greater($open, $close))/($high-$low+1e-12)",
+                "(Less($open, $close)-$low)/$open",
+                "(Less($open, $close)-$low)/($high-$low+1e-12)",
+                "(2*$close-$high-$low)/$open",
+                "(2*$close-$high-$low)/($high-$low+1e-12)",
+            ]
+            names += [
+                "KMID",
+                "KLEN",
+                "KMID2",
+                "KUP",
+                "KUP2",
+                "KLOW",
+                "KLOW2",
+                "KSFT",
+                "KSFT2",
+            ]
+        if "price" in config:
+            windows = config["price"].get("windows", range(5))
+            feature = config["price"].get("feature", ["OPEN", "HIGH", "LOW", "CLOSE", "VWAP"])
+            for field in feature:
+                field = field.lower()
+                fields += ["Ref($%s, %d)/$close" % (field, d) if d != 0 else "$%s/$close" % field for d in windows]
+                names += [field.upper() + str(d) for d in windows]
+        if "volume" in config:
+            windows = config["volume"].get("windows", range(5))
+            fields += ["Ref($volume, %d)/($volume+1e-12)" % d if d != 0 else "$volume/($volume+1e-12)" for d in windows]
+            names += ["VOLUME" + str(d) for d in windows]
+        if "rolling" in config:
+            windows = config["rolling"].get("windows", [5, 10, 20, 30, 60])
+            include = config["rolling"].get("include", None)
+            exclude = config["rolling"].get("exclude", [])
+            # `exclude` in dataset config unnecessary filed
+            # `include` in dataset config necessary field
+
+            def use(x):
+                return x not in exclude and (include is None or x in include)
+
+            # Some factor ref: https://guorn.com/static/upload/file/3/134065454575605.pdf
+            if use("ROC"):
+                # https://www.investopedia.com/terms/r/rateofchange.asp
+                # Rate of change, the price change in the past d days, divided by latest close price to remove unit
+                fields += ["Ref($close, %d)/$close" % d for d in windows]
+                names += ["ROC%d" % d for d in windows]
+            if use("MA"):
+                # https://www.investopedia.com/ask/answers/071414/whats-difference-between-moving-average-and-weighted-moving-average.asp
+                # Simple Moving Average, the simple moving average in the past d days, divided by latest close price to remove unit
+                fields += ["Mean($close, %d)/$close" % d for d in windows]
+                names += ["MA%d" % d for d in windows]
+            if use("STD"):
+                # The standard diviation of close price for the past d days, divided by latest close price to remove unit
+                fields += ["Std($close, %d)/$close" % d for d in windows]
+                names += ["STD%d" % d for d in windows]
+            if use("BETA"):
+                # The rate of close price change in the past d days, divided by latest close price to remove unit
+                # For example, price increase 10 dollar per day in the past d days, then Slope will be 10.
+                fields += ["Slope($close, %d)/$close" % d for d in windows]
+                names += ["BETA%d" % d for d in windows]
+            if use("RSQR"):
+                # The R-sqaure value of linear regression for the past d days, represent the trend linear
+                fields += ["Rsquare($close, %d)" % d for d in windows]
+                names += ["RSQR%d" % d for d in windows]
+            if use("RESI"):
+                # The redisdual for linear regression for the past d days, represent the trend linearity for past d days.
+                fields += ["Resi($close, %d)/$close" % d for d in windows]
+                names += ["RESI%d" % d for d in windows]
+            if use("MAX"):
+                # The max price for past d days, divided by latest close price to remove unit
+                fields += ["Max($high, %d)/$close" % d for d in windows]
+                names += ["MAX%d" % d for d in windows]
+            if use("LOW"):
+                # The low price for past d days, divided by latest close price to remove unit
+                fields += ["Min($low, %d)/$close" % d for d in windows]
+                names += ["MIN%d" % d for d in windows]
+            if use("QTLU"):
+                # The 80% quantile of past d day's close price, divided by latest close price to remove unit
+                # Used with MIN and MAX
+                fields += ["Quantile($close, %d, 0.8)/$close" % d for d in windows]
+                names += ["QTLU%d" % d for d in windows]
+            if use("QTLD"):
+                # The 20% quantile of past d day's close price, divided by latest close price to remove unit
+                fields += ["Quantile($close, %d, 0.2)/$close" % d for d in windows]
+                names += ["QTLD%d" % d for d in windows]
+            if use("RANK"):
+                # Get the percentile of current close price in past d day's close price.
+                # Represent the current price level comparing to past N days, add additional information to moving average.
+                fields += ["Rank($close, %d)" % d for d in windows]
+                names += ["RANK%d" % d for d in windows]
+            if use("RSV"):
+                # Represent the price position between upper and lower resistent price for past d days.
+                fields += ["($close-Min($low, %d))/(Max($high, %d)-Min($low, %d)+1e-12)" % (d, d, d) for d in windows]
+                names += ["RSV%d" % d for d in windows]
+            if use("IMAX"):
+                # The number of days between current date and previous highest price date.
+                # Part of Aroon Indicator https://www.investopedia.com/terms/a/aroon.asp
+                # The indicator measures the time between highs and the time between lows over a time period.
+                # The idea is that strong uptrends will regularly see new highs, and strong downtrends will regularly see new lows.
+                fields += ["IdxMax($high, %d)/%d" % (d, d) for d in windows]
+                names += ["IMAX%d" % d for d in windows]
+            if use("IMIN"):
+                # The number of days between current date and previous lowest price date.
+                # Part of Aroon Indicator https://www.investopedia.com/terms/a/aroon.asp
+                # The indicator measures the time between highs and the time between lows over a time period.
+                # The idea is that strong uptrends will regularly see new highs, and strong downtrends will regularly see new lows.
+                fields += ["IdxMin($low, %d)/%d" % (d, d) for d in windows]
+                names += ["IMIN%d" % d for d in windows]
+            if use("IMXD"):
+                # The time period between previous lowest-price date occur after highest price date.
+                # Large value suggest downward momemtum.
+                fields += ["(IdxMax($high, %d)-IdxMin($low, %d))/%d" % (d, d, d) for d in windows]
+                names += ["IMXD%d" % d for d in windows]
+            if use("CORR"):
+                # The correlation between absolute close price and log scaled trading volume
+                fields += ["Corr($close, Log($volume+1), %d)" % d for d in windows]
+                names += ["CORR%d" % d for d in windows]
+            if use("CORD"):
+                # The correlation between price change ratio and volume change ratio
+                fields += ["Corr($close/Ref($close,1), Log($volume/Ref($volume, 1)+1), %d)" % d for d in windows]
+                names += ["CORD%d" % d for d in windows]
+            if use("CNTP"):
+                # The percentage of days in past d days that price go up.
+                fields += ["Mean($close>Ref($close, 1), %d)" % d for d in windows]
+                names += ["CNTP%d" % d for d in windows]
+            if use("CNTN"):
+                # The percentage of days in past d days that price go down.
+                fields += ["Mean($close<Ref($close, 1), %d)" % d for d in windows]
+                names += ["CNTN%d" % d for d in windows]
+            if use("CNTD"):
+                # The diff between past up day and past down day
+                fields += ["Mean($close>Ref($close, 1), %d)-Mean($close<Ref($close, 1), %d)" % (d, d) for d in windows]
+                names += ["CNTD%d" % d for d in windows]
+            if use("SUMP"):
+                # The total gain / the absolute total price changed
+                # Similar to RSI indicator. https://www.investopedia.com/terms/r/rsi.asp
+                fields += [
+                    "Sum(Greater($close-Ref($close, 1), 0), %d)/(Sum(Abs($close-Ref($close, 1)), %d)+1e-12)" % (d, d)
+                    for d in windows
+                ]
+                names += ["SUMP%d" % d for d in windows]
+            if use("SUMN"):
+                # The total lose / the absolute total price changed
+                # Can be derived from SUMP by SUMN = 1 - SUMP
+                # Similar to RSI indicator. https://www.investopedia.com/terms/r/rsi.asp
+                fields += [
+                    "Sum(Greater(Ref($close, 1)-$close, 0), %d)/(Sum(Abs($close-Ref($close, 1)), %d)+1e-12)" % (d, d)
+                    for d in windows
+                ]
+                names += ["SUMN%d" % d for d in windows]
+            if use("SUMD"):
+                # The diff ratio between total gain and total lose
+                # Similar to RSI indicator. https://www.investopedia.com/terms/r/rsi.asp
+                fields += [
+                    "(Sum(Greater($close-Ref($close, 1), 0), %d)-Sum(Greater(Ref($close, 1)-$close, 0), %d))"
+                    "/(Sum(Abs($close-Ref($close, 1)), %d)+1e-12)" % (d, d, d)
+                    for d in windows
+                ]
+                names += ["SUMD%d" % d for d in windows]
+            if use("VMA"):
+                # Simple Volume Moving average: https://www.barchart.com/education/technical-indicators/volume_moving_average
+                fields += ["Mean($volume, %d)/($volume+1e-12)" % d for d in windows]
+                names += ["VMA%d" % d for d in windows]
+            if use("VSTD"):
+                # The standard deviation for volume in past d days.
+                fields += ["Std($volume, %d)/($volume+1e-12)" % d for d in windows]
+                names += ["VSTD%d" % d for d in windows]
+            if use("WVMA"):
+                # The volume weighted price change volatility
+                fields += [
+                    "Std(Abs($close/Ref($close, 1)-1)*$volume, %d)/(Mean(Abs($close/Ref($close, 1)-1)*$volume, %d)+1e-12)"
+                    % (d, d)
+                    for d in windows
+                ]
+                names += ["WVMA%d" % d for d in windows]
+            if use("VSUMP"):
+                # The total volume increase / the absolute total volume changed
+                fields += [
+                    "Sum(Greater($volume-Ref($volume, 1), 0), %d)/(Sum(Abs($volume-Ref($volume, 1)), %d)+1e-12)"
+                    % (d, d)
+                    for d in windows
+                ]
+                names += ["VSUMP%d" % d for d in windows]
+            if use("VSUMN"):
+                # The total volume increase / the absolute total volume changed
+                # Can be derived from VSUMP by VSUMN = 1 - VSUMP
+                fields += [
+                    "Sum(Greater(Ref($volume, 1)-$volume, 0), %d)/(Sum(Abs($volume-Ref($volume, 1)), %d)+1e-12)"
+                    % (d, d)
+                    for d in windows
+                ]
+                names += ["VSUMN%d" % d for d in windows]
+            if use("VSUMD"):
+                # The diff ratio between total volume increase and total volume decrease
+                # RSI indicator for volume
+                fields += [
+                    "(Sum(Greater($volume-Ref($volume, 1), 0), %d)-Sum(Greater(Ref($volume, 1)-$volume, 0), %d))"
+                    "/(Sum(Abs($volume-Ref($volume, 1)), %d)+1e-12)" % (d, d, d)
+                    for d in windows
+                ]
+                names += ["VSUMD%d" % d for d in windows]
+
+        return fields, names

qlib/contrib/meta/data_selection/dataset.py

@@ -243,7 +243,7 @@ class MetaDatasetDS(MetaTaskDataset):
         trunc_days: int = None,
         rolling_ext_days: int = 0,
         exp_name: Union[str, InternalData],
-        segments: Union[Dict[Text, Tuple], float],
+        segments: Union[Dict[Text, Tuple], float, str],
         hist_step_n: int = 10,
         task_mode: str = MetaTask.PROC_MODE_FULL,
         fill_method: str = "max",
@@ -271,12 +271,16 @@ class MetaDatasetDS(MetaTaskDataset):
             - str: the name of the experiment to store the performance of data
             - InternalData: a prepared internal data
         segments: Union[Dict[Text, Tuple], float]
-            the segments to divide data
-            both left and right
+            if the segment is a Dict
+                the segments to divide data
+                both left and right are included
             if segments is a float:
                 the float represents the percentage of data for training
+            if segments is a string:
+                it will try its best to put its data in training and ensure that the date `segments` is in the test set
         hist_step_n: int
             length of historical steps for the meta infomation
+            Number of steps of the data similarity information
         task_mode : str
             Please refer to the docs of MetaTask
         """
@@ -383,10 +387,30 @@ class MetaDatasetDS(MetaTaskDataset):
         if isinstance(self.segments, float):
             train_task_n = int(len(self.meta_task_l) * self.segments)
             if segment == "train":
-                return self.meta_task_l[:train_task_n]
+                train_tasks = self.meta_task_l[:train_task_n]
+                get_module_logger("MetaDatasetDS").info(f"The first train meta task: {train_tasks[0]}")
+                return train_tasks
             elif segment == "test":
-                return self.meta_task_l[train_task_n:]
+                test_tasks = self.meta_task_l[train_task_n:]
+                get_module_logger("MetaDatasetDS").info(f"The first test meta task: {test_tasks[0]}")
+                return test_tasks
             else:
                 raise NotImplementedError(f"This type of input is not supported")
+        elif isinstance(self.segments, str):
+            train_tasks = []
+            test_tasks = []
+            for t in self.meta_task_l:
+                test_end = t.task["dataset"]["kwargs"]["segments"]["test"][1]
+                if test_end is None or pd.Timestamp(test_end) < pd.Timestamp(self.segments):
+                    train_tasks.append(t)
+                else:
+                    test_tasks.append(t)
+            get_module_logger("MetaDatasetDS").info(f"The first train meta task: {train_tasks[0]}")
+            get_module_logger("MetaDatasetDS").info(f"The first test meta task: {test_tasks[0]}")
+            if segment == "train":
+                return train_tasks
+            elif segment == "test":
+                return test_tasks
+            raise NotImplementedError(f"This type of input is not supported")
         else:
             raise NotImplementedError(f"This type of input is not supported")

qlib/contrib/meta/data_selection/model.py

@@ -53,7 +53,12 @@ class MetaModelDS(MetaTaskModel):
         max_epoch=100,
         seed=43,
         alpha=0.0,
+        loss_skip_thresh=50,
     ):
+        """
+        loss_skip_size: int
+            The number of threshold to skip the loss calculation for each day.
+        """
         self.step = step
         self.hist_step_n = hist_step_n
         self.clip_method = clip_method
@@ -63,6 +68,7 @@ class MetaModelDS(MetaTaskModel):
         self.max_epoch = max_epoch
         self.fitted = False
         self.alpha = alpha
+        self.loss_skip_thresh = loss_skip_thresh
         torch.manual_seed(seed)
 
     def run_epoch(self, phase, task_list, epoch, opt, loss_l, ignore_weight=False):
@@ -88,12 +94,14 @@ class MetaModelDS(MetaTaskModel):
                 criterion = nn.MSELoss()
                 loss = criterion(pred, meta_input["y_test"])
             elif self.criterion == "ic_loss":
-                criterion = ICLoss()
+                criterion = ICLoss(self.loss_skip_thresh)
                 try:
-                    loss = criterion(pred, meta_input["y_test"], meta_input["test_idx"], skip_size=50)
+                    loss = criterion(pred, meta_input["y_test"], meta_input["test_idx"])
                 except ValueError as e:
                     get_module_logger("MetaModelDS").warning(f"Exception `{e}` when calculating IC loss")
                     continue
+            else:
+                raise ValueError(f"Unknown criterion: {self.criterion}")
 
             assert not np.isnan(loss.detach().item()), "NaN loss!"
 

qlib/contrib/meta/data_selection/utils.py

@@ -10,7 +10,11 @@ from qlib.log import get_module_logger
 
 
 class ICLoss(nn.Module):
-    def forward(self, pred, y, idx, skip_size=50):
+    def __init__(self, skip_size=50):
+        super().__init__()
+        self.skip_size = skip_size
+
+    def forward(self, pred, y, idx):
         """forward.
         FIXME:
         - Some times it will be a slightly different from the result from `pandas.corr()`
@@ -33,7 +37,7 @@ class ICLoss(nn.Module):
         skip_n = 0
         for start_i, end_i in zip(diff_point, diff_point[1:]):
             pred_focus = pred[start_i:end_i]  # TODO: just for fake
-            if pred_focus.shape[0] < skip_size:
+            if pred_focus.shape[0] < self.skip_size:
                 # skip some days which have very small amount of stock.
                 skip_n += 1
                 continue
@@ -50,6 +54,7 @@ class ICLoss(nn.Module):
             )
             ic_all += ic_day
         if len(diff_point) - 1 - skip_n <= 0:
+            __import__("ipdb").set_trace()
             raise ValueError("No enough data for calculating IC")
         if skip_n > 0:
             get_module_logger("ICLoss").info(

qlib/contrib/model/catboost_model.py

@@ -33,7 +33,7 @@ class CatBoostModel(Model, FeatureInt):
         verbose_eval=20,
         evals_result=dict(),
         reweighter=None,
-        **kwargs
+        **kwargs,
     ):
         df_train, df_valid = dataset.prepare(
             ["train", "valid"],

qlib/contrib/model/double_ensemble.py

@@ -31,7 +31,7 @@ class DEnsembleModel(Model, FeatureInt):
         sub_weights=None,
         epochs=100,
         early_stopping_rounds=None,
-        **kwargs
+        **kwargs,
     ):
         self.base_model = base_model  # "gbm" or "mlp", specifically, we use lgbm for "gbm"
         self.num_models = num_models  # the number of sub-models

qlib/contrib/model/linear.py

@@ -63,6 +63,7 @@ class LinearModel(Model):
                 df_train = pd.concat([df_train, df_valid])
             except KeyError:
                 get_module_logger("LinearModel").info("include_valid=True, but valid does not exist")
+        df_train = df_train.dropna()
         if df_train.empty:
             raise ValueError("Empty data from dataset, please check your dataset config.")
         if reweighter is not None:

qlib/contrib/model/pytorch_adarnn.py

@@ -56,7 +56,7 @@ class ADARNN(Model):
         n_splits=2,
         GPU=0,
         seed=None,
-        **_
+        **_,
     ):
         # Set logger.
         self.logger = get_module_logger("ADARNN")
@@ -154,10 +154,7 @@ class ADARNN(Model):
         self.model.train()
         criterion = nn.MSELoss()
         dist_mat = torch.zeros(self.num_layers, self.len_seq).to(self.device)
-        len_loader = np.inf
-        for loader in train_loader_list:
-            if len(loader) < len_loader:
-                len_loader = len(loader)
+        out_weight_list = None
         for data_all in zip(*train_loader_list):
             #  for data_all in zip(*train_loader_list):
             self.train_optimizer.zero_grad()
@@ -571,6 +568,7 @@ class TransferLoss:
         Returns:
             [tensor] -- transfer loss
         """
+        loss = None
         if self.loss_type in ("mmd_lin", "mmd"):
             mmdloss = MMD_loss(kernel_type="linear")
             loss = mmdloss(X, Y)

qlib/contrib/model/pytorch_add.py

@@ -63,7 +63,7 @@ class ADD(Model):
         mu=0.05,
         GPU=0,
         seed=None,
-        **kwargs
+        **kwargs,
     ):
         # Set logger.
         self.logger = get_module_logger("ADD")

qlib/contrib/model/pytorch_alstm.py

@@ -52,7 +52,7 @@ class ALSTM(Model):
         optimizer="adam",
         GPU=0,
         seed=None,
-        **kwargs
+        **kwargs,
     ):
         # Set logger.
         self.logger = get_module_logger("ALSTM")

qlib/contrib/model/pytorch_alstm_ts.py

@@ -56,7 +56,7 @@ class ALSTM(Model):
         n_jobs=10,
         GPU=0,
         seed=None,
-        **kwargs
+        **kwargs,
     ):
         # Set logger.
         self.logger = get_module_logger("ALSTM")

qlib/contrib/model/pytorch_gats.py

@@ -56,7 +56,7 @@ class GATs(Model):
         optimizer="adam",
         GPU=0,
         seed=None,
-        **kwargs
+        **kwargs,
     ):
         # Set logger.
         self.logger = get_module_logger("GATs")

qlib/contrib/model/pytorch_gats_ts.py

@@ -73,7 +73,7 @@ class GATs(Model):
         GPU=0,
         n_jobs=10,
         seed=None,
-        **kwargs
+        **kwargs,
     ):
         # Set logger.
         self.logger = get_module_logger("GATs")

qlib/contrib/model/pytorch_general_nn.py

@@ -0,0 +1,358 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+from __future__ import division
+from __future__ import print_function
+
+from torch.utils.data import DataLoader
+
+
+import numpy as np
+import pandas as pd
+from typing import Union
+import copy
+
+import torch
+import torch.optim as optim
+
+from qlib.data.dataset.weight import Reweighter
+
+from .pytorch_utils import count_parameters
+from ...model.base import Model
+from ...data.dataset import DatasetH, TSDatasetH
+from ...data.dataset.handler import DataHandlerLP
+from ...utils import (
+    init_instance_by_config,
+    get_or_create_path,
+)
+from ...log import get_module_logger
+
+from ...model.utils import ConcatDataset
+
+
+class GeneralPTNN(Model):
+    """
+    Motivation:
+        We want to provide a Qlib General Pytorch Model Adaptor
+        You can reuse it for all kinds of Pytorch models.
+        It should include the training and predict process
+
+    Parameters
+    ----------
+    d_feat : int
+        input dimension for each time step
+    metric: str
+        the evaluation metric used in early stop
+    optimizer : str
+        optimizer name
+    GPU : str
+        the GPU ID(s) used for training
+    """
+
+    def __init__(
+        self,
+        n_epochs=200,
+        lr=0.001,
+        metric="",
+        batch_size=2000,
+        early_stop=20,
+        loss="mse",
+        weight_decay=0.0,
+        optimizer="adam",
+        n_jobs=10,
+        GPU=0,
+        seed=None,
+        pt_model_uri="qlib.contrib.model.pytorch_gru_ts.GRUModel",
+        pt_model_kwargs={
+            "d_feat": 6,
+            "hidden_size": 64,
+            "num_layers": 2,
+            "dropout": 0.0,
+        },
+    ):
+        # Set logger.
+        self.logger = get_module_logger("GeneralPTNN")
+        self.logger.info("GeneralPTNN pytorch version...")
+
+        # set hyper-parameters.
+        self.n_epochs = n_epochs
+        self.lr = lr
+        self.metric = metric
+        self.batch_size = batch_size
+        self.early_stop = early_stop
+        self.optimizer = optimizer.lower()
+        self.loss = loss
+        self.weight_decay = weight_decay
+        self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() and GPU >= 0 else "cpu")
+        self.n_jobs = n_jobs
+        self.seed = seed
+
+        self.pt_model_uri, self.pt_model_kwargs = pt_model_uri, pt_model_kwargs
+        self.dnn_model = init_instance_by_config({"class": pt_model_uri, "kwargs": pt_model_kwargs})
+
+        self.logger.info(
+            "GeneralPTNN parameters setting:"
+            "\nn_epochs : {}"
+            "\nlr : {}"
+            "\nmetric : {}"
+            "\nbatch_size : {}"
+            "\nearly_stop : {}"
+            "\noptimizer : {}"
+            "\nloss_type : {}"
+            "\ndevice : {}"
+            "\nn_jobs : {}"
+            "\nuse_GPU : {}"
+            "\nweight_decay : {}"
+            "\nseed : {}"
+            "\npt_model_uri: {}"
+            "\npt_model_kwargs: {}".format(
+                n_epochs,
+                lr,
+                metric,
+                batch_size,
+                early_stop,
+                optimizer.lower(),
+                loss,
+                self.device,
+                n_jobs,
+                self.use_gpu,
+                weight_decay,
+                seed,
+                pt_model_uri,
+                pt_model_kwargs,
+            )
+        )
+
+        if self.seed is not None:
+            np.random.seed(self.seed)
+            torch.manual_seed(self.seed)
+
+        self.logger.info("model:\n{:}".format(self.dnn_model))
+        self.logger.info("model size: {:.4f} MB".format(count_parameters(self.dnn_model)))
+
+        if optimizer.lower() == "adam":
+            self.train_optimizer = optim.Adam(self.dnn_model.parameters(), lr=self.lr, weight_decay=weight_decay)
+        elif optimizer.lower() == "gd":
+            self.train_optimizer = optim.SGD(self.dnn_model.parameters(), lr=self.lr, weight_decay=weight_decay)
+        else:
+            raise NotImplementedError("optimizer {} is not supported!".format(optimizer))
+
+        self.fitted = False
+        self.dnn_model.to(self.device)
+
+    @property
+    def use_gpu(self):
+        return self.device != torch.device("cpu")
+
+    def mse(self, pred, label, weight):
+        loss = weight * (pred - label) ** 2
+        return torch.mean(loss)
+
+    def loss_fn(self, pred, label, weight=None):
+        mask = ~torch.isnan(label)
+
+        if weight is None:
+            weight = torch.ones_like(label)
+
+        if self.loss == "mse":
+            return self.mse(pred[mask], label[mask], weight[mask])
+
+        raise ValueError("unknown loss `%s`" % self.loss)
+
+    def metric_fn(self, pred, label):
+        mask = torch.isfinite(label)
+
+        if self.metric in ("", "loss"):
+            return -self.loss_fn(pred[mask], label[mask])
+
+        raise ValueError("unknown metric `%s`" % self.metric)
+
+    def _get_fl(self, data: torch.Tensor):
+        """
+        get feature and label from data
+        - Handle the different data shape of time series and tabular data
+
+        Parameters
+        ----------
+        data : torch.Tensor
+            input data which maybe 3 dimension or 2 dimension
+            - 3dim: [batch_size, time_step, feature_dim]
+            - 2dim: [batch_size, feature_dim]
+
+        Returns
+        -------
+        Tuple[torch.Tensor, torch.Tensor]
+        """
+        if data.dim() == 3:
+            # it is a time series dataset
+            feature = data[:, :, 0:-1].to(self.device)
+            label = data[:, -1, -1].to(self.device)
+        elif data.dim() == 2:
+            # it is a tabular dataset
+            feature = data[:, 0:-1].to(self.device)
+            label = data[:, -1].to(self.device)
+        else:
+            raise ValueError("Unsupported data shape.")
+        return feature, label
+
+    def train_epoch(self, data_loader):
+        self.dnn_model.train()
+
+        for data, weight in data_loader:
+            feature, label = self._get_fl(data)
+
+            pred = self.dnn_model(feature.float())
+            loss = self.loss_fn(pred, label, weight.to(self.device))
+
+            self.train_optimizer.zero_grad()
+            loss.backward()
+            torch.nn.utils.clip_grad_value_(self.dnn_model.parameters(), 3.0)
+            self.train_optimizer.step()
+
+    def test_epoch(self, data_loader):
+        self.dnn_model.eval()
+
+        scores = []
+        losses = []
+
+        for data, weight in data_loader:
+            feature, label = self._get_fl(data)
+
+            with torch.no_grad():
+                pred = self.dnn_model(feature.float())
+                loss = self.loss_fn(pred, label, weight.to(self.device))
+                losses.append(loss.item())
+
+                score = self.metric_fn(pred, label)
+                scores.append(score.item())
+
+        return np.mean(losses), np.mean(scores)
+
+    def fit(
+        self,
+        dataset: Union[DatasetH, TSDatasetH],
+        evals_result=dict(),
+        save_path=None,
+        reweighter=None,
+    ):
+        ists = isinstance(dataset, TSDatasetH)  # is this time series dataset
+
+        dl_train = dataset.prepare("train", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L)
+        dl_valid = dataset.prepare("valid", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L)
+        if dl_train.empty or dl_valid.empty:
+            raise ValueError("Empty data from dataset, please check your dataset config.")
+
+        if reweighter is None:
+            wl_train = np.ones(len(dl_train))
+            wl_valid = np.ones(len(dl_valid))
+        elif isinstance(reweighter, Reweighter):
+            wl_train = reweighter.reweight(dl_train)
+            wl_valid = reweighter.reweight(dl_valid)
+        else:
+            raise ValueError("Unsupported reweighter type.")
+
+        # Preprocess for data.  To align to Dataset Interface for DataLoader
+        if ists:
+            dl_train.config(fillna_type="ffill+bfill")  # process nan brought by dataloader
+            dl_valid.config(fillna_type="ffill+bfill")  # process nan brought by dataloader
+        else:
+            # If it is a tabular, we convert the dataframe to numpy to be indexable by DataLoader
+            dl_train = dl_train.values
+            dl_valid = dl_valid.values
+
+        train_loader = DataLoader(
+            ConcatDataset(dl_train, wl_train),
+            batch_size=self.batch_size,
+            shuffle=True,
+            num_workers=self.n_jobs,
+            drop_last=True,
+        )
+        valid_loader = DataLoader(
+            ConcatDataset(dl_valid, wl_valid),
+            batch_size=self.batch_size,
+            shuffle=False,
+            num_workers=self.n_jobs,
+            drop_last=True,
+        )
+        del dl_train, dl_valid, wl_train, wl_valid
+
+        save_path = get_or_create_path(save_path)
+
+        stop_steps = 0
+        train_loss = 0
+        best_score = -np.inf
+        best_epoch = 0
+        evals_result["train"] = []
+        evals_result["valid"] = []
+
+        # train
+        self.logger.info("training...")
+        self.fitted = True
+
+        for step in range(self.n_epochs):
+            self.logger.info("Epoch%d:", step)
+            self.logger.info("training...")
+            self.train_epoch(train_loader)
+            self.logger.info("evaluating...")
+            train_loss, train_score = self.test_epoch(train_loader)
+            val_loss, val_score = self.test_epoch(valid_loader)
+            self.logger.info("train %.6f, valid %.6f" % (train_score, val_score))
+            evals_result["train"].append(train_score)
+            evals_result["valid"].append(val_score)
+
+            if step == 0:
+                best_param = copy.deepcopy(self.dnn_model.state_dict())
+            if val_score > best_score:
+                best_score = val_score
+                stop_steps = 0
+                best_epoch = step
+                best_param = copy.deepcopy(self.dnn_model.state_dict())
+            else:
+                stop_steps += 1
+                if stop_steps >= self.early_stop:
+                    self.logger.info("early stop")
+                    break
+
+        self.logger.info("best score: %.6lf @ %d" % (best_score, best_epoch))
+        self.dnn_model.load_state_dict(best_param)
+        torch.save(best_param, save_path)
+
+        if self.use_gpu:
+            torch.cuda.empty_cache()
+
+    def predict(
+        self,
+        dataset: Union[DatasetH, TSDatasetH],
+        batch_size=None,
+        n_jobs=None,
+    ):
+        if not self.fitted:
+            raise ValueError("model is not fitted yet!")
+
+        dl_test = dataset.prepare("test", col_set=["feature", "label"], data_key=DataHandlerLP.DK_I)
+
+        if isinstance(dataset, TSDatasetH):
+            dl_test.config(fillna_type="ffill+bfill")  # process nan brought by dataloader
+            index = dl_test.get_index()
+        else:
+            # If it is a tabular, we convert the dataframe to numpy to be indexable by DataLoader
+            index = dl_test.index
+            dl_test = dl_test.values
+
+        test_loader = DataLoader(dl_test, batch_size=self.batch_size, num_workers=self.n_jobs)
+        self.dnn_model.eval()
+        preds = []
+
+        for data in test_loader:
+            feature, _ = self._get_fl(data)
+            feature = feature.to(self.device)
+
+            with torch.no_grad():
+                pred = self.dnn_model(feature.float()).detach().cpu().numpy()
+
+            preds.append(pred)
+
+        preds_concat = np.concatenate(preds)
+        if preds_concat.ndim != 1:
+            preds_concat = preds_concat.ravel()
+
+        return pd.Series(preds_concat, index=index)

qlib/contrib/model/pytorch_gru.py

@@ -1,25 +1,25 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
 
-
 from __future__ import division
 from __future__ import print_function
+import copy
+from typing import Text, Union
 
 import numpy as np
 import pandas as pd
-from typing import Text, Union
-import copy
-from ...utils import get_or_create_path
-from ...log import get_module_logger
-
 import torch
 import torch.nn as nn
 import torch.optim as optim
 
-from .pytorch_utils import count_parameters
-from ...model.base import Model
+from qlib.workflow import R
+
 from ...data.dataset import DatasetH
 from ...data.dataset.handler import DataHandlerLP
+from ...log import get_module_logger
+from ...model.base import Model
+from ...utils import get_or_create_path
+from .pytorch_utils import count_parameters
 
 
 class GRU(Model):
@@ -52,7 +52,7 @@ class GRU(Model):
         optimizer="adam",
         GPU=0,
         seed=None,
-        **kwargs
+        **kwargs,
     ):
         # Set logger.
         self.logger = get_module_logger("GRU")
@@ -212,16 +212,31 @@ class GRU(Model):
         evals_result=dict(),
         save_path=None,
     ):
-        df_train, df_valid, df_test = dataset.prepare(
-            ["train", "valid", "test"],
-            col_set=["feature", "label"],
-            data_key=DataHandlerLP.DK_L,
-        )
-        if df_train.empty or df_valid.empty:
-            raise ValueError("Empty data from dataset, please check your dataset config.")
+        # prepare training and validation data
+        dfs = {
+            k: dataset.prepare(
+                k,
+                col_set=["feature", "label"],
+                data_key=DataHandlerLP.DK_L,
+            )
+            for k in ["train", "valid"]
+            if k in dataset.segments
+        }
+        df_train, df_valid = dfs.get("train", pd.DataFrame()), dfs.get("valid", pd.DataFrame())
 
+        # check if training data is empty
+        if df_train.empty:
+            raise ValueError("Empty training data from dataset, please check your dataset config.")
+
+        df_train = df_train.dropna()
         x_train, y_train = df_train["feature"], df_train["label"]
-        x_valid, y_valid = df_valid["feature"], df_valid["label"]
+
+        # check if validation data is provided
+        if not df_valid.empty:
+            df_valid = df_valid.dropna()
+            x_valid, y_valid = df_valid["feature"], df_valid["label"]
+        else:
+            x_valid, y_valid = None, None
 
         save_path = get_or_create_path(save_path)
         stop_steps = 0
@@ -235,32 +250,42 @@ class GRU(Model):
         self.logger.info("training...")
         self.fitted = True
 
+        best_param = copy.deepcopy(self.gru_model.state_dict())
         for step in range(self.n_epochs):
             self.logger.info("Epoch%d:", step)
             self.logger.info("training...")
             self.train_epoch(x_train, y_train)
             self.logger.info("evaluating...")
             train_loss, train_score = self.test_epoch(x_train, y_train)
-            val_loss, val_score = self.test_epoch(x_valid, y_valid)
-            self.logger.info("train %.6f, valid %.6f" % (train_score, val_score))
             evals_result["train"].append(train_score)
-            evals_result["valid"].append(val_score)
 
-            if val_score > best_score:
-                best_score = val_score
-                stop_steps = 0
-                best_epoch = step
-                best_param = copy.deepcopy(self.gru_model.state_dict())
-            else:
-                stop_steps += 1
-                if stop_steps >= self.early_stop:
-                    self.logger.info("early stop")
-                    break
+            # evaluate on validation data if provided
+            if x_valid is not None and y_valid is not None:
+                val_loss, val_score = self.test_epoch(x_valid, y_valid)
+                self.logger.info("train %.6f, valid %.6f" % (train_score, val_score))
+                evals_result["valid"].append(val_score)
+
+                if val_score > best_score:
+                    best_score = val_score
+                    stop_steps = 0
+                    best_epoch = step
+                    best_param = copy.deepcopy(self.gru_model.state_dict())
+                else:
+                    stop_steps += 1
+                    if stop_steps >= self.early_stop:
+                        self.logger.info("early stop")
+                        break
 
         self.logger.info("best score: %.6lf @ %d" % (best_score, best_epoch))
         self.gru_model.load_state_dict(best_param)
         torch.save(best_param, save_path)
 
+        # Logging
+        rec = R.get_recorder()
+        for k, v_l in evals_result.items():
+            for i, v in enumerate(v_l):
+                rec.log_metrics(step=i, **{k: v})
+
         if self.use_gpu:
             torch.cuda.empty_cache()
 

qlib/contrib/model/pytorch_gru_ts.py

@@ -54,7 +54,7 @@ class GRU(Model):
         n_jobs=10,
         GPU=0,
         seed=None,
-        **kwargs
+        **kwargs,
     ):
         # Set logger.
         self.logger = get_module_logger("GRU")

qlib/contrib/model/pytorch_hist.py

@@ -59,7 +59,7 @@ class HIST(Model):
         optimizer="adam",
         GPU=0,
         seed=None,
-        **kwargs
+        **kwargs,
     ):
         # Set logger.
         self.logger = get_module_logger("HIST")
@@ -256,7 +256,7 @@ class HIST(Model):
             raise ValueError("Empty data from dataset, please check your dataset config.")
 
         if not os.path.exists(self.stock2concept):
-            url = "http://fintech.msra.cn/stock_data/downloads/qlib_csi300_stock2concept.npy"
+            url = "https://github.com/SunsetWolf/qlib_dataset/releases/download/v0/qlib_csi300_stock2concept.npy"
             urllib.request.urlretrieve(url, self.stock2concept)
 
         stock_index = np.load(self.stock_index, allow_pickle=True).item()

qlib/contrib/model/pytorch_igmtf.py

@@ -55,7 +55,7 @@ class IGMTF(Model):
         optimizer="adam",
         GPU=0,
         seed=None,
-        **kwargs
+        **kwargs,
     ):
         # Set logger.
         self.logger = get_module_logger("IGMTF")

qlib/contrib/model/pytorch_krnn.py

@@ -255,7 +255,7 @@ class KRNN(Model):
         optimizer="adam",
         GPU=0,
         seed=None,
-        **kwargs
+        **kwargs,
     ):
         # Set logger.
         self.logger = get_module_logger("KRNN")

qlib/contrib/model/pytorch_localformer.py

@@ -44,7 +44,7 @@ class LocalformerModel(Model):
         n_jobs=10,
         GPU=0,
         seed=None,
-        **kwargs
+        **kwargs,
     ):
         # set hyper-parameters.
         self.d_model = d_model

qlib/contrib/model/pytorch_localformer_ts.py

@@ -42,7 +42,7 @@ class LocalformerModel(Model):
         n_jobs=10,
         GPU=0,
         seed=None,
-        **kwargs
+        **kwargs,
     ):
         # set hyper-parameters.
         self.d_model = d_model

qlib/contrib/model/pytorch_lstm.py

@@ -51,7 +51,7 @@ class LSTM(Model):
         optimizer="adam",
         GPU=0,
         seed=None,
-        **kwargs
+        **kwargs,
     ):
         # Set logger.
         self.logger = get_module_logger("LSTM")

qlib/contrib/model/pytorch_lstm_ts.py

@@ -53,7 +53,7 @@ class LSTM(Model):
         n_jobs=10,
         GPU=0,
         seed=None,
-        **kwargs
+        **kwargs,
     ):
         # Set logger.
         self.logger = get_module_logger("LSTM")

qlib/contrib/model/pytorch_sandwich.py

@@ -35,7 +35,7 @@ class SandwichModel(nn.Module):
         rnn_layers,
         dropout,
         device,
-        **params
+        **params,
     ):
         """Build a Sandwich model
 
@@ -129,7 +129,7 @@ class Sandwich(Model):
         optimizer="adam",
         GPU=0,
         seed=None,
-        **kwargs
+        **kwargs,
     ):
         # Set logger.
         self.logger = get_module_logger("Sandwich")

qlib/contrib/model/pytorch_sfm.py

@@ -212,7 +212,7 @@ class SFM(Model):
         optimizer="gd",
         GPU=0,
         seed=None,
-        **kwargs
+        **kwargs,
     ):
         # Set logger.
         self.logger = get_module_logger("SFM")

qlib/contrib/model/pytorch_tcn.py

@@ -56,7 +56,7 @@ class TCN(Model):
         optimizer="adam",
         GPU=0,
         seed=None,
-        **kwargs
+        **kwargs,
     ):
         # Set logger.
         self.logger = get_module_logger("TCN")

qlib/contrib/model/pytorch_tcn_ts.py

@@ -54,7 +54,7 @@ class TCN(Model):
         n_jobs=10,
         GPU=0,
         seed=None,
-        **kwargs
+        **kwargs,
     ):
         # Set logger.
         self.logger = get_module_logger("TCN")

qlib/contrib/model/pytorch_tcts.py

@@ -58,7 +58,7 @@ class TCTS(Model):
         mode="soft",
         seed=None,
         lowest_valid_performance=0.993,
-        **kwargs
+        **kwargs,
     ):
         # Set logger.
         self.logger = get_module_logger("TCTS")

qlib/contrib/model/pytorch_transformer.py

@@ -43,7 +43,7 @@ class TransformerModel(Model):
         n_jobs=10,
         GPU=0,
         seed=None,
-        **kwargs
+        **kwargs,
     ):
         # set hyper-parameters.
         self.d_model = d_model

qlib/contrib/model/pytorch_transformer_ts.py

@@ -41,7 +41,7 @@ class TransformerModel(Model):
         n_jobs=10,
         GPU=0,
         seed=None,
-        **kwargs
+        **kwargs,
     ):
         # set hyper-parameters.
         self.d_model = d_model

qlib/contrib/model/xgboost.py

@@ -28,7 +28,7 @@ class XGBModel(Model, FeatureInt):
         verbose_eval=20,
         evals_result=dict(),
         reweighter=None,
-        **kwargs
+        **kwargs,
     ):
         df_train, df_valid = dataset.prepare(
             ["train", "valid"],
@@ -63,7 +63,7 @@ class XGBModel(Model, FeatureInt):
             early_stopping_rounds=early_stopping_rounds,
             verbose_eval=verbose_eval,
             evals_result=evals_result,
-            **kwargs
+            **kwargs,
         )
         evals_result["train"] = list(evals_result["train"].values())[0]
         evals_result["valid"] = list(evals_result["valid"].values())[0]

qlib/contrib/online/manager.py

@@ -4,10 +4,10 @@
 # pylint: skip-file
 # flake8: noqa
 
-import yaml
 import pathlib
 import pandas as pd
 import shutil
+from ruamel.yaml import YAML
 from ...backtest.account import Account
 from .user import User
 from .utils import load_instance, save_instance
@@ -110,7 +110,8 @@ class UserManager:
             raise ValueError("User data for {} already exists".format(user_id))
 
         with config_file.open("r") as fp:
-            config = yaml.safe_load(fp)
+            yaml = YAML(typ="safe", pure=True)
+            config = yaml.load(fp)
         # load model
         model = init_instance_by_config(config["model"])
 

qlib/contrib/online/utils.py

@@ -6,8 +6,8 @@
 
 import pathlib
 import pickle
-import yaml
 import pandas as pd
+from ruamel.yaml import YAML
 from ...data import D
 from ...config import C
 from ...log import get_module_logger
@@ -91,7 +91,8 @@ def prepare(um, today, user_id, exchange_config=None):
     dates.append(get_next_trading_date(dates[-1], future=True))
     if exchange_config:
         with pathlib.Path(exchange_config).open("r") as fp:
-            exchange_paras = yaml.safe_load(fp)
+            yaml = YAML(typ="safe", pure=True)
+            exchange_paras = yaml.load(fp)
     else:
         exchange_paras = {}
     trade_exchange = Exchange(trade_dates=dates, **exchange_paras)

qlib/contrib/report/data/ana.py

@@ -1,5 +1,17 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
+"""
+Here we have a comprehensive set of analysis classes.
+
+Here is an example.
+
+.. code-block:: python
+
+    from qlib.contrib.report.data.ana import FeaMeanStd
+    fa = FeaMeanStd(ret_df)
+    fa.plot_all(wspace=0.3, sub_figsize=(12, 3), col_n=5)
+
+"""
 import pandas as pd
 import numpy as np
 from qlib.contrib.report.data.base import FeaAnalyser
@@ -152,6 +164,7 @@ class FeaSkewTurt(NumFeaAnalyser):
         self._kurt[col].plot(ax=right_ax, label="kurt", color="green")
         right_ax.set_xlabel("")
         right_ax.set_ylabel("kurt")
+        right_ax.grid(None)  # set the grid to None to avoid two layer of grid
 
         h1, l1 = ax.get_legend_handles_labels()
         h2, l2 = right_ax.get_legend_handles_labels()
@@ -171,12 +184,15 @@ class FeaMeanStd(NumFeaAnalyser):
         ax.set_xlabel("")
         ax.set_ylabel("mean")
         ax.legend()
+        ax.tick_params(axis="x", rotation=90)
 
         right_ax = ax.twinx()
 
         self._std[col].plot(ax=right_ax, label="std", color="green")
         right_ax.set_xlabel("")
         right_ax.set_ylabel("std")
+        right_ax.tick_params(axis="x", rotation=90)
+        right_ax.grid(None)  # set the grid to None to avoid two layer of grid
 
         h1, l1 = ax.get_legend_handles_labels()
         h2, l2 = right_ax.get_legend_handles_labels()

qlib/contrib/report/data/base.py

@@ -14,6 +14,24 @@ from qlib.contrib.report.utils import sub_fig_generator
 
 class FeaAnalyser:
     def __init__(self, dataset: pd.DataFrame):
+        """
+
+        Parameters
+        ----------
+        dataset : pd.DataFrame
+
+            We often have multiple columns for dataset. Each column corresponds to one sub figure.
+            There will be a datatime column in the index levels.
+            Aggretation will be used for more summarized metrics overtime.
+            Here is an example of data:
+
+            .. code-block::
+
+                                            return
+                datetime   instrument
+                2007-02-06 equity_tpx     0.010087
+                           equity_spx     0.000786
+        """
         self._dataset = dataset
         with TimeInspector.logt("calc_stat_values"):
             self.calc_stat_values()

qlib/contrib/report/graph.py

@@ -176,7 +176,7 @@ class HeatmapGraph(BaseGraph):
                 x=self._df.columns,
                 y=self._df.index,
                 z=self._df.values.tolist(),
-                **self._graph_kwargs
+                **self._graph_kwargs,
             )
         ]
         return _data
@@ -213,7 +213,7 @@ class SubplotsGraph:
         sub_graph_layout: dict = None,
         sub_graph_data: list = None,
         subplots_kwargs: dict = None,
-        **kwargs
+        **kwargs,
     ):
         """
 
@@ -355,7 +355,7 @@ class SubplotsGraph:
                         df=self._df.loc[:, [column_name]],
                         name_dict={column_name: temp_name},
                         graph_kwargs=_graph_kwargs,
-                    )
+                    ),
                 )
             else:
                 raise TypeError()

qlib/contrib/report/utils.py

@@ -4,7 +4,7 @@ import matplotlib.pyplot as plt
 import pandas as pd
 
 
-def sub_fig_generator(sub_fs=(3, 3), col_n=10, row_n=1, wspace=None, hspace=None, sharex=False, sharey=False):
+def sub_fig_generator(sub_figsize=(3, 3), col_n=10, row_n=1, wspace=None, hspace=None, sharex=False, sharey=False):
     """sub_fig_generator.
     it will return a generator, each row contains <col_n> sub graph
 
@@ -13,7 +13,7 @@ def sub_fig_generator(sub_fs=(3, 3), col_n=10, row_n=1, wspace=None, hspace=None
 
     Parameters
     ----------
-    sub_fs :
+    sub_figsize :
         the figure size of each subgraph in <col_n> * <row_n> subgraphs
     col_n :
         the number of subgraph in each row;  It will generating a new graph after generating <col_n> of subgraphs.
@@ -33,7 +33,7 @@ def sub_fig_generator(sub_fs=(3, 3), col_n=10, row_n=1, wspace=None, hspace=None
 
     while True:
         fig, axes = plt.subplots(
-            row_n, col_n, figsize=(sub_fs[0] * col_n, sub_fs[1] * row_n), sharex=sharex, sharey=sharey
+            row_n, col_n, figsize=(sub_figsize[0] * col_n, sub_figsize[1] * row_n), sharex=sharex, sharey=sharey
         )
         plt.subplots_adjust(wspace=wspace, hspace=hspace)
         axes = axes.reshape(row_n, col_n)

qlib/contrib/rolling/base.py

@@ -2,11 +2,11 @@
 # Licensed under the MIT License.
 from copy import deepcopy
 from pathlib import Path
+from ruamel.yaml import YAML
 from typing import List, Optional, Union
 
 import fire
 import pandas as pd
-import yaml
 
 from qlib import auto_init
 from qlib.log import get_module_logger
@@ -73,8 +73,8 @@ class Rolling:
             The horizon of the prediction target.
             This is used to override the prediction horizon of the file.
         h_path : Optional[str]
-            the dumped data handler;
-            It may come from other data source. It will override the data handler in the config.
+            It is other data source that is dumped as a handler. It will override the data handler section in the config.
+            If it is not given, it will create a customized cache for the handler when `enable_handler_cache=True`
         test_end : Optional[str]
             the test end for the data. It is typically used together with the handler
             You can do the same thing with task_ext_conf in a more complicated way
@@ -117,9 +117,10 @@ class Rolling:
 
     def _raw_conf(self) -> dict:
         with self.conf_path.open("r") as f:
-            return yaml.safe_load(f)
+            yaml = YAML(typ="safe", pure=True)
+            return yaml.load(f)
 
-    def _replace_hanler_with_cache(self, task: dict):
+    def _replace_handler_with_cache(self, task: dict):
         """
         Due to the data processing part in original rolling is slow. So we have to
         This class tries to add more feature
@@ -159,13 +160,20 @@ class Rolling:
             # - get horizon automatically from the expression!!!!
             raise NotImplementedError(f"This type of input is not supported")
         else:
-            self.logger.info("The prediction horizon is overrided")
-            task["dataset"]["kwargs"]["handler"]["kwargs"]["label"] = [
-                "Ref($close, -{}) / Ref($close, -1) - 1".format(self.horizon + 1)
-            ]
+            if enable_handler_cache and self.h_path is not None:
+                self.logger.info("Fail to override the horizon due to data handler cache")
+            else:
+                self.logger.info("The prediction horizon is overrided")
+                if isinstance(task["dataset"]["kwargs"]["handler"], dict):
+                    task["dataset"]["kwargs"]["handler"]["kwargs"]["label"] = [
+                        "Ref($close, -{}) / Ref($close, -1) - 1".format(self.horizon + 1)
+                    ]
+                else:
+                    self.logger.warning("Try to automatically configure the lablel but failed.")
 
-        if enable_handler_cache:
-            task = self._replace_hanler_with_cache(task)
+        if self.h_path is not None or enable_handler_cache:
+            # if we already have provided data source or we want to create one
+            task = self._replace_handler_with_cache(task)
         task = self._update_start_end_time(task)
 
         if self.task_ext_conf is not None:
@@ -173,6 +181,16 @@ class Rolling:
         self.logger.info(task)
         return task
 
+    def run_basic_task(self):
+        """
+        Run the basic task without rolling.
+        This is for fast testing for model tunning.
+        """
+        task = self.basic_task()
+        print(task)
+        trainer = TrainerR(experiment_name=self.exp_name)
+        trainer([task])
+
     def get_task_list(self) -> List[dict]:
         """return a batch of tasks for rolling."""
         task = self.basic_task()

qlib/contrib/rolling/ddgda.py

@@ -80,6 +80,11 @@ class DDGDA(Rolling):
         sim_task_model: UTIL_MODEL_TYPE = "gbdt",
         meta_1st_train_end: Optional[str] = None,
         alpha: float = 0.01,
+        loss_skip_thresh: int = 50,
+        fea_imp_n: Optional[int] = 30,
+        meta_data_proc: Optional[str] = "V01",
+        segments: Union[float, str] = 0.62,
+        hist_step_n: int = 30,
         working_dir: Optional[Union[str, Path]] = None,
         **kwargs,
     ):
@@ -94,6 +99,15 @@ class DDGDA(Rolling):
         alpha: float
             Setting the L2 regularization for ridge
             The `alpha` is only passed to MetaModelDS (it is not passed to sim_task_model currently..)
+        loss_skip_thresh: int
+            The thresh to skip the loss calculation for each day. If the number of item is less than it, it will skip the loss on that day.
+        meta_data_proc : Optional[str]
+            How we process the meta dataset for learning meta model.
+        segments : Union[float, str]
+            if segments is a float:
+                The ratio of training data in the meta task dataset
+            if segments is a string:
+                it will try its best to put its data in training and ensure that the date `segments` is in the test set
         """
         # NOTE:
         # the horizon must match the meaning in the base task template
@@ -104,14 +118,22 @@ class DDGDA(Rolling):
         super().__init__(**kwargs)
         self.working_dir = self.conf_path.parent if working_dir is None else Path(working_dir)
         self.proxy_hd = self.working_dir / "handler_proxy.pkl"
+        self.fea_imp_n = fea_imp_n
+        self.meta_data_proc = meta_data_proc
+        self.loss_skip_thresh = loss_skip_thresh
+        self.segments = segments
+        self.hist_step_n = hist_step_n
 
     def _adjust_task(self, task: dict, astype: UTIL_MODEL_TYPE):
         """
-        some task are use for special purpose.
+        Base on the original task, we need to do some extra things.
+
         For example:
         - GBDT for calculating feature importance
         - Linear or GBDT for calculating similarity
         - Datset (well processed) that aligned to Linear that for meta learning
+
+        So we may need to change the dataset and model for the special purpose and other settings remains the same.
         """
         # NOTE: here is just for aligning with previous implementation
         # It is not necessary for the current implementation
@@ -119,12 +141,16 @@ class DDGDA(Rolling):
         if astype == "gbdt":
             task["model"] = LGBM_MODEL
             if isinstance(handler, dict):
+                # We don't need preprocessing when using GBDT model
                 for k in ["infer_processors", "learn_processors"]:
                     if k in handler.setdefault("kwargs", {}):
                         handler["kwargs"].pop(k)
         elif astype == "linear":
             task["model"] = LINEAR_MODEL
-            handler["kwargs"].update(PROC_ARGS)
+            if isinstance(handler, dict):
+                handler["kwargs"].update(PROC_ARGS)
+            else:
+                self.logger.warning("The handler can't be adjusted.")
         else:
             raise ValueError(f"astype not supported: {astype}")
         return task
@@ -155,12 +181,15 @@ class DDGDA(Rolling):
         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)
+
         # NOTE: adjusting to `self.sim_task_model` just for aligning with previous implementation.
+        # In previous version. The data for proxy model is using sim_task_model's way for processing
         task = self._adjust_task(self.basic_task(enable_handler_cache=False), self.sim_task_model)
         task = replace_task_handler_with_cache(task, self.working_dir)
+        # if self.meta_data_proc is not None:
+        # else:
+        #     # Otherwise, we don't need futher processing
+        #     task = self.basic_task()
 
         dataset = init_instance_by_config(task["dataset"])
         prep_ds = dataset.prepare(slice(None), col_set=["feature", "label"], data_key=DataHandlerLP.DK_L)
@@ -168,12 +197,18 @@ class DDGDA(Rolling):
         feature_df = prep_ds["feature"]
         label_df = prep_ds["label"]
 
-        feature_selected = feature_df.loc[:, col_selected.index]
+        if self.fea_imp_n is not None:
+            fi = self._get_feature_importance()
+            col_selected = fi.nlargest(self.fea_imp_n)
+            feature_selected = feature_df.loc[:, col_selected.index]
+        else:
+            feature_selected = feature_df
 
-        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)
+        if self.meta_data_proc == "V01":
+            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),
@@ -223,7 +258,10 @@ class DDGDA(Rolling):
         # 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
 
-        # the train_start for training meta model does not necessarily align with final rolling
+        # NOTE:
+        # - The train_start for training meta model does not necessarily align with final rolling
+        #   But please select a right time to make sure the finnal rolling tasks are not leaked in the training data.
+        # - The test_start is automatically aligned to the next day of test_end.  Validation is ignored.
         train_start = "2008-01-01" if self.train_start is None else self.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")
@@ -249,9 +287,9 @@ class DDGDA(Rolling):
         kwargs = dict(
             task_tpl=proxy_forecast_model_task,
             step=self.step,
-            segments=0.62,  # keep test period consistent with the dataset yaml
+            segments=self.segments,  # keep test period consistent with the dataset yaml
             trunc_days=1 + self.horizon,
-            hist_step_n=30,
+            hist_step_n=self.hist_step_n,
             fill_method=fill_method,
             rolling_ext_days=0,
         )
@@ -268,7 +306,13 @@ class DDGDA(Rolling):
         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
+                step=self.step,
+                hist_step_n=kwargs["hist_step_n"],
+                lr=0.001,
+                max_epoch=30,
+                seed=43,
+                alpha=self.alpha,
+                loss_skip_thresh=self.loss_skip_thresh,
             )
             mm.fit(md)
             R.save_objects(model=mm)

qlib/contrib/tuner/config.py

@@ -4,9 +4,9 @@
 # pylint: skip-file
 # flake8: noqa
 
-import yaml
 import copy
 import os
+from ruamel.yaml import YAML
 
 
 class TunerConfigManager:
@@ -16,7 +16,8 @@ class TunerConfigManager:
         self.config_path = config_path
 
         with open(config_path) as fp:
-            config = yaml.safe_load(fp)
+            yaml = YAML(typ="safe", pure=True)
+            config = yaml.load(fp)
         self.config = copy.deepcopy(config)
 
         self.pipeline_ex_config = PipelineExperimentConfig(config.get("experiment", dict()), self)

qlib/data/dataset/loader.py

@@ -7,7 +7,7 @@ from pathlib import Path
 import warnings
 import pandas as pd
 
-from typing import Tuple, Union, List
+from typing import Tuple, Union, List, Dict
 
 from qlib.data import D
 from qlib.utils import load_dataset, init_instance_by_config, time_to_slc_point
@@ -41,6 +41,7 @@ class DataLoader(abc.ABC):
         ----------
         instruments : str or dict
             it can either be the market name or the config file of instruments generated by InstrumentProvider.
+            If the value of instruments is None, it means that no filtering is done.
         start_time : str
             start of the time range.
         end_time : str
@@ -50,6 +51,11 @@ class DataLoader(abc.ABC):
         -------
         pd.DataFrame:
             data load from the under layer source
+
+        Raise
+        -----
+        KeyError:
+            if the instruments filter is not supported, raise KeyError
         """
 
 
@@ -247,10 +253,14 @@ class StaticDataLoader(DataLoader, Serializable):
 
     def load(self, instruments=None, start_time=None, end_time=None) -> pd.DataFrame:
         self._maybe_load_raw_data()
+
+        # 1) Filter by instruments
         if instruments is None:
             df = self._data
         else:
             df = self._data.loc(axis=0)[:, instruments]
+
+        # 2) Filter by Datetime
         if start_time is None and end_time is None:
             return df  # NOTE: avoid copy by loc
         # pd.Timestamp(None) == NaT, use NaT as index can not fetch correct thing, so do not change None.
@@ -275,6 +285,61 @@ class StaticDataLoader(DataLoader, Serializable):
             self._data = self._config
 
 
+class NestedDataLoader(DataLoader):
+    """
+    We have multiple DataLoader, we can use this class to combine them.
+    """
+
+    def __init__(self, dataloader_l: List[Dict], join="left") -> None:
+        """
+
+        Parameters
+        ----------
+        dataloader_l : list[dict]
+            A list of dataloader, for exmaple
+
+            .. code-block:: python
+
+                nd = NestedDataLoader(
+                    dataloader_l=[
+                        {
+                            "class": "qlib.contrib.data.loader.Alpha158DL",
+                        }, {
+                            "class": "qlib.contrib.data.loader.Alpha360DL",
+                            "kwargs": {
+                                "config": {
+                                    "label": ( ["Ref($close, -2)/Ref($close, -1) - 1"], ["LABEL0"])
+                                }
+                            }
+                        }
+                    ]
+                )
+        join :
+            it will pass to pd.concat when merging it.
+        """
+        super().__init__()
+        self.data_loader_l = [
+            (dl if isinstance(dl, DataLoader) else init_instance_by_config(dl)) for dl in dataloader_l
+        ]
+        self.join = join
+
+    def load(self, instruments=None, start_time=None, end_time=None) -> pd.DataFrame:
+        df_full = None
+        for dl in self.data_loader_l:
+            try:
+                df_current = dl.load(instruments, start_time, end_time)
+            except KeyError:
+                warnings.warn(
+                    "If the value of `instruments` cannot be processed, it will set instruments to None to get all the data."
+                )
+                df_current = dl.load(instruments=None, start_time=start_time, end_time=end_time)
+            if df_full is None:
+                df_full = df_current
+            else:
+                df_full = pd.merge(df_full, df_current, left_index=True, right_index=True, how=self.join)
+        return df_full.sort_index(axis=1)
+
+
 class DataLoaderDH(DataLoader):
     """DataLoaderDH
     DataLoader based on (D)ata (H)andler

qlib/data/dataset/storage.py

@@ -104,15 +104,24 @@ class HashingStockStorage(BaseHandlerStorage):
         """
 
         stock_selector = slice(None)
+        time_selector = slice(None)  # by default not filter by time.
 
         if level is None:
+            # For directly applying.
             if isinstance(selector, tuple) and self.stock_level < len(selector):
+                # full selector format
                 stock_selector = selector[self.stock_level]
+                time_selector = selector[1 - self.stock_level]
             elif isinstance(selector, (list, str)) and self.stock_level == 0:
+                # only stock selector
                 stock_selector = selector
         elif level in ("instrument", self.stock_level):
             if isinstance(selector, tuple):
+                # NOTE: How could the stock level selector be a tuple?
                 stock_selector = selector[0]
+                raise TypeError(
+                    "I forget why would this case appear. But I think it does not make sense. So we raise a error for that case."
+                )
             elif isinstance(selector, (list, str)):
                 stock_selector = selector
 
@@ -120,7 +129,7 @@ class HashingStockStorage(BaseHandlerStorage):
             raise TypeError(f"stock selector must be type str|list, or slice(None), rather than {stock_selector}")
 
         if stock_selector == slice(None):
-            return self.hash_df
+            return self.hash_df, time_selector
 
         if isinstance(stock_selector, str):
             stock_selector = [stock_selector]
@@ -129,7 +138,7 @@ class HashingStockStorage(BaseHandlerStorage):
         for each_stock in sorted(stock_selector):
             if each_stock in self.hash_df:
                 select_dict[each_stock] = self.hash_df[each_stock]
-        return select_dict
+        return select_dict, time_selector
 
     def fetch(
         self,
@@ -138,10 +147,13 @@ class HashingStockStorage(BaseHandlerStorage):
         col_set: Union[str, List[str]] = DataHandler.CS_ALL,
         fetch_orig: bool = True,
     ) -> pd.DataFrame:
-        fetch_stock_df_list = list(self._fetch_hash_df_by_stock(selector=selector, level=level).values())
+        fetch_stock_df_list, time_selector = self._fetch_hash_df_by_stock(selector=selector, level=level)
+        fetch_stock_df_list = list(fetch_stock_df_list.values())
         for _index, stock_df in enumerate(fetch_stock_df_list):
             fetch_col_df = fetch_df_by_col(df=stock_df, col_set=col_set)
-            fetch_index_df = fetch_df_by_index(df=fetch_col_df, selector=selector, level=level, fetch_orig=fetch_orig)
+            fetch_index_df = fetch_df_by_index(
+                df=fetch_col_df, selector=time_selector, level="datetime", fetch_orig=fetch_orig
+            )
             fetch_stock_df_list[_index] = fetch_index_df
         if len(fetch_stock_df_list) == 0:
             index_names = ("instrument", "datetime") if self.stock_level == 0 else ("datetime", "instrument")

qlib/data/filter.py

@@ -164,6 +164,7 @@ class SeriesDFilter(BaseDFilter):
         timestamp = []
         _lbool = None
         _ltime = None
+        _cur_start = None
         for _ts, _bool in timestamp_series.items():
             # there is likely to be NAN when the filter series don't have the
             # bool value, so we just change the NAN into False

qlib/model/meta/task.py

@@ -51,3 +51,6 @@ class MetaTask:
         Return the **processed** meta_info
         """
         return self.meta_info
+
+    def __repr__(self):
+        return f"MetaTask(task={self.task}, meta_info={self.meta_info})"

qlib/rl/contrib/naive_config_parser.py

@@ -7,8 +7,7 @@ import shutil
 import sys
 import tempfile
 from importlib import import_module
-
-import yaml
+from ruamel.yaml import YAML
 
 
 DELETE_KEY = "_delete_"
@@ -57,7 +56,8 @@ def parse_backtest_config(path: str) -> dict:
                 del sys.modules[tmp_module_name]
             else:
                 with open(tmp_config_file.name) as input_stream:
-                    config = yaml.safe_load(input_stream)
+                    yaml = YAML(typ="safe", pure=True)
+                    config = yaml.load(input_stream)
 
     if "_base_" in config:
         base_file_name = config.pop("_base_")

qlib/rl/contrib/train_onpolicy.py

@@ -8,12 +8,12 @@ import random
 import sys
 import warnings
 from pathlib import Path
+from ruamel.yaml import YAML
 from typing import cast, List, Optional
 
 import numpy as np
 import pandas as pd
 import torch
-import yaml
 from qlib.backtest import Order
 from qlib.backtest.decision import OrderDir
 from qlib.constant import ONE_MIN
@@ -263,6 +263,7 @@ if __name__ == "__main__":
     args = parser.parse_args()
 
     with open(args.config_path, "r") as input_stream:
-        config = yaml.safe_load(input_stream)
+        yaml = YAML(typ="safe", pure=True)
+        config = yaml.load(input_stream)
 
     main(config, run_training=not args.no_training, run_backtest=args.run_backtest)

qlib/utils/__init__.py

@@ -10,7 +10,6 @@ import os
 import re
 import copy
 import json
-import yaml
 import redis
 import bisect
 import struct
@@ -25,6 +24,7 @@ import pandas as pd
 from pathlib import Path
 from typing import List, Union, Optional, Callable
 from packaging import version
+from ruamel.yaml import YAML
 from .file import (
     get_or_create_path,
     save_multiple_parts_file,
@@ -244,12 +244,13 @@ def parse_config(config):
     if not isinstance(config, str):
         return config
     # Check whether config is file
+    yaml = YAML(typ="safe", pure=True)
     if os.path.exists(config):
         with open(config, "r") as f:
-            return yaml.safe_load(f)
+            return yaml.load(f)
     # Check whether the str can be parsed
     try:
-        return yaml.safe_load(config)
+        return yaml.load(config)
     except BaseException as base_exp:
         raise ValueError("cannot parse config!") from base_exp
 
@@ -799,6 +800,7 @@ def fill_placeholder(config: dict, config_extend: dict):
                     )
         return value
 
+    item_keys = None
     while top < tail:
         now_item = item_queue[top]
         top += 1

qlib/utils/index_data.py

@@ -44,7 +44,7 @@ def concat(data_list: Union[SingleData], axis=0) -> MultiData:
         all_index_map = dict(zip(all_index, range(len(all_index))))
 
         # concat all
-        tmp_data = np.full((len(all_index), len(data_list)), np.NaN)
+        tmp_data = np.full((len(all_index), len(data_list)), np.nan)
         for data_id, index_data in enumerate(data_list):
             assert isinstance(index_data, SingleData)
             now_data_map = [all_index_map[index] for index in index_data.index]
@@ -64,7 +64,7 @@ def sum_by_index(data_list: Union[SingleData], new_index: list, fill_value=0) ->
     new_index : list
         the new_index of new SingleData.
     fill_value : float
-        fill the missing values or replace np.NaN.
+        fill the missing values or replace np.nan.
 
     Returns
     -------
@@ -108,6 +108,12 @@ class Index:
             self.index_map = self.idx_list = np.arange(idx_list)
             self._is_sorted = True
         else:
+            # Check if all elements in idx_list are of the same type
+            if not all(isinstance(x, type(idx_list[0])) for x in idx_list):
+                raise TypeError("All elements in idx_list must be of the same type")
+            # Check if all elements in idx_list are of the same datetime64 precision
+            if isinstance(idx_list[0], np.datetime64) and not all(x.dtype == idx_list[0].dtype for x in idx_list):
+                raise TypeError("All elements in idx_list must be of the same datetime64 precision")
             self.idx_list = np.array(idx_list)
             # NOTE: only the first appearance is indexed
             self.index_map = dict(zip(self.idx_list, range(len(self))))
@@ -131,7 +137,12 @@ class Index:
         if self.idx_list.dtype.type is np.datetime64:
             if isinstance(item, pd.Timestamp):
                 # This happens often when creating index based on pandas.DatetimeIndex and query with pd.Timestamp
-                return item.to_numpy()
+                return item.to_numpy().astype(self.idx_list.dtype)
+            elif isinstance(item, np.datetime64):
+                # This happens often when creating index based on np.datetime64 and query with another precision
+                return item.astype(self.idx_list.dtype)
+            # NOTE: It is hard to consider every case at first.
+            # We just try to cover part of cases to make it more user-friendly
         return item
 
     def index(self, item) -> int:
@@ -433,7 +444,7 @@ class IndexData(metaclass=index_data_ops_creator):
         return self.__class__(~self.data.astype(bool), *self.indices)
 
     def abs(self):
-        """get the abs of data except np.NaN."""
+        """get the abs of data except np.nan."""
         tmp_data = np.absolute(self.data)
         return self.__class__(tmp_data, *self.indices)
 
@@ -555,8 +566,8 @@ class SingleData(IndexData):
                 f"The indexes of self and other do not meet the requirements of the four arithmetic operations"
             )
 
-    def reindex(self, index: Index, fill_value=np.NaN) -> SingleData:
-        """reindex data and fill the missing value with np.NaN.
+    def reindex(self, index: Index, fill_value=np.nan) -> SingleData:
+        """reindex data and fill the missing value with np.nan.
 
         Parameters
         ----------
@@ -604,7 +615,7 @@ class SingleData(IndexData):
         return pd.Series(self.data, index=self.index)
 
     def __repr__(self) -> str:
-        return str(pd.Series(self.data, index=self.index))
+        return str(pd.Series(self.data, index=self.index.tolist()))
 
 
 class MultiData(IndexData):
@@ -640,4 +651,4 @@ class MultiData(IndexData):
             )
 
     def __repr__(self) -> str:
-        return str(pd.DataFrame(self.data, index=self.index, columns=self.columns))
+        return str(pd.DataFrame(self.data, index=self.index.tolist(), columns=self.columns.tolist()))

qlib/utils/mod.py

@@ -161,7 +161,13 @@ def init_instance_by_config(
             # path like 'file:///<path to pickle file>/obj.pkl'
             pr = urlparse(config)
             if pr.scheme == "file":
-                pr_path = os.path.join(pr.netloc, pr.path) if bool(pr.path) else pr.netloc
+
+                # To enable relative path like file://data/a/b/c.pkl.  pr.netloc will be data
+                path = pr.path
+                if pr.netloc != "":
+                    path = path.lstrip("/")
+
+                pr_path = os.path.join(pr.netloc, path) if bool(pr.path) else pr.netloc
                 with open(os.path.normpath(pr_path), "rb") as f:
                     return pickle.load(f)
         else:

qlib/utils/paral.py

@@ -1,6 +1,7 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
 
+import threading
 from functools import partial
 from threading import Thread
 from typing import Callable, Text, Union
@@ -9,7 +10,7 @@ from joblib import Parallel, delayed
 from joblib._parallel_backends import MultiprocessingBackend
 import pandas as pd
 
-from queue import Queue
+from queue import Empty, Queue
 import concurrent
 
 from qlib.config import C, QlibConfig
@@ -85,7 +86,17 @@ class AsyncCaller:
 
     def run(self):
         while True:
-            data = self._q.get()
+            # NOTE:
+            # atexit will only trigger when all the threads ended. So it may results in deadlock.
+            # So the child-threading should actively watch the status of main threading to stop itself.
+            main_thread = threading.main_thread()
+            if not main_thread.is_alive():
+                break
+            try:
+                data = self._q.get(timeout=1)
+            except Empty:
+                # NOTE: avoid deadlock. make checking main thread possible
+                continue
             if data == self.STOP_MARK:
                 break
             data()

qlib/workflow/cli.py

@@ -1,18 +1,20 @@
 #  Copyright (c) Microsoft Corporation.
 #  Licensed under the MIT License.
 import logging
-import sys
 import os
 from pathlib import Path
+import sys
+
+import fire
+from jinja2 import Template, meta
+from ruamel.yaml import YAML
 
 import qlib
-import fire
-import ruamel.yaml as yaml
 from qlib.config import C
-from qlib.model.trainer import task_train
-from qlib.utils.data import update_config
 from qlib.log import get_module_logger
+from qlib.model.trainer import task_train
 from qlib.utils import set_log_with_config
+from qlib.utils.data import update_config
 
 set_log_with_config(C.logging_config)
 logger = get_module_logger("qrun", logging.INFO)
@@ -47,6 +49,39 @@ def sys_config(config, config_path):
         sys.path.append(str(Path(config_path).parent.resolve().absolute() / p))
 
 
+def render_template(config_path: str) -> str:
+    """
+    render the template based on the environment
+
+    Parameters
+    ----------
+    config_path : str
+        configuration path
+
+    Returns
+    -------
+    str
+        the rendered content
+    """
+    with open(config_path, "r") as f:
+        config = f.read()
+    # Set up the Jinja2 environment
+    template = Template(config)
+
+    # Parse the template to find undeclared variables
+    env = template.environment
+    parsed_content = env.parse(config)
+    variables = meta.find_undeclared_variables(parsed_content)
+
+    # Get context from os.environ according to the variables
+    context = {var: os.getenv(var, "") for var in variables if var in os.environ}
+    logger.info(f"Render the template with the context: {context}")
+
+    # Render the template with the context
+    rendered_content = template.render(context)
+    return rendered_content
+
+
 # workflow handler function
 def workflow(config_path, experiment_name="workflow", uri_folder="mlruns"):
     """
@@ -67,8 +102,10 @@ def workflow(config_path, experiment_name="workflow", uri_folder="mlruns"):
         market: csi300
 
     """
-    with open(config_path) as fp:
-        config = yaml.safe_load(fp)
+    # Render the template
+    rendered_yaml = render_template(config_path)
+    yaml = YAML(typ="safe", pure=True)
+    config = yaml.load(rendered_yaml)
 
     base_config_path = config.get("BASE_CONFIG_PATH", None)
     if base_config_path:
@@ -90,7 +127,8 @@ def workflow(config_path, experiment_name="workflow", uri_folder="mlruns"):
                 raise FileNotFoundError(f"Can't find the BASE_CONFIG file: {base_config_path}")
 
         with open(path) as fp:
-            base_config = yaml.safe_load(fp)
+            yaml = YAML(typ="safe", pure=True)
+            base_config = yaml.load(fp)
         logger.info(f"Load BASE_CONFIG_PATH succeed: {path.resolve()}")
         config = update_config(base_config, config)
 

qlib/workflow/expm.py

@@ -8,6 +8,7 @@ from mlflow.exceptions import MlflowException, RESOURCE_ALREADY_EXISTS, ErrorCod
 from mlflow.entities import ViewType
 import os
 from typing import Optional, Text
+from pathlib import Path
 
 from .exp import MLflowExperiment, Experiment
 from ..config import C
@@ -233,7 +234,7 @@ class ExpManager:
             # So we supported it in the interface wrapper
             pr = urlparse(self.uri)
             if pr.scheme == "file":
-                with FileLock(os.path.join(pr.netloc, pr.path, "filelock")):  # pylint: disable=E0110
+                with FileLock(Path(os.path.join(pr.netloc, pr.path.lstrip("/"), "filelock"))):  # pylint: disable=E0110
                     return self.create_exp(experiment_name), True
             # NOTE: for other schemes like http, we double check to avoid create exp conflicts
             try:
@@ -421,7 +422,11 @@ class MLflowExpManager(ExpManager):
 
     def list_experiments(self):
         # retrieve all the existing experiments
-        exps = self.client.list_experiments(view_type=ViewType.ACTIVE_ONLY)
+        mlflow_version = int(mlflow.__version__.split(".", maxsplit=1)[0])
+        if mlflow_version >= 2:
+            exps = self.client.search_experiments(view_type=ViewType.ACTIVE_ONLY)
+        else:
+            exps = self.client.list_experiments(view_type=ViewType.ACTIVE_ONLY)  # pylint: disable=E1101
         experiments = dict()
         for exp in exps:
             experiment = MLflowExperiment(exp.experiment_id, exp.name, self.uri)

qlib/workflow/recorder.py

@@ -9,6 +9,7 @@ import shutil
 import pickle
 import tempfile
 import subprocess
+import platform
 from pathlib import Path
 from datetime import datetime
 
@@ -316,7 +317,10 @@ class MLflowRecorder(Recorder):
         This function will return the directory path of this recorder.
         """
         if self.artifact_uri is not None:
-            local_dir_path = Path(self.artifact_uri.lstrip("file:")) / ".."
+            if platform.system() == "Windows":
+                local_dir_path = Path(self.artifact_uri.lstrip("file:").lstrip("/")).parent
+            else:
+                local_dir_path = Path(self.artifact_uri.lstrip("file:")).parent
             local_dir_path = str(local_dir_path.resolve())
             if os.path.isdir(local_dir_path):
                 return local_dir_path

scripts/data_collector/br_index/requirements.txt

@@ -28,7 +28,7 @@ termcolor==1.1.0
 tqdm==4.63.0
 trio==0.20.0
 trio-websocket==0.9.2
-urllib3==1.26.8
+urllib3==1.26.19
 wget==3.2
 wsproto==1.1.0
 yahooquery==2.2.15

setup.py

@@ -1,9 +1,6 @@
-# Copyright (c) Microsoft Corporation.
-# Licensed under the MIT License.
-import os
+from setuptools import setup, Extension
 import numpy
-
-from setuptools import find_packages, setup, Extension
+import os
 
 
 def read(rel_path: str) -> str:
@@ -20,185 +17,25 @@ def get_version(rel_path: str) -> str:
     raise RuntimeError("Unable to find version string.")
 
 
-# Package meta-data.
-NAME = "pyqlib"
-DESCRIPTION = "A Quantitative-research Platform"
-REQUIRES_PYTHON = ">=3.5.0"
+NUMPY_INCLUDE = numpy.get_include()
 
 VERSION = get_version("qlib/__init__.py")
 
-# Detect Cython
-try:
-    import Cython
 
-    ver = Cython.__version__
-    _CYTHON_INSTALLED = ver >= "0.28"
-except ImportError:
-    _CYTHON_INSTALLED = False
-
-if not _CYTHON_INSTALLED:
-    print("Required Cython version >= 0.28 is not detected!")
-    print('Please run "pip install --upgrade cython" first.')
-    exit(-1)
-
-# What packages are required for this module to be executed?
-# `estimator` may depend on other packages. In order to reduce dependencies, it is not written here.
-REQUIRED = [
-    "numpy>=1.12.0, <1.24",
-    "pandas>=0.25.1",
-    "scipy>=1.7.3",
-    "requests>=2.18.0",
-    "sacred>=0.7.4",
-    "python-socketio",
-    "redis>=3.0.1",
-    "python-redis-lock>=3.3.1",
-    "schedule>=0.6.0",
-    "cvxpy>=1.0.21",
-    "hyperopt==0.1.2",
-    "fire>=0.3.1",
-    "statsmodels",
-    "xlrd>=1.0.0",
-    "plotly>=4.12.0",
-    "matplotlib>=3.3",
-    "tables>=3.6.1",
-    "pyyaml>=5.3.1",
-    # To ensure stable operation of the experiment manager, we have limited the version of mlflow,
-    # and we need to verify whether version 2.0 of mlflow can serve qlib properly.
-    "mlflow>=1.12.1, <=1.30.0",
-    # mlflow 1.30.0 requires packaging<22, so we limit the packaging version, otherwise the CI will fail.
-    "packaging<22",
-    "tqdm",
-    "loguru",
-    "lightgbm>=3.3.0",
-    "tornado",
-    "joblib>=0.17.0",
-    # With the upgrading of ruamel.yaml to 0.18, the safe_load method was deprecated,
-    # which would cause qlib.workflow.cli to not work properly,
-    # and no good replacement has been found, so the version of ruamel.yaml has been restricted for now.
-    # Refs: https://pypi.org/project/ruamel.yaml/
-    "ruamel.yaml<=0.17.36",
-    "pymongo==3.7.2",  # For task management
-    "scikit-learn>=0.22",
-    "dill",
-    "dataclasses;python_version<'3.7'",
-    "filelock",
-    "jinja2",
-    "gym",
-    # Installing the latest version of protobuf for python versions below 3.8 will cause unit tests to fail.
-    "protobuf<=3.20.1;python_version<='3.8'",
-    "cryptography",
-]
-
-# Numpy include
-NUMPY_INCLUDE = numpy.get_include()
-
-here = os.path.abspath(os.path.dirname(__file__))
-
-with open(os.path.join(here, "README.md"), encoding="utf-8") as f:
-    long_description = f.read()
-
-
-# Cython Extensions
-extensions = [
-    Extension(
-        "qlib.data._libs.rolling",
-        ["qlib/data/_libs/rolling.pyx"],
-        language="c++",
-        include_dirs=[NUMPY_INCLUDE],
-    ),
-    Extension(
-        "qlib.data._libs.expanding",
-        ["qlib/data/_libs/expanding.pyx"],
-        language="c++",
-        include_dirs=[NUMPY_INCLUDE],
-    ),
-]
-
-# Where the magic happens:
 setup(
-    name=NAME,
     version=VERSION,
-    license="MIT Licence",
-    url="https://github.com/microsoft/qlib",
-    description=DESCRIPTION,
-    long_description=long_description,
-    long_description_content_type="text/markdown",
-    python_requires=REQUIRES_PYTHON,
-    packages=find_packages(exclude=("tests",)),
-    # if your package is a single module, use this instead of 'packages':
-    # py_modules=['qlib'],
-    entry_points={
-        # 'console_scripts': ['mycli=mymodule:cli'],
-        "console_scripts": [
-            "qrun=qlib.workflow.cli:run",
-        ],
-    },
-    ext_modules=extensions,
-    install_requires=REQUIRED,
-    extras_require={
-        "dev": [
-            "coverage",
-            "pytest>=3",
-            "sphinx",
-            "sphinx_rtd_theme",
-            "pre-commit",
-            # CI dependencies
-            "wheel",
-            "setuptools",
-            "black",
-            # Version 3.0 of pylint had problems with the build process, so we limited the version of pylint.
-            "pylint<=2.17.6",
-            # Using the latest versions(0.981 and 0.982) of mypy,
-            # the error "multiprocessing.Value()" is detected in the file "qlib/rl/utils/data_queue.py",
-            # If this is fixed in a subsequent version of mypy, then we will revert to the latest version of mypy.
-            # References: https://github.com/python/typeshed/issues/8799
-            "mypy<0.981",
-            "flake8",
-            "nbqa",
-            "jupyter",
-            "nbconvert",
-            # The 5.0.0 version of importlib-metadata removed the deprecated endpoint,
-            # which prevented flake8 from working properly, so we restricted the version of importlib-metadata.
-            # To help ensure the dependencies of flake8 https://github.com/python/importlib_metadata/issues/406
-            "importlib-metadata<5.0.0",
-            "readthedocs_sphinx_ext",
-            "cmake",
-            "lxml",
-            "baostock",
-            "yahooquery",
-            # 2024-05-30 scs has released a new version: 3.2.4.post2,
-            # this version, causes qlib installation to fail, so we've limited the scs version a bit for now.
-            "scs<=3.2.4",
-            "beautifulsoup4",
-            # In version 0.4.11 of tianshou, the code:
-            # logits, hidden = self.actor(batch.obs, state=state, info=batch.info)
-            # was changed in PR787,
-            # which causes pytest errors(AttributeError: 'dict' object has no attribute 'info') in CI,
-            # so we restricted the version of tianshou.
-            # References:
-            # https://github.com/thu-ml/tianshou/releases
-            "tianshou<=0.4.10",
-            "gym>=0.24",  # If you do not put gym at the end, gym will degrade causing pytest results to fail.
-        ],
-        "rl": [
-            "tianshou<=0.4.10",
-            "torch",
-        ],
-    },
-    include_package_data=True,
-    classifiers=[
-        # Trove classifiers
-        # Full list: https://pypi.python.org/pypi?%3Aaction=list_classifiers
-        # 'License :: OSI Approved :: MIT License',
-        "Operating System :: POSIX :: Linux",
-        "Operating System :: Microsoft :: Windows",
-        "Operating System :: MacOS",
-        "License :: OSI Approved :: MIT License",
-        "Development Status :: 3 - Alpha",
-        "Programming Language :: Python",
-        "Programming Language :: Python :: 3",
-        "Programming Language :: Python :: 3.7",
-        "Programming Language :: Python :: 3.8",
-        "Programming Language :: Python :: 3.9",
+    ext_modules=[
+        Extension(
+            "qlib.data._libs.rolling",
+            ["qlib/data/_libs/rolling.pyx"],
+            language="c++",
+            include_dirs=[NUMPY_INCLUDE],
+        ),
+        Extension(
+            "qlib.data._libs.expanding",
+            ["qlib/data/_libs/expanding.pyx"],
+            language="c++",
+            include_dirs=[NUMPY_INCLUDE],
+        ),
     ],
 )

tests/data_mid_layer_tests/test_dataloader.py

@@ -0,0 +1,81 @@
+# TODO:
+# dump alpha 360 to dataframe and merge it with Alpha158
+
+import sys
+import unittest
+import qlib
+from pathlib import Path
+
+sys.path.append(str(Path(__file__).resolve().parent))
+from qlib.data.dataset.loader import NestedDataLoader, QlibDataLoader
+from qlib.data.dataset.handler import DataHandlerLP
+from qlib.contrib.data.loader import Alpha158DL, Alpha360DL
+from qlib.data import D
+
+
+class TestDataLoader(unittest.TestCase):
+
+    def test_nested_data_loader(self):
+        qlib.init(kernels=1)
+        nd = NestedDataLoader(
+            dataloader_l=[
+                {
+                    "class": "qlib.contrib.data.loader.Alpha158DL",
+                },
+                {
+                    "class": "qlib.contrib.data.loader.Alpha360DL",
+                    "kwargs": {"config": {"label": (["Ref($close, -2)/Ref($close, -1) - 1"], ["LABEL0"])}},
+                },
+            ]
+        )
+        # Of course you can use StaticDataLoader
+
+        dataset = nd.load(start_time="2020-01-01", end_time="2020-01-31")
+
+        assert dataset is not None
+
+        columns = dataset.columns.tolist()
+        columns_list = [tup[1] for tup in columns]
+
+        for col in Alpha158DL.get_feature_config()[1]:
+            assert col in columns_list
+
+        for col in Alpha360DL.get_feature_config()[1]:
+            assert col in columns_list
+
+        assert "LABEL0" in columns_list
+
+        # Then you can use it wth DataHandler;
+        # NOTE: please note that the data processors are missing!!!  You should add based on your requirements
+
+        """
+        dataset.to_pickle("test_df.pkl")
+        nested_data_loader = NestedDataLoader(
+            dataloader_l=[
+                {
+                    "class": "qlib.contrib.data.loader.Alpha158DL",
+                    "kwargs": {"config": {"label": (["Ref($close, -2)/Ref($close, -1) - 1"], ["LABEL0"])}},
+                },
+                {
+                    "class": "qlib.contrib.data.loader.Alpha360DL",
+                },
+                {
+                    "class": "qlib.data.dataset.loader.StaticDataLoader",
+                    "kwargs": {"config": "test_df.pkl"},
+                },
+            ]
+        )
+        data_handler_config = {
+            "start_time": "2008-01-01",
+            "end_time": "2020-08-01",
+            "instruments": "csi300",
+            "data_loader": nested_data_loader,
+        }
+        data_handler = DataHandlerLP(**data_handler_config)
+        data = data_handler.fetch()
+        print(data)
+        """
+
+
+if __name__ == "__main__":
+    unittest.main()

tests/dependency_tests/test_mlflow.py

@@ -1,6 +1,7 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
 import unittest
+import platform
 import mlflow
 import time
 from pathlib import Path
@@ -26,7 +27,10 @@ class MLflowTest(unittest.TestCase):
             _ = mlflow.tracking.MlflowClient(tracking_uri=str(self.TMP_PATH))
         end = time.time()
         elapsed = end - start
-        self.assertLess(elapsed, 1e-2)  # it can be done in less than 10ms
+        if platform.system() == "Linux":
+            self.assertLess(elapsed, 1e-2)  # it can be done in less than 10ms
+        else:
+            self.assertLess(elapsed, 2e-2)
         print(elapsed)
 
 

tests/misc/test_index_data.py

@@ -70,7 +70,7 @@ class IndexDataTest(unittest.TestCase):
         print(sd.loc[:"c"])
 
     def test_corner_cases(self):
-        sd = idd.MultiData([[1, 2], [3, np.NaN]], index=["foo", "bar"], columns=["f", "g"])
+        sd = idd.MultiData([[1, 2], [3, np.nan]], index=["foo", "bar"], columns=["f", "g"])
         print(sd)
 
         self.assertTrue(np.isnan(sd.loc["bar", "g"]))
@@ -94,6 +94,24 @@ class IndexDataTest(unittest.TestCase):
         print(sd)
         self.assertTrue(sd.iloc[0] == 2)
 
+        # test different precisions of time data
+        timeindex = [
+            np.datetime64("2024-06-22T00:00:00.000000000"),
+            np.datetime64("2024-06-21T00:00:00.000000000"),
+            np.datetime64("2024-06-20T00:00:00.000000000"),
+        ]
+        sd = idd.SingleData([1, 2, 3], index=timeindex)
+        self.assertTrue(
+            sd.index.index(np.datetime64("2024-06-21T00:00:00.000000000"))
+            == sd.index.index(np.datetime64("2024-06-21T00:00:00"))
+        )
+        self.assertTrue(sd.index.index(pd.Timestamp("2024-06-21 00:00")) == 1)
+
+        # Bad case: the input is not aligned
+        timeindex[1] = (np.datetime64("2024-06-21T00:00:00.00"),)
+        with self.assertRaises(TypeError):
+            sd = idd.SingleData([1, 2, 3], index=timeindex)
+
     def test_ops(self):
         sd1 = idd.SingleData([1, 2, 3, 4], index=["foo", "bar", "f", "g"])
         sd2 = idd.SingleData([1, 2, 3, 4], index=["foo", "bar", "f", "g"])

tests/model/test_general_nn.py

@@ -0,0 +1,80 @@
+import unittest
+from qlib.tests import TestAutoData
+
+
+class TestNN(TestAutoData):
+    def test_both_dataset(self):
+        try:
+            from qlib.contrib.model.pytorch_general_nn import GeneralPTNN
+            from qlib.data.dataset import DatasetH, TSDatasetH
+            from qlib.data.dataset.handler import DataHandlerLP
+        except ImportError:
+            print("Import error.")
+            return
+
+        data_handler_config = {
+            "start_time": "2008-01-01",
+            "end_time": "2020-08-01",
+            "instruments": "csi300",
+            "data_loader": {
+                "class": "QlibDataLoader",  # Assuming QlibDataLoader is a string reference to the class
+                "kwargs": {
+                    "config": {
+                        "feature": [["$high", "$close", "$low"], ["H", "C", "L"]],
+                        "label": [["Ref($close, -2)/Ref($close, -1) - 1"], ["LABEL0"]],
+                    },
+                    "freq": "day",
+                },
+            },
+            # TODO: processors
+            "learn_processors": [
+                {
+                    "class": "DropnaLabel",
+                },
+                {"class": "CSZScoreNorm", "kwargs": {"fields_group": "label"}},
+            ],
+        }
+        segments = {
+            "train": ["2008-01-01", "2014-12-31"],
+            "valid": ["2015-01-01", "2016-12-31"],
+            "test": ["2017-01-01", "2020-08-01"],
+        }
+        data_handler = DataHandlerLP(**data_handler_config)
+
+        # time-series dataset
+        tsds = TSDatasetH(handler=data_handler, segments=segments)
+
+        # tabular dataset
+        tbds = DatasetH(handler=data_handler, segments=segments)
+
+        model_l = [
+            GeneralPTNN(
+                n_epochs=2,
+                batch_size=32,
+                n_jobs=0,
+                pt_model_uri="qlib.contrib.model.pytorch_gru_ts.GRUModel",
+                pt_model_kwargs={
+                    "d_feat": 3,
+                    "hidden_size": 8,
+                    "num_layers": 1,
+                    "dropout": 0.0,
+                },
+            ),
+            GeneralPTNN(
+                n_epochs=2,
+                batch_size=32,
+                n_jobs=0,
+                pt_model_uri="qlib.contrib.model.pytorch_nn.Net",  # it is a MLP
+                pt_model_kwargs={
+                    "input_dim": 3,
+                },
+            ),
+        ]
+
+        for ds, model in list(zip((tsds, tbds), model_l)):
+            model.fit(ds)  # It works
+            model.predict(ds)  # It works
+
+
+if __name__ == "__main__":
+    unittest.main()

tests/rl/test_logger.py

@@ -1,8 +1,8 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
-
 from random import randint, choice
 from pathlib import Path
+import logging
 
 import re
 from typing import Any, Tuple
@@ -69,6 +69,10 @@ class AnyPolicy(BasePolicy):
 
 def test_simple_env_logger(caplog):
     set_log_with_config(C.logging_config)
+    # In order for caplog to capture log messages, we configure it here:
+    # allow logs from the qlib logger to be passed to the parent logger.
+    C.logging_config["loggers"]["qlib"]["propagate"] = True
+    logging.config.dictConfig(C.logging_config)
     for venv_cls_name in ["dummy", "shmem", "subproc"]:
         writer = ConsoleWriter()
         csv_writer = CsvWriter(Path(__file__).parent / ".output")
@@ -80,13 +84,12 @@ def test_simple_env_logger(caplog):
         output_file = pd.read_csv(Path(__file__).parent / ".output" / "result.csv")
         assert output_file.columns.tolist() == ["reward", "a", "c"]
         assert len(output_file) >= 30
-
     line_counter = 0
     for line in caplog.text.splitlines():
         line = line.strip()
         if line:
             line_counter += 1
-            assert re.match(r".*reward .*  a .* \((4|5|6)\.\d+\)  c .* \((14|15|16)\.\d+\)", line)
+            assert re.match(r".*reward .* {2}a .* \(([456])\.\d+\) {2}c .* \((14|15|16)\.\d+\)", line)
     assert line_counter >= 3
 
 
@@ -137,15 +140,17 @@ class RandomFivePolicy(BasePolicy):
 
 def test_logger_with_env_wrapper():
     with DataQueue(list(range(20)), shuffle=False) as data_iterator:
-        env_wrapper_factory = lambda: EnvWrapper(
-            SimpleSimulator,
-            DummyStateInterpreter(),
-            DummyActionInterpreter(),
-            data_iterator,
-            logger=LogCollector(LogLevel.DEBUG),
-        )
 
-        # loglevel can be debug here because metrics can all dump into csv
+        def env_wrapper_factory():
+            return EnvWrapper(
+                SimpleSimulator,
+                DummyStateInterpreter(),
+                DummyActionInterpreter(),
+                data_iterator,
+                logger=LogCollector(LogLevel.DEBUG),
+            )
+
+        # loglevel can be debugged here because metrics can all dump into csv
         # otherwise, csv writer might crash
         csv_writer = CsvWriter(Path(__file__).parent / ".output", loglevel=LogLevel.DEBUG)
         venv = vectorize_env(env_wrapper_factory, "shmem", 4, csv_writer)
@@ -155,7 +160,7 @@ def test_logger_with_env_wrapper():
 
     output_df = pd.read_csv(Path(__file__).parent / ".output" / "result.csv")
     assert len(output_df) == 20
-    # obs has a increasing trend
+    # obs has an increasing trend
     assert output_df["obs"].to_numpy()[:10].sum() < output_df["obs"].to_numpy()[10:].sum()
     assert (output_df["test_a"] == 233).all()
     assert (output_df["test_b"] == 200).all()

tests/test_pit.py

@@ -8,7 +8,6 @@ import shutil
 import unittest
 import pytest
 import pandas as pd
-import baostock as bs
 from pathlib import Path
 
 from qlib.data import D