docs: replace broken RD-Agent demo links in README (#2150)

Co-authored-by: monkeyjack123 <monkeyjack123@users.noreply.github.com>
Co-authored-by: Linlang Lv (iSoftStone Information) <v-llv@microsoft.com>

.commitlintrc.js

@@ -0,0 +1,21 @@
+module.exports = {
+    extends: ["@commitlint/config-conventional"],
+    rules: {
+        // Configuration Format: [level, applicability, value]
+        // level: Error level, usually expressed as a number:
+        //     0 - disable rule
+        //     1 - Warning (does not prevent commits)
+        //     2 - Error (will block the commit)
+        // applicability: the conditions under which the rule applies, commonly used values:
+        //     “always” - always apply the rule
+        //     “never” - never apply the rule
+        // value: the specific value of the rule, e.g. a maximum length of 100.
+        // Refs: https://commitlint.js.org/reference/rules-configuration.html
+      "header-max-length": [2, "always", 100],
+      "type-enum": [
+        2,
+        "always",
+        ["build", "chore", "ci", "docs", "feat", "fix", "perf", "refactor", "revert", "style", "test", "Release-As"]
+      ]
+    }
+  };

.github/PULL_REQUEST_TEMPLATE.md

@@ -1,3 +1,16 @@
+<!--- Thank you for submitting a Pull Request! In order to make our work smoother. -->
+<!--- please make sure your Pull Request meets the following requirements: -->
+<!---   1. Provide a general summary of your changes in the Title above; -->
+<!---   2. Add appropriate prefixes to titles, such as `build:`, `chore:`, `ci:`, `docs:`, `feat:`, `fix:`, `perf:`, `refactor:`, `revert:`, `style:`, `test:`(Ref: https://www.conventionalcommits.org/). -->
+<!--- Category: -->
+<!--- Patch Updates: `fix:` -->
+<!---   Example: fix(auth): correct login validation issue -->
+<!--- minor update (introduces new functionality): `feat` -->
+<!---   Example: feature(parser): add ability to parse arrays -->
+<!--- major update(destructive update): Include BREAKING CHANGE in the commit message footer, or add `! ` in the commit footer to indicate that there is a destructive update. -->
+<!---   Example: feat(auth)! : remove support for old authentication method -->
+<!--- Other updates: `build:`, `chore:`, `ci:`, `docs:`, `perf:`, `refactor:`, `revert:`, `style:`, `test:`. -->
+
 <!--- Provide a general summary of your changes in the Title above -->
 
 ## Description

.github/labeler.yml

@@ -1,6 +0,0 @@
-documentation:
-- 'docs/**/*'
-- '**/*.md'
-
-waiting for triage:
-- any: ['**/*', '!docs/**/*', '!**/*.md']

.github/workflows/labeler.yml

@@ -1,14 +0,0 @@
-name: "Add label automatically"
-on:
-- pull_request_target
-
-jobs:
-  triage:
-    permissions:
-      contents: read
-      pull-requests: write
-    runs-on: ubuntu-latest
-    steps:
-    - uses: actions/labeler@v4
-      with:
-        repo-token: "${{ secrets.GITHUB_TOKEN }}"

.github/workflows/lint_title.yml

@@ -0,0 +1,35 @@
+name: Lint pull request title
+
+on:
+  pull_request:
+    types:
+      - opened
+      - synchronize
+      - reopened
+      - edited
+
+concurrency:
+  cancel-in-progress: true
+  group: ${{ github.workflow }}-${{ github.ref }}
+
+jobs:
+  lint-title:
+    runs-on: ubuntu-latest
+    steps:
+      # This step is necessary because the lint title uses the .commitlintrc.js file in the project root directory.
+      - name: Checkout Repository
+        uses: actions/checkout@v4
+
+      - name: Setup Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version: '16'
+
+      - name: Install commitlint
+        run: npm install --save-dev @commitlint/{config-conventional,cli}
+
+      - name: Validate PR Title with commitlint
+        env:
+          BODY: ${{ github.event.pull_request.title }}
+        run: |
+          echo "$BODY" | npx commitlint --config .commitlintrc.js

.github/workflows/python-publish.yml

@@ -1,65 +0,0 @@
-# This workflows will upload a Python Package using Twine when a release is created
-# For more information see: https://help.github.com/en/actions/language-and-framework-guides/using-python-with-github-actions#publishing-to-package-registries
-
-name: Upload Python Package
-
-on:
-  release:
-    types: [published]
-
-jobs:
-  deploy_with_bdist_wheel:
-    runs-on: ${{ matrix.os }}
-    strategy:
-      matrix:
-        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@v3
-    - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v4
-      with:
-        python-version: ${{ matrix.python-version }}
-    - name: Install dependencies
-      run: |
-        make dev
-    - name: Build wheel on ${{ matrix.os }}
-      run: |
-        make build
-    - name: Upload to PyPi
-      env:
-        TWINE_USERNAME: __token__
-        TWINE_PASSWORD: ${{ secrets.PYPI_TOKEN }}
-      run: |
-        twine check dist/*.whl
-        twine upload dist/*.whl --verbose
-
-  deploy_with_manylinux:
-    runs-on: ubuntu-latest
-    steps:
-    - 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@v0.7.1-manylinux2014_x86_64
-      with:
-        python-versions: 'cp38-cp38 cp39-cp39 cp310-cp310 cp311-cp311 cp312-cp312'
-        build-requirements: 'numpy cython'
-    - name: Install dependencies
-      run: |
-        python -m pip install twine
-    - name: Upload to PyPi
-      env:
-        TWINE_USERNAME: __token__
-        TWINE_PASSWORD: ${{ secrets.PYPI_TOKEN }}
-      run: |
-        twine check dist/pyqlib-*-manylinux*.whl
-        twine upload dist/pyqlib-*-manylinux*.whl --verbose

.github/workflows/release-drafter.yml

@@ -1,22 +0,0 @@
-name: Release Drafter
-
-on:
-  push:
-    # branches to consider in the event; optional, defaults to all
-    branches:
-      - main
-
-permissions:
-  contents: read
-
-jobs:
-  update_release_draft:
-    permissions:
-      contents: write
-      pull-requests: read
-    runs-on: ubuntu-latest
-    steps:
-      # Drafts your next Release notes as Pull Requests are merged into "master"
-      - uses: release-drafter/release-drafter@v5.11.0
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

.github/workflows/release.yml

@@ -0,0 +1,107 @@
+name: Release
+
+on:
+  push:
+    branches:
+      - main
+
+permissions:
+  contents: read
+
+jobs:
+  release:
+    runs-on: ubuntu-latest
+    outputs:
+      release_created: ${{ steps.release_please.outputs.release_created }}
+
+    steps:
+      - name: Release please
+        id: release_please
+        uses: googleapis/release-please-action@v4
+        with:
+          token: ${{ secrets.PAT }}
+          release-type: simple
+
+  deploy_with_manylinux:
+    needs: release
+    permissions:
+      contents: write
+      pull-requests: read
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: actions/checkout@v4
+        if: needs.release.outputs.release_created == 'true'
+        with:
+          fetch-depth: 0
+
+      - name: Set up Python ${{ matrix.python-version }}
+        if: needs.release.outputs.release_created == 'true'
+        uses: actions/setup-python@v4
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Build wheel on Linux
+        if: needs.release.outputs.release_created == 'true'
+        uses: RalfG/python-wheels-manylinux-build@v0.7.1-manylinux2014_x86_64
+        with:
+          python-versions: 'cp38-cp38 cp39-cp39 cp310-cp310 cp311-cp311 cp312-cp312'
+          build-requirements: 'numpy cython'
+
+      - name: Install dependencies
+        if: needs.release.outputs.release_created == 'true'
+        run: |
+          python -m pip install twine
+
+      - name: Upload to PyPi
+        if: needs.release.outputs.release_created == 'true'
+        env:
+          TWINE_USERNAME: __token__
+          TWINE_PASSWORD: ${{ secrets.TESTPYPI }}
+        run: |
+          twine check dist/pyqlib-*-manylinux*.whl
+          twine upload --repository-url https://test.pypi.org/legacy/ dist/pyqlib-*-manylinux*.whl --verbose
+
+  deploy_with_bdist_wheel:
+    needs: release
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        # After testing, the whl files of pyqlib built by macos-14 and macos-15 in python environments of 3.8, 3.9, 3.10, 3.11, 3.12,
+        # the filenames are exactly duplicated, which will result in the duplicated whl files not being able to be uploaded to pypi,
+        # so we chose to just keep the latest macos-latest. macos-latest currently points to macos-15.
+        # Also, macos-13 will stop being supported on 2025-11-14.
+        # Refs: https://github.blog/changelog/2025-07-11-upcoming-changes-to-macos-hosted-runners-macos-latest-migration-and-xcode-support-policy-updates/
+        os: [windows-latest, macos-latest]
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
+
+    steps:
+      - uses: actions/checkout@v4
+        if: needs.release.outputs.release_created == 'true'
+        with:
+          fetch-depth: 0
+
+      - name: Set up Python ${{ matrix.python-version }}
+        if: needs.release.outputs.release_created == 'true'
+        uses: actions/setup-python@v4
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Install dependencies
+        if: needs.release.outputs.release_created == 'true'
+        run: |
+          make dev
+
+      - name: Build wheel on ${{ matrix.os }}
+        if: needs.release.outputs.release_created == 'true'
+        run: |
+          make build
+
+      - name: Upload to PyPi
+        if: needs.release.outputs.release_created == 'true'
+        env:
+          TWINE_USERNAME: __token__
+          TWINE_PASSWORD: ${{ secrets.TESTPYPI }}
+        run: |
+          twine check dist/*.whl
+          twine upload --repository-url https://test.pypi.org/legacy/ dist/*.whl --verbose

.github/workflows/test_qlib_from_pip.yml

@@ -1,5 +1,9 @@
 name: Test qlib from pip
 
+concurrency:
+  cancel-in-progress: true
+  group: ${{ github.workflow }}-${{ github.ref }}
+
 on:
   push:
     branches: [ main ]
@@ -21,7 +25,9 @@ jobs:
 
     steps:
     - name: Test qlib from pip
-      uses: actions/checkout@v3
+      uses: actions/checkout@v4
+      with:
+        fetch-depth: 0
 
     - name: Set up Python ${{ matrix.python-version }}
       uses: actions/setup-python@v4
@@ -32,19 +38,9 @@ jobs:
       run: |
         python -m pip install --upgrade pip
       
-    # Will cancel this step when the next qlib version is released. The current qlib version is: 0.9.6
-    - name: Installing pywinpt for windows
-      if: ${{ matrix.os == 'windows-latest' }}
-      run: |
-        python -m pip install pywinpty --only-binary=:all:
-
-    # # joblib was released on 2025-05-04 with version 1.5.0, in which _backend_args was removed and replaced by _backend_kwargs.
-    # This change caused the application to fail, so the version of joblib is restricted here. 
-    # This restriction will be removed in the next release. The current qlib version is: 0.9.6
     - name: Qlib installation test
       run: |
         python -m pip install pyqlib
-        python -m pip install "joblib<=1.4.2"
 
     - name: Install Lightgbm for MacOS
       if: ${{ matrix.os == 'macos-14' || matrix.os == 'macos-15' }}
@@ -53,12 +49,10 @@ jobs:
         brew install libomp || brew reinstall libomp
         python -m pip install --no-binary=:all: lightgbm
 
-    # When the new version is released it should be changed to:
-    # python -m qlib.cli.data qlib_data --target_dir ~/.qlib/qlib_data/cn_data --region cn
     - name: Downloads dependencies data
       run: |
         cd ..
-        python -m qlib.run.get_data qlib_data --target_dir ~/.qlib/qlib_data/cn_data --region cn
+        python -m qlib.cli.data qlib_data --target_dir ~/.qlib/qlib_data/cn_data --region cn
         cd qlib
 
     - name: Test workflow by config

.github/workflows/test_qlib_from_source.yml

@@ -1,5 +1,9 @@
 name: Test qlib from source
 
+concurrency:
+  cancel-in-progress: true
+  group: ${{ github.workflow }}-${{ github.ref }}
+
 on:
   push:
     branches: [ main ]
@@ -22,7 +26,9 @@ jobs:
 
     steps:
     - name: Test qlib from source
-      uses: actions/checkout@v3
+      uses: actions/checkout@v4
+      with:
+        fetch-depth: 0
 
     - name: Set up Python ${{ matrix.python-version }}
       uses: actions/setup-python@v4
@@ -74,8 +80,11 @@ jobs:
       run: |
         make mypy
     
+    # Due to issues that cannot be automatically fixed when running `nbqa black . -l 120 --check --diff` on Jupyter notebooks,
+    # we reverted to a version of `black` earlier than 26.1.0 before performing the checks.
     - name: Check Qlib ipynb with nbqa
       run: |
+        python -m pip install "black<26.1"
         make nbqa
 
     - name: Test data downloads

.github/workflows/test_qlib_from_source_slow.yml

@@ -1,5 +1,9 @@
 name: Test qlib from source slow
 
+concurrency:
+  cancel-in-progress: true
+  group: ${{ github.workflow }}-${{ github.ref }}
+
 on:
   push:
     branches: [ main ]
@@ -22,7 +26,9 @@ jobs:
 
     steps:
     - name: Test qlib from source slow
-      uses: actions/checkout@v3
+      uses: actions/checkout@v4
+      with:
+        fetch-depth: 0
 
     - name: Set up Python ${{ matrix.python-version }}
       uses: actions/setup-python@v4
@@ -37,8 +43,6 @@ jobs:
       run: |
         python scripts/get_data.py qlib_data --name qlib_data_simple --target_dir ~/.qlib/qlib_data/cn_data --interval 1d --region cn
 
-    # install.sh file contents from: https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh
-    # brew_install.sh file contents from: https://raw.githubusercontent.com/Microsoft/qlib/main/.github/brew_install.sh
     - name: Install Lightgbm for MacOS
       if: ${{ matrix.os == 'macos-14' || matrix.os == 'macos-15' }}
       run: |

.gitignore

@@ -22,6 +22,7 @@ dist/
 qlib/VERSION.txt
 qlib/data/_libs/expanding.cpp
 qlib/data/_libs/rolling.cpp
+qlib/_version.py
 examples/estimator/estimator_example/
 examples/rl/data/
 examples/rl/checkpoints/

Makefile

@@ -74,34 +74,37 @@ prerequisite:
 
 # Install the package in editable mode.
 dependencies:
-	python -m pip install -e .
+	python -m pip install --no-cache-dir -e .
 
 lightgbm:
-	python -m pip install lightgbm --prefer-binary
+	python -m pip install --no-cache-dir lightgbm --prefer-binary
 
 rl:
-	python -m pip install -e .[rl]
+	python -m pip install --no-cache-dir -e .[rl]
 
 develop:
-	python -m pip install -e .[dev]
+	python -m pip install --no-cache-dir -e .[dev]
 
 lint:
-	python -m pip install -e .[lint]
+	python -m pip install --no-cache-dir -e .[lint]
 
 docs:
-	python -m pip install -e .[docs]
+	python -m pip install --no-cache-dir -e .[docs]
 
 package:
-	python -m pip install -e .[package]
+	python -m pip install --no-cache-dir -e .[package]
 
 test:
-	python -m pip install -e .[test]
+	python -m pip install --no-cache-dir -e .[test]
 
 analysis:
-	python -m pip install -e .[analysis]
+	python -m pip install --no-cache-dir -e .[analysis]
+
+client:
+	python -m pip install --no-cache-dir -e .[client]
 
 all:
-	python -m pip install -e .[pywinpty,dev,lint,docs,package,test,analysis,rl]
+	python -m pip install --no-cache-dir -e .[pywinpty,dev,lint,docs,package,test,analysis,rl]
 
 install: prerequisite dependencies
 
@@ -113,7 +116,7 @@ dev: prerequisite all
 
 # Check lint with black.
 black:
-	black . -l 120 --check --diff
+	black . -l 120 --check --diff --exclude qlib/_version.py
 
 # Check code folder with pylint.
 # TODO: These problems we will solve in the future. Important among them are: W0221, W0223, W0237, E1102

README.md

@@ -17,14 +17,13 @@ We are excited to announce the release of **RD-Agent**📢, a powerful tool that
 
 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.
+To learn more, please visit the [RD-Agent repository](https://github.com/microsoft/RD-Agent). We have prepared several public demo videos for you:
 
-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) |
+| Quant Factor Mining | [YouTube](https://www.youtube.com/watch?v=X4DK2QZKaKY&t=6s) | [YouTube](https://www.youtube.com/watch?v=X4DK2QZKaKY&t=6s) |
+| Quant Factor Mining from reports | [YouTube](https://www.youtube.com/watch?v=ECLTXVcSx-c) | [YouTube](https://www.youtube.com/watch?v=ECLTXVcSx-c) |
+| Quant Model Optimization | [YouTube](https://www.youtube.com/watch?v=dm0dWL49Bc0&t=104s) | [YouTube](https://www.youtube.com/watch?v=dm0dWL49Bc0&t=104s) |
 
 - 📃**Paper**: [R&D-Agent-Quant: A Multi-Agent Framework for Data-Centric Factors and Model Joint Optimization](https://arxiv.org/abs/2505.15155)
 - 👾**Code**: https://github.com/microsoft/RD-Agent/
@@ -324,7 +323,7 @@ We recommend users to prepare their own data if they have a high-quality dataset
     ```
 2. Start a new Docker container
     ```bash
-    docker run -it --name <container name> -v <Mounted local directory>:/app qlib_image_stable
+    docker run -it --name <container name> -v <Mounted local directory>:/app pyqlib/qlib_image_stable:stable
     ```
 3. At this point you are in the docker environment and can run the qlib scripts. An example:
     ```bash

docs/advanced/alpha.rst

@@ -42,7 +42,7 @@ Example
 
     .. math::
 
-        DEA = \frac{EMA(DIF, 9)}{CLOSE}
+        DEA = EMA(DIF, 9)
 
 Users can use ``Data Handler`` to build formulaic alphas `MACD` in qlib:
 
@@ -51,7 +51,7 @@ Users can use ``Data Handler`` to build formulaic alphas `MACD` in qlib:
 .. code-block:: python
 
     >> from qlib.data.dataset.loader import QlibDataLoader
-    >> MACD_EXP = '(EMA($close, 12) - EMA($close, 26))/$close - EMA((EMA($close, 12) - EMA($close, 26))/$close, 9)/$close'
+    >> MACD_EXP = '2 * ((EMA($close, 12) - EMA($close, 26))/$close - EMA((EMA($close, 12) - EMA($close, 26))/$close, 9))'
     >> fields = [MACD_EXP] # MACD
     >> names = ['MACD']
     >> labels = ['Ref($close, -2)/Ref($close, -1) - 1'] # label
@@ -66,17 +66,17 @@ Users can use ``Data Handler`` to build formulaic alphas `MACD` in qlib:
                             feature     label
                                MACD     LABEL
     datetime   instrument
-    2010-01-04 SH600000   -0.011547 -0.019672
-               SH600004    0.002745 -0.014721
-               SH600006    0.010133  0.002911
-               SH600008   -0.001113  0.009818
-               SH600009    0.025878 -0.017758
+    2010-01-04 SH600000    0.008781 -0.019672
+               SH600004    0.006699 -0.014721
+               SH600006    0.005714  0.002911
+               SH600008    0.000798  0.009818
+               SH600009    0.017015 -0.017758
     ...                         ...       ...
-    2017-12-29 SZ300124    0.007306 -0.005074
-               SZ300136   -0.013492  0.056352
-               SZ300144   -0.000966  0.011853
-               SZ300251    0.004383  0.021739
-               SZ300315   -0.030557  0.012455
+    2017-12-29 SZ300124    0.015071 -0.005074
+               SZ300136   -0.015466  0.056352
+               SZ300144    0.013082  0.011853
+               SZ300251   -0.001026  0.021739
+               SZ300315   -0.007559  0.012455
 
 Reference
 =========

docs/conf.py

@@ -21,8 +21,7 @@
 import os
 import sys
 
-import pkg_resources
-
+from importlib.metadata import version as ver
 
 # -- General configuration ------------------------------------------------
 
@@ -63,9 +62,9 @@ author = "Microsoft"
 # built documents.
 #
 # The short X.Y version.
-version = pkg_resources.get_distribution("pyqlib").version
+version = ver("pyqlib")
 # The full version, including alpha/beta/rc tags.
-release = pkg_resources.get_distribution("pyqlib").version
+release = version
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.

docs/start/getdata.rst

@@ -129,7 +129,7 @@ For example, it looks quite long and complicated:
 
 
 But using string is not the only way to implement the expression. You can also implement expression by code.
-Here is an exmaple which does the same thing as above examples.
+Here is an example which does the same thing as above examples.
 
 
 .. code-block:: python

docs/start/integration.rst

@@ -71,7 +71,7 @@ The Custom models need to inherit `qlib.model.base.Model <../reference/api.html#
                 )
 
 - Override the `predict` method
-    - The parameters must include the parameter `dataset`, which will be userd to get the test dataset.
+    - The parameters must include the parameter `dataset`, which will be used to get the test dataset.
     - Return the `prediction score`.
     - Please refer to `Model API <../reference/api.html#module-qlib.model.base>`_ for the parameter types of the fit method.
     - Code Example: In the following example, users need to use `LightGBM` to predict the label(such as `preds`) of test data `x_test` and return it.

examples/benchmarks/TFT/tft.py

@@ -19,7 +19,6 @@ from qlib.model.base import ModelFT
 from qlib.data.dataset import DatasetH
 from qlib.data.dataset.handler import DataHandlerLP
 
-
 # To register new datasets, please add them here.
 ALLOW_DATASET = ["Alpha158", "Alpha360"]
 # To register new datasets, please add their configurations here.

examples/benchmarks/TRA/src/dataset.py

@@ -8,7 +8,6 @@ import pandas as pd
 
 from qlib.data.dataset import DatasetH
 
-
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
 

examples/benchmarks_dynamic/DDG-DA/vis_data.py

@@ -1,9 +1,10 @@
-import pickle
 import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
 import seaborn as sns
 
+from qlib.utils.pickle_utils import restricted_pickle_load
+
 sns.set(color_codes=True)
 plt.rcParams["font.sans-serif"] = "SimHei"
 plt.rcParams["axes.unicode_minus"] = False
@@ -18,7 +19,7 @@ from tqdm.auto import tqdm
 
 # +
 with open("./internal_data_s20.pkl", "rb") as f:
-    data = pickle.load(f)
+    data = restricted_pickle_load(f)
 
 data.data_ic_df.columns.names = ["start_date", "end_date"]
 
@@ -52,7 +53,7 @@ pd.DataFrame(meta_m.tn.twm.linear.weight.detach().numpy()).T[0].rolling(5).mean(
 
 # +
 with open("./tasks_s20.pkl", "rb") as f:
-    tasks = pickle.load(f)
+    tasks = restricted_pickle_load(f)
 
 task_df = {}
 for t in tasks:

examples/highfreq/workflow.py

@@ -4,11 +4,11 @@
 import fire
 
 import qlib
-import pickle
 from qlib.constant import REG_CN
 from qlib.config import HIGH_FREQ_CONFIG
 
 from qlib.utils import init_instance_by_config
+from qlib.utils.pickle_utils import restricted_pickle_load
 from qlib.data.dataset.handler import DataHandlerLP
 from qlib.data.ops import Operators
 from qlib.data.data import Cal
@@ -125,10 +125,10 @@ class HighfreqWorkflow:
         del dataset, dataset_backtest
         ##=============reload dataset=============
         with open("dataset.pkl", "rb") as file_dataset:
-            dataset = pickle.load(file_dataset)
+            dataset = restricted_pickle_load(file_dataset)
 
         with open("dataset_backtest.pkl", "rb") as file_dataset_backtest:
-            dataset_backtest = pickle.load(file_dataset_backtest)
+            dataset_backtest = restricted_pickle_load(file_dataset_backtest)
 
         self._prepare_calender_cache()
         ##=============reinit dataset=============

examples/model_interpreter/feature.py

@@ -9,7 +9,6 @@ from qlib.utils import init_instance_by_config
 from qlib.tests.data import GetData
 from qlib.tests.config import CSI300_GBDT_TASK
 
-
 if __name__ == "__main__":
     # use default data
     provider_uri = "~/.qlib/qlib_data/cn_data"  # target_dir

examples/nested_decision_execution/workflow.py

@@ -95,7 +95,6 @@ pos  0.000000
 [1706497:MainThread](2021-12-07 14:08:30,627) INFO - qlib.timer - [log.py:113] - Time cost: 0.014s | waiting `async_log` Done
 """
 
-
 from copy import deepcopy
 import qlib
 import fire

examples/online_srv/update_online_pred.py

@@ -7,6 +7,7 @@ There are two parts including first_train and update_online_pred.
 Firstly, we will finish the training and set the trained models to the `online` models.
 Next, we will finish updating online predictions.
 """
+
 import copy
 import fire
 import qlib

examples/orderbook_data/README.md

@@ -27,7 +27,7 @@ pip install arctic  # NOTE: pip may fail to resolve the right package dependency
 2. Please follow following steps to download example data
 ```bash
 cd examples/orderbook_data/
-gdown https://drive.google.com/uc?id=15nZF7tFT_eKVZAcMFL1qPS4jGyJflH7e  # Proxies may be necessary here.
+gdown https://drive.google.com/uc?id=15FuUqWn2rkCi8uhJYGEQWKakcEqLJNDG  # Proxies may be necessary here.
 python ../../scripts/get_data.py _unzip --file_path highfreq_orderbook_example_data.zip --target_dir .
 ```
 

examples/orderbook_data/create_dataset.py

@@ -6,6 +6,7 @@ NOTE:
 - !!!!!!!!!!!!!!!TODO!!!!!!!!!!!!!!!!!!!:
     - Its structure is not well designed and very ugly, your contribution is welcome to make importing dataset easier
 """
+
 from datetime import date, datetime as dt
 import os
 from pathlib import Path

examples/rl_order_execution/scripts/merge_orders.py

@@ -1,13 +1,15 @@
-import pickle
 import os
 import pandas as pd
 from tqdm import tqdm
 
+from qlib.utils.pickle_utils import restricted_pickle_load
+
 for tag in ["test", "valid"]:
     files = os.listdir(os.path.join("data/orders/", tag))
     dfs = []
     for f in tqdm(files):
-        df = pickle.load(open(os.path.join("data/orders/", tag, f), "rb"))
+        with open(os.path.join("data/orders/", tag, f), "rb") as fr:
+            df = restricted_pickle_load(fr)
         df = df.drop(["$close0"], axis=1)
         dfs.append(df)
 

examples/rolling_process_data/workflow.py

@@ -3,12 +3,12 @@
 
 import qlib
 import fire
-import pickle
 
 from datetime import datetime
 from qlib.constant import REG_CN
 from qlib.data.dataset.handler import DataHandlerLP
 from qlib.utils import init_instance_by_config
+from qlib.utils.pickle_utils import restricted_pickle_load
 from qlib.tests.data import GetData
 
 
@@ -42,7 +42,7 @@ class RollingDataWorkflow:
 
     def _load_pre_handler(self, path):
         with open(path, "rb") as file_dataset:
-            pre_handler = pickle.load(file_dataset)
+            pre_handler = restricted_pickle_load(file_dataset)
         return pre_handler
 
     def rolling_process(self):

examples/workflow_by_code.py

@@ -7,6 +7,7 @@ Qlib provides two kinds of interfaces.
 
 The interface of (1) is `qrun XXX.yaml`.  The interface of (2) is script like this, which nearly does the same thing as `qrun XXX.yaml`
 """
+
 import qlib
 from qlib.constant import REG_CN
 from qlib.utils import init_instance_by_config, flatten_dict
@@ -15,7 +16,6 @@ from qlib.workflow.record_temp import SignalRecord, PortAnaRecord, SigAnaRecord
 from qlib.tests.data import GetData
 from qlib.tests.config import CSI300_BENCH, CSI300_GBDT_TASK
 
-
 if __name__ == "__main__":
     # use default data
     provider_uri = "~/.qlib/qlib_data/cn_data"  # target_dir

pyproject.toml

@@ -1,5 +1,5 @@
 [build-system]
-requires = ["setuptools", "cython", "numpy>=1.24.0"]
+requires = ["setuptools", "setuptools-scm", "cython", "numpy>=1.24.0"]
 build-backend = "setuptools.build_meta"
 
 [project]
@@ -22,11 +22,15 @@ dynamic = ["version"]
 description = "A Quantitative-research Platform"
 requires-python = ">=3.8.0"
 readme = {file = "README.md", content-type = "text/markdown"}
+license = { text = "MIT" }
 
 dependencies = [
   "pyyaml",
   "numpy",
-  "pandas>=0.24",
+  # Since version 1.1.0, pandas supports the ffill and bfill methods.
+  # Since version 2.1.0, pandas has deprecated the method parameter of the fillna method. 
+  # qlib has updated the fillna method in PR 1987 and limited the minimum version of pandas.
+  "pandas>=1.1",
   # I encoutered an Error that the set_uri does not work when downloading artifacts in mlflow 3.1.1;
   # But earlier versions of mlflow does not have this problem.
   # But when I switch to 2.*.* version, another error occurs, which is even more strange...
@@ -49,6 +53,7 @@ dependencies = [
   "nbconvert",
   "pyarrow",
   "pydantic-settings",
+  "setuptools-scm",
 ]
 
 [project.optional-dependencies]
@@ -64,6 +69,7 @@ rl = [
   "torch",
   "numpy<2.0.0",
 ]
+
 lint = [
   "black",
   "pylint",
@@ -96,6 +102,10 @@ analysis = [
   "plotly",
   "statsmodels",
 ]
+client = [
+  "python-socketio<6",
+  "tables",
+]
 
 # In the process of releasing a new version, when checking the manylinux package with twine, an error is reported:
 # InvalidDistribution: Invalid distribution metadata: unrecognized or malformed field 'license-file'
@@ -108,3 +118,8 @@ license-files = []
 
 [project.scripts]
 qrun = "qlib.cli.run:run"
+
+[tool.setuptools_scm]
+local_scheme = "no-local-version"
+version_scheme = "guess-next-dev"
+write_to = "qlib/_version.py"

qlib/__init__.py

@@ -2,15 +2,22 @@
 # Licensed under the MIT License.
 from pathlib import Path
 
-__version__ = "0.9.7"
+from setuptools_scm import get_version
+
+try:
+    from ._version import version as __version__
+except ImportError:
+    __version__ = get_version(root="..", relative_to=__file__)
 __version__bak = __version__  # This version is backup for QlibConfig.reset_qlib_version
-import os
-import re
-from typing import Union
-from ruamel.yaml import YAML
 import logging
+import os
 import platform
+import re
 import subprocess
+from typing import Union
+
+from ruamel.yaml import YAML
+
 from .log import get_module_logger
 
 
@@ -136,7 +143,10 @@ def _mount_nfs_uri(provider_uri, mount_path, auto_mount: bool = False):
                     _command_log = [line for line in _command_log if _remote_uri in line]
                 if len(_command_log) > 0:
                     for _c in _command_log:
-                        _temp_mount = _c.decode("utf-8").split(" ")[2]
+                        if isinstance(_c, str):
+                            _temp_mount = _c.split(" ")[2]
+                        else:
+                            _temp_mount = _c.decode("utf-8").split(" ")[2]
                         _temp_mount = _temp_mount[:-1] if _temp_mount.endswith("/") else _temp_mount
                         if _temp_mount == _mount_path:
                             _is_mount = True

qlib/backtest/backtest.py

@@ -18,7 +18,6 @@ from tqdm.auto import tqdm
 
 from ..utils.time import Freq
 
-
 PORT_METRIC = Dict[str, Tuple[pd.DataFrame, dict]]
 INDICATOR_METRIC = Dict[str, Tuple[pd.DataFrame, Indicator]]
 

qlib/backtest/profit_attribution.py

@@ -281,13 +281,13 @@ def brinson_pa(
 
     stock_group_field = stock_df[group_field].unstack().T
     # FIXME: some attributes of some suspend stock is NAN.
-    stock_group_field = stock_group_field.fillna(method="ffill")
+    stock_group_field = stock_group_field.ffill()
     stock_group_field = stock_group_field.loc[start_date:end_date]
 
     stock_group = get_stock_group(stock_group_field, bench_stock_weight, group_method, group_n)
 
     deal_price_df = stock_df["deal_price"].unstack().T
-    deal_price_df = deal_price_df.fillna(method="ffill")
+    deal_price_df = deal_price_df.ffill()
 
     # NOTE:
     # The return will be slightly different from the of the return in the report.

qlib/cli/data.py

@@ -4,6 +4,5 @@
 import fire
 from qlib.tests.data import GetData
 
-
 if __name__ == "__main__":
     fire.Fire(GetData)

qlib/cli/run.py

@@ -87,7 +87,7 @@ def workflow(config_path, experiment_name="workflow", uri_folder="mlruns"):
     """
     This is a Qlib CLI entrance.
     User can run the whole Quant research workflow defined by a configure file
-    - the code is located here ``qlib/cli/run.py`
+    - the code is located here ``qlib/cli/run.py``
 
     User can specify a base_config file in your workflow.yml file by adding "BASE_CONFIG_PATH".
     Qlib will load the configuration in BASE_CONFIG_PATH first, and the user only needs to update the custom fields

qlib/config.py

@@ -10,6 +10,7 @@ Two modes are supported
 - server
 
 """
+
 from __future__ import annotations
 
 import os

qlib/contrib/data/dataset.py

@@ -11,7 +11,6 @@ from qlib.utils import init_instance_by_config
 
 from qlib.data.dataset import DatasetH
 
-
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
 

qlib/contrib/evaluate.py

@@ -20,7 +20,6 @@ from ..data import D
 from ..config import C
 from ..data.dataset.utils import get_level_index
 
-
 logger = get_module_logger("Evaluate")
 
 

qlib/contrib/meta/__init__.py

@@ -3,5 +3,4 @@
 
 from .data_selection import MetaTaskDS, MetaDatasetDS, MetaModelDS
 
-
 __all__ = ["MetaTaskDS", "MetaDatasetDS", "MetaModelDS"]

qlib/contrib/meta/data_selection/__init__.py

@@ -4,5 +4,4 @@
 from .dataset import MetaDatasetDS, MetaTaskDS
 from .model import MetaModelDS
 
-
 __all__ = ["MetaDatasetDS", "MetaTaskDS", "MetaModelDS"]

qlib/contrib/model/gbdt.py

@@ -51,7 +51,7 @@ class LGBModel(ModelFT, LightGBMFInt):
                     w = reweighter.reweight(df)
                 else:
                     raise ValueError("Unsupported reweighter type.")
-                ds_l.append((lgb.Dataset(x.values, label=y, weight=w), key))
+                ds_l.append((lgb.Dataset(x.values, label=y, weight=w, free_raw_data=False), key))
         return ds_l
 
     def fit(
@@ -109,8 +109,10 @@ class LGBModel(ModelFT, LightGBMFInt):
             verbose level
         """
         # Based on existing model and finetune by train more rounds
-        dtrain, _ = self._prepare_data(dataset, reweighter)  # pylint: disable=W0632
-        if dtrain.empty:
+        ds_l = self._prepare_data(dataset, reweighter)
+        dtrain, _ = ds_l[0]
+
+        if dtrain.construct().num_data() == 0:
             raise ValueError("Empty data from dataset, please check your dataset config.")
         verbose_eval_callback = lgb.log_evaluation(period=verbose_eval)
         self.model = lgb.train(

qlib/contrib/model/pytorch_nn.py

@@ -10,6 +10,7 @@ import os
 import gc
 import numpy as np
 import pandas as pd
+from packaging import version
 from typing import Callable, Optional, Text, Union
 from sklearn.metrics import roc_auc_score, mean_squared_error
 
@@ -148,7 +149,7 @@ class DNNModelPytorch(Model):
         if scheduler == "default":
             # In torch version 2.7.0, the verbose parameter has been removed. Reference Link:
             # https://github.com/pytorch/pytorch/pull/147301/files#diff-036a7470d5307f13c9a6a51c3a65dd014f00ca02f476c545488cd856bea9bcf2L1313
-            if str(torch.__version__).split("+", maxsplit=1)[0] <= "2.6.0":
+            if version.parse(str(torch.__version__).split("+", maxsplit=1)[0]) <= version.parse("2.6.0"):
                 # Reduce learning rate when loss has stopped decrease
                 self.scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(  # pylint: disable=E1123
                     self.train_optimizer,

qlib/contrib/model/pytorch_tabnet.py

@@ -317,7 +317,7 @@ class TabnetModel(Model):
             feature = x_train_values.float().to(self.device)
             label = y_train_values.float().to(self.device)
             priors = 1 - S_mask
-            (vec, sparse_loss) = self.tabnet_model(feature, priors)
+            vec, sparse_loss = self.tabnet_model(feature, priors)
             f = self.tabnet_decoder(vec)
             loss = self.pretrain_loss_fn(label, f, S_mask)
 
@@ -348,7 +348,7 @@ class TabnetModel(Model):
             S_mask = S_mask.to(self.device)
             priors = 1 - S_mask
             with torch.no_grad():
-                (vec, sparse_loss) = self.tabnet_model(feature, priors)
+                vec, sparse_loss = self.tabnet_model(feature, priors)
                 f = self.tabnet_decoder(vec)
 
                 loss = self.pretrain_loss_fn(label, f, S_mask)

qlib/contrib/online/utils.py

@@ -12,6 +12,7 @@ from ...data import D
 from ...config import C
 from ...log import get_module_logger
 from ...utils import get_next_trading_date
+from ...utils.pickle_utils import restricted_pickle_load
 from ...backtest.exchange import Exchange
 
 log = get_module_logger("utils")
@@ -30,7 +31,7 @@ def load_instance(file_path):
     if not file_path.exists():
         raise ValueError("Cannot find file {}".format(file_path))
     with file_path.open("rb") as fr:
-        instance = pickle.load(fr)
+        instance = restricted_pickle_load(fr)
     return instance
 
 

qlib/contrib/ops/high_freq.py

@@ -135,7 +135,7 @@ class FFillNan(ElemOperator):
 
     def _load_internal(self, instrument, start_index, end_index, freq):
         series = self.feature.load(instrument, start_index, end_index, freq)
-        return series.fillna(method="ffill")
+        return series.ffill()
 
 
 class BFillNan(ElemOperator):
@@ -154,7 +154,7 @@ class BFillNan(ElemOperator):
 
     def _load_internal(self, instrument, start_index, end_index, freq):
         series = self.feature.load(instrument, start_index, end_index, freq)
-        return series.fillna(method="bfill")
+        return series.bfill()
 
 
 class Date(ElemOperator):

qlib/contrib/report/analysis_model/__init__.py

@@ -3,5 +3,4 @@
 
 from .analysis_model_performance import model_performance_graph
 
-
 __all__ = ["model_performance_graph"]

qlib/contrib/report/analysis_position/__init__.py

@@ -7,5 +7,4 @@ from .report import report_graph
 from .rank_label import rank_label_graph
 from .risk_analysis import risk_analysis_graph
 
-
 __all__ = ["cumulative_return_graph", "score_ic_graph", "report_graph", "rank_label_graph", "risk_analysis_graph"]

qlib/contrib/report/analysis_position/parse_position.py

@@ -33,7 +33,7 @@ def parse_position(position: dict = None) -> pd.DataFrame:
 
     position_weight_df = get_stock_weight_df(position)
     # If the day does not exist, use the last weight
-    position_weight_df.fillna(method="ffill", inplace=True)
+    position_weight_df.ffill(inplace=True)
 
     previous_data = {"date": None, "code_list": []}
 

qlib/contrib/report/data/ana.py

@@ -12,6 +12,7 @@ Here is an example.
     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

qlib/contrib/report/data/base.py

@@ -7,6 +7,7 @@ Assumptions
 - The analyse each feature individually
 
 """
+
 import pandas as pd
 from qlib.log import TimeInspector
 from qlib.contrib.report.utils import sub_fig_generator

qlib/contrib/rolling/ddgda.py

@@ -14,6 +14,7 @@ from qlib.model.meta.task import MetaTask
 from qlib.model.trainer import TrainerR
 from qlib.typehint import Literal
 from qlib.utils import init_instance_by_config
+from qlib.utils.pickle_utils import restricted_pickle_load
 from qlib.workflow import R
 from qlib.workflow.task.utils import replace_task_handler_with_cache
 
@@ -298,7 +299,7 @@ class DDGDA(Rolling):
         # but their task test segment are not aligned! It worked in my previous experiment.
         # So the misalignment will not affect the effectiveness of the method.
         with self._internal_data_path.open("rb") as f:
-            internal_data = pickle.load(f)
+            internal_data = restricted_pickle_load(f)
 
         md = MetaDatasetDS(exp_name=internal_data, **kwargs)
 
@@ -360,7 +361,7 @@ class DDGDA(Rolling):
         )
 
         with self._internal_data_path.open("rb") as f:
-            internal_data = pickle.load(f)
+            internal_data = restricted_pickle_load(f)
         mds = MetaDatasetDS(exp_name=internal_data, **kwargs)
 
         # 3) meta model make inference and get new qlib task

qlib/contrib/strategy/__init__.py

@@ -16,7 +16,6 @@ from .rule_strategy import (
 
 from .cost_control import SoftTopkStrategy
 
-
 __all__ = [
     "TopkDropoutStrategy",
     "WeightStrategyBase",

qlib/contrib/strategy/cost_control.py

@@ -1,101 +1,117 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
-"""
-This strategy is not well maintained
-"""
-
 
 from .order_generator import OrderGenWInteract
 from .signal_strategy import WeightStrategyBase
-import copy
 
 
 class SoftTopkStrategy(WeightStrategyBase):
     def __init__(
         self,
-        model,
-        dataset,
-        topk,
+        model=None,
+        dataset=None,
+        topk=None,
         order_generator_cls_or_obj=OrderGenWInteract,
         max_sold_weight=1.0,
+        trade_impact_limit=None,
         risk_degree=0.95,
         buy_method="first_fill",
-        trade_exchange=None,
-        level_infra=None,
-        common_infra=None,
         **kwargs,
     ):
         """
+        Refactored SoftTopkStrategy with a budget-constrained rebalancing engine.
+
         Parameters
         ----------
         topk : int
-            top-N stocks to buy
+            The number of top-N stocks to be held in the portfolio.
+        trade_impact_limit : float
+            Maximum weight change for each stock in one trade. If None, fallback to max_sold_weight.
+        max_sold_weight : float
+            Backward-compatible alias for trade_impact_limit. Use 1.0 to effectively disable the limit.
         risk_degree : float
-            position percentage of total value buy_method:
-
-                rank_fill: assign the weight stocks that rank high first(1/topk max)
-                average_fill: assign the weight to the stocks rank high averagely.
+            The target percentage of total value to be invested.
         """
         super(SoftTopkStrategy, self).__init__(
-            model, dataset, order_generator_cls_or_obj, trade_exchange, level_infra, common_infra, **kwargs
+            model=model, dataset=dataset, order_generator_cls_or_obj=order_generator_cls_or_obj, **kwargs
         )
+
         self.topk = topk
-        self.max_sold_weight = max_sold_weight
+        self.trade_impact_limit = trade_impact_limit if trade_impact_limit is not None else max_sold_weight
         self.risk_degree = risk_degree
         self.buy_method = buy_method
 
     def get_risk_degree(self, trade_step=None):
-        """get_risk_degree
-        Return the proportion of your total value you will used in investment.
-        Dynamically risk_degree will result in Market timing
-        """
-        # It will use 95% amount of your total value by default
         return self.risk_degree
 
-    def generate_target_weight_position(self, score, current, trade_start_time, trade_end_time):
+    def generate_target_weight_position(self, score, current, trade_start_time, trade_end_time, **kwargs):
         """
-        Parameters
-        ----------
-        score:
-            pred score for this trade date, pd.Series, index is stock_id, contain 'score' column
-        current:
-            current position, use Position() class
-        trade_date:
-            trade date
-
-            generate target position from score for this date and the current position
-
-            The cache is not considered in the position
+        Generates target position using Proportional Budget Allocation.
+        Ensures deterministic sells and synchronized buys under impact limits.
         """
-        # TODO:
-        # If the current stock list is more than topk(eg. The weights are modified
-        # by risk control), the weight will not be handled correctly.
-        buy_signal_stocks = set(score.sort_values(ascending=False).iloc[: self.topk].index)
-        cur_stock_weight = current.get_stock_weight_dict(only_stock=True)
 
-        if len(cur_stock_weight) == 0:
-            final_stock_weight = {code: 1 / self.topk for code in buy_signal_stocks}
-        else:
-            final_stock_weight = copy.deepcopy(cur_stock_weight)
-            sold_stock_weight = 0.0
-            for stock_id in final_stock_weight:
-                if stock_id not in buy_signal_stocks:
-                    sw = min(self.max_sold_weight, final_stock_weight[stock_id])
-                    sold_stock_weight += sw
-                    final_stock_weight[stock_id] -= sw
-            if self.buy_method == "first_fill":
-                for stock_id in buy_signal_stocks:
-                    add_weight = min(
-                        max(1 / self.topk - final_stock_weight.get(stock_id, 0), 0.0),
-                        sold_stock_weight,
-                    )
-                    final_stock_weight[stock_id] = final_stock_weight.get(stock_id, 0.0) + add_weight
-                    sold_stock_weight -= add_weight
-            elif self.buy_method == "average_fill":
-                for stock_id in buy_signal_stocks:
-                    final_stock_weight[stock_id] = final_stock_weight.get(stock_id, 0.0) + sold_stock_weight / len(
-                        buy_signal_stocks
-                    )
-            else:
-                raise ValueError("Buy method not found")
-        return final_stock_weight
+        if self.topk is None or self.topk <= 0:
+            return {}
+
+        def apply_impact_limit(weight):
+            return weight if self.trade_impact_limit is None else min(weight, self.trade_impact_limit)
+
+        ideal_per_stock = self.risk_degree / self.topk
+        ideal_list = score.sort_values(ascending=False).iloc[: self.topk].index.tolist()
+
+        cur_weights = current.get_stock_weight_dict(only_stock=True)
+        initial_total_weight = sum(cur_weights.values())
+
+        # --- Case A: Cold Start ---
+        if not cur_weights:
+            fill = apply_impact_limit(ideal_per_stock)
+            return {code: fill for code in ideal_list}
+
+        # --- Case B: Rebalancing ---
+        all_tickers = set(cur_weights.keys()) | set(ideal_list)
+        next_weights = {t: cur_weights.get(t, 0.0) for t in all_tickers}
+
+        # Phase 1: Deterministic Sell Phase
+        released_cash = 0.0
+        for t in list(next_weights.keys()):
+            cur = next_weights[t]
+            if cur <= 1e-8:
+                continue
+
+            if t not in ideal_list:
+                sell = apply_impact_limit(cur)
+                next_weights[t] -= sell
+                released_cash += sell
+            elif cur > ideal_per_stock + 1e-8:
+                excess = cur - ideal_per_stock
+                sell = apply_impact_limit(excess)
+                next_weights[t] -= sell
+                released_cash += sell
+
+        # Phase 2: Budget Calculation
+        # Budget = Cash from sells + Available space from target risk degree
+        total_budget = released_cash + (self.risk_degree - initial_total_weight)
+
+        # Phase 3: Proportional Buy Allocation
+        if total_budget > 1e-8:
+            shortfalls = {
+                t: (ideal_per_stock - next_weights.get(t, 0.0))
+                for t in ideal_list
+                if next_weights.get(t, 0.0) < ideal_per_stock - 1e-8
+            }
+
+            if shortfalls:
+                total_shortfall = sum(shortfalls.values())
+                # Normalize total_budget to not exceed total_shortfall
+                available_to_spend = min(total_budget, total_shortfall)
+
+                for t, shortfall in shortfalls.items():
+                    # Every stock gets its fair share based on its distance to target
+                    share_of_budget = (shortfall / total_shortfall) * available_to_spend
+
+                    # Capped by impact limit
+                    max_buy_cap = apply_impact_limit(shortfall)
+
+                    next_weights[t] += min(share_of_budget, max_buy_cap)
+
+        return {k: v for k, v in next_weights.items() if v > 1e-8}

qlib/contrib/strategy/optimizer/__init__.py

@@ -5,5 +5,4 @@ from .base import BaseOptimizer
 from .optimizer import PortfolioOptimizer
 from .enhanced_indexing import EnhancedIndexingOptimizer
 
-
 __all__ = ["BaseOptimizer", "PortfolioOptimizer", "EnhancedIndexingOptimizer"]

qlib/contrib/strategy/optimizer/enhanced_indexing.py

@@ -9,7 +9,6 @@ from typing import Union, Optional, Dict, Any, List
 from qlib.log import get_module_logger
 from .base import BaseOptimizer
 
-
 logger = get_module_logger("EnhancedIndexingOptimizer")
 
 

qlib/contrib/strategy/order_generator.py

@@ -4,6 +4,7 @@
 """
 This order generator is for strategies based on WeightStrategyBase
 """
+
 from ...backtest.position import Position
 from ...backtest.exchange import Exchange
 

qlib/contrib/torch.py

@@ -5,6 +5,7 @@ This module is not a necessary part of Qlib.
 They are just some tools for convenience
 It is should not imported into the core part of qlib
 """
+
 import torch
 import numpy as np
 import pandas as pd

qlib/contrib/tuner/launcher.py

@@ -13,7 +13,6 @@ import yaml
 
 from .config import TunerConfigManager
 
-
 args_parser = argparse.ArgumentParser(prog="tuner")
 args_parser.add_argument(
     "-c",

qlib/contrib/tuner/space.py

@@ -6,7 +6,6 @@
 
 from hyperopt import hp
 
-
 TopkAmountStrategySpace = {
     "topk": hp.choice("topk", [30, 35, 40]),
     "buffer_margin": hp.choice("buffer_margin", [200, 250, 300]),

qlib/contrib/tuner/tuner.py

@@ -8,7 +8,6 @@ import os
 import yaml
 import json
 import copy
-import pickle
 import logging
 import importlib
 import subprocess
@@ -18,6 +17,7 @@ import numpy as np
 from abc import abstractmethod
 
 from ...log import get_module_logger, TimeInspector
+from ...utils.pickle_utils import restricted_pickle_load
 from hyperopt import fmin, tpe
 from hyperopt import STATUS_OK, STATUS_FAIL
 
@@ -136,7 +136,7 @@ class QLibTuner(Tuner):
         exp_result_dir = os.path.join(self.ex_dir, QLibTuner.EXP_RESULT_DIR.format(estimator_ex_id))
         exp_result_path = os.path.join(exp_result_dir, QLibTuner.EXP_RESULT_NAME)
         with open(exp_result_path, "rb") as fp:
-            analysis_df = pickle.load(fp)
+            analysis_df = restricted_pickle_load(fp)
 
         # 4. Get the backtest factor which user want to optimize, if user want to maximize the factor, then reverse the result
         res = analysis_df.loc[self.optim_config.report_type].loc[self.optim_config.report_factor]

qlib/contrib/workflow/__init__.py

@@ -3,5 +3,4 @@
 from .record_temp import MultiSegRecord
 from .record_temp import SignalMseRecord
 
-
 __all__ = ["MultiSegRecord", "SignalMseRecord"]

qlib/data/__init__.py

@@ -36,7 +36,6 @@ from .cache import (
     MemoryCalendarCache,
 )
 
-
 __all__ = [
     "D",
     "CalendarProvider",

qlib/data/cache.py

@@ -30,6 +30,7 @@ from ..utils import (
     normalize_cache_fields,
     normalize_cache_instruments,
 )
+from ..utils.pickle_utils import restricted_pickle_load
 
 from ..log import get_module_logger
 from .base import Feature
@@ -225,7 +226,7 @@ class CacheUtils:
             cache_path = Path(cache_path)
             meta_path = cache_path.with_suffix(".meta")
             with meta_path.open("rb") as f:
-                d = pickle.load(f)
+                d = restricted_pickle_load(f)
             with meta_path.open("wb") as f:
                 try:
                     d["meta"]["last_visit"] = str(time.time())
@@ -592,7 +593,7 @@ class DiskExpressionCache(ExpressionCache):
 
         with CacheUtils.writer_lock(self.r, f"{str(C.dpm.get_data_uri())}:expression-{cache_uri}"):
             with meta_path.open("rb") as f:
-                d = pickle.load(f)
+                d = restricted_pickle_load(f)
             instrument = d["info"]["instrument"]
             field = d["info"]["field"]
             freq = d["info"]["freq"]
@@ -959,7 +960,7 @@ class DiskDatasetCache(DatasetCache):
         im = DiskDatasetCache.IndexManager(cp_cache_uri)
         with CacheUtils.writer_lock(self.r, f"{str(C.dpm.get_data_uri())}:dataset-{cache_uri}"):
             with meta_path.open("rb") as f:
-                d = pickle.load(f)
+                d = restricted_pickle_load(f)
             instruments = d["info"]["instruments"]
             fields = d["info"]["fields"]
             freq = d["info"]["freq"]

qlib/data/client.py

@@ -2,15 +2,15 @@
 # Licensed under the MIT License.
 
 
-from __future__ import division
-from __future__ import print_function
+from __future__ import division, print_function
+
+import json
 
 import socketio
 
 import qlib
-from ..config import C
+
 from ..log import get_module_logger
-import pickle
 
 
 class Client:
@@ -96,7 +96,7 @@ class Client:
         self.logger.debug("connected")
         # The pickle is for passing some parameters with special type(such as
         # pd.Timestamp)
-        request_content = {"head": head_info, "body": pickle.dumps(request_content, protocol=C.dump_protocol_version)}
+        request_content = {"head": head_info, "body": json.dumps(request_content, default=str)}
         self.sio.on(request_type + "_response", request_callback)
         self.logger.debug("try sending")
         self.sio.emit(request_type + "_request", request_content)

qlib/data/dataset/handler.py

@@ -19,7 +19,6 @@ from .loader import DataLoader
 from . import processor as processor_module
 from . import loader as data_loader_module
 
-
 DATA_KEY_TYPE = Literal["raw", "infer", "learn"]
 
 

qlib/data/dataset/loader.py

@@ -2,7 +2,6 @@
 # Licensed under the MIT License.
 
 import abc
-import pickle
 from pathlib import Path
 import warnings
 import pandas as pd
@@ -11,6 +10,7 @@ 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
+from qlib.utils.pickle_utils import restricted_pickle_load
 from qlib.log import get_module_logger
 from qlib.utils.serial import Serializable
 
@@ -283,7 +283,7 @@ class StaticDataLoader(DataLoader, Serializable):
                 self._data = pd.read_parquet(self._config, engine="pyarrow")
             else:
                 with Path(self._config).open("rb") as f:
-                    self._data = pickle.load(f)
+                    self._data = restricted_pickle_load(f)
         elif isinstance(self._config, pd.DataFrame):
             self._data = self._config
 

qlib/data/dataset/storage.py

@@ -67,7 +67,6 @@ class NaiveDFStorage(BaseHandlerStorage):
         col_set: Union[str, List[str]] = DataHandler.CS_ALL,
         fetch_orig: bool = True,
     ) -> pd.DataFrame:
-
         # Following conflicts may occur
         # - Does [20200101", "20210101"] mean selecting this slice or these two days?
         # To solve this issue

qlib/data/filter.py

@@ -168,7 +168,7 @@ class SeriesDFilter(BaseDFilter):
         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
-            if _bool == np.nan:
+            if np.isnan(_bool):
                 _bool = False
             if _lbool is None:
                 _cur_start = _ts

qlib/data/pit.py

@@ -13,6 +13,7 @@ The calculation of both <period_time, feature> and <observe_time, feature> data
 2) concatenate all th collasped data, we will get data with format <observe_time, feature>.
 Qlib will use the operator `P` to perform the collapse.
 """
+
 import numpy as np
 import pandas as pd
 from qlib.data.ops import ElemOperator

qlib/data/storage/__init__.py

@@ -3,5 +3,4 @@
 
 from .storage import CalendarStorage, InstrumentStorage, FeatureStorage, CalVT, InstVT, InstKT
 
-
 __all__ = ["CalendarStorage", "InstrumentStorage", "FeatureStorage", "CalVT", "InstVT", "InstKT"]

qlib/data/storage/file_storage.py

@@ -156,7 +156,7 @@ class FileCalendarStorage(FileStorageMixin, CalendarStorage):
     def index(self, value: CalVT) -> int:
         self.check()
         calendar = self._read_calendar()
-        return int(np.argwhere(calendar == value)[0])
+        return calendar.index(value)
 
     def insert(self, index: int, value: CalVT):
         calendar = self._read_calendar()

qlib/model/__init__.py

@@ -5,5 +5,4 @@ import warnings
 
 from .base import Model
 
-
 __all__ = ["Model", "warnings"]

qlib/model/meta/__init__.py

@@ -4,5 +4,4 @@
 from .task import MetaTask
 from .dataset import MetaTaskDataset
 
-
 __all__ = ["MetaTask", "MetaTaskDataset"]

qlib/model/riskmodel/__init__.py

@@ -6,7 +6,6 @@ from .poet import POETCovEstimator
 from .shrink import ShrinkCovEstimator
 from .structured import StructuredCovEstimator
 
-
 __all__ = [
     "RiskModel",
     "POETCovEstimator",

qlib/rl/contrib/naive_config_parser.py

@@ -9,7 +9,6 @@ import tempfile
 from importlib import import_module
 from ruamel.yaml import YAML
 
-
 DELETE_KEY = "_delete_"
 
 

qlib/rl/data/native.py

@@ -2,17 +2,18 @@
 # Licensed under the MIT License.
 from __future__ import annotations
 
+import os
 from pathlib import Path
-from typing import cast, List
+from typing import List, cast
 
 import cachetools
 import pandas as pd
-import pickle
-import os
 
 from qlib.backtest import Exchange, Order
 from qlib.backtest.decision import TradeRange, TradeRangeByTime
 from qlib.constant import EPS_T
+from qlib.utils.pickle_utils import restricted_pickle_load
+
 from .base import BaseIntradayBacktestData, BaseIntradayProcessedData, ProcessedDataProvider
 
 
@@ -162,7 +163,7 @@ class HandlerIntradayProcessedData(BaseIntradayProcessedData):
         path = os.path.join(data_dir, "backtest" if backtest else "feature", f"{stock_id}.pkl")
         start_time, end_time = date.replace(hour=0, minute=0, second=0), date.replace(hour=23, minute=59, second=59)
         with open(path, "rb") as fstream:
-            dataset = pickle.load(fstream)
+            dataset = restricted_pickle_load(fstream)
         data = dataset.handler.fetch(pd.IndexSlice[stock_id, start_time:end_time], level=None)
 
         if index_only:

qlib/utils/data.py

@@ -3,6 +3,7 @@
 """
 This module covers some utility functions that operate on data or basic object
 """
+
 from copy import deepcopy
 from typing import List, Union
 

qlib/utils/mod.py

@@ -11,7 +11,6 @@ import contextlib
 import importlib
 import os
 from pathlib import Path
-import pickle
 import pkgutil
 import re
 import sys
@@ -20,6 +19,7 @@ from typing import Any, Dict, List, Tuple, Union
 from urllib.parse import urlparse
 
 from qlib.typehint import InstConf
+from qlib.utils.pickle_utils import restricted_pickle_load
 
 
 def get_module_by_module_path(module_path: Union[str, ModuleType]):
@@ -161,7 +161,6 @@ def init_instance_by_config(
             # path like 'file:///<path to pickle file>/obj.pkl'
             pr = urlparse(config)
             if pr.scheme == "file":
-
                 # To enable relative path like file://data/a/b/c.pkl.  pr.netloc will be data
                 path = pr.path
                 if pr.netloc != "":
@@ -169,10 +168,10 @@ def init_instance_by_config(
 
                 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)
+                    return restricted_pickle_load(f)
         else:
             with config.open("rb") as f:
-                return pickle.load(f)
+                return restricted_pickle_load(f)
 
     klass, cls_kwargs = get_callable_kwargs(config, default_module=default_module)
 

qlib/utils/objm.py

@@ -6,6 +6,7 @@ import tempfile
 from pathlib import Path
 
 from qlib.config import C
+from qlib.utils.pickle_utils import restricted_pickle_load
 
 
 class ObjManager:
@@ -116,7 +117,7 @@ class FileManager(ObjManager):
 
     def load_obj(self, name):
         with (self.path / name).open("rb") as f:
-            return pickle.load(f)
+            return restricted_pickle_load(f)
 
     def exists(self, name):
         return (self.path / name).exists()

qlib/utils/pickle_utils.py

@@ -0,0 +1,171 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+"""
+Secure pickle utilities to prevent arbitrary code execution through deserialization.
+
+This module provides a secure alternative to pickle.load() and pickle.loads()
+that restricts deserialization to a whitelist of safe classes.
+"""
+
+import io
+import pickle
+from typing import Any, BinaryIO, Set, Tuple
+
+# Whitelist of safe classes that are allowed to be unpickled
+# These are common data types used in qlib that should be safe to deserialize
+SAFE_PICKLE_CLASSES: Set[Tuple[str, str]] = {
+    # python builtins
+    ("builtins", "slice"),
+    ("builtins", "range"),
+    ("builtins", "dict"),
+    ("builtins", "list"),
+    ("builtins", "tuple"),
+    ("builtins", "set"),
+    ("builtins", "frozenset"),
+    ("builtins", "bytearray"),
+    ("builtins", "bytes"),
+    ("builtins", "str"),
+    ("builtins", "int"),
+    ("builtins", "float"),
+    ("builtins", "bool"),
+    ("builtins", "complex"),
+    ("builtins", "type"),
+    ("builtins", "property"),
+    # common utility classes
+    ("datetime", "datetime"),
+    ("datetime", "date"),
+    ("datetime", "time"),
+    ("datetime", "timedelta"),
+    ("datetime", "timezone"),
+    ("decimal", "Decimal"),
+    ("collections", "OrderedDict"),
+    ("collections", "defaultdict"),
+    ("collections", "Counter"),
+    ("collections", "namedtuple"),
+    ("enum", "Enum"),
+    ("pathlib", "Path"),
+    ("pathlib", "PosixPath"),
+    ("pathlib", "WindowsPath"),
+    ("qlib.data.dataset.handler", "DataHandler"),
+    ("qlib.data.dataset.handler", "DataHandlerLP"),
+    ("qlib.data.dataset.loader", "StaticDataLoader"),
+}
+
+
+TRUSTED_MODULE_PREFIXES = (
+    "pandas",
+    "numpy",
+)
+
+
+class RestrictedUnpickler(pickle.Unpickler):
+    """Custom unpickler that only allows safe classes to be deserialized.
+
+    This prevents arbitrary code execution through malicious pickle files by
+    restricting deserialization to a whitelist of safe classes.
+
+    Example:
+        >>> with open("data.pkl", "rb") as f:
+        ...     data = RestrictedUnpickler(f).load()
+    """
+
+    def find_class(self, module: str, name: str):
+        """Override find_class to restrict allowed classes.
+
+        Args:
+            module: Module name of the class
+            name: Class name
+
+        Returns:
+            The class object if it's in the whitelist
+
+        Raises:
+            pickle.UnpicklingError: If the class is not in the whitelist
+        """
+        if module.startswith(TRUSTED_MODULE_PREFIXES):
+            return super().find_class(module, name)
+
+        # 2. explicit whitelist (qlib internal)
+        if (module, name) in SAFE_PICKLE_CLASSES:
+            return super().find_class(module, name)
+
+        raise pickle.UnpicklingError(
+            f"Forbidden class: {module}.{name}. "
+            f"Only whitelisted classes are allowed for security reasons. "
+            f"This is to prevent arbitrary code execution through pickle deserialization."
+        )
+
+
+def restricted_pickle_load(file: BinaryIO) -> Any:
+    """Safely load a pickle file with restricted classes.
+
+    This is a drop-in replacement for pickle.load() that prevents
+    arbitrary code execution by only allowing whitelisted classes.
+
+    Args:
+        file: An opened file object in binary mode
+
+    Returns:
+        The unpickled Python object
+
+    Raises:
+        pickle.UnpicklingError: If the pickle contains forbidden classes
+
+    Example:
+        >>> with open("data.pkl", "rb") as f:
+        ...     data = restricted_pickle_load(f)
+    """
+    return RestrictedUnpickler(file).load()
+
+
+def restricted_pickle_loads(data: bytes) -> Any:
+    """Safely load a pickle from bytes with restricted classes.
+
+    This is a drop-in replacement for pickle.loads() that prevents
+    arbitrary code execution by only allowing whitelisted classes.
+
+    Args:
+        data: Bytes object containing pickled data
+
+    Returns:
+        The unpickled Python object
+
+    Raises:
+        pickle.UnpicklingError: If the pickle contains forbidden classes
+
+    Example:
+        >>> data = b'\\x80\\x04\\x95...'
+        >>> obj = restricted_pickle_loads(data)
+    """
+    file_like = io.BytesIO(data)
+    return RestrictedUnpickler(file_like).load()
+
+
+def add_safe_class(module: str, name: str) -> None:
+    """Add a class to the whitelist of safe classes for unpickling.
+
+    Use this function to extend the whitelist if your code needs to deserialize
+    additional classes. However, be very careful when adding classes, as this
+    could potentially introduce security vulnerabilities.
+
+    Args:
+        module: Module name of the class (e.g., 'my_package.my_module')
+        name: Class name (e.g., 'MyClass')
+
+    Warning:
+        Only add classes that you fully control and trust. Adding arbitrary
+        classes from external packages could introduce security risks.
+
+    Example:
+        >>> add_safe_class('my_package.models', 'CustomModel')
+    """
+    SAFE_PICKLE_CLASSES.add((module, name))
+
+
+def get_safe_classes() -> Set[Tuple[str, str]]:
+    """Get a copy of the current whitelist of safe classes.
+
+    Returns:
+        A set of (module, name) tuples representing allowed classes
+    """
+    return SAFE_PICKLE_CLASSES.copy()

qlib/utils/resam.py

@@ -109,7 +109,7 @@ def resam_ts_data(
     """
     Resample value from time-series data
 
-        - If `feature` has MultiIndex[instrument, datetime], apply the `method` to each instruemnt data with datetime in [start_time, end_time]
+        - If `feature` has MultiIndex[instrument, datetime], apply the `method` to each instrument data with datetime in [start_time, end_time]
             Example:
 
             .. code-block::
@@ -222,7 +222,7 @@ def get_valid_value(series, last=True):
     Nan | float
         the first/last valid value
     """
-    return series.fillna(method="ffill").iloc[-1] if last else series.fillna(method="bfill").iloc[0]
+    return series.ffill().iloc[-1] if last else series.bfill().iloc[0]
 
 
 def _ts_data_valid(ts_feature, last=False):

qlib/utils/time.py

@@ -3,6 +3,7 @@
 """
 Time related utils are compiled in this script
 """
+
 import bisect
 from datetime import datetime, time, date, timedelta
 from typing import List, Optional, Tuple, Union
@@ -14,7 +15,6 @@ import pandas as pd
 from qlib.config import C
 from qlib.constant import REG_CN, REG_TW, REG_US
 
-
 CN_TIME = [
     datetime.strptime("9:30", "%H:%M"),
     datetime.strptime("11:30", "%H:%M"),

qlib/workflow/expm.py

@@ -16,7 +16,6 @@ from .recorder import Recorder
 from ..log import get_module_logger
 from ..utils.exceptions import ExpAlreadyExistError
 
-
 logger = get_module_logger("workflow")
 
 

qlib/workflow/record_temp.py

@@ -22,7 +22,6 @@ from ..utils.data import deepcopy_basic_type
 from ..utils.exceptions import QlibException
 from ..contrib.eva.alpha import calc_ic, calc_long_short_return, calc_long_short_prec
 
-
 logger = get_module_logger("workflow", logging.INFO)
 
 
@@ -476,7 +475,13 @@ class PortAnaRecord(ACRecordTemp):
         if self.backtest_config["start_time"] is None:
             self.backtest_config["start_time"] = dt_values.min()
         if self.backtest_config["end_time"] is None:
-            self.backtest_config["end_time"] = get_date_by_shift(dt_values.max(), 1)
+            self.backtest_config["end_time"] = get_date_by_shift(dt_values.max(), -1)
+            warnings.warn(
+                "No explicit backtest end_time provided. "
+                "Qlib requires one extra calendar step to determine the right boundary of a bar. "
+                "Therefore the end_time is shifted backward by one trading day from "
+                f"{dt_values.max()} -> {self.backtest_config['end_time']}."
+            )
 
         artifact_objects = {}
         # custom strategy and get backtest

qlib/workflow/task/gen.py

@@ -3,6 +3,7 @@
 """
 TaskGenerator module can generate many tasks based on TaskGen and some task templates.
 """
+
 import abc
 import copy
 import pandas as pd
@@ -106,15 +107,13 @@ def handler_mod(task: dict, rolling_gen):
         rg (RollingGen): an instance of RollingGen
     """
     try:
-        interval = rolling_gen.ta.cal_interval(
-            task["dataset"]["kwargs"]["handler"]["kwargs"]["end_time"],
-            task["dataset"]["kwargs"]["segments"][rolling_gen.test_key][1],
-        )
-        # if end_time < the end of test_segments, then change end_time to allow load more data
-        if interval < 0:
-            task["dataset"]["kwargs"]["handler"]["kwargs"]["end_time"] = copy.deepcopy(
-                task["dataset"]["kwargs"]["segments"][rolling_gen.test_key][1]
-            )
+        handler_kwargs = task["dataset"]["kwargs"]["handler"]["kwargs"]
+        handler_end_time = handler_kwargs.get("end_time")
+        test_seg_end_time = task["dataset"]["kwargs"]["segments"][rolling_gen.test_key][1]
+        # if the end of test_segments is None (open-ended segment, i.e., "until now") or end_time < the end of test_segments,
+        # then change end_time to allow load more data
+        if test_seg_end_time is None or rolling_gen.ta.cal_interval(handler_end_time, test_seg_end_time) < 0:
+            handler_kwargs["end_time"] = copy.deepcopy(test_seg_end_time)
     except KeyError:
         # Maybe dataset do not have handler, then do nothing.
         pass

qlib/workflow/task/manage.py

@@ -12,6 +12,7 @@ A task in TaskManager consists of 3 parts
 - tasks status: the status of the task
 - tasks result: A user can get the task with the task description and task result.
 """
+
 import concurrent
 import pickle
 import time
@@ -28,6 +29,7 @@ from tqdm.cli import tqdm
 
 from .utils import get_mongodb
 from ...config import C
+from ...utils.pickle_utils import restricted_pickle_loads
 
 
 class TaskManager:
@@ -131,7 +133,7 @@ class TaskManager:
         for prefix in self.ENCODE_FIELDS_PREFIX:
             for k in list(task.keys()):
                 if k.startswith(prefix):
-                    task[k] = pickle.loads(task[k])
+                    task[k] = restricted_pickle_loads(task[k])
         return task
 
     def _dict_to_str(self, flt):

scripts/check_data_health.py

@@ -1,12 +1,12 @@
-from loguru import logger
 import os
 from typing import Optional
 
 import fire
 import pandas as pd
-import qlib
+from loguru import logger
 from tqdm import tqdm
 
+import qlib
 from qlib.data import D
 
 
@@ -36,6 +36,7 @@ class DataHealthChecker:
         self.large_step_threshold_price = large_step_threshold_price
         self.large_step_threshold_volume = large_step_threshold_volume
         self.missing_data_num = missing_data_num
+        self.qlib_dir = os.path.abspath(os.path.expanduser(qlib_dir))
 
         if csv_path:
             assert os.path.isdir(csv_path), f"{csv_path} should be a directory."
@@ -68,6 +69,43 @@ class DataHealthChecker:
             self.data[instrument] = df
         print(df)
 
+    # NOTE:
+    # This check is added due to a known issue in Qlib where feature paths
+    # are constructed using lowercased instrument names. On case-sensitive
+    # file systems (e.g. Linux), uppercase directory names under `features/`
+    # will cause data loading failures.
+    #
+    # See: https://github.com/microsoft/qlib/issues/2053
+    def check_features_dir_lowercase(self) -> Optional[pd.DataFrame]:
+        """
+        Check whether all subdirectories under `<qlib_dir>/features` are named in lowercase.
+
+        This validation helps prevent data loading issues on case-sensitive
+        file systems caused by uppercase instrument directory names.
+        """
+        if not self.qlib_dir:
+            return None
+
+        features_dir = os.path.join(self.qlib_dir, "features")
+        if not os.path.isdir(features_dir):
+            logger.warning(f"`features` directory not found under {self.qlib_dir}")
+            return None
+
+        bad_dirs = []
+        for name in os.listdir(features_dir):
+            full_path = os.path.join(features_dir, name)
+            if os.path.isdir(full_path) and name != name.lower():
+                bad_dirs.append(name)
+
+        if bad_dirs:
+            result_df = pd.DataFrame({"non_lowercase_dir": bad_dirs})
+            return result_df
+        else:
+            logger.info(
+                f"✅ All subdirectories under `{os.path.join(self.qlib_dir, 'features')}` are named in lowercase."
+            )
+            return None
+
     def check_missing_data(self) -> Optional[pd.DataFrame]:
         """Check if any data is missing in the DataFrame."""
         result_dict = {
@@ -177,11 +215,13 @@ class DataHealthChecker:
         check_large_step_changes_result = self.check_large_step_changes()
         check_required_columns_result = self.check_required_columns()
         check_missing_factor_result = self.check_missing_factor()
+        check_features_dir_case_result = self.check_features_dir_lowercase()
         if (
             check_large_step_changes_result is not None
             or check_large_step_changes_result is not None
             or check_required_columns_result is not None
             or check_missing_factor_result is not None
+            or check_features_dir_case_result is not None
         ):
             print(f"\nSummary of data health check ({len(self.data)} files checked):")
             print("-------------------------------------------------")
@@ -197,6 +237,11 @@ class DataHealthChecker:
             if isinstance(check_missing_factor_result, pd.DataFrame):
                 logger.warning(f"The factor column does not exist or is empty")
                 print(check_missing_factor_result)
+            if isinstance(check_features_dir_case_result, pd.DataFrame):
+                logger.warning(
+                    f"Some subdirectories under `{os.path.join(self.qlib_dir, 'features')}` contain uppercase letters, please rename them to lowercase manually."
+                )
+                print(check_features_dir_case_result)
 
 
 if __name__ == "__main__":

scripts/collect_info.py

@@ -28,32 +28,32 @@ class InfoCollector:
         """collect qlib related info"""
         print("Qlib version: {}".format(qlib.__version__))
         REQUIRED = [
+            "setuptools",
+            "wheel",
+            "cython",
+            "pyyaml",
             "numpy",
             "pandas",
-            "scipy",
-            "requests",
-            "sacred",
-            "python-socketio",
-            "redis",
-            "python-redis-lock",
-            "schedule",
-            "cvxpy",
-            "hyperopt",
-            "fire",
-            "statsmodels",
-            "xlrd",
-            "plotly",
-            "matplotlib",
-            "tables",
-            "pyyaml",
             "mlflow",
-            "tqdm",
-            "loguru",
-            "lightgbm",
-            "tornado",
-            "joblib",
+            "filelock",
+            "redis",
+            "dill",
             "fire",
             "ruamel.yaml",
+            "python-redis-lock",
+            "tqdm",
+            "pymongo",
+            "loguru",
+            "lightgbm",
+            "gym",
+            "cvxpy",
+            "joblib",
+            "matplotlib",
+            "jupyter",
+            "nbconvert",
+            "pyarrow",
+            "pydantic-settings",
+            "setuptools-scm",
         ]
 
         for package in REQUIRED:

scripts/data_collector/baostock_5min/collector.py

@@ -172,7 +172,7 @@ class BaostockNormalizeHS3005min(BaseNormalize):
     @staticmethod
     def calc_change(df: pd.DataFrame, last_close: float) -> pd.Series:
         df = df.copy()
-        _tmp_series = df["close"].fillna(method="ffill")
+        _tmp_series = df["close"].ffill()
         _tmp_shift_series = _tmp_series.shift(1)
         if last_close is not None:
             _tmp_shift_series.iloc[0] = float(last_close)

scripts/data_collector/base.py

@@ -280,11 +280,20 @@ class Normalize:
         self._symbol_field_name = symbol_field_name
         self._end_date = kwargs.get("end_date", None)
         self._max_workers = max_workers
+        self.interval = kwargs.get("interval", "1d")
 
         self._normalize_obj = normalize_class(
             date_field_name=date_field_name, symbol_field_name=symbol_field_name, **kwargs
         )
 
+    def format_data(self, df: pd.DataFrame):
+        if self.interval == "1d":
+            try:
+                pd.to_datetime(df.iloc[-1]["date"], format="%Y-%m-%d", errors="raise")
+            except Exception:
+                df = df.iloc[:-1]
+        return df
+
     def _executor(self, file_path: Path):
         file_path = Path(file_path)
 
@@ -300,14 +309,18 @@ class Normalize:
             keep_default_na=False,
             na_values={col: symbol_na if col == self._symbol_field_name else default_na for col in columns},
         )
+        df = self.format_data(df=df)
 
-        # NOTE: It has been reported that there may be some problems here, and the specific issues will be dealt with when they are identified.
-        df = self._normalize_obj.normalize(df)
-        if df is not None and not df.empty:
-            if self._end_date is not None:
-                _mask = pd.to_datetime(df[self._date_field_name]) <= pd.Timestamp(self._end_date)
-                df = df[_mask]
-            df.to_csv(self._target_dir.joinpath(file_path.name), index=False)
+        if not df.empty:
+            # NOTE: It has been reported that there may be some problems here, and the specific issues will be dealt with when they are identified.
+            df = self._normalize_obj.normalize(df)
+            if df is not None and not df.empty:
+                if self._end_date is not None:
+                    _mask = pd.to_datetime(df[self._date_field_name]) <= pd.Timestamp(self._end_date)
+                    df = df[_mask]
+                df.to_csv(self._target_dir.joinpath(file_path.name), index=False)
+        else:
+            logger.warning(f"{file_path.stem} source data is empty and will not undergo normalization processing.")
 
     def normalize(self):
         logger.info("normalize data......")

scripts/data_collector/cn_index/collector.py

@@ -22,7 +22,6 @@ from data_collector.index import IndexBase
 from data_collector.utils import get_calendar_list, get_trading_date_by_shift, deco_retry
 from data_collector.utils import get_instruments
 
-
 NEW_COMPANIES_URL = (
     "https://oss-ch.csindex.com.cn/static/html/csindex/public/uploads/file/autofile/cons/{index_code}cons.xls"
 )

scripts/data_collector/crypto/collector.py

@@ -19,7 +19,6 @@ from time import mktime
 from datetime import datetime as dt
 import time
 
-
 _CG_CRYPTO_SYMBOLS = None
 
 

scripts/data_collector/us_index/collector.py

@@ -7,13 +7,14 @@ import sys
 from pathlib import Path
 from concurrent.futures import ThreadPoolExecutor
 from typing import List
+from io import StringIO
 
 import fire
 import requests
 import pandas as pd
 from tqdm import tqdm
 from loguru import logger
-
+from fake_useragent import UserAgent
 
 CUR_DIR = Path(__file__).resolve().parent
 sys.path.append(str(CUR_DIR.parent.parent))
@@ -22,7 +23,6 @@ from data_collector.index import IndexBase
 from data_collector.utils import deco_retry, get_calendar_list, get_trading_date_by_shift
 from data_collector.utils import get_instruments
 
-
 WIKI_URL = "https://en.wikipedia.org/wiki"
 
 WIKI_INDEX_NAME_MAP = {
@@ -51,6 +51,7 @@ class WIKIIndex(IndexBase):
         )
 
         self._target_url = f"{WIKI_URL}/{WIKI_INDEX_NAME_MAP[self.index_name.upper()]}"
+        self._ua = UserAgent()
 
     @property
     @abc.abstractmethod
@@ -112,7 +113,8 @@ class WIKIIndex(IndexBase):
         return _calendar_list
 
     def _request_new_companies(self) -> requests.Response:
-        resp = requests.get(self._target_url, timeout=None)
+        headers = {"User-Agent": self._ua.random}
+        resp = requests.get(self._target_url, timeout=None, headers=headers)
         if resp.status_code != 200:
             raise ValueError(f"request error: {self._target_url}")
 
@@ -128,7 +130,7 @@ class WIKIIndex(IndexBase):
     def get_new_companies(self):
         logger.info(f"get new companies {self.index_name} ......")
         _data = deco_retry(retry=self._request_retry, retry_sleep=self._retry_sleep)(self._request_new_companies)()
-        df_list = pd.read_html(_data.text)
+        df_list = pd.read_html(StringIO(_data.text))
         for _df in df_list:
             _df = self.filter_df(_df)
             if (_df is not None) and (not _df.empty):
@@ -226,7 +228,11 @@ class SP500Index(WIKIIndex):
     def get_changes(self) -> pd.DataFrame:
         logger.info(f"get sp500 history changes......")
         # NOTE: may update the index of the table
-        changes_df = pd.read_html(self.WIKISP500_CHANGES_URL)[-1]
+        # Add headers to avoid 403 Forbidden error from Wikipedia
+        headers = {"User-Agent": self._ua.random}
+        response = requests.get(self.WIKISP500_CHANGES_URL, headers=headers, timeout=None)
+        response.raise_for_status()
+        changes_df = pd.read_html(StringIO(response.text))[-1]
         changes_df = changes_df.iloc[:, [0, 1, 3]]
         changes_df.columns = [self.DATE_FIELD_NAME, self.ADD, self.REMOVE]
         changes_df[self.DATE_FIELD_NAME] = pd.to_datetime(changes_df[self.DATE_FIELD_NAME])

scripts/data_collector/us_index/requirements.txt

@@ -3,3 +3,4 @@ requests
 pandas
 lxml
 loguru
+fake-useragent

scripts/data_collector/utils.py

@@ -7,7 +7,6 @@ import importlib
 import time
 import bisect
 import pickle
-import random
 import requests
 import functools
 from pathlib import Path
@@ -21,6 +20,9 @@ from tqdm import tqdm
 from functools import partial
 from concurrent.futures import ProcessPoolExecutor
 from bs4 import BeautifulSoup
+import baostock as bs
+
+from qlib.utils.pickle_utils import restricted_pickle_load
 
 HS_SYMBOLS_URL = "http://app.finance.ifeng.com/hq/list.php?type=stock_a&class={s_type}"
 
@@ -67,9 +69,16 @@ def get_calendar_list(bench_code="CSI300") -> List[pd.Timestamp]:
 
     logger.info(f"get calendar list: {bench_code}......")
 
-    def _get_calendar(url):
-        _value_list = requests.get(url, timeout=None).json()["data"]["klines"]
-        return sorted(map(lambda x: pd.Timestamp(x.split(",")[0]), _value_list))
+    def _get_calendar(end_date):
+        bs.login()
+        rs = bs.query_trade_dates(start_date="2005-01-01", end_date=end_date)
+        data_list = []
+        while (rs.error_code == "0") & rs.next():
+            data_list.append(rs.get_row_data())
+        bs.logout()
+        df = pd.DataFrame(data_list, columns=rs.fields)
+        trade_days = df[df["is_trading_day"] == "1"]["calendar_date"]
+        return sorted(map(pd.Timestamp, trade_days.to_list()))
 
     calendar = _CALENDAR_MAP.get(bench_code, None)
     if calendar is None:
@@ -80,30 +89,17 @@ def get_calendar_list(bench_code="CSI300") -> List[pd.Timestamp]:
             calendar = df.index.get_level_values(level="date").map(pd.Timestamp).unique().tolist()
         else:
             if bench_code.upper() == "ALL":
+                import akshare as ak  # pylint: disable=C0415
 
-                @deco_retry
-                def _get_calendar(month):
-                    _cal = []
-                    try:
-                        resp = requests.get(
-                            SZSE_CALENDAR_URL.format(month=month, random=random.random), timeout=None
-                        ).json()
-                        for _r in resp["data"]:
-                            if int(_r["jybz"]):
-                                _cal.append(pd.Timestamp(_r["jyrq"]))
-                    except Exception as e:
-                        raise ValueError(f"{month}-->{e}") from e
-                    return _cal
-
-                month_range = pd.date_range(start="2000-01", end=pd.Timestamp.now() + pd.Timedelta(days=31), freq="M")
-                calendar = []
-                for _m in month_range:
-                    cal = _get_calendar(_m.strftime("%Y-%m"))
-                    if cal:
-                        calendar += cal
-                calendar = list(filter(lambda x: x <= pd.Timestamp.now(), calendar))
+                trade_date_df = ak.tool_trade_date_hist_sina()
+                trade_date_list = trade_date_df["trade_date"].tolist()
+                trade_date_list = [pd.Timestamp(d) for d in trade_date_list]
+                dates = pd.DatetimeIndex(trade_date_list)
+                filtered_dates = dates[(dates >= "2000-01-04") & (dates <= pd.Timestamp.today().normalize())]
+                calendar = filtered_dates.tolist()
             else:
-                calendar = _get_calendar(CALENDAR_BENCH_URL_MAP[bench_code])
+                end_date = time.strftime("%Y-%m-%d", time.localtime())
+                calendar = _get_calendar(end_date=end_date)
         _CALENDAR_MAP[bench_code] = calendar
     logger.info(f"end of get calendar list: {bench_code}.")
     return calendar
@@ -280,7 +276,7 @@ def get_hs_stock_symbols() -> list:
         symbol_cache_path.parent.mkdir(parents=True, exist_ok=True)
         if symbol_cache_path.exists():
             with symbol_cache_path.open("rb") as fp:
-                cache_symbols = pickle.load(fp)
+                cache_symbols = restricted_pickle_load(fp)
                 symbols |= cache_symbols
         with symbol_cache_path.open("wb") as fp:
             pickle.dump(symbols, fp)
@@ -297,20 +293,14 @@ def get_us_stock_symbols(qlib_data_path: [str, Path] = None) -> list:
     -------
         stock symbols
     """
+    import akshare as ak  # pylint: disable=C0415
+
     global _US_SYMBOLS  # pylint: disable=W0603
 
     @deco_retry
     def _get_eastmoney():
-        url = "http://4.push2.eastmoney.com/api/qt/clist/get?pn=1&pz=10000&fs=m:105,m:106,m:107&fields=f12"
-        resp = requests.get(url, timeout=None)
-        if resp.status_code != 200:
-            raise ValueError("request error")
-
-        try:
-            _symbols = [_v["f12"].replace("_", "-P") for _v in resp.json()["data"]["diff"].values()]
-        except Exception as e:
-            logger.warning(f"request error: {e}")
-            raise
+        df = ak.get_us_stock_name()
+        _symbols = df["symbol"].to_list()
 
         if len(_symbols) < 8000:
             raise ValueError("request error")

scripts/data_collector/yahoo/collector.py

@@ -371,7 +371,7 @@ class YahooNormalize(BaseNormalize):
     @staticmethod
     def calc_change(df: pd.DataFrame, last_close: float) -> pd.Series:
         df = df.copy()
-        _tmp_series = df["close"].fillna(method="ffill")
+        _tmp_series = df["close"].ffill()
         _tmp_shift_series = _tmp_series.shift(1)
         if last_close is not None:
             _tmp_shift_series.iloc[0] = float(last_close)
@@ -459,7 +459,7 @@ class YahooNormalize1d(YahooNormalize, ABC):
         df.set_index(self._date_field_name, inplace=True)
         if "adjclose" in df:
             df["factor"] = df["adjclose"] / df["close"]
-            df["factor"] = df["factor"].fillna(method="ffill")
+            df["factor"] = df["factor"].ffill()
         else:
             df["factor"] = 1
         for _col in self.COLUMNS:
@@ -613,10 +613,6 @@ class YahooNormalize1min(YahooNormalize, ABC):
     def symbol_to_yahoo(self, symbol):
         raise NotImplementedError("rewrite symbol_to_yahoo")
 
-    @abc.abstractmethod
-    def _get_1d_calendar_list(self) -> Iterable[pd.Timestamp]:
-        raise NotImplementedError("rewrite _get_1d_calendar_list")
-
 
 class YahooNormalizeUS:
     def _get_calendar_list(self) -> Iterable[pd.Timestamp]:

scripts/data_collector/yahoo/requirements.txt

@@ -9,4 +9,5 @@ yahooquery
 joblib
 beautifulsoup4
 bs4
-soupsieve
+soupsieve
+akshare

scripts/get_data.py

@@ -4,6 +4,5 @@
 import fire
 from qlib.tests.data import GetData
 
-
 if __name__ == "__main__":
     fire.Fire(GetData)