fix: trying to fix the error in github action by limiting the version of gymnasium

.github/workflows/test_qlib_from_pip.yml

@@ -1,9 +1,5 @@
 name: Test qlib from pip
 
-concurrency:
-  cancel-in-progress: true
-  group: ${{ github.workflow }}-${{ github.ref }}
-
 on:
   push:
     branches: [ main ]

.github/workflows/test_qlib_from_source.yml

@@ -1,9 +1,5 @@
 name: Test qlib from source
 
-concurrency:
-  cancel-in-progress: true
-  group: ${{ github.workflow }}-${{ github.ref }}
-
 on:
   push:
     branches: [ main ]
@@ -80,11 +76,8 @@ 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

@@ -1,9 +1,5 @@
 name: Test qlib from source slow
 
-concurrency:
-  cancel-in-progress: true
-  group: ${{ github.workflow }}-${{ github.ref }}
-
 on:
   push:
     branches: [ main ]

.gitignore

@@ -22,7 +22,6 @@ 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

@@ -74,37 +74,34 @@ prerequisite:
 
 # Install the package in editable mode.
 dependencies:
-	python -m pip install --no-cache-dir -e .
+	python -m pip install -e .
 
 lightgbm:
-	python -m pip install --no-cache-dir lightgbm --prefer-binary
+	python -m pip install lightgbm --prefer-binary
 
 rl:
-	python -m pip install --no-cache-dir -e .[rl]
+	python -m pip install -e .[rl]
 
 develop:
-	python -m pip install --no-cache-dir -e .[dev]
+	python -m pip install -e .[dev]
 
 lint:
-	python -m pip install --no-cache-dir -e .[lint]
+	python -m pip install -e .[lint]
 
 docs:
-	python -m pip install --no-cache-dir -e .[docs]
+	python -m pip install -e .[docs]
 
 package:
-	python -m pip install --no-cache-dir -e .[package]
+	python -m pip install -e .[package]
 
 test:
-	python -m pip install --no-cache-dir -e .[test]
+	python -m pip install -e .[test]
 
 analysis:
-	python -m pip install --no-cache-dir -e .[analysis]
-
-client:
-	python -m pip install --no-cache-dir -e .[client]
+	python -m pip install -e .[analysis]
 
 all:
-	python -m pip install --no-cache-dir -e .[pywinpty,dev,lint,docs,package,test,analysis,rl]
+	python -m pip install -e .[pywinpty,dev,lint,docs,package,test,analysis,rl]
 
 install: prerequisite dependencies
 
@@ -116,7 +113,7 @@ dev: prerequisite all
 
 # Check lint with black.
 black:
-	black . -l 120 --check --diff --exclude qlib/_version.py
+	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

README.md

@@ -17,13 +17,14 @@ 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 the [RD-Agent repository](https://github.com/microsoft/RD-Agent). We have prepared several public demo videos for you:
+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 | [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) |
+| 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) |
 
 - 📃**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/
@@ -323,7 +324,7 @@ 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 pyqlib/qlib_image_stable:stable
+    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

docs/advanced/alpha.rst

@@ -42,7 +42,7 @@ Example
 
     .. math::
 
-        DEA = EMA(DIF, 9)
+        DEA = \frac{EMA(DIF, 9)}{CLOSE}
 
 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 = '2 * ((EMA($close, 12) - EMA($close, 26))/$close - EMA((EMA($close, 12) - EMA($close, 26))/$close, 9))'
+    >> MACD_EXP = '(EMA($close, 12) - EMA($close, 26))/$close - EMA((EMA($close, 12) - EMA($close, 26))/$close, 9)/$close'
     >> fields = [MACD_EXP] # MACD
     >> names = ['MACD']
     >> labels = ['Ref($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    0.008781 -0.019672
-               SH600004    0.006699 -0.014721
-               SH600006    0.005714  0.002911
-               SH600008    0.000798  0.009818
-               SH600009    0.017015 -0.017758
+    2010-01-04 SH600000   -0.011547 -0.019672
+               SH600004    0.002745 -0.014721
+               SH600006    0.010133  0.002911
+               SH600008   -0.001113  0.009818
+               SH600009    0.025878 -0.017758
     ...                         ...       ...
-    2017-12-29 SZ300124    0.015071 -0.005074
-               SZ300136   -0.015466  0.056352
-               SZ300144    0.013082  0.011853
-               SZ300251   -0.001026  0.021739
-               SZ300315   -0.007559  0.012455
+    2017-12-29 SZ300124    0.007306 -0.005074
+               SZ300136   -0.013492  0.056352
+               SZ300144   -0.000966  0.011853
+               SZ300251    0.004383  0.021739
+               SZ300315   -0.030557  0.012455
 
 Reference
 =========

docs/conf.py

@@ -21,7 +21,8 @@
 import os
 import sys
 
-from importlib.metadata import version as ver
+import pkg_resources
+
 
 # -- General configuration ------------------------------------------------
 
@@ -62,9 +63,9 @@ author = "Microsoft"
 # built documents.
 #
 # The short X.Y version.
-version = ver("pyqlib")
+version = pkg_resources.get_distribution("pyqlib").version
 # The full version, including alpha/beta/rc tags.
-release = version
+release = pkg_resources.get_distribution("pyqlib").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 example which does the same thing as above examples.
+Here is an exmaple 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 used to get the test dataset.
+    - The parameters must include the parameter `dataset`, which will be userd 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

@@ -19,6 +19,7 @@ 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

@@ -8,6 +8,7 @@ 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

@@ -1,10 +1,9 @@
+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
@@ -19,7 +18,7 @@ from tqdm.auto import tqdm
 
 # +
 with open("./internal_data_s20.pkl", "rb") as f:
-    data = restricted_pickle_load(f)
+    data = pickle.load(f)
 
 data.data_ic_df.columns.names = ["start_date", "end_date"]
 
@@ -53,7 +52,7 @@ pd.DataFrame(meta_m.tn.twm.linear.weight.detach().numpy()).T[0].rolling(5).mean(
 
 # +
 with open("./tasks_s20.pkl", "rb") as f:
-    tasks = restricted_pickle_load(f)
+    tasks = 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 = restricted_pickle_load(file_dataset)
+            dataset = pickle.load(file_dataset)
 
         with open("dataset_backtest.pkl", "rb") as file_dataset_backtest:
-            dataset_backtest = restricted_pickle_load(file_dataset_backtest)
+            dataset_backtest = pickle.load(file_dataset_backtest)
 
         self._prepare_calender_cache()
         ##=============reinit dataset=============

examples/model_interpreter/feature.py

@@ -9,6 +9,7 @@ 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

@@ -95,6 +95,7 @@ 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

@@ -7,7 +7,6 @@ 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/create_dataset.py

@@ -6,7 +6,6 @@ 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

@@ -1,15 +1,13 @@
+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):
-        with open(os.path.join("data/orders/", tag, f), "rb") as fr:
-            df = restricted_pickle_load(fr)
+        df = pickle.load(open(os.path.join("data/orders/", tag, f), "rb"))
         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 = restricted_pickle_load(file_dataset)
+            pre_handler = pickle.load(file_dataset)
         return pre_handler
 
     def rolling_process(self):

examples/workflow_by_code.py

@@ -7,7 +7,6 @@ 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
@@ -16,6 +15,7 @@ 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

@@ -45,7 +45,7 @@ dependencies = [
   "pymongo",
   "loguru",
   "lightgbm",
-  "gym",
+  "gymnasium<=0.26.2",
   "cvxpy",
   "joblib",
   "matplotlib",
@@ -69,7 +69,6 @@ rl = [
   "torch",
   "numpy<2.0.0",
 ]
-
 lint = [
   "black",
   "pylint",
@@ -102,10 +101,6 @@ 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'
@@ -122,4 +117,3 @@ 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

@@ -4,10 +4,7 @@ from pathlib import Path
 
 from setuptools_scm import get_version
 
-try:
-    from ._version import version as __version__
-except ImportError:
-    __version__ = get_version(root="..", relative_to=__file__)
+__version__ = get_version(root="..", relative_to=__file__)
 __version__bak = __version__  # This version is backup for QlibConfig.reset_qlib_version
 import logging
 import os
@@ -143,10 +140,7 @@ 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:
-                        if isinstance(_c, str):
-                            _temp_mount = _c.split(" ")[2]
-                        else:
-                            _temp_mount = _c.decode("utf-8").split(" ")[2]
+                        _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

@@ -18,6 +18,7 @@ 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/cli/data.py

@@ -4,5 +4,6 @@
 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

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

qlib/contrib/data/dataset.py

@@ -11,6 +11,7 @@ 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

@@ -20,6 +20,7 @@ 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

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

qlib/contrib/meta/data_selection/__init__.py

@@ -4,4 +4,5 @@
 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, free_raw_data=False), key))
+                ds_l.append((lgb.Dataset(x.values, label=y, weight=w), key))
         return ds_l
 
     def fit(
@@ -109,10 +109,8 @@ class LGBModel(ModelFT, LightGBMFInt):
             verbose level
         """
         # Based on existing model and finetune by train more rounds
-        ds_l = self._prepare_data(dataset, reweighter)
-        dtrain, _ = ds_l[0]
-
-        if dtrain.construct().num_data() == 0:
+        dtrain, _ = self._prepare_data(dataset, reweighter)  # pylint: disable=W0632
+        if dtrain.empty:
             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

@@ -10,7 +10,6 @@ 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
 
@@ -149,7 +148,7 @@ 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 version.parse(str(torch.__version__).split("+", maxsplit=1)[0]) <= version.parse("2.6.0"):
+            if str(torch.__version__).split("+", maxsplit=1)[0] <= "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

@@ -12,7 +12,6 @@ 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")
@@ -31,7 +30,7 @@ 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 = restricted_pickle_load(fr)
+        instance = pickle.load(fr)
     return instance
 
 

qlib/contrib/report/analysis_model/__init__.py

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

qlib/contrib/report/analysis_position/__init__.py

@@ -7,4 +7,5 @@ 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/data/ana.py

@@ -12,7 +12,6 @@ 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

@@ -7,7 +7,6 @@ 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

@@ -14,7 +14,6 @@ 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
 
@@ -299,7 +298,7 @@ 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 = restricted_pickle_load(f)
+            internal_data = pickle.load(f)
 
         md = MetaDatasetDS(exp_name=internal_data, **kwargs)
 
@@ -361,7 +360,7 @@ class DDGDA(Rolling):
         )
 
         with self._internal_data_path.open("rb") as f:
-            internal_data = restricted_pickle_load(f)
+            internal_data = 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

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

qlib/contrib/strategy/cost_control.py

@@ -1,117 +1,101 @@
 # 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=None,
-        dataset=None,
-        topk=None,
+        model,
+        dataset,
+        topk,
         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
-            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.
+            top-N stocks to buy
         risk_degree : float
-            The target percentage of total value to be invested.
+            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.
         """
         super(SoftTopkStrategy, self).__init__(
-            model=model, dataset=dataset, order_generator_cls_or_obj=order_generator_cls_or_obj, **kwargs
+            model, dataset, order_generator_cls_or_obj, trade_exchange, level_infra, common_infra, **kwargs
         )
-
         self.topk = topk
-        self.trade_impact_limit = trade_impact_limit if trade_impact_limit is not None else max_sold_weight
+        self.max_sold_weight = 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, **kwargs):
+    def generate_target_weight_position(self, score, current, trade_start_time, trade_end_time):
         """
-        Generates target position using Proportional Budget Allocation.
-        Ensures deterministic sells and synchronized buys under impact limits.
+        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
         """
+        # 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 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}
+        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

qlib/contrib/strategy/optimizer/__init__.py

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

qlib/contrib/strategy/optimizer/enhanced_indexing.py

@@ -9,6 +9,7 @@ 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

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

qlib/contrib/torch.py

@@ -5,7 +5,6 @@ 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

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

qlib/contrib/tuner/space.py

@@ -6,6 +6,7 @@
 
 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

@@ -8,6 +8,7 @@ import os
 import yaml
 import json
 import copy
+import pickle
 import logging
 import importlib
 import subprocess
@@ -17,7 +18,6 @@ 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 = restricted_pickle_load(fp)
+            analysis_df = 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

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

qlib/data/__init__.py

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

qlib/data/cache.py

@@ -30,7 +30,6 @@ 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
@@ -226,7 +225,7 @@ class CacheUtils:
             cache_path = Path(cache_path)
             meta_path = cache_path.with_suffix(".meta")
             with meta_path.open("rb") as f:
-                d = restricted_pickle_load(f)
+                d = pickle.load(f)
             with meta_path.open("wb") as f:
                 try:
                     d["meta"]["last_visit"] = str(time.time())
@@ -593,7 +592,7 @@ 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 = restricted_pickle_load(f)
+                d = pickle.load(f)
             instrument = d["info"]["instrument"]
             field = d["info"]["field"]
             freq = d["info"]["freq"]
@@ -960,7 +959,7 @@ 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 = restricted_pickle_load(f)
+                d = 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, print_function
-
-import json
+from __future__ import division
+from __future__ import print_function
 
 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": json.dumps(request_content, default=str)}
+        request_content = {"head": head_info, "body": pickle.dumps(request_content, protocol=C.dump_protocol_version)}
         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

@@ -19,6 +19,7 @@ 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

@@ -2,6 +2,7 @@
 # Licensed under the MIT License.
 
 import abc
+import pickle
 from pathlib import Path
 import warnings
 import pandas as pd
@@ -10,7 +11,6 @@ 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 = restricted_pickle_load(f)
+                    self._data = pickle.load(f)
         elif isinstance(self._config, pd.DataFrame):
             self._data = self._config
 

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 np.isnan(_bool):
+            if _bool == np.nan:
                 _bool = False
             if _lbool is None:
                 _cur_start = _ts

qlib/data/pit.py

@@ -13,7 +13,6 @@ 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

@@ -3,4 +3,5 @@
 
 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 calendar.index(value)
+        return int(np.argwhere(calendar == value)[0])
 
     def insert(self, index: int, value: CalVT):
         calendar = self._read_calendar()

qlib/model/__init__.py

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

qlib/model/meta/__init__.py

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

qlib/model/riskmodel/__init__.py

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

qlib/rl/contrib/naive_config_parser.py

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

qlib/rl/data/native.py

@@ -2,18 +2,17 @@
 # Licensed under the MIT License.
 from __future__ import annotations
 
-import os
 from pathlib import Path
-from typing import List, cast
+from typing import cast, List
 
 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
 
 
@@ -163,7 +162,7 @@ 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 = restricted_pickle_load(fstream)
+            dataset = pickle.load(fstream)
         data = dataset.handler.fetch(pd.IndexSlice[stock_id, start_time:end_time], level=None)
 
         if index_only:

qlib/rl/interpreter.py

@@ -5,9 +5,9 @@ from __future__ import annotations
 
 from typing import Any, Generic, TypeVar
 
-import gym
+import gymnasium as gym
 import numpy as np
-from gym import spaces
+from gymnasium import spaces
 
 from qlib.typehint import final
 from .simulator import ActType, StateType

qlib/rl/order_execution/interpreter.py

@@ -8,7 +8,7 @@ from typing import Any, List, Optional, cast
 
 import numpy as np
 import pandas as pd
-from gym import spaces
+from gymnasium import spaces
 
 from qlib.constant import EPS
 from qlib.rl.data.base import ProcessedDataProvider

qlib/rl/order_execution/policy.py

@@ -6,11 +6,11 @@ from __future__ import annotations
 from pathlib import Path
 from typing import Any, Dict, Generator, Iterable, Optional, OrderedDict, Tuple, cast
 
-import gym
+import gymnasium as gym
 import numpy as np
 import torch
 import torch.nn as nn
-from gym.spaces import Discrete
+from gymnasium.spaces import Discrete
 from tianshou.data import Batch, ReplayBuffer, to_torch
 from tianshou.policy import BasePolicy, PPOPolicy, DQNPolicy
 

qlib/rl/utils/env_wrapper.py

@@ -6,8 +6,8 @@ from __future__ import annotations
 import weakref
 from typing import Any, Callable, cast, Dict, Generic, Iterable, Iterator, Optional, Tuple
 
-import gym
-from gym import Space
+import gymnasium as gym
+from gymnasium import Space
 
 from qlib.rl.aux_info import AuxiliaryInfoCollector
 from qlib.rl.interpreter import ActionInterpreter, ObsType, PolicyActType, StateInterpreter

qlib/rl/utils/finite_env.py

@@ -13,7 +13,7 @@ import warnings
 from contextlib import contextmanager
 from typing import Any, Callable, Dict, Generator, List, Optional, Set, Tuple, Type, Union, cast
 
-import gym
+import gymnasium as gym
 import numpy as np
 from tianshou.env import BaseVectorEnv, DummyVectorEnv, ShmemVectorEnv, SubprocVectorEnv
 

qlib/utils/data.py

@@ -3,7 +3,6 @@
 """
 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

@@ -11,6 +11,7 @@ import contextlib
 import importlib
 import os
 from pathlib import Path
+import pickle
 import pkgutil
 import re
 import sys
@@ -19,7 +20,6 @@ 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]):
@@ -168,10 +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 restricted_pickle_load(f)
+                    return pickle.load(f)
         else:
             with config.open("rb") as f:
-                return restricted_pickle_load(f)
+                return pickle.load(f)
 
     klass, cls_kwargs = get_callable_kwargs(config, default_module=default_module)
 

qlib/utils/objm.py

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

qlib/utils/pickle_utils.py

@@ -1,171 +0,0 @@
-# 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/time.py

@@ -3,7 +3,6 @@
 """
 Time related utils are compiled in this script
 """
-
 import bisect
 from datetime import datetime, time, date, timedelta
 from typing import List, Optional, Tuple, Union
@@ -15,6 +14,7 @@ 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

@@ -16,6 +16,7 @@ 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

@@ -22,6 +22,7 @@ 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)
 
 
@@ -475,13 +476,7 @@ 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)
-            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']}."
-            )
+            self.backtest_config["end_time"] = get_date_by_shift(dt_values.max(), 1)
 
         artifact_objects = {}
         # custom strategy and get backtest

qlib/workflow/task/gen.py

@@ -3,7 +3,6 @@
 """
 TaskGenerator module can generate many tasks based on TaskGen and some task templates.
 """
-
 import abc
 import copy
 import pandas as pd
@@ -107,13 +106,15 @@ def handler_mod(task: dict, rolling_gen):
         rg (RollingGen): an instance of RollingGen
     """
     try:
-        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)
+        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]
+            )
     except KeyError:
         # Maybe dataset do not have handler, then do nothing.
         pass

qlib/workflow/task/manage.py

@@ -12,7 +12,6 @@ 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
@@ -29,7 +28,6 @@ from tqdm.cli import tqdm
 
 from .utils import get_mongodb
 from ...config import C
-from ...utils.pickle_utils import restricted_pickle_loads
 
 
 class TaskManager:
@@ -133,7 +131,7 @@ class TaskManager:
         for prefix in self.ENCODE_FIELDS_PREFIX:
             for k in list(task.keys()):
                 if k.startswith(prefix):
-                    task[k] = restricted_pickle_loads(task[k])
+                    task[k] = 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
-from loguru import logger
+import qlib
 from tqdm import tqdm
 
-import qlib
 from qlib.data import D
 
 
@@ -36,7 +36,6 @@ 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."
@@ -69,43 +68,6 @@ 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 = {
@@ -215,13 +177,11 @@ 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("-------------------------------------------------")
@@ -237,11 +197,6 @@ 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

@@ -45,7 +45,7 @@ class InfoCollector:
             "pymongo",
             "loguru",
             "lightgbm",
-            "gym",
+            "gymnasium",
             "cvxpy",
             "joblib",
             "matplotlib",

scripts/data_collector/base.py

@@ -280,20 +280,11 @@ 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)
 
@@ -309,18 +300,14 @@ 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)
 
-        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.")
+        # 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)
 
     def normalize(self):
         logger.info("normalize data......")

scripts/data_collector/cn_index/collector.py

@@ -22,6 +22,7 @@ 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

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

scripts/data_collector/us_index/collector.py

@@ -7,14 +7,13 @@ 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))
@@ -23,6 +22,7 @@ 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 = {
@@ -51,7 +51,6 @@ class WIKIIndex(IndexBase):
         )
 
         self._target_url = f"{WIKI_URL}/{WIKI_INDEX_NAME_MAP[self.index_name.upper()]}"
-        self._ua = UserAgent()
 
     @property
     @abc.abstractmethod
@@ -113,8 +112,7 @@ class WIKIIndex(IndexBase):
         return _calendar_list
 
     def _request_new_companies(self) -> requests.Response:
-        headers = {"User-Agent": self._ua.random}
-        resp = requests.get(self._target_url, timeout=None, headers=headers)
+        resp = requests.get(self._target_url, timeout=None)
         if resp.status_code != 200:
             raise ValueError(f"request error: {self._target_url}")
 
@@ -130,7 +128,7 @@ 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(StringIO(_data.text))
+        df_list = pd.read_html(_data.text)
         for _df in df_list:
             _df = self.filter_df(_df)
             if (_df is not None) and (not _df.empty):
@@ -228,11 +226,7 @@ class SP500Index(WIKIIndex):
     def get_changes(self) -> pd.DataFrame:
         logger.info(f"get sp500 history changes......")
         # NOTE: may update the index of the table
-        # 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 = pd.read_html(self.WIKISP500_CHANGES_URL)[-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

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

scripts/data_collector/utils.py

@@ -7,6 +7,7 @@ import importlib
 import time
 import bisect
 import pickle
+import random
 import requests
 import functools
 from pathlib import Path
@@ -20,9 +21,6 @@ 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}"
 
@@ -69,16 +67,9 @@ def get_calendar_list(bench_code="CSI300") -> List[pd.Timestamp]:
 
     logger.info(f"get calendar list: {bench_code}......")
 
-    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()))
+    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))
 
     calendar = _CALENDAR_MAP.get(bench_code, None)
     if calendar is None:
@@ -89,17 +80,30 @@ 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
 
-                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()
+                @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))
             else:
-                end_date = time.strftime("%Y-%m-%d", time.localtime())
-                calendar = _get_calendar(end_date=end_date)
+                calendar = _get_calendar(CALENDAR_BENCH_URL_MAP[bench_code])
         _CALENDAR_MAP[bench_code] = calendar
     logger.info(f"end of get calendar list: {bench_code}.")
     return calendar
@@ -276,7 +280,7 @@ 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 = restricted_pickle_load(fp)
+                cache_symbols = pickle.load(fp)
                 symbols |= cache_symbols
         with symbol_cache_path.open("wb") as fp:
             pickle.dump(symbols, fp)
@@ -293,14 +297,20 @@ 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():
-        df = ak.get_us_stock_name()
-        _symbols = df["symbol"].to_list()
+        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
 
         if len(_symbols) < 8000:
             raise ValueError("request error")

scripts/data_collector/yahoo/collector.py

@@ -613,6 +613,10 @@ 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

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

scripts/get_data.py

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

setup.py

@@ -2,11 +2,22 @@ import os
 
 import numpy
 from setuptools import Extension, setup
+from setuptools_scm import get_version
+
+
+def read(rel_path: str) -> str:
+    here = os.path.abspath(os.path.dirname(__file__))
+    with open(os.path.join(here, rel_path), encoding="utf-8") as fp:
+        return fp.read()
+
 
 NUMPY_INCLUDE = numpy.get_include()
 
 
+VERSION = get_version(root=".", relative_to=__file__)
+
 setup(
+    version=VERSION,
     ext_modules=[
         Extension(
             "qlib.data._libs.rolling",

tests/backtest/test_soft_topk_strategy.py

@@ -1,56 +0,0 @@
-import pandas as pd
-import pytest
-from qlib.contrib.strategy.cost_control import SoftTopkStrategy
-
-
-class MockPosition:
-    def __init__(self, weights):
-        self.weights = weights
-
-    def get_stock_weight_dict(self, only_stock=True):
-        return self.weights
-
-
-def test_soft_topk_logic():
-    # Initial: A=0.8, B=0.2 (Total=1.0). Target Risk=0.95.
-    # Scores: A and B are low, C and D are topk.
-    scores = pd.Series({"C": 0.9, "D": 0.8, "A": 0.1, "B": 0.1})
-    current_pos = MockPosition({"A": 0.8, "B": 0.2})
-
-    topk = 2
-    risk_degree = 0.95
-    impact_limit = 0.1  # Max change per step
-
-    def create_test_strategy(impact_limit_value):
-        strat = SoftTopkStrategy.__new__(SoftTopkStrategy)
-        strat.topk = topk
-        strat.risk_degree = risk_degree
-        strat.trade_impact_limit = impact_limit_value
-        return strat
-
-    # 1. With impact limit: Expect deterministic sell and limited buy
-    strat_i = create_test_strategy(impact_limit)
-    res_i = strat_i.generate_target_weight_position(scores, current_pos, None, None)
-
-    # A should be exactly 0.8 - 0.1 = 0.7
-    assert abs(res_i["A"] - 0.7) < 1e-8
-    # B should be exactly 0.2 - 0.1 = 0.1
-    assert abs(res_i["B"] - 0.1) < 1e-8
-    # Total sells = 0.2 released. New budget = 0.2 + (0.95 - 1.0) = 0.15.
-    # C and D share 0.15 -> 0.075 each.
-    assert abs(res_i["C"] - 0.075) < 1e-8
-    assert abs(res_i["D"] - 0.075) < 1e-8
-
-    # 2. Without impact limit: Expect full liquidation and full target fill
-    strat_c = create_test_strategy(1.0)
-    res_c = strat_c.generate_target_weight_position(scores, current_pos, None, None)
-
-    # A, B not in topk -> Liquidated
-    assert "A" not in res_c and "B" not in res_c
-    # C, D should reach ideal_per_stock (0.95/2 = 0.475)
-    assert abs(res_c["C"] - 0.475) < 1e-8
-    assert abs(res_c["D"] - 0.475) < 1e-8
-
-
-if __name__ == "__main__":
-    pytest.main([__file__])

tests/backtest/test_soft_topk_strategy_cold_start.py

@@ -1,38 +0,0 @@
-import pandas as pd
-import pytest
-
-from qlib.contrib.strategy.cost_control import SoftTopkStrategy
-
-
-class MockPosition:
-    def __init__(self, weights):
-        self.weights = weights
-
-    def get_stock_weight_dict(self, only_stock=True):
-        return self.weights
-
-
-def create_test_strategy(topk, risk_degree, impact_limit):
-    strat = SoftTopkStrategy.__new__(SoftTopkStrategy)
-    strat.topk = topk
-    strat.risk_degree = risk_degree
-    strat.trade_impact_limit = impact_limit
-    return strat
-
-
-@pytest.mark.parametrize(
-    ("impact_limit", "expected_fill"),
-    [
-        (0.1, 0.1),
-        (1.0, 0.475),
-    ],
-)
-def test_soft_topk_cold_start_impact_limit(impact_limit, expected_fill):
-    scores = pd.Series({"C": 0.9, "D": 0.8, "A": 0.1, "B": 0.1})
-    current_pos = MockPosition({})
-
-    strat = create_test_strategy(topk=2, risk_degree=0.95, impact_limit=impact_limit)
-    res = strat.generate_target_weight_position(scores, current_pos, None, None)
-
-    assert abs(res["C"] - expected_fill) < 1e-8
-    assert abs(res["D"] - expected_fill) < 1e-8

tests/data_mid_layer_tests/test_handler.py

@@ -1,10 +1,10 @@
 import os
+import pickle
+import shutil
 import unittest
-
+from qlib.tests import TestAutoData
 from qlib.data import D
 from qlib.data.dataset.handler import DataHandlerLP
-from qlib.tests import TestAutoData
-from qlib.utils.pickle_utils import restricted_pickle_load
 
 
 class HandlerTests(TestAutoData):
@@ -23,7 +23,7 @@ class HandlerTests(TestAutoData):
         dh.to_pickle(fname, dump_all=True)
 
         with open(fname, "rb") as f:
-            dh_d = restricted_pickle_load(f)
+            dh_d = pickle.load(f)
 
         self.assertTrue(dh_d._data.equals(df))
         self.assertTrue(dh_d._infer is dh_d._data)

tests/rl/test_finite_env.py

@@ -3,7 +3,7 @@
 
 from collections import Counter
 
-import gym
+import gymnasium as gym
 import numpy as np
 from tianshou.data import Batch, Collector
 from tianshou.policy import BasePolicy
@@ -17,6 +17,7 @@ from qlib.rl.utils.finite_env import (
     generate_nan_observation,
 )
 
+
 _test_space = gym.spaces.Dict(
     {
         "sensors": gym.spaces.Dict(

tests/rl/test_logger.py

@@ -7,10 +7,10 @@ import logging
 import re
 from typing import Any, Tuple
 
-import gym
+import gymnasium as gym
 import numpy as np
 import pandas as pd
-from gym import spaces
+from gymnasium import spaces
 from tianshou.data import Collector, Batch
 from tianshou.policy import BasePolicy
 

tests/rl/test_trainer.py

@@ -7,7 +7,7 @@ import pytest
 
 import torch
 import torch.nn as nn
-from gym import spaces
+from gymnasium import spaces
 from tianshou.policy import PPOPolicy
 
 from qlib.config import C

tests/test_contrib_workflow.py

@@ -13,6 +13,7 @@ from qlib.workflow import R
 from qlib.tests import TestAutoData
 from qlib.tests.config import GBDT_MODEL, get_dataset_config, CSI300_MARKET
 
+
 CSI300_GBDT_TASK = {
     "model": GBDT_MODEL,
     "dataset": get_dataset_config(

tests/test_dump_data.py

@@ -16,6 +16,7 @@ sys.path.append(str(Path(__file__).resolve().parent.parent.joinpath("scripts")))
 from get_data import GetData
 from dump_bin import DumpDataAll, DumpDataFix
 
+
 DATA_DIR = Path(__file__).parent.joinpath("test_dump_data")
 SOURCE_DIR = DATA_DIR.joinpath("source")
 SOURCE_DIR.mkdir(exist_ok=True, parents=True)

tests/test_pit.py

@@ -19,6 +19,7 @@ from dump_pit import DumpPitData
 sys.path.append(str(Path(__file__).resolve().parent.parent.joinpath("scripts/data_collector/pit")))
 from collector import Run
 
+
 pd.set_option("display.width", 1000)
 pd.set_option("display.max_columns", None)