Qlib上手学习记录

最新推荐文章于 2025-06-01 09:07:38 发布

原创最新推荐文章于 2025-06-01 09:07:38 发布 · 1.8k 阅读

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CC 4.0 BY-SA版权

文章标签：

#人工智能 #财务预测 #量化研究 #机器学习

Qlib 是一个面向人工智能的量化投资平台，其目标是通过在量化投资中运用AI技术来发掘潜力、赋能研究并创造价值，从探索投资策略到实现产品化部署。该平台支持多种机器学习建模范式，包括有监督学习、市场动态建模以及强化学习等。

真的是走了很多弯路

step 1. 使用 ananconda 建立环境，要主要必须是 python 3.8

(a308) E:\hw2024\stock>python --version
Python 3.8.20

失败的

git clone https://github.com/microsoft/qlib.git

下了以后进入 glib folder 后

执行
 pip install .
是会报错的，不管你还什么source 都一样，整了很久

PS D:\ProgramData\git\qlib> pip install .
Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
Processing d:\programdata\git\qlib
Installing build dependencies ... error
error: subprocess-exited-with-error

× pip subprocess to install build dependencies did not run successfully.
│ exit code: 1
╰─> [9 lines of output]
Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
WARNING: Retrying (Retry(total=4, connect=None, read=None, redirect=None, status=None)) after connection broken by 'SSLError(SSLZeroReturnError(6, 'TLS/SSL connection has been closed (EOF) (_ssl.c:1149)'))': /simple/setuptools/
WARNING: Retrying (Retry(total=3, connect=None, read=None, redirect=None, status=None)) after connection broken by 'SSLError(SSLZeroReturnError(6, 'TLS/SSL connection has been closed (EOF) (_ssl.c:1149)'))': /simple/setuptools/
WARNING: Retrying (Retry(total=2, connect=None, read=None, redirect=None, status=None)) after connection broken by 'SSLError(SSLZeroReturnError(6, 'TLS/SSL connection has been closed (EOF) (_ssl.c:1149)'))': /simple/setuptools/
WARNING: Retrying (Retry(total=1, connect=None, read=None, redirect=None, status=None)) after connection broken by 'SSLError(SSLZeroReturnError(6, 'TLS/SSL connection has been closed (EOF) (_ssl.c:1149)'))': /simple/setuptools/
WARNING: Retrying (Retry(total=0, connect=None, read=None, redirect=None, status=None)) after connection broken by 'SSLError(SSLZeroReturnError(6, 'TLS/SSL connection has been closed (EOF) (_ssl.c:1149)'))': /simple/setuptools/
Could not fetch URL https://pypi.tuna.tsinghua.edu.cn/simple/setuptools/: There was a problem confirming the ssl certificate: HTTPSConnectionPool(host='pypi.tuna.tsinghua.edu.cn', port=443): Max retries exceeded with url: /simple/setuptools/ (Caused by SSLError(SSLZeroReturnError(6, 'TLS/SSL connection has been closed (EOF) (_ssl.c:1149)'))) - skipping
ERROR: Could not find a version that satisfies the requirement setuptools (from versions: none)
ERROR: No matching distribution found for setuptools
[end of output]

note: This error originates from a subprocess, and is likely not a problem with pip.
error: subprocess-exited-with-error

× pip subprocess to install build dependencies did not run successfully.
│ exit code: 1
╰─> See above for output.

note: This error originates from a subprocess, and is likely not a problem with pip.
PS D:\ProgramData\git\qlib> pip install. -i https://pypi.org/simple/
ERROR: unknown command "install." - maybe you meant "install"
PS D:\ProgramData\git\qlib> pip install . -i https://pypi.org/simple/
Looking in indexes: https://pypi.org/simple/

使用前最好看下这个

021 年之前发布的功能未在此列出。

Qlib 是一个开源的、面向 AI 的量化投资平台，旨在通过 AI 技术实现量化投资的潜力，赋能研究，并创造价值，从探索想法到实施生产。Qlib 支持多种机器学习建模范式，包括监督学习、市场动态建模和强化学习。

越来越多的 SOTA 量化研究工作/论文在 Qlib 中发布，以协作解决量化投资中的关键挑战。例如，1) 使用监督学习从丰富和异构的金融数据中挖掘市场的复杂非线性模式，2) 使用自适应概念漂移技术建模金融市场的动态特性，3) 使用强化学习建模连续投资决策并协助投资者优化其交易策略。

它包含了完整的机器学习流程，包括数据处理、模型训练、回测；并涵盖了量化投资的整个链条：阿尔法挖掘、风险建模、投资组合优化和订单执行。更多详情，请参阅我们的论文 "Qlib: 一个面向 AI 的量化投资平台"。

框架、教程、数据与 DevOps 量化研究中的主要挑战与解决方案
- 计划 - 量化研究中的主要挑战与解决方案
- Qlib 框架 - 预测：寻找有价值的信号/模式
- 快速开始 - 适应市场动态
- 安装 - 强化学习：建模连续决策
- 数据准备 - 量化模型（论文）库
- 自动量化研究工作流程 - 运行单个模型
- 通过代码构建定制量化研究工作流程 - 运行多个模型
- 量化数据集库
- 学习框架
- 更多关于 Qlib
- 离线模式与在线模式
- Qlib 数据服务器的性能
- 相关报告
- 联系我们
- 贡献

计划

正在开发中的新功能（按预计发布时间排序）。您的反馈对这些功能非常重要。

Qlib 框架

Qlib 的高级框架如上所示（用户可以在深入了解时找到 Qlib 设计的详细框架）。这些组件被设计为松耦合模块，每个组件都可以独立使用。

Qlib 提供了强大的基础设施来支持量化研究。数据始终是一个重要部分。设计了一个强大的学习框架来支持多样化的学习范式（例如强化学习、监督学习)和不同层次的模式（例如市场动态建模)。通过建模市场，交易策略将生成交易决策并执行。不同层次或粒度的多个交易策略和执行器可以嵌套在一起进行优化和运行。最后，将提供全面的分析，模型可以在低成本下在线服务。

快速开始

本快速开始指南试图展示

使用 Qlib 构建完整的量化研究工作流程并尝试您的想法非常容易。
尽管使用公共数据和简单模型，机器学习技术在实际量化投资中表现非常出色。

以下是一个快速**演示**，展示了如何安装 Qlib 并使用 qrun 运行 LightGBM。但是，请确保您已按照说明准备好数据。

安装

此表展示了 Qlib 支持的 Python 版本：

使用 pip 安装从源码安装绘图
Python 3.7 ✔️ ✔️ ✔️
Python 3.8 ✔️ ✔️ ✔️
Python 3.9 ❌ ✔️ ❌

注意:

Conda 建议用于管理您的 Python 环境。在某些情况下，在 conda 环境之外使用 Python 可能会导致缺少头文件，从而导致某些包的安装失败。
请注意，在 Python 3.6 中安装 cython 时，从源码安装 Qlib 会引发一些错误。如果用户在机器上使用 Python 3.6，建议升级 Python 到 3.7 版本或使用 conda 的 Python 从源码安装 Qlib。
对于 Python 3.9，Qlib 支持运行训练模型、回测和绘制大部分相关图表（包括 notebook 中的图表）。然而，目前不支持绘制模型性能图表，我们将在未来依赖包升级时修复此问题。
Qlib 需要 tables 包，tables 中的 hdf5 不支持 python3.9。

使用 pip 安装

用户可以轻松地通过以下命令使用 pip 安装 Qlib。
  pip install pyqlib
注意：pip 将安装最新的稳定版 qlib。然而，qlib 的主分支目前正在积极开发中。如果您想测试主分支中的最新脚本或功能，请使用以下方法安装 qlib。

从源代码安装

此外，用户可以根据以下步骤，通过源代码安装最新的开发版 Qlib：
在从源代码安装 Qlib 之前，用户需要安装一些依赖项：
pip install numpy
pip install --upgrade cython
按如下方式克隆仓库并安装 Qlib。
git clone https://github.com/microsoft/qlib.git && cd qlib
pip install .  # 推荐开发时使用 `pip install -e .[dev]`。详细内容请查阅文档 docs/developer/code_standard_and_dev_guide.rst
注意：您也可以使用 python setup.py install 来安装 Qlib。但这不是推荐的方法。它将跳过 pip 并可能导致不明显的问题。例如，只有使用命令 pip install . 可以覆盖通过 pip install pyqlib 安装的稳定版，而使用命令 python setup.py install 不能。
提示：如果在您的环境中无法安装 Qlib 或运行示例运行失败，比较您的步骤和 CI 工作流可能帮助您找到问题。

Mac 用户提示：如果您使用的是配备 M1 芯片的 Mac，构建 LightGBM 的 wheel 包时可能会遇到问题，这是因为缺少来自 OpenMP 的依赖项。要解决这个问题，首先使用 brew install libomp 安装 op.enmp，然后运行 pip install . 进行构建。

框架、教程、数据与 DevOps	量化研究中的主要挑战与解决方案
- 计划	- 量化研究中的主要挑战与解决方案
- Qlib 框架	- 预测：寻找有价值的信号/模式
- 快速开始	- 适应市场动态
- 安装	- 强化学习：建模连续决策
- 数据准备	- 量化模型（论文）库
- 自动量化研究工作流程	- 运行单个模型
- 通过代码构建定制量化研究工作流程	- 运行多个模型
- 量化数据集库
- 学习框架
- 更多关于 Qlib
- 离线模式与在线模式
- Qlib 数据服务器的性能
- 相关报告
- 联系我们
- 贡献

	使用 pip 安装	从源码安装	绘图
Python 3.7	✔️	✔️	✔️
Python 3.8	✔️	✔️	✔️
Python 3.9	❌	✔️	❌

pip install numpy
pip install --upgrade cython

==》要改成 conda install cython

最后，不要使用魔法

pip install pyqlib

安装起来了，装的依赖真心不少

(a308) E:\hw2024\stock>pip install pyqlib
Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
Requirement already satisfied: pyqlib in d:\programdata\envs\a308\lib\site-packages\pyqlib-0.9.5.99-py3.8-win-amd64.egg (0.9.5.99)
Collecting numpy<1.24,>=1.12.0 (from pyqlib)
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/4c/42/6274f92514fbefcb1caa66d56d82ac7ac89f7652c0cef1e159a4b79e09f1/numpy-1.23.5-cp38-cp38-win_amd64.whl (14.7 MB)
---------------------------------------- 14.7/14.7 MB 17.1 MB/s eta 0:00:00
Requirement already satisfied: pandas>=0.25.1 in d:\programdata\envs\a308\lib\site-packages (from pyqlib) (2.0.3)
Collecting scipy>=1.7.3 (from pyqlib)
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/32/8e/7f403535ddf826348c9b8417791e28712019962f7e90ff845896d6325d09/scipy-1.10.1-cp38-cp38-win_amd64.whl (42.2 MB)
---------------------------------------- 42.2/42.2 MB 19.3 MB/s eta 0:00:00
Collecting scs<=3.2.4 (from pyqlib)
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/6a/9f/d18c7f5ef2764c7ea10cf0077b6a953fc56afade1bb641571916143255dd/scs-3.2.4-cp38-cp38-win_amd64.whl (8.4 MB)
---------------------------------------- 8.4/8.4 MB 15.8 MB/s eta 0:00:00
Requirement already satisfied: requests>=2.18.0 in d:\programdata\envs\a308\lib\site-packages (from pyqlib) (2.32.3)
Collecting sacred>=0.7.4 (from pyqlib)
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/01/d9/67a53f465395e0be45ac8f780938e0a551d82d4b864e5a9394ab66168432/sacred-0.8.7-py2.py3-none-any.whl (108 kB)
Collecting python-socketio (from pyqlib)
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/7e/9a/52b94c8c9516e07844d3da3d0da3e68649f172aeeace8d7a1becca9e6111/python_socketio-5.11.4-py3-none-any.whl (76 kB)
Collecting redis>=3.0.1 (from pyqlib)
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/3c/5f/fa26b9b2672cbe30e07d9a5bdf39cf16e3b80b42916757c5f92bca88e4ba/redis-5.2.1-py3-none-any.whl (261 kB)
Collecting python-redis-lock>=3.3.1 (from pyqlib)
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/09/70/c5dfaec2085d9be10792704f108543ba1802e228bf040632c673066d8e78/python_redis_lock-4.0.0-py3-none-any.whl (12 kB)
Collecting schedule>=0.6.0 (from pyqlib)
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/20/a7/84c96b61fd13205f2cafbe263cdb2745965974bdf3e0078f121dfeca5f02/schedule-1.2.2-py3-none-any.whl (12 kB)
Collecting cvxpy>=1.0.21 (from pyqlib)
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/a1/68/44958a503a7882f032a55006eee8da037018cd0d755b6a1618c78ff757f1/cvxpy-1.5.2-cp38-cp38-win_amd64.whl (1.1 MB)
---------------------------------------- 1.1/1.1 MB 2.5 MB/s eta 0:00:00
Collecting hyperopt==0.1.2 (from pyqlib)
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/63/12/704382c3081df3ae3f9d96fe6afb62efa2fa9749be20c301cd2797fb0b52/hyperopt-0.1.2-py3-none-any.whl (115 kB)
Collecting fire>=0.3.1 (from pyqlib)
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/6b/b6/82c7e601d6d3c3278c40b7bd35e17e82aa227f050aa9f66cb7b7fce29471/fire-0.7.0.tar.gz (87 kB)
Preparing metadata (setup.py) ... done
Collecting statsmodels (from pyqlib)
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/15/93/62c79bb548988201078242d903de47666a08167de8e4beceb32157d75d5f/statsmodels-0.14.1-cp38-cp38-win_amd64.whl (10.0 MB)
---------------------------------------- 10.0/10.0 MB 9.3 MB/s eta 0:00:00
Collecting xlrd>=1.0.0 (from pyqlib)
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/a6/0c/c2a72d51fe56e08a08acc85d13013558a2d793028ae7385448a6ccdfae64/xlrd-2.0.1-py2.py3-none-any.whl (96 kB)
Collecting plotly>=4.12.0 (from pyqlib)
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/e5/ae/580600f441f6fc05218bd6c9d5794f4aef072a7d9093b291f1c50a9db8bc/plotly-5.24.1-py3-none-any.whl (19.1 MB)
---------------------------------------- 19.1/19.1 MB 18.2 MB/s eta 0:00:00
Collecting matplotlib>=3.3 (from pyqlib)
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/16/51/58b0b9de42fe1e665736d9286f88b5f1556a0e22bed8a71f468231761083/matplotlib-3.7.5-cp38-cp38-win_amd64.whl (7.5 MB)
---------------------------------------- 7.5/7.5 MB 11.0 MB/s eta 0:00:00
Collecting tables>=3.6.1 (from pyqlib)
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/6a/44/4c397ecf3140bef6fb1a4e0397130c50d0bf94282033f1209b0259656679/tables-3.8.0-cp38-cp38-win_amd64.whl (3.6 MB)
---------------------------------------- 3.6/3.6 MB 8.5 MB/s eta 0:00:00
Collecting pyyaml>=5.3.1 (from pyqlib)
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/75/8a/ee831ad5fafa4431099aa4e078d4c8efd43cd5e48fbc774641d233b683a9/PyYAML-6.0.2-cp38-cp38-win_amd64.whl (162 kB)
Collecting mlflow<=1.30.0,>=1.12.1 (from pyqlib)
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/33/fe/c2c9b72585609344e70606b23c642c8f069997cc63aee55f746527ecd053/mlflow-1.30.0-py3-none-any.whl (17.0 MB)
---------------------------------------- 17.0/17.0 MB 14.9 MB/s eta 0:00:00
Collecting packaging<22 (from pyqlib)
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/05/8e/8de486cbd03baba4deef4142bd643a3e7bbe954a784dc1bb17142572d127/packaging-21.3-py3-none-any.whl (40 kB)
Collecting tqdm (from pyqlib)
Using cached https://pypi.tuna.tsinghua.edu.cn/packages/d0/30/dc54f88dd4a2b5dc8a0279bdd7270e735851848b762aeb1c1184ed1f6b14/tqdm-4.67.1-py3-none-any.whl (78 kB)
Collecting loguru (from pyqlib)
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/0c/29/0348de65b8cc732daa3e33e67806420b2ae89bdce2b04af740289c5c6c8c/loguru-0.7.3-py3-none-any.whl (61 kB)
Collecting lightgbm>=3.3.0 (from pyqlib)
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/d9/28/3be76b591a2e14a031b681b8283acf1dec2ad521f6f1701b7957df68c466/lightgbm-4.5.0-py3-none-win_amd64.whl (1.4 MB)
---------------------------------------- 1.4/1.4 MB 2.7 MB/s eta 0:00:00
Collecting tornado (from pyqlib)
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/61/cc/58b1adeb1bb46228442081e746fcdbc4540905c87e8add7c277540934edb/tornado-6.4.2-cp38-abi3-win_amd64.whl (438 kB)
Collecting joblib>=0.17.0 (from pyqlib)
Using cached https://pypi.tuna.tsinghua.edu.cn/packages/91/29/df4b9b42f2be0b623cbd5e2140cafcaa2bef0759a00b7b70104dcfe2fb51/joblib-1.4.2-py3-none-any.whl (301 kB)
Collecting ruamel.yaml<=0.17.36 (from pyqlib)
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/c3/06/20feed488ef1551074657d334cafd8aa094f7f1a6ae9d70df13c76bf5491/ruamel.yaml-0.17.36-py3-none-any.whl (106 kB)
Collecting pymongo==3.7.2 (from pyqlib)
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/0f/3a/b90cfa7e27fa92244925826538fa2cf80fed3cbd20a413fd0c1b9705d820/pymongo-3.7.2.tar.gz (628 kB)
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Preparing metadata (setup.py) ... done
Collecting scikit-learn>=0.22 (from pyqlib)
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/52/2d/ad6928a578c78bb0e44e34a5a922818b14c56716b81d145924f1f291416f/scikit_learn-1.3.2-cp38-cp38-win_amd64.whl (9.3 MB)
---------------------------------------- 9.3/9.3 MB 14.8 MB/s eta 0:00:00
Requirement already satisfied: dill in d:\programdata\envs\a308\lib\site-packages\dill-0.3.9-py3.8.egg (from pyqlib) (0.3.9)
Requirement already satisfied: filelock in d:\programdata\envs\a308\lib\site-packages (from pyqlib) (3.16.1)
Requirement already satisfied: jinja2 in d:\programdata\envs\a308\lib\site-packages (from pyqlib) (3.1.4)
Requirement already satisfied: gym in d:\programdata\envs\a308\lib\site-packages\gym-0.26.2-py3.8.egg (from pyqlib) (0.26.2)
Requirement already satisfied: cryptography in d:\programdata\envs\a308\lib\site-packages\cryptography-44.0.0-py3.8-win-amd64.egg (from pyqlib) (44.0.0)
Requirement already satisfied: protobuf<=3.20.1 in d:\programdata\envs\a308\lib\site-packages\protobuf-3.20.1-py3.8.egg (from pyqlib) (3.20.1)
Requirement already satisfied: six in d:\programdata\envs\a308\lib\site-packages (from hyperopt==0.1.2->pyqlib) (1.17.0)
Requirement already satisfied: networkx in d:\programdata\envs\a308\lib\site-packages (from hyperopt==0.1.2->pyqlib) (3.1)
Collecting future (from hyperopt==0.1.2->pyqlib)
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/da/71/ae30dadffc90b9006d77af76b393cb9dfbfc9629f339fc1574a1c52e6806/future-1.0.0-py3-none-any.whl (491 kB)
Collecting osqp>=0.6.2 (from cvxpy>=1.0.21->pyqlib)
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/64/5a/d860cb1679cd97a38d9227be9247853568b76b32751b83052899eaede73a/osqp-0.6.7.post3-cp38-cp38-win_amd64.whl (292 kB)
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Collecting prometheus-client (from prometheus-flask-exporter<1->mlflow<=1.30.0,>=1.12.1->pyqlib)
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/ff/c2/ab7d37426c179ceb9aeb109a85cda8948bb269b7561a0be870cc656eefe4/prometheus_client-0.21.1-py3-none-any.whl (54 kB)
Collecting simple-websocket>=0.10.0 (from python-engineio>=4.8.0->python-socketio->pyqlib)
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/52/59/0782e51887ac6b07ffd1570e0364cf901ebc36345fea669969d2084baebb/simple_websocket-1.1.0-py3-none-any.whl (13 kB)
Collecting greenlet!=0.4.17 (from sqlalchemy<2,>=1.4.0->mlflow<=1.30.0,>=1.12.1->pyqlib)
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/d3/50/7b7a3e10ed82c760c1fd8d3167a7c95508e9fdfc0b0604f05ed1a9a9efdc/greenlet-3.1.1-cp38-cp38-win_amd64.whl (298 kB)
Collecting smmap<6,>=3.0.1 (from gitdb<5,>=4.0.1->gitpython<4,>=2.1.0->mlflow<=1.30.0,>=1.12.1->pyqlib)
Using cached https://pypi.tuna.tsinghua.edu.cn/packages/a7/a5/10f97f73544edcdef54409f1d839f6049a0d79df68adbc1ceb24d1aaca42/smmap-5.0.1-py3-none-any.whl (24 kB)
Collecting wsproto (from simple-websocket>=0.10.0->python-engineio>=4.8.0->python-socketio->pyqlib)
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/78/58/e860788190eba3bcce367f74d29c4675466ce8dddfba85f7827588416f01/wsproto-1.2.0-py3-none-any.whl (24 kB)
Collecting h11<1,>=0.9.0 (from wsproto->simple-websocket>=0.10.0->python-engineio>=4.8.0->python-socketio->pyqlib)
Using cached https://pypi.tuna.tsinghua.edu.cn/packages/95/04/ff642e65ad6b90db43e668d70ffb6736436c7ce41fcc549f4e9472234127/h11-0.14.0-py3-none-any.whl (58 kB)
Building wheels for collected packages: pymongo, fire
Building wheel for pymongo (setup.py) ... done
Created wheel for pymongo: filename=pymongo-3.7.2-cp38-cp38-win_amd64.whl size=312427 sha256=9eb798a0a168ba950cf6489f3039b10b9730fadbe6f2b5bd5b3c00217953755b
Stored in directory: c:\users\administrator\appdata\local\pip\cache\wheels\b4\a6\14\10536b68552027ca1f7e89df8b999520caaaa05ed154f0feda
Building wheel for fire (setup.py) ... done
Created wheel for fire: filename=fire-0.7.0-py3-none-any.whl size=114262 sha256=ee2f359ccbe9387c05af684f664788b387395e3d9d5031d0e4cf8ca981fdc117
Stored in directory: c:\users\administrator\appdata\local\pip\cache\wheels\1d\05\c2\9eacbbffa3b9f5b1446a30b222097dbccc5314ade2ea1ddb40
Successfully built pymongo fire
Installing collected packages: pywin32, pytz, pymongo, py-cpuinfo, gym_notices, zipp, xlrd, wrapt, win32-setctime, Werkzeug, websocket-client, waitress, tornado, threadpoolctl, termcolor, tenacity, tabulate, sqlparse, smmap, schedule, ruamel.yaml.clib, querystring-parser, pyyaml, pyparsing, pyjwt, pycparser, pybind11, prometheus-client, pillow, oauthlib, numpy, munch, msgpack, Mako, kiwisolver, jsonpickle, joblib, itsdangerous, h11, greenlet, future, fonttools, entrypoints, docopt-ng, cycler, colorama, cloudpickle, blinker, bidict, async-timeout, wsproto, tqdm, sqlalchemy, scipy, ruamel.yaml, redis, patsy, pandas, packaging, numexpr, loguru, importlib-resources, importlib-metadata, gitdb, fire, contourpy, click, cffi, blosc2, tables, statsmodels, simple-websocket, scs, scikit-learn, qdldl, python-redis-lock, plotly, matplotlib, lightgbm, hyperopt, gitpython, Flask, ecos, docker, databricks-cli, clarabel, alembic, sacred, python-engineio, prometheus-flask-exporter, osqp, python-socketio, mlflow, cvxpy
Attempting uninstall: pytz
Found existing installation: pytz 2024.2
Uninstalling pytz-2024.2:
Successfully uninstalled pytz-2024.2
Attempting uninstall: numpy
Found existing installation: numpy 1.24.4
Uninstalling numpy-1.24.4:
Successfully uninstalled numpy-1.24.4
WARNING: Failed to remove contents in a temporary directory 'D:\ProgramData\envs\a308\Lib\site-packages\~umpy'.
You can safely remove it manually.
Attempting uninstall: pandas
Found existing installation: pandas 2.0.3
Uninstalling pandas-2.0.3:
Successfully uninstalled pandas-2.0.3
WARNING: Failed to remove contents in a temporary directory 'D:\ProgramData\envs\a308\Lib\site-packages\~andas'.
You can safely remove it manually.
Attempting uninstall: packaging
Found existing installation: packaging 24.2
Uninstalling packaging-24.2:
Successfully uninstalled packaging-24.2
Successfully installed Flask-2.3.3 Mako-1.3.7 Werkzeug-3.0.6 alembic-1.14.0 async-timeout-5.0.1 bidict-0.23.1 blinker-1.8.2 blosc2-2.0.0 cffi-1.17.1 clarabel-0.9.0 click-8.1.7 cloudpickle-2.2.1 colorama-0.4.6 contourpy-1.1.1 cvxpy-1.4.4 cycler-0.12.1 databricks-cli-0.18.0 docker-6.1.3 docopt-ng-0.9.0 ecos-2.0.14 entrypoints-0.4 fire-0.7.0 fonttools-4.55.2 future-1.0.0 gitdb-4.0.11 gitpython-3.1.43 greenlet-3.1.1 gym_notices-0.0.8 h11-0.14.0 hyperopt-0.1.2 importlib-metadata-5.2.0 importlib-resources-6.4.5 itsdangerous-2.2.0 joblib-1.4.2 jsonpickle-4.0.0 kiwisolver-1.4.7 lightgbm-4.5.0 loguru-0.7.3 matplotlib-3.7.5 mlflow-1.30.0 msgpack-1.1.0 munch-4.0.0 numexpr-2.8.6 numpy-1.23.5 oauthlib-3.2.2 osqp-0.6.7.post3 packaging-21.3 pandas-1.5.3 patsy-1.0.1 pillow-10.4.0 plotly-5.24.1 prometheus-client-0.21.1 prometheus-flask-exporter-0.23.1 py-cpuinfo-9.0.0 pybind11-2.13.6 pycparser-2.22 pyjwt-2.9.0 pymongo-3.7.2 pyparsing-3.1.4 python-engineio-4.10.1 python-redis-lock-4.0.0 python-socketio-5.11.4 pytz-2022.7.1 pywin32-308 pyyaml-6.0.2 qdldl-0.1.7.post4 querystring-parser-1.2.4 redis-5.2.1 ruamel.yaml-0.17.36 ruamel.yaml.clib-0.2.8 sacred-0.8.7 schedule-1.2.2 scikit-learn-1.3.2 scipy-1.10.1 scs-3.2.4 simple-websocket-1.1.0 smmap-5.0.1 sqlalchemy-1.4.54 sqlparse-0.5.2 statsmodels-0.14.1 tables-3.8.0 tabulate-0.9.0 tenacity-9.0.0 termcolor-2.4.0 threadpoolctl-3.5.0 tornado-6.4.2 tqdm-4.67.1 waitress-2.1.2 websocket-client-1.8.0 win32-setctime-1.1.0 wrapt-1.17.0 wsproto-1.2.0 xlrd-2.0.1 zipp-3.20.2

最后记录下安装后的环境

(a308) E:\hw2024\stock>pip list

Package Version
------------------------- -----------
alembic 1.14.0
async-timeout 5.0.1
baostock 0.8.9
bidict 0.23.1
blinker 1.8.2
blosc2 2.0.0
certifi 2024.8.30
cffi 1.17.1
charset-normalizer 3.4.0
clarabel 0.9.0
click 8.1.7
cloudpickle 2.2.1
colorama 0.4.6
contourpy 1.1.1
cryptography 44.0.0
cvxpy 1.4.4
cycler 0.12.1
Cython 3.0.11
databricks-cli 0.18.0
dill 0.3.9
docker 6.1.3
docopt-ng 0.9.0
ecos 2.0.14
entrypoints 0.4
filelock 3.16.1
fire 0.7.0
Flask 2.3.3
fonttools 4.55.2
fsspec 2024.10.0
future 1.0.0
gitdb 4.0.11
GitPython 3.1.43
greenlet 3.1.1
gym 0.26.2
gym-notices 0.0.8
h11 0.14.0
hyperopt 0.1.2
idna 3.10
importlib-metadata 5.2.0
importlib_resources 6.4.5
itsdangerous 2.2.0
Jinja2 3.1.4
joblib 1.4.2
jsonpickle 4.0.0
kiwisolver 1.4.7
lightgbm 4.5.0
loguru 0.7.3
Mako 1.3.7
MarkupSafe 2.1.5
matplotlib 3.7.5
mlflow 1.30.0
mpmath 1.3.0
msgpack 1.1.0
munch 4.0.0
networkx 3.1
numexpr 2.8.6
numpy 1.23.5
oauthlib 3.2.2
osqp 0.6.7.post3
packaging 21.3
pandas 1.5.3
patsy 1.0.1
pillow 10.4.0
pip 24.2
plotly 5.24.1
prometheus_client 0.21.1
prometheus_flask_exporter 0.23.1
protobuf 3.20.1
py-cpuinfo 9.0.0
pybind11 2.13.6
pycparser 2.22
PyJWT 2.9.0
pymongo 3.7.2
pyparsing 3.1.4
pyqlib 0.9.5.99
python-dateutil 2.9.0.post0
python-engineio 4.10.1
python-redis-lock 4.0.0
python-socketio 5.11.4
pytz 2022.7.1
pywin32 308
PyYAML 6.0.2
qdldl 0.1.7.post4
qlib 0.0.2.dev20
querystring-parser 1.2.4
redis 5.2.1
requests 2.32.3
ruamel.yaml 0.17.36
ruamel.yaml.clib 0.2.8
sacred 0.8.7
schedule 1.2.2
scikit-learn 1.3.2
scipy 1.10.1
scs 3.2.4
setuptools 75.1.0
simple-websocket 1.1.0
six 1.17.0
smmap 5.0.1
SQLAlchemy 1.4.54
sqlparse 0.5.2
statsmodels 0.14.1
sympy 1.13.3
tables 3.8.0
tabulate 0.9.0
tenacity 9.0.0
termcolor 2.4.0
threadpoolctl 3.5.0
torch 2.4.1
tornado 6.4.2
tqdm 4.67.1
typing_extensions 4.12.2
tzdata 2024.2
urllib3 2.2.3
waitress 2.1.2
websocket-client 1.8.0
Werkzeug 3.0.6
wheel 0.44.0
win32-setctime 1.1.0
wrapt 1.17.0
wsproto 1.2.0
xlrd 2.0.1
zipp 3.20.2