semvec/pypi-packages huggingface dataset:
egarcia-poc 0.1.0
Security testing package - Authorized pentest only
License: MIT
Requires Python: >=3.7

# egarcia-poc

⚠️ **WARNING: SECURITY TESTING PACKAGE**

This package is designed for **authorized penetration testing only**.

## Purpose

This package is used to test:
- Python package installation security
- Remote Code Execution via pip install
- Dependency confusion vulnerabilities
- Supply chain attack vectors

## Usage

This package should only be installed in authorized testing environments:
```bash
pip install egarcia-poc
```

## Disclaimer

This package executes code during installation as part of authorized security testing.
Use only with explicit written authorization.

## Contact

For security concerns: pentest@example.com

## License

MIT License - For authorized security testing only
vnpy_rohon 6.5.1.22
Rohon gateway for vn.py quant trading framework.
License: MIT
Requires Python: >=3.10
Keywords: quant, quantitative, investment, trading, algotrading, ctp

# VeighNa框架的融航资管系统交易接口

<p align="center">
  <img src ="https://vnpy.oss-cn-shanghai.aliyuncs.com/vnpy-logo.png"/>
</p>

<p align="center">
    <img src ="https://img.shields.io/badge/version-6.5.1.22-blueviolet.svg"/>
    <img src ="https://img.shields.io/badge/platform-windows|linux-yellow.svg"/>
    <img src ="https://img.shields.io/badge/python-3.10|3.11|3.12|3.13-blue.svg" />
    <img src ="https://img.shields.io/github/license/vnpy/vnpy.svg?color=orange"/>
</p>

## 说明

基于融航期货资管系统的6.5.1接口封装开发，接口中自带的是【穿透式实盘环境】的dll文件。

## 安装

安装环境推荐基于4.0.0版本以上的【[**VeighNa Studio**](https://www.vnpy.com)】。

直接使用pip命令：

```
pip install vnpy_rohon
```


或者下载源代码后，解压后在cmd中运行：

```
pip install .
```

使用源代码安装时需要进行C++编译，因此在执行上述命令之前请确保已经安装了【Visual Studio（Windows）】或者【GCC（Linux）】编译器。

如果需要以**开发模式**安装到当前Python环境，可以使用下述命令：

```
pip install -e . --no-build-isolation --config-settings=build-dir=.\vnpy_rohon\api
```

## 使用

以脚本方式启动（script/run.py）：

```
from vnpy.event import EventEngine
from vnpy.trader.engine import MainEngine
from vnpy.trader.ui import MainWindow, create_qapp

from vnpy_rohon import RohonGateway


def main():
    """主入口函数"""
    qapp = create_qapp()

    event_engine = EventEngine()
    main_engine = MainEngine(event_engine)
    main_engine.add_gateway(RohonGateway)
    
    main_window = MainWindow(main_engine, event_engine)
    main_window.showMaximized()

    qapp.exec()


if __name__ == "__main__":
    main()
```

## 连接
 
如果启动时报错“ImportError: DLL load failed: 找不到指定的模块。”，可自行下载vcredist2010解决。

## Linux运行

Linux系统下运行API时，融航接口需要加载两个so文件：librohonbase.so 和 libLinuxDataCollect.so。

为了确保这两个文件能被正常加载，需要将其放置到系统lib目录下。

因此在Linux系统上进行安装时，推荐git clone当前仓库后，使用脚本执行安装：

> sudo bash install_rohon.sh
slearn 0.2.9
A package linking symbolic representation with sklearn for time series prediction
License: MIT
Requires Python: >=3.7

.. image:: https://app.travis-ci.com/nla-group/slearn.svg?token=SziD2n1qxpnRwysssUVq&branch=master
    :target: https://app.travis-ci.com/github/nla-group/slearn
    :alt: Build Status
.. image:: https://badge.fury.io/py/slearn.svg
    :target: https://badge.fury.io/py/slearn
    :alt: PyPI version
.. image:: https://img.shields.io/pypi/pyversions/slearn.svg
    :target: https://pypi.python.org/pypi/slearn/
    :alt: PyPI pyversions
.. image:: https://img.shields.io/badge/License-MIT-yellow.svg
    :target: https://pypi.python.org/pypi/slearn/
    :alt: PyPI pyversions    
.. image:: https://readthedocs.org/projects/slearn/badge/?version=latest
    :target: https://slearn.readthedocs.io/en/latest/?badge=latest
    :alt: Documentation Status

*A package linking symbolic representation with scikit-learn machine learning for time series prediction.*

Symbolic representations of time series have proved their usefulness in the field of time series motif discovery, clustering, classification, forecasting, anomaly detection, etc.  Symbolic time series representation methods do not only reduce the dimensionality of time series but also speedup the downstream time series task. It has been demonstrated by [S. Elsworth and S. Güttel, Time series forecasting using LSTM networks: a symbolic approach, arXiv, 2020] that symbolic forecasting has greatly reduce the sensitivity of hyperparameter settings for Long Short Term Memory networks. How to appropriately deploy machine learning algorithm on the level of symbols instead of raw time series poses a challenge to the interest of applications. To boost the development of research community on symbolic representation, we develop this Python library to simplify the process of machine learning algorithm practice on symbolic representation. 

---------
Install
---------

Install the slearn package simply by

.. code:: bash
    
    pip install slearn