semvec/pypi-packages huggingface dataset:
iflow-mcp_wisecon 0.1.10
A quant Python package.
License: MIT
Requires Python: >=3.10
Keywords: Financial, MCP, Quant

<div align="center">

<h1> WisEcon </h1>

[![Python package](https://img.shields.io/pypi/v/wisecon)](https://pypi.org/project/wisecon/)
[![Python](https://img.shields.io/pypi/pyversions/wisecon.svg)](https://pypi.python.org/pypi/wisecon/)
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[![Doc](https://img.shields.io/badge/Doc-online-green)](https://wisecon-llm.github.io/wisecon-doc/)
[![Issue](https://img.shields.io/github/issues/wisecon-llm/wisecon)](https://github.com/CaoChensy/wisecon/issues/new/choose)
[![Discussions](https://img.shields.io/github/discussions/wisecon-llm/wisecon)](https://github.com/CaoChensy/wisecon/issues/new/choose)
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[![License: MIT](https://img.shields.io/github/license/wisecon-llm/wisecon)](https://github.com/CaoChensy/wisecon/blob/main/LICENSE)

</div>

## WisEcon是什么?

WisEcon 是一款专注于金融市场的量化分析工具，旨在为投资者、研究人员和金融分析师提供全面的数据分析和决策支持。以下是其主要特点和功能：

1. **数据支持** WisEcon 提供多种类型的金融数据，包括：

   - 股票数据：实时和历史股票价格、交易量、财务报表等。
   - 基金数据：各类基金的净值、收益率、风险指标等。
   - 期货数据：期货合约的价格、成交量、持仓量等。
   - 宏观经济数据：包括GDP、通货膨胀率、失业率等关键经济指标。

2. **大模型支持** WisEcon 集成了 ZLAI-Agent 工具链，这是一种强大的人工智能驱动的分析工具。 
   - 通过机器学习和自然语言处理，ZLAI-Agent 可以有效地分析海量数据，发掘潜在的投资机会，并提供智能化的建议。

3. **功能特点**
   - 数据可视化：直观的图表和仪表盘，帮助用户快速理解数据趋势和模式。
   - 策略回测：用户可以根据历史数据测试自己的交易策略，以评估其有效性。
   - 风险管理：提供多种风险评估工具，帮助用户识别和管理投资风险。
   - 实时监控：用户可以设置警报，实时监控市场变化，及时作出决策。

4. **应用场景**
   - 个人投资者：帮助个人投资者制定投资策略，提高投资回报。
   - 机构投资者：为机构提供深入的市场分析和预测，支持决策过程。
   - 学术研究：为金融研究人员提供丰富的数据和分析工具，支持学术研究。

[详细文档](https://caochensy.github.io/wisecon/)

-----

## 如何安装？

```bash
pip install wisecon
```

## MCP-Server

> 研报MCP



> 股票数据MCP

```json
{
  "mcpServers": {
    "wisecon-mcp": {
      "command": "uvx",
      "args": [
         "--from",
         "wisecon",
         "wisecon-stock-server"
      ]
    }
  }
}
```

[Quick Start](https://caochensy.github.io/wisecon/)

-----

<div align="center">

> Wechat

<center>
<img src="https://raw.githubusercontent.com/zlai-llm/wisecon/master/assets/wechat.jpg" width="160px">
<h5>微信群</h5>
</center>

</div>

-----
@2024/03/27
cartalyzer 0.1.1
Fetch and analyze e-commerce cart data
License: MIT
Requires Python: >=3.11

# Cartalyzer

**Cartalyzer** is a Python-based tool designed to streamline data ingestion, transformation, and synchronization for multi-source e-commerce datasets. It provides a flexible and extensible pipeline structure that enables developers to automate complex ETL (Extract, Transform, Load) operations with minimal setup.

---

## 🧩 Project Purpose

The goal of **Cartalyzer** is to simplify and standardize how product data is processed across different vendors and platforms.
It ensures:

* Consistent data formats across all sources
* Reliable handling of pipeline configuration and package data
* Easy integration into other Python or CLI-based workflows

This makes it ideal for developers working on data automation, analytics pipelines, or marketplace integrations.

---

## ⚙️ Installation

You can install the package directly from PyPI:

```bash
pip install cartalyzer
```

---

## 🚀 Usage Guide

Once installed, you can run the tool from your command line using the provided entry point:

```bash
cartalyzer
```

---

## 🧠 Handling the Package Data (.cfg file)

Python packages often face a challenge when trying to access data files (like `.cfg` configuration files) included within the package — especially when installed via `pip`.

To solve this, **Cartalyzer** uses the `importlib.resources` module (introduced in Python 3.9+) for safely reading data files bundled within the package.

Example:

```python
from importlib import resources

cfg_path = resources.files('pipeline').joinpath('pipeline.cfg')
with cfg_path.open("r") as f:
    config = configparser.ConfigParser()
    config.read_file(f)
```

This approach:

* Works both in development and after installation from PyPI.
* Avoids deprecated methods like `pkg_resources` and `importlib.resources.open_text()`.
* Ensures compatibility with modern Python packaging standards.

---

## 🧾 License

This project is licensed under the **MIT License**.