semvec/pypi-packages huggingface dataset:
neuralgc 0.0.2
Neural Granger Causality
Requires Python: >=3.8

Clone from https://github.com/atong01/Neural-GC

# Neural Granger Causality

The `Neural-GC` repository contains code for a deep learning-based approach to discovering Granger causality networks in multivariate time series. The methods implemented here are described in [this paper](https://arxiv.org/abs/1802.05842).

## Installation

To install the code, please clone the repository. All you need is `Python 3`, `PyTorch (>= 0.4.0)`, `numpy` and `scipy`.

## Usage

See examples of how to apply our approach in the notebooks `cmlp_lagged_var_demo.ipynb`, `clstm_lorenz_demo.ipynb`, and `crnn_lorenz_demo.ipynb`.

## How it works

The models implemented in this repository, called the cMLP, cLSTM and cRNN, are neural networks that model multivariate time series by forecasting each time series separately. During training, sparse penalties on the input layer's weight matrix set groups of parameters to zero, which can be interpreted as discovering Granger non-causality.

The cMLP model can be trained with three different penalties: group lasso, group sparse group lasso, and hierarchical. The cLSTM and cRNN models both use a group lasso penalty, and they differ from one another only in the type of RNN cell they use.

Training models with non-convex loss functions and non-smooth penalties requires a specialized optimization strategy, and we use a proximal gradient descent approach (ISTA). Our paper finds that ISTA provides comparable performance to two other approaches: proximal gradient descent with a line search (GISTA), which guarantees convergence to a local minimum, and Adam, which converges faster (although it requires an additional thresholding parameter).

## Other information

- Selecting the right regularization strength can be difficult and time consuming. To get results for many regularization strengths, you may want to run parallel training jobs or use a warm start strategy.
- Pretraining (training without regularization) followed by ISTA can lead to a different result than training directly with ISTA. Given the non-convex objective function, this is unsurprising, because the initialization from pretraining is very different than a random initialization. You may need to experiment to find what works best for you.
- If you want to train a debiased model with the learned sparsity pattern, use the `cMLPSparse`, `cLSTMSparse`, and `cRNNSparse` classes.

## Authors

- Ian Covert (<icovert@cs.washington.edu>)
- Alex Tank
- Nicholas Foti
- Ali Shojaie
- Emily Fox

## References

- Alex Tank, Ian Covert, Nicholas Foti, Ali Shojaie, Emily Fox. "Neural Granger Causality." *Transactions on Pattern Analysis and Machine Intelligence*, 2021.
iflow-mcp_twjackysu_twstockmcpserver 0.1.0
A MCPServer for partial APIs of the Taiwan Stock Exchange OpenAPI.
Requires Python: >=3.13

# 🚀 TWStockMCPServer

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Python](https://img.shields.io/badge/python-3.13+-blue.svg)](https://www.python.org/downloads/)
[![MCP](https://img.shields.io/badge/MCP-Compatible-green.svg)](https://modelcontextprotocol.io/)
[![API Tests](https://github.com/twjackysu/TWStockMCPServer/actions/workflows/api-tests.yml/badge.svg)](https://github.com/twjackysu/TWStockMCPServer/actions/workflows/api-tests.yml)

一個全面的**模型上下文協議 (MCP) 伺服器**，專為台灣證券交易所 (TWSE) 數據分析設計，提供即時股票資訊、財務報表、ESG 數據和趨勢分析功能。

<a href="https://glama.ai/mcp/servers/@twjackysu/TWSEMCPServer">
  <img width="380" height="200" src="https://glama.ai/mcp/servers/@twjackysu/TWSEMCPServer/badge" />
</a>

## 🌏 語言版本

- [English](README_en-us.md) | **繁體中文**

## 🎬 示範影片

### VSCode Copilot demo
![VSCode Copilot demo](./staticFiles/sample-ezgif.com-resize.gif)

### Gemini CLI demo
![Gemini CLI demo](./staticFiles/gemini-cli-demo.gif)

*觀看 TWStockMCPServer 功能展示*

## ✨ 五大投資分析情境

### 📊 **個股趨勢研判**
短中長期技術面、基本面、籌碼面綜合分析
> *"分析台積電(2330)最近的走勢" / "鴻海(2317)適合長期投資嗎？"*

### 💰 **外資投資解讀**
外資持股、產業流向、個股進出追蹤
> *"外資最近在買什麼股票？" / "半導體業外資投資趨勢如何？"*

### 🔥 **市場熱點捕捉**
重大訊息、異常成交、權證活躍度監控
> *"今天有什麼重大消息？" / "哪些股票交易量異常活躍？"*

### 💎 **股利投資規劃**
高殖利率篩選、除權息行事曆、配息穩定性分析
> *"推薦一些高殖利率股票" / "下個月有哪些公司要除權息？"*

### 🎯 **投資標的篩選**
價值股/成長股篩選、ESG風險評估
> *"幫我找一些被低估的價值股" / "ESG表現好的公司有哪些？"*

## 📈 API 整合進度

目前進度：**143/143 (100.0%)** 已完成 ✅

想了解詳細的API覆蓋情況？查看我們的 **[API TODO List](API_TODO.md)** 追蹤實作進度！

> 💡 執行 `python generate_todo.py` 可自動更新進度統計

## ⚙️ 快速開始

### 🚀 線上使用（推薦）
```json
{
  "twstockmcpserver": {
    "transport": "streamable_http",
    "url": "https://TW-Stock-MCP-Server.fastmcp.app/mcp"
  }
}
```

### 🔧 本地安裝
```bash
git clone https://github.com/twjackysu/TWStockMCPServer.git
cd TWStockMCPServer
uv sync && uv run fastmcp dev server.py
```

## 🤝 參與貢獻
歡迎PR！查看現有[API列表](API_TODO.md)了解可擴展的功能。

## 📄 授權 & 免責聲明
MIT授權 | 僅供參考，不構成投資建議
iflow-mcp_abhishekloiwal_mcp-file-server 0.1.1
An MCP server for reading and writing files from your local file system
Requires Python: >=3.10

# MCP File Server

An MCP server for reading and writing files from your local file system. This server can be used with Claude for Desktop or any other MCP client to provide file system access for AI assistants.

## Features

- List files and directories
- Read file contents
- Write content to files
- Delete files and directories

## Prerequisites

- Docker installed on your system
- Git (optional, for cloning the repository)

## Setup and Deployment

### Option 1: Using Docker Compose (Recommended)

1. Clone this repository:
   ```bash
   git clone https://github.com/abhishekloiwal/mcp-file-server.git
   cd mcp-file-server
   ```

2. Edit the `docker-compose.yml` file to update the volume mount path if needed. By default, it's set to:
   ```yaml
   volumes:
     - /Users/abhishekloiwal/CascadeProjects/ClaudeProjects:/data
   ```
   Replace with your desired local path if different.

3. Deploy with Docker Compose:
   ```bash
   docker-compose up -d
   ```

### Option 2: Using Docker directly

1. Clone the repository:
   ```bash
   git clone https://github.com/abhishekloiwal/mcp-file-server.git
   cd mcp-file-server
   ```

2. Build the Docker image:
   ```bash
   docker build -t mcp-file-server .
   ```

3. Run the container with your local directory mounted:
   ```bash
   docker run -d --name mcp-file-server -v /Users/abhishekloiwal/CascadeProjects/ClaudeProjects:/data mcp-file-server
   ```
   Replace the path with your desired local directory path.

## Connecting to Claude for Desktop

1. Create or update your Claude for Desktop configuration file at:
   - Mac: `~/Library/Application Support/Claude/claude_desktop_config.json`
   - Windows: `%AppData%\Claude\claude_desktop_config.json`

2. Add the mcp-file-server to your configuration:
   ```json
   {
     "mcpServers": {
       "file-server": {
         "command": "docker",
         "args": ["exec", "-i", "mcp-file-server", "python", "server.py"]
       }
     }
   }
   ```

3. Restart Claude for Desktop.

4. You should now see the file-server tools available in Claude.

## Available Tools

The following tools are available through this MCP server:

- `list_files`: List all files in a directory
- `read_file`: Read the contents of a file
- `write_file`: Write content to a file
- `delete_file`: Delete a file or directory

## License

MIT

## Troubleshooting

- If Claude for Desktop doesn't connect to the server, check the Docker container status:
  ```bash
  docker ps -a | grep mcp-file-server
  ```

- View server logs:
  ```bash
  docker logs mcp-file-server
  ```

- Make sure the volume is correctly mounted:
  ```bash
  docker inspect mcp-file-server | grep -A 10 Mounts
  ```
iflow-mcp_wukan1986_akshare_mcp 0.1.3
AKShare MCP Server
Requires Python: >=3.10

# akshare_mcp

`AKShare`数据接口的`MCP Server`封装

## 项目背景

`AKShare`提供了上千个数据接口，但`Github`上提供的服务都只支持少量接口，所以本项目将所有接口都暴露出来，方便用户使用。

## 安装

1. 在虚拟环境下执行`pip install akshare_mcp`或`uv pip install akshare_mcp`，
2. 运行`python -m akshare_mcp -h`检查是否安装成功，留意打印的配置文件路径

## 配置

```json
{
  "mcpServers": {
    "akshare_mcp": {
      "command": "D:\\Users\\Kan\\miniconda3\\envs\\py312\\python.exe",
      "args": [
        "-m",
        "akshare_mcp",
        "--format",
        "markdown"
      ]
    }
  }
}
```

## 注意

1. 1000+接口，全部暴露成工具，将会消耗大量`Token`
2. 部分MCP客户端只支持少量的工具，例如`Trae`最多支持40个工具

所以，请一定在使用前配置需要使用的接口。

1. 运行`python -m akshare_mcp -h`，查看配置文件的位置。例如`d:\Users\Kan\miniconda3\envs\py312\Lib\site-packages\akshare_mcp\config.py`
2. 编辑配置文件`config.py`，添加需要使用的接口。接口名参考`https://akshare.akfamily.xyz/data/index.html`
3. MCP客户端刷新重连服务端

如果`site-packages\akshare_mcp\config.py`无权限修改，可以从`github`上复制`config.py`文件到任意路径，然后使用`--config`参数

```commandline
python -m akshare_mcp --config D:\config.py
```