semvec/pypi-packages huggingface dataset:
voly 0.0.261
Options & volatility research package
License: MIT
Requires Python: >=3.9

# Voly - Options & Volatility Research Package

Voly is a Python package for options data analysis, volatility surface modeling, and risk-neutral density estimation. It provides a simple interface for handling common options research tasks, including data collection, model fitting, and visualization.

## Features

- **Data Collection**: Fetch options data from exchanges (currently supports Deribit)
- **Volatility Surface Modeling**: Fit SVI (Stochastic Volatility Inspired) model to market data
- **Risk-Neutral Density**: Calculate and analyze risk-neutral density distributions
- **Surface Interpolation**: Interpolate volatility surfaces across different expiries
- **Options Pricing & Greeks**: Calculate Black-Scholes prices and all major Greeks
- **Visualizations**: Generate interactive plots using Plotly

## Installation

You can install Voly using pip:

```bash
pip install voly
```

## Quick Start

```python
import pandas as pd
from voly import VolyClient

# Initialize the client
voly = VolyClient()

# Fetch options data (or load your own data)
option_chain = voly.get_option_chain(exchange='deribit', currency='BTC')

# Fit an SVI model to the data with visualization
fit_results = voly.fit_model(option_chain, plot=True)

# Calculate risk-neutral density
rnd_results = voly.rnd(fit_results, spot_price=option_chain['underlying_price'].iloc[0], plot=True)

# Calculate probability of price above a target
probability = voly.probability(rnd_results, target_price=32000, direction='above')
print(f"Probability of price above 32000: {probability:.2%}")

# Calculate option Greeks
greeks = voly.greeks(s=30000, k=32000, r=0.05, vol=0.6, t=0.25, option_type='call')
print(f"Option Greeks: {greeks}")
```

## Example: Visualizing the Volatility Surface

```python
import pandas as pd
from voly import VolyClient

# Initialize the client
voly = VolyClient()

# Load your own data or fetch from exchange
# The DataFrame should have columns for:
# - log_moneyness
# - strike
# - mark_iv (implied volatility)
# - yte (years to expiry)
# - dte (days to expiry)
# - maturity_name (identifier for the expiry)
data = pd.read_csv('your_options_data.csv')

# Fit the SVI model
fit_results = voly.fit_model(data, plot=True)

# Display the 3D volatility surface
surface_fig = fit_results['plots']['surface_3d']
surface_fig.show()
```

## Documentation

For full documentation, visit [https://docs.voly.io](https://docs.voly.io)

## Development

### Setting up the development environment

```bash
# Clone the repository
git clone https://github.com/manudc22/voly.git
cd voly

# Install development dependencies & activate venv
./env_setup.sh --all
```

## License

This project is licensed under the MIT License - see the LICENSE file for details.
mysqlconnect-mcp-server 0.1.0
MCP Server to connect the MySQL database
License: MIT
Requires Python: >=3.13

# MySQL MCP Server Documentation

## Overview
This repository contains the MySQL database configuration for the MCP (Model Context Protocol) server. This MCP server handles data fetching from the connected database.

## Prerequisites
- Python 3.10 or above
- pip or uv package installer
- mysql-connector-python

## Installation Steps
1. Install the MCP package using either pip or uv package installer. The MCP package version should be 1.6.0 or higher with the CLI components included.
2. Install the required MySQL connector package. This package is essential as it establishes the connection between your MCP server and the MySQL database, enabling data querying capabilities.
3. Set up your environment variables by creating a .env file in your project directory. This file should contain your database connection details including host, username, password, and database name.
4. After completing the installation and configuration, you can start the server and connect it to any MCP client for use. The server will handle database operations through the configured MySQL connection.
For detailed setup instructions and additional configuration options, refer to the official MCP Python SDK documentation.

## Environment Variables
Required environment variables for database connection:
- `DB_HOST` - Database host address
- `DB_USER` - Database username
- `DB_PASSWORD` - Database password
- `DB_NAME` - Target database name