# Rogers-Satchell Volatility Calculator MCP for AI Agents AI Agent Connect

> Rogers-Satchell Volatility Calculator MCP provides drift-independent volatility estimation for financial assets. It uses high, low, open, and close price data to calculate robust historical volatility measures that remain accurate even during strong price trends, offering a more reliable alternative to standard estimators.

## Overview
- **Category:** finance
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_N4jLFPSx7eO4lHHqyXSriTtbGeoa20SD4BTzI19F/ai-agent-connect
- **Tags:** volatility, finance, quantitative, ohlc, statistics

## Description

Calculating volatility in trending markets is notoriously difficult because standard models often mistake price drift for actual risk. This MCP solves that problem by using the Rogers-Satchell model, which stays accurate even when an asset is moving aggressively in one direction. Instead of relying on simple close-to-close changes, it looks at the relationship between the high, low, open, and close prices to get a much clearer picture of true market movement. You can use it to run direct volatility estimates, compare different mathematical models to see which fits your data best, or check where current market turbulence sits compared to historical norms. By connecting this to your AI client through the Vinkius catalog, you turn your agent into a quantitative researcher that can process OHLC data and output precise risk metrics without you ever having to touch a spreadsheet or write a line of Python. It's about getting a reliable volatility reading that doesn't break just because a stock is rallying or crashing.

## Tools

### calculate_rs_volatility
Calculates the deterministic Rogers-Satchell volatility for a specific price series. It provides a robust measure that ignores price drift.

### calculate_volatility_comparison
Compares Rogers-Satchell results against Parkinson and Garman-Klass models. This helps you see how different estimators interpret the same data.

### get_volatility_percentile
Finds the percentile rank of recent volatility within a historical window. It shows if current market movement is extreme or normal.

## Prompt Examples

**Prompt:** 
```
What is the Rogers-Satchell volatility for these prices: [{'open': 100, 'high': 105, 'low': 95, 'close': 102}, {'open': 102, 'high': 108, 'low': 101, 'close': 107}]?
```

**Response:** 
```
The Rogers-Satchell volatility for the provided price series is **0.1542**.
```

**Prompt:** 
```
Compare the different volatility models for this data: [{'open': 50, 'high': 55, 'low': 48, 'close': 52}, {'open': 52, 'high': 54, 'low': 50, 'close': 51}]
```

**Response:** 
```
Here is the comparison of the volatility estimators for your data:

| Model | Volatility |
| :--- | :--- |
| **Rogers-Satchell** | **0.124** |
| Parkinson | 0.115 |
| Garman-Klass | 0.128 |

*Lookback period used: 2*
```

**Prompt:** 
```
Is a volatility of 0.25 high compared to this history: [0.1, 0.15, 0.2, 0.12]?
```

**Response:** 
```
Yes, a volatility of **0.25** is extremely high relative to that history. It sits at the **100th percentile**, meaning it is higher than all previous values in that series.
```

## Capabilities

### Estimate drift-independent volatility
Get accurate volatility readings that aren't skewed by strong upward or downward price trends.

### Compare different volatility models
Check how Rogers-Satchell results stack up against Parkinson or Garman-Klass estimators.

### Rank current market turbulence
Determine where today's volatility sits as a percentile compared to a historical window of data.

## Use Cases

### Analyzing trending stocks
A trader asks their agent to calculate volatility for a stock in a massive bull run. The agent uses calculate_rs_volatility to ensure the trend doesn't inflate the risk estimate.

### Comparing risk models
A quant needs to know if the Parkinson model is underestimating risk. They ask their agent to run calculate_volatility_comparison on a recent dataset.

### Detecting volatility spikes
A risk manager wants to know if today's movement is unusual. They ask their agent to use get_volatility_percentile against the last 30 days of data.

### Automated risk reporting
An analyst provides a week of OHLC data and asks for a volatility summary. The agent uses the MCP to provide precise, drift-independent numbers.

## Benefits

- Eliminate drift bias by using Rogers-Satchell via calculate_rs_volatility for more accurate risk assessment in trending markets.
- Validate model selection by using calculate_volatility_comparison to see how different estimators treat your price data.
- Identify market extremes by using get_volatility_percentile to see if current volatility is a statistical outlier.
- Reduce manual math by letting your agent handle complex OHLC calculations instantly.
- Improve backtesting accuracy by using volatility measures that don't break during strong price trends.

## How It Works

The bottom line is you provide price data and get back mathematically robust volatility metrics.

1. Provide a series of OHLC (Open, High, Low, Close) price data to your AI client.
2. The MCP processes the price series using the Rogers-Satchell mathematical model.
3. Your agent returns the calculated volatility value or a comparison report.

## Frequently Asked Questions

**How does the Rogers-Satchell MCP handle trending markets?**
It uses a drift-independent formula that separates price direction from actual volatility, ensuring your risk estimates stay accurate even when prices are moving strongly in one direction.

**Can I use this Rogers-Satchell MCP to compare different risk models?**
Yes, you can ask your agent to compare the Rogers-Satchell results against other common estimators like Parkinson or Garman-Klass to see how they differ for your specific data.

**How do I know if current volatility is an outlier using this MCP?**
You can use the percentile tool to compare the most recent volatility value against a historical window, which tells you exactly where the current movement ranks.

**What kind of data does the Rogers-Satchell MCP need?**
The MCP requires OHLC data, which includes the Open, High, Low, and Close prices for each period in your series.

**Can I use this Rogers-Satchell MCP with Claude or Cursor?**
Yes, as long as your client is MCP-compatible, you can connect this to Claude, Cursor, Windsurf, or VS Code to perform these calculations.