# Kaufman Adaptive Moving Average MCP for AI Agents AI Agent Connect

> Kaufman Adaptive Moving Average (KAMA) Calculator MCP for technical analysis. This tool calculates adaptive moving averages that automatically adjust their smoothing speed based on market volatility. It helps you distinguish between trending and ranging markets by using an Efficiency Ratio to filter out noise during sideways movement while staying responsive during strong price trends.

## Overview
- **Category:** finance
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_QeHHBMLTwx6vQSFHoSo9kqveZm5WAXFYa3h5Xtzj/ai-agent-connect
- **Tags:** kama, moving-average, volatility, trading, trend-following

## Description

Trading involves a constant battle against market noise. Standard moving averages often lag too much during fast trends or give too many false signals when the market is just drifting sideways. This MCP solves that by providing a deterministic engine for Kaufman Adaptive Moving Average calculations. Instead of a fixed smoothing period, it uses an Efficiency Ratio to sense how much a price is actually moving in a specific direction. When the market is trending strongly, the average speeds up to track the price closely. When the market is choppy or sideways, it slows down to avoid being whipped around by insignificant fluctuations.

You can use this to generate complete trend datasets for backtesting, pull the most recent trend metrics to check your current position, or identify whether the market has entered a new volatility regime. By connecting this to your AI client through the Vinkius catalog, you turn a standard chat interface into a sophisticated quantitative analysis workstation. You stop guessing if a move is a real trend or just noise and start working with mathematical certainty about market efficiency.

## Tools

### analyze_kama_volatility_regime
Determines if the market is currently in a trending or ranging regime. It uses recent movement efficiency to classify the volatility state.

### calculate_kama_series
Computes a full sequence of KAMA values and trend metrics. It processes a provided set of prices into a complete dataset.

### get_latest_kama_status
Extracts the most recent trend and volatility data. It pulls current metrics from a previously calculated series.

## Prompt Examples

**Prompt:** 
```
Calculate the KAMA series for these closing prices: [150.2, 151.5, 152.1, 150.8, 153.4, 154.2, 155.0, 154.5, 156.2, 157.5]
```

**Response:** 
```
| Index | KAMA | ER | Slope | Trend |
| :--- | :--- | :--- | :--- | :--- |
| 1 | 150.20 | 1.00 | 0.00 | NEUTRAL |
| 2 | 150.85 | 0.75 | 0.45 | UP |
| 3 | 151.40 | 0.60 | 0.55 | UP |
| ... | ... | ... | ... | ... |

**Summary:** The trend is currently **UP** with increasing efficiency.
```

**Prompt:** 
```
Is the market currently in a trending or ranging regime based on these prices: [10, 11, 10, 11, 10, 11, 10, 11, 10, 11]?
```

**Response:** 
```
The market is currently in a **RANGING** regime.

* **Average Efficiency Ratio:** 0.10
* **Status:** Low volatility/Sideways movement

This indicates that price movements are highly inefficient, suggesting you should avoid trend-following strategies right now.
```

**Prompt:** 
```
What is the latest trend status for this KAMA data: [{"kama": 100, "slope": 0.5, "trend": "UP"}]?
```

**Response:** 
```
The most recent trend data shows:

* **Current KAMA:** 100
* **Current Slope:** 0.5
* **Trend Direction:** UP
* **Is Trending:** Yes

The upward slope suggests strong momentum is currently present.
```

## Capabilities

### Generate trend datasets
Create a full sequence of KAMA values and trend metrics from a list of prices.

### Identify market regimes
Determine if the current market environment is trending or ranging based on movement efficiency.

### Extract real-time metrics
Pull the most recent trend and volatility data from existing price series.

### Filter market noise
Use the Efficiency Ratio to adjust smoothing speeds automatically during sideways movement.

## Use Cases

### Filtering choppy markets
A trader asks their agent to check if a stock is trending or just ranging. The agent uses analyze_kama_volatility_regime to confirm a sideways market, preventing a premature trend entry.

### Automated trend following
An analyst provides a week of closing prices. The agent uses calculate_kama_series to build a trend profile, identifying exactly when the momentum shifted.

### Real-time monitoring
A user wants to know the current trend direction. The agent uses get_latest_kama_status to pull the most recent slope and trend data from a large dataset.

### Backtesting volatility-adjusted strategies
A developer uses the agent to run KAMA calculations over historical data to see how an adaptive average would have performed compared to a standard SMA.

## Benefits

- Reduce false signals by using the Efficiency Ratio to ignore sideways market noise.
- Catch breakouts faster by letting the smoothing speed increase during strong trends via calculate_kama_series.
- Identify regime shifts instantly using analyze_kama_volatility_regime to switch between strategies.
- Get immediate trend updates with get_latest_kama_status for rapid decision making.
- Eliminate manual math by letting your agent handle all KAMA calculations directly.

## How It Works

The bottom line is you get mathematically adaptive trend indicators that react to volatility in real-time.

1. Connect your AI client to the KAMA MCP via Vinkius.
2. Provide a series of price data points to your agent.
3. Receive calculated trend values, efficiency ratios, and regime classifications.

## Frequently Asked Questions

**How does the Kaufman Adaptive Moving Average MCP help with trading?**
It provides an adaptive trend indicator that automatically speeds up during strong trends and slows down during sideways markets, helping you avoid false signals.

**Can I use this Kaufman Adaptive Moving Average MCP in Claude?**
Yes, once you connect this MCP through Vinkius, you can use it directly within Claude or any other MCP-compatible client.

**How does this MCP distinguish between trending and ranging markets?**
It uses an Efficiency Ratio to measure how much price movement is actual progress versus noise, allowing it to classify the market regime.

**Is this Kaufman Adaptive Moving Average MCP useful for backtesting?**
Absolutely. You can provide historical price series to your agent to generate full datasets of adaptive trend values for testing strategies.

**What makes KAMA different from a standard moving average?**
Unlike standard averages that have a fixed lag, KAMA adjusts its smoothing speed based on market volatility to be more responsive when it matters most.