MCP For Claude Marketplace
Kaufman Adaptive Moving Average (KAMA) Calculator

Kaufman Adaptive Moving Average (KAMA) Calculator MCP Connector for Claude

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Calculate adaptive moving averages that adjust to market volatility.

3 tools Official Updated Oct 1, 2026 Official Vinkius Partner
More Details Connect to Claude

This MCP server provides a deterministic engine for calculating the Kaufman Adaptive Moving Average (KAMA). Unlike standard moving averages, KAMA uses an Efficiency Ratio (ER) to adjust its smoothing speed, allowing it to filter out market noise during sideways movement and react quickly during strong trends. Use calculate_kama_series to generate full trend datasets, get_latest_kama_status to extract current trend metrics, or analyze_kama_volatility_regime to identify if the market is trending or ranging.

kamamoving-averagevolatilitytradingtrend-following

3 tools expose this connector's capabilities to your AI agent.

calculate_kama_series

Computes the full sequence of KAMA values and trend metrics for a provided set of prices

analyze_kama_volatility_regime

Determines whether the market is currently in a trending or ranging regime based on the efficiency of recent movements

get_latest_kama_status

Extracts the most recent trend and volatility data from a pre-calculated series

See how to talk to your AI agent using Kaufman Adaptive Moving Average (KAMA) Calculator.

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]

[{"kama": 151.2, "er": 0.85, "slope": 0.4, "trend": "UP"}, ...]

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

{"averageER": 0.1, "regime": "RANGING"}

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

{"currentKama": 100, "currentSlope": 0.5, "currentTrend": "UP", "isTrending": true}

Standard EMAs use a constant smoothing factor. KAMA uses an Efficiency Ratio to change its smoothing speed based on market volatility, making it more responsive to trends and less sensitive to noise.

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