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Help Center/Indicators/KAMA (Kaufman Adaptive Moving Average)

KAMA (Kaufman Adaptive Moving Average)

📈Indicators
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KAMA (Kaufman Adaptive Moving Average)

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What is KAMA?

The Kaufman Adaptive Moving Average (KAMA) automatically adjusts its smoothing factor based on market volatility. In trending markets it speeds up to follow the price closely; in choppy or ranging markets it slows down to avoid whipsaws. Developed by Perry Kaufman.

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How it works

Efficiency Ratio (ER) = |Close - Close_N_bars_ago| / Sum(|Close_i - Close_{i-1}|, N)
Smoothing Constant (SC) = (ER × (fast_k - slow_k) + slow_k)^2
  where fast_k = 2/(fast+1), slow_k = 2/(slow+1)
KAMA = KAMA_prev + SC × (Close - KAMA_prev)
  • ER measures how "efficient" the price movement is (0 = noise, 1 = clean trend).
  • When ER is high (trending), SC approaches the fast constant and KAMA reacts quickly.
  • When ER is low (choppy), SC approaches the slow constant and KAMA barely moves.
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Key features

  • Self-adapting — no need to manually switch parameters for different market conditions.
  • Noise-resistant — stays flat during sideways chop, reducing false signals.
  • Trend-following — accelerates when a real trend develops.
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Trading signals

Trend direction

  • Price above KAMA — bullish.
  • Price below KAMA — bearish.

Flat KAMA

  • When KAMA is nearly flat, the market is range-bound — avoid trend-following trades.

Breakout confirmation

  • If KAMA starts rising/falling sharply after being flat, a new trend may be starting.
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Parameters

| Parameter | Default | Description | |-----------|---------|-------------| | Period | 10 | ER lookback length | | Fast | 2 | Fast EMA constant period | | Slow | 30 | Slow EMA constant period |

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Example conditions

| Condition | Meaning | |-----------|---------| | close > KAMA(10) | Price above adaptive average — bullish | | close cross_over KAMA(10) | Price breaks above KAMA — potential trend start | | KAMA(10) cross_over SMA(50) | KAMA crosses above SMA — strong bullish confirmation |

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Tips

  • KAMA is one of the best "set and forget" moving averages thanks to its adaptivity.
  • Default parameters (10, 2, 30) work well across most markets and timeframes.
  • Compare KAMA to ALMA — both reduce noise, but KAMA adapts dynamically while ALMA uses static Gaussian weights.
  • KAMA slope changes are more meaningful than price crossovers of KAMA in some strategies.
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