Overlap Studies
Kaufman's Adaptive Moving Average (KAMA)
Kaufman Adaptive Moving Average
Deep Dive
Everything You Need to Know
Under the Hood
How It Works
KAMA is an adaptive moving average developed by Perry Kaufman that automatically adjusts its smoothing constant based on market volatility using an Efficiency Ratio (ER). When price movement is trending strongly (high ER), KAMA acts like a fast EMA; during choppy sideways markets (low ER), it slows down to filter noise. The calculation uses fast_period (default 2) and slow_period (default 30) constants, with time_period (default 30) controlling the ER lookback. This self-adjusting behavior reduces false signals in range-bound markets while maintaining trend-following responsiveness.
In Practice
How Traders Use It
Cryptocurrency traders use KAMA to adapt automatically to changing market conditions without manual parameter adjustments. It excels in volatile crypto markets by speeding up during trends and slowing during consolidation - reducing whipsaws that plague fixed-period moving averages. KAMA generates buy signals when price crosses above and sell signals when crossing below. It works particularly well for swing trading and position trading where reducing false signals improves profitability. KAMA combines effectively with momentum oscillators (RSI, MACD) for confirmation, and with volume indicators (OBV, AD) to validate breakouts. Popular among algorithmic traders seeking adaptive trend-following systems.
Highlights
KAMA at a Glance
- Adaptive moving average adjusting to volatility automatically
- Fast during strong trends, slow during sideways markets
- Uses Efficiency Ratio to measure market noise
- Default 30-period with fast=2, slow=30 constants
- Reduces false signals in choppy markets
- Buy when price crosses above, sell when below
- Developed by Perry Kaufman in 1995
- Best for swing and position trading strategies
- Combine with RSI, MACD, or volume indicators
- Popular among algorithmic traders
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FAQ
Frequently Asked Questions
What is the Kaufman's Adaptive Moving Average (KAMA) indicator?
Kaufman Adaptive Moving Average
How does KAMA work?
KAMA is an adaptive moving average developed by Perry Kaufman that automatically adjusts its smoothing constant based on market volatility using an Efficiency Ratio (ER). When price movement is trending strongly (high ER), KAMA acts like a fast EMA; during choppy sideways markets (low ER), it slows down to filter noise. The calculation uses fast_period (default 2) and slow_period (default 30) constants, with time_period (default 30) controlling the ER lookback. This self-adjusting behavior reduces false signals in range-bound markets while maintaining trend-following responsiveness.
How do traders use KAMA in their strategies?
Cryptocurrency traders use KAMA to adapt automatically to changing market conditions without manual parameter adjustments. It excels in volatile crypto markets by speeding up during trends and slowing during consolidation - reducing whipsaws that plague fixed-period moving averages. KAMA generates buy signals when price crosses above and sell signals when crossing below. It works particularly well for swing trading and position trading where reducing false signals improves profitability. KAMA combines effectively with momentum oscillators (RSI, MACD) for confirmation, and with volume indicators (OBV, AD) to validate breakouts. Popular among algorithmic traders seeking adaptive trend-following systems.
What are the key points to know about KAMA?
Adaptive moving average adjusting to volatility automatically Fast during strong trends, slow during sideways markets Uses Efficiency Ratio to measure market noise Default 30-period with fast=2, slow=30 constants Reduces false signals in choppy markets Buy when price crosses above, sell when below Developed by Perry Kaufman in 1995 Best for swing and position trading strategies Combine with RSI, MACD, or volume indicators Popular among algorithmic traders
Can I use KAMA with Cryptorobot.ai?
Yes. Kaufman's Adaptive Moving Average (KAMA) is available as a built-in indicator in Cryptorobot.ai. You can add it to any automated strategy using the no-code strategy builder, backtest it against historical data, and deploy it live on supported exchanges.