Kaufman's Adaptive Moving Average (KAMA)

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.

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.