// Licensed under the Apache License, Version 2.0
// © mihakralj
//@version=6
indicator("Kaufman's Adaptive Moving Average (KAMA)", "KAMA", overlay=true)

//@function Calculates KAMA using adaptive smoothing based on market volatility
//@param source Series to calculate KAMA from
//@param period Length of the efficiency ratio lookback period
//@param fast_alpha Fastest EMA constant (2/(2+1))
//@param slow_alpha Slowest EMA constant (2/(30+1))
//@returns KAMA value with efficiency ratio-based adaptive smoothing
//@optimized Uses efficiency ratio calculation for O(n) complexity per bar due to lookback sum
kama(series float source, simple int period, simple float fast_alpha=0.666667, simple float slow_alpha=0.0645) =>
    var float kama_state = na
    float current_kama = na
    if not na(source)
        float change_val = math.abs(source - source[period])
        float volatility_val = math.sum(math.abs(source - source[1]), period)
        float er = 0.0
        if not na(change_val) and not na(volatility_val) and volatility_val != 0.0
            er := change_val / volatility_val
        float sc = math.pow(er * (fast_alpha - slow_alpha) + slow_alpha, 2)
        kama_state := na(kama_state) ? source : kama_state + sc * (source - kama_state)
        current_kama := kama_state
    current_kama

// ---------- Main loop ----------

// Inputs
i_period = input.int(10, "Period", minval=1)
i_source = input.source(close, "Source")
i_fast = input.int(2, "Fast EMA Period", minval=2)
i_slow = input.int(30, "Slow EMA Period", minval=2)

// Calculation
float fast_alpha = 2.0 / (float(i_fast) + 1.0)
float slow_alpha = 2.0 / (float(i_slow) + 1.0)
kama_value = kama(i_source, i_period, fast_alpha, slow_alpha)

// Plot
plot(kama_value, "KAMA", color=color.yellow, linewidth=2)