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https://github.com/mihakralj/QuanTAlib.git
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Miha Kralj
35a6702b06
Deep review of all indicator categories verified .md headers against .cs WarmupPeriod, parameters, inputs, and outputs. Fixes include warmup corrections, parameter documentation, output type accuracy, and Pine Script alignment.
42 lines
1.7 KiB
Plaintext
42 lines
1.7 KiB
Plaintext
// Licensed under the Apache License, Version 2.0
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// © mihakralj
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//@version=6
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indicator("Kaufman's Adaptive Moving Average (KAMA)", "KAMA", overlay=true)
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//@function Calculates KAMA using adaptive smoothing based on market volatility
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//@param source Series to calculate KAMA from
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//@param period Length of the efficiency ratio lookback period
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//@param fast_alpha Fastest EMA constant (2/(2+1))
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//@param slow_alpha Slowest EMA constant (2/(30+1))
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//@returns KAMA value with efficiency ratio-based adaptive smoothing
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//@optimized Uses efficiency ratio calculation for O(n) complexity per bar due to lookback sum
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kama(series float source, simple int period, simple float fast_alpha=0.666667, simple float slow_alpha=0.0645) =>
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var float kama_state = na
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float current_kama = na
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if not na(source)
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float change_val = math.abs(source - source[period])
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float volatility_val = math.sum(math.abs(source - source[1]), period)
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float er = 0.0
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if not na(change_val) and not na(volatility_val) and volatility_val != 0.0
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er := change_val / volatility_val
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float sc = math.pow(er * (fast_alpha - slow_alpha) + slow_alpha, 2)
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kama_state := na(kama_state) ? source : kama_state + sc * (source - kama_state)
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current_kama := kama_state
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current_kama
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// ---------- Main loop ----------
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// Inputs
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i_period = input.int(10, "Period", minval=1)
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i_source = input.source(close, "Source")
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i_fast = input.int(2, "Fast EMA Period", minval=2)
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i_slow = input.int(30, "Slow EMA Period", minval=2)
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// Calculation
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float fast_alpha = 2.0 / (float(i_fast) + 1.0)
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float slow_alpha = 2.0 / (float(i_slow) + 1.0)
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kama_value = kama(i_source, i_period, fast_alpha, slow_alpha)
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// Plot
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plot(kama_value, "KAMA", color=color.yellow, linewidth=2)
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