feat: add new indicators (Decay, Edecay, MinusDi, MinusDm, PlusDi, PlusDm, Maxindex, Minindex, Sarext) and update pine scripts, core libs, validation tests, and python bindings
2026-03-09 13:45:46 -07:00
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// Licensed under the Apache License, Version 2.0
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2026-01-18 19:02:03 -08:00
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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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