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("Double Exponential Moving Average (DEMA)", "DEMA", overlay=true)
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//@function Calculates DEMA using double exponential smoothing with compensator
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//@param source Series to calculate DEMA from
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//@param period Lookback period for DEMA calculation
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//@param alpha Optional smoothing factor (overrides period if provided)
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//@returns DEMA value from first bar with proper compensation
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//@optimized Uses exponential warmup compensator on both EMA stages for O(1) complexity
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dema(series float source, simple int period=0, simple float alpha=0) =>
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float a = alpha > 0 ? alpha : 2.0 / (period + 1)
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float beta = 1.0 - a
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var bool warmup = true
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var float e = 1.0
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var float ema1_raw = 0.0
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var float ema2_raw = 0.0
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var float ema1 = source
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var float ema2 = source
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ema1_raw := a * (source - ema1_raw) + ema1_raw
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if warmup
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e *= beta
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float c = 1.0 / (1.0 - e)
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ema1 := c * ema1_raw
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ema2_raw := a * (ema1 - ema2_raw) + ema2_raw
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ema2 := c * ema2_raw
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warmup := e > 1e-10
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else
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ema1 := ema1_raw
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ema2_raw := a * (ema1 - ema2_raw) + ema2_raw
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ema2 := ema2_raw
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2 * ema1 - ema2
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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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// Calculation
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dema_value = dema(i_source, period=i_period)
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// Plot
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plot(dema_value, "DEMA", color=color.yellow, linewidth=2)
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