mirror of
https://github.com/mihakralj/QuanTAlib.git
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Miha Kralj
55 lines
1.9 KiB
Plaintext
55 lines
1.9 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("Adaptive Price Channel (APCHANNEL)", "APCHANNEL", overlay=true)
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//@function Calculates the Adaptive Price Channel using dual EMA on highs and lows.
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//@doc The channel applies exponential smoothing to price highs and lows independently,
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// creating a dynamic envelope that "remembers" significant extremes while gradually
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// fading their influence. Unlike fixed-window Donchian channels that drop extremes
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// abruptly, APCHANNEL decays them smoothly (leaky integration).
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//@param alpha Smoothing factor (0 < alpha <= 1). Higher = faster decay, shorter memory.
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//@returns tuple of [middle, upper, lower] band values
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//@optimized O(1) per bar via EMA recursion; uses FMA pattern: decay*prev + alpha*new
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apchannel(simple float alpha) =>
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if alpha <= 0.0 or alpha > 1.0
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runtime.error("Alpha must be > 0 and <= 1")
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[float(na), float(na), float(na)]
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float decay = 1.0 - alpha
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// EMA of highs (upper band)
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var float high_ema = na
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float valid_high = nz(high, nz(high_ema, 0.0))
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if na(high_ema)
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high_ema := valid_high
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else
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high_ema := decay * high_ema + alpha * valid_high
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// EMA of lows (lower band)
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var float low_ema = na
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float valid_low = nz(low, nz(low_ema, 0.0))
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if na(low_ema)
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low_ema := valid_low
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else
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low_ema := decay * low_ema + alpha * valid_low
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// Midpoint
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float mid = (high_ema + low_ema) / 2.0
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[mid, high_ema, low_ema]
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// ---------- Main loop ----------
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// Inputs
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i_alpha = input.float(0.2, "Alpha (smoothing factor)", minval=0.01, maxval=1.0, step=0.01)
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// Calculation
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[middle, upper, lower] = apchannel(i_alpha)
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// Plot
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plot(middle, "Middle", color=color.yellow, linewidth=2)
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p1 = plot(upper, "Upper", color=color.new(color.blue, 50), linewidth=1)
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p2 = plot(lower, "Lower", color=color.new(color.blue, 50), linewidth=1)
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fill(p1, p2, color=color.new(color.blue, 90))
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