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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.
70 lines
2.4 KiB
Plaintext
70 lines
2.4 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 algorithm (C) 2013 John F. Ehlers
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indicator("Ehlers Trendflex (TRENDFLEX)", "TRENDFLEX", overlay=false)
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//@function Calculates Ehlers Trendflex using SuperSmoother pre-filtering and cumulative slope with RMS normalization
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//@param source Series to calculate Trendflex from
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//@param period Lookback period for trend measurement (>= 1)
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//@returns Normalized Trendflex value centered around zero
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//@optimized Uses O(1) running sum for cumulative slope instead of O(N) loop, with RMS normalization
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trendflex(series float source, simple int period) =>
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if period <= 0
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runtime.error("Period must be positive")
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float src = nz(source)
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// SuperSmoother (2-pole Butterworth lowpass) coefficients
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float halfPeriod = period * 0.5
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float a1 = math.exp(-1.414 * math.pi / halfPeriod)
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float b1 = 2.0 * a1 * math.cos(1.414 * math.pi / halfPeriod)
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float c2 = b1
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float c3 = -(a1 * a1)
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float c1 = 1.0 - c2 - c3
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// SuperSmoother filter state
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var float filt = 0.0
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var float filt1 = 0.0
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float new_filt = bar_index < 2 ? src : c1 * (src + nz(src[1])) * 0.5 + c2 * filt + c3 * filt1
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filt1 := filt
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filt := new_filt
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// O(1) cumulative slope via circular buffer and running sum
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// Sum = Σ(Filt - Filt[i]) for i=1..N = N × Filt - Σ(Filt[i])
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var array<float> buf = array.new_float(period, 0.0)
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var int head = 0
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var float running_sum = 0.0
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var int count = 0
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int n = math.min(count, period)
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float slope_sum = n > 0 ? (n * new_filt - running_sum) / period : 0.0
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float oldest = array.get(buf, head)
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running_sum -= oldest
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running_sum += new_filt
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array.set(buf, head, new_filt)
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head := (head + 1) % period
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if count < period
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count += 1
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// RMS normalization via exponential mean-square
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var float ms = 0.0
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ms := 0.04 * slope_sum * slope_sum + 0.96 * ms
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float result = ms > 0 ? slope_sum / math.sqrt(ms) : 0.0
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na(source) ? na : result
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// ---------- Main loop ----------
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// Inputs
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i_period = input.int(20, "Period", minval=1, tooltip="Lookback period for trend measurement")
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i_source = input.source(close, "Source")
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// Calculation
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trendflex_value = trendflex(i_source, i_period)
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// Plot
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plot(trendflex_value, "TRENDFLEX", color=color.yellow, linewidth=2)
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hline(0, "Zero", color=color.gray, linestyle=hline.style_dotted)
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