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https://github.com/mihakralj/QuanTAlib.git
synced 2026-08-05 04:27:43 +00:00
Miha Kralj
7253f61299
- Implemented the TRAMA (Trend Regularity Adaptive Moving Average) class with adaptive EMA logic. - Added unit tests for TRAMA functionality, including constructor validation, basic calculations, state management, and robustness checks. - Created validation tests to ensure consistency across different modes of operation (streaming, batch, and static calculations). - Enhanced documentation for TRAMA, including performance profiles and quality metrics. - Updated workspace configuration by removing unnecessary folder references.
70 lines
2.4 KiB
Plaintext
70 lines
2.4 KiB
Plaintext
// The MIT License (MIT)
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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 Indicator (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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