mirror of
https://github.com/mihakralj/QuanTAlib.git
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Miha Kralj
47 lines
2.1 KiB
Plaintext
47 lines
2.1 KiB
Plaintext
// This Pine Script™ code is subject to the terms of the Mozilla Public License 2.0
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// https://mozilla.org/MPL/2.0/
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// © QuanTAlib
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//@version=6
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indicator("TRIM: Trimmed Mean Moving Average", shorttitle="TRIM", overlay=true)
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// @function Calculates the Trimmed Mean Moving Average.
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// Sorts the lookback window, discards the lowest and highest trimPct%
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// of values, and averages the remaining middle portion.
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// This robust estimator reduces the influence of outliers/spikes
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// while preserving more information than a pure median.
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// trimPct=0 → SMA, trimPct=50 → Median.
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// @param src Series to smooth.
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// @param period Window length. Must be >= 3.
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// @param trimPct Percentage of values to trim from each tail (0-49). Default 10.
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// @returns The trimmed mean value.
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trim(series float src, simple int period, simple int trimPct) =>
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// Number of values to discard from each end
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int trimCount = math.max(int(period * trimPct / 100.0), 0)
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int keepCount = period - 2 * trimCount
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if keepCount < 1
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keepCount := 1
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trimCount := (period - 1) / 2
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// Collect values into array and sort
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float[] vals = array.new_float(period)
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for i = 0 to period - 1
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array.set(vals, i, nz(src[i]))
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array.sort(vals, order.ascending)
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// Average the middle portion
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float sum = 0.0
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for i = trimCount to trimCount + keepCount - 1
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sum += array.get(vals, i)
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sum / keepCount
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// ── Inputs ──────────────────────────────────────────────
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p = input.int(20, "Period", minval=3)
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t = input.int(10, "Trim %", minval=0, maxval=49, tooltip="Percentage trimmed from each tail. 0=SMA, 50=Median")
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// ── Calculation ─────────────────────────────────────────
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result = trim(close, p, t)
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// ── Plot ────────────────────────────────────────────────
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plot(result, "TRIM", color=color.yellow, linewidth=2)
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