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https://github.com/mihakralj/QuanTAlib.git
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Miha Kralj
74 lines
2.3 KiB
Plaintext
74 lines
2.3 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("Standard Error of Regression (STDERR)", "STDERR", overlay=false, precision=8)
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//@function Calculates the standard error of the linear regression estimate over the specified period.
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//@param src {series float} Source series.
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//@param len {simple int} Lookback length. `len` >= 3.
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//@returns {series float} Standard error of regression for `len` bars back. Returns `na` if not enough data.
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stderr(series float src, simple int len) =>
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if len < 3
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runtime.error("Period must be at least 3")
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var int p = math.max(3, len)
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var array<float> buffer = array.new_float(p, na)
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var int head = 0, var int count = 0
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// Update circular buffer
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float oldest = array.get(buffer, head)
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if not na(oldest)
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count -= 1
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float val = nz(src)
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array.set(buffer, head, val)
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count += 1
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head := (head + 1) % p
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if count < 3
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na
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else
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// Calculate regression coefficients
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int n = count
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int start = count < p ? 0 : head
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float sumX = 0.0, float sumY = 0.0
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float sumXY = 0.0, float sumX2 = 0.0
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for i = 0 to n - 1
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int idx = (start + i) % p
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float y_val = array.get(buffer, idx)
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float x_val = float(i)
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sumX += x_val
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sumY += y_val
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sumXY += x_val * y_val
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sumX2 += x_val * x_val
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float nf = float(n)
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float denom = nf * sumX2 - sumX * sumX
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if denom == 0
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0.0
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else
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float slope = (nf * sumXY - sumX * sumY) / denom
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float intercept = (sumY - slope * sumX) / nf
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// Calculate sum of squared residuals
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float ssr = 0.0
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for i = 0 to n - 1
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int idx = (start + i) % p
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float y_val = array.get(buffer, idx)
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float predicted = intercept + slope * float(i)
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float residual = y_val - predicted
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ssr += residual * residual
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math.sqrt(ssr / (nf - 2.0))
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// ---------- Main loop ----------
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// Inputs
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i_period = input.int(14, "Period", minval=3)
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i_source = input.source(close, "Source")
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// Calculation
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stderr_value = stderr(i_source, i_period)
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// Plot
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plot(stderr_value, "Stderr", color=color.yellow, linewidth=2)
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