mirror of
https://github.com/mihakralj/QuanTAlib.git
synced 2026-07-27 17:27:43 +00:00
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.
121 lines
4.6 KiB
Plaintext
121 lines
4.6 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("Autoregressive FIR Moving Average (AFIRMA)", "AFIRMA", overlay=true)
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//@function Calculates AFIRMA using various windowing functions with optional least squares cubic spline fitting
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//@param source Series to calculate AFIRMA from
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//@param period Lookback period - window size
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//@param windowType Window function type (1:Hanning, 2:Hamming, 3:Blackman, 4:Blackman-Harris)
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//@param leastSquares Apply least squares linear regression fitting for autoregressive prediction
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//@returns AFIRMA value, calculates from first bar using available data
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//@optimized Uses windowing functions with O(n) complexity; least squares adds O(n) for polynomial fitting
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afirma(series float src, simple int period, simple int windowType=4, simple bool leastSquares=false) =>
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float result = src
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if period <= 0
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runtime.error("Period must be greater than 0")
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if windowType < 1 or windowType > 4
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runtime.error("WindowType should be in range [1-4]")
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int p = math.min(bar_index + 1, period)
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if p > 1
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var array<float> coefs = array.new_float(1, 0.0)
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var int prevPeriod = 0
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var int prevWindowType = -1
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if p != prevPeriod or windowType != prevWindowType
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coefs := array.new_float(p, 0.0)
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float a0 = 0.35875
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float a1 = -0.48829
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float a2 = 0.14128
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float a3 = -0.01168
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if windowType == 1
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a0 := 0.50
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a1 := -0.50
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else if windowType == 2
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a0 := 0.54
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a1 := -0.46
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else if windowType == 3
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a0 := 0.42
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a1 := -0.50
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a2 := 0.08
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float TWO_PI = 6.28318530718
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float twoPiDivP = TWO_PI / p
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for k = 0 to p - 1
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float kTwoPiDivP = k * twoPiDivP
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float coef = a0 + a1 * math.cos(kTwoPiDivP)
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if a2 != 0.0
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coef += a2 * math.cos(2.0 * kTwoPiDivP)
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if a3 != 0.0
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coef += a3 * math.cos(3.0 * kTwoPiDivP)
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array.set(coefs, k, coef)
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prevPeriod := p
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prevWindowType := windowType
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float sum = 0.0
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float weightSum = 0.0
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int validCount = 0
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for i = 0 to p - 1
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float price = src[i]
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if not na(price)
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float coef = array.get(coefs, i)
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sum += price * coef
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weightSum += coef
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validCount += 1
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result := validCount > 0 and weightSum > 0 ? sum / weightSum : src
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if leastSquares and p > 2
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int n = math.min(math.floor((p - 1) / 2), 50)
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if n >= 2
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var float sx = 0.0
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var float sx2 = 0.0
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var int prevN = 0
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if n != prevN
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sx := 0.0
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sx2 := 0.0
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for i = 0 to n - 1
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sx += i
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sx2 += i * i
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prevN := n
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float sy = 0.0
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float sxy = 0.0
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for i = 0 to n - 1
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float yi = nz(src[i])
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sy += yi
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sxy += i * yi
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float denom = n * sx2 - sx * sx
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if math.abs(denom) > 1e-10
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float slope = (n * sxy - sx * sy) / denom
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float intercept = (sy - slope * sx) / n
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var array<float> fittedBuffer = array.new_float(p, na)
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for i = 0 to n - 1
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float fitted = intercept + slope * i
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array.set(fittedBuffer, i, fitted)
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float lsSum = 0.0
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float lsCount = 0.0
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for i = 0 to p - 1
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float val = i < n ? array.get(fittedBuffer, i) : nz(src[i])
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if not na(val)
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lsSum += val
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lsCount += 1.0
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result := lsCount > 0 ? lsSum / lsCount : result
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result
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// ---------- Main loop ----------
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// Inputs
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i_period = input.int(20, "Period", minval=1)
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i_source = input.source(close, "Source")
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i_windowType = input.int(4, "Window Function", minval=1, maxval=4, tooltip="1:Hanning, 2:Hamming, 3:Blackman, 4:Blackman-Harris")
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i_leastSquares = input.bool(false, "Least Squares Method", tooltip="Enable linear regression fitting for autoregressive prediction")
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// Calculation
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afirma_value = afirma(i_source, i_period, i_windowType, i_leastSquares)
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// Plot
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plot(afirma_value, "AFIRMA", color=color.yellow, linewidth=2)
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