mirror of
https://github.com/mihakralj/QuanTAlib.git
synced 2026-07-27 17:27:43 +00:00
Miha Kralj
49 lines
1.7 KiB
Plaintext
49 lines
1.7 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("Convolution Moving Average (CONV)", "CONV", overlay=true)
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//@function Calculates a convolution MA using any custom kernel
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//@param source Series to calculate CONV from
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//@param kernel Array of weights to use as convolution kernel
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//@returns CONV value, calculates from first bar using available data
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//@optimized Uses custom kernel convolution with O(n) complexity per bar due to lookback loop
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conv(series float source, simple array<float> kernel) =>
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int kernel_size = array.size(kernel)
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if kernel_size <= 0
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runtime.error("Kernel must not be empty")
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var array<float> norm_kernel = array.new_float(1, 1.0)
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var int last_kernel_size = 1
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if last_kernel_size != kernel_size
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norm_kernel := array.copy(kernel)
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float kernel_sum = 0.0
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for i = 0 to kernel_size - 1
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kernel_sum += array.get(kernel, i)
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if kernel_sum != 0.0
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float inv_sum = 1.0 / kernel_sum
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for i = 0 to kernel_size - 1
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array.set(norm_kernel, i, array.get(kernel, i) * inv_sum)
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last_kernel_size := kernel_size
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int p = math.min(bar_index + 1, kernel_size)
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float sum = 0.0
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float weight_sum = 0.0
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for i = 0 to p - 1
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float price = source[i]
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if not na(price)
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float w = array.get(norm_kernel, i)
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sum += price * w
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weight_sum += w
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nz(sum / weight_sum, source)
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// ---------- Main loop ----------
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// Inputs
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i_source = input.source(close, "Source")
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i_kernel = array.from(1.0, 2.5, -3.14, 0.0, 1.0)
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// Calculation
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conv_value = conv(i_source, i_kernel)
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// Plot
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plot(conv_value, "CONV", color=color.yellow, linewidth=2)
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