mirror of
https://github.com/mihakralj/QuanTAlib.git
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Miha Kralj
61 lines
2.0 KiB
Plaintext
61 lines
2.0 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("Least Squares Moving Average (LSMA)", "LSMA", overlay=true)
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//@function Calculates LSMA by fitting a linear regression line to price data
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//@param source Series to calculate LSMA from
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//@param period Lookback period for the linear regression
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//@returns LSMA value, calculates from first bar using available data
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//@optimized Uses circular buffer with linear regression for O(n) complexity per bar
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lsma(series float source, simple int period) =>
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if period <= 1
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runtime.error("Period must be greater than 1")
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source
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else
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int p = math.min(bar_index + 1, period)
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if p <= 1
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source
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else
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var array<float> buffer = array.new_float(period, na)
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var int head = 0
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array.set(buffer, head, source)
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head := (head + 1) % period
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float sum_y = 0.0
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float sum_xy = 0.0
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float sum_x = 0.0
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float sum_x2 = 0.0
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float count = 0.0
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int idx = (head - 1 + period) % period
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for i = 0 to p - 1
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float val = array.get(buffer, idx)
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if not na(val)
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sum_x += i
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sum_y += val
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sum_xy += i * val
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sum_x2 += i * i
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count += 1.0
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idx := (idx - 1 + period) % period
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if count <= 1.0
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source
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else
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float denom = count * sum_x2 - sum_x * sum_x
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if denom == 0.0
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source
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else
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float slope = (count * sum_xy - sum_x * sum_y) / denom
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float intercept = (sum_y - slope * sum_x) / count
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intercept
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// ---------- Main loop ----------
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// Inputs
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i_period = input.int(10, "Period", minval=1)
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i_source = input.source(close, "Source")
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// Calculation
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lsma_value = lsma(i_source, i_period)
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// Plot
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plot(lsma_value, "LSMA", color=color.yellow, linewidth=2)
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