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https://github.com/mihakralj/QuanTAlib.git
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Miha Kralj
24e86d762a
- Updated BBWN, BBWP, CCV, CV, CVI, EWMA, GKV, HLV, HV, Jvolty, JVOLTYN, MASSI, NATR, RSV, RV, RVI, TR, UI, VOV, VR, YZV indicators with documentation links. - Added documentation links for Aberration, Acceleration Bands, Andrews' Pitchfork, Adaptive Price Zone, ATR Bands, Bollinger Bands, Center of Gravity, Donchian Channels, Decay Min-Max Channel, Detrended Synthetic Price, EACP, EBSW, HOMOD, Jurik Volatility Bands, Keltner Channel, MA Envelope, Min-Max Channel, Price Channel, Regression Channels, Standard Deviation Channel, Stoller Average Range Channel, Super Trend Bands, Ultimate Bands, Ultimate Channel, VWAP Bands, and VWAP with Standard Deviation Bands.
61 lines
2.0 KiB
Plaintext
61 lines
2.0 KiB
Plaintext
// The MIT License (MIT)
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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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