mirror of
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.
84 lines
2.8 KiB
Plaintext
84 lines
2.8 KiB
Plaintext
// The MIT License (MIT)
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// © mihakralj
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//@version=6
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indicator("Skewness (SKEW)", "SKEW", overlay=false, precision=6)
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//@function Calculates the skewness of a source series over a specified period.
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// Skewness is a measure of the asymmetry of the probability distribution of a real-valued random variable about its mean.
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// This implementation calculates the population skewness (Fisher-Pearson coefficient g1).
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//@param src The source series.
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//@param len The lookback period. Must be > 2.
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//@returns The skewness value.
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//@optimized Uses efficient rolling calculations for mean, variance, and the third central moment.
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skew(series float src, simple int len) =>
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if len <= 2
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runtime.error("Length must be greater than 2 for Skewness calculation.")
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var float sum_m = 0.0
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var array<float> buffer_m = array.new_float(len)
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var int head_m = 0
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if bar_index >= len
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sum_m -= array.get(buffer_m, head_m)
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float current_src_nz = nz(src)
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sum_m += current_src_nz
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array.set(buffer_m, head_m, current_src_nz)
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head_m := (head_m + 1) % len
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float mean_val = na
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if bar_index >= len - 1
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mean_val := sum_m / len
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else
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mean_val := sum_m / (bar_index + 1)
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float dev = src - mean_val
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var float sum_v = 0.0
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var array<float> buffer_v = array.new_float(len)
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var int head_v = 0
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float dev_sq_nz = nz(math.pow(dev, 2))
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if bar_index >= len
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sum_v -= array.get(buffer_v, head_v)
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sum_v += dev_sq_nz
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array.set(buffer_v, head_v, dev_sq_nz)
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head_v := (head_v + 1) % len
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float variance_val = na
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if bar_index >= len - 1
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variance_val := sum_v / len
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else
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variance_val := sum_v / (bar_index + 1)
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float stddev_val = variance_val > 1e-9 ? math.sqrt(variance_val) : 0.0
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var float sum_s3 = 0.0
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var array<float> buffer_s3 = array.new_float(len)
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var int head_s3 = 0
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float dev_cubed_nz = nz(math.pow(dev, 3))
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if bar_index >= len
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sum_s3 -= array.get(buffer_s3, head_s3)
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sum_s3 += dev_cubed_nz
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array.set(buffer_s3, head_s3, dev_cubed_nz)
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head_s3 := (head_s3 + 1) % len
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float m3 = na
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if bar_index >= len - 1
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m3 := sum_s3 / len
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else
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m3 := sum_s3 / (bar_index + 1)
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float skew_val = na
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if not na(m3) and not na(stddev_val)
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if stddev_val > 1e-9
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float stddev_cubed = math.pow(stddev_val, 3)
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if stddev_cubed != 0
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skew_val := m3 / stddev_cubed
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else
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skew_val := 0.0
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else
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skew_val := 0.0
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skew_val
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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_length = input.int(20, "Length", minval=3) // Minval 3 for skewness
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// Calculation
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skewValue = skew(i_source, i_length)
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// Plot
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plot(skewValue, "Skewness", color=color.yellow, linewidth=2)
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