mirror of
https://github.com/mihakralj/QuanTAlib.git
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Miha Kralj
86fe32a682
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com> Co-authored-by: aider (openrouter/anthropic/claude-sonnet-4) <aider@aider.chat> Co-authored-by: Warp <agent@warp.dev>
63 lines
2.4 KiB
Plaintext
63 lines
2.4 KiB
Plaintext
// The MIT License (MIT)
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// © mihakralj
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//@version=6
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indicator("Standardization (Z-score)", "STANDARDIZE", overlay=false, precision=4)
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//@function Calculates the Z-score of a series over a lookback period.
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//@doc https://github.com/mihakralj/pinescript/blob/main/indicators/numerics/standardize.md
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//@param src series float Input data series.
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//@param len simple int Lookback period for calculating mean and standard deviation (must be > 1 for sample stdev).
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//@returns series float The Z-score of the current data point, or na if issues like insufficient data or zero stdev for a non-mean current value.
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standardize(series float src, simple int len) =>
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if len <= 1
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float(na)
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var S1 = 0.0
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var S2 = 0.0
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var N = 0
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var float[] window_data_vals = array.new_float(0)
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var bool[] window_data_is_na = array.new_bool(0)
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float x_new = src[0]
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bool x_new_is_na = na(x_new)
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if array.size(window_data_vals) == len
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float x_old_val = array.get(window_data_vals, 0)
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bool x_old_was_na = array.get(window_data_is_na, 0)
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array.shift(window_data_vals)
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array.shift(window_data_is_na)
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if not x_old_was_na
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S1 -= x_old_val
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S2 -= x_old_val * x_old_val
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N -= 1
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array.push(window_data_vals, x_new_is_na ? 0.0 : x_new)
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array.push(window_data_is_na, x_new_is_na)
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if not x_new_is_na
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S1 += x_new
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S2 += x_new * x_new
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N += 1
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float z_score = na
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if N < 2
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z_score := na
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else
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float mean_val = S1 / N
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float variance_pop = (S2 / N) - (mean_val * mean_val)
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variance_pop := variance_pop < 1e-10 ? 0.0 : variance_pop
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float variance_sample = variance_pop * N / (N - 1)
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float stdev_val = math.sqrt(variance_sample)
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if x_new_is_na
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z_score := na
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else if stdev_val > 1e-10
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z_score := (x_new - mean_val) / stdev_val
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else
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z_score := (math.abs(x_new - mean_val) < 1e-10) ? 0.0 : na
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z_score
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// Inputs
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i_source = input.source(close, title="Source")
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i_length = input.int(20, title="Lookback Period", minval=2, tooltip="Period for mean and standard deviation. Must be at least 2 for stdev.")
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// Calculation
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z_score_value = standardize(i_source, i_length) // Renamed for clarity
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// Plot
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plot(z_score_value, "Z-score", color=color.yellow, linewidth=2)
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hline(0, "Zero Line", color=color.gray, linestyle=hline.style_dashed)
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