mirror of
https://github.com/mihakralj/QuanTAlib.git
synced 2026-08-03 03:47:42 +00:00
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.
76 lines
2.6 KiB
Plaintext
76 lines
2.6 KiB
Plaintext
// The MIT License (MIT)
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// © mihakralj
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//@version=6
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indicator("One-Sample t-Test (ZTEST)", "t-TEST", overlay=false)
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//@function Calculates the t-statistic for a one-sample hypothesis test
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//@param source Source series to test (use returns for meaningful results)
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//@param period Lookback period for calculating sample mean and standard deviation
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//@param mu0 Hypothesized population mean to test against
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//@returns t-statistic (positive values indicate sample mean > mu0, negative indicate sample mean < mu0)
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//@optimized Uses circular buffer with running sums for O(1) complexity, Bessel correction for sample variance
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ztest(series float source, simple int period, simple float mu0) =>
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if period <= 1
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runtime.error("Period must be greater than 1")
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int p = period
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var array<float> buffer = array.new_float(0)
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var int head = 0
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var int n = 0
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var float sum = 0.0
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var float sumSq = 0.0
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if array.size(buffer) != p
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buffer := array.new_float(p, na)
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head := 0
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n := 0
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sum := 0.0
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sumSq := 0.0
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float oldest = array.get(buffer, head)
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if not na(oldest)
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sum -= oldest
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sumSq -= oldest * oldest
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n -= 1
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if not na(source)
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sum += source
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sumSq += source * source
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n += 1
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array.set(buffer, head, source)
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else
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array.set(buffer, head, na)
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head := (head + 1) % p
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float result = na
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if n >= 2
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float nf = float(n)
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float mean = sum / nf
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float variance = math.max(0.0, (sumSq / nf) - (mean * mean)) * (nf / (nf - 1.0))
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float stddev = math.sqrt(variance)
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float standard_error = stddev / math.sqrt(nf)
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if standard_error > 1e-10
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result := (mean - mu0) / standard_error
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result
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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_period = input.int(30, "Period", minval=2, tooltip="Minimum 30 recommended for z-critical approximation")
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i_mu0 = input.float(0.0, "Hypothesized Mean (μ₀)", step=0.01, tooltip="Use 0 when testing returns")
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// Calculation
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t_stat = ztest(i_source, i_period, i_mu0)
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// Plot
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plot(t_stat, "t-statistic", color=color.yellow, linewidth=2)
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hline(0, "Zero Line", color=color.gray, linestyle=hline.style_dotted)
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hline(2.04, "95% ~t(30)", color=color.new(color.green, 70), linestyle=hline.style_dashed)
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hline(-2.04, "95% ~t(30)", color=color.new(color.green, 70), linestyle=hline.style_dashed)
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hline(2.75, "99% ~t(30)", color=color.new(color.red, 70), linestyle=hline.style_dashed)
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hline(-2.75, "99% ~t(30)", color=color.new(color.red, 70), linestyle=hline.style_dashed)
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