How to get the standard deviation from a TradingView Pine Script array?

How to get the standard deviation from a TradingView Pine Script array?

TL;DR
Keep data in a float array, ensure it has at least two elements, and use array.stdev(myArray) to measure dispersion for any custom sample set.

At a Glance

Difficulty	Beginner
Time to implement	10-15 min
Category	Arrays

Quick Actions

• Open in AI Editor
• Browse PineScript Examples
• Connect to Live Trading

Quick Start

//@version=5
indicator("Array stdev demo", overlay=false)

var float[] samples = array.new_float()
if barstate.isnew
    array.unshift(samples, close)
    if array.size(samples) > 30
        array.pop(samples)

float sampleStdev = na
if array.size(samples) > 1
    sampleStdev := array.stdev(samples)

plot(sampleStdev, "Sample stdev", color=color.new(color.orange, 0))

Tip. Standard deviation requires at least two elements. Guard with `array.size(arr) > 1` before calling `array.stdev()` to avoid runtime errors.

Why It Matters

Built-in indicators like ta.stdev() operate on chart series. Arrays let you feed in anything—custom swing measurements, session returns, latency samples—and array.stdev() reports how dispersed that custom data is. That dispersion can drive regime detection, adaptive thresholds, or quality-control checks.

What You’ll Learn

• Persist arrays with var so values accumulate across bars.
• Maintain a rolling window while keeping array size in check.
• Combine array.avg() and array.stdev() to track both centre and spread.
• Visualise and alert on volatility bursts captured from bespoke datasets.

Quick Reference

Call	Purpose
array.new_float()	Builds a float array to hold samples.
array.unshift(id, value)	Inserts the newest value at the front of the array.
array.pop(id)	Removes the oldest element to keep the window bounded.
array.avg(id)	Returns the mean of the array elements.
array.stdev(id)	Returns the sample standard deviation of the array elements.

Implementation Blueprint

1. Persist and bound the array
   Declare with var and enforce a maximum size each bar.

   var float[] buffer = array.new_float()
   array.unshift(buffer, close)
   if array.size(buffer) > 50
       array.pop(buffer)

2. Compute the statistics safely
   Only call the statistics functions when enough samples exist.

   float mean = na
   float stdev = na
   if array.size(buffer) > 1
       mean  := array.avg(buffer)
       stdev := array.stdev(buffer)

3. Use the dispersion insight
   Plot, alert, or adjust logic when volatility shifts.

   plot(stdev, "Dispersion", color=color.new(color.teal, 0))
   if not na(stdev) and stdev > mean * 0.02
       alert("Array dispersion above threshold")

Example Playbook

//@version=5
indicator("Session range quality", overlay=false, max_labels_count=500)

window      = input.int(20, "Rolling sessions", minval=2)
threshMult  = input.float(1.5, "Std dev multiplier", minval=0.1, step=0.1)

var float[] ranges = array.new_float()

sessionOpen  = request.security(syminfo.tickerid, "D", open)
sessionClose = request.security(syminfo.tickerid, "D", close)
sessionRange = math.abs(sessionClose - sessionOpen)

if barstate.isconfirmed
    array.unshift(ranges, sessionRange)
    if array.size(ranges) > window
        array.pop(ranges)

float avgRange  = na
float stdevRange = na

if array.size(ranges) > 1
    avgRange  := array.avg(ranges)
    stdevRange := array.stdev(ranges)

plot(avgRange, "Average range", color=color.new(color.blue, 0))
plot(stdevRange, "Std dev range", color=color.new(color.orange, 0))
plot(avgRange + stdevRange * threshMult, "High-volatility band", color=color.new(color.red, 40), style=plot.style_circles)

isExtreme = not na(stdevRange) and sessionRange > avgRange + stdevRange * threshMult

if isExtreme
    label.new(bar_index, sessionRange,
        "Extreme session\nRange: " + str.tostring(sessionRange, format.mintick),
        textcolor=color.white,
        bgcolor=color.new(color.red, 65))

Why this works.

• The array holds session ranges independent of intraday timeframe.
• `array.avg()` and `array.stdev()` track both central tendency and spread.
• Labels and thresholds highlight when a session moves beyond its normal volatility envelope.

Pro Tips & Pitfalls

• array.stdev() returns the sample standard deviation (dividing by n - 1). If you need population deviation, adjust manually.
• Arrays consume memory; keep windows tight (20–200 elements) for real-time responsiveness.
• Mix and match with other array functions (array.percentile, array.sort) when profiling distributions.
• Reset arrays (e.g., on session change) by assigning a new array or calling array.clear() if you need segment-by-segment stats.

Troubleshooting & FAQ

I get “Cannot call array.stdev with an empty array”

Insert at least two values before calling the function, or guard with a size check. Empty arrays cannot form variance.

Values explode to na

na propagates if you insert na into the array. Filter out invalid numbers before pushing them into the buffer.

Can I blend chart series and custom data?

Yes—push any numeric value into the array. For example, store high - low for custom ranges or strategy.closedtrades.profit(size) for trade-level stats.

Key Takeaways

• Arrays make it easy to compute statistics on bespoke data sets beyond built-in series.
• Guard array.stdev() with a size check and keep the array bounded for performance.
• Pair standard deviation with the average to flag abnormal readings quickly.
• Use labels, alerts, or adaptive logic to act on volatility information immediately.

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Keep Going

• Open AI Editor to apply the pattern with guided assistance
• Browse Strategy Examples for ready-to-run templates
• Connect to Live Trading when you are ready to automate

Adapted from tradingcode.net, optimised for Algo Trade Analytics users.