Direct Answer
Price percentile rank is the percentage of closes in a lookback window that are at or below the current price. A rank of 90 means price is higher than or equal to 90% of the closes in the window. High ranks mean price is near the top of where it has traded in the window, low ranks near the bottom.
Price Percentile Rank: Formula, Meaning, Signals, Examples and How to Use It
How Is Price Percentile Rank Calculated?
Price percentile rank is the percentage of closes in a lookback window that are at or below the current price. A rank of 90 means price is higher than or equal to 90% of the closes in the window.
Formula: Rank = 100 x (number of closes in the window <= current close) / N
Inputs:
- Closing prices over the window
- Lookback length N
- Current close
Worked example. If 189 of the last 252 closes were at or below the current close, the percentile rank is 75.0%. The inputs are illustrative numbers, not market data.
Reading it. High ranks mean price is near the top of where it has traded in the window, low ranks near the bottom. Unlike a position within the high-low range, it counts how often price traded at each level.
Limits. Results depend on the window length, and a trending series will naturally produce high ranks as it makes new highs. Rank changes with data revisions, and the tie-handling convention varies.
What is Price Percentile Rank?
Price percentile rank is the percentage of closes in a lookback window that are at or below the current price. A rank of 90 means price is higher than or equal to 90% of the closes in the window.
How to use Price Percentile Rank
High ranks mean price is near the top of where it has traded in the window, low ranks near the bottom. Unlike a position within the high-low range, it counts how often price traded at each level. A reading alone is never a buy or sell signal; it needs context from price structure, volume, volatility and the broader market regime.
Strengths
- Ranking the close against its own recent closes gives a 0 to 100 scale that assumes nothing about the shape of the price distribution.
- A count of closes at or below the current one is easy to verify, and each of the N observations carries equal weight.
- Reproducible when the formula and data convention are fixed.
Weaknesses and limitations
- The rank is bounded, so a price far above the window's earlier closes and one barely above them both read near 100.
- After a few outsized closes enter the window, ordinary prices rank low and recover only as those closes age out.
- Correlated indicators can create false confidence by repeating the same underlying information.
Editorial and risk note
This page is educational content, not individualized investment advice. It should not imply guaranteed prediction, accuracy or outperformance.
Frequently Asked Questions
Is Price Percentile Rank a buy or sell signal?
No. High ranks mean price is near the top of where it has traded in the window, low ranks near the bottom. Results depend on the window length, and a trending series will naturally produce high ranks as it makes new highs. A trade built on it still needs a trigger, an invalidation rule, a position size and realistic execution assumptions.
What is the best setting for Price Percentile Rank?
Price Percentile Rank takes these inputs: closing prices over the window; lookback length N; current close. There is no universal best setting for the tunable ones, so test them across instruments, regimes and out-of-sample periods and favor stable parameter regions over one historical winner.
Can Price Percentile Rank be used by itself?
Results depend on the window length, and a trending series will naturally produce high ranks as it makes new highs. Rank changes with data revisions, and the tie-handling convention varies. For that reason it is read next to measures of direction, participation, valuation, liquidity and risk, not alone.
Does Price Percentile Rank work on every timeframe?
The calculation can be run on any bar size, but behavior is not portable: a reading on a five-minute chart describes a very different market window from the same reading on a daily chart. Validate the exact timeframe before relying on it.
Why does Price Percentile Rank give false signals?
The rank is bounded, so a price far above the window's earlier closes and one barely above them both read near 100. After a few outsized closes enter the window, ordinary prices rank low and recover only as those closes age out. Lag, noise and regime changes add to this, because market participants react to new information after the reading is calculated.
How should Price Percentile Rank be backtested?
Rebuild Price Percentile Rank from closing prices over the window using point-in-time data, write the rules before testing, include delisted securities where relevant, model realistic fills and costs, reserve a validation sample and compare with a simpler baseline.