Correlations answers one simple question about any two tickers: when one of them moves, what does the other usually do?
Two tickers, one relationship
You pick a Primary Ticker and a ticker to Compare Against. Everything on the page describes the relationship between that pair. Swap either one and every number changes.
Two tickers can sit far apart in price and still move together. What correlation measures is how they move.
It compares moves, not prices
This is the part most people get wrong. The tool never compares the two price levels. A $700 ETF and a $12 ETF have nothing meaningful to say to each other in dollar terms.
Every number on the page is calculated from paired daily returns. Absolute price levels play no role at all. That is why a $700 index ETF and a $12 volatility ETF can be compared directly. The tool uses simple daily returns (nothing compounded) so each day remains an independent observation.
Direction
The first block of results is a plain count. Out of every shared day, how often did the two close on the same side?
Move together counts the days both closed green or both closed red. Those days are split into two groups:
both closed green
both closed red
Move opposite counts the days one closed green while the other closed red. Those days are split into two groups:
Direction tells you whether they agree. Beta tells you by how much.
The question beta answers
Beta is the average move in one ticker for every 1% move in the other.
beta = correlation × (volatility of A ÷ volatility of B)
Reversing the pair flips the volatility ratio, which is why the numbers change.
Why the two rows are not reciprocals
This confuses a lot of people. On SPY versus QQQ the tool shows:
For every 1% move in QQQ, SPY moves about +0.81%
For every 1% move in SPY, QQQ moves about +1.05%
Many people expect the second number to simply be the inverse of the first (1 ÷ 0.81 ≈ 1.23). It isn't.
The reason is simple: the two tickers do not move in perfect lockstep. Part of each ticker's daily move is unique to it and is not shared with the other.
Neither one is a pure inverse of the other.
When you multiply the two numbers (0.81 × 1.05 = 0.85), you get an estimate of how much movement they actually share. In this case, about 85% of the daily moves are common to both SPY and QQQ. The remaining 15% is unique to each one.
When the two rows look nothing alike
SPY versus UVXY is an extreme example:
For every 1% move in UVXY, SPY moves about −0.11%
For every 1% move in SPY, UVXY moves about −5.36%
The big difference in size comes from volatility. UVXY swings much harder than SPY. A 1% move in UVXY is relatively small for it, so SPY barely reacts. But a 1% move in SPY is a big deal for UVXY, which is why that number is so large.
The first chart breaks the correlation down into calendar years. Each year has two columns: the share of days the pair moved together and the share they moved opposite.
Rolling correlation
The second chart tracks the correlation over a trailing 30-day window. Above the zero line the pair is moving together, below it they are moving opposite. The chart also highlights the highest and lowest readings with the dates they happened.
The VIX Regime Filter
This tool includes an optional volatility filter. It lets you look only at days that began in a specific volatility environment.
How the filter works
Each day is grouped by where the VIX opened that morning: under 20, between 20 and 30, or above 30. That opening level defines the market environment for the day. Only the days that match the regime you select are kept. All other days are removed from the calculation.
What it reveals
In calm markets, stocks often move for their own reasons. One company may report strong earnings while another struggles, so their prices can go in opposite directions.
When volatility rises and fear increases, behavior changes. Investors pay less attention to individual companies and more attention to overall risk. Selling or buying tends to happen in broad waves. As a result, even stocks that normally have little in common start moving together more often.
High-volatility periods therefore show higher same-direction rates across the market, including pairs that usually look unrelated.
Use the buttons below to open the tool with different VIX filters and compare the results.
How closely two tickers' daily percentage moves agree. This tool scores that from paired daily returns, not from price levels. Two names can move in the same direction most days and still travel very different distances.
Beta
How far one ticker typically moves for every 1% move in the other. The tool shows it in both directions, because reversing the pair flips the volatility ratio and the two numbers are not reciprocals.
Session
One trading day. The tool pairs sessions both tickers were open and drops the rest. Time is measured in sessions rather than calendar days, so weekends and holidays are skipped.
Candle (OHLC)
One bar of price data: the open, high, low and close. This tool uses each session's close to compute that day's percentage move.
The Filter and Charts
VIX
The Cboe Volatility Index. The optional filter groups each day by where VIX opened that morning: under 20, between 20 and 30, or above 30. Only the days in the regime you select are kept.
Lookback Window
How far back a calculation measures. The rolling chart uses a trailing 30-day window, redrawn each session.
Realized Volatility
How much a ticker actually moved. The two beta rows differ when one ticker is much more volatile than the other, the way UVXY is versus SPY.
