Quick Answer: Calculate Bitcoin–Nasdaq correlation from paired returns over a stated period, using the same timestamps for both assets. Select a BTC price series and a Nasdaq-100 index or futures series, convert each to percentage returns, align observations, and compute Pearson correlation across a rolling window. A single day when both fall is not a correlation measurement. Results can change with the asset proxy, time zone, frequency, and window length.
Which two price series should you compare?
Decide whether your question concerns the Nasdaq-100 cash index, Nasdaq-100 futures, or an exchange-traded fund tracking the index. These are related but have different trading schedules. Bitcoin trades throughout weekends and holidays; the U.S. cash index does not. Label the chosen proxy in your chart and do not silently switch between them.
For a daily comparison with the cash index, one practical method is to sample BTC at the U.S. equity close on each equity trading day, then use the index’s matching close. Remove weekends and U.S. holidays from both return series. For an intraday comparison, pair BTC and equity or futures prices at identical UTC timestamps. A 24-hour BTC change cannot be directly paired with one U.S. cash session and called a synchronized return.
What calculation should go into a spreadsheet?
For each asset, compute a simple return from consecutive aligned prices: return = current price ÷ previous aligned price − 1. Place BTC returns in one column and Nasdaq-100 returns in another. Then calculate Pearson correlation for a chosen set of paired rows. In a spreadsheet, that is typically CORREL(BTC_return_range, Nasdaq_return_range).
Choose the window before viewing the result. A 30-trading-day rolling estimate uses the most recent 30 paired returns at each point; a 90-day estimate is smoother but slower to reflect change. Missing prices, contract rolls, data-vendor corrections, and index holidays should be handled consistently. If you use Nasdaq-100 futures, specify the contract and how you handle rolls.
How should you interpret the result?
Correlation ranges from −1 to +1. A positive value means the paired returns tended to move in the same direction within the selected sample. It does not mean the Nasdaq caused BTC to move, nor does it prove that the relationship will persist. A coefficient close to zero may hide short bursts of co-movement. To claim a “spike,” compare the current rolling value with values computed by the same method in earlier windows.
The November 6 BTC recap describes a joint selloff but does not supply a calculated coefficient. This tutorial provides the method needed before assigning a number to that relationship.
Phemex offers a NAS100/USDT perpetual market for eligible traders to inspect or trade. It is a leveraged derivative with funding, margin and liquidation risk, not the Nasdaq-100 cash index used in the sample calculation. Use an appropriate index or CME futures data source for a research-grade coefficient. This educational calculation is not a trade signal.
A reproducible spreadsheet workflow
Create a sheet with a date/time column, one BTC price column and one Nasdaq-100 price column. Write the source, market symbol, time zone and sampling rule above the data. If you are comparing daily cash-index closes, keep only U.S. equity trading days, then sample Bitcoin at the matching U.S. close. The first return row needs a prior aligned price for both assets; do not calculate BTC's Saturday-to-Monday move against the index's Friday-to-Monday move without noting the unequal intervals.
In the next two columns, calculate each asset's return from the immediately previous aligned row. For example, if BTC moves from $75,000 to $76,500, its return is 76,500/75,000 - 1 = 2%. If the index moves from 20,000 to 20,200 across the same aligned observations, its return is 1%. Those two numbers form one paired observation. Repeat across the full sample and run CORREL on equal-length return columns, not on the raw price levels.
Price-level correlation can appear high simply because both series trend over time, even if their day-to-day returns are weakly related. A rolling return correlation addresses a different question: whether recent changes have tended to move together. Report the number of paired returns, window endpoints and whether you used simple or log returns. If one series is missing an observation, remove the pair, rather than shifting the remaining series by a row.
How can you test whether a reported spike is meaningful?
Calculate a rolling series using the same rule for every window. A coefficient of 0.6 over the latest 30 paired trading days could be higher than the prior 30-day value, but the word “spike” should be grounded in that comparison. Then check a longer window, such as 90 paired days, to see whether the short-window change persists. The two answers can disagree because they describe different horizons.
Ask whether the result survives a reasonable alternative data choice: BTC sampled at the equity close versus BTC sampled at midnight UTC, or Nasdaq-100 cash index versus Nasdaq-100 futures during matched hours. Do not switch the proxy after seeing which choice produces the strongest headline. A chart of the paired returns can reveal one extreme day dominating the coefficient; report that sensitivity rather than hiding it.
What can a correlation study inform—and what can it not?
It can describe how two return series moved together in a specified sample. It can help a portfolio owner test whether BTC provided diversification over that period. It cannot assign causation, prove that an equity selloff triggered a BTC liquidation, or determine the next trade. Correlations are especially unstable during rare stress events, when common liquidity constraints may push several assets in the same direction at once.
A coefficient also does not tell you how large either asset's moves were. BTC and the Nasdaq-100 could show positive correlation while BTC fluctuates much more widely. For portfolio risk, look at each series' volatility and the size of the exposures alongside the coefficient. Do not turn a 0.7 correlation into a claim that a 1% index move implies a 0.7% BTC move; that requires a different regression analysis and still would describe a historical sample, not a fixed future response.
FAQ
Can I calculate correlation from only two daily moves?
No meaningful rolling relationship can be inferred from a single paired move. Use a defined multi-observation window and report its size.
Why can two analysts get different coefficients?
They may choose different BTC exchanges, equity proxies, timestamps, return frequencies, and rolling windows. Their figures are comparable only when the methods match.
