How to Calculate DXY–Bitcoin Correlation in Excel
To calculate DXY–Bitcoin correlation, align the two datasets by date, convert their prices into returns, and run a correlation calculation on those paired returns. The answer depends on your sample period and closing-time convention. A number without those details is difficult to interpret.
This guide explains a reproducible Excel method. It does not claim a current correlation reading or use live market prices.
Choose the dollar series before collecting Bitcoin prices
The ICE U.S. Dollar Index measures the dollar against a fixed basket of six currencies. A different dollar index or a dollar-index futures contract is a different input. Write down exactly which series you use. ICE describes the USDX composition and its underlying index.
For Bitcoin, choose one consistent BTC/USD price series. Record its provider, timezone and closing timestamp. Switching exchanges halfway through a sample can introduce differences unrelated to the relationship you want to measure.
The broader interpretation is covered in our DXY and Bitcoin relationship guide. Here, the task is to measure the relationship rather than assume it is always negative.
Match observation dates and return intervals
Bitcoin trades through weekends, while dollar-market data may have no new weekend observation. Do not pair a fresh Sunday Bitcoin price with a repeated Friday DXY value and treat both as equivalent new observations.
One practical convention is to use dates on which both datasets have observations. For each retained date, calculate the return from the previous retained date in both series. A Monday return will then span Friday to Monday in both columns, rather than one column spanning three days and the other only one day.
You also need a closing-time rule. A Bitcoin close at midnight UTC and a dollar-index observation several hours earlier are not perfectly synchronized. If matching timestamps are unavailable, disclose the difference instead of describing the calculation as an exact simultaneous relationship.
Set up the Excel calculation
Arrange the matched observations in these columns:
- Column A: observation date.
- Column B: DXY value.
- Column C: BTC/USD price.
- Column D: DXY return.
- Column E: Bitcoin return.
With your first price observation in row 2, enter =B3/B2-1 in D3 and =C3/C2-1 in E3. Fill both formulas down. Format the return columns as percentages, but keep the underlying numeric values.
For 30 paired return observations in rows 3 through 32, use =CORREL(D3:D32,E3:E32). That requires 31 paired price observations. Microsoft documents the CORREL function and its input requirements.
A coefficient nearer +1 indicates a stronger positive linear association; nearer −1 indicates a stronger negative one. A value near zero indicates a weak linear association in this sample. It does not rule out every possible relationship.
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Compare windows without hiding the date range
Repeat the calculation for a longer sample, such as 90 paired observations, if you have sufficient data. Use the same dataset and timing convention.
A 30-observation reading and a 90-observation reading can differ because they summarize different periods. Thirty matched observations also do not necessarily mean 30 calendar days.
For a rolling 30-observation calculation, move both return ranges down one row at a time. Compare the resulting sequence with the single full-sample result. Report the start date, end date, observation count and whether you used simple or logarithmic returns.
Use our DXY–Bitcoin relationship worksheet to keep your assumptions beside the number you calculated.
Four mistakes that make the result misleading
- Correlating raw price levels and presenting the result as a relationship between daily moves.
- Pairing different dates or return intervals.
- Selecting a start date only because it produces the result you expected.
- Treating an observed association as evidence that DXY caused Bitcoin to move.
If you examine several windows, show them rather than presenting only the most dramatic coefficient. If the result changes materially when you adjust the sample, that sensitivity belongs in the explanation.
Does negative correlation mean Bitcoin will rise when DXY falls?
No. Correlation describes paired observations in a specified sample. It is not a rule for the next observation, a price target or a complete trading strategy.
For example, a hypothetical negative coefficient would not tell you the size of Bitcoin's next move or when it would occur. Those questions require information that the coefficient alone does not contain.
Can I use hourly prices instead?
Yes, provided you align timestamps and return intervals consistently and explain how you handle gaps. Do not mix hourly Bitcoin returns with daily DXY changes and label the result an hourly correlation.
A useful calculation is one another reader can reproduce. Keep the input source, date convention, formulas and window length together, and treat the result as one piece of market context.
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