Live prices are currently unavailable — the exchange feed could not be reached and no recent cached reading is held.

Bitcoin Data GuideBitcoin Reference

Research · Altcoins · Strand D

The SOL-BTC correlation record

SOL and BTC have spent most of their shared history moving in the same direction, and the interesting part of the record is where that breaks. This page sets out how the relationship is measured, which windows it holds in, and the event periods that interrupt it.

Last reviewed 2026-09-21Source: CoinGecko daily close series for SOL and BTC; Coin Metrics community network dataMethod and window definitions only. No correlation coefficient is asserted here that the reader cannot recompute from the cited daily series.

What a correlation coefficient measures

A correlation coefficient summarises how two return series moved together across a set of observations. It is calculated from returns rather than from price levels, and that distinction matters more than it first appears. Two series that both trend upward over a multi-year window will show a high correlation in levels almost regardless of how they behave day to day, because the trend dominates the arithmetic. Differencing the series removes the trend and leaves the question this page is actually asking: on a given day, did the two assets move together?

The window over which the coefficient is computed changes the answer as much as the data does. A thirty-day rolling correlation and a three-year correlation over the same pair can differ substantially, and neither is wrong. The short window responds quickly to a regime change and is noisy; the long window is stable and slow to reflect anything new. A correlation figure quoted without its window is close to meaningless, which is why every figure on this page is stated with its window.

There is a second measurement choice that is easy to miss. Returns can be computed at daily, weekly or monthly frequency, and the coefficient generally rises as the sampling interval lengthens. That is not evidence of a stronger relationship; it is an artefact of averaging away short-horizon noise. Two series that are only loosely related day to day can look tightly related month to month.

What the rolling window shows

The table below describes the character of the relationship in each phase of the record rather than asserting a coefficient. The qualitative description is what the cited daily series support; a reader who wants the exact figure for a given window can compute it from the same source.

Phases of the SOL-BTC daily-return relationship, with the window each covers and the character of the co-movement observed.
PhaseWindowCharacter of co-movement
Early marketAug 2020 – Dec 2020Thin and erratic. SOL's price discovery was still forming, and daily moves were driven by its own listing and liquidity conditions as much as by the wider market.
2021 expansionJan 2021 – Nov 2021Strongly positive on a rolling 90-day basis for most of the window. Both assets rose through the same liquidity cycle, and the shared factor dominates the daily record.
2022 contractionNov 2021 – Dec 2022Positive but with visible breaks. The relationship tightened during broad risk-off moves and loosened around SOL-specific events, when idiosyncratic news moved SOL without a matching BTC move.
2023 recoveryJan 2023 – Dec 2023Positive and comparatively stable. With fewer asset-specific shocks, the common market factor accounts for more of the daily variation in both series.
Recent periodJan 2024 – review datePositive on the rolling window, with short-lived decouplings around network events and around periods when the two assets responded differently to the same macro news.

Last reviewed 2026-09-21Source: CoinGecko daily close series for SOL and BTCPhase boundaries are editorial and follow the cycle dates used elsewhere on this site; the underlying daily series is the cited source.

The periods that interrupt it

A high correlation is not a constant. The record contains several stretches in which SOL moved in a way BTC did not, and those stretches are more informative than the average. The clearest are the periods when Solana's own network conditions were the dominant input: the congestion episodes of 2021 and early 2022, the validator-outage period in September 2021, and the aftermath of the November 2022 events that affected the wider market through a specific counterparty rather than through a general repricing.

During those windows the rolling coefficient falls, sometimes sharply, because SOL is responding to information that does not enter BTC's price at all. That is the correct reading of the statistic. A falling correlation is not evidence that the two assets have become independent in any structural sense; it is evidence that for that window, a factor specific to one asset was large enough to dominate the common factor.

The reverse also appears. There are windows in which the correlation rises above its longer-run level, typically during broad risk-asset repricings when both assets are being sold or bought as part of the same portfolio decision. In those windows the asset-specific factors are small relative to the market factor, and the coefficient reflects that.

What this page does not claim

A correlation coefficient measures the strength of a linear association between two series over a stated window. It does not identify a cause, it does not establish that one asset leads the other, and it does not tell a reader what will happen next. Two series can be highly correlated because one drives the other, because a third factor drives both, or because both are responding to the same news at the same time. The statistic cannot distinguish between those cases.

In this record the most plausible common factor is the broad risk-asset cycle. Both SOL and BTC trade as high-beta instruments against the same liquidity conditions, and when those conditions change, both reprice. That shared exposure is enough to produce a positive daily correlation without any direct relationship between the two networks. Nothing in the data here supports the stronger claim that Bitcoin's price moves cause Solana's.

There is also a timing problem that a daily correlation hides. If one asset responds to news within minutes and the other over hours, a daily close-to-close calculation will understate the relationship and a weekly one will overstate it. The window length is therefore part of the result, not a detail of the presentation.

Dataset, period, method and limitations

Dataset
CoinGecko daily close series for SOL and BTC, cross-checked against Coin Metrics community network data for the periods both cover.
Period
August 2020, SOL's first liquid market, to the last complete month before the review date. SOL did not trade before 2020, so there is no earlier record to draw on.
Method
Pearson's r computed on daily log returns over a rolling window, with the window length stated wherever a figure appears. Returns rather than price levels, so the shared trend does not dominate the result.
Limitations
The record is roughly five years, one Bitcoin cycle and not enough to say anything durable about how the relationship behaves across regimes. The series are venue-aggregated closes, and the aggregation method is not identical across the two assets. Correlation is not stable and not predictive.

Where this sits in the wider record

The correlation record is one of six pages in this strand that measure Solana against Bitcoin rather than describing it on its own terms. The SOL/BTC ratio across cycles covers the relative-price record that this page's daily returns are derived from, and the drawdown comparison covers the downside behaviour that a correlation coefficient does not describe.

For the general treatment of what a drawdown is and how it is measured, see drawdown explained. For the architectural comparison between the two networks, see Bitcoin vs Solana architecture.

Sources and references

Every source below is named and linked. Where a page describes a method rather than a figure, the source is the specification or documentation that defines the method. Last reviewed 2026-09-21.

Related reading