Tracking Digital Asset Markets Beyond Crypto-Native Venues

insights.glassnodePubblicato 2026-05-21Pubblicato ultima volta 2026-05-21

Introduzione

Glassnode has expanded its analytics platform to track digital asset markets beyond crypto-native venues. New data includes daily price and volume for U.S. spot ETFs and publicly-listed digital asset treasury (DAT) companies. This allows users to compare the behavior of these listed equities against underlying crypto assets, assessing which stocks act as strong proxies. ETF trading volume data provides insights into institutional participation and liquidity, helping to distinguish genuine market trends from thin, speculative moves. These new metrics offer a unified view of crypto exposure through traditional finance instruments, bridging the gap between on-chain and traditional market data.

Glassnode has continued to expand its Traditional Finance coverage in Studio, helping users connect crypto-native market activity with the growing universe of listed instruments tied to digital assets.

This expansion reflects the growing role of ETFs, treasury companies, and other listed vehicles in digital asset market analysis, as institutional exposure increasingly moves beyond spot markets.

Two new metric families are now live:

US Spot ETF Price & Volume
US Digital Asset Treasury Company Price & Volume.

For each selected crypto asset, users can now retrieve daily price and traded volume across the relevant US spot ETFs and publicly listed treasury companies, with one time series per ticker. This gives investors a more complete view of how crypto exposure is being expressed through traditional equity markets.

DATs: Analyzing Equity Behavior Relative to Crypto

For US Digital Asset Treasury (DAT) companies, the data makes it easier to compare how listed equities behave relative to the underlying crypto asset and assess which names exhibit stronger or weaker equity beta to crypto market movements.

For example, users can compare BTC performance against companies holding BTC on their balance sheet to identify which stocks behave more like direct crypto proxies, and which names trade with a more idiosyncratic equity profile.

US Spot ETF Volume: Assessing Participation and Liquidity Conditions

The addition of trading volume is particularly useful for US spot ETFs. Trading volume provides insight into liquidity, participation, and conviction.

For example, a sharp spike in ETF trading volume around a major price move or macro event can signal heavy institutional repositioning — and comparing ETF volume against the underlying spot market shows how much real flow is moving through TradFi venues versus crypto-native ones. Sustained volume can confirm genuine demand behind a price trend, while thin volume on a move may warrant more caution.

Together, these new metrics strengthen Glassnode’s ability to bridge crypto-native data with traditional market instruments, giving users a unified view of spot assets, ETFs, and listed treasury vehicles directly in Studio.

Explore the new metrics in Studio

DAT Price Companies
DAT Trading Volume Companies
US Spot ETF Price
US Spot ETF Trading Volume

Follow us on X for timely market updates and analysis
Join our Telegram channel for regular market insights
For on-chain metrics, dashboards, and alerts, visit Glassnode Studio

Disclaimer: This report is for informational and educational purposes only. The analysis represents a limited case study with significant constraints and should not be interpreted as investment advice or definitive trading signals. Past performance patterns do not guarantee future results. Always conduct thorough due diligence and consider multiple factors before making investment decisions.

Domande pertinenti

QWhat new metric families has Glassnode introduced in Studio?

AGlassnode has introduced two new metric families: US Spot ETF Price & Volume and US Digital Asset Treasury Company Price & Volume.

QWhat is the primary purpose of adding the US Spot ETF trading volume data?

AThe primary purpose of adding US Spot ETF trading volume data is to provide insight into liquidity, participation, and conviction, helping users assess the significance of price trends and the flow of capital between traditional and crypto-native venues.

QHow can the data for US Digital Asset Treasury (DAT) companies be used?

AThe data for US Digital Asset Treasury (DAT) companies can be used to compare how listed equities behave relative to the underlying crypto asset and to assess which stocks exhibit a stronger or weaker equity beta to crypto market movements, identifying which ones act more as direct crypto proxies.

QWhat overall capability do these new metrics strengthen for Glassnode?

AThese new metrics strengthen Glassnode's ability to bridge crypto-native data with traditional market instruments, giving users a unified view of spot assets, ETFs, and listed treasury vehicles directly within its Studio platform.

QWhat disclaimer does the article provide regarding its content?

AThe article provides a disclaimer stating that the report is for informational and educational purposes only, the analysis is a limited case study, it should not be interpreted as investment advice or definitive trading signals, and users should always conduct thorough due diligence before making investment decisions.

Letture associate

Near Returns to the AI Stage: Transformation into a Public Chain Due to 'Payroll Difficulties,' Agent and Privacy Emerge as New Growth Narratives

NEAR Returns to AI Origins: From Payroll Struggles to Blockchain, Now Focusing on AI Agents and Privacy NEAR Protocol's journey began not with grand blockchain ambitions, but from a practical hurdle: its AI startup founders, including Transformer paper co-author Illia Polosukhin, couldn't efficiently pay international developers in 2017. This led them to pivot and build a high-performance, scalable blockchain. After years navigating various crypto narratives like sharding and cross-chain interoperability, NEAR is now leveraging its AI roots to re-enter the AI arena. A key driver is its "NEAR Intents" layer, which abstracts complex cross-chain transactions. Users simply state their goal (e.g., swap BTC for ETH), and a solver network finds the optimal route. This system has processed over $20B in cross-chain volume, generating significant fee revenue. A major growth area is private transactions via "Confidential Intents/Swaps," which hide trade details until settlement to protect against MEV and front-running. Remarkably, private swaps recently accounted for over 40% of NEAR's transaction volume, highlighting strong demand but also potential regulatory scrutiny. With its AI-founder pedigree, NEAR is positioning itself at the intersection of blockchain, AI agents, and privacy, aiming to become infrastructure for the emerging agent economy while navigating the challenges of its rapid adoption.

marsbit1 h fa

Near Returns to the AI Stage: Transformation into a Public Chain Due to 'Payroll Difficulties,' Agent and Privacy Emerge as New Growth Narratives

marsbit1 h fa

From Ethereum to AI's 'CROPS': What Exactly is This Set of 'Slow Variables' That Vitalik Repeatedly Emphasizes?

In recent discussions, Vitalik Buterin has frequently emphasized the concept of "CROPS," a framework defining core values for Ethereum's development. CROPS stands for Censorship Resistance, Capture Resistance, Open Source, Privacy, and Security. Initially outlined in the Ethereum Foundation's "EF Mandate," it represents a commitment to user sovereignty, ensuring that the network resists external control, remains open, protects privacy, and prioritizes security. The relevance of CROPS extends beyond Ethereum's foundational principles, becoming crucial in the context of AI integration. As AI agents begin handling wallet operations and automated transactions, the risk increases that users may cede control over their digital assets, privacy, and intentions to centralized AI service providers. A "CROPS AI" would therefore emphasize local execution where possible, privacy-preserving remote model calls (e.g., using zero-knowledge proofs), and transparent, verifiable processes to maintain user agency. Vitalik highlights a significant convergence between "CROPS Ethereum access layer" and "CROPS AI." Both address the same fundamental challenge: how users can access powerful services—be it blockchain data via RPCs or AI models—without exposing sensitive information or relinquishing ultimate control. This intersection points toward a future digital entry point that is more private, secure, and user-controlled. Ultimately, CROPS is not merely an abstract ideal but a practical guidepost. It steers development—from protocol resilience and wallet design to AI agent safety—towards a future where users retain self-sovereignty even as digital systems grow more complex and powerful. In an era of accelerating AI adoption, these "slow variables" of censorship resistance, openness, privacy, and security may define Ethereum's enduring value.

marsbit1 h fa

From Ethereum to AI's 'CROPS': What Exactly is This Set of 'Slow Variables' That Vitalik Repeatedly Emphasizes?

marsbit1 h fa

Zcash Bug Could Have Minted Unlimited ZEC Undetected

A critical vulnerability in Zcash's Orchard shielded pool, discovered by researcher Taylor Hornby on May 29, 2026, could have allowed an attacker to create an unlimited amount of undetectable counterfeit ZEC. The flaw, involving an under-constrained element in the Orchard circuit, existed from the pool's 2022 activation until an emergency fix was deployed by June 2, 2026. Hornby identified the bug using AI-assisted auditing tools and confirmed its exploitability in a test environment. Due to Orchard's privacy features, which hide transaction amounts and history, there is no cryptographic way to prove whether the vulnerability was exploited before the fix. While Shielded Labs assesses prior exploitation as unlikely, this uncertainty has sparked a debate on proving supply integrity in privacy-preserving systems. In response, Shielded Labs and other developers are exploring a network upgrade, potentially involving a new shielded pool and formal verification of the circuit rules to prevent future vulnerabilities and allow verification of the ZEC supply's integrity. ZEC's price fell nearly 45% following the disclosure.

bitcoinist1 h fa

Zcash Bug Could Have Minted Unlimited ZEC Undetected

bitcoinist1 h fa

Silicon Valley 'Startup Guru' Steve Hoffman: Web3 + AI Could Be a Trap

Silicon Valley investor and "Godfather of Startups" Steve Hoffman warns that combining Web3 with AI is likely a trap, not a promising venture. In an interview, Hoffman argues that while AI is a foundational technology touching all industries, Web3 adds complexity, friction, and regulatory risk without solving mainstream consumer or business needs. He advises founders to focus on deep, specialized applications where startups can out-iterate giants, rather than on generic features easily replicated by large tech companies. Hoffman observes that Silicon Valley will lead foundational AI research, while China excels at rapid, large-scale application and commercialization, particularly in robotics. He stresses that AI-driven autonomous agents capable of collaborative, multi-step tasks are 2-4 years away, which will cause significant job displacement. The solution is not to slow AI but to redesign business models around human-AI collaboration and reform social systems like education and retraining. For startups, Hoffman recommends focusing on vertical, expertise-heavy domains to build defensibility. He sees major opportunities in AI fraud detection and cybersecurity. Key founder mindsets include systemic thinking over feature-focus, relentless customer centricity, building adaptive teams, and deeply understanding AI's capabilities and limits. Hoffman is also leading a non-profit initiative to establish university centers aimed at training future leaders in responsible, human-value-aligned AI innovation.

marsbit2 h fa

Silicon Valley 'Startup Guru' Steve Hoffman: Web3 + AI Could Be a Trap

marsbit2 h fa

Token Inefficient, Economy Tokenless

The article "Tokens Aren't Economical, Economics Aren't Tokenized" analyzes a pivotal shift in the AI industry from a technology-driven narrative to one dominated by capital efficiency. It highlights two concurrent trends: a severe capital shortage due to the exorbitant and recurring costs of compute (e.g., OpenAI's high burn rate) and a wave of corporate spin-offs where major tech companies are separating their AI units (like Kuaishou's Kling and Baidu's Kunlunxin). The core argument is that AI's "anti-internet" business model, where user growth increases costs rather than profits, has created a disconnect between high valuations and actual cash flow. Spin-offs address this by allowing AI assets to be valued independently. Within a parent company, they are seen as cost centers, but as standalone entities, they are priced based on their growth potential and scarcity in the primary market, leading to massive valuation premiums (e.g., Kling's estimated value tripling post-spin-off). The industry is at an inflection point, moving from "model worship" to "value realization." The competition is evolving from a pure compute (GPU) race to a broader focus on systemic efficiency and full-stack engineering (involving CPUs and orchestration) to achieve viable commercialization. The year 2026 is framed as a critical moment where the industry must definitively answer how to economically translate AI capability into tangible business value, reshaping the sector's future power structure.

marsbit2 h fa

marsbit2 h fa

Trading

Spot

Futures