Strategy Watch #2

insights.glassnode2026-03-24 tarihinde yayınlandı2026-03-24 tarihinde güncellendi

Özet

Strategy Watch #2 provides institutional analysis of digital asset fund performance and allocation trends. The report covers six key areas: institutional capital flows, fund performance, macro strategies, on-chain vault returns, manager sentiment, and allocation updates. In February and early March, Bitcoin and Ethereum saw significant net outflows (-$9.6B and -$3.2B respectively), while stablecoins attracted inflows (+$6.2B), suggesting a defensive shift toward lower-risk assets. BTC ETF flows turned positive in March, though institutional conviction remains cautious. DeFi Total Value Locked (TVL) on Ethereum declined sharply, with peak outflows of -$23.7B in February, indicating reduced participation in on-chain yield strategies. CME basis yields continued to tighten, reflecting lower institutional leverage and softer derivatives demand. The report also highlights performance trends, with only one strategy type generating positive returns in February. Over 300 managers shared their three-month market outlook, and a new $750M fund of funds was announced.

The full report is freely available in PDF format.

Download PDF version

Welcome to Strategy Watch #2

Strategy Watch was built to address a clear demand for high-signal, impartial analysis of fund-level performance and allocation trends in digital assets.

The inaugural edition confirmed this demand, reaching a broad audience of key decision makers, from leading global asset managers, hedge funds, major investment banks, top-tier crypto-native funds and Fund of Funds.

Building on this early momentum, our objective is straightforward — to make Strategy Watch a must-read monthly publication for the digital asset investment community.

This publication is strengthened by direct input from market participants. Funds and allocators that contribute data and insights help shape a more complete and valuable view of the landscape.

If you have insights, data, or allocation updates worth sharing, we welcome your contribution.

Present your latest initiatives and updates to a curated audience of institutional allocators.

Share updates ↗

Inside the Latest Strategy Watch

The report is structured across six core sections, each focused on a distinct dimension of institutional activity in digital assets:

01 Institutional Flow Monitor |

02 Fund and SMA Performance | February was a bloodbath, check this section to see the only strategy type that returned positively last month.

03 Strategy Deep Dive: Macro Strategies | Hear firsthand from a macro portfolio manager on how they are navigating the current geopolitical challenges. 

04 On-chain Vault Performance | Are ETH curators underperforming ETH staking yield?

05 Manager Monitor | Find out how more than 300 managers are expecting the crypto market to perform over the next three months.

06 Allocation Updates | New USD 750m Fund of Funds is launching. Check this section to learn more.

in partnership with

The Premier Digital Assets Allocator Platform. Learn more


Institutional Flow Monitor

Capital flows into BTC and ETH remained deeply negative through February and into March, with stablecoins turning net positive in early March as investors rotated toward lower-risk positioning.

Bitcoin and Ethereum continued to experience sustained net outflows, with BTC and ETH capital flows registering -$9.6B and -$3.2B. However, stablecoins broke from the broader negative trend, flipping to +$6.2B in net inflows. Rather than signaling a broad return of risk appetite, this rotation likely reflects a defensive repositioning, with capital migrating into dollar-denominated on-chain instruments while conviction in spot crypto assets remains under pressure heading into mid-Q1 2026.

ETF & DAT Net Flows

After sustained February outflows, BTC ETF and DAT flows returned to positive, while ETH institutional flows showed early signs of stabilization.

After sustained February outflows, Bitcoin ETF and DAT flows returned to positive territory, recovering to +28k BTC and +46.8k BTC respectively. Ethereum flows were more measured, with ETF flows near neutral at +46.5k ETH and DAT flows stabilizing at +295.8k ETH. While the directional shift is encouraging, the recovery remains early-stage and uneven, and it would be premature to characterize this as a broad resumption of institutional conviction.

DeFi TVL & Stablecoin Cap

DeFi TVL on Ethereum saw sharp contraction through February before showing early signs of stabilization in March, though the broader declining trend since August 2025 remains intact.

Total Value Locked on Ethereum recorded peak outflows of -$23.7B per month in February, implying sustained withdrawal by larger allocators from on-chain activities such as liquidity provisioning and yield strategies. This slowdown alone is insufficient to signal a reversal, and diminished conviction in DeFi risk-adjusted returns continues to point to shallower liquidity depth across the ecosystem.

CME Basis Yield

CME Basis Yield compressed even further in February, reinforcing waning incentives for market-neutral strategies compared to January.

The monthly dollar value captured by institutions via cash-and-carry trades declined notably for both Bitcoin and Ethereum, building on the sharp compression seen since August 2025. This tightening basis reflects reduced leverage deployment, softer futures demand, and continued pullback in balance-sheet commitment amid tighter liquidity conditions persisting from January into early March 2026.


Disclaimer: This report does not provide any investment advice. All data is provided for information and educational purposes only. No investment decision shall be based on the information provided here and you are solely responsible for your own investment decisions.
Exchange balances presented are derived from Glassnode’s comprehensive database of address labels, which are amassed through both officially published exchange information and proprietary clustering algorithms. While we strive to ensure the utmost accuracy in representing exchange balances, it is important to note that these figures might not always encapsulate the entirety of an exchange’s reserves, particularly when exchanges refrain from disclosing their official addresses. We urge users to exercise caution and discretion when utilizing these metrics. Glassnode shall not be held responsible for any discrepancies or potential inaccuracies. Please read our Transparency Notice when using exchange data.

İlgili Sorular

QWhat is the primary purpose of the Strategy Watch publication?

AStrategy Watch was built to address a clear demand for high-signal, impartial analysis of fund-level performance and allocation trends in digital assets, aiming to be a must-read monthly publication for the digital asset investment community.

QWhich was the only strategy type that returned positively in February according to the report?

AThe report indicates that readers should check the 'Fund and SMA Performance' section to see the only strategy type that returned positively in February, though the specific strategy name is not stated in the provided text.

QWhat was the trend in capital flows for Bitcoin (BTC) and Ethereum (ETH) through February and into March?

ACapital flows into BTC and ETH remained deeply negative, with BTC and ETH capital flows registering -$9.6B and -$3.2B, respectively.

QHow did stablecoin capital flows behave in contrast to BTC and ETH in early March?

AStablecoins broke from the broader negative trend, flipping to +$6.2B in net inflows, which likely reflects a defensive repositioning into lower-risk, dollar-denominated on-chain instruments.

QWhat does the sharp compression in CME Basis Yield indicate about market conditions?

AThe compression in CME Basis Yield reflects reduced leverage deployment, softer futures demand, and a continued pullback in balance-sheet commitment amid tighter liquidity conditions, indicating waning incentives for market-neutral strategies.

İlgili Okumalar

a16z: AI's 'Amnesia', Can Continuous Learning Cure It?

The article "a16z: AI's 'Amnesia' – Can Continual Learning Cure It?" explores the limitations of current large language models (LLMs), which, like the protagonist in the film *Memento*, are trapped in a perpetual present—unable to form new memories after training. While methods like in-context learning (ICL), retrieval-augmented generation (RAG), and external scaffolding (e.g., chat history, prompts) provide temporary solutions, they fail to enable true internalization of new knowledge. The authors argue that compression—the core of learning during training—is halted at deployment, preventing models from generalizing, discovering novel solutions (e.g., mathematical proofs), or handling adversarial scenarios. The piece introduces *continual learning* as a critical research direction to address this, categorizing approaches into three paths: 1. **Context**: Scaling external memory via longer context windows, multi-agent systems, and smarter retrieval. 2. **Modules**: Using pluggable adapters or external memory layers for specialization without full retraining. 3. **Weights**: Enabling parameter updates through sparse training, test-time training, meta-learning, distillation, and reinforcement learning from feedback. Challenges include catastrophic forgetting, safety risks, and auditability, but overcoming these could unlock models that learn iteratively from experience. The conclusion emphasizes that while context-based methods are effective, true breakthroughs require models to compress new information into weights post-deployment, moving from mere retrieval to genuine learning.

marsbit2 saat önce

a16z: AI's 'Amnesia', Can Continuous Learning Cure It?

marsbit2 saat önce

Can a Hair Dryer Earn $34,000? Deciphering the Reflexivity Paradox in Prediction Markets

An individual manipulated a weather sensor at Paris Charles de Gaulle Airport with a portable heat source, causing a Polymarket weather market to settle at 22°C and earning $34,000. This incident highlights a fundamental issue in prediction markets: when a market aims to reflect reality, it also incentivizes participants to influence that reality. Prediction markets operate on two layers: platform rules (what outcome counts as a win) and data sources (what actually happened). While most focus on rules, the real vulnerability lies in the data source. If reality is recorded through a specific source, influencing that source directly affects market settlement. The article categorizes markets by their vulnerability: 1. **Single-point physical data sources** (e.g., weather stations): Easily manipulated through physical interference. 2. **Insider information markets** (e.g., MrBeast video details): Insiders like team members use non-public information to trade. Kalshi fined a剪辑师 $20,000 for insider trading. 3. **Actor-manipulated markets** (e.g., Andrew Tate’s tweet counts): The subject of the market can control the outcome. Evidence suggests Tate’sociated accounts coordinated to profit. 4. **Individual-action markets** (e.g., WNBA disruptions): A single person can execute an event to profit from their pre-placed bets. Kalshi and Polymarket handle these issues differently. Kalshi enforces strict KYC, publicly penalizes insider trading, and reports to regulators. Polymarket, with its anonymous wallet-based system, has historically been more permissive, arguing that insider information improves market accuracy. However, it cooperated with authorities in the "Van Dyke case," where a user traded on classified government information. The core paradox is reflexivity: prediction markets are designed to discover truth, but their financial incentives can distort reality. The more valuable a prediction becomes, the more likely participants are to influence the event itself. The market ceases to be a mirror of reality and instead shapes it.

marsbit3 saat önce

Can a Hair Dryer Earn $34,000? Deciphering the Reflexivity Paradox in Prediction Markets

marsbit3 saat önce

İşlemler

Spot
Futures
活动图片