Glassnode
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Glassnode
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World leading onchain & financial metrics, charts, data & insights for Bitcoin & digital assets.
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Strategy Watch #3The full report is freely available in PDF format.
Download PDF version Welcome to Strategy Watch #3
Strategy Watch was built to address a clear demand for high-signal, impartial analysis of fund-level performance and allocation trends in digital assets.
Our objective is straightforward — to make Strategy Watch a must-read monthly publication for the digital asset investment community.
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 | Early stabilization as BTC/ETH outflows improve and ETF demand recovers, but conviction in spot markets remains under pressure.
02 Fund and SMA Performance | Market-neutral strategies delivered consistent gains; directional performance remains highly dispersed.
03 Strategy Deep Dive: Quant Trend Following | What’s driving quant trend performance in a difficult environment for directional strategies? Hear directly from a fund manager.
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 | A $6B pension fund increases crypto exposure as new funds and institutional strategies continue to launch.
in partnership with
The Premier Digital Assets Allocator Platform. Learn more
Institutional Flow Monitor
BTC and ETH capital flows remained negative through March but continued to recover from February lows, while stablecoin inflows moderated alongside the broader stabilization.
Bitcoin and Ethereum continued to register net outflows through March, with capital flows closing the month at -$7.0B and -$1.6B respectively, a notable improvement from the -$9.6B and -$3.2B readings seen in mid-February. Stablecoin inflows also moderated to +$2.6B by month-end, easing from the +$6.2B peak earlier in March. The overall picture is one of gradual stabilization rather than recovery, with the acute phase of institutional de-risking losing momentum but conviction in spot assets remaining under pressure.
ETF & DAT Net Flows
BTC ETF and DAT flows swung decisively positive through March, with ETH channels following at a more measured pace before both eased into month-end.
Bitcoin ETF and DAT flows finally turned positive through March, reaching intra-month highs of +30.6k BTC and +46.8k BTC respectively mid-month before settling back to +17.6k BTC and +30.9k BTC by month-end. Ethereum flows mirrored the directional shift with less intensity, as ETF flows reached +46.6k ETH and DAT flows peaked at +295.9k ETH before easing to +261.9k ETH at close. The mid-month surge followed by minor pullback, suggests demand remains sensitive to wider market conditions, rather than a true sustained structural shift in institutional positioning.
DeFi TVL & Stablecoin Cap
DeFi TVL flows on Ethereum staged a significant recovery through March, reversing from peak February outflows to near-neutral territory by month-end.
After registering peak monthly outflows of $17.8B at end of February, Ethereum DeFi TVL flows recovered sharply through March, turning positive in mid-month and closing the period near neutral at -$0.75B. The pace of recovery was notable, with flows moving from double-digit outflows in early March to briefly positive readings around $4.9B by mid-month before settling back. While the trend shift is meaningful, a single month of stabilization is insufficient to declare a reversal of the broader contraction that has persisted since August 2025, and sustained inflows would be required to confirm a genuine return of allocator conviction in on-chain yield strategies.
CME Basis Yield
BTC CME basis yield turned negative through March, erasing the carry trade entirely, while ETH basis yield remained subdued but showed tentative signs of recovery by month-end.
Here we measure return available to institutions running cash-and-carry trades. After compressing through February to $17.3M/month, BTC basis yield crossed into negative territory mid-March and closed the month at -$3.9M, reflecting a full inversion of the carry premium. This signals futures are trading at a discount to spot, removing the economic rationale for market-neutral strategies entirely. ETH basis yield, already negative at end of February, oscillated in a narrow range before recovering modestly to +0.9M by month-end. Taken together, the carry environment for both assets remains structurally challenged, with meaningful recovery contingent on a sustained rebuilding of futures premium above spot.
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.
Strategy Watch #3
The full report is freely available in PDF format.
Download PDF version Welcome to Strategy Watch #3
Strategy Watch was built to address a clear demand for high-signal, impartial analysis of fund-level performance and allocation trends in digital assets.
Our objective is straightforward — to make Strategy Watch a must-read monthly publication for the digital asset investment community.
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 | Early stabilization as BTC/ETH outflows improve and ETF demand recovers, but conviction in spot markets remains under pressure.
02 Fund and SMA Performance | Market-neutral strategies delivered consistent gains; directional performance remains highly dispersed.
03 Strategy Deep Dive: Quant Trend Following | What’s driving quant trend performance in a difficult environment for directional strategies? Hear directly from a fund manager.
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 | A $6B pension fund increases crypto exposure as new funds and institutional strategies continue to launch.
in partnership with
The Premier Digital Assets Allocator Platform. Learn more
Institutional Flow Monitor
BTC and ETH capital flows remained negative through March but continued to recover from February lows, while stablecoin inflows moderated alongside the broader stabilization.
Bitcoin and Ethereum continued to register net outflows through March, with capital flows closing the month at -$7.0B and -$1.6B respectively, a notable improvement from the -$9.6B and -$3.2B readings seen in mid-February. Stablecoin inflows also moderated to +$2.6B by month-end, easing from the +$6.2B peak earlier in March. The overall picture is one of gradual stabilization rather than recovery, with the acute phase of institutional de-risking losing momentum but conviction in spot assets remaining under pressure.
ETF & DAT Net Flows
BTC ETF and DAT flows swung decisively positive through March, with ETH channels following at a more measured pace before both eased into month-end.
Bitcoin ETF and DAT flows finally turned positive through March, reaching intra-month highs of +30.6k BTC and +46.8k BTC respectively mid-month before settling back to +17.6k BTC and +30.9k BTC by month-end. Ethereum flows mirrored the directional shift with less intensity, as ETF flows reached +46.6k ETH and DAT flows peaked at +295.9k ETH before easing to +261.9k ETH at close. The mid-month surge followed by minor pullback, suggests demand remains sensitive to wider market conditions, rather than a true sustained structural shift in institutional positioning.
DeFi TVL & Stablecoin Cap
DeFi TVL flows on Ethereum staged a significant recovery through March, reversing from peak February outflows to near-neutral territory by month-end.
After registering peak monthly outflows of $17.8B at end of February, Ethereum DeFi TVL flows recovered sharply through March, turning positive in mid-month and closing the period near neutral at -$0.75B. The pace of recovery was notable, with flows moving from double-digit outflows in early March to briefly positive readings around $4.9B by mid-month before settling back. While the trend shift is meaningful, a single month of stabilization is insufficient to declare a reversal of the broader contraction that has persisted since August 2025, and sustained inflows would be required to confirm a genuine return of allocator conviction in on-chain yield strategies.
CME Basis Yield
BTC CME basis yield turned negative through March, erasing the carry trade entirely, while ETH basis yield remained subdued but showed tentative signs of recovery by month-end.
Here we measure return available to institutions running cash-and-carry trades. After compressing through February to $17.3M/month, BTC basis yield crossed into negative territory mid-March and closed the month at -$3.9M, reflecting a full inversion of the carry premium. This signals futures are trading at a discount to spot, removing the economic rationale for market-neutral strategies entirely. ETH basis yield, already negative at end of February, oscillated in a narrow range before recovering modestly to +0.9M by month-end. Taken together, the carry environment for both assets remains structurally challenged, with meaningful recovery contingent on a sustained rebuilding of futures premium above spot.
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.
Download PDF version Welcome to Strategy Watch #3
Strategy Watch was built to address a clear demand for high-signal, impartial analysis of fund-level performance and allocation trends in digital assets.
Our objective is straightforward — to make Strategy Watch a must-read monthly publication for the digital asset investment community.
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 | Early stabilization as BTC/ETH outflows improve and ETF demand recovers, but conviction in spot markets remains under pressure.
02 Fund and SMA Performance | Market-neutral strategies delivered consistent gains; directional performance remains highly dispersed.
03 Strategy Deep Dive: Quant Trend Following | What’s driving quant trend performance in a difficult environment for directional strategies? Hear directly from a fund manager.
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 | A $6B pension fund increases crypto exposure as new funds and institutional strategies continue to launch.
in partnership with
The Premier Digital Assets Allocator Platform. Learn more
Institutional Flow Monitor
BTC and ETH capital flows remained negative through March but continued to recover from February lows, while stablecoin inflows moderated alongside the broader stabilization.
Bitcoin and Ethereum continued to register net outflows through March, with capital flows closing the month at -$7.0B and -$1.6B respectively, a notable improvement from the -$9.6B and -$3.2B readings seen in mid-February. Stablecoin inflows also moderated to +$2.6B by month-end, easing from the +$6.2B peak earlier in March. The overall picture is one of gradual stabilization rather than recovery, with the acute phase of institutional de-risking losing momentum but conviction in spot assets remaining under pressure.
ETF & DAT Net Flows
BTC ETF and DAT flows swung decisively positive through March, with ETH channels following at a more measured pace before both eased into month-end.
Bitcoin ETF and DAT flows finally turned positive through March, reaching intra-month highs of +30.6k BTC and +46.8k BTC respectively mid-month before settling back to +17.6k BTC and +30.9k BTC by month-end. Ethereum flows mirrored the directional shift with less intensity, as ETF flows reached +46.6k ETH and DAT flows peaked at +295.9k ETH before easing to +261.9k ETH at close. The mid-month surge followed by minor pullback, suggests demand remains sensitive to wider market conditions, rather than a true sustained structural shift in institutional positioning.
DeFi TVL & Stablecoin Cap
DeFi TVL flows on Ethereum staged a significant recovery through March, reversing from peak February outflows to near-neutral territory by month-end.
After registering peak monthly outflows of $17.8B at end of February, Ethereum DeFi TVL flows recovered sharply through March, turning positive in mid-month and closing the period near neutral at -$0.75B. The pace of recovery was notable, with flows moving from double-digit outflows in early March to briefly positive readings around $4.9B by mid-month before settling back. While the trend shift is meaningful, a single month of stabilization is insufficient to declare a reversal of the broader contraction that has persisted since August 2025, and sustained inflows would be required to confirm a genuine return of allocator conviction in on-chain yield strategies.
CME Basis Yield
BTC CME basis yield turned negative through March, erasing the carry trade entirely, while ETH basis yield remained subdued but showed tentative signs of recovery by month-end.
Here we measure return available to institutions running cash-and-carry trades. After compressing through February to $17.3M/month, BTC basis yield crossed into negative territory mid-March and closed the month at -$3.9M, reflecting a full inversion of the carry premium. This signals futures are trading at a discount to spot, removing the economic rationale for market-neutral strategies entirely. ETH basis yield, already negative at end of February, oscillated in a narrow range before recovering modestly to +0.9M by month-end. Taken together, the carry environment for both assets remains structurally challenged, with meaningful recovery contingent on a sustained rebuilding of futures premium above spot.
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.
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Agentic Workflows for Crypto ResearchAI coding agents are changing the way analysts and researchers interact with data. Instead of writing scripts line by line, you provide a hypothesis or research question to an AI agent and – it writes the code, fetches the data, runs the analysis, and returns results.
In this article we present a step-by-step real-world example: Asking an AI agent to download data via the Glassnode CLI, run a statistical analysis, and generate publication-ready charts, all from natural-language prompts.
What you will need
Access to an AI agent
We use Claude Code in this walkthrough, but any agent able to execute Python and shell commands will work, including ChatGPT's Codex, Cursor, Github Copilot, Google Gemini CLI, OpenClaw, or similar tools.
The Glassnode CLI ( gn)
A command-line interface for the Glassnode API. Install it and configure your API key by following the Glassnode CLI docs. An API key is required.
The prompt
We will evaluate the following hypothesis: Extreme BTC exchange inflow events are predictive of 7-day forward drawdowns. To do that, we will instruct Claude Code using the following prompt:
Using the Glassnode CLI, download BTC daily exchange inflows and closing price for the last year. Analyze whether inflow spikes (days with inflows > 2 standard deviations above the mean) predict drawdowns in the following 7 days. Show me a summary with statistics and results.
That's it. One sentence describing the question, and another sentence defining the methodology. The agent takes it from there.
A simple prompt for the AI agent What the agent does
Behind the scenes, the agent executes a sequence of steps:
Discovers the right metrics by running gn metric list and gn metric describe to find the correct metric paths and valid parameters.
Downloads the data via gn metric get, saving CSV files for both exchange inflows ( transactions/transfers_volume_to_exchanges_sum) and closing price ( market/price_usd_close).
Writes and runs a Python analysis that computes the spike threshold, identifies spike days, calculates forward 7-day max drawdowns, and compares spike days to normal days.
The agent comes back with a readable summary:
While this is just an illustrative example, our experiment does reveal a moderate association between exchange inflow spikes and subsequent drawdowns. Spike days see roughly 1.9 percentage points more drawdown on average. That said, with only 10 spike days in the sample and the effect concentrated in two volatile periods, the signal is suggestive rather than statistically robust. A rigorous backtest would need to account for overlapping windows, control for volatility regimes, use point-in-time data, and validate out-of-sample.
Visualizing the results
Visualizing the data is a good way to validate whether the numbers hold up. In this process, a simple follow-up prompt is enough:
Create a visualization that shows the data as a timeseries.
From here, you can keep iterating: adjust the chart, refine the analysis, or take the research in a different direction, all through natural language conversation.
The AI-generated visualisation of Glassnode data Get started with AI Crypto Research on Glassnode Data
The Glassnode CLI requires an API key, available to Glassnode Professional subscribers.
Install the Glassnode CLI and configure your API key. See documentation
Open your preferred AI coding agent (Claude Code, Codex, Cursor, Gemini CLI, OpenClaw, etc.)
Start prompting. Try questions such as:
"Download ETH staking deposits for the last 6 months and plot the trend"
"Compare BTC and ETH exchange netflows over the last 90 days"
"Find which metric has the highest correlation with BTC 30-day returns"
The Glassnode CLI allows agents to discover and retrieve metric data without requiring manual API lookup or writing boilerplate code. Combined with an AI coding agent, the Glassnode CLI turns a research question into results in minutes.
Agentic Workflows for Crypto Research
AI coding agents are changing the way analysts and researchers interact with data. Instead of writing scripts line by line, you provide a hypothesis or research question to an AI agent and – it writes the code, fetches the data, runs the analysis, and returns results.
In this article we present a step-by-step real-world example: Asking an AI agent to download data via the Glassnode CLI, run a statistical analysis, and generate publication-ready charts, all from natural-language prompts.
What you will need
Access to an AI agent
We use Claude Code in this walkthrough, but any agent able to execute Python and shell commands will work, including ChatGPT's Codex, Cursor, Github Copilot, Google Gemini CLI, OpenClaw, or similar tools.
The Glassnode CLI ( gn)
A command-line interface for the Glassnode API. Install it and configure your API key by following the Glassnode CLI docs. An API key is required.
The prompt
We will evaluate the following hypothesis: Extreme BTC exchange inflow events are predictive of 7-day forward drawdowns. To do that, we will instruct Claude Code using the following prompt:
Using the Glassnode CLI, download BTC daily exchange inflows and closing price for the last year. Analyze whether inflow spikes (days with inflows > 2 standard deviations above the mean) predict drawdowns in the following 7 days. Show me a summary with statistics and results.
That's it. One sentence describing the question, and another sentence defining the methodology. The agent takes it from there.
A simple prompt for the AI agent What the agent does
Behind the scenes, the agent executes a sequence of steps:
Discovers the right metrics by running gn metric list and gn metric describe to find the correct metric paths and valid parameters.
Downloads the data via gn metric get, saving CSV files for both exchange inflows ( transactions/transfers_volume_to_exchanges_sum) and closing price ( market/price_usd_close).
Writes and runs a Python analysis that computes the spike threshold, identifies spike days, calculates forward 7-day max drawdowns, and compares spike days to normal days.
The agent comes back with a readable summary:
While this is just an illustrative example, our experiment does reveal a moderate association between exchange inflow spikes and subsequent drawdowns. Spike days see roughly 1.9 percentage points more drawdown on average. That said, with only 10 spike days in the sample and the effect concentrated in two volatile periods, the signal is suggestive rather than statistically robust. A rigorous backtest would need to account for overlapping windows, control for volatility regimes, use point-in-time data, and validate out-of-sample.
Visualizing the results
Visualizing the data is a good way to validate whether the numbers hold up. In this process, a simple follow-up prompt is enough:
Create a visualization that shows the data as a timeseries.
From here, you can keep iterating: adjust the chart, refine the analysis, or take the research in a different direction, all through natural language conversation.
The AI-generated visualisation of Glassnode data Get started with AI Crypto Research on Glassnode Data
The Glassnode CLI requires an API key, available to Glassnode Professional subscribers.
Install the Glassnode CLI and configure your API key. See documentation
Open your preferred AI coding agent (Claude Code, Codex, Cursor, Gemini CLI, OpenClaw, etc.)
Start prompting. Try questions such as:
"Download ETH staking deposits for the last 6 months and plot the trend"
"Compare BTC and ETH exchange netflows over the last 90 days"
"Find which metric has the highest correlation with BTC 30-day returns"
The Glassnode CLI allows agents to discover and retrieve metric data without requiring manual API lookup or writing boilerplate code. Combined with an AI coding agent, the Glassnode CLI turns a research question into results in minutes.
In this article we present a step-by-step real-world example: Asking an AI agent to download data via the Glassnode CLI, run a statistical analysis, and generate publication-ready charts, all from natural-language prompts.
What you will need
Access to an AI agent
We use Claude Code in this walkthrough, but any agent able to execute Python and shell commands will work, including ChatGPT's Codex, Cursor, Github Copilot, Google Gemini CLI, OpenClaw, or similar tools.
The Glassnode CLI ( gn)
A command-line interface for the Glassnode API. Install it and configure your API key by following the Glassnode CLI docs. An API key is required.
The prompt
We will evaluate the following hypothesis: Extreme BTC exchange inflow events are predictive of 7-day forward drawdowns. To do that, we will instruct Claude Code using the following prompt:
Using the Glassnode CLI, download BTC daily exchange inflows and closing price for the last year. Analyze whether inflow spikes (days with inflows > 2 standard deviations above the mean) predict drawdowns in the following 7 days. Show me a summary with statistics and results.
That's it. One sentence describing the question, and another sentence defining the methodology. The agent takes it from there.
A simple prompt for the AI agent What the agent does
Behind the scenes, the agent executes a sequence of steps:
Discovers the right metrics by running gn metric list and gn metric describe to find the correct metric paths and valid parameters.
Downloads the data via gn metric get, saving CSV files for both exchange inflows ( transactions/transfers_volume_to_exchanges_sum) and closing price ( market/price_usd_close).
Writes and runs a Python analysis that computes the spike threshold, identifies spike days, calculates forward 7-day max drawdowns, and compares spike days to normal days.
The agent comes back with a readable summary:
While this is just an illustrative example, our experiment does reveal a moderate association between exchange inflow spikes and subsequent drawdowns. Spike days see roughly 1.9 percentage points more drawdown on average. That said, with only 10 spike days in the sample and the effect concentrated in two volatile periods, the signal is suggestive rather than statistically robust. A rigorous backtest would need to account for overlapping windows, control for volatility regimes, use point-in-time data, and validate out-of-sample.
Visualizing the results
Visualizing the data is a good way to validate whether the numbers hold up. In this process, a simple follow-up prompt is enough:
Create a visualization that shows the data as a timeseries.
From here, you can keep iterating: adjust the chart, refine the analysis, or take the research in a different direction, all through natural language conversation.
The AI-generated visualisation of Glassnode data Get started with AI Crypto Research on Glassnode Data
The Glassnode CLI requires an API key, available to Glassnode Professional subscribers.
Install the Glassnode CLI and configure your API key. See documentation
Open your preferred AI coding agent (Claude Code, Codex, Cursor, Gemini CLI, OpenClaw, etc.)
Start prompting. Try questions such as:
"Download ETH staking deposits for the last 6 months and plot the trend"
"Compare BTC and ETH exchange netflows over the last 90 days"
"Find which metric has the highest correlation with BTC 30-day returns"
The Glassnode CLI allows agents to discover and retrieve metric data without requiring manual API lookup or writing boilerplate code. Combined with an AI coding agent, the Glassnode CLI turns a research question into results in minutes.
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