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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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Wisdom Tree + Glassnode: Reframing Blockchains As Economic SystemsThe digital asset ecosystem is increasingly being evaluated not as a collection of speculative assets, but as an emerging financial system with its own infrastructure, economic inputs, and market structure.
In our joint analysis with Wisdom Tree, we reframe blockchain networks and digital assets as a multi-layered economic system that increasingly mirrors traditional markets in structure, incentives, and economic function.
For financial professionals, this framing demonstrates how digital assets can be analyzed through observable data and compared against traditional macroeconomic benchmarks, offering a more robust foundation for valuation and risk assessment than a price-centric analysis alone.
Key Takeaways
Transaction fees provide a direct, price-independent measure of a blockchain's growth, health and maturity. The transparency of blockchain data allows analysts to quantify economic activity with a level of fidelity that is difficult to achieve in other financial systems.
In blockchain systems, transaction security and settlement finality are enforced through economic incentives built into the protocol. Bitcoin represents an energy-backed security model, while Ethereum is an example of a capital-backed security model.
Mining has evolved into an industrialized commodity business: Cost structures, margin dispersion, and consolidation dynamics increasingly resemble traditional energy and extractive industries.
Crypto exchanges and custodians have become systemically relevant: they function as the primary financial gateways, linking on-chain activity to traditional capital markets, and driving the transition from retail-dominated ownership to institutional participation through regulated channels.
Stablecoins underpin settlement and liquidity across the ecosystem. Acting as the primary unit of account, stablecoins enable real-time clearing and continuous capital mobility across global markets.
DeFi and tokenization extend financial functionality on-chain. Trading, lending, and capital deployment are increasingly executed via rules-based systems, with growing integration into traditional finance.
The full report is freely available for download in PDF format.
Download report PDF Quantifying Network Demand
At the foundation of the blockchain economy is blockspace, a scarce digital resource consumed by every transaction, smart contract execution, and settlement event, and priced dynamically through transaction fees.
Transaction fees, in this context, are not incidental costs but market-based pricing for network usage, offering a direct lens into network demand. Unlike traditional economic data, this activity is observable in near real time, enabling a high-fidelity view of system utilization.
While fee levels remain cyclical and sensitive to market conditions, the longer-term trend in the data points toward sustained growth in infrastructure-level demand. This dynamic positions blockspace as a core economic primitive for evaluating the ecosystem.
Security as an Economic Input
Blockchain networks replace institutional trust with explicitly funded security models, where participants are economically incentivized to validate transactions and maintain system integrity.
In Bitcoin’s Proof-of-Work model, this relationship is observable through hashrate, or the total computational power securing the network, which reflects the aggregate energy and capital deployed to maintain transaction finality. As the economic value of the network grows, so too does the incentive to invest in this security layer.
In practice, this has given rise to increasingly industrialised operations (most notably in Bitcoin mining), where cost efficiency, scale, and capital access shape competitive positioning.
Bitcoin Hashrate vs. BTC Price Financial Gateways and Institutional Integration
If blockchain networks form the economic substrate, then exchanges and custodians operate as the primary and systemically important financial gateways to traditional capital markets.
As infrastructure has matured, so too has the investor base. Ownership is increasingly shifting toward institutional and regulated capital, marking a transition from early adopters toward participants operating within formal mandates, governance frameworks, and risk constraints.
Institutional & Corporate Ownership of Bitcoin (Time Series) The System’s Monetary Layer
Stablecoins have emerged as the core liquidity and settlement instrument within the blockchain economy. Functionally, they operate as the primary unit of account, medium of exchange, and collateral asset across both centralized and decentralized venues. Their ability to enable instant, final settlement makes them a growing rail for cross-border value transfer.
Growth in stablecoin supply reflects a broader structural shift: capital migrating from traditional banking infrastructure onto blockchain rails to support trading, settlement, and financial activity.
Stablecoin Supply Over Time Extending Markets Beyond Traditional Rails
DeFi today represents an alternative financial system, where trading, lending, and capital deployment are executed through rules-based protocols rather than intermediaries.
This architecture enables continuous, transparent market activity, with mechanisms such as automated market making and on-chain credit replacing traditional structures.
At the same time, tokenization is extending this system outward: bringing traditional financial assets on-chain and deepening the integration between blockchain infrastructure and global capital markets.
Tokenized Assets by Category
To read the full analysis, download the report in PDF format.
Download report PDF
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. We urge users to exercise caution and discretion when utilizing these metrics. Glassnode shall not be held responsible for any discrepancies or potential inaccuracies.
Follow us on X for timely market updates and analysis
Join our Telegram channel
For on-chain metrics, dashboards, and alerts, visit Glassnode Studio
Wisdom Tree + Glassnode: Reframing Blockchains As Economic Systems
The digital asset ecosystem is increasingly being evaluated not as a collection of speculative assets, but as an emerging financial system with its own infrastructure, economic inputs, and market structure.
In our joint analysis with Wisdom Tree, we reframe blockchain networks and digital assets as a multi-layered economic system that increasingly mirrors traditional markets in structure, incentives, and economic function.
For financial professionals, this framing demonstrates how digital assets can be analyzed through observable data and compared against traditional macroeconomic benchmarks, offering a more robust foundation for valuation and risk assessment than a price-centric analysis alone.
Key Takeaways
Transaction fees provide a direct, price-independent measure of a blockchain's growth, health and maturity. The transparency of blockchain data allows analysts to quantify economic activity with a level of fidelity that is difficult to achieve in other financial systems.
In blockchain systems, transaction security and settlement finality are enforced through economic incentives built into the protocol. Bitcoin represents an energy-backed security model, while Ethereum is an example of a capital-backed security model.
Mining has evolved into an industrialized commodity business: Cost structures, margin dispersion, and consolidation dynamics increasingly resemble traditional energy and extractive industries.
Crypto exchanges and custodians have become systemically relevant: they function as the primary financial gateways, linking on-chain activity to traditional capital markets, and driving the transition from retail-dominated ownership to institutional participation through regulated channels.
Stablecoins underpin settlement and liquidity across the ecosystem. Acting as the primary unit of account, stablecoins enable real-time clearing and continuous capital mobility across global markets.
DeFi and tokenization extend financial functionality on-chain. Trading, lending, and capital deployment are increasingly executed via rules-based systems, with growing integration into traditional finance.
The full report is freely available for download in PDF format.
Download report PDF Quantifying Network Demand
At the foundation of the blockchain economy is blockspace, a scarce digital resource consumed by every transaction, smart contract execution, and settlement event, and priced dynamically through transaction fees.
Transaction fees, in this context, are not incidental costs but market-based pricing for network usage, offering a direct lens into network demand. Unlike traditional economic data, this activity is observable in near real time, enabling a high-fidelity view of system utilization.
While fee levels remain cyclical and sensitive to market conditions, the longer-term trend in the data points toward sustained growth in infrastructure-level demand. This dynamic positions blockspace as a core economic primitive for evaluating the ecosystem.
Security as an Economic Input
Blockchain networks replace institutional trust with explicitly funded security models, where participants are economically incentivized to validate transactions and maintain system integrity.
In Bitcoin’s Proof-of-Work model, this relationship is observable through hashrate, or the total computational power securing the network, which reflects the aggregate energy and capital deployed to maintain transaction finality. As the economic value of the network grows, so too does the incentive to invest in this security layer.
In practice, this has given rise to increasingly industrialised operations (most notably in Bitcoin mining), where cost efficiency, scale, and capital access shape competitive positioning.
Bitcoin Hashrate vs. BTC Price Financial Gateways and Institutional Integration
If blockchain networks form the economic substrate, then exchanges and custodians operate as the primary and systemically important financial gateways to traditional capital markets.
As infrastructure has matured, so too has the investor base. Ownership is increasingly shifting toward institutional and regulated capital, marking a transition from early adopters toward participants operating within formal mandates, governance frameworks, and risk constraints.
Institutional & Corporate Ownership of Bitcoin (Time Series) The System’s Monetary Layer
Stablecoins have emerged as the core liquidity and settlement instrument within the blockchain economy. Functionally, they operate as the primary unit of account, medium of exchange, and collateral asset across both centralized and decentralized venues. Their ability to enable instant, final settlement makes them a growing rail for cross-border value transfer.
Growth in stablecoin supply reflects a broader structural shift: capital migrating from traditional banking infrastructure onto blockchain rails to support trading, settlement, and financial activity.
Stablecoin Supply Over Time Extending Markets Beyond Traditional Rails
DeFi today represents an alternative financial system, where trading, lending, and capital deployment are executed through rules-based protocols rather than intermediaries.
This architecture enables continuous, transparent market activity, with mechanisms such as automated market making and on-chain credit replacing traditional structures.
At the same time, tokenization is extending this system outward: bringing traditional financial assets on-chain and deepening the integration between blockchain infrastructure and global capital markets.
Tokenized Assets by Category
To read the full analysis, download the report in PDF format.
Download report PDF
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. We urge users to exercise caution and discretion when utilizing these metrics. Glassnode shall not be held responsible for any discrepancies or potential inaccuracies.
Follow us on X for timely market updates and analysis
Join our Telegram channel
For on-chain metrics, dashboards, and alerts, visit Glassnode Studio
In our joint analysis with Wisdom Tree, we reframe blockchain networks and digital assets as a multi-layered economic system that increasingly mirrors traditional markets in structure, incentives, and economic function.
For financial professionals, this framing demonstrates how digital assets can be analyzed through observable data and compared against traditional macroeconomic benchmarks, offering a more robust foundation for valuation and risk assessment than a price-centric analysis alone.
Key Takeaways
Transaction fees provide a direct, price-independent measure of a blockchain's growth, health and maturity. The transparency of blockchain data allows analysts to quantify economic activity with a level of fidelity that is difficult to achieve in other financial systems.
In blockchain systems, transaction security and settlement finality are enforced through economic incentives built into the protocol. Bitcoin represents an energy-backed security model, while Ethereum is an example of a capital-backed security model.
Mining has evolved into an industrialized commodity business: Cost structures, margin dispersion, and consolidation dynamics increasingly resemble traditional energy and extractive industries.
Crypto exchanges and custodians have become systemically relevant: they function as the primary financial gateways, linking on-chain activity to traditional capital markets, and driving the transition from retail-dominated ownership to institutional participation through regulated channels.
Stablecoins underpin settlement and liquidity across the ecosystem. Acting as the primary unit of account, stablecoins enable real-time clearing and continuous capital mobility across global markets.
DeFi and tokenization extend financial functionality on-chain. Trading, lending, and capital deployment are increasingly executed via rules-based systems, with growing integration into traditional finance.
The full report is freely available for download in PDF format.
Download report PDF Quantifying Network Demand
At the foundation of the blockchain economy is blockspace, a scarce digital resource consumed by every transaction, smart contract execution, and settlement event, and priced dynamically through transaction fees.
Transaction fees, in this context, are not incidental costs but market-based pricing for network usage, offering a direct lens into network demand. Unlike traditional economic data, this activity is observable in near real time, enabling a high-fidelity view of system utilization.
While fee levels remain cyclical and sensitive to market conditions, the longer-term trend in the data points toward sustained growth in infrastructure-level demand. This dynamic positions blockspace as a core economic primitive for evaluating the ecosystem.
Security as an Economic Input
Blockchain networks replace institutional trust with explicitly funded security models, where participants are economically incentivized to validate transactions and maintain system integrity.
In Bitcoin’s Proof-of-Work model, this relationship is observable through hashrate, or the total computational power securing the network, which reflects the aggregate energy and capital deployed to maintain transaction finality. As the economic value of the network grows, so too does the incentive to invest in this security layer.
In practice, this has given rise to increasingly industrialised operations (most notably in Bitcoin mining), where cost efficiency, scale, and capital access shape competitive positioning.
Bitcoin Hashrate vs. BTC Price Financial Gateways and Institutional Integration
If blockchain networks form the economic substrate, then exchanges and custodians operate as the primary and systemically important financial gateways to traditional capital markets.
As infrastructure has matured, so too has the investor base. Ownership is increasingly shifting toward institutional and regulated capital, marking a transition from early adopters toward participants operating within formal mandates, governance frameworks, and risk constraints.
Institutional & Corporate Ownership of Bitcoin (Time Series) The System’s Monetary Layer
Stablecoins have emerged as the core liquidity and settlement instrument within the blockchain economy. Functionally, they operate as the primary unit of account, medium of exchange, and collateral asset across both centralized and decentralized venues. Their ability to enable instant, final settlement makes them a growing rail for cross-border value transfer.
Growth in stablecoin supply reflects a broader structural shift: capital migrating from traditional banking infrastructure onto blockchain rails to support trading, settlement, and financial activity.
Stablecoin Supply Over Time Extending Markets Beyond Traditional Rails
DeFi today represents an alternative financial system, where trading, lending, and capital deployment are executed through rules-based protocols rather than intermediaries.
This architecture enables continuous, transparent market activity, with mechanisms such as automated market making and on-chain credit replacing traditional structures.
At the same time, tokenization is extending this system outward: bringing traditional financial assets on-chain and deepening the integration between blockchain infrastructure and global capital markets.
Tokenized Assets by Category
To read the full analysis, download the report in PDF format.
Download report PDF
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. We urge users to exercise caution and discretion when utilizing these metrics. Glassnode shall not be held responsible for any discrepancies or potential inaccuracies.
Follow us on X for timely market updates and analysis
Join our Telegram channel
For on-chain metrics, dashboards, and alerts, visit Glassnode Studio
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