ІНФОРМАЦІЙНЕ ЗАБЕЗПЕЧЕННЯ ОНЧЕЙН-АНАЛІЗУ РИНКУ ЦИФРОВИХ АКТИВІВ: КЛАСИФІКАЦІЯ ДЖЕРЕЛ ТА АЛГОРИТМ ЇХ ЗАСТОСУВАННЯ
Saved in:
| Date: | 2026 |
|---|---|
| Main Author: | |
| Format: | Article |
| Language: | English |
| Published: |
ДКС Центр
2026
|
| Online Access: | https://www.nayka.com.ua/index.php/agrosvit/article/view/10325 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Journal Title: | Agrosvit |
| Download file: |  |
Institution
Agrosvit| _version_ | 1867479139673440256 |
|---|---|
| author | Ганусич, В. О. |
| author_facet | Ганусич, В. О. |
| author_institution_txt_mv | [
{
"author": "В. О. Ганусич",
"institution": null
}
] |
| author_sort | Ганусич, В. О. |
| baseUrl_str | https://www.nayka.com.ua/index.php/agrosvit/oai |
| collection | OJS |
| datestamp_date | 2026-06-08T07:46:11Z |
| doi_str_mv | 10.32702/2306-6792.2026.10.260 |
| first_indexed | 2026-06-09T01:01:31Z |
| format | Article |
| fulltext |
260
АГРОСВІТ № 10, 2026
ISSN 2306-6792Copyright © The Author(s). This is an open access article distributed under the terms of the Creative
Commons Attribution License 4.0 (https://creativecommons.org/licenses/by/4.0/).
УДК 336.76:004.031.4
V. Hanusych,
PhD in Economics, Associate Professor,
Associate Professor of the Department of Accounting and Auditing,
Ferenc Rakoczi II Transcarpathian Hungarian University
ORCID ID: https://orcid.org/0000-0001-6902-6303
INFORMATION SUPPORT FOR ON-CHAIN
ANALYSIS OF DIGITAL ASSET MARKETS:
CLASSIFICATION OF SOURCES
AND APPLICATION ALGORITHM
DOI: 10.32702/2306-6792.2026.10.260
В. О. Ганусич,
к. е. н, доцент, доцент кафедри обліку і аудиту,
Закарпатський угорський університет ім. Ф.Ракоці ІІ
ІНФОРМАЦІЙНЕ ЗАБЕЗПЕЧЕННЯ ОНЧЕЙН-АНАЛІЗУ РИНКУ ЦИФРОВИХ АКТИВІВ:
КЛАСИФІКАЦІЯ ДЖЕРЕЛ ТА АЛГОРИТМ ЇХ ЗАСТОСУВАННЯ
The rapid development of blockchain technologies, decentralised financial ecosystems and digital assets has
significantly transformed the informational environment of financial analysis. Unlike traditional financial systems,
where access to transaction-level information is often fragmented, delayed or institutionally restricted, blockchain
networks provide publicly accessible and continuously updated records of economic activity. This has created
fundamentally new opportunities for analysing capital flows, liquidity conditions, ownership structures,
behavioural patterns of market participants and internal transformations of decentralised financial systems
through the direct interpretation of distributed ledger data. Under such conditions, on-chain analysis has gradually
evolved into an independent direction of digital asset market research.
The purpose of the article is to develop a systematic methodological approach to the informational support of
on-chain analysis of digital asset markets through the classification of blockchain-based data sources and the
construction of an algorithm for their integrated application within the analytical research process. The article
systematises the principal groups of information sources used in on-chain analysis, including primary blockchain
ledgers, blockchain explorers, on-chain metric aggregators, address attribution platforms, DeFi analytical systems,
exchange and derivatives platforms, and custom analytical environments. Their functional purpose, level of data
processing and role in the formation of analytical conclusions are substantiated.
The study proposes a multidimensional classification model of data sources for on-chain analysis that
incorporates the origin of data, the level of analytical transformation, functional purpose, level of market
observation and degree of methodological transparency. An algorithm for the application of data sources in on-
chain analysis is developed, combining the stages of blockchain-data verification, transaction-activity assessment,
liquidity analysis, supply-structure evaluation, behavioural analysis of market participants, DeFi activity
assessment and derivatives-market interpretation. It is demonstrated that the effectiveness of on-chain analysis
is determined not by isolated metrics, but by the integrated interpretation of multi-level information sources.
The obtained results form a methodological basis for the further development of systematic digital asset market
analysis, the construction of integrated analytical models and the improvement of approaches to the interpretation
of blockchain data within financial research.
АГРОСВІТ № 10, 2026
261
ISSN 2306-6792 Copyright © The Author(s). This is an open access article distributed under the terms of the Creative
Commons Attribution License 4.0 (https://creativecommons.org/licenses/by/4.0/).
GENERAL STATEMENT OF THE PROBLEM
AND ITS CONNECTION WITH IMPORTANT
SCIENTIFIC OR PRACTICAL TASKS
The rapid development of blockchain
technologies and the institutionalisation of digital
asset markets have substantially transformed the
informational foundations of financial analysis.
Unlike traditional financial systems, where market
information is usually mediated by financial
institutions, reporting standards and trading
platforms, blockchain networks generate con-
tinuously updated records of transactions, owner-
ship movements and network interactions. This
creates new opportunities for observing capital
flows and behavioural patterns of market
participants directly within distributed ledger
infrastructures. At the same time, the increasing
availability of blockchain data does not auto-
matically ensure their analytical reliability or
methodological consistency.
The main scientific problem lies in the frag-
mentation of the informational environment used
in on-chain analysis. In contemporary analytical
practice, researchers, investors and market
Стрімкий розвиток блокчейн-технологій, децентралізованих фінансових екосистем та цифрових активів суттє-
во трансформував інформаційне середовище фінансового аналізу. На відміну від традиційних фінансових систем, у
яких доступ до інформації про транзакції часто є фрагментованим або повністю обмеженим, блокчейн-мережі за-
безпечують відкриті та безперервно оновлювані записи економічної активності. Це створило принципово нові мож-
ливості для дослідження руху капіталу, ліквідності, структури власності, поведінкових моделей учасників ринку та
внутрішніх трансформацій децентралізованих фінансових систем на основі безпосередньої інтерпретації даних роз-
поділених реєстрів. За таких умов ончейн-аналіз поступово сформувався як самостійний напрям дослідження ринку
цифрових активів.
Метою статті є розробка системного методологічного підходу до інформаційного забезпечення ончейн-аналізу
ринку цифрових активів шляхом класифікації джерел блокчейн-даних та побудови алгоритму їх інтегрованого зас-
тосування у процесі аналітичного дослідження. У статті систематизовано основні групи джерел інформації для он-
чейн-аналізу, зокрема первинні блокчейн-реєстри, блокчейн-експлорери, агрегатори ончейн-метрик, платформи
адресної атрибуції, DeFi-аналітичні системи, біржові та деривативні платформи, а також кастомізовані аналітичні
середовища. Визначено їх функціональне призначення, рівень обробки даних та роль у процесі формування аналі-
тичних висновків.
У дослідженні запропоновано багатовимірну класифікаційну модель джерел даних для ончейн-аналізу, яка вра-
ховує походження даних, рівень їх аналітичної трансформації, функціональне призначення, рівень ринкового спос-
тереження та ступінь методологічної прозорості. Розроблено алгоритм застосування джерел даних в ончейн-аналізі,
що поєднує етапи верифікації блокчейн-даних, оцінки транзакційної активності, аналізу ліквідності, структури про-
позиції, поведінкових характеристик учасників ринку, DeFi-активності та деривативних індикаторів. Доведено, що
ефективність ончейн-аналізу визначається не окремими метриками, а комплексною інтеграцією різнорівневих інфор-
маційних джерел. Отримані результати формують методологічну основу для подальшого розвитку системного аналізу
ринку цифрових активів, побудови інтегрованих аналітичних моделей та вдосконалення підходів до інтерпретації
блокчейн-даних у фінансових дослідженнях.
Ключові слова: ончейн-аналіз, блокчейн-аналітика, цифрові активи, джерела блокчейн-да-
них, DeFi-аналітика, криптовалютний ринок.
Key words: on-chain analysis, blockchain analytics, digital assets, blockchain data sources, DeFi
analytics, cryptocurrency market.
participants rely on a wide range of heterogeneous
data sources, including primary blockchain le-
dgers, blockchain explorers, on-chain metric
aggregators, address attribution platforms, DeFi
analytical systems, exchange and derivatives plat-
forms. Each of these sources performs a different
function in the process of collecting, verifying,
transforming and interpreting blockchain data.
However, the methodological boundaries between
raw data, aggregated indicators, attributed entities
and market-context variables often remain
insufficiently defined. As a result, on-chain analysis
may be reduced to the mechanical use of platform-
specific metrics without proper verification of
their origin, calculation logic and analytical
purpose.
This problem is particularly important because
digital asset markets are characterised by high
volatility, informational asymmetry, rapid techno-
logical change and growing institutional partici-
pation. Under such conditions, the quality of analy-
tical conclusions depends not only on the selected
metrics, but also on the correct choice and combi-
nation of data sources. Misinterpretation of
262
АГРОСВІТ № 10, 2026
ISSN 2306-6792Copyright © The Author(s). This is an open access article distributed under the terms of the Creative
Commons Attribution License 4.0 (https://creativecommons.org/licenses/by/4.0/).
exchange flows, wallet activity, supply distri-
bution, DeFi indicators or derivatives-market data
may lead to distorted assessments of liquidity
conditions, accumulation and distribution phases,
investor behaviour and market-cycle dynamics.
Therefore, the systematisation of information
sources becomes not a purely technical task of data
collection, but an essential methodological
prerequisite for reliable on-chain research.
LITERATURE REVIEW
In contemporary scientific literature, several
interconnected research directions have emerged
concerning blockchain analytics, cryptocurrency
market infrastructure and the methodological
interpretation of on-chain data. One group of
studies focuses on the structural characteristics of
blockchain networks, transaction flows and supply
distribution mechanisms within cryptocurrency
ecosystems. Celig T., Ockenga T. A., Schoder D.
[1], Ciaian P., Kancs d'Artis, Rajcaniova M. [2],
Rudd M. A., Porter D. [3], and Biais B., Bisiеre C.,
Bouvard M., Casamatta C., Menkveld A. J. [4]
investigate the relationships between supply dy-
namics, market equilibrium, demand factors and
Bitcoin price formation, emphasising the role of
blockchain-level activity in the development of
digital asset markets. Their studies demonstrate that
transaction structures, ownership concentration
and market participation significantly influence
cryptocurrency valuation and market stability.
Another important direction of research
concerns blockchain transaction networks, address
identification and the technological mechanisms
underlying blockchain analytics. Xia L., Zhu T.,
Jing Z., Wang Q., Ma Z., Huang Z., Yin Z. [5] ana-
lyse transaction-network structures and propose
methods for cryptocurrency address identity
recognition, highlighting the growing methodo-
logical importance of behavioural clustering and
entity attribution in blockchain research. Misic V.
B., Misic J., Chang X. [6] examine the optimisation
of transaction messaging mechanisms within Bit-
coin infrastructure, while Kim D., Ryu D., Webb R. I.
[7] investigate the determinants of equilibrium
transaction fees in the Bitcoin network. These
studies contribute to understanding the technical
and informational architecture of blockchain
systems and provide a foundation for the further
development of on-chain analytical methodologies.
A separate strand of scientific literature is
devoted to decentralised finance, derivatives
markets and trading activity within cryptocur-
rency ecosystems. Schar F. [8] explores the
conceptual foundations of decentralised finance
and the transformation of financial markets
through blockchain— and smart contract-based
infrastructures. Aleti S., Mizrach B. [9] and
Hung J.-C., Liu H.-C., Yang J. J. [10] investigate
Bitcoin spot and futures market microstructure,
trading activity and price discovery processes,
demonstrating the growing importance of
derivatives and liquidity-related indicators for
interpreting cryptocurrency market behaviour.
Sun W., Jin H., Jin F., Kong L., Peng Y., Dai Z. [11]
analyse the spatial structure of global Bitcoin
mining activity, thereby extending the analytical
framework of blockchain research to infra-
structure-level and geographical dimensions of
digital asset ecosystems.
Another important direction concerns the
broader financial characteristics, risk structure
and investment properties of cryptocurrencies. Liu Y.,
Tsyvinski A. [12] examine the risk-return profile
of cryptocurrencies and demonstrate that digital
assets possess distinctive financial characteristics
compared with traditional asset classes. Their
findings strengthen the argument that crypto-
currency markets require specialised analytical
approaches capable of integrating blockchain-
level information, behavioural indicators and
market-structure variables.
Existing studies predominantly focus either on
individual metrics, market behaviour or techno-
logical aspects of blockchain systems, whereas the
informational architecture of on-chain analytics,
including the classification of data sources, their
methodological functions and the algorithm of their
practical application, remains insufficiently
developed in academic research. This determines the
necessity of constructing a systematic metho-
dological framework for the classification and
integrated use of information sources within the
process of on-chain analysis of digital asset markets.
FORMULATION OF THE ARTICLE'S
OBJECTIVES (SETTING THE TASK)
The purpose of the article is to develop a
systematic methodological framework for the
informational support of on-chain analysis of
digital asset markets through the classification of
blockchain-based data sources and the construc-
tion of an algorithm for their integrated appli-
cation within the analytical research process.
In order to achieve this purpose, the following
tasks are defined: to clarify the economic and
methodological essence of information sources used
in on-chain analysis; to develop a classification of
information sources for on-chain analysis of digital
asset markets; to construct a multidimensional
classification model of information sources for on-
chain analysis of digital asset markets; to develop
АГРОСВІТ № 10, 2026
263
ISSN 2306-6792 Copyright © The Author(s). This is an open access article distributed under the terms of the Creative
Commons Attribution License 4.0 (https://creativecommons.org/licenses/by/4.0/).
an algorithm for the application of blockchain-
based information sources within the process of on-
chain research, including the selection of relevant
metrics, verification procedures and the formation
of analytical conclusions.
PRESENTATION OF THE MAIN RESEARCH
MATERIAL
The rapid expansion of blockchain techno-
logies and decentralised financial infrastructures
has fundamentally transformed the informational
environment of digital asset markets. Unlike
traditional financial systems, where access to
transaction-level information is often fragmented,
delayed or institutionally restricted, blockchain
networks provide publicly accessible and con-
tinuously updated records of economic activity.
This technological transparency has created
fundamentally new opportunities for analysing
capital movements, ownership redistribution,
liquidity conditions and behavioural transfor-
mations of market participants through direct
interpretation of distributed ledger data.
Under such conditions, on-chain analysis has
gradually evolved into an independent analytical
direction within the broader system of digital asset
market research. The methodological significance of
on-chain analysis is determined not only by the
availability of large-scale blockchain datasets, but also
by the possibility of observing structural market
processes directly at the network level. Transaction
histories, wallet interactions, exchange flows, smart-
contract activity and network participation indicators
form a multidimensional informational environment
capable of reflecting internal transformations within
decentralised financial ecosystems.
At the same time, the growing practical appli-
cation of blockchain analytics has generated a
significant increase in the number of platforms,
databases, aggregators and analytical services
providing access to on-chain information. In mo-
dern analytical practice, researchers and investors
simultaneously use blockchain explorers, metric
aggregators, exchange monitoring systems, wallet
attribution platforms, decentralised finance
dashboards and custom analytical infrastructures
based on APIs and SQL-query environments.
However, despite the widespread use of these
instruments, the informational architecture of on-
chain analysis remains insufficiently systematised
in academic research. From a methodological
perspective, the informational environment of on-
chain analysis should be interpreted as a multi-
level system of interconnected data sources
performing different analytical functions within
the process of blockchain research. Certain
sources provide direct access to raw blockchain
records, others aggregate transaction-level infor-
mation into structured indicators, while additional
analytical systems perform behavioural interpre-
tation, entity attribution or cross-network
integration of blockchain activity. Consequently,
the analytical reliability of on-chain research
depends not only on the interpretation of indi-
vidual metrics, but also on the correct selection,
verification and integration of information sources
used throughout the analytical process.
The growing complexity of blockchain ecosystems
and the increasing institutionalisation of digital asset
markets further strengthen the importance of
developing a structured methodological approach to
the classification of on-chain data sources. Syste-
matisation of these sources creates the foundation for
improving the consistency of blockchain-based market
research, enhancing comparability of analytical results
and reducing the risks associated with incorrect
interpretation of aggregated indicators. In this regard,
the classification of information sources becomes an
important methodological component of on-chain
analysis itself rather than merely a technical aspect of
data collection (Table 1).
Primary blockchain ledgers constitute the
fundamental informational basis of on-chain
analysis. Their main function is to provide access
to original transaction records, block data and
network-level activity stored directly within
distributed ledger systems. These sources serve as
the primary layer of data verification, since all
subsequent analytical platforms derive, process or
interpret information that ultimately originates
from blockchain records.
Blockchain explorers perform a verification
and inspection function within the on-chain
analytical process. They enable researchers to
examine individual transactions, wallet addresses,
blocks, token transfers and smart-contract
interactions. Their role is especially important
when analytical conclusions require confirmation
at the level of specific blockchain events rather
than aggregated indicators. In this sense, explorers
provide a transparent link between raw blockchain
data and applied market interpretation.
On-chain metric aggregators transform raw
blockchain records into structured analytical
indicators. Their functional purpose consists in
systematising large volumes of distributed ledger
data into metrics that describe transaction activity,
liquidity conditions, supply distribution, network
usage and investor behaviour. These platforms are
particularly important for identifying market-
cycle dynamics, structural changes and long-term
regularities in digital asset markets.
264
АГРОСВІТ № 10, 2026
ISSN 2306-6792Copyright © The Author(s). This is an open access article distributed under the terms of the Creative
Commons Attribution License 4.0 (https://creativecommons.org/licenses/by/4.0/).
Address attribution platforms provide an
additional behavioural and institutional layer of
interpretation. Their function is to associate
blockchain addresses with specific entities, such as
exchanges, funds, protocols, large holders or
institutional wallets. This makes it possible to move
beyond anonymous transaction flows and analyse
the behaviour of particular categories of market
participants, including whale activity, exchange
movements and institutional positioning.
DeFi analytical platforms are designed to
capture economic activity within decentralised
finance ecosystems. Their functional purpose is to
provide data on total value locked, protocol
revenues, transaction volumes, liquidity pools,
lending markets and other indicators of protocol-
level performance. These sources expand on-chain
analysis beyond individual blockchain transactions
and allow researchers to evaluate the functioning
of decentralised financial infrastructures as
independent market segments.
Exchange and derivatives platforms comple-
ment classical on-chain data with information from
the trading and leverage environment. Their role
is to provide indicators such as exchange reserves,
funding rates, open interest, liquidation volumes
and derivatives market positioning. These data are
important because the behaviour of digital asset
markets is shaped not only by blockchain-level
activity, but also by liquidity conditions, leverage
structures and speculative pressure in exchange-
based trading systems.
Custom analytical environments perform a
methodological and research-design function.
They allow researchers to construct independent
datasets, formulate SQL queries, use APIs and
develop customised dashboards adapted to specific
research objectives. Their importance lies in the
possibility of moving from the use of predefined
platform indicators toward reproducible, flexible
and author-defined analytical models of block-
chain activity.
Taken together, these groups of sources form
a multi-level information infrastructure for on-
chain analysis. Primary ledgers provide the original
data layer, explorers support verification,
aggregators transform blockchain records into
indicators, attribution platforms add behavioural
interpretation, DeFi platforms expand the analysis
to protocol ecosystems, exchange and derivatives
Source
category
Analytical content
Representative
platforms and
infrastructures
Analytical role
in on-chain research
Primary
blockchain
ledgers
Direct records of blockchain
transactions and network
activity stored within
distributed ledger systems
Bitcoin
blockchain,
Ethereum
blockchain
Foundational level of
blockchain data
verification
Blockchain
explorers
Interfaces for examining
transactions, wallet
addresses, blocks and smart-
contract interactions
Etherscan,
Blockchain.com,
Blockchair,
Mempool.space
Verification of individual
transactions, addresses
and blockchain events
On-chain
metric
aggregators
Analytical platforms
providing aggregated
blockchain indicators and
market metrics
Glassnode,
CryptoQuant,
CoinMetrics,
IntoTheBlock
Analysis of metric
dynamics, market cycles
and structural blockchain
indicators
Address
attribution
platforms
Services for identifying
wallets, entities and
behavioural relationships
between market participants
Arkham,
Nansen,
Breadcrumbs
Analysis of wallet
behaviour, institutional
activity, exchange flows
and whale positioning
DeFi
analytical
platforms
Infrastructures providing
data on decentralised
finance protocols, total
value locked, transaction
volumes and protocol
revenues
DeFiLlama,
Token Terminal,
Dune
Analysis of decentralised
financial ecosystems and
protocol-level economic
activity
Exchange and
derivatives
platforms
Sources of data related to
reserves, funding rates, open
interest and derivatives
market conditions
Coinglass,
CryptoQuant,
cryptocurrency
exchanges
Complementing on-chain
analysis with liquidity
and derivatives market
indicators
Custom
analytical
environments
User-defined analytical
infrastructures based on
APIs, SQL queries and
blockchain datasets
Dune, Flipside,
APIs
Development of
customised analytical
methodologies and
blockchain research
models
Table 1. Classification of information sources for on-chain analysis of digital asset markets
according to their analytical function and data processing level
Source: developed by the author.
АГРОСВІТ № 10, 2026
265
ISSN 2306-6792 Copyright © The Author(s). This is an open access article distributed under the terms of the Creative
Commons Attribution License 4.0 (https://creativecommons.org/licenses/by/4.0/).
platforms connect on-chain data with market
liquidity, while custom analytical environments
enable the construction of independent research
methodologies.
Publicly available web-traffic estimates
indicate that the largest audience reach within the
ecosystem of on-chain analytical platforms is not
necessarily concentrated among the most
methodologically complex research terminals.
Instead, the highest visibility is often associated
with platforms that provide rapid access to
frequently used verification, monitoring and
trading-context information. According to
Similarweb estimates for April 2026 (Fig. 1),
CoinGlass recorded approximately 12.9 million
monthly visits, while Etherscan reached about
3.7 million visits, Blockchain.com about 2.8 million,
Blockchair about 1.4 million, Mempool.space
about 1.3 million and DeFiLlama about 1.2 million
visits [13]. These figures suggest that mass demand
in the field of on-chain analytics is largely shaped
by platforms with low access barriers, high
practical utility and repeated everyday use in
transaction verification, blockchain monitoring
and market-oriented information search.
At the same time, website traffic should not be
interpreted as the only indicator of analytical
significance. Official platform disclosures show
that some infrastructures with lower public web-
traffic visibility may possess deeper professional,
institutional or methodological embeddedness
within the digital asset analytical ecosystem. Dune
states that its on-chain data platform is used by
more than 1 million users and trusted by more than
20,000 companies, while also providing access to
more than 100 blockchains and more than 1.5
million datasets [14]. Coin Metrics reports more
than 21,000 weekly Community API users and
more than 11,000 monthly Charts users [15].
Nansen discloses more than 500 million labelled
wallets or addresses within its blockchain
intelligence infrastructure [16], which should be
interpreted as an indicator of address-attribution
coverage rather than as a user-count measure.
Blockchain.com reports more than 39 million
verified retail users, more than 1,500 engaged
institutional clients and 100 million wallets created
[17].
Therefore, the popularity of on-chain data
platforms should be interpreted as a functionally
differentiated phenomenon. Blockchain explorers
tend to attract mass verification traffic because
they are used for checking transactions, addresses
and blocks. Derivatives and market-structure
dashboards concentrate trading-oriented traffic
because they provide time-sensitive information
on funding rates, liquidations, open interest and
exchange-related indicators. Institutional ter-
minals, address-attribution systems and API-
oriented analytical environments may appear less
prominent in public web-traffic statistics, but they
can remain highly influential in research,
compliance, risk assessment and investment
decision-making processes. For this reason, web
traffic should be treated as an auxiliary proxy of
public visibility and adoption, rather than as a
direct measure of methodological quality,
analytical reliability or scientific value.
The growing complexity of blockchain eco-
systems and the rapid expansion of analytical
infrastructures within digital asset markets nece-
ssitate the development of a systematic methodo-
logical approach to the interpretation of on-chain
information sources. In contemporary analytical
practice, blockchain data are no longer repre-
sented solely by isolated transaction records or
individual analytical platforms. Instead, they form
a multilayered informational environment in
which raw blockchain data undergo successive
stages of verification, aggregation, attribution and
Fig. 1. Monthly website visits for crypto data platforms (April 2026)
Source: compiled on the basis of [13].
266
АГРОСВІТ № 10, 2026
ISSN 2306-6792Copyright © The Author(s). This is an open access article distributed under the terms of the Creative
Commons Attribution License 4.0 (https://creativecommons.org/licenses/by/4.0/).
analytical transformation before becoming
suitable for economic interpretation and market
research. Under such conditions, the informational
infrastructure of on-chain analysis should be
considered as an integrated system of intercon-
nected analytical layers that differ according to
their origin of data, degree of processing, func-
tional purpose and level of market observation. In
order to systematise these relationships, a multi-
dimensional classification model of data sources
for on-chain analysis of digital asset markets is
proposed (Fig. 2).
The model reflects the sequential transfor-
mation of blockchain information from primary
distributed-ledger records to advanced resear-
cher-defined analytical environments and si-
multaneously demonstrates the methodological
interconnections between verification mecha-
nisms, metric aggregation systems, attribution
platforms, liquidity-context infrastructures and
custom analytical models.
In order to systematise the practical
application of blockchain-based informational
infrastructures within the research process, the
algorithm for applying data sources in on-chain
analysis of digital asset markets is proposed in
Table 2.
The proposed algorithm demonstrates that the
use of data sources in on-chain analysis should not
be reduced to the mechanical extraction of
indicators from analytical platforms. Each stage
performs a specific methodological function within
the research process. Primary blockchain ledgers
and explorers provide the factual basis for
verification, while metric aggregators transform
raw blockchain records into indicators suitable for
structural interpretation. Address attribution
platforms add behavioural and institutional
meaning to transaction flows, DeFi analytical sys-
tems expand the scope of research to protocol-
level economic activity, and exchange or deriva-
tives platforms provide the market context nece-
ssary for interpreting liquidity and leverage con-
ditions.
A distinctive feature of the proposed algorithm
is the connection between the source of data, the
selected metric and the analytical conclusion
derived at each stage. Transaction metrics make it
possible to identify changes in the intensity of
network activity. Liquidity and exchange-flow
indicators support conclusions about accumu-
lation, distribution and potential selling pressure.
Supply distribution metrics reveal the structure of
ownership and the behaviour of long-term and
short-term holders. Address attribution data allow
the researcher to distinguish between anonymous
capital movements and the activity of exchanges,
institutional participants or large holders. DeFi and
derivatives indicators provide additional context
for evaluating protocol sustainability, market
leverage and speculative pressure.
Thus, the algorithm establishes a sequential but
flexible methodological framework for conducting
on-chain research. Its application allows the
researcher to move from primary blockchain veri-
fication to multidimensional market interpre-
Fig. 2. Classification model of data sources for on-chain analysis of digital asset markets
Source: developed by the author.
АГРОСВІТ № 10, 2026
267
ISSN 2306-6792 Copyright © The Author(s). This is an open access article distributed under the terms of the Creative
Commons Attribution License 4.0 (https://creativecommons.org/licenses/by/4.0/).
Stage
Analytical
objective
Data sources
used
Key metrics or data
extracted
Analytical decision
or conclusion
1. Definition
of research
objective
To determine the
analytical focus
of the study
Research design,
previous market
data, selected
blockchain
ecosystem
Asset, blockchain
network, time horizon,
level of analysis
Selection of the
analytical direction:
transaction activity,
liquidity, holder
behaviour, DeFi activity
or market-cycle
diagnostics
2. Primary
data
verification
To confirm the
factual basis of
blockchain
events
Primary
blockchain
ledgers,
blockchain
explorers
Transactions, blocks,
wallet addresses,
token transfers, smart-
contract events
Verification of the
reliability of the
empirical basis and
exclusion of incorrectly
identified or irrelevant
blockchain events
3. Transaction
activity
assessment
To evaluate the
intensity of
capital
movement
within the
network
Blockchain
explorers,
Glassnode, Coin
Metrics,
CryptoQuant
Transaction count,
transfer volume,
average transaction
size, active addresses
Identification of
expansion or contraction
in network-level
economic activity
4. Liquidity
and exchange-
flow analysis
To assess the
availability of
assets for market
circulation and
potential selling
pressure
CryptoQuant,
Glassnode,
CoinGlass,
exchange data
Exchange inflows and
outflows, exchange
reserves, stablecoin
flows, liquid and
illiquid supply
Determination of whether
the market is
characterised by
accumulation,
distribution, liquidity
expansion or liquidity
contraction
5. Supply
distribution
analysis
To examine
ownership
structure and
concentration of
assets
Glassnode, Coin
Metrics,
Etherscan,
Nansen, Arkham
Holder concentration,
long-term holder
supply, short-term
holder supply, whale
balances, realised
capitalisation
Assessment of ownership
concentration, strategic
accumulation and
redistribution of assets
between market
participants
6. Address
attribution and
behavioural
analysis
To identify the
behaviour of
specific
categories of
market
participants
Arkham,
Nansen,
Breadcrumbs,
Etherscan
Exchange wallets,
institutional wallets,
whale transactions,
labelled addresses,
wallet clusters
Identification of
behavioural patterns of
exchanges, funds, large
holders and institutional
participants
7. DeFi
ecosystem
analysis
To evaluate
protocol-level
economic
activity and
decentralised
financial
interactions
DeFiLlama,
Token Terminal,
Dune
Total value locked,
protocol revenue,
transaction volume,
lending activity,
liquidity pool
dynamics
Evaluation of the
sustainability, growth or
contraction of
decentralised financial
ecosystems
8. Derivatives
and market-
context
analysis
To complement
on-chain signals
with trading-
market
conditions
CoinGlass,
CryptoQuant,
exchange
platforms
Funding rates, open
interest, liquidation
volumes, long/short
ratios, futures basis
Assessment of leverage,
speculative pressure,
market overheating or
risk of forced
liquidations
9. Cross-
source
verification
To compare
signals from
different types of
data sources
Explorers,
aggregators,
attribution
platforms, DeFi
dashboards,
exchange
platforms
Consistency of
transaction data,
metric dynamics,
labelled entities and
liquidity indicators
Confirmation, rejection
or adjustment of
preliminary analytical
conclusions
10. Integrated
interpretation
To combine
metrics into a
coherent market
diagnosis
All selected
sources
according to the
research
objective
Combined set of
transaction, liquidity,
supply, behavioural,
DeFi and derivatives
indicators
Formulation of the final
analytical conclusion
regarding market phase,
capital flow direction,
accumulation or
distribution regime,
liquidity condition and
behavioural structure of
participants
Table 2. Algorithm for applying data sources in on-chain analysis
of digital asset markets
Source: developed by the author.
268
АГРОСВІТ № 10, 2026
ISSN 2306-6792Copyright © The Author(s). This is an open access article distributed under the terms of the Creative
Commons Attribution License 4.0 (https://creativecommons.org/licenses/by/4.0/).
tation, where final conclusions are formed not on
the basis of isolated metrics, but through the
combined analysis of transaction activity, liquidity
conditions, ownership structure, behavioural
signals and market-context indicators.
CONCLUSIONS AND PROSPECTS FOR
FURTHER RESEARCH IN THIS AREA
The conducted research demonstrates that the
informational environment of on-chain analysis
should be interpreted as a complex multi-level
analytical infrastructure rather than as a frag-
mented collection of independent blockchain
platforms and digital services. The rapid expansion
of blockchain ecosystems, the increasing institu-
tionalisation of digital asset markets and the
growing practical use of blockchain analytics have
substantially transformed the methodological
foundations of cryptocurrency market research.
Under such conditions, the reliability and
analytical depth of on-chain analysis depend not
only on the interpretation of individual blockchain
metrics, but also on the correct selection,
verification and integration of information sources
throughout the analytical process. The proposed
classification of information sources systematises
blockchain-based analytical infrastructures
according to their analytical function, degree of
data processing and role within the research
process. The study substantiates that primary
blockchain ledgers, blockchain explorers, on-
chain metric aggregators, address attribution
platforms, DeFi analytical systems, exchange and
derivatives platforms, and custom analytical
environments perform fundamentally different
methodological functions within the architecture
of on-chain analysis. Their combined application
forms a sequential informational chain in which
raw blockchain records are transformed into
structured analytical indicators, behavioural
interpretations and integrated market diagnostics.
An important result of the research is the
development of a multidimensional classification
model of data sources for on-chain analysis of digital
asset markets. The proposed model demonstrates
that blockchain-based informational infrastructures
differ not only by technological form, but also by the
level of market observation, methodological
transparency and analytical purpose. This allows on-
chain analysis to be interpreted as a structured system
of interconnected analytical layers encompassing
transaction verification, liquidity assessment,
behavioural attribution, ecosystem analysis and
market-context interpretation.
The research additionally develops an algorithm
for the practical application of information sources
within the process of on-chain analysis. The proposed
algorithm establishes a methodological sequence
linking data sources, selected metrics, analytical
procedures and final research conclusions. Unlike
fragmented approaches based on isolated indicators,
the developed framework emphasises the integrated
interpretation of blockchain activity, liquidity
conditions, supply distribution, behavioural signals,
DeFi indicators and derivatives-market data. Such an
approach significantly expands the analytical
possibilities of blockchain research and improves the
methodological consistency of digital asset market
analysis. Prospects for further research are associated
with the practical application of the proposed
methodological framework to empirical analysis of
cryptocurrency markets.
Література:
1. Celig T., Ockenga T. A., Schoder D.
Distributional equality in ethereum? on-chain
analysis of Ether Supply Distribution and Supply
Dynamics. Humanities and Social Sciences
Communications. 2025. № 12 (1). DOI: https://
doi.org/10.1057/s41599-025-04728-9
2. Ciaian P., Kancs d'Artis, Rajcaniova M. On-
and off-chain demand and supply drivers of Bitcoin
Price. Economics. 2026. № 20 (1). DOI: https://
doi.org/10.1515/econ-2025-0169
3. Rudd M. A., Porter D. Bitcoin supply,
demand, and Price Dynamics. Journal of Risk and
Financial Management. 2025. № 18 (10). pp. 570.
DOI: https://doi.org/10.3390/jrfm18100570
4. Biais B., Bisiеre C., Bouvard M., Casamatta
C., Menkveld A. J. Equilibrium bitcoin pricing. The
Journal of Finance. № 78 (2). pp. 967—1014. DOI:
https://doi.org/10.1111/jofi.13206
5. Xia L., Zhu T., Jing Z., Wang Q., Ma Z.,
Huang Z., Yin Z. A two-layer transaction network-
based method for virtual currency address identity
recognition. Cryptography. 2025. № 9(4). DOI:
https://doi.org/10.3390/cryptography9040065
6. Misic V. B., Misic J., Chang X. Reducing the
number of transaction messages in bitcoin. Peer-
to-Peer Networking and Applications. 2022. № 15
(1). pp. 768—782. DOI: https://doi.org/10.1007/
s12083-021-01278-0
7. Kim D., Ryu D., Webb R. I. Determination of
equilibrium transaction fees in the Bitcoin Network:
A rank-order contest. International Review of
Financial Analysis. 2023. № 86. pp. 102487. DOI:
https://doi.org/10.1016/j.irfa.2023.102487
8. Schar F. Decentralized finance: on block-
chain-and smart contract-based financial markets.
Federal Reserve Bank of St. Louis Review. 2022.
№ 103 (2). pp. 153—174. DOI: https://doi.org/
10.20955/r.103.153-74
АГРОСВІТ № 10, 2026
269
ISSN 2306-6792 Copyright © The Author(s). This is an open access article distributed under the terms of the Creative
Commons Attribution License 4.0 (https://creativecommons.org/licenses/by/4.0/).
9. Aleti S., Mizrach B. Bitcoin spot and futures
market microstructure. Journal of Futures
Markets. 2021. № 41 (2). pp. 194—225. DOI:
https://doi.org/10.1002/fut.22163
10. Hung J.-C., Liu H.-C., Yang J. J. Trading
activity and price discovery in Bitcoin
futuresmarkets. Journal of Empirical Finance.
2021. № 62. pp. 107—120. DOI: https://doi.org/
10.1016/j.jempfin.2021.03.001
11. Sun W., Jin H., Jin F., Kong L., Peng Y., Dai Z.
Spatial Analysis of Global Bitcoin Mining.
Scientific Reports. 2022. № 12 (1). DOI: https://
doi.org/10.1038/s41598-022-14987-0
12. Liu Y., Tsyvinski A. Risks and returns of
cryptocurrency. The Review of Financial Studies.
2020. № 34 (6). pp. 2689—2727. DOI: https://
doi.org/10.1093/rfs/hhaa113
13. Website Traffic Checker: Traffic Analytics,
Ranking and Audience Data. Similarweb. 2026.
URL: https://www.similarweb.com/website/
14. Make onchain data work for you. Dune.
2026. URL: https://dune.com/
15. Crypto data for individuals. Coin Metrics.
2026. URL: https://coinmetrics.io/individuals/
16. Nansen: The leading blockchain intelligence tool
for onchain data in 2025. Nansen. 2025. URL: https://
www.nansen.ai/post/nansen-the-leading-blockchain-
intelligence-tool-for-onchain-data-in-2025
17. About Blockchain.com. Blockchain.com.
2026. URL: https://www.blockchain.com/about
References:
1. Celig, T., Ockenga, T.A. and Schoder, D.
(2025), "Distributional equality in ethereum? On-
chain analysis of Ether supply distribution and
supply dynamics", Humanities and Social Sciences
Communications, vol. 12, no. 1. https://doi.org/
10.1057/s41599-025-04728-9
2. Ciaian, P., Kancs, d'Artis and Rajcaniova, M.
(2026), "On- and off-chain demand and supply
drivers of Bitcoin price", Economics, vol. 20, no.
1. https://doi.org/10.1515/econ-2025-0169
3. Rudd, M.A. and Porter, D. (2025), "Bitcoin
supply, demand, and price dynamics", Journal of
Risk and Financial Management, vol. 18, no. 10,
article 570. https://doi.org/10.3390/jrfm18100570
4. Biais, B., Bisiеre, C., Bouvard, M., Casamatta,
C. and Menkveld, A.J. (2023), "Equilibrium bitcoin
pricing", The Journal of Finance, vol. 78, no. 2,
pp. 967—1014. https://doi.org/10.1111/jofi.13206
5. Xia, L., Zhu, T., Jing, Z., Wang, Q., Ma, Z.,
Huang, Z. and Yin, Z. (2025), "A two-layer
transaction network-based method for virtual
currency address identity recognition", Crypto-
graphy, vol. 9, no. 4. https://doi.org/10.3390/
cryptography9040065
6. Misic, V.B., Misic, J. and Chang, X. (2022),
"Reducing the number of transaction messages in
bitcoin", Peer-to-Peer Networking and Appli-
cations, vol. 15, no. 1, pp. 768—782. https://
doi.org/10.1007/s12083-021-01278-0
7. Kim, D., Ryu, D. and Webb, R.I. (2023),
"Determination of equilibrium transaction fees in
the Bitcoin Network: A rank-order contest",
International Review of Financial Analysis, vol. 86,
article 102487. https://doi.org/10.1016/j.irfa.-
2023.102487
8. Schar, F. (2022), "Decentralized finance: On
blockchain— and smart contract-based financial
markets", Federal Reserve Bank of St. Louis
Review, vol. 103, no. 2, pp. 153—174. https://doi.-
org/10.20955/r.103.153-74
9. Aleti, S. and Mizrach, B. (2021), "Bitcoin spot
and futures market microstructure", Journal of
Futures Markets, vol. 41, no. 2, pp. 194—225.
https://doi.org/10.1002/fut.22163
10. Hung, J.-C., Liu, H.-C. and Yang, J.J. (2021),
"Trading activity and price discovery in Bitcoin
futures markets", Journal of Empirical Finance,
vol. 62, pp. 107—120. https://doi.org/10.1016/
j.jempfin.2021.03.001
11. Sun, W., Jin, H., Jin, F., Kong, L., Peng, Y.
and Dai, Z. (2022), "Spatial analysis of global
Bitcoin mining", Scientific Reports, vol. 12,
no. 1. https://doi.org/10.1038/s41598-022-
14987-0
12. Liu, Y. and Tsyvinski, A. (2020), "Risks and
returns of cryptocurrency", The Review of Finan-
cial Studies, vol. 34, no. 6, pp. 2689—2727. https:/
/doi.org/10.1093/rfs/hhaa113
13. Similarweb (2026), "Website Traffic
Checker: Traffic Analytics, Ranking and Audience
Data", available at: https://www.similarweb.com/
website/ (Accessed 8 May 2026).
14. Dune (2026), "Make onchain data work for
you", available at: https://dune.com/ (Accessed
8 May 2026).
15. Coin Metrics (2026), "Crypto Data for
Individuals", available at: https://coinmetrics.io/
individuals/ (Accessed 8 May 2026).
16. Nansen (2025), "Nansen: The Leading
Blockchain Intelligence Tool for Onchain Data in
2025", available at: https://www.nansen.ai/post/
nansen-the-leading-blockchain-intelligence-
tool-for-onchain-data-in-2025 (Accessed 8 May
2026).
17. Blockchain.com (2026), "About Block-
chain.com", available at: https://www.block-
chain.com/about (Accessed 8 May 2026).
Отримано редакцією журналу / Received: 08.05.26
Прорецензовано / Revised: 15.05.26
Дата публікації / Published: 21.05.26
|
| id | www_nayka_com_ua-article-10325 |
| institution | Agrosvit |
| keywords_txt_mv | keywords |
| language | English |
| last_indexed | 2026-06-09T01:01:31Z |
| publishDate | 2026 |
| publisher | ДКС Центр |
| record_format | ojs |
| resource_txt_mv | wwwnaykacomua/e6/9c36590f6cdeacb015ee04ce911d77e6.pdf |
| spelling | www_nayka_com_ua-article-103252026-06-08T07:46:11Z INFORMATION SUPPORT FOR ON-CHAIN ANALYSIS OF DIGITAL ASSET MARKETS: CLASSIFICATION OF SOURCES AND APPLICATION ALGORITHM ІНФОРМАЦІЙНЕ ЗАБЕЗПЕЧЕННЯ ОНЧЕЙН-АНАЛІЗУ РИНКУ ЦИФРОВИХ АКТИВІВ: КЛАСИФІКАЦІЯ ДЖЕРЕЛ ТА АЛГОРИТМ ЇХ ЗАСТОСУВАННЯ Ганусич, В. О. ДКС Центр 2026-05-21 Article Article application/pdf https://www.nayka.com.ua/index.php/agrosvit/article/view/10325 10.32702/2306-6792.2026.10.260 Журнал "Агросвіт"; № 10 (2026): АГРОСВІТ; 260-269 Agrosvit; No. 10 (2026): AGROSVIT; 260-269 2306-6792 10.32702/2306-6792.2026.10 en https://www.nayka.com.ua/index.php/agrosvit/article/view/10325/10469 Авторське право (c) 2026 Журнал "Агросвіт" |
| spellingShingle | Ганусич, В. О. ІНФОРМАЦІЙНЕ ЗАБЕЗПЕЧЕННЯ ОНЧЕЙН-АНАЛІЗУ РИНКУ ЦИФРОВИХ АКТИВІВ: КЛАСИФІКАЦІЯ ДЖЕРЕЛ ТА АЛГОРИТМ ЇХ ЗАСТОСУВАННЯ |
| title | ІНФОРМАЦІЙНЕ ЗАБЕЗПЕЧЕННЯ ОНЧЕЙН-АНАЛІЗУ РИНКУ ЦИФРОВИХ АКТИВІВ: КЛАСИФІКАЦІЯ ДЖЕРЕЛ ТА АЛГОРИТМ ЇХ ЗАСТОСУВАННЯ |
| title_alt | INFORMATION SUPPORT FOR ON-CHAIN ANALYSIS OF DIGITAL ASSET MARKETS: CLASSIFICATION OF SOURCES AND APPLICATION ALGORITHM |
| title_full | ІНФОРМАЦІЙНЕ ЗАБЕЗПЕЧЕННЯ ОНЧЕЙН-АНАЛІЗУ РИНКУ ЦИФРОВИХ АКТИВІВ: КЛАСИФІКАЦІЯ ДЖЕРЕЛ ТА АЛГОРИТМ ЇХ ЗАСТОСУВАННЯ |
| title_fullStr | ІНФОРМАЦІЙНЕ ЗАБЕЗПЕЧЕННЯ ОНЧЕЙН-АНАЛІЗУ РИНКУ ЦИФРОВИХ АКТИВІВ: КЛАСИФІКАЦІЯ ДЖЕРЕЛ ТА АЛГОРИТМ ЇХ ЗАСТОСУВАННЯ |
| title_full_unstemmed | ІНФОРМАЦІЙНЕ ЗАБЕЗПЕЧЕННЯ ОНЧЕЙН-АНАЛІЗУ РИНКУ ЦИФРОВИХ АКТИВІВ: КЛАСИФІКАЦІЯ ДЖЕРЕЛ ТА АЛГОРИТМ ЇХ ЗАСТОСУВАННЯ |
| title_short | ІНФОРМАЦІЙНЕ ЗАБЕЗПЕЧЕННЯ ОНЧЕЙН-АНАЛІЗУ РИНКУ ЦИФРОВИХ АКТИВІВ: КЛАСИФІКАЦІЯ ДЖЕРЕЛ ТА АЛГОРИТМ ЇХ ЗАСТОСУВАННЯ |
| title_sort | інформаційне забезпечення ончейн-аналізу ринку цифрових активів: класифікація джерел та алгоритм їх застосування |
| url | https://www.nayka.com.ua/index.php/agrosvit/article/view/10325 |
| work_keys_str_mv | AT ganusičvo informationsupportforonchainanalysisofdigitalassetmarketsclassificationofsourcesandapplicationalgorithm AT ganusičvo ínformacíjnezabezpečennâončejnanalízurinkucifrovihaktivívklasifíkacíâdžereltaalgoritmíhzastosuvannâ |