Point-In-Time
Accumulation Addresses
GET https://api.glassnode.com/v1/metrics/addresses/accumulation_count_pit
The number of unique accumulation addresses. Accumulation addresses are defined as addresses that have at least 2 incoming non-dust transfers and have never spent funds. Exchange addresses and addresses receiving from coinbase transactions (miner addresses) are discarded. To account for lost coins, addresses that were last active more than 7 years ago are omitted as well.
This is the Point-in-Time (PiT) variant of Accumulation Addresses. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Accumulation Balance
GET https://api.glassnode.com/v1/metrics/addresses/accumulation_balance_pit
The total amount of funds held in accumulation addresses. Accumulation addresses are defined as addresses that have at least 2 incoming non-dust transfers and have never spent funds. Exchange addresses and addresses receiving from coinbase transactions (miner addresses) are discarded. To account for lost coins, addresses that were last active more than 7 years ago are omitted as well.
This is the Point-in-Time (PiT) variant of Accumulation Balance. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Accumulation Trend Score
GET https://api.glassnode.com/v1/metrics/indicators/accumulation_trend_score_pit
The Accumulation Trend Score is an indicator that reflects the relative size of entities that are actively accumulating coins on-chain in terms of their BTC holdings. The scale of the Accumulation Trend Score represents both the size of the entities balance (their participation score), and the amount of new coins they have acquired/sold over the last month (their balance change score). An Accumulation Trend Score of closer to 1 indicates that on aggregate, larger entities (or a big part of the network) are accumulating, and a value closer to 0 indicates they are distributing or not accumulating. This provides insight into the balance size of market participants, and their accumulation behavior over the last month. For more information see the metric description in Glassnode Academy.
This is the Point-in-Time (PiT) variant of Accumulation Trend Score. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Active Entities
GET https://api.glassnode.com/v1/metrics/entities/active_count_pit
The number of unique entities that were active either as a sender or receiver. Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses. For more information see this article.
This is the Point-in-Time (PiT) variant of Active Entities. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Asia Year-over-Year Supply Change
GET https://api.glassnode.com/v1/metrics/supply/apac_1y_supply_change_pit
This metric aims at giving an estimate for the year-over-year change in the share of the Bitcoin supply to be held/traded in Asia.
Geolocation of Bitcoin supply is performed probabilistically at the entity level. The timestamps of all transactions created by an entity are correlated with the working hours of different geographical regions to determine the probabilities for each entity being located in the US, Europe, or Asia. Working hours are defined as:
US: 8am to 8pm Eastern Time (13:00-01:00 UTC)
EU: 8am to 8pm Central European Time (07:00-19:00 UTC)
Asia: 8am to 8pm China Standard Time (00:00-12:00 UTC)
An entity's balance will only contribute to the supply in the respective region if the location can be determined with a high certainty. Supply held on exchanges wallets are excluded.
This is the Point-in-Time (PiT) variant of Asia Year-over-Year Supply Change. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Bridges Deposits By Chain
GET https://api.glassnode.com/v1/metrics/bridges/deposits_by_chain_pit
This metric measures the USD value which is deposited into bridge smart contracts on Ethereum, and is therefore flowing out of the Ethereum blockchain, and into target blockchains. Deposit Volume is computed daily by multiplying the number of tokens deposited into bridges by the latest daily price of each token.
Bridges are protocols that enable digital assets to be transferred from one blockchain to another. When an asset is transferred out of Ethereum, it gets deposited and locked into a bridge smart contract. When the asset is transferred back to Ethereum, it is withdrawn and released from the smart contract.
This metric only includes bridge contracts on the Ethereum side. The bridges included in this metric cover bridge deposits into both L1 and L2 blockchains, providing information on the value transferred to both L1 competitors, and L2 scaling solutions. Each bridge included in this metric represents a single blockchain, except the ones labeled as multichain. That label is used to represent bridges that allow transferring assets across multiple different chains.
This is the Point-in-Time (PiT) variant of Bridges Deposits By Chain. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Bridges Net Flow By Chain
GET https://api.glassnode.com/v1/metrics/bridges/net_volume_by_chain_pit
This metric shows the net USD value flowing into, or out of Ethereum bridge smart contracts, calculated as bridge deposits minus bridge withdrawals. It can also be considered to represent the net USD value flowing in, or out of the Ethereum blockchain via bridges. A positive value means that there is more value being deposited into bridges, which translates into a net value outflow from Ethereum. On the other hand, a negative value means that there is more USD value being withdrawn from bridges, which translates into more USD value flowing back into Ethereum.
Bridges are protocols that enable digital assets to be transferred from one blockchain to another. When an asset is transferred out of Ethereum, it gets deposited and locked into a bridge smart contract. When the asset is transferred back to Ethereum, it is withdrawn and released from the smart contract.
This metric only includes bridge contracts on the Ethereum side. The bridges included in this metric cover bridge deposits into both L1 and L2 blockchains, providing information on the value transferred to both L1 competitors, and L2 scaling solutions. Each bridge included in this metric represents a single blockchain, except the ones labeled as multichain. That label is used to represent bridges that allow transferring assets across multiple different chains.
This is the Point-in-Time (PiT) variant of Bridges Net Flow By Chain. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Bridges Transactions (Absolute)
GET https://api.glassnode.com/v1/metrics/transactions/transfers_count_bridges_pit
The number of transactions (transaction count) in the Ethereum network by contracts that allow transfer of tokens between different blockchains.
This is the Point-in-Time (PiT) variant of Bridges Transactions (Absolute). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Bridges Transactions (Relative)
GET https://api.glassnode.com/v1/metrics/transactions/transfers_count_bridges_relative_pit
The relative amount (share) of transactions in the Ethereum network by contracts that allow transfer of tokens between different blockchains.
This is the Point-in-Time (PiT) variant of Bridges Transactions (Relative). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Bridges TVL
GET https://api.glassnode.com/v1/metrics/bridges/total_value_locked_by_chain_pit
The Total Value Locked (TVL) in bridges measures the total USD value that is locked within the Ethereum side of bridge smart contracts. Locked tokens are not available on the Ethereum chain, but are available on the target blockchains. An increasing TVL means that value is flowing out of Ethereum and into other target blockchains, whilst a decreasing TVL means the value is flowing back into Ethereum. Bridge TVL is computed daily, by multiplying the number of tokens locked within the bridge smart contracts, by the latest daily price for each token.
Bridges are protocols that enable digital assets to be transferred from one blockchain to another. When an asset is transferred out of Ethereum, it gets deposited and locked into a bridge smart contract. When the asset is transferred back to Ethereum, it is withdrawn and released from the smart contract.
This metric only includes bridge contracts on the Ethereum side. The bridges included in this metric cover bridge deposits into both L1 and L2 blockchains, providing information on the value transferred to both L1 competitors, and L2 scaling solutions. Each bridge included in this metric represents a single blockchain, except the ones labeled as multichain. That label is used to represent bridges that allow transferring assets across multiple different chains.
This is the Point-in-Time (PiT) variant of Bridges TVL. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Bridges TVL Relative
GET https://api.glassnode.com/v1/metrics/bridges/total_value_locked_by_chain_relative_pit
This metric presents the Relative Total Value Locked (TVL dominance) of each target blockchain bridge compared to the total TVL across all bridges. A rising relative TVL indicates that the target blockchain is growing in USD denominated TVL dominance compared to the others (and vice versa). Bridge TVL is computed daily, by multiplying the number of tokens locked within the bridge smart contract, by the latest daily price of each token. Relative TVL is then computed by dividing the TVL of each bridge by the total TVL across all bridges.
Bridges are protocols that enable digital assets to be transferred from one blockchain to another. When an asset is transferred out of Ethereum, it gets deposited and locked into a bridge smart contract. When the asset is transferred back to Ethereum, it is withdrawn and released from the smart contract.
This metric only includes bridge contracts on the Ethereum side. The bridges included in this metric cover bridge deposits into both L1 and L2 blockchains, providing information on the value transferred to both L1 competitors, and L2 scaling solutions. Each bridge included in this metric represents a single blockchain, except the ones labeled as multichain. That label is used to represent bridges that allow transferring assets across multiple different chains.
This is the Point-in-Time (PiT) variant of Bridges TVL Relative. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Bridges Withdrawals By Chain
GET https://api.glassnode.com/v1/metrics/bridges/withdrawals_by_chain_pit
This metric measures the USD value which is withdrawn from bridge smart contracts on Ethereum, and is therefore flowing into the Ethereum blockchain, and out of target blockchains. Withdrawal Volume is computed daily by multiplying the number of tokens withdrawn from bridges by the latest daily price of each token.
Bridges are protocols that enable digital assets to be transferred from one blockchain to another. When an asset is transferred out of Ethereum, it gets deposited and locked into a bridge smart contract. When the asset is transferred back to Ethereum, it is withdrawn and released from the smart contract.
This metric only includes bridge contracts on the Ethereum side. The bridges included in this metric cover bridge deposits into both L1 and L2 blockchains, providing information on the value transferred to both L1 competitors, and L2 scaling solutions. Each bridge included in this metric represents a single blockchain, except the ones labeled as multichain. That label is used to represent bridges that allow transferring assets across multiple different chains.
This is the Point-in-Time (PiT) variant of Bridges Withdrawals By Chain. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Coinjoin Output Count
GET https://api.glassnode.com/v1/metrics/transactions/transfers_from_coinjoins_count_pit
The total count of indistinguishable outputs in coinjoin transactions. The metric is an aggregate of different coinjoin providers. Note that coinjoin metrics rely on heuristics and statistical information that change over time. Therefore these metrics are mutable â the data is stable, but especially most recent data points are subject to slight fluctuations as time progresses.
This is the Point-in-Time (PiT) variant of Coinjoin Output Count. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Coinjoin Output Volume
GET https://api.glassnode.com/v1/metrics/transactions/transfers_volume_from_coinjoins_sum_pit
The total amount of indistinguishable outputs in coinjoin transactions, i.e. the volume of coins mixed by different coinjoin providers. Note that coinjoin metrics rely on heuristics and statistical information that change over time. Therefore these metrics are mutable â the data is stable, but especially most recent data points are subject to slight fluctuations as time progresses.
This is the Point-in-Time (PiT) variant of Coinjoin Output Volume. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
DeFi Transactions (Absolute)
GET https://api.glassnode.com/v1/metrics/transactions/transfers_count_defi_pit
The number of transactions (transaction count) in the Ethereum network by on-chain financial instruments and protocols implemented as smart contracts, including decentralized exchanges (DEXs).
This is the Point-in-Time (PiT) variant of DeFi Transactions (Absolute). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
DeFi Transactions (Relative)
GET https://api.glassnode.com/v1/metrics/transactions/transfers_count_defi_relative_pit
The relative amount (share) of transactions in the Ethereum network by on-chain financial instruments and protocols implemented as smart contracts, including decentralized exchanges (DEXs).
This is the Point-in-Time (PiT) variant of DeFi Transactions (Relative). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Depositing Addresses
GET https://api.glassnode.com/v1/metrics/addresses/sending_to_exchanges_count_pit
The number of unique addresses that appeared as a sender in a transaction sending funds to exchanges.
This is the Point-in-Time (PiT) variant of Depositing Addresses. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Supported asset symbols: BTC, ETH, 1INCH, AAVE, ABT, AMP, AMPL, ANT, APE, BADGER, BAL, BAND, BAT, BNT, BOBA, BOND, BORG, BUSD, CAKE, CELR, COMP, CREAM, CRO, CRV, CVC, CVP, CVX, CVXCRV, DAI, DDX, DENT, DHT, DODO, DPI, DRGN, ELF, ENG, ENJ, ETHDYDX, EURS, FET, FLX, FRAX, FTM, FTT, FUN, FXS, GNO, GUSD, HEGIC, HOT, HT, HUSD, IMX, INDEX, KCS, LAMB, LBA, LDO, LEO, LINK, LOOM, LRC, MANA, MATIC, MCB, METIS, MIR, MKR, MLN, MTA, MTL, NDX, NEXO, NFTX, NMR, Nsure, OCEAN, OKB, OMG, PAY, PERP, PICKLE, PNK, PNT, POLY, POWR, PPT, PYUSD, QASH, QKC, QNT, RAI, RDN, REN, REP, rETH, RLC, ROOK, RPL, RSR, SAND, SFRXETH, SHIB, SNT, SNX, SSV, STAKE, stETH, STORJ, sUSD, SUSHI, TEL, TUSD, UBT, UMA, UNI, USDC, USDD, USDK, USDP, USDT, UTK, VERI, WBTC, WETH, wNXM, YAM, YFI, ZRX
Entities Net Growth
GET https://api.glassnode.com/v1/metrics/entities/net_growth_count_pit
The net growth of unique entities in the network. This metric is defined as the difference between new entities and "disappearing" entities (entities with a zero balance that had a nonâzero balance at the previous timestamp). Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses. For more information see this article.
The computation of this metric requires statistical information from several days, and is therefore only available with a lag of one week.
This is the Point-in-Time (PiT) variant of Entities Net Growth. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entities Supply Distribution
GET https://api.glassnode.com/v1/metrics/entities/supply_distribution_relative_pit
Relative distribution of the circulating supply held by entities with specific balance bands.
This is the Point-in-Time (PiT) variant of Entities Supply Distribution. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity- and Supply-Adjusted CYD
GET https://api.glassnode.com/v1/metrics/indicators/cyd_account_based_supply_adjusted_pit
Coin Years Destroyed (CYD) is defined as the 365 day rolling sum of Coin Days Destroyed (CDD), and shows the amount of coin days that have been destroyed over the past year. It is indicative of long-term holder behaviour. This version is entity-adjusted, meaning that transactions within addresses controlled by the same network participant are discarded (see this article for more information), as well as supply-adjusted to account for the increasing baseline of the metric over time. This metric was first put forward by ARK Invest and further developed by Glassnode by adjusting for the circulating supply.
This is the Point-in-Time (PiT) variant of Entity- and Supply-Adjusted CYD. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted 90D Coin Days Destroyed (eCDD-90)
GET https://api.glassnode.com/v1/metrics/indicators/cdd90_account_based_age_adjusted_pit
90D Coin Days Destroyed is the 90 day rolling sum of Coin Days Destroyed (CDD) and shows the amount of coin days that have been destroyed over the past year. This version is entity-adjusted, meaning that transactions within addresses controlled by the same network participant are discarded (see this article for more information), as well as age-adjusted meaning that we normalize by time in order to account for the increasing baseline as time goes by.
This is the Point-in-Time (PiT) variant of Entity-Adjusted 90D Coin Days Destroyed (eCDD-90). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted ASOL
GET https://api.glassnode.com/v1/metrics/indicators/asol_account_based_pit
Entity-adjusted ASOL is an improved variant of ASOL that discards transactions between addresses of the same entity ("in-house" transactions). Entity-adjusted ASOL therefore accounts for real economic activity only, and provides an improved market signal compared to its raw UTXO-based counterpart. For detailed information read this article.
This is the Point-in-Time (PiT) variant of Entity-Adjusted ASOL. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted CDD
GET https://api.glassnode.com/v1/metrics/indicators/cdd_account_based_pit
Entity-adjusted CDD is an improved variant of CDD that discards transactions between addresses of the same entity ("in-house" transactions). Entity-adjusted CDD therefore accounts for real economic activity only, and provides an improved market signal compared to its raw UTXO-based counterpart. For detailed information read this article.
This is the Point-in-Time (PiT) variant of Entity-Adjusted CDD. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted CYD
GET https://api.glassnode.com/v1/metrics/indicators/cyd_account_based_pit
Coin Years Destroyed (CYD) is defined as the 365 day rolling sum of Coin Days Destroyed (CDD), and shows the amount of coin days that have been destroyed over the past year. It is indicative of long-term holder behaviour. This version is entity-adjusted, meaning that transactions within addresses controlled by the same network participant are discarded (see this article for more information). This metric was first put forward by ARK Invest.
This is the Point-in-Time (PiT) variant of Entity-Adjusted CYD. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted Dormancy
GET https://api.glassnode.com/v1/metrics/indicators/dormancy_account_based_pit
Entity-adjusted Dormancy is an improved variant of Average Coin Dormancy that discards transactions between addresses of the same entity ("in-house" transactions). Entity-adjusted Dormancy therefore accounts for real economic activity only, and provides an improved market signal compared to its raw UTXO-based counterpart. For detailed information read this article.
This is the Point-in-Time (PiT) variant of Entity-Adjusted Dormancy. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted Dormancy Flow
GET https://api.glassnode.com/v1/metrics/indicators/dormancy_flow_pit
Entity-adjusted Dormancy Flow is the ratio of the current market capitalization and the annualized dormancy value (measured in USD). Entity-adjusted Dormancy Flow can be used to time market lows and assess whether the bull market remains in relatively normal conditions. It helps confirm whether Bitcoin is in a bullish or bearish primary trend. This metric was put forward by David Puell. For more information please read his introductory article.
This is the Point-in-Time (PiT) variant of Entity-Adjusted Dormancy Flow. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted Liveliness
GET https://api.glassnode.com/v1/metrics/indicators/liveliness_account_based_pit
Entity-adjusted Liveliness is an improved variant of Liveliness that discards transactions between addresses of the same entity ("in-house" transactions). Entity-adjusted Liveliness therefore accounts for real economic activity only, and provides an improved market signal compared to its raw UTXO-based counterpart. For detailed information read this article.
This is the Point-in-Time (PiT) variant of Entity-Adjusted Liveliness. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted Long-Term Holder ASOL
GET https://api.glassnode.com/v1/metrics/indicators/asol_lth_account_based_pit
Long-Term Holder variant of Entity-Adjusted ASOL. Average Spent Output Lifespan (ASOL) is the average age (in days) of spent transaction outputs. Transactions between addresses of the same entity ("in-house" transactions) are discarded. Long- and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days.
Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses. For more information see this article.
This is the Point-in-Time (PiT) variant of Entity-Adjusted Long-Term Holder ASOL. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted Long-Term Holder CDD
GET https://api.glassnode.com/v1/metrics/indicators/cdd_lth_account_based_pit
Long-Term Holder variant of Entity-Adjusted CDD. Coin Days Destroyed (CDD) for any given transaction is calculated by taking the number of coins in a transaction and multiplying it by the number of days it has been since those coins were last spent. Transactions between addresses of the same entity ("in-house" transactions) are discarded. Long- and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days.
Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses. For more information see this article.
This is the Point-in-Time (PiT) variant of Entity-Adjusted Long-Term Holder CDD. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted Long-Term Holder Dormancy
GET https://api.glassnode.com/v1/metrics/indicators/dormancy_lth_account_based_pit
Long-Term Holder variant of Entity-Adjusted Dormancy. Dormancy is the average number of days destroyed per coin transacted, and is defined as the ratio of coin days destroyed and total transfer volume. Transactions between addresses of the same entity ("in-house" transactions) are discarded. Long- and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days.
Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses. For more information see this article.
This is the Point-in-Time (PiT) variant of Entity-Adjusted Long-Term Holder Dormancy. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted LTH Realized Loss
GET https://api.glassnode.com/v1/metrics/indicators/realized_loss_lth_account_based_pit
Entity-Adjusted variant of Realized Loss for Long-Term Holders, which denotes the total profit (in USD) of all moved coins whose price at their last movement was lower than the price at the current movement. Long- and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days.
Volume transferred between addresses owned by the same entity cluster is excluded. As such, no value is realized during internal or âin-houseâ transfers.
Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses. For more information see this article.
This is the Point-in-Time (PiT) variant of Entity-Adjusted LTH Realized Loss. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted LTH Realized Loss to Exchanges
GET https://api.glassnode.com/v1/metrics/indicators/realized_loss_lth_to_exchanges_account_based_pit
Entity-Adjusted variant of Realized Loss for coins that are sent from Long-Term Holders to exchanges. Realized loss denotes the total loss (in USD) of all moved coins whose price at their last movement was higher than the price at the current movement. Long- and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days.
Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses. For more information see this article.
Note that exchange metrics are based on our labeled data of exchange addresses that we constantly keep updating, as well as data science techniques and statistical information that changes over time. Therefore these metrics are mutable â the data is stable, but especially most recent data points are subject to slight fluctuations as time progresses.
This is the Point-in-Time (PiT) variant of Entity-Adjusted LTH Realized Loss to Exchanges. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted LTH Realized Profit
GET https://api.glassnode.com/v1/metrics/indicators/realized_profit_lth_account_based_pit
Entity-Adjusted variant of Realized Profit for Long-Term Holders, which denotes the total profit (in USD) of all moved coins whose price at their last movement was lower than the price at the current movement. Long- and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days.
Volume transferred between addresses owned by the same entity cluster is excluded. As such, no value is realized during internal or âin-houseâ transfers.
Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses. For more information see this article.
This is the Point-in-Time (PiT) variant of Entity-Adjusted LTH Realized Profit. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted LTH Realized Profit to Exchanges
GET https://api.glassnode.com/v1/metrics/indicators/realized_profit_lth_to_exchanges_account_based_pit
Entity-Adjusted variant of Realized Profit for coins that are sent from Long-Term Holders to exchanges. Realized profit denotes the total profit (in USD) of all moved coins whose price at their last movement was lower than the price at the current movement. Long- and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days.
Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses. For more information see this article.
Note that exchange metrics are based on our labeled data of exchange addresses that we constantly keep updating, as well as data science techniques and statistical information that changes over time. Therefore these metrics are mutable â the data is stable, but especially most recent data points are subject to slight fluctuations as time progresses.
This is the Point-in-Time (PiT) variant of Entity-Adjusted LTH Realized Profit to Exchanges. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted LTH Transfer Volume
GET https://api.glassnode.com/v1/metrics/transactions/transfers_volume_entity_adjusted_from_lth_sum_pit
The total estimated amount of coins moved by long-term holders. Volume transferred within addresses of the same entity is excluded. Long- and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days.
Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses. For more information see this article.
This is the Point-in-Time (PiT) variant of Entity-Adjusted LTH Transfer Volume. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted LTH Transfer Volume in Loss
GET https://api.glassnode.com/v1/metrics/transactions/transfers_volume_entity_adjusted_from_lth_loss_sum_pit
The total estimated amount of coins moved by long-term holders in loss. Volume transferred within addresses of the same entity is excluded. Coins are considered to be in loss when the price at the time the coins are spent is lower than the entity's average on-chain acquisition price for its funds. Long- and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days.
Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses. For more information see this article.
This is the Point-in-Time (PiT) variant of Entity-Adjusted LTH Transfer Volume in Loss. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted LTH Transfer Volume in Profit
GET https://api.glassnode.com/v1/metrics/transactions/transfers_volume_entity_adjusted_from_lth_profit_sum_pit
The total estimated amount of coins moved by long-term holders in profit. Volume transferred within addresses of the same entity is excluded. Coins are considered to be in profit when the price at the time the coins are spent is higher than the entity's average on-chain acquisition price for its funds. Long- and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days.
Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses. For more information see this article.
This is the Point-in-Time (PiT) variant of Entity-Adjusted LTH Transfer Volume in Profit. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted LTH-NUPL
GET https://api.glassnode.com/v1/metrics/indicators/nupl_more_155_account_based_pit
Entity-adjusted LTH-NUPL is an improved variant of Long-Term Holders Net Unrealized Profit/Loss (LTH-NUPL) that discards transactions between addresses of the same entity ("in-house" transactions). Entity-adjusted LTH NUPL therefore accounts for real economic activity only, and provides an improved market signal compared to its raw UTXO-based counterpart. For detailed information read this article. An entity is considered as a Long-Term Holder if the time since its averaged purchasing date is more than 155 days.
This is the Point-in-Time (PiT) variant of Entity-Adjusted LTH-NUPL. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted LTH/STH Transfer Volume in Profit/Loss
GET https://api.glassnode.com/v1/metrics/transactions/transfers_volume_entity_adjusted_from_lth_sth_profit_loss_relative_pit
The relative amount of coins moved by by long- and short-term holders in profit/loss. Volume transferred within addresses of the same entity is excluded. Coins are considered to be in profit/loss when the price at the time the coins are spent is higher/lower than the entity's average on-chain acquisition price for its funds. Long- and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days.
Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses. For more information see this article.
This is the Point-in-Time (PiT) variant of Entity-Adjusted LTH/STH Transfer Volume in Profit/Loss. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted MSOL
GET https://api.glassnode.com/v1/metrics/indicators/msol_account_based_pit
Entity-adjusted MSOL is an improved variant of MSOL that discards transactions between addresses of the same entity ("in-house" transactions). Entity-adjusted MSOL therefore accounts for real economic activity only, and provides an improved market signal compared to its raw UTXO-based counterpart. For detailed information read this article.
This is the Point-in-Time (PiT) variant of Entity-Adjusted MSOL. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted MVRV
GET https://api.glassnode.com/v1/metrics/indicators/mvrv_account_based_pit
Entity-adjusted MVRV is an improved variant of MVRV Ratio that discards transactions between addresses of the same entity ("in-house" transactions). Entity-adjusted MVRV therefore accounts for real economic activity only, and provides an improved market signal compared to its raw UTXO-based counterpart. For detailed information read this article.
This is the Point-in-Time (PiT) variant of Entity-Adjusted MVRV. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted NUPL
GET https://api.glassnode.com/v1/metrics/indicators/net_unrealized_profit_loss_account_based_pit
Entity-adjusted NUPL is an improved variant of Net Unrealized Profit/Loss (NUPL) that discards transactions between addresses of the same entity ("in-house" transactions). Entity-adjusted NUPL therefore accounts for real economic activity only, and provides an improved market signal compared to its raw UTXO-based counterpart. For detailed information read this article.
This is the Point-in-Time (PiT) variant of Entity-Adjusted NUPL. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted NVT
GET https://api.glassnode.com/v1/metrics/indicators/nvt_entity_adjusted_pit
The Network Value to Transactions (NVT) Ratio is computed by dividing the market cap by the transferred on-chain volume measured in USD. This entity-adjusted version of the NVT Ratio uses entity-adjusted on-chain volume and is therefore more accurate as it accounts for actual economic throughput.
This is the Point-in-Time (PiT) variant of Entity-Adjusted NVT. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted Realized Cap
GET https://api.glassnode.com/v1/metrics/indicators/rcap_account_based_pit
Entity-adjusted Realized Cap is an improved variant of Realized Cap that discards transactions between addresses of the same entity ("in-house" transactions). Entity-adjusted Realized Cap therefore accounts for real economic activity only, and provides an improved market signal compared to its raw UTXO-based counterpart. For detailed information read this article.
This is the Point-in-Time (PiT) variant of Entity-Adjusted Realized Cap. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted Realized Loss
GET https://api.glassnode.com/v1/metrics/indicators/realized_loss_account_based_pit
Entity-Adjusted variant of Realized Loss, which denotes the total profit (in USD) of all moved coins whose price at their last movement was lower than the price at the current movement.
Volume transferred between addresses owned by the same entity cluster is excluded. As such, no value is realized during internal or âin-houseâ transfers.
Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses. For more information see this article.
This is the Point-in-Time (PiT) variant of Entity-Adjusted Realized Loss. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted Realized Loss to Exchanges
GET https://api.glassnode.com/v1/metrics/indicators/realized_loss_to_exchanges_account_based_pit
Entity-Adjusted variant of Realized Loss for coins that are sent to exchanges. Realized loss denotes the total loss (in USD) of all moved coins whose price at their last movement was higher than the price at the current movement.
Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses. For more information see this article.
Note that exchange metrics are based on our labeled data of exchange addresses that we constantly keep updating, as well as data science techniques and statistical information that changes over time. Therefore these metrics are mutable â the data is stable, but especially most recent data points are subject to slight fluctuations as time progresses.
This is the Point-in-Time (PiT) variant of Entity-Adjusted Realized Loss to Exchanges. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted Realized Profit
GET https://api.glassnode.com/v1/metrics/indicators/realized_profit_account_based_pit
Entity-Adjusted variant of Realized Profit, which denotes the total profit (in USD) of all moved coins whose price at their last movement was lower than the price at the current movement.
Volume transferred between addresses owned by the same entity cluster is excluded. As such, no value is realized during internal or âin-houseâ transfers.
Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses. For more information see this article.
This is the Point-in-Time (PiT) variant of Entity-Adjusted Realized Profit. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted Realized Profit to Exchanges
GET https://api.glassnode.com/v1/metrics/indicators/realized_profit_to_exchanges_account_based_pit
Entity-Adjusted variant of Realized Profit for coins that are sent to exchanges. Realized profit denotes the total profit (in USD) of all moved coins whose price at their last movement was lower than the price at the current movement.
Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses. For more information see this article.
Note that exchange metrics are based on our labeled data of exchange addresses that we constantly keep updating, as well as data science techniques and statistical information that changes over time. Therefore these metrics are mutable â the data is stable, but especially most recent data points are subject to slight fluctuations as time progresses.
This is the Point-in-Time (PiT) variant of Entity-Adjusted Realized Profit to Exchanges. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted Short-Term Holder ASOL
GET https://api.glassnode.com/v1/metrics/indicators/asol_sth_account_based_pit
Short-Term Holder variant of Entity-Adjusted ASOL. Average Spent Output Lifespan (ASOL) is the average age (in days) of spent transaction outputs. Transactions between addresses of the same entity ("in-house" transactions) are discarded. Long- and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days.
Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses. For more information see this article.
This is the Point-in-Time (PiT) variant of Entity-Adjusted Short-Term Holder ASOL. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted Short-Term Holder CDD
GET https://api.glassnode.com/v1/metrics/indicators/cdd_sth_account_based_pit
Short-Term Holder variant of Entity-Adjusted CDD. Coin Days Destroyed (CDD) for any given transaction is calculated by taking the number of coins in a transaction and multiplying it by the number of days it has been since those coins were last spent. Transactions between addresses of the same entity ("in-house" transactions) are discarded. Long- and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days.
Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses. For more information see this article.
This is the Point-in-Time (PiT) variant of Entity-Adjusted Short-Term Holder CDD. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted Short-Term Holder Dormancy
GET https://api.glassnode.com/v1/metrics/indicators/dormancy_sth_account_based_pit
Short-Term Holder variant of Entity-Adjusted Dormancy. Dormancy is the average number of days destroyed per coin transacted, and is defined as the ratio of coin days destroyed and total transfer volume. Transactions between addresses of the same entity ("in-house" transactions) are discarded. Long- and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days.
Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses. For more information see this article.
This is the Point-in-Time (PiT) variant of Entity-Adjusted Short-Term Holder Dormancy. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted SOPR
GET https://api.glassnode.com/v1/metrics/indicators/sopr_account_based_pit
Entity-adjusted SOPR is an improved variant of SOPR that discards transactions between addresses of the same entity ("in-house" transactions). Entity-adjusted SOPR therefore accounts for real economic activity only, and provides an improved market signal compared to its raw UTXO-based counterpart. For detailed information read this article.
This is the Point-in-Time (PiT) variant of Entity-Adjusted SOPR. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted Spent Volume Age Bands (SVAB)
GET https://api.glassnode.com/v1/metrics/indicators/svab_entity_adjusted_pit
Spent Volume Age Bands (SVAB) is a separation of the on-chain transfer volume based on the coins' age. Each band represents the percentage of spent volume that was previously moved within the time period denoted in the legend. This metric is entity-adjusted and discards transactions between addresses of the same entity ("in-house" transactions).
Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses. For more information see this article.
This is the Point-in-Time (PiT) variant of Entity-Adjusted Spent Volume Age Bands (SVAB). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted Spent Volume Lifespan < 24h
GET https://api.glassnode.com/v1/metrics/indicators/svl_entity_adjusted_24h_pit
The total transfer volume of coins younger than 24 hours. This metric is entity-adjusted and discards transactions between addresses of the same entity ("in-house" transactions).
Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses. For more information see this article.
This is the Point-in-Time (PiT) variant of Entity-Adjusted Spent Volume Lifespan < 24h. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted Spent Volume Lifespan > 10y
GET https://api.glassnode.com/v1/metrics/indicators/svl_entity_adjusted_more_10y_pit
The total transfer volume of coins that were last active more than 10 years ago. This metric is entity-adjusted and discards transactions between addresses of the same entity ("in-house" transactions).
Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses. For more information see this article.
This is the Point-in-Time (PiT) variant of Entity-Adjusted Spent Volume Lifespan > 10y. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted Spent Volume Lifespan 1d-1w
GET https://api.glassnode.com/v1/metrics/indicators/svl_entity_adjusted_1d_1w_pit
The total transfer volume of coins that were last active between 1d and 1w ago. This metric is entity-adjusted and discards transactions between addresses of the same entity ("in-house" transactions).
Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses. For more information see this article.
This is the Point-in-Time (PiT) variant of Entity-Adjusted Spent Volume Lifespan 1d-1w. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted Spent Volume Lifespan 1m-3m
GET https://api.glassnode.com/v1/metrics/indicators/svl_entity_adjusted_1m_3m_pit
The total transfer volume of coins that were last active between 1m and 3m ago. This metric is entity-adjusted and discards transactions between addresses of the same entity ("in-house" transactions).
Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses. For more information see this article.
This is the Point-in-Time (PiT) variant of Entity-Adjusted Spent Volume Lifespan 1m-3m. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted Spent Volume Lifespan 1w-1m
GET https://api.glassnode.com/v1/metrics/indicators/svl_entity_adjusted_1w_1m_pit
The total transfer volume of coins that were last active between 1w and 1m ago. This metric is entity-adjusted and discards transactions between addresses of the same entity ("in-house" transactions).
Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses. For more information see this article.
This is the Point-in-Time (PiT) variant of Entity-Adjusted Spent Volume Lifespan 1w-1m . PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted Spent Volume Lifespan 1y-2y
GET https://api.glassnode.com/v1/metrics/indicators/svl_entity_adjusted_1y_2y_pit
The total transfer volume of coins that were last active between 1y and 2y ago. This metric is entity-adjusted and discards transactions between addresses of the same entity ("in-house" transactions).
Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses. For more information see this article.
This is the Point-in-Time (PiT) variant of Entity-Adjusted Spent Volume Lifespan 1y-2y. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted Spent Volume Lifespan 2y-3y
GET https://api.glassnode.com/v1/metrics/indicators/svl_entity_adjusted_2y_3y_pit
The total transfer volume of coins that were last active between 2y and 3y ago. This metric is entity-adjusted and discards transactions between addresses of the same entity ("in-house" transactions).
Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses. For more information see this article.
This is the Point-in-Time (PiT) variant of Entity-Adjusted Spent Volume Lifespan 2y-3y. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted Spent Volume Lifespan 3m-6m
GET https://api.glassnode.com/v1/metrics/indicators/svl_entity_adjusted_3m_6m_pit
The total transfer volume of coins that were last active between 3m and 6m ago. This metric is entity-adjusted and discards transactions between addresses of the same entity ("in-house" transactions).
Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses. For more information see this article.
This is the Point-in-Time (PiT) variant of Entity-Adjusted Spent Volume Lifespan 3m-6m. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted Spent Volume Lifespan 3y-5y
GET https://api.glassnode.com/v1/metrics/indicators/svl_entity_adjusted_3y_5y_pit
The total transfer volume of coins that were last active between 3y and 5y ago. This metric is entity-adjusted and discards transactions between addresses of the same entity ("in-house" transactions).
Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses. For more information see this article.
This is the Point-in-Time (PiT) variant of Entity-Adjusted Spent Volume Lifespan 3y-5y. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted Spent Volume Lifespan 5y-7y
GET https://api.glassnode.com/v1/metrics/indicators/svl_entity_adjusted_5y_7y_pit
The total transfer volume of coins that were last active between 5y and 7y ago. This metric is entity-adjusted and discards transactions between addresses of the same entity ("in-house" transactions).
Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses. For more information see this article.
This is the Point-in-Time (PiT) variant of Entity-Adjusted Spent Volume Lifespan 5y-7y. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted Spent Volume Lifespan 6m-12m
GET https://api.glassnode.com/v1/metrics/indicators/svl_entity_adjusted_6m_12m_pit
The total transfer volume of coins that were last active between 6m and 12m ago. This metric is entity-adjusted and discards transactions between addresses of the same entity ("in-house" transactions).
Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses. For more information see this article.
This is the Point-in-Time (PiT) variant of Entity-Adjusted Spent Volume Lifespan 6m-12m . PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted Spent Volume Lifespan 7y-10y
GET https://api.glassnode.com/v1/metrics/indicators/svl_entity_adjusted_7y_10y_pit
The total transfer volume of coins that were last active between 7y and 10y ago. This metric is entity-adjusted and discards transactions between addresses of the same entity ("in-house" transactions).
Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses. For more information see this article.
This is the Point-in-Time (PiT) variant of Entity-Adjusted Spent Volume Lifespan 7y-10y. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted STH Realized Loss
GET https://api.glassnode.com/v1/metrics/indicators/realized_loss_sth_account_based_pit
Entity-Adjusted variant of Realized Loss for Short-Term Holders, which denotes the total profit (in USD) of all moved coins whose price at their last movement was lower than the price at the current movement. Long- and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days.
Volume transferred between addresses owned by the same entity cluster is excluded. As such, no value is realized during internal or âin-houseâ transfers.
Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses. For more information see this article.
This is the Point-in-Time (PiT) variant of Entity-Adjusted STH Realized Loss. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted STH Realized Loss to Exchanges
GET https://api.glassnode.com/v1/metrics/indicators/realized_loss_sth_to_exchanges_account_based_pit
Entity-Adjusted variant of Realized Loss for coins that are sent from Short-Term Holders to exchanges. Realized loss denotes the total loss (in USD) of all moved coins whose price at their last movement was higher than the price at the current movement. Long- and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days.
Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses. For more information see this article.
Note that exchange metrics are based on our labeled data of exchange addresses that we constantly keep updating, as well as data science techniques and statistical information that changes over time. Therefore these metrics are mutable â the data is stable, but especially most recent data points are subject to slight fluctuations as time progresses.
This is the Point-in-Time (PiT) variant of Entity-Adjusted STH Realized Loss to Exchanges. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted STH Realized Profit
GET https://api.glassnode.com/v1/metrics/indicators/realized_profit_sth_account_based_pit
Entity-Adjusted variant of Realized Profit for Short-Term Holders, which denotes the total profit (in USD) of all moved coins whose price at their last movement was lower than the price at the current movement. Long- and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days.
Volume transferred between addresses owned by the same entity cluster is excluded. As such, no value is realized during internal or âin-houseâ transfers.
Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses. For more information see this article.
This is the Point-in-Time (PiT) variant of Entity-Adjusted STH Realized Profit. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted STH Realized Profit to Exchanges
GET https://api.glassnode.com/v1/metrics/indicators/realized_profit_sth_to_exchanges_account_based_pit
Entity-Adjusted variant of Realized Profit for coins that are sent from Short-Term Holders to exchanges. Realized profit denotes the total profit (in USD) of all moved coins whose price at their last movement was lower than the price at the current movement. Long- and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days.
Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses. For more information see this article.
Note that exchange metrics are based on our labeled data of exchange addresses that we constantly keep updating, as well as data science techniques and statistical information that changes over time. Therefore these metrics are mutable â the data is stable, but especially most recent data points are subject to slight fluctuations as time progresses.
This is the Point-in-Time (PiT) variant of Entity-Adjusted STH Realized Profit to Exchanges. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted STH Transfer Volume
GET https://api.glassnode.com/v1/metrics/transactions/transfers_volume_entity_adjusted_from_sth_sum_pit
The total estimated amount of coins moved by short-term holders. Volume transferred within addresses of the same entity is excluded. Long- and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days.
Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses. For more information see this article.
This is the Point-in-Time (PiT) variant of Entity-Adjusted STH Transfer Volume. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted STH Transfer Volume in Loss
GET https://api.glassnode.com/v1/metrics/transactions/transfers_volume_entity_adjusted_from_sth_loss_sum_pit
The total estimated amount of coins moved by short-term holders in loss. Volume transferred within addresses of the same entity is excluded. Coins are considered to be in loss when the price at the time the coins are spent is lower than the entity's average on-chain acquisition price for its funds. Long- and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days.
Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses. For more information see this article.
This is the Point-in-Time (PiT) variant of Entity-Adjusted STH Transfer Volume in Loss. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted STH Transfer Volume in Profit
GET https://api.glassnode.com/v1/metrics/transactions/transfers_volume_entity_adjusted_from_sth_profit_sum_pit
The total estimated amount of coins moved by short-term holders in profit. Volume transferred within addresses of the same entity is excluded. Coins are considered to be in profit when the price at the time the coins are spent is higher than the entity's average on-chain acquisition price for its funds. Long- and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days.
Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses. For more information see this article.
This is the Point-in-Time (PiT) variant of Entity-Adjusted STH Transfer Volume in Profit. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted STH-NUPL
GET https://api.glassnode.com/v1/metrics/indicators/nupl_less_155_account_based_pit
Entity-adjusted STH-NUPL is an improved variant of Shot-Term Holders Net Unrealized Profit/Loss (STH-NUPL) that discards transactions between addresses of the same entity ("in-house" transactions). Entity-adjusted STH NUPL therefore accounts for real economic activity only, and provides an improved market signal compared to its raw UTXO-based counterpart. For detailed information read this article. An entity is considered as a Short-Term Holder if the time since its averaged purchasing date is less than 155 days.
This is the Point-in-Time (PiT) variant of Entity-Adjusted STH-NUPL. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted Transaction Count
GET https://api.glassnode.com/v1/metrics/transactions/entity_adjusted_count_pit
The estimated entity-adjusted number of transactions is defined as the number of transactions between different entities, i.e. the total number of transactions excluding transactions within addresses of the same entity. Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses.
For more information this article.
This is the Point-in-Time (PiT) variant of Entity-Adjusted Transaction Count. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted Unrealized Loss
GET https://api.glassnode.com/v1/metrics/indicators/unrealized_loss_account_based_pit
Entity-adjusted Relative Unrealized Loss is an improved variant of Unrealized Loss that discards transactions between addresses of the same entity ("in-house" transactions). Entity-adjusted Relative Unrealized Loss therefore accounts for real economic activity only, and provides an improved market signal compared to its raw UTXO-based counterpart. For detailed information read this article.
This is the Point-in-Time (PiT) variant of Entity-Adjusted Unrealized Loss. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted Unrealized Profit
GET https://api.glassnode.com/v1/metrics/indicators/unrealized_profit_account_based_pit
Entity-adjusted Relative Unrealized Profit is an improved variant of Unrealized Profit that discards transactions between addresses of the same entity ("in-house" transactions). Entity-adjusted Relative Unrealized Proft therefore accounts for real economic activity only, and provides an improved market signal compared to its raw UTXO-based counterpart. For detailed information read this article.
This is the Point-in-Time (PiT) variant of Entity-Adjusted Unrealized Profit. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted URPD
GET https://api.glassnode.com/v1/metrics/indicators/urpd_entity_adjusted_pit
UTXO Realized Price Distribution (URPD) shows at which prices the current set of Bitcoin UTXOs were created, i.e. each bar shows the amount of existing bitcoins that last moved within that specified price bucket. This version is entity-adjusted, meaning that for each entity the average purchasing price is used to sort its full balance into a bucket. In this calculation we discard coin movements between addresses controlled by the same entity, as such transfers do not correspond to real purchasing events and would distort the actual mean purchasing price. Further, we exclude all supply that is on exchanges, because a single averaged price for the funds of millions of users would be misleading and give rise to unwanted artifacts in the data.
This is the Point-in-Time (PiT) variant of Entity-Adjusted URPD. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted Volume (Mean)
GET https://api.glassnode.com/v1/metrics/transactions/transfers_volume_entity_adjusted_mean_pit
The mean estimated amount of coins moved between different entities, i.e. excluding the volume transferred within addresses of the same entity. Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses.
For more information this article.
This is the Point-in-Time (PiT) variant of Entity-Adjusted Volume (Mean). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted Volume (Median)
GET https://api.glassnode.com/v1/metrics/transactions/transfers_volume_entity_adjusted_median_pit
The median estimated amount of coins moved between different entities, i.e. excluding volume transferred within addresses of the same entity. Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses.
For more information this article.
This is the Point-in-Time (PiT) variant of Entity-Adjusted Volume (Median). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Entity-Adjusted Volume (Total)
GET https://api.glassnode.com/v1/metrics/transactions/transfers_volume_entity_adjusted_sum_pit
The total estimated amount of coins moved between different entities, i.e. the total volumed transferred excluding the volume transferred within addresses of the same entity. Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses.
For more information this article.
This is the Point-in-Time (PiT) variant of Entity-Adjusted Volume (Total). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
ERC-20 Transactions (Absolute)
GET https://api.glassnode.com/v1/metrics/transactions/transfers_count_erc20_pit
The number of transactions (transaction count) in the Ethereum network by transactions calling ERC20 contracts. Stablecoins contracts are excluded here.
This is the Point-in-Time (PiT) variant of ERC-20 Transactions (Absolute). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
ERC-20 Transactions (Relative)
GET https://api.glassnode.com/v1/metrics/transactions/transfers_count_erc20_relative_pit
The relative amount (share) of transactions in the Ethereum network by transactions calling ERC20 contracts. Stablecoins contracts are excluded here.
This is the Point-in-Time (PiT) variant of ERC-20 Transactions (Relative). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
ETH 2.0 Total Value Staked by Provider
GET https://api.glassnode.com/v1/metrics/eth2/deposited_by_provider_volume_sum_pit
The total amount of ETH transferred to the ETH2 deposit contract via staking providers.
This is the Point-in-Time (PiT) variant of ETH 2.0 Total Value Staked by Provider. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
EU Year-over-Year Supply Change
GET https://api.glassnode.com/v1/metrics/supply/emea_1y_supply_change_pit
This metric aims at giving an estimate for the year-over-year change in the share of the Bitcoin supply to be held/traded in Europe.
Geolocation of Bitcoin supply is performed probabilistically at the entity level. The timestamps of all transactions created by an entity are correlated with the working hours of different geographical regions to determine the probabilities for each entity being located in the US, Europe, or Asia. Working hours are defined as:
US: 8am to 8pm Eastern Time (13:00-01:00 UTC)
EU: 8am to 8pm Central European Time (07:00-19:00 UTC)
Asia: 8am to 8pm China Standard Time (00:00-12:00 UTC)
An entity's balance will only contribute to the supply in the respective region if the location can be determined with a high certainty. Supply held on exchanges wallets are excluded.
This is the Point-in-Time (PiT) variant of EU Year-over-Year Supply Change. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Exchange Balance (Percent)
GET https://api.glassnode.com/v1/metrics/distribution/balance_exchanges_relative_pit
The percent supply held on exchange addresses. Note that exchange metrics are based on our labeled data of exchange addresses that we constantly keep updating, as well as data science techniques and statistical information that changes over time. Therefore these metrics are mutable â the data is stable, but especially most recent data points are subject to slight fluctuations as time progresses.
This is the Point-in-Time (PiT) variant of Exchange Balance (Percent). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Supported asset symbols: BTC, ETH, 1INCH, AAVE, ABT, AMP, ANT, APE, BADGER, BAL, BAND, BAT, BNT, BOBA, BOND, BORG, BUSD, CAKE, CELR, COMP, CREAM, CRO, CRV, CVC, CVP, CVX, CVXCRV, DAI, DENT, DHT, DODO, DPI, DRGN, ELF, ENG, ENJ, ETHDYDX, EURS, FET, FLX, FRAX, FTM, FTT, FUN, FXS, GNO, GUSD, HEGIC, HOT, HT, HUSD, IMX, KCS, LAMB, LBA, LDO, LEO, LINK, LOOM, LRC, MANA, MATIC, MCB, METIS, MIR, MKR, MLN, MTA, MTL, NEXO, NFTX, NMR, Nsure, OCEAN, OKB, OMG, PAY, PERP, PICKLE, PNK, PNT, POLY, POWR, PPT, PYUSD, QASH, QKC, QNT, RAI, RDN, REN, REP, rETH, RLC, ROOK, RSR, SAND, SFRXETH, SHIB, SNT, SNX, SSV, STAKE, stETH, STORJ, sUSD, SUSHI, TEL, TUSD, UBT, UMA, UNI, USDC, USDD, USDK, USDP, USDT, UTK, VERI, WBTC, WETH, wNXM, YAM, YFI, ZRX
Exchange Balance (Total)
GET https://api.glassnode.com/v1/metrics/distribution/balance_exchanges_pit
The total amount of coins held on exchange addresses. Note that exchange metrics are based on our labeled data of exchange addresses that we constantly keep updating, as well as data science techniques and statistical information that changes over time. Therefore these metrics are mutable â the data is stable, but especially most recent data points are subject to slight fluctuations as time progresses.
This is the Point-in-Time (PiT) variant of Exchange Balance (Total). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Supported asset symbols: BTC, ETH, 1INCH, AAVE, ABT, AMP, ANT, APE, BADGER, BAL, BAND, BAT, BNT, BOBA, BOND, BORG, BUSD, CAKE, CELR, COMP, CREAM, CRO, CRV, CVC, CVP, CVX, CVXCRV, DAI, DENT, DHT, DODO, DPI, DRGN, ELF, ENG, ENJ, ETHDYDX, EURS, FET, FLX, FRAX, FTM, FTT, FUN, FXS, GNO, GUSD, HEGIC, HOT, HT, HUSD, IMX, KCS, LAMB, LBA, LDO, LEO, LINK, LOOM, LRC, MANA, MATIC, MCB, METIS, MIR, MKR, MLN, MTA, MTL, NEXO, NFTX, NMR, Nsure, OCEAN, OKB, OMG, PAY, PERP, PICKLE, PNK, PNT, POLY, POWR, PPT, PYUSD, QASH, QKC, QNT, RAI, RDN, REN, REP, rETH, RLC, ROOK, RSR, SAND, SFRXETH, SHIB, SNT, SNX, SSV, STAKE, stETH, STORJ, sUSD, SUSHI, TEL, TUSD, UBT, UMA, UNI, USDC, USDD, USDK, USDP, USDT, UTK, VERI, WBTC, WETH, wNXM, YFI, ZRX
Exchange Deposits
GET https://api.glassnode.com/v1/metrics/transactions/transfers_to_exchanges_count_pit
The total count of transfers to exchange addresses, i.e. the number of on-chain deposits to exchanges. Note that exchange metrics are based on our labeled data of exchange addresses that we constantly keep updating, as well as data science techniques and statistical information that changes over time. Therefore these metrics are mutable â the data is stable, but especially most recent data points are subject to slight fluctuations as time progresses.
This is the Point-in-Time (PiT) variant of Exchange Deposits. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Supported asset symbols: BTC, ETH, 1INCH, AAVE, ABT, AMP, AMPL, ANT, APE, BADGER, BAL, BAND, BAT, BNT, BOBA, BOND, BORG, BUSD, CAKE, CELR, COMP, CREAM, CRO, CRV, CVC, CVP, CVX, CVXCRV, DAI, DENT, DHT, DODO, DPI, DRGN, ELF, ENG, ENJ, ETHDYDX, EURS, FET, FLX, FRAX, FTM, FTT, FUN, FXS, GNO, GUSD, HEGIC, HOT, HT, HUSD, IMX, KCS, LAMB, LBA, LDO, LEO, LINK, LOOM, LRC, MANA, MATIC, MCB, METIS, MIR, MKR, MLN, MTA, MTL, NEXO, NFTX, NMR, Nsure, OCEAN, OKB, OMG, PAY, PERP, PICKLE, PNK, PNT, POLY, POWR, PPT, PYUSD, QASH, QKC, QNT, RAI, RDN, REN, REP, rETH, RLC, ROOK, RSR, SAND, SFRXETH, SHIB, SNT, SNX, SSV, STAKE, stETH, STORJ, sUSD, SUSHI, TEL, TUSD, UBT, UMA, UNI, USDC, USDD, USDK, USDP, USDT, UTK, VERI, WBTC, WETH, wNXM, YAM, YFI, ZRX
Exchange Fee Dominance
GET https://api.glassnode.com/v1/metrics/fees/exchanges_relative_pit
The Exchange Fee Dominance metric is defined as the percent amount of total fees paid in transactions related to on-chain exchange activity.
Deposits: Transactions that include an exchange address as the receiver of funds.
Withdrawals: Transactions that include an exchange address as the sender of funds.
In-House: Transactions that include addresses of a single exchange as both the sender and receiver of funds.
Inter-Exchange: Transactions that include addresses of (distinct) exchanges as both the sender and receiver of funds.
If a transaction can be categorized into multiple of these categories (e.g. a transaction that sends funds externally as well as in-house), the fees are split into percentages according to the volume transferred.
This is the Point-in-Time (PiT) variant of Exchange Fee Dominance. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Exchange Fees (Mean)
GET https://api.glassnode.com/v1/metrics/fees/exchanges_mean_pit
The mean amount of fees paid in transactions related to on-chain exchange activity. Note that the mean is computed over transfers, not transactions.
Deposits: Transactions that include an exchange address as the receiver of funds.
Withdrawals: Transactions that include an exchange address as the sender of funds.
In-House: Transactions that include addresses of a single exchange as both the sender and receiver of funds.
Inter-Exchange: Transactions that include addresses of (distinct) exchanges as both the sender and receiver of funds.
If a transaction can be categorized into multiple of these categories (e.g. a transaction that sends funds externally as well as in-house), the fees are split into percentages according to the volume transferred.
This is the Point-in-Time (PiT) variant of Exchange Fees (Mean). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Exchange Fees (Total)
GET https://api.glassnode.com/v1/metrics/fees/exchanges_sum_pit
The total amount of fees paid in transactions related to on-chain exchange activity.
Deposits: Transactions that include an exchange address as the receiver of funds.
Withdrawals: Transactions that include an exchange address as the sender of funds.
In-House: Transactions that include addresses of a single exchange as both the sender and receiver of funds.
Inter-Exchange: Transactions that include addresses of (distinct) exchanges as both the sender and receiver of funds.
If a transaction can be categorized into multiple of these categories (e.g. a transaction that sends funds externally as well as in-house), the fees are split into percentages according to the volume transferred.
This is the Point-in-Time (PiT) variant of Exchange Fees (Total). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Exchange Inflow Volume (Mean)
GET https://api.glassnode.com/v1/metrics/transactions/transfers_volume_to_exchanges_mean_pit
The mean value of a transfer to exchanges addresses. Only successful transfers are counted. Note that exchange metrics are based on our labeled data of exchange addresses that we constantly keep updating, as well as data science techniques and statistical information that changes over time. Therefore these metrics are mutable â the data is stable, but especially most recent data points are subject to slight fluctuations as time progresses.
This is the Point-in-Time (PiT) variant of Exchange Inflow Volume (Mean). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Supported asset symbols: BTC, ETH, 1INCH, AAVE, ABT, AMP, ANT, APE, BADGER, BAL, BAND, BAT, BNT, BOBA, BOND, BORG, BUSD, CAKE, CELR, COMP, CREAM, CRO, CRV, CVC, CVP, CVX, CVXCRV, DAI, DENT, DHT, DODO, DPI, DRGN, ELF, ENG, ENJ, ETHDYDX, EURS, FET, FLX, FRAX, FTM, FTT, FUN, FXS, GNO, GUSD, HEGIC, HOT, HT, HUSD, IMX, KCS, LAMB, LBA, LDO, LEO, LINK, LOOM, LRC, MANA, MATIC, MCB, METIS, MIR, MKR, MLN, MTA, MTL, NEXO, NFTX, NMR, Nsure, OCEAN, OKB, OMG, PAY, PERP, PICKLE, PNK, PNT, POLY, POWR, PPT, PYUSD, QASH, QKC, QNT, RAI, RDN, REN, REP, rETH, RLC, ROOK, RSR, SAND, SFRXETH, SHIB, SNT, SNX, SSV, STAKE, stETH, STORJ, sUSD, SUSHI, TEL, TUSD, UBT, UMA, UNI, USDC, USDD, USDK, USDP, USDT, UTK, VERI, WBTC, WETH, wNXM, YAM, YFI, ZRX
Exchange Inflow Volume (Total)
GET https://api.glassnode.com/v1/metrics/transactions/transfers_volume_to_exchanges_sum_pit
The total amount of coins transferred to exchange addresses. Note that exchange metrics are based on our labeled data of exchange addresses that we constantly keep updating, as well as data science techniques and statistical information that changes over time. Therefore these metrics are mutable â the data is stable, but especially most recent data points are subject to slight fluctuations as time progresses.
This is the Point-in-Time (PiT) variant of Exchange Inflow Volume (Total). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Supported asset symbols: BTC, ETH, 1INCH, AAVE, ABT, AMP, ANT, APE, BADGER, BAL, BAND, BAT, BNT, BOBA, BOND, BORG, BUSD, CAKE, CELR, COMP, CREAM, CRO, CRV, CVC, CVP, CVX, CVXCRV, DAI, DENT, DHT, DODO, DPI, DRGN, ELF, ENG, ENJ, ETHDYDX, EURS, FET, FLX, FRAX, FTM, FTT, FUN, FXS, GNO, GUSD, HEGIC, HOT, HT, HUSD, IMX, KCS, LAMB, LBA, LDO, LEO, LINK, LOOM, LRC, MANA, MATIC, MCB, METIS, MIR, MKR, MLN, MTA, MTL, NEXO, NFTX, NMR, Nsure, OCEAN, OKB, OMG, PAY, PERP, PICKLE, PNK, PNT, POLY, POWR, PPT, PYUSD, QASH, QKC, QNT, RAI, RDN, REN, REP, rETH, RLC, ROOK, RSR, SAND, SFRXETH, SHIB, SNT, SNX, SSV, STAKE, stETH, STORJ, sUSD, SUSHI, TEL, TUSD, UBT, UMA, UNI, USDC, USDD, USDK, USDP, USDT, UTK, VERI, WBTC, WETH, wNXM, YAM, YFI, ZRX
Exchange Net Position Change
GET https://api.glassnode.com/v1/metrics/distribution/exchange_net_position_change_pit
The 30d change of the supply held in exchange wallets.
This is the Point-in-Time (PiT) variant of Exchange Net Position Change. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Supported asset symbols: BTC, ETH, 1INCH, AAVE, ABT, AMP, ANT, APE, BADGER, BAL, BAND, BAT, BNT, BOBA, BOND, BORG, BUSD, CAKE, CELR, COMP, CREAM, CRO, CRV, CVC, CVP, CVX, CVXCRV, DAI, DENT, DHT, DODO, DPI, DRGN, ELF, ENG, ENJ, ETHDYDX, EURS, FET, FLX, FRAX, FTM, FTT, FUN, FXS, GNO, GUSD, HEGIC, HOT, HT, HUSD, IMX, KCS, LAMB, LBA, LDO, LEO, LINK, LOOM, LRC, MANA, MATIC, MCB, METIS, MIR, MKR, MLN, MTA, MTL, NEXO, NFTX, NMR, Nsure, OCEAN, OKB, OMG, PAY, PERP, PICKLE, PNK, PNT, POLY, POWR, PPT, PYUSD, QASH, QKC, QNT, RAI, RDN, REN, REP, rETH, RLC, ROOK, RSR, SAND, SFRXETH, SHIB, SNT, SNX, SSV, STAKE, stETH, STORJ, sUSD, SUSHI, TEL, TUSD, UBT, UMA, UNI, USDC, USDD, USDK, USDP, USDT, UTK, VERI, WBTC, WETH, wNXM, YAM, YFI, ZRX
Exchange Netflow Volume
GET https://api.glassnode.com/v1/metrics/transactions/transfers_volume_exchanges_net_pit
The difference in volume flowing into exchanges and out of exchanges, i.e. the net flow of coins into/out of exchanges. Note that exchange metrics are based on our labeled data of exchange addresses that we constantly keep updating, as well as data science techniques and statistical information that changes over time. Therefore these metrics are mutable â the data is stable, but especially most recent data points are subject to slight fluctuations as time progresses.
This is the Point-in-Time (PiT) variant of Exchange Netflow Volume. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Supported asset symbols: BTC, ETH, 1INCH, AAVE, ABT, AMP, ANT, APE, BADGER, BAL, BAND, BAT, BNT, BOBA, BOND, BORG, BUSD, CAKE, CELR, COMP, CREAM, CRO, CRV, CVC, CVP, CVX, CVXCRV, DAI, DENT, DHT, DODO, DPI, DRGN, ELF, ENG, ENJ, ETHDYDX, EURS, FET, FLX, FRAX, FTM, FTT, FUN, FXS, GNO, GUSD, HEGIC, HOT, HT, HUSD, IMX, KCS, LAMB, LBA, LDO, LEO, LINK, LOOM, LRC, MANA, MATIC, MCB, METIS, MIR, MKR, MLN, MTA, MTL, NEXO, NFTX, NMR, Nsure, OCEAN, OKB, OMG, PAY, PERP, PICKLE, PNK, PNT, POLY, POWR, PPT, PYUSD, QASH, QKC, QNT, RAI, RDN, REN, REP, rETH, RLC, ROOK, RSR, SAND, SFRXETH, SHIB, SNT, SNX, SSV, STAKE, stETH, STORJ, sUSD, SUSHI, TEL, TUSD, UBT, UMA, UNI, USDC, USDD, USDK, USDP, USDT, UTK, VERI, WBTC, WETH, wNXM, YAM, YFI, ZRX
Exchange Netflow Volume by Size
GET https://api.glassnode.com/v1/metrics/transactions/transfers_volume_exchanges_net_by_size_pit
Breakdown of the net flow of coins into/out of exchanges by the USD value of the transactions. Note that exchange metrics are based on our labeled data of exchange addresses that we constantly keep updating, as well as data science techniques and statistical information that changes over time. Therefore these metrics are mutable - the data is stable, but especially most recent data points are subject to slight fluctuations as time progresses.
This is the Point-in-Time (PiT) variant of Exchange Netflow Volume by Size. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Exchange Outflow Volume (Mean)
GET https://api.glassnode.com/v1/metrics/transactions/transfers_volume_from_exchanges_mean_pit
The mean value of a transfer from exchanges addresses. Only successful transfers are counted. Note that exchange metrics are based on our labeled data of exchange addresses that we constantly keep updating, as well as data science techniques and statistical information that changes over time. Therefore these metrics are mutable â the data is stable, but especially most recent data points are subject to slight fluctuations as time progresses.
This is the Point-in-Time (PiT) variant of Exchange Outflow Volume (Mean). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Supported asset symbols: BTC, ETH, 1INCH, AAVE, ABT, AMP, ANT, APE, BADGER, BAL, BAND, BAT, BNT, BOBA, BOND, BORG, BUSD, CAKE, CELR, COMP, CREAM, CRO, CRV, CVC, CVP, CVX, CVXCRV, DAI, DENT, DHT, DODO, DPI, DRGN, ELF, ENG, ENJ, ETHDYDX, EURS, FET, FLX, FRAX, FTM, FTT, FUN, FXS, GNO, GUSD, HEGIC, HOT, HT, HUSD, IMX, KCS, LAMB, LBA, LDO, LEO, LINK, LOOM, LRC, MANA, MATIC, MCB, METIS, MIR, MKR, MLN, MTA, MTL, NEXO, NFTX, NMR, Nsure, OCEAN, OKB, OMG, PAY, PERP, PICKLE, PNK, PNT, POLY, POWR, PPT, PYUSD, QASH, QKC, QNT, RAI, RDN, REN, REP, rETH, RLC, ROOK, RSR, SAND, SFRXETH, SHIB, SNT, SNX, SSV, STAKE, stETH, STORJ, sUSD, SUSHI, TEL, TUSD, UBT, UMA, UNI, USDC, USDD, USDK, USDP, USDT, UTK, VERI, WBTC, WETH, wNXM, YAM, YFI, ZRX
Exchange Outflow Volume (Total)
GET https://api.glassnode.com/v1/metrics/transactions/transfers_volume_from_exchanges_sum_pit
The total amount of coins transferred from exchange addresses. Note that exchange metrics are based on our labeled data of exchange addresses that we constantly keep updating, as well as data science techniques and statistical information that changes over time. Therefore these metrics are mutable â the data is stable, but especially most recent data points are subject to slight fluctuations as time progresses.
This is the Point-in-Time (PiT) variant of Exchange Outflow Volume (Total). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Supported asset symbols: BTC, ETH, 1INCH, AAVE, ABT, AMP, ANT, APE, BADGER, BAL, BAND, BAT, BNT, BOBA, BOND, BORG, BUSD, CAKE, CELR, COMP, CREAM, CRO, CRV, CVC, CVP, CVX, CVXCRV, DAI, DENT, DHT, DODO, DPI, DRGN, ELF, ENG, ENJ, ETHDYDX, EURS, FET, FLX, FRAX, FTM, FTT, FUN, FXS, GNO, GUSD, HEGIC, HOT, HT, HUSD, IMX, KCS, LAMB, LBA, LDO, LEO, LINK, LOOM, LRC, MANA, MATIC, MCB, METIS, MIR, MKR, MLN, MTA, MTL, NEXO, NFTX, NMR, Nsure, OCEAN, OKB, OMG, PAY, PERP, PICKLE, PNK, PNT, POLY, POWR, PPT, PYUSD, QASH, QKC, QNT, RAI, RDN, REN, REP, rETH, RLC, ROOK, RSR, SAND, SFRXETH, SHIB, SNT, SNX, SSV, STAKE, stETH, STORJ, sUSD, SUSHI, TEL, TUSD, UBT, UMA, UNI, USDC, USDD, USDK, USDP, USDT, UTK, VERI, WBTC, WETH, wNXM, YAM, YFI, ZRX
Exchange Withdrawals
GET https://api.glassnode.com/v1/metrics/transactions/transfers_from_exchanges_count_pit
The total count of transfers from exchange addresses, i.e. the number of on-chain withdrawals from exchanges. Note that exchange metrics are based on our labeled data of exchange addresses that we constantly keep updating, as well as data science techniques and statistical information that changes over time. Therefore these metrics are mutable â the data is stable, but especially most recent data points are subject to slight fluctuations as time progresses.
This is the Point-in-Time (PiT) variant of Exchange Withdrawals. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Supported asset symbols: BTC, ETH, 1INCH, AAVE, ABT, AMP, AMPL, ANT, APE, BADGER, BAL, BAND, BAT, BNT, BOBA, BOND, BORG, BUSD, CAKE, CELR, COMP, CREAM, CRO, CRV, CVC, CVP, CVX, CVXCRV, DAI, DENT, DHT, DODO, DPI, DRGN, ELF, ENG, ENJ, ETHDYDX, EURS, FET, FLX, FRAX, FTM, FTT, FUN, FXS, GNO, GUSD, HEGIC, HOT, HT, HUSD, IMX, KCS, LAMB, LBA, LDO, LEO, LINK, LOOM, LRC, MANA, MATIC, MCB, METIS, MIR, MKR, MLN, MTA, MTL, NEXO, NFTX, NMR, Nsure, OCEAN, OKB, OMG, PAY, PERP, PICKLE, PNK, PNT, POLY, POWR, PPT, PYUSD, QASH, QKC, QNT, RAI, RDN, REN, REP, rETH, RLC, ROOK, RSR, SAND, SFRXETH, SHIB, SNT, SNX, SSV, STAKE, stETH, STORJ, sUSD, SUSHI, TEL, TUSD, UBT, UMA, UNI, USDC, USDD, USDK, USDP, USDT, UTK, VERI, WBTC, WETH, wNXM, YAM, YFI, ZRX
Gas Usage by Bridges (Absolute)
GET https://api.glassnode.com/v1/metrics/fees/gas_used_sum_bridges_pit
The amount of gas consumed by the Ethereum network by contracts that allow transfer of tokens between different blockchains.
This is the Point-in-Time (PiT) variant of Gas Usage by Bridges (Absolute). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Gas Usage by Bridges (Relative)
GET https://api.glassnode.com/v1/metrics/fees/gas_used_sum_bridges_relative_pit
The relative amount (share) of gas consumed by the Ethereum network by contracts that allow transfer of tokens between different blockchains.
This is the Point-in-Time (PiT) variant of Gas Usage by Bridges (Relative). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Gas Usage by DeFi (Absolute)
GET https://api.glassnode.com/v1/metrics/fees/gas_used_sum_defi_pit
The amount of gas consumed by the Ethereum network by on-chain financial instruments and protocols implemented as smart contracts, including decentralized exchanges (DEXs).
This is the Point-in-Time (PiT) variant of Gas Usage by DeFi (Absolute). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Gas Usage by DeFi (Relative)
GET https://api.glassnode.com/v1/metrics/fees/gas_used_sum_defi_relative_pit
The relative amount (share) of gas consumed by the Ethereum network by on-chain financial instruments and protocols implemented as smart contracts, including decentralized exchanges (DEXs).
This is the Point-in-Time (PiT) variant of Gas Usage by DeFi (Relative). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Gas Usage by ERC-20 Tokens (Absolute)
GET https://api.glassnode.com/v1/metrics/fees/gas_used_sum_erc20_pit
The amount of gas consumed by the Ethereum network by transactions calling ERC20 contracts. Stablecoins contracts are excluded here.
This is the Point-in-Time (PiT) variant of Gas Usage by ERC-20 Tokens (Absolute). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Gas Usage by ERC-20 Tokens (Relative)
GET https://api.glassnode.com/v1/metrics/fees/gas_used_sum_erc20_relative_pit
The relative amount (share) of gas consumed by the Ethereum network by transactions calling ERC20 contracts. Stablecoins contracts are excluded here.
This is the Point-in-Time (PiT) variant of Gas Usage by ERC-20 Tokens (Relative). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Gas Usage by NFTs (Absolute)
GET https://api.glassnode.com/v1/metrics/fees/gas_used_sum_nfts_pit
The amount of gas consumed by the Ethereum network by transactions interacting with non-fungible tokens. This category includes of both token contract standards (ERC721, ERC1155), as well as NFT marketplaces (OpenSea, Blur, LooksRare, Rarible, SuperRare) for trading those.
This is the Point-in-Time (PiT) variant of Gas Usage by NFTs (Absolute). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Gas Usage by NFTs (Relative)
GET https://api.glassnode.com/v1/metrics/fees/gas_used_sum_nfts_relative_pit
The relative amount (share) of gas consumed by the Ethereum network by transactions interacting with non-fungible tokens. This category includes of both token contract standards (ERC721, ERC1155), as well as NFT marketplaces (OpenSea, Blur, LooksRare, Rarible, SuperRare) for trading those.
This is the Point-in-Time (PiT) variant of Gas Usage by NFTs (Relative). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Gas Usage by Stablecoins (Absolute)
GET https://api.glassnode.com/v1/metrics/fees/gas_used_sum_stablecoins_pit
The amount of gas consumed by the Ethereum network by stablecoin transactions. Stablecoin are fungible tokens that have their value pegged to an off-chain asset, either by the issuer or by an algorithm. We include 150+ stablecoins in this category, with USDT, USDC, UST, BUSD, and DAI being the most prominent ones.
This is the Point-in-Time (PiT) variant of Gas Usage by Stablecoins (Absolute). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Gas Usage by Stablecoins (Relative)
GET https://api.glassnode.com/v1/metrics/fees/gas_used_sum_stablecoins_relative_pit
The relative amount (share) of gas consumed by the Ethereum network by stablecoin transactions. Stablecoin are fungible tokens that have their value pegged to an off-chain asset, either by the issuer or by an algorithm. We include 150+ stablecoins in this category, with USDT, USDC, UST, BUSD, and DAI being the most prominent ones.
This is the Point-in-Time (PiT) variant of Gas Usage by Stablecoins (Relative). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Gas Usage by Transaction Type (Absolute)
GET https://api.glassnode.com/v1/metrics/fees/tx_types_breakdown_sum_pit
The amount of gas consumed by the Ethereum network by category. Transactions are classified into the following categories:
Vanilla: Pure ETH transfers between Externally Owned Accounts (EOAs), with no contracts being called.
ERC20: All transactions calling ERC20 contracts. Contracts in the Stablecoins category are excluded here.
Stablecoins: Fungible tokens that have their value pegged to an off-chain asset, either by the issuer or by an algorithm. We include 150+ stablecoins in this category, with USDT, USDC, UST, BUSD, and DAI being the most prominent ones.
DeFi: On-chain financial instruments and protocols implemented as smart contracts, including decentralized exchanges (DEXs). We include over 90+ DeFi protocols in this category, such as Uniswap, Etherdelta, 1inch, Sushiswap, Aave, and 0x.
Bridges: Contracts allowing transfer of tokens between different blockchains. We include 50+ bridges in this category, such as Ronin, Polygon, Optimism, and Arbitrum.
NFTs: Transactions interacting with non-fungible tokens. This category includes of both token contract standards (ERC721, ERC1155), as well as NFT marketplaces (OpenSea, Blur, LooksRare, Rarible, SuperRare) for trading those.
MEV Bots: Miner Extractable Value (MEV) bots execute transactions for profit by reordering, inserting, and censoring transactions within blocks.
Other: This category includes all other transactions in the Ethereum network that are not included in categories listed above.
This is the Point-in-Time (PiT) variant of Gas Usage by Transaction Type (Absolute). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Gas Usage by Transaction Type (Relative)
GET https://api.glassnode.com/v1/metrics/fees/tx_types_breakdown_relative_pit
The relative amount (share) of gas consumed by the Ethereum network by category. Transactions are classified into one of the following categories:
Vanilla: Pure ETH transfers between Externally Owned Accounts (EOAs), with no contracts being called.
ERC20: All transactions calling ERC20 contracts. Contracts in the Stablecoins category are excluded here.
Stablecoins: Fungible tokens that have their value pegged to an off-chain asset, either by the issuer or by an algorithm. We include 150+ stablecoins in this category, with USDT, USDC, UST, BUSD, and DAI being the most prominent ones.
DeFi: On-chain financial instruments and protocols implemented as smart contracts, including decentralized exchanges (DEXs). We include over 90+ DeFi protocols in this category, such as Uniswap, Etherdelta, 1inch, Sushiswap, Aave, and 0x.
Bridges: Contracts allowing transfer of tokens between different blockchains. We include 50+ bridges in this category, such as Ronin, Polygon, Optimism, and Arbitrum.
NFTs: Transactions interacting with non-fungible tokens. This category includes of both token contract standards (ERC721, ERC1155), as well as NFT marketplaces (OpenSea, Blur, LooksRare, Rarible, SuperRare) for trading those.
MEV Bots: Miner Extractable Value (MEV) bots execute transactions for profit by reordering, inserting, and censoring transactions within blocks.
Other: This category includes all other transactions in the Ethereum network that are not included in categories listed above.
This is the Point-in-Time (PiT) variant of Gas Usage by Transaction Type (Relative). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Gas Usage by Vanilla Transactions (Absolute)
GET https://api.glassnode.com/v1/metrics/fees/gas_used_sum_vanilla_pit
The amount of gas consumed by the Ethereum network by vanilla transactions. Vanilla transactions are pure ETH transfers between Externally Owned Accounts (EOAs), with no contracts being called. Note that occasionally the value of the last datapoint can slightly change as some addresses initially transact as "vanilla" before their associated smart contract deployment is observed.
This is the Point-in-Time (PiT) variant of Gas Usage by Vanilla Transactions (Absolute). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Gas Usage by Vanilla Transactions (Relative)
GET https://api.glassnode.com/v1/metrics/fees/gas_used_sum_vanilla_relative_pit
The relative amount (share) of gas consumed by the Ethereum network by vanilla transactions. Vanilla transactions are pure ETH transfers between Externally Owned Accounts (EOAs), with no contracts being called. Note that occasionally the value of the last datapoint can slightly change as some addresses initially transact as "vanilla" before their associated smart contract deployment is observed.
This is the Point-in-Time (PiT) variant of Gas Usage by Vanilla Transactions (Relative). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Gini Coefficient
GET https://api.glassnode.com/v1/metrics/distribution/gini_pit
The gini coefficient for the distribution of coins over addresses. Exchange addresses, smart contract addresses, and other special asset-specific addresses (e.g. team fund addresses) are excluded for the computation of the gini.
This is the Point-in-Time (PiT) variant of Gini Coefficient. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Herfindahl Index
GET https://api.glassnode.com/v1/metrics/distribution/herfindahl_pit
Originally used as a measure of competition, we adapt the Herfindahl Index as a metric for decentralization. It measures the addresses' shares of the current supply, and is defined as the sum of weighted address balances in the network. A large score indicates high concentration of supply, whereas a small score is an indicator for more evenly distributed funds across addresses. Exchange addresses, smart contract addresses and other special asset-specific addresses (e.g. team fund addresses) are excluded.
This is the Point-in-Time (PiT) variant of Herfindahl Index. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Highly Liquid Supply
GET https://api.glassnode.com/v1/metrics/supply/highly_liquid_sum_pit
The total supply held by "highly liquid" entities. The liquidity of an entity is defined as the ratio of cumulative outflows and cumulative inflows over the entity's lifespan. An entity is considered to be illiquid / liquid / highly liquid if its liquidity L is Ⲡ0.25 / 0.25 ⲠL Ⲡ0.75 / 0.75 ⲠL, respectively. For more information see our introductory article on Bitcoin liquidity.
This is the Point-in-Time (PiT) variant of Highly Liquid Supply. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Illiquid Supply
GET https://api.glassnode.com/v1/metrics/supply/illiquid_sum_pit
The total supply held by illiquid entities. The liquidity of an entity is defined as the ratio of cumulative outflows and cumulative inflows over the entity's lifespan. An entity is considered to be illiquid / liquid / highly liquid if its liquidity L is Ⲡ0.25 / 0.25 ⲠL Ⲡ0.75 / 0.75 ⲠL, respectively. For more information see our introductory article on Bitcoin liquidity.
This is the Point-in-Time (PiT) variant of Illiquid Supply. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Illiquid Supply Change
GET https://api.glassnode.com/v1/metrics/supply/illiquid_change_pit
The monthly (30d) net change of supply held by illiquid entities. For more information see our introductory article on Bitcoin liquidity.
This is the Point-in-Time (PiT) variant of Illiquid Supply Change. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
In-House Exchange Volume
GET https://api.glassnode.com/v1/metrics/transactions/transfers_volume_within_exchanges_sum_pit
The total amount of coins transferred within wallets of the same exchange. Note that exchange metrics are based on our labeled data of exchange addresses that we constantly keep updating, as well as data science techniques and statistical information that changes over time. Therefore these metrics are mutable â the data is stable, but especially most recent data points are subject to slight fluctuations as time progresses.
This is the Point-in-Time (PiT) variant of In-House Exchange Volume. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Supported asset symbols: BTC, ETH, 1INCH, AAVE, ABT, AMP, ANT, APE, BADGER, BAL, BAND, BAT, BNT, BOBA, BOND, BORG, BUSD, CAKE, CELR, COMP, CREAM, CRO, CRV, CVC, CVP, CVX, CVXCRV, DAI, DENT, DHT, DODO, DPI, DRGN, ELF, ENG, ENJ, ETHDYDX, EURS, FET, FLX, FRAX, FTM, FTT, FUN, FXS, GNO, GUSD, HEGIC, HOT, HT, HUSD, IMX, KCS, LAMB, LBA, LDO, LEO, LINK, LOOM, LRC, MANA, MATIC, MCB, METIS, MIR, MKR, MLN, MTA, MTL, NEXO, NFTX, NMR, Nsure, OCEAN, OKB, OMG, PAY, PERP, PICKLE, PNK, PNT, POLY, POWR, PPT, PYUSD, QASH, QKC, QNT, RAI, RDN, REN, REP, rETH, RLC, ROOK, RSR, SAND, SFRXETH, SHIB, SNT, SNX, SSV, STAKE, stETH, STORJ, sUSD, SUSHI, TEL, TUSD, UBT, UMA, UNI, USDC, USDD, USDK, USDP, USDT, UTK, VERI, WBTC, WETH, wNXM, YAM, YFI, ZRX
Inter-Exchange Transfers
GET https://api.glassnode.com/v1/metrics/transactions/transfers_between_exchanges_count_pit
The total count of transfers between exchanges. Note that exchange metrics are based on our labeled data of exchange addresses that we constantly keep updating, as well as data science techniques and statistical information that changes over time. Therefore these metrics are mutable â the data is stable, but especially most recent data points are subject to slight fluctuations as time progresses.
This is the Point-in-Time (PiT) variant of Inter-Exchange Transfers. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Supported asset symbols: BTC, ETH, 1INCH, AAVE, ABT, AMP, AMPL, ANT, APE, BADGER, BAL, BAND, BAT, BNT, BOBA, BOND, BORG, BUSD, CAKE, CELR, COMP, CREAM, CRO, CRV, CVC, CVP, CVX, CVXCRV, DAI, DENT, DHT, DODO, DPI, DRGN, ELF, ENG, ENJ, ETHDYDX, EURS, FET, FLX, FRAX, FTM, FTT, FUN, FXS, GNO, GUSD, HEGIC, HOT, HT, HUSD, IMX, KCS, LAMB, LBA, LDO, LEO, LINK, LOOM, LRC, MANA, MATIC, MCB, METIS, MIR, MKR, MLN, MTA, MTL, NEXO, NFTX, NMR, Nsure, OCEAN, OKB, OMG, PAY, PERP, PICKLE, PNK, PNT, POLY, POWR, PPT, PYUSD, QASH, QKC, QNT, RAI, RDN, REN, REP, rETH, RLC, ROOK, RSR, SAND, SFRXETH, SHIB, SNT, SNX, SSV, STAKE, stETH, STORJ, sUSD, SUSHI, TEL, TUSD, UBT, UMA, UNI, USDC, USDD, USDK, USDP, USDT, UTK, VERI, WBTC, WETH, wNXM, YAM, YFI, ZRX
Inter-Exchange Volume
GET https://api.glassnode.com/v1/metrics/transactions/transfers_volume_between_exchanges_sum_pit
The total amount of coins transferred between exchanges. Note that exchange metrics are based on our labeled data of exchange addresses that we constantly keep updating, as well as data science techniques and statistical information that changes over time. Therefore these metrics are mutable â the data is stable, but especially most recent data points are subject to slight fluctuations as time progresses.
This is the Point-in-Time (PiT) variant of Inter-Exchange Volume. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Supported asset symbols: BTC, ETH, 1INCH, AAVE, ABT, AMP, AMPL, ANT, APE, BADGER, BAL, BAND, BAT, BNT, BOBA, BOND, BORG, BUSD, CAKE, CELR, COMP, CREAM, CRO, CRV, CVC, CVP, CVX, CVXCRV, DAI, DENT, DHT, DODO, DPI, DRGN, ELF, ENG, ENJ, ETHDYDX, EURS, FET, FLX, FRAX, FTM, FTT, FUN, FXS, GNO, GUSD, HEGIC, HOT, HT, HUSD, IMX, KCS, LAMB, LBA, LDO, LEO, LINK, LOOM, LRC, MANA, MATIC, MCB, METIS, MIR, MKR, MLN, MTA, MTL, NEXO, NFTX, NMR, Nsure, OCEAN, OKB, OMG, PAY, PERP, PICKLE, PNK, PNT, POLY, POWR, PPT, PYUSD, QASH, QKC, QNT, RAI, RDN, REN, REP, rETH, RLC, ROOK, RSR, SAND, SFRXETH, SHIB, SNT, SNX, SSV, STAKE, stETH, STORJ, sUSD, SUSHI, TEL, TUSD, UBT, UMA, UNI, USDC, USDD, USDK, USDP, USDT, UTK, VERI, WBTC, WETH, wNXM, YAM, YFI, ZRX
Liquid and Illiquid Supply
GET https://api.glassnode.com/v1/metrics/supply/liquid_illiquid_sum_pit
The total supply held by illiquid, liquid, and highly liquid entities. The liquidity of an entity is defined as the ratio of cumulative outflows and cumulative inflows over the entity's lifespan. An entity is considered to be illiquid / liquid / highly liquid if its liquidity L is Ⲡ0.25 / 0.25 ⲠL Ⲡ0.75 / 0.75 ⲠL, respectively. For more information see our introductory article on Bitcoin liquidity.
This is the Point-in-Time (PiT) variant of Liquid and Illiquid Supply. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Liquid Supply
GET https://api.glassnode.com/v1/metrics/supply/liquid_sum_pit
The total supply held by "liquid" entities. The liquidity of an entity is defined as the ratio of cumulative outflows and cumulative inflows over the entity's lifespan. An entity is considered to be illiquid / liquid / highly liquid if its liquidity L is Ⲡ0.25 / 0.25 ⲠL Ⲡ0.75 / 0.75 ⲠL, respectively. For more information see our introductory article on Bitcoin liquidity.
This is the Point-in-Time (PiT) variant of Liquid Supply. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Liquid Supply Change
GET https://api.glassnode.com/v1/metrics/supply/liquid_change_pit
The monthly (30d) net change of supply held by liquid and highly liquid entities. For more information see our introductory article on Bitcoin liquidity.
This is the Point-in-Time (PiT) variant of Liquid Supply Change. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Long-Term Holder in Loss to Exchanges (Volume)
GET https://api.glassnode.com/v1/metrics/transactions/transfers_volume_lth_to_exchanges_loss_sum_pit
The total amount of coins transferred from long-term holders in loss to exchange wallets. Only direct transfers are counted. Coins are considered to be in loss when the price at the time the coins are spent is lower than the entity's average on-chain acquisition price for its funds. Long- and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days.
Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses. For more information see this article.
Note that exchange metrics are based on our labeled data of exchange addresses that we constantly keep updating, as well as data science techniques and statistical information that changes over time. Therefore these metrics are mutable â the data is stable, but especially most recent data points are subject to slight fluctuations as time progresses.
This is the Point-in-Time (PiT) variant of Long-Term Holder in Loss to Exchanges (Volume). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Long-Term Holder in Profit to Exchanges (Volume)
GET https://api.glassnode.com/v1/metrics/transactions/transfers_volume_lth_to_exchanges_profit_sum_pit
The total amount of coins transferred from long-term holders in profit to exchange wallets. Only direct transfers are counted. Coins are considered to be in profit when the price at the time the coins are spent is higher than the entity's average on-chain acquisition price for its funds. Long- and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days.
Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses. For more information see this article.
Note that exchange metrics are based on our labeled data of exchange addresses that we constantly keep updating, as well as data science techniques and statistical information that changes over time. Therefore these metrics are mutable â the data is stable, but especially most recent data points are subject to slight fluctuations as time progresses.
This is the Point-in-Time (PiT) variant of Long-Term Holder in Profit to Exchanges (Volume). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Long-Term Holder Position Change
GET https://api.glassnode.com/v1/metrics/supply/lth_net_change_pit
The monthly net position change of long term holders, i.e. the 30 day change in supply held by long term holders. Long- and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days.
This is the Point-in-Time (PiT) variant of Long-Term Holder Position Change. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Long-Term Holder Supply
GET https://api.glassnode.com/v1/metrics/supply/lth_sum_pit
The total amount of circulating supply held by long term holders. Long- and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days.
This is the Point-in-Time (PiT) variant of Long-Term Holder Supply. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Long-Term Holder Supply in Loss
GET https://api.glassnode.com/v1/metrics/supply/lth_loss_sum_pit
The total amount of circulating supply that is currently at loss and held by long term holders. Long- and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days.
This is the Point-in-Time (PiT) variant of Long-Term Holder Supply in Loss. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Long-Term Holder Supply in Profit
GET https://api.glassnode.com/v1/metrics/supply/lth_profit_sum_pit
The total amount of circulating supply that is currently in profit and held by long term holders. Long- and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days.
This is the Point-in-Time (PiT) variant of Long-Term Holder Supply in Profit. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Long-Term Holder to Exchanges (Volume)
GET https://api.glassnode.com/v1/metrics/transactions/transfers_volume_lth_to_exchanges_sum_pit
The total amount of coins transferred from long-term holders to exchange wallets. Only direct transfers are counted. Long- and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days.
Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses. For more information see this article.
Note that exchange metrics are based on our labeled data of exchange addresses that we constantly keep updating, as well as data science techniques and statistical information that changes over time. Therefore these metrics are mutable â the data is stable, but especially most recent data points are subject to slight fluctuations as time progresses.
This is the Point-in-Time (PiT) variant of Long-Term Holder to Exchanges (Volume). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
LTH/STH Transfer Volume in Profit/Loss to Exchanges
GET https://api.glassnode.com/v1/metrics/transactions/transfers_volume_lth_sth_to_exchanges_profit_loss_relative_pit
The relative amount of coins moved by by long- and short-term holders in profit/loss to exchanges. Only direct transfers are counted. Coins are considered to be in profit/loss when the price at the time the coins are spent is higher/lower than the entity's average on-chain acquisition price for its funds. Long- and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days.
Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses. For more information see this article.
Note that exchange metrics are based on our labeled data of exchange addresses that we constantly keep updating, as well as data science techniques and statistical information that changes over time. Therefore these metrics are mutable â the data is stable, but especially most recent data points are subject to slight fluctuations as time progresses.
This is the Point-in-Time (PiT) variant of LTH/STH Transfer Volume in Profit/Loss to Exchanges. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Miner Balance
GET https://api.glassnode.com/v1/metrics/distribution/balance_miners_sum_pit
The total supply held in miner addresses.
This is the Point-in-Time (PiT) variant of Miner Balance. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Miner Balance (Stacked)
GET https://api.glassnode.com/v1/metrics/distribution/balance_miners_all_pit
The total supply held in miner addresses.
This is the Point-in-Time (PiT) variant of Miner Balance (Stacked). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Miner Incoming Transfers
GET https://api.glassnode.com/v1/metrics/transactions/transfers_to_miners_count_pit
The total number of transfers in which the receiver is a miners' address.
This is the Point-in-Time (PiT) variant of Miner Incoming Transfers. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Miner Inflow Volume
GET https://api.glassnode.com/v1/metrics/transactions/transfers_volume_to_miners_sum_pit
The total amount of coins transferred to miner addresses.
This is the Point-in-Time (PiT) variant of Miner Inflow Volume. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Miner Net Position Change
GET https://api.glassnode.com/v1/metrics/distribution/balance_miners_change_pit
The 30d change of the supply held in miner addresses.
This is the Point-in-Time (PiT) variant of Miner Net Position Change. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Miner Netflow Volume
GET https://api.glassnode.com/v1/metrics/transactions/transfers_volume_miners_net_pit
The difference between miner's inflow and outflow, i.e the net flow of coins into/out of miner addresses.
This is the Point-in-Time (PiT) variant of Miner Netflow Volume. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Miner Outflow Volume
GET https://api.glassnode.com/v1/metrics/transactions/transfers_volume_from_miners_sum_pit
The total amount of coins transferred from miner addresses.
This is the Point-in-Time (PiT) variant of Miner Outflow Volume. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Miner Outgoing Transfers
GET https://api.glassnode.com/v1/metrics/transactions/transfers_from_miners_count_pit
The total number of transfers in which the sender is a miners' address.
This is the Point-in-Time (PiT) variant of Miner Outgoing Transfers. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Miner Revenue (Block Rewards)
GET https://api.glassnode.com/v1/metrics/mining/volume_mined_sum_pit
The total amount of newly minted coins, i.e. block rewards.
This is the Point-in-Time (PiT) variant of Miner Revenue (Block Rewards). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Miner Revenue (Total)
GET https://api.glassnode.com/v1/metrics/mining/revenue_sum_pit
The total miner revenue, i.e. fees plus newly minted coins.
This is the Point-in-Time (PiT) variant of Miner Revenue (Total). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Miners to Exchanges
GET https://api.glassnode.com/v1/metrics/transactions/transfers_volume_miners_to_exchanges_pit
The total amount of coins transferred from miners to exchange wallets. Only direct transfers are counted. Note that exchange metrics are based on our labeled data of exchange addresses that we constantly keep updating, as well as data science techniques and statistical information that changes over time. Therefore these metrics are mutable â the data is stable, but especially most recent data points are subject to slight fluctuations as time progresses.
This is the Point-in-Time (PiT) variant of Miners to Exchanges. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Miners to Exchanges (Stacked)
GET https://api.glassnode.com/v1/metrics/transactions/transfers_volume_miners_to_exchanges_all_pit
The total amount of coins transferred from miners to exchange wallets. Only direct transfers are counted. Note that exchange metrics are based on our labeled data of exchange addresses that we constantly keep updating, as well as data science techniques and statistical information that changes over time. Therefore these metrics are mutable â the data is stable, but especially most recent data points are subject to slight fluctuations as time progresses.
This is the Point-in-Time (PiT) variant of Miners to Exchanges (Stacked). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Mt. Gox Balance
GET https://api.glassnode.com/v1/metrics/distribution/balance_mtgox_trustee_pit
The Mt. Gox Trustee Balance corresponds to the amount of BTC held in addresses controlled by Nobuaki Kobayashi, the trustee overseeing the Mt. Gox civil rehabilitation proceedings.
This is the Point-in-Time (PiT) variant of Mt. Gox Balance. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
New Entities
GET https://api.glassnode.com/v1/metrics/entities/new_count_pit
The number of unique entities that appeared for the first time in a transaction of the native coin in the network. Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses. For more information see this article.
The computation of this metric requires statistical information from several days, and is therefore only available with a lag of one week.
This is the Point-in-Time (PiT) variant of New Entities. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
NFT Transfers on Marketplaces
GET https://api.glassnode.com/v1/metrics/transactions/transfers_nft_marketplaces_count_all_pit
The number of NFT transfers on different marketplaces which are paid using Ether or WETH. Please note that multiple NFTs can be transferred within the same transaction, and those would only be counted as one transaction. We do this to avoid extreme outlier events, where a user transfers millions of NFTs within one single transaction for a negligible price and distorts the metric. At the date of release of this metric, 98.5% of the transactions made transferred just one NFT.
This is the Point-in-Time (PiT) variant of NFT Transfers on Marketplaces. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
NFT Volume on Marketplaces
GET https://api.glassnode.com/v1/metrics/transactions/transfers_nft_marketplaces_volume_sum_all_pit
The volume of NFT transfers on different marketplaces which are paid using Ether or WETH.
This is the Point-in-Time (PiT) variant of NFT Volume on Marketplaces. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
NFTs Transactions (Absolute)
GET https://api.glassnode.com/v1/metrics/transactions/transfers_count_nfts_pit
The number of transactions (transaction count) in the Ethereum network by transactions interacting with non-fungible tokens. This category includes of both token contract standards (ERC721, ERC1155), as well as NFT marketplaces (OpenSea, Blur, LooksRare, Rarible, SuperRare) for trading those.
This is the Point-in-Time (PiT) variant of NFTs Transactions (Absolute). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
NFTs Transactions (Relative)
GET https://api.glassnode.com/v1/metrics/transactions/transfers_count_nfts_relative_pit
The relative amount (share) of transactions in the Ethereum network by transactions interacting with non-fungible tokens. This category includes of both token contract standards (ERC721, ERC1155), as well as NFT marketplaces (OpenSea, Blur, LooksRare, Rarible, SuperRare) for trading those.
This is the Point-in-Time (PiT) variant of NFTs Transactions (Relative). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Number of Whales
GET https://api.glassnode.com/v1/metrics/entities/min_1k_count_pit
The number of unique entities holding at least 1k coins.Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses. For more information see this article.
This is the Point-in-Time (PiT) variant of Number of Whales. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
OTC Desks Holdings
GET https://api.glassnode.com/v1/metrics/distribution/balance_otc_desks_pit
The total amount of coins held on OTC desk addresses. This data is based on three different OTC desks. Note that OTC metrics are based on our labeled data that we constantly keep updating, as well as data science techniques and statistical information that changes over time. Therefore these metrics are mutable â the data is stable, but especially most recent data points are subject to slight fluctuations as time progresses.
This is the Point-in-Time (PiT) variant of OTC Desks Holdings. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
OTC Desks Incoming Transactions
GET https://api.glassnode.com/v1/metrics/transactions/transfers_to_otc_desks_count_pit
The total count of transfers to OTC desk addresses. This data is based on three different OTC desks. Note that OTC metrics are based on our labeled data that we constantly keep updating, as well as data science techniques and statistical information that changes over time. Therefore these metrics are mutable â the data is stable, but especially most recent data points are subject to slight fluctuations as time progresses.
This is the Point-in-Time (PiT) variant of OTC Desks Incoming Transactions. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
OTC Desks Inflows
GET https://api.glassnode.com/v1/metrics/transactions/transfers_volume_to_otc_desks_sum_pit
The total amount of coins transferred to OTC desk addresses. This data is based on three different OTC desks. Note that OTC metrics are based on our labeled data that we constantly keep updating, as well as data science techniques and statistical information that changes over time. Therefore these metrics are mutable â the data is stable, but especially most recent data points are subject to slight fluctuations as time progresses.
This is the Point-in-Time (PiT) variant of OTC Desks Inflows. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
OTC Desks Outflows
GET https://api.glassnode.com/v1/metrics/transactions/transfers_volume_from_otc_desks_sum_pit
The total amount of coins transferred from OTC desk addresses. This data is based on three different OTC desks. Note that OTC metrics are based on our labeled data that we constantly keep updating, as well as data science techniques and statistical information that changes over time. Therefore these metrics are mutable â the data is stable, but especially most recent data points are subject to slight fluctuations as time progresses.
This is the Point-in-Time (PiT) variant of OTC Desks Outflows. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
OTC Desks Outgoing Transactions
GET https://api.glassnode.com/v1/metrics/transactions/transfers_from_otc_desks_count_pit
The total count of transfers from OTC desk addresses. This data is based on three different OTC desks. Note that OTC metrics are based on our labeled data that we constantly keep updating, as well as data science techniques and statistical information that changes over time. Therefore these metrics are mutable â the data is stable, but especially most recent data points are subject to slight fluctuations as time progresses.
This is the Point-in-Time (PiT) variant of OTC Desks Outgoing Transactions. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Percent Entities in Profit
GET https://api.glassnode.com/v1/metrics/entities/profit_relative_pit
The percentage of entities in the network that are currently in profit, e.g. the entities whose funds where on average bought at prices lower than the current price. "Buy price" is here defined as the price at the time coins were transferred into addresses controlled by the entity. Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses. For more information this article.
This is the Point-in-Time (PiT) variant of Percent Entities in Profit. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Receiving Entities
GET https://api.glassnode.com/v1/metrics/entities/receiving_count_pit
The number of unique entities that were active as a receiver. Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses. For more information see this article.
This is the Point-in-Time (PiT) variant of Receiving Entities. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Relative Long/Short-Term Holder Supply
GET https://api.glassnode.com/v1/metrics/supply/lth_sth_profit_loss_relative_pit
The relative amount of circulating supply of held by long- and short-term holders in profit/loss. Long- and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days.
This is the Point-in-Time (PiT) variant of Relative Long/Short-Term Holder Supply. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Relative LTH/STH Realized Profit/Loss
GET https://api.glassnode.com/v1/metrics/indicators/realized_profit_loss_lth_sth_relative_pit
Relative distribution of the total profit and loss (USD value) of all coins moved by long- and short-term holders. Long- and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days.
Volume transferred between addresses owned by the same entity cluster is excluded. As such, no value is realized during internal or âin-houseâ transfers.
Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses. For more information see this article.
This is the Point-in-Time (PiT) variant of Relative LTH/STH Realized Profit/Loss. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Relative LTH/STH Realized Profit/Loss to Exchanges
GET https://api.glassnode.com/v1/metrics/indicators/realized_profit_loss_lth_sth_to_exchanges_relative_pit
Relative distribution of the total profit and loss (USD value) of all coins moved by long- and short-term holders to exchanges. Realized profit/loss denotes the total profit/loss (in USD) of all moved coins whose price at their last movement was lower/higher than the price at the current movement. Long- and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days.
Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses. For more information see this article.
Note that exchange metrics are based on our labeled data of exchange addresses that we constantly keep updating, as well as data science techniques and statistical information that changes over time. Therefore these metrics are mutable â the data is stable, but especially most recent data points are subject to slight fluctuations as time progresses.
This is the Point-in-Time (PiT) variant of Relative LTH/STH Realized Profit/Loss to Exchanges. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Relative Transfer Volume by Size (Entity-Adjusted)
GET https://api.glassnode.com/v1/metrics/transactions/transfers_volume_by_size_entity_adjusted_relative_pit
Entity-adjusted relative on-chain volume breakdown by the USD value of the transfers.
This is the Point-in-Time (PiT) variant of Relative Transfer Volume by Size (Entity-Adjusted). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Sending Entities
GET https://api.glassnode.com/v1/metrics/entities/sending_count_pit
The number of unique entities that were active as a sender. Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses. For more information see this article.
This is the Point-in-Time (PiT) variant of Sending Entities. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Short-Term Holder in Loss to Exchanges (Volume)
GET https://api.glassnode.com/v1/metrics/transactions/transfers_volume_sth_to_exchanges_loss_sum_pit
The total amount of coins transferred from short-term holders in loss to exchange wallets. Only direct transfers are counted. Coins are considered to be in loss when the price at the time the coins are spent is lower than the entity's average on-chain acquisition price for its funds. Long- and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days.
Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses. For more information see this article.
Note that exchange metrics are based on our labeled data of exchange addresses that we constantly keep updating, as well as data science techniques and statistical information that changes over time. Therefore these metrics are mutable â the data is stable, but especially most recent data points are subject to slight fluctuations as time progresses.
This is the Point-in-Time (PiT) variant of Short-Term Holder in Loss to Exchanges (Volume). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Short-Term Holder in Profit to Exchanges (Volume)
GET https://api.glassnode.com/v1/metrics/transactions/transfers_volume_sth_to_exchanges_profit_sum_pit
The total amount of coins transferred from short-term holders in profit to exchange wallets. Only direct transfers are counted. Coins are considered to be in profit when the price at the time the coins are spent is higher than the entity's average on-chain acquisition price for its funds. Long- and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days.
Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses. For more information see this article.
Note that exchange metrics are based on our labeled data of exchange addresses that we constantly keep updating, as well as data science techniques and statistical information that changes over time. Therefore these metrics are mutable â the data is stable, but especially most recent data points are subject to slight fluctuations as time progresses.
This is the Point-in-Time (PiT) variant of Short-Term Holder in Profit to Exchanges (Volume). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Short-Term Holder Profit/Loss Ratio
GET https://api.glassnode.com/v1/metrics/supply/sth_profit_loss_ratio_pit
The ratio of the Short-Term Holder Supply in Profit and the Short-Term Holder Supply in Loss. Similar to SOPR, it detects local bottoms in bull markets and local tops in bear markets. This metric was first put forward by ARK Invest.
This is the Point-in-Time (PiT) variant of Short-Term Holder Profit/Loss Ratio. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Short-Term Holder Supply
GET https://api.glassnode.com/v1/metrics/supply/sth_sum_pit
The total amount of circulating supply held by short term holders. Long- and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days.
This is the Point-in-Time (PiT) variant of Short-Term Holder Supply. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Short-Term Holder Supply in Loss
GET https://api.glassnode.com/v1/metrics/supply/sth_loss_sum_pit
The total amount of circulating supply that is currently at loss and held by short term holders. Long- and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days.
This is the Point-in-Time (PiT) variant of Short-Term Holder Supply in Loss. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Short-Term Holder Supply in Profit
GET https://api.glassnode.com/v1/metrics/supply/sth_profit_sum_pit
The total amount of circulating supply that is currently in profit and held by short term holders. Long- and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days.
This is the Point-in-Time (PiT) variant of Short-Term Holder Supply in Profit. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Short-Term Holder to Exchanges (Volume)
GET https://api.glassnode.com/v1/metrics/transactions/transfers_volume_sth_to_exchanges_sum_pit
The total amount of coins transferred from short-term holders to exchange wallets. Only direct transfers are counted. Long- and Short-Term Holder supply is defined with respect to the entity's averaged purchasing date with weights given by a logistic function centered at an age of 155 days and a transition width of 10 days.
Entities are defined as a cluster of addresses that are controlled by the same network entity and are estimated through advanced heuristics and Glassnode's proprietary clustering algorithms. Note that entityâbased metrics are based on data science techniques and statistical information that changes over time and are therefore mutable â the data is stable, but most recent data points are subject to slight fluctuations as time progresses. For more information see this article.
Note that exchange metrics are based on our labeled data of exchange addresses that we constantly keep updating, as well as data science techniques and statistical information that changes over time. Therefore these metrics are mutable â the data is stable, but especially most recent data points are subject to slight fluctuations as time progresses.
This is the Point-in-Time (PiT) variant of Short-Term Holder to Exchanges (Volume). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Stablecoins Transactions (Absolute)
GET https://api.glassnode.com/v1/metrics/transactions/transfers_count_stablecoins_pit
The number of transactions (transaction count) in the Ethereum network by stablecoin transactions. Stablecoin are fungible tokens that have their value pegged to an off-chain asset, either by the issuer or by an algorithm. We include 150+ stablecoins in this category, with USDT, USDC, UST, BUSD, and DAI being the most prominent ones.
This is the Point-in-Time (PiT) variant of Stablecoins Transactions (Absolute). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Stablecoins Transactions (Relative)
GET https://api.glassnode.com/v1/metrics/transactions/transfers_count_stablecoins_relative_pit
The relative amount (share) of transactions in the Ethereum network by stablecoin transactions. Stablecoin are fungible tokens that have their value pegged to an off-chain asset, either by the issuer or by an algorithm. We include 150+ stablecoins in this category, with USDT, USDC, UST, BUSD, and DAI being the most prominent ones.
This is the Point-in-Time (PiT) variant of Stablecoins Transactions (Relative). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Supply Held by Entities with Balance < 0.001
GET https://api.glassnode.com/v1/metrics/entities/supply_balance_less_0001_pit
The total circulating supply held by entities with balance lower than 0.001 coins.
This is the Point-in-Time (PiT) variant of Supply Held by Entities with Balance < 0.001. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Supply Held by Entities with Balance > 100k
GET https://api.glassnode.com/v1/metrics/entities/supply_balance_more_100k_pit
The total circulating supply held by entities with balance of at least 100,000 coins.
This is the Point-in-Time (PiT) variant of Supply Held by Entities with Balance > 100k. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Supply Held by Entities with Balance 0.001 - 0.01
GET https://api.glassnode.com/v1/metrics/entities/supply_balance_0001_001_pit
The total circulating supply held by entities with balance between 0.001 and 0.01 coins.
This is the Point-in-Time (PiT) variant of Supply Held by Entities with Balance 0.001 - 0.01. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Supply Held by Entities with Balance 0.01 - 0.1
GET https://api.glassnode.com/v1/metrics/entities/supply_balance_001_01_pit
The total circulating supply held by entities with balance between 0.01 and 0.1 coins.
This is the Point-in-Time (PiT) variant of Supply Held by Entities with Balance 0.01 - 0.1. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Supply Held by Entities with Balance 0.1 - 1
GET https://api.glassnode.com/v1/metrics/entities/supply_balance_01_1_pit
The total circulating supply held by entities with balance between 0.1 and 1 coins.
This is the Point-in-Time (PiT) variant of Supply Held by Entities with Balance 0.1 - 1. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Supply Held by Entities with Balance 1 - 10
GET https://api.glassnode.com/v1/metrics/entities/supply_balance_1_10_pit
The total circulating supply held by entities with balance between 1 and 10 coins.
This is the Point-in-Time (PiT) variant of Supply Held by Entities with Balance 1 - 10. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Supply Held by Entities with Balance 10 - 100
GET https://api.glassnode.com/v1/metrics/entities/supply_balance_10_100_pit
The total circulating supply held by entities with balance between 10 and 100 coins.
This is the Point-in-Time (PiT) variant of Supply Held by Entities with Balance 10 - 100. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Supply Held by Entities with Balance 100 - 1k
GET https://api.glassnode.com/v1/metrics/entities/supply_balance_100_1k_pit
The total circulating supply held by entities with balance between 100 and 1,000 coins.
This is the Point-in-Time (PiT) variant of Supply Held by Entities with Balance 100 - 1k. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Supply Held by Entities with Balance 10k - 100k
GET https://api.glassnode.com/v1/metrics/entities/supply_balance_10k_100k_pit
The total circulating supply held by entities with balance between 10,000 and 100,000 coins.
This is the Point-in-Time (PiT) variant of Supply Held by Entities with Balance 10k - 100k. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Supply Held by Entities with Balance 1k - 10k
GET https://api.glassnode.com/v1/metrics/entities/supply_balance_1k_10k_pit
The total circulating supply held by entities with balance between 1,000 and 10,000 coins.
This is the Point-in-Time (PiT) variant of Supply Held by Entities with Balance 1k - 10k. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Supply in Smart Contracts
GET https://api.glassnode.com/v1/metrics/distribution/supply_contracts_pit
The percent of total supply of the token that is held in smart contracts.
This is the Point-in-Time (PiT) variant of Supply in Smart Contracts. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Supply of Top 1% Addresses
GET https://api.glassnode.com/v1/metrics/distribution/balance_1pct_holders_pit
The percentage of supply held by the top 1% addresses. Exchange addresses, smart contract addresses, and other special asset-specific addresses (e.g. team fund addresses) are excluded.
This is the Point-in-Time (PiT) variant of Supply of Top 1% Addresses. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Total Transfer Volume by Size (Entity-Adjusted)
GET https://api.glassnode.com/v1/metrics/transactions/transfers_volume_by_size_entity_adjusted_sum_pit
Entity-adjusted on-chain volume breakdown by the USD value of the transfers.
This is the Point-in-Time (PiT) variant of Total Transfer Volume by Size (Entity-Adjusted). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Transaction Type Breakdown (Absolute)
GET https://api.glassnode.com/v1/metrics/transactions/tx_types_breakdown_count_pit
The number of transactions (transaction count) in the Ethereum network by category. Transactions are classified into the following categories:
Vanilla: Pure ETH transfers between Externally Owned Accounts (EOAs), with no contracts being called.
ERC20: All transactions calling ERC20 contracts. Contracts in the Stablecoins category are excluded here.
Stablecoins: Fungible tokens that have their value pegged to an off-chain asset, either by the issuer or by an algorithm. We include 150+ stablecoins in this category, with USDT, USDC, UST, BUSD, and DAI being the most prominent ones.
DeFi: On-chain financial instruments and protocols implemented as smart contracts, including decentralized exchanges (DEXs). We include over 90+ DeFi protocols in this category, such as Uniswap, Etherdelta, 1inch, Sushiswap, Aave, and 0x.
Bridges: Contracts allowing transfer of tokens between different blockchains. We include 50+ bridges in this category, such as Ronin, Polygon, Optimism, and Arbitrum.
NFTs: Transactions interacting with non-fungible tokens. This category includes of both token contract standards (ERC721, ERC1155), as well as NFT marketplaces (OpenSea, Blur, LooksRare, Rarible, SuperRare) for trading those.
MEV Bots: Miner Extractable Value (MEV) bots execute transactions for profit by reordering, inserting, and censoring transactions within blocks.
Other: This category includes all other transactions in the Ethereum network that are not included in categories listed above.
This is the Point-in-Time (PiT) variant of Transaction Type Breakdown (Absolute). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Transaction Type Breakdown (Relative)
GET https://api.glassnode.com/v1/metrics/transactions/tx_types_breakdown_relative_pit
The relative amount (share) of transactions in the Ethereum network by category. Transactions are classified into one of the following categories:
Vanilla: Pure ETH transfers between Externally Owned Accounts (EOAs), with no contracts being called.
ERC20: All transactions calling ERC20 contracts. Contracts in the Stablecoins category are excluded here.
Stablecoins: Fungible tokens that have their value pegged to an off-chain asset, either by the issuer or by an algorithm. We include 150+ stablecoins in this category, with USDT, USDC, UST, BUSD, and DAI being the most prominent ones.
DeFi: On-chain financial instruments and protocols implemented as smart contracts, including decentralized exchanges (DEXs). We include over 90+ DeFi protocols in this category, such as Uniswap, Etherdelta, 1inch, Sushiswap, Aave, and 0x.
Bridges: Contracts allowing transfer of tokens between different blockchains. We include 50+ bridges in this category, such as Ronin, Polygon, Optimism, and Arbitrum.
NFTs: Transactions interacting with non-fungible tokens. This category includes of both token contract standards (ERC721, ERC1155), as well as NFT marketplaces (OpenSea, Blur, LooksRare, Rarible, SuperRare) for trading those.
MEV Bots: Miner Extractable Value (MEV) bots execute transactions for profit by reordering, inserting, and censoring transactions within blocks.
Other: This category includes all other transactions in the Ethereum network that are not included in categories listed above.
This is the Point-in-Time (PiT) variant of Transaction Type Breakdown (Relative). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
US Year-over-Year Supply Change
GET https://api.glassnode.com/v1/metrics/supply/amer_1y_supply_change_pit
This metric aims at giving an estimate for the year-over-year change in the share of the Bitcoin supply to be held/traded in the US.
Geolocation of Bitcoin supply is performed probabilistically at the entity level. The timestamps of all transactions created by an entity are correlated with the working hours of different geographical regions to determine the probabilities for each entity being located in the US, Europe, or Asia. Working hours are defined as:
US: 8am to 8pm Eastern Time (13:00-01:00 UTC)
EU: 8am to 8pm Central European Time (07:00-19:00 UTC)
Asia: 8am to 8pm China Standard Time (00:00-12:00 UTC)
An entity's balance will only contribute to the supply in the respective region if the location can be determined with a high certainty. Supply held on exchanges wallets are excluded.
This is the Point-in-Time (PiT) variant of US Year-over-Year Supply Change. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Vanilla Transactions (Absolute)
GET https://api.glassnode.com/v1/metrics/transactions/transfers_count_vanilla_pit
The amount of gas consumed by the Ethereum network by vanilla transactions. Vanilla transactions are pure ETH transfers between Externally Owned Accounts (EOAs), with no contracts being called. If at least one of the EOAs participating in the transaction (the receiver, the sender, or both) belongs to an exchange, the amount will be represented in the exchange cohort. Note that occasionally the value of the last datapoint can slightly change as some addresses initially transact as "vanilla" before their associated smart contract deployment is observed.
This is the Point-in-Time (PiT) variant of Vanilla Transactions (Absolute). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Vanilla Transactions (Relative)
GET https://api.glassnode.com/v1/metrics/transactions/transfers_count_vanilla_relative_pit
The relative amount (share) of gas consumed by the Ethereum network by vanilla transactions. Vanilla transactions are pure ETH transfers between Externally Owned Accounts (EOAs), with no contracts being called. If at least one of the EOAs participating in the transaction (the receiver, the sender, or both) belongs to an exchange, the amount will be represented in the exchange cohort. Note that occasionally the value of the last datapoint can slightly change as some addresses initially transact as "vanilla" before their associated smart contract deployment is observed.
This is the Point-in-Time (PiT) variant of Vanilla Transactions (Relative). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Whale Deposits to Exchanges (Counts)
GET https://api.glassnode.com/v1/metrics/transactions/transfers_whales_to_exchanges_count_pit
The total count of transfers from whales to exchange addresses. Whales are defined as network entities (cluster of addresses) that hold at least 1,000 BTC. Note that exchange metrics are based on our labeled data of exchange addresses that we constantly keep updating, as well as data science techniques and statistical information that changes over time. Therefore these metrics are mutable â the data is stable, but especially most recent data points are subject to slight fluctuations as time progresses.
This is the Point-in-Time (PiT) variant of Whale Deposits to Exchanges (Counts). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Whale Deposits to Exchanges (Volume)
GET https://api.glassnode.com/v1/metrics/transactions/transfers_volume_whales_to_exchanges_sum_pit
The total amount of coins transferred from whales to exchange wallets. Only direct transfers are counted. Whales are defined as network entities (cluster of addresses) that hold at least 1,000 BTC. Note that exchange metrics are based on our labeled data of exchange addresses that we constantly keep updating, as well as data science techniques and statistical information that changes over time. Therefore these metrics are mutable â the data is stable, but especially most recent data points are subject to slight fluctuations as time progresses.
This is the Point-in-Time (PiT) variant of Whale Deposits to Exchanges (Volume). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Whale Withdrawals from Exchanges (Counts)
GET https://api.glassnode.com/v1/metrics/transactions/transfers_exchanges_to_whales_count_pit
The total count of transfers from exchange addresses to whales. Whales are defined as network entities (cluster of addresses) that hold at least 1,000 BTC. Note that exchange metrics are based on our labeled data of exchange addresses that we constantly keep updating, as well as data science techniques and statistical information that changes over time. Therefore these metrics are mutable â the data is stable, but especially most recent data points are subject to slight fluctuations as time progresses.
This is the Point-in-Time (PiT) variant of Whale Withdrawals from Exchanges (Counts). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Whale Withdrawals from Exchanges (Volume)
GET https://api.glassnode.com/v1/metrics/transactions/transfers_volume_exchanges_to_whales_sum_pit
The total amount of coins transferred from exchange wallets to whale entities. Only direct transfers are counted. Whales are defined as network entities (cluster of addresses) that hold at least 1,000 BTC. Note that exchange metrics are based on our labeled data of exchange addresses that we constantly keep updating, as well as data science techniques and statistical information that changes over time. Therefore these metrics are mutable â the data is stable, but especially most recent data points are subject to slight fluctuations as time progresses.
This is the Point-in-Time (PiT) variant of Whale Withdrawals from Exchanges (Volume). PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Withdrawing Addresses
GET https://api.glassnode.com/v1/metrics/addresses/receiving_from_exchanges_count_pit
The number of unique addresses that appeared as a receiver in a transaction receiving funds from an exchanges.
This is the Point-in-Time (PiT) variant of Withdrawing Addresses. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
Supported asset symbols: BTC, ETH, 1INCH, AAVE, ABT, AMP, AMPL, ANT, APE, BADGER, BAL, BAND, BAT, BNT, BOBA, BOND, BORG, BUSD, CAKE, CELR, COMP, CREAM, CRO, CRV, CVC, CVP, CVX, CVXCRV, DAI, DDX, DENT, DHT, DODO, DPI, DRGN, ELF, ENG, ENJ, ETHDYDX, EURS, FET, FLX, FRAX, FTM, FTT, FUN, FXS, GNO, GUSD, HEGIC, HOT, HT, HUSD, IMX, INDEX, KCS, LAMB, LBA, LDO, LEO, LINK, LOOM, LRC, MANA, MATIC, MCB, METIS, MIR, MKR, MLN, MTA, MTL, NDX, NEXO, NFTX, NMR, Nsure, OCEAN, OKB, OMG, PAY, PERP, PICKLE, PNK, PNT, POLY, POWR, PPT, PYUSD, QASH, QKC, QNT, RAI, RDN, REN, REP, rETH, RLC, ROOK, RPL, RSR, SAND, SFRXETH, SHIB, SNT, SNX, SSV, STAKE, stETH, STORJ, sUSD, SUSHI, TEL, TUSD, UBT, UMA, UNI, USDC, USDD, USDK, USDP, USDT, UTK, VERI, WBTC, WETH, wNXM, YAM, YFI, ZRX
Wrapped BTC (WBTC) Balance
GET https://api.glassnode.com/v1/metrics/distribution/balance_wbtc_pit
Wrapped Bitcoin (WBTC) is the first ERC20 token backed 1:1 with Bitcoin and designed to act as representation of Bitcoin on the Ethereum blockchain. The WBTC supply listed here corresponds to the amount of Bitcoin held by BitGo, the custodian responsible for minting new WBTC ERC20 tokens and guaranteeing backing of new ERC20 tokens by actual BTC.
This is the Point-in-Time (PiT) variant of Wrapped BTC (WBTC) Balance. PiT metrics are strictly append-only and their history is immutable. The historic data does not necessarily reflect the best current knowledge, but the information at the time when a data point was first computed. PiT metrics are ideal candidates for applications in model backtesting and related quantitative purposes. Read our article on PiT metrics for more information.
Query Parameters
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