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Datasets > Bitcoin Metadata

Datasets

Explore free and paid datasets available on QuantConnect covering fundamentals, pricing, and alternative options.

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US Equity Security Master

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Financial articles and news publications condensed into a news feed with titles and article bodies for sentiment analysis

  • 1,250 Posts/Day, 8,000 Stocks
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News releases from 120 different news providers for sentiment analysis.

  • 10,000 US Equities
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Alert for upcoming split and reverse split events of primary US Equities common shares with split date, and split factor.

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Trading activity of Congresspeople for potential insider trading signals based on early access to regulation changes.

  • 1,800 US Equities
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  • 6,000 US Equities
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US Equity buyback announcements and transactions scraped from SEC reports and secondary sources.

  • 3,000 US Equities
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US Regulatory Alerts - Financial Sector

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  • 400,000 Alerts
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Estimates of company financials including EPS, revenues, and macroeconomic indicators based on 100,000+ crowdsourced predictions.

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Trade and quote data for the Bybit Crypto exchange, collected by CoinAPI.

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Trade and quote data for the Bitfinex Crypto exchange, collected by CoinAPI.

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Bitcoin Metadata

Bitcoin processing fundamental data such as hash rate, miner revenue, and number of transactions.

  • Bitcoin blockchain
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Datasets >

Bitcoin Metadata

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Bitcoin Metadata

Dataset by Blockchain

  • About
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  • Pricing

Introduction

The Bitcoin Metadata dataset by Blockchain provides 23 fundamental metadata of Bitcoin directly fetched from the Bitcoin blockchain. The data starts in January 2009 and delivered on a daily frequency. This dataset contains mining statistics like hash rate and miner revenue; transaction metadata like transaction per block, transaction fee, and number of addresses; and blockchain metadata like blockchain size and block size.

About the Provider

Blockchain is a website that publishes data related to Bitcoin. It has been online since 2011 and publishes the Bitcoin Metadata history back to 2009.

Getting Started

The following snippet demonstrates how to request data from the Bitcoin Metadata dataset:

Select Language:
from QuantConnect.DataSource import *

self.btcusd = self.add_crypto("BTCUSD", Resolution.DAILY, Market.BITFINEX).symbol
self.dataset_symbol = self.add_data(BitcoinMetadata, self.btcusd).symbol
using QuantConnect.DataSource;

_symbol = AddCrypto("BTCUSD", Resolution.Daily, Market.Bitfinex).Symbol;
_datasetSymbol = AddData<BitcoinMetadata>(_symbol).Symbol; 

Data Summary

The following table describes the dataset properties:

Property Value
Start Date January 2009
Coverage Bitcoin blockchain
Data Density Regular
Resolution Daily
Timezone UTC

Example Applications

The Bitcoin Metadata dataset enables you to incorporate metadata from the Bitcoin blockchain into your strategies. Examples include the following strategies:

  • Comparing mining and transaction statistics to provide insight on the supply-demand relationship of the Bitcoin blockchain service.
  • Measuring the activity and popularity of the Bitcoin blockchain to predict the price movements of the Cryptocurrency.

For more example algorithms, see Examples.

Data Point Attributes

The Bitcoin Metadata dataset provides BitcoinMetadata objects, which have the following attributes:

BitcoinMetadata
    A relative measure of how difficult it is to find a new block. The difficulty is adjusted periodically as a function of how much hashing power has been deployed by the network of miners.
  • difficulty: decimal
  • Number of wallets hosts using our My Wallet Service.
  • my_wallet_numberof_users: decimal
  • The average block size in MB.
  • average_block_size: decimal
  • The total size of all block headers and transactions. Not including database indexes.
  • blockchain_size: decimal
  • The median time for a transaction to be accepted into a mined block and added to the public ledger (note: only includes transactions with miner fees).
  • median_transaction_confirmation_time: decimal
  • Total value of coinbase block rewards and transaction fees paid to miners.
  • miners_revenue: decimal
  • The estimated number of tera hashes per second (trillions of hashes per second) the Bitcoin network is performing
  • hash_rate: decimal
  • The miners revenue divided by the number of transactions.
  • cost_per_transaction: decimal
  • The miners revenue as percentage of the transaction volume.
  • cost_percentof_transaction_volume: decimal
  • The Estimated Transaction Value in USD value.
  • estimated_transaction_volume_usd: decimal
  • The total estimated value of transactions on the Bitcoin blockchain (does not include coins returned to sender as change).
  • estimated_transaction_volume: decimal
  • The total value of all transaction outputs per day (includes coins returned to the sender as change).
  • total_output_volume: decimal
  • The average number of transactions per block.
  • numberof_transactionper_block: decimal
  • The total number of unique addresses used on the Bitcoin blockchain.
  • numberof_unique_bitcoin_addresses_used: decimal
  • The total number of Bitcoin transactions, excluding those involving any of the network's 100 most popular addresses.
  • numberof_transactions_excluding_popular_addresses: decimal
  • The Total Number of transactions.
  • total_numberof_transactions: decimal
  • The number of daily confirmed Bitcoin transactions.
  • numberof_transactions: decimal
  • The total value of all transaction fees in USD paid to miners (not including the coinbase value of block rewards).
  • total_transaction_fees_usd: decimal
  • The total value of all transaction fees in Bitcoin paid to miners (not including the coinbase value of block rewards).
  • total_transaction_fees: decimal
  • The total USD value of bitcoin supply in circulation, as calculated by the daily average market price across major exchanges.
  • market_capitalization: decimal
  • The total number of bitcoins that have already been mined; in other words, the current supply of bitcoins on the network.
  • total_bitcoins: decimal
  • Number of transactions made by My Wallet Users per day.
  • my_wallet_numberof_transaction_per_day: decimal
  • 24hr Transaction Volume of our web wallet service.
  • my_wallet_transaction_volume: decimal
  • Market Data Type of this data - does it come in individual price packets or is it grouped into OHLC.
  • data_type: MarketDataType
  • True if this is a fill forward piece of data
  • is_fill_forward: bool
  • Current time marker of this data packet.
  • time: DateTime
  • The end time of this data. Some data covers spans (trade bars) and as such we want to know the entire time span covered
  • end_time: DateTime
  • Symbol representation for underlying Security
  • symbol: Symbol
  • Value representation of this data packet. All data requires a representative value for this moment in time. For streams of data this is the price now, for OHLC packets this is the closing price.
  • value: decimal
  • As this is a backtesting platform we'll provide an alias of value as price.
  • price: decimal

 


Requesting Data

To add Bitcoin Metadata data to your algorithm, call the AddDataadd_data method with the BTCUSD Symbol. Save a reference to the dataset Symbol so you can access the data later in your algorithm.

Select Language:
class BlockchainBitcoinMetadataAlgorithm(QCAlgorithm):

    def initialize(self) -> None:
        self.set_start_date(2019, 1, 1)
        self.set_end_date(2020, 6, 1)
        self.set_cash(100000)

        self.btcusd = self.add_crypto("BTCUSD", Resolution.DAILY, Market.BITFINEX).symbol
        self.dataset_symbol = self.add_data(BitcoinMetadata, self.btcusd).symbol 
namespace QuantConnect
{
    public class BlockchainBitcoinMetadataAlgorithm: QCAlgorithm
    {
        private Symbol _symbol, _datasetSymbol;

        public override void Initialize()
        {
            SetStartDate(2019, 1, 1);
            SetEndDate(2020, 6, 1);
            SetCash(100000);

            _symbol = AddCrypto("BTCUSD", Resolution.Daily, Market.Bitfinex).Symbol;
            _datasetSymbol = AddData<BitcoinMetadata>(_symbol).Symbol;
        }
    }
}

Accessing Data

To get the current Bitcoin Metadata data, index the current Slice with the dataset Symbol. Slice objects deliver unique events to your algorithm as they happen, but the Slice may not contain data for your dataset at every time step. To avoid issues, check if the Slice contains the data you want before you index it.

Select Language:
def on_data(self, slice: Slice) -> None:
    if slice.contains_key(self.dataset_symbol):
        data_point = slice[self.dataset_symbol]
        self.log(f"{self.dataset_symbol} miner revenue at {slice.time}: {data_point.miners_revenue}")
public override void OnData(Slice slice)
{
    if (slice.ContainsKey(_datasetSymbol))
    {
        var dataPoint = slice[_datasetSymbol];
        Log($"{_datasetSymbol} miner revenue at {slice.Time}: {dataPoint.MinersRevenue}");
    }
}

To iterate through all of the dataset objects in the current Slice, call the Getget method.

Select Language:
def on_data(self, slice: Slice) -> None:
    for dataset_symbol, data_point in slice.get(BlockchainBitcoinData).items():
        self.log(f"{dataset_symbol} miner revenue at {slice.time}: {data_point.miners_revenue}")
public override void OnData(Slice slice)
{
    foreach (var kvp in slice.Get<BlockchainBitcoinData>())
    {
        var datasetSymbol = kvp.Key;
        var dataPoint = kvp.Value;
        Log($"{datasetSymbol} miner revenue at {slice.Time}: {dataPoint.MinersRevenue}");
    }
}

Historical Data

To get historical Bitcoin Metadata data, call the Historyhistory method with the dataset Symbol. If there is no data in the period you request, the history result is empty.

Select Language:
# DataFrame
history_df = self.history(self.dataset_symbol, 100, Resolution.DAILY)

# Dataset objects
history_bars = self.history[BlockchainBitcoinData](self.dataset_symbol, 100, Resolution.DAILY)
var history = History<BlockchainBitcoinData>(_datasetSymbol, 100, Resolution.Daily);

For more information about historical data, see History Requests.

Remove Subscriptions

To remove a subscription, call the RemoveSecurityremove_security method.

Select Language:
self.remove_security(self.dataset_symbol)
RemoveSecurity(_datasetSymbol);

Data Point Attributes

The Bitcoin Metadata dataset provides BitcoinMetadata objects, which have the following attributes:

BitcoinMetadata
    A relative measure of how difficult it is to find a new block. The difficulty is adjusted periodically as a function of how much hashing power has been deployed by the network of miners.
  • difficulty: decimal
  • Number of wallets hosts using our My Wallet Service.
  • my_wallet_numberof_users: decimal
  • The average block size in MB.
  • average_block_size: decimal
  • The total size of all block headers and transactions. Not including database indexes.
  • blockchain_size: decimal
  • The median time for a transaction to be accepted into a mined block and added to the public ledger (note: only includes transactions with miner fees).
  • median_transaction_confirmation_time: decimal
  • Total value of coinbase block rewards and transaction fees paid to miners.
  • miners_revenue: decimal
  • The estimated number of tera hashes per second (trillions of hashes per second) the Bitcoin network is performing
  • hash_rate: decimal
  • The miners revenue divided by the number of transactions.
  • cost_per_transaction: decimal
  • The miners revenue as percentage of the transaction volume.
  • cost_percentof_transaction_volume: decimal
  • The Estimated Transaction Value in USD value.
  • estimated_transaction_volume_usd: decimal
  • The total estimated value of transactions on the Bitcoin blockchain (does not include coins returned to sender as change).
  • estimated_transaction_volume: decimal
  • The total value of all transaction outputs per day (includes coins returned to the sender as change).
  • total_output_volume: decimal
  • The average number of transactions per block.
  • numberof_transactionper_block: decimal
  • The total number of unique addresses used on the Bitcoin blockchain.
  • numberof_unique_bitcoin_addresses_used: decimal
  • The total number of Bitcoin transactions, excluding those involving any of the network's 100 most popular addresses.
  • numberof_transactions_excluding_popular_addresses: decimal
  • The Total Number of transactions.
  • total_numberof_transactions: decimal
  • The number of daily confirmed Bitcoin transactions.
  • numberof_transactions: decimal
  • The total value of all transaction fees in USD paid to miners (not including the coinbase value of block rewards).
  • total_transaction_fees_usd: decimal
  • The total value of all transaction fees in Bitcoin paid to miners (not including the coinbase value of block rewards).
  • total_transaction_fees: decimal
  • The total USD value of bitcoin supply in circulation, as calculated by the daily average market price across major exchanges.
  • market_capitalization: decimal
  • The total number of bitcoins that have already been mined; in other words, the current supply of bitcoins on the network.
  • total_bitcoins: decimal
  • Number of transactions made by My Wallet Users per day.
  • my_wallet_numberof_transaction_per_day: decimal
  • 24hr Transaction Volume of our web wallet service.
  • my_wallet_transaction_volume: decimal
  • Market Data Type of this data - does it come in individual price packets or is it grouped into OHLC.
  • data_type: MarketDataType
  • True if this is a fill forward piece of data
  • is_fill_forward: bool
  • Current time marker of this data packet.
  • time: DateTime
  • The end time of this data. Some data covers spans (trade bars) and as such we want to know the entire time span covered
  • end_time: DateTime
  • Symbol representation for underlying Security
  • symbol: Symbol
  • Value representation of this data packet. All data requires a representative value for this moment in time. For streams of data this is the price now, for OHLC packets this is the closing price.
  • value: decimal
  • As this is a backtesting platform we'll provide an alias of value as price.
  • price: decimal

 


Classic Algorithm Example

The following example algorithm tracks the transaction-to-hash-rate ratio of the Bitcoin network. The algorithm holds Bitcoin when the ratio increases. Otherwise, it holds dollars.

Select Language:
from AlgorithmImports import *
from QuantConnect.DataSource import *

class BlockchainBitcoinMetadataAlgorithm(QCAlgorithm):
    
    def initialize(self) -> None:
        self.set_start_date(2019, 1, 1)   # Set Start Date
        self.set_end_date(2020, 12, 31)    # Set End Date
        self.set_cash(100000)

        # Request BTCUSD as the trading vehicle on Bitcoin Metadata
        self.btcusd = self.add_crypto("BTCUSD", Resolution.MINUTE).symbol
        # Request Bitcoin Metadata for trade signal generation
        self.bitcoin_metadata_symbol = self.add_data(BitcoinMetadata, self.btcusd).symbol

        # Historical data
        history = self.history(BitcoinMetadata, self.bitcoin_metadata_symbol, 60, Resolution.DAILY)
        self.debug(f"We got {len(history)} items from our history request for {self.btcusd} Blockchain Bitcoin Metadata")

        # Cache the last supply-demand ratio for comparison
        self.last_demand_supply = None

    def on_data(self, slice: Slice) -> None:
        # Trade only based on updated Bitcoin Metadata
        data = slice.get(BitcoinMetadata)
        
        if self.bitcoin_metadata_symbol in data and data[self.bitcoin_metadata_symbol] != None:
            # Calculate the supply-demand ratio to estimate the microeconomy structure of Bitcoin for scalp-trading
            # Transaction number as demand, hash production rate as supply
            current_demand_supply = data[self.bitcoin_metadata_symbol].numberof_transactions / data[self.bitcoin_metadata_symbol].hash_rate

            # Comparing the average transaction-to-hash-rate ratio changes, buy Bitcoin if demand is higher than supply, sell vice versa
            if self.last_demand_supply != None and current_demand_supply > self.last_demand_supply:
                self.set_holdings(self.btcusd, 1)
            else:
                self.set_holdings(self.btcusd, 0)

            self.last_demand_supply = current_demand_supply
using QuantConnect.DataSource;

namespace QuantConnect.Algorithm.CSharp
{
    public class BlockchainBitcoinMetadataAlgorithm : QCAlgorithm
    {
        private Symbol _bitcoinMetadataSymbol;
        private Symbol _btcSymbol;
        // Cache the last supply-demand ratio for comparison
        private decimal? _lastDemandSupply = null;

        public override void Initialize()
        {
            SetStartDate(2019, 1, 1);  //Set Start Date
            SetEndDate(2020, 12, 31);    //Set End Date
            SetCash(100000);

            // Request BTCUSD as the trading vehicle on Bitcoin Metadata
            _btcSymbol = AddCrypto("BTCUSD", Resolution.Minute, Market.Bitfinex).Symbol; 
            // Request Bitcoin Metadata for trade signal generation
            _bitcoinMetadataSymbol = AddData<BitcoinMetadata>(_btcSymbol).Symbol;

            // Historical data
            var history = History(new[]{_bitcoinMetadataSymbol}, 60, Resolution.Daily);
            Debug($"We got {history.Count()} items from our history request for {_btcSymbol} Blockchain Bitcoin Metadata");
        }

        public override void OnData(Slice slice)
        {
            // Trade only based on updated Bitcoin Metadata
            var data = slice.Get<BitcoinMetadata>();
            if (!data.IsNullOrEmpty())
            {
                // Calculate the supply-demand ratio to estimate the microeconomy structure of Bitcoin for scalp-trading
                // Transaction number as demand, hash production rate as supply
                var currentDemandSupply = data[_bitcoinMetadataSymbol].NumberofTransactions / data[_bitcoinMetadataSymbol].HashRate;

                // Comparing the average transaction-to-hash-rate ratio changes, buy Bitcoin if demand is higher than supply, sell vice versa
                if (_lastDemandSupply != null && currentDemandSupply > _lastDemandSupply)
                {
                    SetHoldings(_btcSymbol, 1);
                }
                else
                {
                    SetHoldings(_btcSymbol, 0);
                }

                _lastDemandSupply = currentDemandSupply;
            }
        }
    }
}

Framework Algorithm Example

The following example algorithm tracks the transaction-to-hash-rate ratio of the Bitcoin network. The algorithm holds Bitcoin when the ratio increases. Otherwise, it holds dollars.

Select Language:
from AlgorithmImports import *
from QuantConnect.DataSource import *

class BlockchainBitcoinMetadataFrameworkAlgorithm(QCAlgorithm):
    
    def initialize(self) -> None:
        self.set_start_date(2019, 1, 1)   # Set Start Date
        self.set_end_date(2020, 12, 31)    # Set End Date
        self.set_cash(100000)

        # Universe contains only BTCUSD as the trading vehicle on Bitcoin Metadata
        self.add_universe_selection(
            ManualUniverseSelectionModel(
            Symbol.create("BTCUSD", SecurityType.CRYPTO, Market.BITFINEX)
        ))
        # Custom alpha model that emit insights based on Bitcoin Metadata
        self.add_alpha(BlockchainBitcoinMetadataAlphaModel())
        # Equally invest to evenly dissipate the capital concentration risk from non-sysmtematic risky events
        self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())
        
class BlockchainBitcoinMetadataAlphaModel(AlphaModel):
    
    def __init__(self) -> None:
        self.bitcoin_metadata_symbol_by_symbol = {}
        # Cache the last supply-demand ratio for comparison
        self.last_demand_supply = {}

    def update(self, algorithm:QCAlgorithm, slice: Slice) -> List[Insight]:
        insights = []
        
        # Trade only based on updated Bitcoin Metadata
        data = slice.Get(BitcoinMetadata)
        
        for symbol, bitcoin_metadata_symbol in self.bitcoin_metadata_symbol_by_symbol.items():
            if data.contains_key(bitcoin_metadata_symbol) and data[bitcoin_metadata_symbol] != None: 
                # Calculate the supply-demand ratio to estimate the microeconomy structure of the crypto pair for scalp-trading
                # Transaction number as demand, hash production rate as supply
                current_demand_supply = data[bitcoin_metadata_symbol].numberof_transactions / data[bitcoin_metadata_symbol].hash_rate

                # Comparing the average transaction-to-hash-rate ratio changes, buy coin if demand is higher than supply
                if symbol in self.last_demand_supply and current_demand_supply > self.last_demand_supply[symbol]:
                    insights.append(Insight.price(symbol, timedelta(1), InsightDirection.UP))

                self.last_demand_supply[symbol] = current_demand_supply
                
        return insights

    def on_securities_changed(self, algorithm: QCAlgorithm, changes: SecurityChanges) -> None:
        for security in changes.added_securities:
            symbol = security.symbol
            
            # Request Bitcoin Metadata for trade signal generation
            bitcoin_metadata_symbol = algorithm.add_data(BitcoinMetadata, symbol).symbol

            self.bitcoin_metadata_symbol_by_symbol[symbol] = bitcoin_metadata_symbol

            # Historical data
            history = algorithm.history(BitcoinMetadata, bitcoin_metadata_symbol, 60, Resolution.DAILY)
            algorithm.debug(f"We got {len(history)} items from our history request for {symbol} Blockchain Bitcoin Metadata")
using QuantConnect.DataSource;

namespace QuantConnect.Algorithm.CSharp
{
    public class BlockchainBitcoinMetadataFrameworkAlgorithm : QCAlgorithm
    {
        public override void Initialize()
        {
            SetStartDate(2019, 1, 1);  //Set Start Date
            SetEndDate(2020, 12, 31);    //Set End Date
            SetCash(100000);

            // Universe contains only BTCUSD as the trading vehicle on Bitcoin Metadata
            AddUniverseSelection(
                new ManualUniverseSelectionModel(
                QuantConnect.Symbol.Create("BTCUSD", SecurityType.Crypto, Market.Bitfinex)
            ));
            // Custom alpha model that emit insights based on Bitcoin Metadata
            AddAlpha(new BlockchainBitcoinMetadataAlphaModel());
            // Equally invest to evenly dissipate the capital concentration risk from non-sysmtematic risky events
            SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
        }
    }

    public class BlockchainBitcoinMetadataAlphaModel: AlphaModel
    {
        private Dictionary<Symbol, Symbol> _bitcoinMetadataSymbolBySymbol = new Dictionary<Symbol, Symbol>();
        // Cache the last supply-demand ratio for comparison
        private Dictionary<Symbol, decimal> _lastDemandSupply = new Dictionary<Symbol, decimal>();

        public BlockchainBitcoinMetadataAlphaModel(){}

        public override IEnumerable<Insight> Update(QCAlgorithm algorithm, Slice slice)
        {
            var insights = new List<Insight>();
            
            // Trade only based on updated Bitcoin Metadata
            var data = slice.Get<BitcoinMetadata>();
            if (!data.IsNullOrEmpty())
            {
                foreach(var kvp in _bitcoinMetadataSymbolBySymbol)
                {
                    var symbol = kvp.Key;
                    var bitcoinMetadataSymbol = kvp.Value;

                    // Calculate the supply-demand ratio to estimate the microeconomy structure of the crypto pair for scalp-trading
                    // Transaction number as demand, hash production rate as supply
                    var currentDemandSupply = data[bitcoinMetadataSymbol].NumberofTransactions / data[bitcoinMetadataSymbol].HashRate;

                    // Comparing the average transaction-to-hash-rate ratio changes, buy coin if demand is higher than supply
                    if (_lastDemandSupply.ContainsKey(symbol) && currentDemandSupply > _lastDemandSupply[symbol])
                    {
                        insights.Add(Insight.Price(symbol, TimeSpan.FromDays(1), InsightDirection.Up));
                    }

                    _lastDemandSupply[symbol] = currentDemandSupply;
                }
            }
            
            return insights;
        }

        public override void OnSecuritiesChanged(QCAlgorithm algorithm, SecurityChanges changes)
        {
            foreach (var security in changes.AddedSecurities)
            {
                var symbol = security.Symbol;
                // Request Bitcoin Metadata for trade signal generation
                var bitcoinMetadataSymbol = algorithm.AddData<BitcoinMetadata>(symbol).Symbol;

                _bitcoinMetadataSymbolBySymbol.Add(symbol, bitcoinMetadataSymbol);

                // Historical data
                var history = algorithm.History(new[]{bitcoinMetadataSymbol}, 60, Resolution.Daily);
                algorithm.Debug($"We got {history.Count()} items from our history request for {symbol} Blockchain Bitcoin Metadata");
            }
        }
    }
}

 


Licensing Available

Cloud Usage

Cloud Usage

Bitcoin Metadata is allowed to be used in the cloud for personal and commercial projects for free. The data is permissioned for use within the licensed organization only

Free | Documentation

Live trading license available

LEAN CLI Downloads Usage

Bitcoin Metadata can be downloaded on premise with the LEAN CLI, for a charge per file downloaded. This download is for the licensed organization's internal LEAN use only and cannot be redistributed or converted in any format.

Starting at 5 QCC/file | Learn More


About Lean CLI

LEAN CLI is a cross-platform wrapper on the QuantConnect algorithmic trading engine called LEAN. The CLI makes using LEAN incredibly easy, reducing most of the pain points of developing and managing an algorithmic trading strategy to a few lines of bash.

Using the CLI you can download the same data QuantConnect hosts in the cloud for a small fee. These fees are per file downloaded, and are paid for in QuantConnect-Credits (QCC). We recommend purchasing credits to enable downloading.

CLI Command Generator

The CLI command generator is a helpful tool to generate a copy-paste command to download this dataset from the form below.

Select OS:
lean data download \
	--dataset "Bitcoin Metadata" \
	--ticker "BTCUSD" 
lean data download `
	--dataset "Bitcoin Metadata" `
	--ticker "BTCUSD" 

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Using Bitcoin Metadata dataset in the QuantConnect Cloud for your backtesting and live trading purposes.

  • Curated, clean data
  • Updated nightly at 1am (UTC 5am)
  • Mapped to Cryptos data

PRICE

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Download On Premise edit edit

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Bitcoin Metadata archived in LEAN format for on premise backtesting and research.

  • Ownership of the data
  • Data in LEAN format
  • Local compute resources

PRICE

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Pricing

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Starting Date edit edit

  • Jan 2009

Coverage edit edit

  • Bitcoin blockchain

Delivery Methods edit edit

  • Download
  • Cloud

About the Provider

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  • Contact the Provider

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