In This Page

Introduction

Trades

Quotes

Ticks

Slices

Universes

Alternative Data

Indicators

Examples

Asset Classes

Crypto

Introduction

This page explains how to get historical data for Crypto. Some of the data you can get include prices, indicators, and universe selection data.

Trades

To get historical trade data, call the History<TradeBar> method with a security's Symbol.

To get historical trade data, call the history method with the TradeBar type and a security's Symbol. This method returns a DataFrame with columns for the open, high, low, close, and volume.

Select Language:

public class CryptoTradeBarHistoryAlgorithm : QCAlgorithm
{
    public override void Initialize()
    {
        SetStartDate(2024, 12, 19);
        // Get the Symbol of a security.
        var symbol = AddCrypto("BTCUSD").Symbol;
        // Get the 5 trailing daily TradeBar objects of the security. 
        var history = History<TradeBar>(symbol, 5, Resolution.Daily);
        // Iterate through each TradeBar and calculate its dollar volume.
        foreach (var bar in history)
        {
            var t = bar.EndTime;
            var dollarVolume = bar.Close * bar.Volume;
        }
    }
}

class CryptoTradeBarHistoryAlgorithm(QCAlgorithm):

    def initialize(self) -> None:
        self.set_start_date(2024, 12, 19)
        # Get the Symbol of a security.
        symbol = self.add_crypto('BTCUSD').symbol
        # Get the 5 trailing daily TradeBar objects of the security in DataFrame format. 
        history = self.history(TradeBar, symbol, 5, Resolution.DAILY)

		close	high	low	open	volume
symbol	time
BTCUSD	2024-12-15	101399.99	102650.00	100600.00	101423.26	4054.541500
	2024-12-16	104439.88	105100.00	101221.34	101400.00	7216.743790
	2024-12-17	106099.81	107857.79	103289.21	104445.15	22263.157625
	2024-12-18	106150.00	108388.88	105337.97	106099.98	11729.293641
	2024-12-19	100150.73	106528.13	99939.82	106147.77	21659.470502

# Calculate the daily returns.
daily_returns = history.close.pct_change().iloc[1:]

symbol  time      
BTCUSD  2024-12-16    0.029979
        2024-12-17    0.015894
        2024-12-18    0.000473
        2024-12-19   -0.056517
Name: close, dtype: float64

If you intend to use the data in the DataFrame to create TradeBar objects, request that the history request returns the data type you need. Otherwise, LEAN consumes unnecessary computational resources populating the DataFrame. To get a list of TradeBar objects instead of a DataFrame, call the history[TradeBar] method.

# Get the 5 trailing daily TradeBar objects of the security in TradeBar format. 
history = self.history[TradeBar](symbol, 5, Resolution.DAILY)
# Iterate through the TradeBar objects and access their volumes.
for trade_bar in history:
    t = trade_bar.end_time
    volume = trade_bar.volume

Quotes

To get historical quote data, call the History<QuoteBar> method with a security's Symbol.

To get historical quote data, call the history method with the QuoteBar type and a security's Symbol. This method returns a DataFrame with columns for the open, high, low, close, and size of the bid and ask quotes. The columns that don't start with "bid" or "ask" are the mean of the quote prices on both sides of the market.

Select Language:

public class CryptoQuoteBarHistoryAlgorithm : QCAlgorithm
{
    public override void Initialize()
    {
        SetStartDate(2024, 12, 19);
        // Get the Symbol of a security.
        var symbol = AddCrypto("BTCUSD", market: Market.Bitfinex).Symbol;
        // Get the 5 trailing minute QuoteBar objects of the security. 
        var history = History<QuoteBar>(symbol, 5, Resolution.Minute);
        // Iterate through the QuoteBar objects and calculate the spread.
        foreach (var bar in history)
        {
            var t = bar.EndTime;
            var spread = bar.Ask.Close - bar.Bid.Close;
        }
    }
}

class CryptoQuoteBarHistoryAlgorithm(QCAlgorithm):

    def initialize(self) -> None:
        self.set_start_date(2024, 12, 19)
        # Get the Symbol of a security.
        symbol = self.add_crypto('BTCUSD', market=Market.BITFINEX).symbol
        # Get the 5 trailing minute QuoteBar objects of the security in DataFrame format. 
        history = self.history(QuoteBar, symbol, 5, Resolution.MINUTE)

		askclose	askhigh	asklow	askopen	asksize	bidclose	bidhigh	bidlow	bidopen	bidsize	close	high	low	open
symbol	time
BTCUSD	2024-12-19 04:56:00	100830.0	100830.0	100830.0	100830.0	0.496728	100820.0	100820.0	100820.0	100820.0	0.050586	100825.0	100825.0	100825.0	100825.0
	2024-12-19 04:57:00	100810.0	100830.0	100810.0	100830.0	1.200384	100800.0	100820.0	100800.0	100820.0	0.130504	100805.0	100825.0	100805.0	100825.0
	2024-12-19 04:58:00	100760.0	100810.0	100760.0	100810.0	0.520363	100750.0	100800.0	100750.0	100800.0	0.275894	100755.0	100805.0	100755.0	100805.0
	2024-12-19 04:59:00	100710.0	100760.0	100710.0	100760.0	1.716247	100700.0	100750.0	100700.0	100750.0	0.270080	100705.0	100755.0	100705.0	100755.0
	2024-12-19 05:00:00	100710.0	100710.0	100710.0	100710.0	0.712784	100700.0	100700.0	100700.0	100700.0	0.389266	100705.0	100705.0	100705.0	100705.0

# Calculate the spread at each minute.
spread = history.askclose - history.bidclose

symbol  time               
BTCUSD  2024-12-19 04:56:00    10.0
        2024-12-19 04:57:00    10.0
        2024-12-19 04:58:00    10.0
        2024-12-19 04:59:00    10.0
        2024-12-19 05:00:00    10.0
dtype: float64

If you intend to use the data in the DataFrame to create QuoteBar objects, request that the history request returns the data type you need. Otherwise, LEAN consumes unnecessary computational resources populating the DataFrame. To get a list of QuoteBar objects instead of a DataFrame, call the history[QuoteBar] method.

# Get the 5 trailing minute QuoteBar objects of the security in QuoteBar format. 
history = self.history[QuoteBar](symbol, 5, Resolution.MINUTE)
# Iterate through each QuoteBar and calculate the dollar volume on the bid.
for quote_bar in history:
    t = quote_bar.end_time
    bid_dollar_volume = quote_bar.last_bid_size * quote_bar.bid.close

Ticks

To get historical tick data, call the History<Tick> method with a security's Symbol and Resolution.Tick.

To get historical tick data, call the history method with a security's Symbol and Resolution.TICK. This method returns a DataFrame that contains data on bids, asks, and last trade prices.

Select Language:

public class CryptoTickHistoryAlgorithm : QCAlgorithm
{
    public override void Initialize()
    {
        SetStartDate(2024, 12, 19);
        // Get the Symbol of a security.
        var symbol = AddCrypto("BTCUSD", market: Market.Bitfinex).Symbol;
        // Get the trailing 2 days of ticks for the security.
        var history = History<Tick>(symbol, TimeSpan.FromDays(2), Resolution.Tick);
        // Select the ticks that represent trades, excluding the quote ticks.
        var trades = history.Where(tick => tick.TickType == TickType.Trade);
    }
}

class CryptoTickHistoryAlgorithm(QCAlgorithm):

    def initialize(self) -> None:
        self.set_start_date(2024, 12, 19)
        # Get the Symbol of a security.
        symbol = self.add_crypto('BTCUSD', market=Market.BITFINEX).symbol
        # Get the trailing 2 days of ticks for the security in DataFrame format.
        history = self.history(symbol, timedelta(2), Resolution.TICK)

		askprice	asksize	bidprice	bidsize	lastprice	quantity
symbol	time
BTCUSD	2024-12-17 05:00:00.915569	106390.0	1.072804	106380.0	16.521214	106385.0	0.0
	2024-12-17 05:00:01.119192	106390.0	1.302212	106380.0	16.521214	106385.0	0.0
	2024-12-17 05:00:05.981284	106390.0	1.131937	106380.0	16.521214	106385.0	0.0
	2024-12-17 05:00:07.302974	106390.0	1.498471	106380.0	16.521214	106385.0	0.0
	2024-12-17 05:00:07.514624	106390.0	1.631937	106380.0	16.521214	106385.0	0.0

# Select the rows in the DataFrame that represent trades. Drop the bid/ask columns since they are NaN.
trade_ticks = history[history.quantity > 0].dropna(axis=1)

		lastprice	quantity
symbol	time
BTCUSD	2024-12-17 05:00:13.647	106380.0	0.201870
	2024-12-17 05:01:13.130	106390.0	0.000747
	2024-12-17 05:01:23.446	106380.0	0.197494
	2024-12-17 05:01:31.421	106390.0	0.031909
	2024-12-17 05:01:31.460	106390.0	0.000086

If you intend to use the data in the DataFrame to create Tick objects, request that the history request returns the data type you need. Otherwise, LEAN consumes unnecessary computational resources populating the DataFrame. To get a list of Tick objects instead of a DataFrame, call the history[Tick] method.

# Get the trailing 2 days of ticks for the security in Tick format. 
history = self.history[Tick](symbol, timedelta(2), Resolution.TICK)
# Iterate through each quote tick and calculate the quote size.
for tick in history:
    if tick.tick_type == TickType.Quote:
        t = tick.end_time
        size = max(tick.bid_size, tick.ask_size)

Ticks are a sparse dataset, so request ticks over a trailing period of time or between start and end times.

Slices

To get historical Slice data, call the Historyhistory method without passing any Symbol objects. This method returns Slice objects, which contain data points from all the datasets in your algorithm. If you omit the resolution argument, it uses the resolution that you set for each security and dataset when you created the subscriptions.

Select Language:

public class SliceHistoryAlgorithm : QCAlgorithm
{
    public override void Initialize()
    {
        SetStartDate(2024, 12, 1);
        // Add some securities and datasets.
        AddCrypto("BTCUSD");
        // Get the historical Slice objects over the last 5 days for all the subcriptions in your algorithm.
        var history = History(5, Resolution.Daily);
        // Iterate through each historical Slice.
        foreach (var slice in history)
        {
            // Iterate through each TradeBar in this Slice.
            foreach (var kvp in slice.Bars)
            {
                var symbol = kvp.Key;
                var bar = kvp.Value;
            }
        }
    }
}

class SliceHistoryAlgorithm(QCAlgorithm):

    def initialize(self) -> None:
        self.set_start_date(2024, 12, 1)
        # Add some securities and datasets.
        self.add_crypto('BTCUSD')
        # Get the historical Slice objects over the last 5 days for all the subcriptions in your algorithm.
        history = self.history(5, Resolution.DAILY)
        # Iterate through each historical Slice.
        for slice_ in history:
            # Iterate through each TradeBar in this Slice.
            for symbol, trade_bar in slice_.bars.items():
                close = trade_bar.close

Universes

To get historical universe data, call the History method with the Universe object. This method doesn't apply your selection function. It returns all of the universe data.

To get historical universe data, call the history method with the Universe object. This method doesn't apply your selection function. It returns all of the universe data in a DataFrame with columns for the data point attributes.

Select Language:

public class CryptoUniverseHistoryAlgorithm : QCAlgorithm
{
    public override void Initialize()
    {
        SetStartDate(2024, 12, 23);
        // Add a universe of Cryptocurrencies on Coinbase.
        var universe = AddUniverse(CryptoUniverse.Coinbase());
        // Get 5 days of history for the universe.
        var history = History(universe, TimeSpan.FromDays(5));
        // Iterate through each day of the universe history.
        foreach (var constituents in history)
        {
            // Select the 2 assets with the smallest weights in the ETF on this day.
            var dailyLargest = constituents.Select(c => c as ETFConstituentData).OrderByDescending(c => c.Weight).Take(2);
        }
    }
}

class CryptoUniverseHistoryAlgorithm(QCAlgorithm):

    def initialize(self) -> None:
        self.set_start_date(2024, 12, 23)
        # Add a universe of Cryptocurrencies on Coinbase.
        universe = self.add_universe(CryptoUniverse.coinbase())
        # Get 5 days of history for the universe.
        history = self.history(universe, timedelta(5), flatten=True)

		close	high	low	open	price	volume	volumeinusd
time	symbol
2024-12-19	00USD 2XR	0.050300	0.051500	0.049200	0.051500	0.050300	3.173495e+06	1.596268e+05
	00USDC 2XR	0.050300	0.051500	0.049200	0.051500	0.050300	3.173495e+06	1.596268e+05
	1INCHBTC 2XR	0.000004	0.000005	0.000004	0.000005	0.000004	9.815380e+03	4.584371e+03
	1INCHEUR 2XR	0.446000	0.467000	0.442000	0.465000	0.446000	9.007325e+04	4.215390e+04
	1INCHGBP 2XR	0.369000	0.385000	0.366000	0.381000	0.369000	3.932220e+04	1.845807e+04
...	...	...	...	...	...	...	...	...
2024-12-23	ZETAUSDC 2XR	0.574500	0.638600	0.562600	0.598400	0.574500	1.522481e+06	8.746654e+05
	ZROUSD 2XR	5.487000	6.363000	5.393000	5.635000	5.487000	1.312069e+05	7.199321e+05
	ZROUSDC 2XR	5.487000	6.363000	5.393000	5.635000	5.487000	1.312069e+05	7.199321e+05
	ZRXUSD 2XR	0.449944	0.509425	0.441590	0.476001	0.449944	3.371118e+06	1.516814e+06
	ZRXUSDC 2XR	0.449944	0.509425	0.441590	0.476001	0.449944	3.371118e+06	1.516814e+06

# Select the 2 assets with the largest dollar volume each day.
most_traded = history.groupby('time').apply(lambda x: x.nlargest(2, 'volumeinusd')).reset_index(level=1, drop=True).volumeinusd

time        symbol     
2024-12-19  BTCUSD         1.245065e+09
            BTCUSDC 2XR    1.245065e+09
2024-12-20  BTCUSD         2.169212e+09
            BTCUSDC 2XR    2.169212e+09
2024-12-21  BTCUSD         2.231721e+09
            BTCUSDC 2XR    2.231721e+09
2024-12-22  BTCUSD         2.077999e+09
            BTCUSDC 2XR    2.077999e+09
2024-12-23  BTCUSD         6.169765e+08
            BTCUSDC 2XR    6.169765e+08
Name: volumeinusd, dtype: float64

To get the data in the format of the objects that you receive in your universe filter function instead of a DataFrame, use flatten=False. This call returns a Series where the values are lists of the universe data objects.

# Get the historical universe data over the last 30 days in a Series where
# the values in the series are lists of the universe selection objects.
history = self.history(universe, timedelta(30), flatten=False)
# Iterate through each day of universe selection.
for (universe_symbol, end_time), constituents in history.items():
    # Select the 10 assets with the largest dollar volume this day.
    most_liquid = sorted(constituents, key=lambda c: c.volume_in_usd)[-10:]

Alternative Data

To get historical alternative data, call the History<alternativeDataClass> method with the dataset Symbol.

To get historical alternative data, call the history method with the dataset Symbol. This method returns a DataFrame that contains the data point attributes.

Select Language:

public class CryptoAlternativeDataHistoryAlgorithm : QCAlgorithm
{
    public override void Initialize()
    {
        SetStartDate(2024, 12, 19);
        // Get the Symbol of an asset.
        var symbol = AddEquity("BTCUSD").Symbol;
        // Add the alternative dataset and save a reference to its Symbol.
        var datasetSymbol = AddData<BitcoinMetadata>(symbol).Symbol;
        // Get the trailing 5 days of alternative data for the asset.
        var history = History<BitcoinMetadata>(datasetSymbol, 5, Resolution.Daily);
        // Iterate each data point and access its attributes.
        foreach (var dataPoint in history)
        {
            var t = dataPoint.EndTime;
            var marketCap = dataPoint.MarketCapitalization;
        }
    }
}

class CryptoAlternativeDataHistoryAlgorithm(QCAlgorithm):

    def initialize(self) -> None:
        self.set_start_date(2024, 12, 19)
        # Get the Symbol of an asset.
        symbol = self.add_crypto('BTCUSD').symbol
        # Add the alternative dataset and save a reference to its Symbol.
        dataset_symbol = self.add_data(BitcoinMetadata, symbol).symbol
        # Get the trailing 5 days of alternative data for the asset in DataFrame format.
        history = self.history(dataset_symbol, 5, Resolution.DAILY)

		averageblocksize	blockchainsize	costpercentoftransactionvolume	costpertransaction	difficulty	estimatedtransactionvolume	estimatedtransactionvolumeusd	hashrate	marketcapitalization	mediantransactionconfirmationtime	...	mywalletnumberofusers	numberoftransactionperblock	numberoftransactions	numberoftransactionsexcludingpopularaddresses	numberofuniquebitcoinaddressesused	totalbitcoins	totalnumberoftransactions	totaloutputvolume	totaltransactionfees	totaltransactionfeesusd
symbol	time
BTCUSD.BitcoinMetadata	2024-12-15	1.5554	622522.4629	0.9645	99.7845	1.039196e+14	52757.2240	5.353586e+09	8.213738e+08	1.999814e+12	7.5167	...	89780467.0	3254.6038	517482.0	517269.0	547370.0	1.979622e+07	1.131425e+09	430661.4198	11.9933	1.216796e+06
	2024-12-16	1.5014	622769.7569	0.6667	118.2001	1.039196e+14	66749.2576	6.822123e+09	7.077246e+08	2.038652e+12	7.8167	...	89784782.0	2762.9708	378527.0	378325.0	540459.0	1.979658e+07	1.131942e+09	435592.1612	9.5660	9.890319e+05
	2024-12-17	1.5993	622975.3876	0.2560	120.9025	1.082427e+14	185055.0677	1.948991e+10	7.963548e+08	2.096909e+12	7.6167	...	89788382.0	2788.8311	412747.0	412506.0	628257.0	1.979710e+07	1.132320e+09	944149.5981	12.3276	1.295375e+06
	2024-12-18	1.6135	623212.2483	0.2910	111.2104	1.085226e+14	150308.1866	1.603829e+10	7.336779e+08	2.099723e+12	8.2167	...	89792154.0	3086.0809	419707.0	419484.0	587955.0	1.979750e+07	1.132733e+09	899983.4775	12.7376	1.359282e+06
	2024-12-19	1.5915	623431.5971	0.3013	116.3402	1.085226e+14	154047.1023	1.600095e+10	7.768354e+08	2.034833e+12	8.3833	...	89797436.0	2877.5417	414366.0	414128.0	583495.0	1.979795e+07	1.133152e+09	863124.9378	13.1134	1.363842e+06

# Calculate the growth in market cap.
growth = history.marketcapitalization.pct_change().iloc[1:]

symbol                  time      
BTCUSD.BitcoinMetadata  2024-12-16    0.019420
                        2024-12-17    0.028577
                        2024-12-18    0.001342
                        2024-12-19   -0.030904
Name: marketcapitalization, dtype: float64

For information on historical data for other alternative datasets, see the documentation in the Dataset Market.

Indicators

To get historical indicator values, call the IndicatorHistoryindicator_history method with an indicator and the security's Symbol.

Select Language:

public class CryptoIndicatorHistoryAlgorithm : QCAlgorithm
{
    public override void Initialize()
    {
        SetStartDate(2024, 12, 19);
        // Get the Symbol of a security.
        var symbol = AddCrypto("BTCUSD").Symbol;
        // Get the 21-day SMA values of the security for the last 5 trading days. 
        var history = IndicatorHistory(new SimpleMovingAverage(21), symbol, 5, Resolution.Daily);
        // Get the maximum of the SMA values.
        var maxSMA = history.Max(indicatorDataPoint => indicatorDataPoint.Current.Value);
    }
}

class CryptoIndicatorHistoryAlgorithm(QCAlgorithm):

    def initialize(self) -> None:
        self.set_start_date(2024, 12, 19)
        # Get the Symbol of a security.
        symbol = self.add_crypto('BTCUSD').symbol
        # Get the 21-day SMA values of the security for the last 5 trading days. 
        history = self.indicator_history(SimpleMovingAverage(21), symbol, 5, Resolution.DAILY)

To organize the data into a DataFrame, use the data_frame property of the result.

# Organize the historical indicator data into a DataFrame to enable pandas wrangling.
history_df = history.data_frame

	current	rollingsum
2024-12-15	97734.062857	2052415.32
2024-12-16	98039.381905	2058827.02
2024-12-17	98663.181905	2071926.82
2024-12-18	99340.640952	2086153.46
2024-12-19	99540.619048	2090353.00

# Get the maximum of the SMA values.
sma_max = history_df.current.max()

The IndicatorHistoryindicator_history method resets your indicator, makes a history request, and updates the indicator with the historical data. Just like with regular history requests, the IndicatorHistoryindicator_history method supports time periods based on a trailing number of bars, a trailing period of time, or a defined period of time. If you don't provide a resolution argument, it defaults to match the resolution of the security subscription.

To make the IndicatorHistoryindicator_history method update the indicator with an alternative price field instead of the close (or mid-price) of each bar, pass a selector argument.

Select Language:

// Get the historical values of an indicator over the last 30 days, applying the indicator to the security's volume.
var history = IndicatorHistory(indicator, symbol, TimeSpan.FromDays(30), selector: Field.Volume);

# Get the historical values of an indicator over the last 30 days, applying the indicator to the security's volume.
history = self.indicator_history(indicator, symbol, timedelta(30), selector=Field.VOLUME)

Some indicators require the prices of two securities to compute their value (for example, Beta). In this case, pass a list of the Symbol objects to the method.

Select Language:

public class CryptoMultiAssetIndicatorHistoryAlgorithm : QCAlgorithm
{
    public override void Initialize()
    {
        SetStartDate(2024, 12, 19);
        // Add the target and reference securities.
        var targetSymbol = AddCrypto("DOGEUSD").Symbol;
        var referenceSymbol = AddCrypto("BTCUSD").Symbol;
        // Create a 21-period Beta indicator.
        var beta = new Beta("", targetSymbol, referenceSymbol, 21);
        // Get the historical values of the indicator over the last 10 trading days.
        var history = IndicatorHistory(beta, new[] {targetSymbol, referenceSymbol}, 10, Resolution.Daily);
        // Get the average Beta value.
        var avgBeta = history.Average(indicatorDataPoint => indicatorDataPoint.Current.Value);
    }
}

class CryptoMultiAssetIndicatorHistoryAlgorithm(QCAlgorithm):

    def initialize(self) -> None:
        self.set_start_date(2024, 12, 19)
        # Add the target and reference securities.
        target_symbol = self.add_crypto('DOGEUSD').symbol
        reference_symbol = self.add_crypto('BTCUSD').symbol
        # Create a 21-period Beta indicator.
        beta = Beta("", target_symbol, reference_symbol, 21)
        # Get the historical values of the indicator over the last 10 trading days.
        history = self.indicator_history(beta, [target_symbol, reference_symbol], 10, Resolution.DAILY)
        # Get the average Beta value.
        beta_avg = history.data_frame.mean()

If you already have a list of Slice objects, you can pass them to the IndicatorHistoryindicator_history method to avoid the internal history request.

var slices = History(new[] {symbol}, 30, Resolution.Daily);
var history = IndicatorHistory(indicator, slices);

Examples

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