Crypto
Handling Data
Introduction
LEAN passes the data you request to the on_data
method so you can make trading decisions. The Slice
object that the on_data
method receives groups all the data together at a single moment in time. To access the Slice
outside of the on_data
method, use the current_slice
property of your algorithm.
All the data formats use DataDictionary
objects to group data by Symbol
and provide easy access to information. The plural of the type denotes the collection of objects. For instance, the TradeBars
DataDictionary
is made up of TradeBar
objects. To access individual data points in the dictionary, you can index the dictionary with the security ticker or symbol
, but we recommend you use the symbol
.
To view the resolutions that are available for Crypto data, see Resolutions.
Trades
TradeBar
objects are price bars that consolidate individual trades from the exchanges. They contain the open, high, low, close, and volume of trading activity over a period of time.

To get the TradeBar
objects in the Slice
, index the Slice
or index the bars
property of the Slice
with the security symbol
. If the security doesn't actively trade or you are in the same time step as when you added the security subscription, the Slice
may not contain data for your symbol
. To avoid issues, check if the Slice
contains data for your security before you index the Slice
with the security symbol
.
def on_data(self, slice: Slice) -> None: # Obtain the mapped TradeBar of the symbol if any trade_bar = slice.bars.get(self._symbol) # None if not found
You can also iterate through the TradeBars
dictionary. The keys of the dictionary are the Symbol
objects and the values are the TradeBar
objects.
def on_data(self, slice: Slice) -> None: # Iterate all received Symbol-TradeBar key-value pairs for symbol, trade_bar in slice.bars.items(): close_price = trade_bar.close
TradeBar
objects have the following properties:
Quotes
QuoteBar
objects are bars that consolidate NBBO quotes from the exchanges. They contain the open, high, low, and close prices of the bid and ask. The open
, high
, low
, and close
properties of the QuoteBar
object are the mean of the respective bid and ask prices. If the bid or ask portion of the QuoteBar
has no data, the open
, high
, low
, and close
properties of the QuoteBar
copy the values of either the bid
or ask
instead of taking their mean.

To get the QuoteBar
objects in the Slice
, index the QuoteBars
property of the Slice
with the security symbol
. If the security doesn't actively get quotes or you are in the same time step as when you added the security subscription, the Slice
may not contain data for your symbol
. To avoid issues, check if the Slice
contains data for your security before you index the Slice
with the security symbol
.
def on_data(self, slice: Slice) -> None: # Obtain the mapped QuoteBar of the symbol if any quote_bar = slice.quote_bars.get(self._symbol) # None if not found
You can also iterate through the QuoteBars
dictionary. The keys of the dictionary are the Symbol
objects and the values are the QuoteBar
objects.
def on_data(self, slice: Slice) -> None: # Iterate all received Symbol-QuoteBar key-value pairs for symbol, quote_bar in slice.quote_bars.items(): ask_price = quote_bar.ask.close
QuoteBar
objects let LEAN incorporate spread costs into your simulated trade fills to make backtest results more realistic.
QuoteBar
objects have the following properties:
Ticks
Tick
objects represent a single trade or quote at a moment in time. A trade tick is a record of a transaction for the security. A quote tick is an offer to buy or sell the security at a specific price.
Trade ticks have a non-zero value for the quantity
and price
properties, but they have a zero value for the bid_price
, bid_size
, ask_price
, and ask_size
properties. Quote ticks have non-zero values for bid_price
and bid_size
properties or have non-zero values for ask_price
and ask_size
properties. To check if a tick is a trade or a quote, use the ticktype
property.
In backtests, LEAN groups ticks into one millisecond buckets. In live trading, LEAN groups ticks into ~70-millisecond buckets. To get the Tick
objects in the Slice
, index the Ticks
property of the Slice
with a symbol
. If the security doesn't actively trade or you are in the same time step as when you added the security subscription, the Slice
may not contain data for your symbol
. To avoid issues, check if the Slice
contains data for your security before you index the Slice
with the security symbol
.
def on_data(self, slice: Slice) -> None: ticks = slice.ticks.get(self._symbol, []) # Empty if not found for tick in ticks: price = tick.price
You can also iterate through the Ticks
dictionary. The keys of the dictionary are the Symbol
objects and the values are the list[Tick]
objects.
def on_data(self, slice: Slice) -> None: for symbol, ticks in slice.ticks.items(): for tick in ticks: price = tick.price
Tick data is raw and unfiltered, so it can contain bad ticks that skew your trade results. For example, some ticks come from dark pools, which aren't tradable. We recommend you only use tick data if you understand the risks and are able to perform your own online tick filtering.
Tick
objects have the following properties:
Examples
The following examples demonstrate some common practices for handling Crypto data.
Example 1: Dollar Cost Average BTC
Dollar cost averaging (DCA) is where you consistently invest a fixed dollar amount into an asset on a regular basis (for example, monthly), regardless of the asset's price. It can reduce the volatility in your PnL due to slowly increasing the position size over time. The following algorithm demonstrates a DCA investment into BTC. It buys $10,000 USD worth of BTC every midnight for 30 consecutive days.
class CryptoExampleAlgorithm(QCAlgorithm): def initialize(self) -> None: # Set a day count variable for counting the days of the DCA trade. self.day_count = 0 # Set the brokerage and account type to match your brokerage environment for accurate fee and margin behavior. self.set_brokerage_model(BrokerageName.BITFINEX, AccountType.CASH) # For daily DCA purchases, subscribe to daily asset data. self._symbol = self.add_crypto("BTCUSD", Resolution.DAILY, Market.BITFINEX).symbol def on_data(self, slice: Slice) -> None: # If you haven't invested for 30 consecutive days yet, continue buying. if self._symbol in slice.bars and self.day_count < 30: # Calculate the order size for $10,000 USD using the current price. size = 10000 / slice.bars[self._symbol].close self.market_order(self._symbol, size) # Increase the day count. self.day_count += 1
Example 2: Inter-Market Spread
There is always a small discrepancy in the price of the same Crypto pair trading on different exchanges. Although you can't currently live trade on algorithm with multiple brokerages, you can study the cointegration pattern and implement 2 live nodes to capture the arbitrage opportunity. The following algorithm demonstrates how to obtain the spread between the BTCUSD pair on Kraken and on Coinbase:
class CryptoExampleAlgorithm(QCAlgorithm): def initialize(self) -> None: # Subscribe to BTC/USD on 2 different exchanges. self.kraken_btc = self.add_crypto("BTCUSD", market=Market.KRAKEN).symbol self.coinbase_btc = self.add_crypto("BTCUSD", market=Market.COINBASE).symbol def on_data(self, slice: Slice) -> None: # Only calculate the spread if the prices on both exchanges are in the current Slice. if self.kraken_btc in slice.bars and self.coinbase_btc in slice.bars: # Calculate the spread between the 2 exchanges, making sure the comparison is always in the same direction. spread = slice.bars[self.kraken_btc].close - slice.bars[self.coinbase_btc].close # Plot the spread between the 2 exchanges in a custom plot. self.plot("BTC Close Spread", "Spread", spread)