Crypto Futures
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 Futures 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:
Margin Interest Rates
MarginInterestRate
objects contain the margin interest rate, which is a cost associated with trading on margin.
To get the MarginInterestRate
objects in the Slice
, index the margin_interest_rates
property of the Slice
with the Crypto Future Symbol
.
The margin_interest_rates
property of the Slice
may not contain data for your Symbol
.
To avoid issues, check if the property contains data for your Crypto Future before you index it with the Crypto Future Symbol
.
def on_data(self, slice: Slice) -> None: margin_interest_rate = slice.margin_interest_rates.get(self._symbol) if margin_interest_rate: interest_rate = margin_interest_rate.interest_rate
You can also iterate through the margin_interest_rates
dictionary. The keys of the dictionary are the Symbol
objects and the values are the margin_interest_rate
objects.
def on_data(self, slice: Slice) -> None: for symbol, margin_interest_rate in slice.margin_interest_rates.items(): interest_rate = margin_interest_rate.interest_rate
MarginInterestRate
objects have the following properties:
Examples
The following examples demonstrate some common practices for handling Crypto Futures data.
Example 1: Adjust Holdings on Margin Payments
Perpetual futures have a mechanism called funding , where if you're holding a position at certain times (the funding timestamp), you might have to pay or receive funding based on the difference between the perpetual contract price and the spot price. This example demonstrates how to adjust your position in Crypto Future based on the funding rate. The funding is settled in the quote currency, which is USDT in this case. When you receive USDT, the algorithm increases its position size in BTCUSDT. When you pay USDT, the algorithm decreases its position size.
class CryptoFutureAlgorithm(QCAlgorithm): def initialize(self) -> None: self.set_start_date(2020, 4, 1) self.set_end_date(2024, 10, 1) # Set brokerage and account type to match your brokerage environment for accurate fee and margin behavior. self.set_brokerage_model(BrokerageName.BINANCE, AccountType.MARGIN) # In the Binance brokerage, you can't trade with USD. # Set the account currency as USDT and add the starting cash. self.set_account_currency("USDT", 1000000) # Subscribe to the BTCUSDT perpetual Future contract. btcusdt = self.add_crypto_future("BTCUSDT") self._symbol = btcusdt.symbol # Save the lot size to avoid order errors. self._lot_size = btcusdt.symbol_properties.lot_size # Set the starting BTC balance to 10. btcusdt.base_currency.set_amount(10) # Create a member to track the current day. self._day = -1 def on_data(self, slice: Slice) -> None: # Only place orders when the market is open since market on open orders aren't supported. if not self.portfolio.invested and self.is_market_open(self._symbol): # Open a long position in the perpetual Crypto Future. self.market_order(self._symbol, 10) # Only run the following logic once per day. if self._day == self.time.day: return # Get the current margin interest rate. interest_rate = slice.margin_interest_rates.get(self._symbol) if not interest_rate: return # Calculate the funding payment. holding = self.portfolio[self._symbol] position_value = holding.get_quantity_value(holding.quantity).amount interest_rate = slice.margin_interest_rates[self._symbol].interest_rate funding = interest_rate * position_value / holding.security.price # Increase/decrease the position size based on the funding payment. quantity = -funding // self._lot_size * self._lot_size if quantity: self.market_order(self._symbol, quantity) # Plot the portfolio state. self.plot("CashBook", "USDT", self.portfolio.cash_book['USDT'].amount) self.plot("CashBook", "BTC", self.portfolio.cash_book['BTC'].amount) self.plot("Quantity", "BTCUSDT", self.portfolio[self._symbol].quantity) self._day = self.time.day
Example 2: Future-Spot Arbitrage
Long-short arbitrage involves simultaneously trading BTCUSDT in the spot market and BTCUSDT Futures with the same size. The following algorithm monitors the spread between these markets. If the spot price exceeds the front-month Future price by a threshold, it shorts the spot market and buys the Future contract (and vice vera for the other way around), assuming their discrepancies will be wiped out within a short period due to market efficiency. It closes the positions after the spread inverts.
class CryptoFutureAlgorithm(QCAlgorithm): def initialize(self) -> None: self.set_start_date(2022, 1, 1) self.set_end_date(2023, 1, 1) # Set brokerage and account type to match your brokerage environment for accurate fee and margin behavior. self.set_brokerage_model(BrokerageName.BINANCE, AccountType.MARGIN) # In the Binance brokerage, you can't trade with USD. # Set the account currency as USDT and add the starting cash. self.set_account_currency("USDT", 50000000) # Add the BTCUSDT spot and Futures markets. future = self.add_crypto_future("BTCUSDT", market=Market.BINANCE) self.multiplier = future.symbol_properties.contract_multiplier self._future = future.symbol self._spot = self.add_crypto("BTCUSDT", market=Market.BINANCE).symbol def on_data(self, slice: Slice) -> None: # Get the price of each market. if not (self._spot in slice.bars and self._future in slice.bars): return spot_price = slice.bars[self._spot].price future_price = slice.bars[self._future].price # Buy low sell high: if one's price is above another by the set threshold, sell it and buy the other security. # The threshold ensures there is enough profit potential to overcome the fees and slippage. # Make sure to equalize the actual order size with the contract multiplier. if spot_price >= future_price * 1.02: quantity = self.calculate_order_quantity(self._future, 0.5) self.market_order(self._spot, -quantity * self.multiplier) self.market_order(self._future, quantity) elif spot_price * 1.02 <= future_price: quantity = self.calculate_order_quantity(self._future, 0.5) self.market_order(self._spot, quantity * self.multiplier) self.market_order(self._future, -quantity) # When the mispricing converges, close both positions to earn the spread. elif ((round(self.portfolio[self._spot].quantity, 2) < 0 and spot_price < future_price) or (round(self.portfolio[self._spot].quantity, 2) > 0 and spot_price > future_price)): self.liquidate()