CFD
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 QuoteBars
DataDictionary
is made up of QuoteBar
objects. To access individual data points in the dictionary, you can index the dictionary with the CFD ticker or symbol
, but we recommend you use the symbol
.
To view the resolutions that are available for CFD data, see Resolutions.
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 CFD symbol
. If the CFD doesn't actively get quotes or you are in the same time step as when you added the CFD subscription, the Slice
may not contain data for your symbol
. To avoid issues, check if the Slice
contains data for your CFD before you index the Slice
with the CFD 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 CFD. A quote tick is an offer to buy or sell the CFD 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 CFD doesn't actively trade or you are in the same time step as when you added the CFD subscription, the Slice
may not contain data for your symbol
. To avoid issues, check if the Slice
contains data for your CFD before you index the Slice
with the CFD 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 common practices for handling CFD data.
Example 1: Buy And Hold
The following algorithm is a buy-and-hold strategy on the DE30EUR CFD contract. We only place orders when quote data is available to avoid stale fills. Also, we set the algorithm time zone to Berlin and the account currency to Euros for a more convenient comparison.
class CfdExampleAlgorithm(QCAlgorithm): def initialize(self) -> None: self.set_start_date(2024, 1, 1) self.set_end_date(2025, 1, 1) # Set the timezone as Berlin to conveniently compare the data. self.set_time_zone(TimeZones.BERLIN) # Set the account currency to EUR to trade the DAX CFD. self.set_account_currency("EUR", 10000) # Request the CFD data to trade. self._dax = self.add_cfd("DE30EUR").symbol def on_data(self, slice: Slice) -> None: # Trade based on updated data; CFDs only have quote data. bar = slice.quote_bars.get(self._dax) if bar: # Buy and hold DAX CFD. if not self.portfolio[self._dax].is_long: self.set_holdings(self._dax, 0.5)
Example 2: Global Indices
The following algorithm buys and holds various Index CFDs that trade in different market hours and currencies during their opening hours.
class CfdExampleAlgorithm(QCAlgorithm): def initialize(self) -> None: self.set_start_date(2024, 1, 1) self.set_end_date(2025, 1, 1) # Request the CFD data to trade. # We include CFD contracts that trade in different market hours to stay in the market. self._dax = self.add_cfd("DE30EUR").symbol self._sgx = self.add_cfd("SG30SGD").symbol self._dow = self.add_cfd("US30USD").symbol # Set scheduled events to hold each CFD contract in their market opening hours. # Buy and hold after the market opens. self.schedule.on( self.date_rules.every_day(self._dax), self.time_rules.after_market_open(self._dax, 1), lambda: self.set_holdings(self._dax, 0.3) ) self.schedule.on( self.date_rules.every_day(self._sgx), self.time_rules.after_market_open(self._sgx, 1), lambda: self.set_holdings(self._sgx, 0.3) ) self.schedule.on( self.date_rules.every_day(self._dow), self.time_rules.after_market_open(self._dow, 1), lambda: self.set_holdings(self._dow, 0.3) ) # Liquidate before market close. self.schedule.on( self.date_rules.every_day(self._dax), self.time_rules.before_market_close(self._dax, 1), lambda: self.liquidate(self._dax) ) self.schedule.on( self.date_rules.every_day(self._sgx), self.time_rules.before_market_close(self._sgx, 1), lambda: self.liquidate(self._sgx) ) self.schedule.on( self.date_rules.every_day(self._dow), self.time_rules.before_market_close(self._dow, 1), lambda: self.liquidate(self._dow) )