Forex
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 Forex pair ticker or symbol
, but we recommend you use the symbol
.
To view the resolutions that are available for Forex 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 Forex pair symbol
. If the Forex pair doesn't actively get quotes or you are in the same time step as when you added the Forex pair subscription, the Slice
may not contain data for your symbol
. To avoid issues, check if the Slice
contains data for your Forex pair before you index the Slice
with the Forex pair 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 Forex pair. A quote tick is an offer to buy or sell the Forex pair 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 Forex pair doesn't actively trade or you are in the same time step as when you added the Forex pair subscription, the Slice
may not contain data for your symbol
. To avoid issues, check if the Slice
contains data for your Forex pair before you index the Slice
with the Forex pair 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 Forex data.
Example 1: Trade the USDJPY Spread
USDJPY is a Forex pair where the reserve banks of the base currency and quote currency act at different time, which results in a 12-hour period of seasonal peak volume. To reduce slippage costs, place orders when bid-ask spread is relatively low. The following algorithm demonstrates how to utilize the Minimum indicator to check for a narrow spread.
class ForexExampleAlgorithm(QCAlgorithm): def initialize(self) -> None: self.set_start_date(2004, 1, 1) # Add the USDJPY trading pair. self._forex = self.add_forex("USDJPY") # Create a Minimum indicator to track the lowest bid-ask spread for the past 12 hours. self._forex.spread_low = Minimum(12*60) # Warm up the indicator so it's immediately ready to use. self.warm_up_indicator(self._forex.symbol, self._forex.spread_low) def on_data(self, slice: Slice) -> None: # Ensure we have quote data for USDJPY in the current slice. if self._forex.symbol not in slice.quote_bars: return quote_bar = slice.quote_bars[self._forex.symbol] # Bid-ask spread = Ask price - Bid price bid_ask_spread = quote_bar.ask.close = quote_bar.bid.close # Update the spread minimum indicator to calculate the lowest bid-ask spread over the last 12 hours. self._forex.spread_low.update(quote_bar.end_time, bid_ask_spread) # Trade if the current spread is the lowest bid-ask spread, since it is the most efficient, # liquid price with lowest slippage. if not self.portfolio.invested and bid_ask_spread == self._forex.spread_low.current.value: self.market_order(self._forex.symbol, 1000) # Plot the bid-ask spread to validate the minimum bid-ask spread calculation. self.plot("Bid-Ask Spread", "spread", bid_ask_spread)
Example 2: 5-Minute EURUSD Trend Reversal
The following algorithm applies a mean reveral strategy to EURUSD. To smooth out the noise of minute resolution data, it consolidates the data into 5-minute bars and then calculates their 200-bar Exponential Moving Average (EMA). After every time the indicator updates, the algorithm checks for a trading opportuniity. When the current EURUSD price is below the EMA, it takes a long position. Otherwise, it takes a short position.
class ForexExampleAlgorithm(QCAlgorithm): def initialize(self) -> None: self.set_start_date(2003, 1, 1) self.set_end_date(2005, 1, 1) # Add EURUSD minute data. self._forex = self.add_forex("EURUSD") # Create an EMA indicator to estimate the trend of EURUSD. self._forex.ema = ExponentialMovingAverage(200) # Attach an event handler to the indicator to track its updates. self._forex.ema.updated += self.on_updated # Create a consolidator that produces 5-minute quote bars to reduce reduce noise. consolidator = QuoteBarConsolidator(timedelta(minutes=5)) # Define the consolidator handler so it updates the EMA with the consoildated bars. consolidator.DataConsolidated += lambda _, bar: self._forex.ema.update(bar.end_time, bar.close) # Warm up the indicator with historical data. for bar in self.history[QuoteBar](self._forex.symbol, 1000): consolidator.update(bar) # Subscribe the consolidator for automatic updates. self.subscription_manager.add_consolidator(self._forex.symbol, consolidator) def on_updated(self, sender: object, point: IndicatorDataPoint) -> None: # Wait until the indicator is ready. if not self._forex.ema.is_ready: return # Rebalance the portfolio. If the current price is below the EMA, long. Otherwise, short. self.set_holdings(self._forex.symbol, 0.5 if self._forex.price <= point.value else -0.5)
Example 3: New York Time Alignment
The following algorithm creates daily Forex bars that close at 4:30 PM Eastern Time (ET). This technique is helpful when you want to align the daily bars with the close of the New York.
class ForexExampleAlgorithm(QCAlgorithm): def initialize(self) -> None: # Add EURUSD minute data. self._forex = self.add_forex("EURUSD") # Create a daily bar consolidator with a custom period (4:30 PM-4:30 PM Eastern Time). consolidator = QuoteBarConsolidator(self._consolidation_period) # Attach a consolidation handler to plot the consolidated bars. consolidator.data_consolidated += lambda _, bar: self.plot("Consolidated Bar", "Close", bar.close) # Subscribe the consolidator for automatic updates. self.subscription_manager.add_consolidator(self._forex.symbol, consolidator) def _consolidation_period(self, dt: datetime) -> CalendarInfo: # Set the start time to be 4:30 PM of the previous day. start = dt.replace(hour=16, minute=30, second=0, microsecond=0) period = timedelta(1) if dt.hour < 17: start -= period # Set the period to 1 day to make daily bars. return CalendarInfo(start, period)