US Equity

To create an Equity subscription, in the Initializeinitialize method, call the AddEquityadd_equity method. The AddEquityadd_equity method returns an Equity object, which contains a Symbolsymbol property. Save a reference to the Symbolsymbol so you can use it in OnDataon_data to access the security data in the Slice.

_symbol = AddEquity("SPY").Symbol;

self._symbol = self.add_equity("SPY").symbol

The AddEquityadd_equity method creates a subscription for a single Equity asset and adds it to your user-defined universe. To create a dynamic universe of Equities, add an Equity universe or an Equity Universe Selection model.

To view the supported assets in the US Equities dataset, see the Data Explorer. It doesn't include Over-the-Counter (OTC) stocks.

The following table shows the available resolutions and data formats for Equity subscriptions:

Resolution	TradeBar	QuoteBar	Trade Tick	Quote Tick
TickTICK
SecondSECOND
MinuteMINUTE
HourHOUR
DailyDAILY

The default resolution for Equity subscriptions is Resolution.MinuteResolution.MINUTE. To change the resolution, pass a resolution argument to the AddEquityadd_equity method.

_symbol = AddEquity("SPY", Resolution.Daily).Symbol;

self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol

To create custom resolution periods, see Consolidating Data.

When a stock like Apple is listed, it’s listed on Nasdaq. The open auction tick on Nasdaq is the price that’s used as the official open of the day. NYSE, BATS, and other exchanges also have opening auctions, but the only official opening price for Apple is the opening auction on the exchange where it was listed.

LEAN groups all of the US Equity exchanges under Market.USA. In live mode, the brokerage routes the orders to the exchange that provides the best price.

To set the market for a security, pass a market argument to the AddEquityadd_equity method.

_symbol = AddEquity("SPY", market: Market.USA).Symbol;

self._symbol = self.add_equity("SPY", market=Market.USA).symbol

The brokerage models have a default market for each asset class. If you set a brokerage model, you may not need to specify the market to use.

Fill forward means if there is no data point for the current slice, LEAN uses the previous data point. Fill forward is the default data setting. If you disable fill forward, you may get stale fills or you may see trade volume as zero.

To disable fill forward for a security, set the fillForwardfill_forward argument to false when you create the security subscription.

AddEquity("SPY", fillForward: false);

self.add_equity("SPY", fill_forward=False)

LEAN models buying power and margin calls to ensure your algorithm stays within the margin requirements. In backtests, the default leverage for margin accounts is 2x leverage and leverage is not available for cash accounts. To change the amount of leverage you can use for a security, pass a leverage argument to the AddEquityadd_equity method.

_symbol = AddEquity("SPY", leverage: 3).Symbol;

self._symbol = self.add_equity("SPY", leverage=3).symbol

In live trading, the brokerage determines how much leverage you may use. For more information about the leverage they provide, see Brokerages.

By default, your security subscriptions only cover regular trading hours. To subscribe to pre and post-market trading hours for a specific asset, enable the extendedMarketHoursextended_market_hours argument when you create the security subscription.

AddEquity("SPY", extendedMarketHours: true);

self.add_equity("SPY", extended_market_hours=True)

You only receive extended market hours data if you create the subscription with minute, second, or tick resolution. If you create the subscription with daily or hourly resolution, the bars only reflect the regular trading hours.

To view the schedule of regular and extended market hours, see Market Hours.

The data normalization mode defines how historical data is adjusted for corporate actions. The data normalization mode affects the data that LEAN passes to OnDataon_data and the data from history requests. By default, LEAN adjusts US Equity data for splits and dividends to produce a smooth price curve, but the following data normalization modes are available for Equity subscriptions:

If you use AdjustedADJUSTED, SplitAdjustedSPLIT_ADJUSTED, or TotalReturnTOTAL_RETURN, we use the entire split and dividend history to adjust historical prices. This process ensures you get the same adjusted prices, regardless of the backtest end date.

To set the data normalization mode for a security, pass a dataNormalizationModedata_normalization_mode argument to the AddEquityadd_equity method.

_symbol = AddEquity("SPY", dataNormalizationMode: DataNormalizationMode.Raw).Symbol;

self._symbol = self.add_equity("SPY", data_normalization_mode=DataNormalizationMode.RAW).symbol

The ScaledRawSCALED_RAW data normalization is only for history requests. When you use ScaledRawSCALED_RAW, we use the split and dividend history before the algorithm's current time to adjust historical prices. The ScaledRawSCALED_RAW data normalization model enables you to warm up indicators with adjusted data when you subscribe to RawRAW security data.

The AddEquityadd_equity method returns an Equity object, which have the following properties:

The following examples demonstrate some common practices for requesting US Equity data.

Example 1: Add Multiple Assets

The following algorithm adds multiple US Equities with extended market hours data. Extended market hours data enables you to monitor price movements during pre-market and after-hours trading sessions.

public class IndexDemoAlgorithm : QCAlgorithm
{
    private List<Equity> _equities = new();

    public override void Initialize()
    {
        // Create a list of tickers you want to add.
        var tickers = new string[] { "AAPL", "TSLA", "GOOG" };
        // Add extended market hours data for each US Equity.
        _equities = tickers.Select(ticker => AddEquity(ticker, extendedMarketHours: true)).ToList();
    }
}

class USEquityDemoAlgorithm(QCAlgorithm):

    def initialize(self):
        # Create a list of tickers you want to add.
        tickers = ["AAPL", "TSLA", "GOOG"]
        # Add extended market hours data for each US Equity.
        self._equities = [self.add_equity(ticker, extended_market_hours=True) for ticker in tickers]

Example 2: Import External Trade Signals

Say you source trading signals from outside your algorithm and organize them into a CSV file on Dropbox with the following structure:

#ticker,source1,source2,source3,source4,source5
AAPL,1.25,1.10,1.32,1.13,1.21
AMZN,1.15,1.05,1.07,1.11,1.03
GOOG,0.97,0.99,0.98,1.04,0.95
META,1.04,1.03,1.10,1.04,0.98
MSFT,0.92,0.96,0.98,1.01,0.98

The following algorithm uses a Scheduled Event to periodically pull the CSV file from Dropbox , parse its signals, and place trades. To determine the target weight for each asset, it takes the mean score from all the sources. If the mean score is greater than 1, it allocates 20% of the portfolio to the asset. Otherwise, it allocates -20% to the asset.

public class USEquityDemoAlgorithm : QCAlgorithm
{
    public override void Initialize()
    {
        SetStartDate(2020, 1, 10);
        // Add the US Equities.
        foreach (var ticker in new[] { "AAPL", "AMZN", "GOOG", "META", "MSFT" })
        {
            AddEquity(ticker, Resolution.Daily);
        }
        // Set up a Scheduled Event to download the data file and place orders.
        Schedule.On(DateRules.MonthStart(), TimeRules.Midnight, DownloadDataAndPlaceOrders);
    }

    private void DownloadDataAndPlaceOrders()
    {
        // Download the file from DropBox.
        var file = Download("<DropBoxUrl>&dl=1");

        // Convert the text file into trading signals.
        var signalByTicker = new Dictionary<string, decimal>();
        foreach (var line in file.Split(new[] { '\n', '\r' }, StringSplitOptions.RemoveEmptyEntries).Skip(1))
        {
            var values = line.Split(',');
            signalByTicker[values[0]] = values.Skip(1).Select(decimal.Parse).ToArray().Average();
        }

        // Place orders according to the signals.
        var targets = signalByTicker.Select(kvp => new PortfolioTarget(kvp.Key, kvp.Value >= 1m ? 0.2m : -0.2m));
        SetHoldings(targets.ToList(), true);
    }
}

from io import StringIO


class USEquityDemoAlgorithm(QCAlgorithm):
        
    def initialize(self):
        self.set_start_date(2020, 1, 10)

        # Add the US Equities.
        for ticker in ["AAPL", "AMZN", "GOOG", "META", "MSFT"]:
            self.add_equity(ticker, Resolution.DAILY)
        
        # Set up a Scheduled Event to download the data file and place orders.
        self.schedule.on(
            self.date_rules.month_start(),
            self.time_rules.midnight,
            self._download_data_and_place_orders
        )

    def _download_data_and_place_orders(self):
        # Download the file from DropBox.
        file = self.download("<DropBoxUrl>&dl=1")

        # Convert the text file into trading signals.
        signals = pd.read_csv(StringIO(file), index_col=0).mean(axis=1)

        # Place orders according to the signals.
        targets = [
            PortfolioTarget(ticker, 0.2 if signal >= 1 else -0.2)
            for ticker, signal in signals.items()
        ]
        self.set_holdings(targets, True)