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Securities

Key Concepts

Introduction

A security is an individual financial asset that you can trade on an exchange. LEAN models these unique assets with a Security object, which the add_equity, add_crypto, and similar methods return. The securities property of the QCAlgorithm class is a dictionary where the keys are Symbol objects and the values are Security objects. The securities dictionary contains all of the securities that have been in the algorithm during its lifetime.

Select Language: 
# Add the SPY.
self._symbol = self.add_equity("SPY").symbol

Price

To get the current price of a security, index the securities dictionary and then access the price member.

Select Language: 
# Get the price of the security.
price = self.securities[self._symbol].price

Holdings

The Holdings property of a Security object contains information about the investment state and history of the security.

Select Language: 
# Use the holdings object to get information on the asset position, such as the quantity and average price.
holdings = self.securities[self._symbol].holdings
quantity = holdings.quantity

Alternatively, you can get the current holdings of a security with the portfolio dictionary.

Select Language: 
# Get the quantity of a security you hold through the portfolio dictionary.
holdings = self.portfolio[self._symbol]
quantity = holdings.quantity

Reality Models

Security objects contain references to the security level reality models. To customize the behavior of each security, configure its reality models.

Tradable Status

The is_tradable property shows whether you can trade a security. The property value is true when the security is in the universe, even if the data starts at a later day. Indices, canonical Option securities, and continuous Futures contracts are not tradable. In live mode, custom data objects are also not tradable, even if the custom data represents a tradable asset.

Select Language: 
# Check if the security is tradable.
tradable = self.securities[self._symbol].is_tradable

Symbol Properties

The symbol_properties property of a Security contains properties for that specific security, such as the lot size and the minimum price variation.

Select Language: 
# Round orders to a brokerage's lot size and minimum price variation using the symbol_properties.
symbol_properties = self.securities[self._symbol].symbol_properties
lot_size = symbol_properties.lot_size
minimum_price_variation = symbol_properties.minimum_price_variation

Market Hours

The exchange property of a Security contains information about the exchange that lists the security. For example, the exchange.hours property contains information on the trading days and hours.

Select Language: 
# Using the exchange object, check if the exchange's regular trading hours are open at the given time.
exchange = self.securities[self._symbol].exchange
hours = exchange.hours
is_open_now = hours.is_open(self.time, extended_market_hours=False)

Custom Security Properties

You can add attributes to the Security object. For example, you can add an exponential moving average.

Select Language: 
# Add an EMA indicator to the SPY Equity object.
equity = self.add_equity("SPY")
equity.ema = self.ema(equity.symbol, 10, Resolution.DAILY)

This feature is helpful because you can get the Security object from the securities object.

Select Language: 
# Get the SPY Equity object from the securities object to get the EMA indicator.
ema = self.securities["SPY"].ema.current.value

Properties

The Security type has the following properties:

Security Select Language:
    This securities QuantConnect.Interfaces.IShortableProvider
  • shortable_provider: IShortableProvider
    • Gets all the subscriptions for this security
  • subscriptions: List<SubscriptionDataConfig>
    • QuantConnect.Securities.Security.Symbol for the asset.
  • symbol: Symbol
    • Gets the Cash object used for converting the quote currency to the account currency
  • quote_currency: Cash
    • Gets the symbol properties for this security
  • symbol_properties: SymbolProperties
    • Type of the security.
  • type: SecurityType
    • There has been at least one datapoint since our algorithm started running for us to determine price.
  • has_data: bool
    • Gets or sets whether or not this security should be considered tradable
  • is_tradable: bool
    • True if the security has been delisted from exchanges and is no longer tradable
  • is_delisted: bool
    • Data cache for the security to store previous price information.
  • cache: SecurityCache
    • Holdings class contains the portfolio, cash and processes order fills.
  • holdings: SecurityHolding
    • Exchange class contains the market opening hours, along with pre-post market hours.
  • exchange: SecurityExchange
    • Fee model used to compute order fees for this security
  • fee_model: IFeeModel
    • Fill model used to produce fill events for this security
  • fill_model: IFillModel
    • Slippage model use to compute slippage of market orders
  • slippage_model: ISlippageModel
    • Gets the portfolio model used by this security
  • portfolio_model: ISecurityPortfolioModel
    • Gets the buying power model used for this security
  • buying_power_model: IBuyingPowerModel
    • Gets the buying power model used for this security, an alias for QuantConnect.Securities.Security.BuyingPowerModel
  • margin_model: IBuyingPowerModel
    • Gets or sets the margin interest rate model
  • margin_interest_rate_model: IMarginInterestRateModel
    • Gets the settlement model used for this security
  • settlement_model: ISettlementModel
    • Gets the volatility model used for this security
  • volatility_model: IVolatilityModel
    • Customizable data filter to filter outlier ticks before they are passed into user event handlers. By default all ticks are passed into the user algorithms.
  • data_filter: ISecurityDataFilter
    • Customizable price variation model used to define the minimum price variation of this security. By default minimum price variation is a constant find in the symbol-properties-database.
  • price_variation_model: IPriceVariationModel
    • Provides dynamic access to data in the cache
  • data: object
    • Read only property that checks if we currently own stock in the company.
  • hold_stock: bool
    • Alias for HoldStock - Do we have any of this security
  • invested: bool
    • Local time for this market
  • local_time: DateTime
    • Get the current value of the security.
  • price: decimal
    • Leverage for this Security.
  • leverage: decimal
    • If this uses tradebar data, return the most recent high.
  • high: decimal
    • If this uses tradebar data, return the most recent low.
  • low: decimal
    • If this uses tradebar data, return the most recent close.
  • close: decimal
    • If this uses tradebar data, return the most recent open.
  • open: decimal
    • Access to the volume of the equity today
  • volume: decimal
    • Gets the most recent bid price if available
  • bid_price: decimal
    • Gets the most recent bid size if available
  • bid_size: decimal
    • Gets the most recent ask price if available
  • ask_price: decimal
    • Gets the most recent ask size if available
  • ask_size: decimal
    • Access to the open interest of the security today
  • open_interest: int
    • Gets the fundamental data associated with the security if there is any, otherwise null.
  • fundamentals: Fundamental
    • Gets or sets the specified custom property through the indexer. This is a wrapper around the QuantConnect.Securities.Security.Get``1(System.String) and QuantConnect.Securities.Security.Add(System.String,System.Object) methods.
  • item: object
    • A null security leverage value
  • NULL_LEVERAGE: decimal

Examples

The following examples demonstrate some common practices for securities.

Example 1: Accessing Properties

The following example shows how to access various propeties of the Security object.

Select Language: 
class SecuritiesUsageAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self.set_start_date(2023, 1, 1)
        self.set_end_date(2023, 1, 2)

        self.spy = self.add_equity("SPY")

    def on_data(self, slice: Slice) -> None:
        # OHLCV data.
        price = self.spy.close
        volume = self.spy.volume
        # Quote data.
        quote_size = self.spy.bid_size
        quote_price = self.spy.ask_price
        # Symbol Properties.
        lot_size = self.spy.symbol_properties.lot_size
        # Fundamentals cached data.
        pe_ratio = self.spy.fundamentals.valuation_ratios.pe_ratio
        # Properties, e.g. is tradeble, is market open, leverage, ...
        market_open = self.spy.is_market_open
        leverage = self.spy.leverage
        # Models, e.g. margin model, buying power model, ...
        margin_model = self.spy.margin_model
        # Portfolio Holdings reference.
        holding = self.spy.holdings

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