How to Avoid Look-Ahead Bias with FuncSecuritySeeder in QuantConnect

To correctly set the starting price for assets in your QuantConnect algorithm—ensuring they have a valid price immediately upon initialization rather than waiting for the first market data tick—you should use a Security Initializer with a Security Seeder.

By default, LEAN initializes security prices to $0. This can cause issues if you attempt to calculate margin, buying power, or place trades immediately inside initialize or before the first data slice arrives.

The Standard Solution: Security Seeder

The most robust way to handle this is to configure the security_initializer in your initialize method. This tells the engine to look up the last known price for every asset added to your universe (manually or dynamically) and seed the price model.

Here is the implementation using the Python PEP8 API:

# region imports
from AlgorithmImports import *
# endregion

class AssetPriceSeedingAlgorithm(QCAlgorithm):

    def initialize(self):
        self.set_start_date(2020, 1, 1)
        self.set_cash(100000)

        # ------------------------------------------------------------
        # SET SECURITY INITIALIZER
        # ------------------------------------------------------------
        # This line ensures that any security added to the algorithm 
        # (via AddEquity or Universe Selection) gets its price seeded 
        # using the last known data available before the Start Date.
        self.set_security_initializer(
            BrokerageModelSecurityInitializer(
                self.brokerage_model, 
                FuncSecuritySeeder(self.get_last_known_prices)
            )
        )

        # Example: Adding an equity
        self.spy = self.add_equity("SPY", Resolution.MINUTE).symbol
        
        # Because of the initializer above, we can access the price immediately
        # even though OnData has not run yet.
        self.debug(f"Initial SPY Price: {self.securities[self.spy].price}")

    def on_data(self, data):
        if not self.portfolio.invested:
            self.set_holdings("SPY", 1.0)

Explanation of Components

1. self.set_security_initializer(...): This sets the model used to initialize new securities.
2. BrokerageModelSecurityInitializer: This is the standard model that ensures securities are set up with the correct leverage, fee models, and buying power models according to your selected brokerage (e.g., Interactive Brokers, Bitfinex, etc.).
3. FuncSecuritySeeder(self.get_last_known_prices): This is the critical part for pricing.
   • FuncSecuritySeeder: A helper class that executes a function to determine the seed price.
   • self.get_last_known_prices: A built-in method in QCAlgorithm that queries the data history for the most recent close price before the current algorithm time.

Alternative: Manual History Call

If you do not want to apply this globally to all assets and only need the starting price for a specific calculation inside initialize, you can make a direct history request:

    def initialize(self):
        self.set_start_date(2020, 1, 1)
        self.spy = self.add_equity("SPY", Resolution.MINUTE).symbol

        # Manually fetch the last price (e.g., last daily close)
        history = self.history(self.spy, 1, Resolution.DAILY)
        
        if not history.empty:
            start_price = history['close'].iloc[-1]
            self.debug(f"Manual History Price: {start_price}")

Common Pitfalls

• Zero Division Errors: Without a seeder, indicators or logic that divide by self.securities[symbol].price will crash if called before the first data slice.
• Margin Calls on Start: If you have existing cash positions or holdings (e.g., in live trading) and the price is $0, the portfolio value calculation may be incorrect, potentially triggering a margin call warning immediately.
• Universe Selection: When using dynamic universes, assets are added constantly. The set_security_initializer method is preferred because it automatically handles every new asset added by the universe without requiring manual history calls for each one.

Q&A

Q: Does get_last_known_prices use look-ahead bias?
A: No. It retrieves the last available data point before the algorithm's current time. For example, if your start date is Jan 1st, it will look for the closing price of Dec 31st (or the last trading day).

Q: Can I use this for custom data?
A: Yes, provided your custom data type supports history requests and has data available prior to the start date. However, get_last_known_prices is optimized for standard asset classes (Equity, Forex, Crypto, etc.).

Q: Why is my price still 0 after using the seeder?
A: This usually happens if the asset is highly illiquid or new, and there is no historical data available in the LEAN data folder for the period immediately preceding your start date.

Q: Does this affect Live Trading?
A: In Live Trading, LEAN attempts to load your actual holdings and prices from the brokerage. However, using the SecuritySeeder is still best practice to ensure that any new assets added during execution (that you don't currently hold) have valid reference prices.