Volatility trading

Here is a beginner-friendly QuantConnect Python algorithm template for backtesting a strategy from YouTube (such as your referenced video). This code is well-commented, uses daily data, covers a recent 5-year period, and provides clear placeholders to customize logic once you gather the strategy's specific rules.

from AlgorithmImports import *

class YoutubeStrategyTemplate(QCAlgorithm):
    def initialize(self) -> None:
        """
        Initialize the algorithm and set default parameters.
        """
        # 1. Backtest configuration ------------------------------------------
        self.set_start_date(2019, 1, 1)   # Last 5-year period; adjust as needed
        self.set_end_date(2024, 1, 1)
        self.set_cash(100000)            # Starting capital

        # 2. Asset universe ---------------------------------------------------
        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol

        # 3. Warm-up period for indicator calculations ------------------------
        #    Placeholder for custom indicator periods
        self._fast_period: int = 10
        self._slow_period: int = 30

        #    Example indicators (replace with your own)
        self._fast_sma = self.sma(self._symbol, self._fast_period, Resolution.DAILY)
        self._slow_sma = self.sma(self._symbol, self._slow_period, Resolution.DAILY)

        #    Warm-up ensures the indicators are “ready” before we begin
        self.set_warm_up(self._slow_period)

        # 4. Trade tracking variables ----------------------------------------
        self._in_position: bool = False  # Tracks if we currently hold the asset

        # 5. Logging ----------------------------------------------------------
        self.debug("Initialization complete. Ready to run backtest.")

    def on_data(self, data: Slice) -> None:
        """
        Event-driven method called for each new data point in the subscribed resolution.
        """
        # Skip logic until indicators are warmed up
        if self.is_warming_up:
            return

        # ---------------------------------------------------------------------
        # 1. Custom indicator logic (update / compute here)
        #    Replace the SMA example with your own indicator calculations.
        # ---------------------------------------------------------------------
        fast_value: float = float(self._fast_sma.current.value)
        slow_value: float = float(self._slow_sma.current.value)

        # ---------------------------------------------------------------------
        # 2. Entry rules (modify as needed)
        #    Example: Enter long when fast SMA crosses above slow SMA
        # ---------------------------------------------------------------------
        entry_signal: bool = False
        if not self._in_position and fast_value > slow_value:
            entry_signal = True

        # ---------------------------------------------------------------------
        # 3. Exit rules (modify as needed)
        #    Example: Exit when fast SMA crosses back below slow SMA
        # ---------------------------------------------------------------------
        exit_signal: bool = False
        if self._in_position and fast_value < slow_value:
            exit_signal = True

        # ---------------------------------------------------------------------
        # 4. Portfolio management
        # ---------------------------------------------------------------------
        if entry_signal:
            self.debug(f"{self.time} | ENTRY | {self._symbol} | fast: {fast_value:.2f} slow: {slow_value:.2f}")
            # Enter the trade (full allocation; change sizing as required)
            self.set_holdings(self._symbol, 1.0)
            self._in_position = True

        elif exit_signal:
            self.debug(f"{self.time} | EXIT  | {self._symbol} | fast: {fast_value:.2f} slow: {slow_value:.2f}")
            # Exit the trade
            self.liquidate(self._symbol)
            self._in_position = False

    def on_end_of_algorithm(self) -> None:
        """
        Called at the end of the backtest for final logging or custom statistics.
        """
        self.debug("Backtest complete.")

How to proceed:

• Watch the strategy rules in your YouTube video.
• Replace the SMA logic and entry/exit signal conditions with the strategy's logic from the video.
• Adjust the universe, indicator types, periods, and portfolio rules as needed.

If you post or summarize the strategy steps from the video, I can help you implement those exact rules inside this template!

Sources: Basic Template Continuous Future With Extended Market Algorithm, Indicator Suite Algorithm, Basic Template Algorithm, Liquid E T F Universe Framework Algorithm, Basic Template Index Daily Algorithm