15 min or 30 min tradebar with add equity and add option for universe of symbols

To help Manoj implement a strategy where he checks if a stock's 30-minute price is greater than its 9 EMA and 13 EMA and trades its ATM option from a universe that changes hourly, here is a step-by-step guide:

Step-by-Step Implementation

1. Initialize the Algorithm: Set the start and end dates, initial cash, and define the universe with hourly resolution using add_universe.

2. Define Universe Selection: Implement a selection function that updates the universe every hour, choosing symbols based on your criteria.

3. Add Indicators: Use the EMA method to create 9-period and 13-period EMA indicators for each symbol in the universe at a 30-minute resolution.

4. Check Conditions in OnData: In the on_data method, check if the current price of each symbol is greater than both its 9 EMA and 13 EMA.

5. Add Options: Use the AddOption method to add ATM options for the symbols that meet the condition.

6. Trade Options: Implement logic to trade the ATM options when the conditions are met, ensuring to handle entry and exit points.

7. Manage Portfolio: Include logic to manage the portfolio, such as liquidating positions when conditions are no longer met or at the end of the trading day.

Example Code

Here's a simplified example to illustrate these steps:

class MyOptionTradingAlgorithm(QCAlgorithm):
    def initialize(self):
        self.set_start_date(2021, 1, 1)
        self.set_end_date(2021, 12, 31)
        self.set_cash(100000)
        self.universe_settings.resolution = Resolution.HOUR
        self.add_universe('my_universe', Resolution.HOUR, self.selection)
        self.ema9 = {}
        self.ema13 = {}

    def selection(self, time):
        # Define your universe selection logic here
        return ["AAPL", "MSFT", "GOOG"]  # Example symbols

    def on_securities_changed(self, changes):
        for security in changes.added_securities:
            symbol = security.symbol
            self.ema9[symbol] = self.ema(symbol, 9, Resolution.MINUTE, lambda x: x.close)
            self.ema13[symbol] = self.ema(symbol, 13, Resolution.MINUTE, lambda x: x.close)

    def on_data(self, data):
        for symbol in self.ema9:
            if symbol not in data:
                continue

            price = data[symbol].close
            if price > self.ema9[symbol].current.value and price > self.ema13[symbol].current.value:
                self.trade_atm_option(symbol)

    def trade_atm_option(self, symbol):
        option = self.add_option(symbol, Resolution.MINUTE)
        chain = self.option_chain_provider.get_option_contract_list(option, self.time)
        atm_option = sorted(chain, key=lambda x: abs(x.strike - self.securities[symbol].price))[0]
        self.add_option_contract(atm_option, Resolution.MINUTE)
        self.set_holdings(atm_option, 0.1)  # Example allocation

    def set_holdings(self, symbol, fraction):
        super().set_holdings(symbol, fraction)

Key Methods and Concepts

• AddOption: Adds an option security to the algorithm.
• EMA: Creates an Exponential Moving Average indicator.
• OnData: Main event handler for new data.
• SetHoldings: Sets the holdings for a given security.

This example provides a basic framework to get started with the strategy Manoj wants to implement. Adjust the universe selection, trading logic, and risk management according to specific needs and preferences.

Sources: Basic Template Options Hourly Algorithm, Futures Fast Trend Following, with Trend Strength, Basic Template Options Algorithm, Basic Template Options Framework Algorithm, Basic Template Options Daily Algorithm