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How to Place an Iron Condor Order With Lumibot?

Introduction

In the realm of options trading, there are a multitude of strategies available to fit different market scenarios and trader objectives. One of the more popular strategies, especially during periods of low volatility, is the Iron Condor. This advanced options strategy is known for its ability to generate consistent returns with limited risk. However, executing and managing an Iron Condor position manually can be time-consuming and error-prone, especially for traders with multiple positions or complex portfolios. This is where Lumibot, an algorithmic trading platform, becomes a game-changer.

Lumibot empowers traders to automate the execution of advanced strategies like the Iron Condor, eliminating manual effort and ensuring that trades are placed systematically based on predefined criteria. 

In this blog, we’ll take a detailed look at how you can place an Iron Condor order using Lumibot, what the strategy entails, and how to optimize its use for your trading objectives.

What Is an Iron Condor?

An Iron Condor is an advanced options strategy that allows traders to profit from a stock or index that is expected to have low volatility. It involves the simultaneous selling of two option spreads: a bull put spread and a bear call spread. This creates a “range-bound” strategy where the trader expects the price of the underlying asset to stay between two strike prices until the options’ expiration date.

The overall goal of an Iron Condor is for the stock or index to stay between the two middle strike prices (A and C) by the time the options expire. The strategy is designed to take advantage of the time decay in options pricing (theta), meaning you benefit if the options lose value as the expiration date nears and the price stays within a defined range.

Why Use Lumibot for Iron Condor Trading?

Trading an Iron Condor manually, while effective, can be time-consuming and may require frequent attention, especially as market prices change or expiration dates approach. This is where Lumibot offers a clear advantage. Lumibot is an algorithmic trading platform that allows you to automate your Iron Condor strategy, saving you time and improving execution accuracy. Here are some of the key benefits of using Lumibot for Iron Condor Trading: 

1. Automated Execution

With Lumibot, once your strategy parameters are set (strike prices, expiration dates, risk levels), the platform automatically places the trades for you. This means you don’t need to be constantly watching the markets to ensure your strategy is executed correctly. Automation also helps eliminate human error in placing trades, ensuring precision with every order.

2. Backtesting Capabilities

One of Lumibot’s core strengths is its ability to backtest strategies using historical data. You can evaluate how an Iron Condor strategy would have performed in different market environments before committing real capital. This allows you to fine-tune the parameters (strike distances, expiration dates, etc.) for better results based on past performance.

3. Live Trading with Multiple Brokers

Lumibot connects with popular brokers like Interactive Brokers, Alpaca, and TD Ameritrade, allowing seamless integration with your existing trading account. Once connected, you can deploy your Iron Condor strategy in real-time, with Lumibot managing the trades on your behalf.

4. Customization and Flexibility

The beauty of Lumibot lies in its flexibility. Whether you’re new to options trading or a seasoned pro, Lumibot allows you to fully customize your trading logic. From defining profit-taking rules to setting up automatic exit strategies when your position hits a certain threshold, you can build a strategy that aligns with your risk tolerance and trading goals.

5. Efficiency in Trade Management

Managing multiple legs of an Iron Condor position can be overwhelming. Lumibot simplifies this by providing real-time monitoring of your positions and automatically executing adjustments based on market conditions. Whether you need to close one leg, roll a position to a new expiration date, or exit the entire trade, Lumibot does it all programmatically.

    Overall, Lumibot empowers you to focus on strategy optimization while it handles the tedious tasks of executing and managing trades.

    A Step-by-Step Guide to Placing an Iron Condor Order With Lumibot

    Step 1: Import Necessary Classes

    The code starts by importing necessary modules to define the strategy and interact with time and asset entities. This sets up the foundation by bringing in essential classes like Strategy and Asset that will be used to define and execute the trading algorithm.

    from datetime import datetime
    from lumibot.strategies.strategy import Strategy
    from lumibot.entities import Asset

    Step 2: Parameters Setup

    Within the IronCondorStrategy class, a dictionary called parameters is defined. This dictionary contains the options strategy setup: the stock symbol (e.g., SPY), expiration date, strike prices for the calls and puts, and the quantity of contracts. These parameters define the four legs of the Iron Condor and the amount of contracts to be traded.

    class IronCondorStrategy(Strategy):
        parameters = {
            "symbol": "SPY",  # Underlying stock symbol
            "expiration_date": "2024-01-19",  # Option expiration date
            "short_call_strike": 155,  # Strike price for short call
            "long_call_strike": 160,   # Strike price for long call
            "short_put_strike": 145,   # Strike price for short put
            "long_put_strike": 140,    # Strike price for long put
            "quantity": 1               # Number of contracts for each leg
        }

    Step 3: Initialize Function

    In the initialize method, the sleeptime variable is set. This determines how often the strategy will run, in this case, every day (“1D”). This setup ensures that the strategy will execute daily to monitor the market or manage positions.

     def initialize(self):
            self.sleeptime = "1D"  # Adjust as needed

    Step 4: On Trading Iteration

    This function is called during each iteration of the strategy (daily, in this case). It performs the core functions like setting the current date/time and defining the four legs of the Iron Condor. This part of the code defines the four legs of the Iron Condor:

    • Short call option (Sell call)
    • Long call option (Buy call)
    • Short put option (Sell put)
    • Long put option (Buy put)

    Each Asset object is created with the stock symbol (SPY), option expiration date, strike price, and whether it’s a call or put option.

    def on_trading_iteration(self):
            """Places an Iron Condor once and manages the position"""
    
            # Get current time and default expiration date to today
            current_time = self.get_datetime().date()
            self.log_message(f"Current datetime: {current_time}")
            
            # Set expiration date to today
            expiration_date = current_time
    
            # Fetch the current price of the underlying asset
            underlying_asset = Asset(
                symbol=self.parameters["symbol"],
                asset_type=Asset.AssetType.STOCK
            )
            underlying_price = self.get_last_price(underlying_asset.symbol)
    
            # Automatically determine the strike prices
            short_call_strike = round(underlying_price * 1.05, 2)  # Short call strike 5% above current price
            long_call_strike = round(underlying_price * 1.10, 2)   # Long call strike 10% above current price
            short_put_strike = round(underlying_price * 0.95, 2)   # Short put strike 5% below current price
            long_put_strike = round(underlying_price * 0.90, 2)    # Long put strike 10% below current price
    
            # Define the four legs of the Iron Condor
            short_call = Asset(
                symbol=self.parameters["symbol"],
                asset_type=Asset.AssetType.OPTION,
                expiration=expiration_date,
                strike=short_call_strike,
                right=Asset.OptionRight.CALL
            )
    
            long_call = Asset(
                symbol=self.parameters["symbol"],
                asset_type=Asset.AssetType.OPTION,
                expiration=expiration_date,
                strike=long_call_strike,
                right=Asset.OptionRight.CALL
            )
    
            short_put = Asset(
                symbol=self.parameters["symbol"],
                asset_type=Asset.AssetType.OPTION,
                expiration=expiration_date,
                strike=short_put_strike,
                right=Asset.OptionRight.PUT
            )
    
            long_put = Asset(
                symbol=self.parameters["symbol"],
                asset_type=Asset.AssetType.OPTION,
                expiration=expiration_date,
                strike=long_put_strike,
                right=Asset.OptionRight.PUT
            )

    Step 5: Logging Option Prices

    For each of the option legs, the strategy logs the price using get_last_price() and adds it to a graph for tracking.

    # Log option details
            for option in [short_call, long_call, short_put, long_put]:
                option_price = self.get_last_price(option.symbol)
                self.log_message(f"Price of {option.symbol}: {option_price}")
                self.add_line(f"{option.symbol} Price", option_price)

    Step 6: Check for Existing Positions

    The strategy checks if the Iron Condor position has already been placed by looking at the current positions. If there are no significant positions (other than cash), the Iron Condor order is created and placed.

    # Check if Iron Condor is already placed
            current_positions = self.get_positions()
            if len(current_positions) <= 1:  # Only USD present
                quantity = self.parameters["quantity"]
    
                # Create orders for each leg
                orders = [
                    self.create_order(short_call.symbol, quantity, "sell"),
                    self.create_order(long_call.symbol, quantity, "buy"),
                    self.create_order(short_put.symbol, quantity, "sell"),
                    self.create_order(long_put.symbol, quantity, "buy")
                ]
    
                # Submit all orders
                for order in orders:
                    self.submit_order(order)

    Backtesting Your Strategy With Lumibot

    The following code snippet demonstrates how to run the IronCondorStrategy using the Alpaca broker with Lumibot. This setup allows traders to leverage the capabilities of Lumibot to automate their options trading strategies effectively.

    if __name__ == "__main__":
      
        from lumibot.brokers import Alpaca
        from lumibot.traders import Trader
    
        ALPACA_CONFIG = {
                "API_KEY": "",  # Add your Alpaca API Key here
                "API_SECRET": "",  # Add your Alpaca API Secret here
                "PAPER": True,  # Set to True for paper trading, False for live trading
            }
            
           
            # Set up the Alpaca broker and run the strategy
        broker = Alpaca(ALPACA_CONFIG)
        strategy = IronCondorStrategy(broker=broker)
        trader = Trader()
        trader.add_strategy(strategy)
        trader.run_all()

    Key Points in the Code

    • Broker Configuration: The ALPACA_CONFIG dictionary contains the necessary API credentials for the Alpaca brokerage account.

    API_KEY: Your unique API key provided by Alpaca, which authorizes your trading requests.

    API_SECRET: Your secret key associated with the API key, used for authentication.

    PAPER: This boolean flag indicates whether to use paper trading (simulated environment) or live trading. Setting this to True enables you to test your strategy without risking real capital.

    • Broker Initialization: An instance of the Alpaca broker is created using the configuration defined in ALPACA_CONFIG. This broker instance will handle the trading operations on the Alpaca platform.
    • Strategy Setup: The IronCondorStrategy is instantiated with the broker instance passed as an argument. This links the strategy to the broker, enabling it to place trades based on the Iron Condor parameters defined earlier in the code.
    • Trader Instance: A Trader instance is created. The Trader class is responsible for managing the execution of strategies. By adding the IronCondorStrategy to the trader, it becomes part of the automated trading system.

    Complete Code

    from lumibot.strategies.strategy import Strategy
    from lumibot.entities import Asset
    
    class IronCondorStrategy(Strategy):
        parameters = {
            "symbol": "SPY",  # Underlying stock symbol
            "quantity": 1               # Number of contracts for each leg
        }
    
        def initialize(self):
            self.sleeptime = "1D"  # Adjust as needed
    
        def on_trading_iteration(self):
            """Places an Iron Condor once and manages the position"""
    
            # Get current time and default expiration date to today
            current_time = self.get_datetime().date()
            self.log_message(f"Current datetime: {current_time}")
            
            # Set expiration date to today
            expiration_date = current_time
    
            # Fetch the current price of the underlying asset
            underlying_asset = Asset(
                symbol=self.parameters["symbol"],
                asset_type=Asset.AssetType.STOCK
            )
            underlying_price = self.get_last_price(underlying_asset.symbol)
    
            # Automatically determine the strike prices
            short_call_strike = round(underlying_price * 1.05, 2)  # Short call strike 5% above current price
            long_call_strike = round(underlying_price * 1.10, 2)   # Long call strike 10% above current price
            short_put_strike = round(underlying_price * 0.95, 2)   # Short put strike 5% below current price
            long_put_strike = round(underlying_price * 0.90, 2)    # Long put strike 10% below current price
    
            # Define the four legs of the Iron Condor
            short_call = Asset(
                symbol=self.parameters["symbol"],
                asset_type=Asset.AssetType.OPTION,
                expiration=expiration_date,
                strike=short_call_strike,
                right=Asset.OptionRight.CALL
            )
    
            long_call = Asset(
                symbol=self.parameters["symbol"],
                asset_type=Asset.AssetType.OPTION,
                expiration=expiration_date,
                strike=long_call_strike,
                right=Asset.OptionRight.CALL
            )
    
            short_put = Asset(
                symbol=self.parameters["symbol"],
                asset_type=Asset.AssetType.OPTION,
                expiration=expiration_date,
                strike=short_put_strike,
                right=Asset.OptionRight.PUT
            )
    
            long_put = Asset(
                symbol=self.parameters["symbol"],
                asset_type=Asset.AssetType.OPTION,
                expiration=expiration_date,
                strike=long_put_strike,
                right=Asset.OptionRight.PUT
            )
    
            # Log option details
            for option in [short_call, long_call, short_put, long_put]:
                option_price = self.get_last_price(option.symbol)
                self.log_message(f"Price of {option.symbol}: {option_price}")
                self.add_line(f"{option.symbol} Price", option_price)
    
            # Check if Iron Condor is already placed
            current_positions = self.get_positions()
            if len(current_positions) <= 1:  # Only USD present
                quantity = self.parameters["quantity"]
    
                # Create orders for each leg
                orders = [
                    self.create_order(short_call.symbol, quantity, "sell"),
                    self.create_order(long_call.symbol, quantity, "buy"),
                    self.create_order(short_put.symbol, quantity, "sell"),
                    self.create_order(long_put.symbol, quantity, "buy")
                ]
    
                # Submit all orders
                for order in orders:
                    self.submit_order(order)
    
    if __name__ == "__main__":
        from lumibot.brokers import Alpaca
        from lumibot.traders import Trader
    
        ALPACA_CONFIG = {
            "API_KEY": "",  # Add your Alpaca API Key here
            "API_SECRET": "",  # Add your Alpaca API Secret here
            "PAPER": True,  # Set to True for paper trading, False for live trading
        }
            
        # Set up the Alpaca broker and run the strategy
        broker = Alpaca(ALPACA_CONFIG)
        strategy = IronCondorStrategy(broker=broker)
        trader = Trader()
        trader.add_strategy(strategy)
        trader.run_all()

    Output

    Conclusion

    While its complexity may intimidate some traders, platforms like Lumibot simplify the process by automating trade execution and management. With Lumibot, you can easily set up, backtest, and execute Iron Condor trades without needing to monitor the market constantly. Its integration with multiple brokers and robust customization options make it a valuable tool for both novice and experienced traders. 

    Overall, using Lumibot to automate the Iron Condor strategy allows you to make smarter, more efficient trading decisions while managing your risk effectively. So, whether you’re looking to add a new options strategy to your toolkit or automate your existing trades, Lumibot can help you trade the Iron Condor with greater ease and precision.

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    Integrating Lumibot with Tradier: A Practical Guide

    Introduction

    In the fast-paced world of financial markets, automation is becoming essential for traders seeking to optimize their strategies, minimize risk, and capitalize on market opportunities 24/7. The rise of algorithmic trading has ushered in a new era, allowing traders to harness powerful tools and technologies to build sophisticated trading systems. Two key players enabling this transformation are Lumibot and Tradier, each offering unique capabilities that, when combined, create a robust platform for automated trading.

    Lumibot provides a flexible, Python-based framework designed for building custom algorithmic trading bots, enabling you to program, test, and deploy complex strategies with ease. On the other hand, Tradier is a versatile brokerage service that not only offers access to an array of trading instruments but also provides a cutting-edge API for seamless trade execution.

    In this guide, we’ll take you step-by-step through the process of connecting these platforms, ensuring you can effortlessly automate your trading strategies and stay ahead in today’s competitive market.

    Why Integrating Lumibot With Tradier Enhances Trading Strategies?

    Integrating Lumibot with Tradier takes your trading to the next level by seamlessly combining the strengths of both platforms. Lumibot provides a powerful, customizable framework for building sophisticated algorithmic trading strategies using Python. This flexibility allows traders to implement complex rules and data-driven approaches tailored to their specific needs. Meanwhile, Tradier’s brokerage API offers fast, reliable, and scalable execution capabilities, enabling real-time market access and order fulfillment.

    Together, this integration empowers you to fully automate your trading strategies, ensuring trades are executed according to precise rules without manual intervention. This minimizes human error, reduces emotional decision-making, and optimizes the efficiency of trading operations. Additionally, the ability to run backtests and fine-tune strategies using historical data before deploying them live gives you a reliable environment to refine your approach, ultimately improving trading performance and consistency.

    The Powerful Advantages of Integrating Lumibot with Tradier

    Let’s explore the compelling advantages of integrating Lumibot with Tradier and how this combination can significantly elevate your trading strategies.

    Cost-Efficiency

    Tradier stands out for its cost-effective pricing structure, offering competitive commissions and flexible monthly subscription models tailored to different types of traders. Whether you’re a retail trader managing a personal portfolio or a professional running larger accounts, Tradier provides an affordable solution that can scale according to your trading needs.

    Comprehensive API

    Tradier’s robust API offers a wide array of functionalities essential for successful algorithmic trading. From retrieving real-time and historical market data to managing trade orders, positions, and accounts, Tradier provides an all-encompassing solution. This API integrates seamlessly with Lumibot, enabling the execution of even the most complex algorithmic strategies. With this integration, traders can build, test, and deploy strategies without needing to worry about the technical complexity. 

    Flexibility

    Lumibot offers traders unparalleled flexibility by leveraging Python, one of the most widely-used programming languages in the financial world. Python’s extensive libraries and simple syntax allow traders to customize strategies to their unique needs. When paired with Tradier’s execution engine, traders can design, iterate, and deploy their Python-based strategies directly in the live markets, making the integration highly versatile and accessible. 

    Scalability

    Both Lumibot and Tradier are built with scalability in mind. Tradier’s infrastructure is designed to handle a high volume of trading requests and orders, ensuring that as your strategies evolve and grow, the platform can keep up with the demand without any degradation in performance. Similarly, Lumibot allows you to run multiple strategies simultaneously, monitor different markets, and make split-second trading decisions. 

    Security

    Security is a top priority for both Lumibot and Tradier. Tradier implements industry-standard secure protocols, including encryption, to safeguard sensitive information such as your account data and transaction details. At the same time, Lumibot ensures that all communication between your trading bots and the broker is encrypted and secure. This high level of security ensures that your trading operations remain confidential and protected from any unauthorized access. 

    Step-by-step Guide For Integrating Lumibot With Tradier

    Here’s a step-by-step guide to walk you through the entire integration process, from setting up your accounts to implementing your first trading strategy. 

    Step 1: Importing Libraries and Dependencies

    The first step is importing necessary libraries and modules that provide the foundation for the strategy. The datetime module is imported to handle date and time-related operations. Next, Strategy is imported from lumibot.strategies.strategy, which allows you to create a custom trading strategy based on Lumibot’s framework. Additionally, the Asset class is imported, which will be used to define and manage option-related assets, such as strike prices and expiration dates. 

    from datetime import datetime
    from lumibot.strategies.strategy import Strategy
    from lumibot.entities import Asset  # Assuming Asset is imported from lumibot

    Step 2: Defining the “BuyAndHoldOption” Strategy Class

    This step defines the BuyAndHoldOption strategy class, which extends Lumibot’s base Strategy class. The parameters dictionary is used to configure key aspects of the options trade, such as the underlying stock symbol (SPY), the expiration date of the option, the strike price, and whether the option is a call or a put. 

    class BuyAndHoldOption(Strategy):
        parameters = {
            "symbol": "SPY",  # Underlying stock symbol
            "expiration_date": "2024-01-19",  # Option expiration date
            "strike_price": 150,  # Strike price
            "option_type": "call",  # "call" or "put"
        }

    Step 3: Initializing the Strategy

    The initialize method is called once at the start of the strategy execution. Here, the sleeptime is set to “1M”, which indicates that the strategy will check and perform its operations every minute. This step sets the timing and frequency of the trading bot’s iteration loop, ensuring the strategy continuously monitors the market conditions and the option’s price in real time. The on_trading_iteration method contains the core logic of the strategy and runs every minute as set by the sleeptime.

    def initialize(self):
            self.sleeptime = "1M"
    
        def on_trading_iteration(self):
            """Buys the specified option once, then holds it"""

    Step 4: Logging the Current Time and Creating the Option Asset

    The first action it performs is to log the current time using self.get_datetime() for debugging and transparency purposes. It then creates an option_asset object, defining the option to be traded using the parameters such as the underlying symbol (SPY), expiration date, strike price, and the type of option (call or put). 

    current_time = self.get_datetime()
            self.log_message(f"Current datetime: {current_time}")
    
            # Create option object
            option_asset = Asset(
                symbol=self.parameters["symbol"],
                asset_type=Asset.AssetType.OPTION,
                expiration=self.parameters["expiration_date"],
                strike=self.parameters["strike_price"],
                right=Asset.OptionRight.CALL if self.parameters["option_type"] == "call" else Asset.OptionRight.PUT
            )

    Step 5: Fetching the Option Price and Logging It

    Once the option asset is created, the next step is to fetch its latest market price using self.get_last_price(option_asset.symbol). This method retrieves the most recent price of the option and logs it for tracking. Logging the price is crucial for keeping a record of the market data at the time of execution. The strategy also adds the option price to a chart using self.add_line(), providing a visual representation of price movement, which can be useful for reviewing the performance of the trade.

    option_price = self.get_last_price(option_asset.symbol)
            self.log_message(f"The price of option {option_asset.symbol} is {option_price}")
    
            self.add_line(f"{option_asset.symbol} Price", option_price)

    
    
                quantity_to_buy = 1
    
                buy_order = self.create_order(option_asset.symbol, quantity_to_buy, "buy")
                self.submit_order(buy_order)

    Backtesting Integrating Lumibot With Tradier

    One of the standout features of integrating Lumibot with Tradier is the capability to backtest your trading strategies, allowing you to assess their effectiveness using historical market data before committing real capital.

    if __name__ == "__main__":
    
      TRADIER_CONFIG = {
          'ACCESS_TOKEN': "",
          'ACCOUNT_NUMBER': "",
          "PAPER": True,  # Change to True if using paper trading
      }
      
      from lumibot.brokers import Tradier
      from lumibot.traders import Trader
      
      broker = Tradier(TRADIER_CONFIG)
      
      strategy = BuyAndHoldOption(broker=broker)
      
      trader = Trader()
      trader.add_strategy(strategy)
      trader.run_all()

    Key Points in the Code

    • IS_BACKTESTING Flag: Thisvariable determines the operational mode of the script. When set to True, the script will execute in backtesting mode, utilizing historical data to simulate trades. Conversely, when set to False, the script will engage in live trading, directly interacting with the Tradier brokerage for executing actual trades.
    • Backtest Period: The backtesting phase is set to analyze the performance of the trading strategy from January 1, 2024, to September 1, 2024. This time frame allows traders to assess how the strategy would have reacted to market movements during this period, providing a comprehensive evaluation of its viability.
    • Polygon Data Backtesting: When backtesting is enabled, the code imports the PolygonDataBacktesting class, which facilitates the simulation of trades based on historical market data. This class is instrumental in generating realistic trading scenarios for testing strategies.

    Broker Configuration: In live trading mode, the TRADIER_CONFIG dictionary holds essential information, including the access token, account number, and a flag indicating whether the trading is paper (simulated) or real. This configuration is crucial for establishing a secure connection with the Tradier API

    Complete Code

    from datetime import datetime
    from lumibot.strategies.strategy import Strategy
    from lumibot.entities import Asset  # Assuming Asset is imported from lumibot
    
    
    class BuyAndHoldOption(Strategy):
        parameters = {
            "symbol": "AAPL",  # Underlying stock symbol
            "expiration_date": "2024-12-20",  # Option expiration date
            "strike_price": 150,  # Strike price
            "option_type": "call",  # "call" or "put"
        }
    
        def initialize(self):
            self.sleeptime = "1M"
    
        def on_trading_iteration(self):
            """Buys the specified option once, then holds it"""
    
            current_time = self.get_datetime()
            self.log_message(f"Current datetime: {current_time}")
    
            # Create option object
            option_asset = Asset(
                symbol=self.parameters["symbol"],
                asset_type=Asset.AssetType.OPTION,
                expiration=self.parameters["expiration_date"],
                strike=self.parameters["strike_price"],
                right=Asset.OptionRight.CALL if self.parameters["option_type"] == "call" else Asset.OptionRight.PUT
            )
    
            option_price = self.get_last_price(option_asset.symbol)
            self.log_message(f"The price of option {option_asset.symbol} is {option_price}")
    
            self.add_line(f"{option_asset.symbol} Price", option_price)
    
            quantity_to_buy = 1 
    
    
            buy_order = self.create_order(option_asset.symbol, quantity_to_buy, "buy")
            self.submit_order(buy_order)
            print(buy_order)
    
    
    if __name__ == "__main__":
        
      TRADIER_CONFIG = {
          'ACCESS_TOKEN': "",
          'ACCOUNT_NUMBER': "",
          "PAPER": True,  # Change to True if using paper trading
      }
    
      from lumibot.brokers import Tradier
      from lumibot.traders import Trader
    
      broker = Tradier(TRADIER_CONFIG)
    
      strategy = BuyAndHoldOption(broker=broker)
    
      trader = Trader()
      trader.add_strategy(strategy)
      trader.run_all()

    Order is Placed

    Integrating Lumibot with Tradier presents a powerful opportunity for traders seeking to enhance their trading strategies through automation and efficiency. By leveraging Lumibot’s flexible algorithmic trading framework alongside Tradier’s brokerage services, you can build, test, and deploy customized trading strategies tailored to your unique goals. Whether you are a novice looking to automate your trades or a seasoned professional aiming to optimize your strategies, this integration equips you with the tools necessary to navigate today’s fast-paced financial markets successfully.

    Integrating Lumibot with Tradier_ A Practical Guide