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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.

    Categories
    Algorithmic Trading

    Placing Stock Orders Using Lumibot: A Practical Approach

    Introduction

    Algorithmic trading has revolutionized the stock market by offering several advantages over manual trading. Trading bots have become a critical tool for traders who are looking to gain a competitive edge. Platforms like Lumibot provide a robust solution by allowing traders to automate their strategies with speed and precision that manual traders cannot match by responding instantly to market conditions and taking advantage of fleeting opportunities.  

    At its core, Lumibot is a Python-based open-source trading platform designed for those who want to automate their stock trades using algorithms. It integrates with various brokers like Alpaca and Interactive Brokers, making it accessible to a wide range of users. In this blog, we’ll explore the practical steps to place stock orders using Lumibot. 

    Let’s explore the installation process, how to connect with your brokerage and the different types of stock orders you can place, such as market, limit, and stop orders. 

    Why is Lumibot a Smart Choice For Placing Stock Orders?

    Lumibot is a highly flexible algorithmic trading framework that provides distinct advantages for those looking to enhance their stock trading experience. Let’s see some of its powerful benefits below: 

    1. Automation of Trading Strategies

    Lumibot ensures consistent execution of a pre-defined logic without constant monitoring. Once a strategy is coded and deployed, it can operate 24/7, maximizing opportunities in the stock market. For example, strategies like “buy low, sell high” or “buy and hold” can be automatically executed based on real-time market conditions, without the need for human intervention.

    2. Speed and Precision in Order Execution

    Timing is critical in the stock market. Lumibot’s algorithmic approach ensures that trades are placed almost instantly as soon as market conditions trigger the predefined criteria. This speed and precision allow traders to capitalize on short-lived opportunities, such as sudden market shifts or price gaps, that would be impossible to execute manually.

    Moreover, Lumibot integrates with multiple brokers, including Alpaca and Interactive Brokers, ensuring that orders are routed through highly reliable platforms, reducing the risk of trade failure or slippage.

      3. Backtesting for Strategy Optimization

      One of the most powerful features of Lumibot is its ability to backtest trading strategies. Backtesting allows traders to evaluate their strategies against historical market data, providing a clear picture of the strategy’s performance before it is deployed in live markets. By using historical data to simulate past market conditions, traders can identify the strengths and weaknesses of their strategies, refine them, and improve profitability.

      4. Integration with Various Brokers and Data Providers

      Another advantage of using Lumibot is its seamless integration with popular brokerage platforms such as Alpaca, Interactive Brokers, and others. This allows traders to connect their trading bots directly to their brokerage accounts, making it easy to execute trades across different asset classes including stocks, ETFs, and options.

        5. Paper Trading for Low-Risk Strategy Testing

        For those hesitant to dive into live trading with real money, Lumibot offers a paper trading feature. This allows users to simulate real-world trading conditions without putting actual capital at risk. By executing their strategies in paper trading mode, traders can gain confidence in their algorithms, observe how they perform in real-time markets, and make adjustments before switching to live trading.

        The Prerequisites Before Placing a Stock Order

        To start placing stock orders using Lumibot, a user needs to follow the preparatory steps mentioned below. This installation process is quite straightforward if you are familiar with Python

        Step 1: Install Python

        The user must install Python in their system with version 3.10 or above. You can download the recent version of Python here

        Step 2: Set Up Lumibot

        The user must first install Lumibot using pip: pip install Lumibot from the terminal.

        pip install lumibot

        Step 3: Run Python 

        Once Lumibot has been installed, you have to run the Python file by importing the classes below. 

        import lumibot
        from lumibot.strategies import Strategy
        from lumibot.entities import Asset
        from lumibot.traders import Trader
        from datetime import datetime

        Step 4: Create ALPACA_CONFIG 

        Finally, the user has to create ALPACA_CONFIG with API KEY and API SECRET by logging in or signing up at https://alpaca.markets/

        Steps to Placing Stock Orders Using Lumibot

        Step 1: Import the Required Libraries

        Begin by importing necessary modules, such as datetime to handle date and time functionalities, and Strategy from the Lumibot framework to define the trading strategy.

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

        Step 2: Define the Buy-and-Hold Strategy Class

        Create a class BuyAndHoldStock that inherits from the Strategy class provided by Lumibot. This class contains the core logic for the strategy.

        class BuyAndHoldStock(Strategy):

        Step 3: Set Parameters for the Strategy

        Define a parameters dictionary to specify the stock symbol you want to buy. In this example, the stock symbol is set to “AAPL” by default.

        # Parameters for the strategy, currently only the stock symbol to buy
            parameters = {
                "buy_symbol": "AAPL",  # The stock symbol to buy, default is AAPL
            }

        Step 4: Initialize the Strategy

        Define the initialize method to set how often the trading loop will run. Here, the bot will execute the strategy iteration every minute, which is indicated by the “1M” sleeptime.

        def initialize(self):
                # Set how often the trading iteration will run (e.g., every 1 minute)
                self.sleeptime = "1M"

        Step 5: Implement the Trading Logic in on_trading_iteration

        The on_trading_iteration method is the core function where the strategy executes its logic on each trading iteration. In this step:

        • Log Current Time: The bot logs the current datetime to track when the trade is being executed.
         def on_trading_iteration(self):
                """Buys the specified stock once, then holds it"""   
        # Log the current datetime
                current_time = self.get_datetime()
                self.log_message(f"Current datetime: {current_time}")
        • Retrieve Stock Symbol: Extract the stock symbol (e.g., “AAPL”) from the parameters.
        # Retrieve the stock symbol from the parameters
                stock_symbol = self.parameters["buy_symbol"]
        • Fetch Current Stock Price: The bot gets the latest stock price for the specified symbol and logs it.
        # Get the latest price of the stock
                stock_price = self.get_last_price(stock_symbol)
                self.log_message(f"The price of {stock_symbol} is {stock_price}")
        • Visualize Stock Price: Add the stock price to a custom line chart for monitoring and visualization purposes.
        # Add the stock price to a custom line chart for visualization
                self.add_line(f"{stock_symbol} Price", stock_price)

        Step 6: Check Existing Portfolio Positions

        Check the current positions in the portfolio.

        # Get the current portfolio positions
                current_positions = self.get_positions()

        Step 7: Calculate Quantity and Place a Buy Order

        If there are no stock holdings, the bot calculates how many shares of the stock to buy based on the available portfolio value. It then creates and submits a buy order.

        • Calculate Quantity: Divide the portfolio value by the stock price to determine how many shares to buy.
        # If the only position is USD, buy the stock
                if len(current_positions) <= 1:  # Assuming only USD is present
                    # Calculate how much stock to buy based on available portfolio value
                    quantity_to_buy = int(self.portfolio_value // stock_price)
                    self.log_message(f"Quantity to buy: {quantity_to_buy}")
        • Create and Submit the Buy Order: Once the quantity is calculated, the bot creates and submits the buy order for execution.
        # Create and submit the buy order
                    buy_order = self.create_order(stock_symbol, quantity_to_buy, "buy")
                    self.submit_order(buy_order)

        This will allow the bot to monitor the market automatically and place a buy order for the stock, holding it after the first purchase.

        Backtesting Your Strategy With Lumibot

        One of the greatest advantages of using Lumibot is the ability to backtest your strategies before deploying them in live trading environments. The backtest will simulate how the strategy would have performed over a historical period.

        if __name__ == "__main__":
            IS_BACKTESTING = True
            
            if IS_BACKTESTING:
                from lumibot.backtesting import PolygonDataBacktesting
                
                # Set up the backtest period
                backtest_start = datetime(2024, 1, 1)
                backtest_end = datetime(2024, 9, 1)
                
                # Run the backtest with SPY as the benchmark asset
                results = BuyAndHoldStock.run_backtest(
                    PolygonDataBacktesting,
                    backtest_start,
                    backtest_end,
                    benchmark_asset="SPY",
                    polygon_api_key="",  # Add your Polygon API key here
                )
                print(results) 
            else:
                # Check if Alpaca API keys are set
                ALPACA_CONFIG = {
                    "API_KEY": "",  # Add your Alpaca API Key here
                    "API_SECRET": "",  # Add your Alpaca API Secret here
                    "PAPER": False,  # Set to True for paper trading, False for live trading
                }
                
                # Throw an error if the API keys are not set
                if not ALPACA_CONFIG["API_KEY"] or not ALPACA_CONFIG["API_SECRET"]:
                    raise ValueError("Please set your Alpaca API key and secret in the ALPACA_CONFIG.")
                
                from lumibot.brokers import Alpaca
                from lumibot.traders import Trader
                
                # Set up the Alpaca broker and run the strategy
                broker = Alpaca(ALPACA_CONFIG)
                strategy = BuyAndHoldStock(broker=broker)
                trader = Trader()
                trader.add_strategy(strategy)
                trader.run_all()

        Key Points in the Backtesting Code

        • IS_BACKTESTING = True: his flag determines whether the script runs in backtesting mode or live trading mode. When set to backtesting mode, it uses historical data to simulate trades. In live mode, the strategy interacts with a real broker to execute actual trades.
        • Backtest Period: The strategy is tested between January 1, 2023, and September 1, 2024. 

        Complete Code

        from datetime import datetime
        from lumibot.strategies.strategy import Strategy
        
        class BuyAndHoldStock(Strategy):
           # Parameters for the strategy, currently only the stock symbol to buy
           parameters = {
           	"buy_symbol": "AAPL",  # The stock symbol to buy, default is AAPL
           }
        
           def initialize(self):
           	# Set how often the trading iteration will run (e.g., every 1 minute)
               self.sleeptime = "1M"
        
           def on_trading_iteration(self):
           	"""Buys the specified stock once, then holds it"""
           	
           	# Log the current datetime
               current_time = self.get_datetime()
               self.log_message(f"Current datetime: {current_time}")
           	
           	# Retrieve the stock symbol from the parameters
               stock_symbol = self.parameters["buy_symbol"]
           	
           	# Get the latest price of the stock
               stock_price = self.get_last_price(stock_symbol)
               self.log_message(f"The price of {stock_symbol} is {stock_price}")
           	
           	# Add the stock price to a custom line chart for visualization
               self.add_line(f"{stock_symbol} Price", stock_price)
           	
           	# Get the current portfolio positions
               current_positions = self.get_positions()
           	
           	# If the only position is USD, buy the stock
           	if len(current_positions) <= 1:  # Assuming only USD is present
               	# Calculate how much stock to buy based on available portfolio value
                   quantity_to_buy = int(self.portfolio_value // stock_price)
                   self.log_message(f"Quantity to buy: {quantity_to_buy}")
               	
               	# Create and submit the buy order
                   buy_order = self.create_order(stock_symbol, quantity_to_buy, "buy")
                   self.submit_order(buy_order)
        
        if __name__ == "__main__":
           IS_BACKTESTING = True
          
           if IS_BACKTESTING:
           	from lumibot.backtesting import PolygonDataBacktesting
           	
           	# Set up the backtest period
               backtest_start = datetime(2024, 1, 1)
               backtest_end = datetime(2024, 9, 1)
           	
           	# Run the backtest with SPY as the benchmark asset
               results = BuyAndHoldStock.run_backtest(
                   PolygonDataBacktesting,
                   backtest_start,
                   backtest_end,
                   benchmark_asset="SPY",
                   polygon_api_key="",  # Add your Polygon API key here
               )
           	print(results)
          
           else:
           	# Check if Alpaca API keys are set
               ALPACA_CONFIG = {
               	"API_KEY": "",  # Add your Alpaca API Key here
               	"API_SECRET": "",  # Add your Alpaca API Secret here
               	"PAPER": False,  # Set to True for paper trading, False for live trading
               }
           	
           	# Throw an error if the API keys are not set
           	if not ALPACA_CONFIG["API_KEY"] or not ALPACA_CONFIG["API_SECRET"]:
               	raise ValueError("Please set your Alpaca API key and secret in the ALPACA_CONFIG.")
           	
           	from lumibot.brokers import Alpaca
           	from lumibot.traders import Trader
           	
           	# Set up the Alpaca broker and run the strategy
               broker = Alpaca(ALPACA_CONFIG)
               strategy = BuyAndHoldStock(broker=broker)
               trader = Trader()
               trader.add_strategy(strategy)
               trader.run_all()

        Output and Key Metrics Explained

        After completing the backtest, Lumibot produces several key output files that provide in-depth insights into the strategy’s performance. These files include a tearsheet, indicators, and a trades file. Below is a summary of each file’s contents and how to understand the results.

        1. Tearsheet.html / Tearsheet.csv

        The tearsheet offers a detailed report on the strategy’s performance, featuring essential metrics that assess profitability and risk. Key metrics include:

        • Total Return: The overall profit generated by the strategy during the backtesting period.
        • CAGR (Compound Annual Growth Rate): The average annual growth rate over the backtest duration.
        • Sharpe Ratio: A metric that evaluates risk-adjusted returns.
        • Max Drawdown: The largest drop in portfolio value from its highest point to its lowest.
        • Sortino Ratio: A modified version of the Sharpe Ratio that focuses specifically on downside risk.

        Output for Stock Order Backtesting

        2. Indicators.html / Indicators.csv

        The indicators file logs all technical indicators utilized in the strategy. Reviewing these values allows you to see how the strategy responds to different market conditions. Key contents of indicators file include: 

        • Indicator Values Over Time:This section includes moving averages, RSI, MACD, Bollinger Bands, and other indicators used by the strategy, showing how these values change over time and relate to the strategy’s buy/sell actions.
          • Custom Strategy Metrics: If the strategy tracks custom metrics such as portfolio value, cash position, or specific thresholds, these will also be included in the file.

          Output for Stock Order Backtesting

          3. Trades.html / Trades.csv

          The trades file documents every trade made by the strategy during the backtest, detailing information such as the trade timestamp, symbol, buy/sell action, trade price, and the profit or loss for each transaction. Key contents of trades file include:

          • Trade Timestamp: The date and time each trade was executed.
          • Symbol: The asset being traded (e.g., QQQ).
          • Buy/Sell Action: Indicates if the trade was a buy or sell.
          • Trade Price: The price at which the asset was traded.
          • Quantity: The number of units (e.g., shares or contracts) involved.
          • Profit/Loss: The profit or loss from each trade, helping identify successful trades.
          • Order Type: Specifies whether the order was market, limit, or another type.

          Output for Stock Order Backtesting

          Conclusion

          Placing stock orders using Lumibot can be a highly efficient way to automate your trading strategies. Whether you are executing simple market orders or more complex limit and stop orders, Lumibot provides the flexibility and precision that is needed to optimize your trading approach. Moreover, it has features like backtesting and paper trading, making it possible for you to refine your strategies without risking capital.

          By understanding the different types of stock orders, setting up your brokerage account, and utilizing Lumibot’s rich functionality, you can take advantage of algorithmic trading and improve your overall market performance.

          Categories
          Algorithmic Trading

          Backtesting Strategies Using Polygon.io Data with Lumibot

          Introduction

          Backtesting is a crucial process for algorithmic traders, as it allows them to evaluate the performance of a strategy using historical data. By testing strategies in a simulated environment, traders can gain valuable insights into how their trading algorithm would perform without putting real money at risk. In this article, we will explore how to implement a simple Buy-and-Hold strategy using Lumibot and historical data from Polygon.io. This strategy involves purchasing a single asset and holding it over time, which is ideal for long-term investors.

          Polygon.io offers reliable, high-quality financial data for backtesting, including minute-level and daily historical data. Unlike Yahoo Finance, which is limited to daily data, Polygon.io is well-suited for traders looking to backtest strategies with higher-frequency data. In this guide, we’ll use Polygon.io data to backtest a Buy-and-Hold strategy on an asset of your choice and analyze how it performs compared to a benchmark such as the S&P 500.

          Let’s dive into the process of setting up Lumibot with Polygon.io data, implementing a Buy-and-Hold strategy, and performing a backtest to evaluate the strategy’s performance.

          Implementing the Buy-and-Hold Strategy Using Lumibot and Polygon.io Data

          This section explains how to set up and backtest a simple Buy-and-Hold strategy using Polygon.io data with the Lumibot algorithmic trading framework. The strategy buys a single asset and holds it for the entire backtesting period, aiming to simulate long-term investing behavior. Below is a breakdown of the key components of the code.

          from datetime import datetime
          from lumibot.strategies.strategy import Strategy
          
          """
          Strategy Description
          
          Simply buys one asset and holds onto it.
          """
          
          class BuyAndHold(Strategy):
              parameters = {
                  "buy_symbol": "AAPL",  # Change this to the asset symbol of your choice
              }
          
              # =====Overloading lifecycle methods=============
          
              def initialize(self):
                  # Set the sleep time to one day (the strategy will run once per day)
                  self.sleeptime = "1D"
          
              def on_trading_iteration(self):
                  """Buys the self.buy_symbol once, then never again"""
          
                  # Get the current datetime and log it
                  dt = self.get_datetime()  # Used to get the time in the backtesting environment
                  self.log_message(f"Current datetime: {dt}")
          
                  # Get the symbol to buy from the parameters
                  buy_symbol = self.parameters["buy_symbol"]
          
                  # Get the current value of the symbol and log it
                  current_value = self.get_last_price(buy_symbol)
                  self.log_message(f"The value of {buy_symbol} is {current_value}")
          
                  # Add a line to the indicator chart
                  self.add_line(f"{buy_symbol} Value", current_value)
          
                  # Get all the positions that we have
                  all_positions = self.get_positions()
          
                  # If we don't own anything (other than USD), buy the asset
                  if len(all_positions) <= 1:  # Because we always have a cash position (USD)
          
                      # Calculate the quantity to buy
                      quantity = int(self.portfolio_value // current_value)
          
                      # Create the order and submit it
                      purchase_order = self.create_order(buy_symbol, quantity, "buy")
                      self.submit_order(purchase_order)
          

          Key Components of the Strategy Code

          • dt = self.get_datetime(): This method retrieves the current date and time during the backtesting session. It ensures that the trades or actions taken by the algorithm are aligned with specific historical market events.
          • self.add_line(f”{buy_symbol} Value”, current_value): This function adds the asset’s price data to a visual chart during backtesting. It tracks the value of the chosen asset over time, providing insight into its performance.
          • all_positions = self.get_positions(): This function fetches all current positions held by the strategy, including cash. Since this is a Buy-and-Hold strategy, it checks if the portfolio already contains the chosen asset. If it doesn’t, it buys the asset and holds it throughout the backtesting period.

          Backtesting the Strategy with Polygon.io Data

          In this section, we’ll cover how to set up the backtest using historical data from Polygon.io. This data source provides more granular historical price data (such as minute-level data), which allows us to simulate the strategy with higher accuracy. Below is the code for setting up and running the backtest.

          if __name__ == "__main__":
              IS_BACKTESTING = True
          
              if IS_BACKTESTING:
                  from lumibot.backtesting import PolygonDataBacktesting
          
                  # Backtest this strategy
                  backtesting_start = datetime(2023, 1, 1)
                  backtesting_end = datetime(2024, 9, 1)
          
                  results = BuyAndHold.run_backtest(
                      PolygonDataBacktesting,
                      backtesting_start,
                      backtesting_end,
                      benchmark_asset="SPY",  # Use S&P 500 as a benchmark
                  )
          
                  # Print the results
                  print(results)
              else:
                  POLYGON_CONFIG = {
                      "API_KEY": "YOUR_API_KEY",
                  }
          
                  from lumibot.brokers import Polygon
                  from lumibot.traders import Trader
          
                  trader = Trader()
          
                  broker = Polygon(POLYGON_CONFIG)
          
                  strategy = BuyAndHold(broker=broker)
          
                  trader.add_strategy(strategy)
                  strategy_executors = trader.run_all()
          

          Key Points in the Backtesting Code

          • IS_BACKTESTING = True: This flag determines whether the script will run in backtesting mode or live trading mode. When True, the strategy will backtest with historical data. In live trading mode, it connects to a real broker and executes live trades.
          • PolygonDataBacktesting: This method loads historical price data from Polygon.io. Unlike Yahoo Finance, Polygon.io supports minute-level data, making it suitable for testing short-term strategies. However, in this case, we use daily data for a Buy-and-Hold strategy.
          • Backtest Period: The strategy is tested between January 1, 2023, and September 1, 2024. During this period, the performance of the asset will be compared to a benchmark like the S&P 500.

          Complete Code

          from datetime import datetime
           
          from lumibot.strategies.strategy import Strategy
           
          """
          Strategy Description
           
          Simply buys one asset and holds onto it.
          """
           
           
          class BuyAndHold(Strategy):
              parameters = {
                  "buy_symbol": "QQQ",
              }
           
              # =====Overloading lifecycle methods=============
           
              def initialize(self):
                  # Set the sleep time to one day (the strategy will run once per day)
                  self.sleeptime = "1M"
           
              def on_trading_iteration(self):
                  """Buys the self.buy_symbol once, then never again"""
           
                  # Get the current datetime and log it
                  dt = self.get_datetime() # We use this function so that we get the time in teh backtesting environment
                  self.log_message(f"Current datetime: {dt}")
           
                  # Get the symbol to buy from the parameters
                  buy_symbol = self.parameters["buy_symbol"]
           
                  # Get the current value of the symbol and log it
                  current_value = self.get_last_price(buy_symbol)
                  self.log_message(f"The value of {buy_symbol} is {current_value}")
           
                  # Add a line to the indicator chart
                  self.add_line(f"{buy_symbol} Value", current_value)
           
                  # Get all the positions that we have
                  all_positions = self.get_positions()
           
                  # If we don't own anything (other than USD), buy the asset
                  if len(all_positions) <= 1:  # Because we always have a cash position (USD)
           
                      # Calculate the quantity to buy
                      quantity = int(self.portfolio_value // current_value)
           
                      # Create the order and submit it
                      purchase_order = self.create_order(buy_symbol, quantity, "buy")
                      self.submit_order(purchase_order)
           
           
          if __name__ == "__main__":
              IS_BACKTESTING = True
           
              if IS_BACKTESTING:
                  from lumibot.backtesting import PolygonDataBacktesting
           
                  # Backtest this strategy
                  backtesting_start = datetime(2024, 1, 1)
                  backtesting_end = datetime(2024, 9, 1)
           
                  results = BuyAndHold.run_backtest(
                      PolygonDataBacktesting,
                      backtesting_start,
                      backtesting_end,
                      benchmark_asset="SPY",
                      polygon_api_key="tYtRp9IBM_t8NbsE6cKGEF33XwlFprCv",
                  )
           
                  # Print the results
                  print(results)
              else:
                  ALPACA_CONFIG = {
                      "API_KEY": "YOUR_API_KEY",
                      "API_SECRET": "YOUR_API_SECRET",
                      "PAPER": True,
                  }
           
                  from lumibot.brokers import Alpaca
                  from lumibot.traders import Trader
           
                  trader = Trader()
           
                  broker = Alpaca(ALPACA_CONFIG)
           
                  strategy = BuyAndHold(broker=broker)
           
                  trader.add_strategy(strategy)
                  strategy_executors = trader.run_all()

          Output Files and Key Metrics Explained

          Once the backtest is complete, Lumibot generates several important output files. These files contain detailed information about the performance of the strategy, including a tearsheet, indicators, and a trades file. Below is an overview of what each file contains and how to interpret the results.

          1. Tearsheet.html / Tearsheet.csv

          The tearsheet file provides a comprehensive report on the strategy’s performance, including various metrics that measure profitability and risk. Some key metrics include:

          • Total Return: The overall return generated by the strategy during the backtesting period.
          • CAGR (Compound Annual Growth Rate): The average annual growth rate over the backtest period.
          • Sharpe Ratio: A measure of risk-adjusted return.
          • Max Drawdown: The maximum decline in portfolio value from peak to trough.
          • Sortino Ratio: A variation of the Sharpe Ratio that penalizes only downside risk.

          Output for Polygon.io Backtesting

          2. Indicators.html / Indicators.csv

          The indicators file records all technical indicators used in the strategy. These values can be reviewed to understand how the strategy reacts to various market conditions.

          Output for Polygon.io Backtesting

          3. Trades.html / Trades.csv

          The trades file logs every trade executed by the strategy during the backtest. It includes data like the trade timestamp, symbol, buy/sell action, trade price, and the profit or loss for each transaction.

          Output for Polygon.io Backtesting

          Conclusion

          By using Polygon.io’s comprehensive data and Lumibot’s robust backtesting framework, we can evaluate the effectiveness of a simple Buy-and-Hold strategy. Polygon.io’s minute-level data is particularly valuable for testing strategies that require more granularity than daily data, making it ideal for a wide range of strategies beyond just long-term investing.

          The Buy-and-Hold strategy is a great starting point for understanding backtesting, but you can build more complex strategies using Lumibot and Polygon.io data. Whether you are a beginner or an experienced algorithmic trader, this combination offers the tools and data needed to test and improve your strategies efficiently.

          Categories
          Algorithmic Trading

          Backtesting Strategies Using Yahoo Finance Data with Lumibot

          Introduction

          Backtesting is an essential part of algorithmic trading that allows traders to simulate a strategy’s performance using historical data. By testing strategies on past market conditions, traders can gain valuable insights into how their approach might behave in real-world scenarios without risking actual capital. One of the simplest yet effective long-term strategies is the Buy-and-Hold approach, where an investor purchases an asset and holds it regardless of market fluctuations. This strategy is favored by investors who believe in the long-term appreciation of the asset’s value.

          In this article, we will explore how to implement a Buy-and-Hold strategy using Lumibot, an algorithmic trading framework, and data from Yahoo Finance. Lumibot provides the infrastructure needed to test and deploy strategies, while Yahoo Finance offers a rich source of historical market data. We will backtest the strategy on the QQQ ETF, which tracks the performance of the Nasdaq 100 over a defined period to see how it performs compared to a benchmark like the S&P 500.

          Let’s read on to learn how to set up Lumibot, fetch historical data from Yahoo Finance, implement the strategy, and analyze its performance through backtesting. Whether you’re a beginner or an experienced trader, this guide will help you understand the basics of building and testing trading strategies using Python and Lumibot.

          Implementing the Buy-and-Hold Strategy Using Lumibot and Yahoo Finance Data

          In this section, we will break down the steps involved in implementing and backtesting a simple Buy-and-Hold strategy using Lumibot with Yahoo Finance data. This strategy buys a single asset (in this case, the QQQ ETF) and holds it throughout the entire backtest. We will explain the code, focusing on key components like the get_datetime() function, how to add data to indicator charts, and the backtesting logic.

          Strategy Breakdown

          The Buy-and-Hold strategy is one of the simplest trading strategies, where an asset is purchased and held over time without selling. Here, we are using Lumibot’s framework to automate this strategy, allowing it to buy the QQQ ETF once and hold it throughout the backtesting period. Below are the key steps and components of the code.

          from datetime import datetime
          from lumibot.strategies.strategy import Strategy
          
          """
          Strategy Description
          Simply buys one asset and holds onto it.
          """
          
          class BuyAndHold(Strategy):
              parameters = {
                  "buy_symbol": "QQQ",
              }
          
              # =====Overloading lifecycle methods=============
              def initialize(self):
                  # Set the sleep time to one day (the strategy will run once per day)
                  self.sleeptime = "1D"
          
              def on_trading_iteration(self):
                  """Buys the self.buy_symbol once, then never again"""
          
                  # Get the current datetime and log it
                  dt = self.get_datetime()  # Used to get the time in the backtesting environment
                  self.log_message(f"Current datetime: {dt}")
          
                  # Get the symbol to buy from the parameters
                  buy_symbol = self.parameters["buy_symbol"]
          
                  # Get the current value of the symbol and log it
                  current_value = self.get_last_price(buy_symbol)
                  self.log_message(f"The value of {buy_symbol} is {current_value}")
          
                  # Add a line to the indicator chart
                  self.add_line(f"{buy_symbol} Value", current_value)
          
                  # Get all the positions that we have
                  all_positions = self.get_positions()
          
                  # If we don't own anything (other than USD), buy the asset
                  if len(all_positions) <= 1:  # We always have a cash position (USD)
          
                      # Calculate the quantity to buy
                      quantity = int(self.portfolio_value // current_value)
          
                      # Create the order and submit it
                      purchase_order = self.create_order(buy_symbol, quantity, "buy")
                      self.submit_order(purchase_order)
          
          

          Key Components

          • dt = self.get_datetime(): This method fetches the current datetime within the backtesting environment, allowing the strategy to log or perform actions based on specific times. It’s especially useful when you need to align trades or actions with historical market events.
          • self.add_line(f”{buy_symbol} Value”, current_value): This line adds data to the indicator chart for tracking purposes. In this case, it logs the value of the asset (QQQ) over time, allowing you to visually monitor its price movements during the backtest.
          • all_positions = self.get_positions(): This retrieves all the positions currently held by the strategy. Since the strategy is designed to buy and hold only one asset (besides cash), we use this to check whether the asset is already in the portfolio. If not, the strategy will purchase it.

          Backtesting the Strategy

          The backtesting section of the code allows us to simulate how the strategy would have performed over a specific historical period using Yahoo Finance data. Backtesting is crucial to ensure the strategy’s viability without risking real capital.

          Backtesting the Strategy

          The backtesting section of the code allows us to simulate how the strategy would have performed over a specific historical period using Yahoo Finance data. Backtesting is crucial to ensure the strategy’s viability without risking real capital.

          if __name__ == "__main__":
              IS_BACKTESTING = True
          
              if IS_BACKTESTING:
                  from lumibot.backtesting import YahooDataBacktesting
          
                  # Backtest this strategy
                  backtesting_start = datetime(2023, 1, 1)
                  backtesting_end = datetime(2024, 9, 1)
          
                  results = BuyAndHold.run_backtest(
                      YahooDataBacktesting,
                      backtesting_start,
                      backtesting_end,
                      benchmark_asset="SPY",
                  )
          
                  # Print the results
                  print(results)
              else:
                  ALPACA_CONFIG = {
                      "API_KEY": "YOUR_API_KEY",
                      "API_SECRET": "YOUR_API_SECRET",
                      "PAPER": True,
                  }
          
                  from lumibot.brokers import Alpaca
                  from lumibot.traders import Trader
          
                  trader = Trader()
                  broker = Alpaca(ALPACA_CONFIG)
          
                  strategy = BuyAndHold(broker=broker)
                  trader.add_strategy(strategy)
                  strategy_executors = trader.run_all()
          
          

          Key Points in the Backtesting Code

          • IS_BACKTESTING = True: This flag tells the script whether to run in backtesting mode or live trading mode. In backtesting mode, historical data is used to simulate trades. In live mode, the strategy would execute trades with a real broker.
          • Yahoo Finance Data: In backtesting, we use YahooDataBacktesting, which provides daily historical price data. Yahoo Finance supports daily data (not minute-level data), which is why we use a sleep time of 1D in the strategy to run once per day. This is perfect for long-term strategies like Buy-and-Hold, but not suitable for high-frequency or minute-based trading strategies.
          • Backtest Period: The strategy is tested between January 1, 2023, and September 1, 2024. During this period, the performance is compared against the S&P 500 benchmark (represented by the symbol SPY).

          Complete Code

          
          from datetime import datetime
           
          from lumibot.strategies.strategy import Strategy
           
          """
          Strategy Description
           
          Simply buys one asset and holds onto it.
          """
           
           
          class BuyAndHold(Strategy):
              parameters = {
                  "buy_symbol": "QQQ",
              }
           
              # =====Overloading lifecycle methods=============
           
              def initialize(self):
                  # Set the sleep time to one day (the strategy will run once per day)
                  self.sleeptime = "1D"
           
              def on_trading_iteration(self):
                  """Buys the self.buy_symbol once, then never again"""
           
                  # Get the current datetime and log it
                  dt = self.get_datetime() # We use this function so that we get the time in teh backtesting environment
                  self.log_message(f"Current datetime: {dt}")
           
                  # Get the symbol to buy from the parameters
                  buy_symbol = self.parameters["buy_symbol"]
           
                  # Get the current value of the symbol and log it
                  current_value = self.get_last_price(buy_symbol)
                  self.log_message(f"The value of {buy_symbol} is {current_value}")
           
                  # Add a line to the indicator chart
                  self.add_line(f"{buy_symbol} Value", current_value)
           
                  # Get all the positions that we have
                  all_positions = self.get_positions()
           
                  # If we don't own anything (other than USD), buy the asset
                  if len(all_positions) <= 1:  # Because we always have a cash position (USD)
           
                      # Calculate the quantity to buy
                      quantity = int(self.portfolio_value // current_value)
           
                      # Create the order and submit it
                      purchase_order = self.create_order(buy_symbol, quantity, "buy")
                      self.submit_order(purchase_order)
           
           
          if __name__ == "__main__":
              IS_BACKTESTING = True
           
              if IS_BACKTESTING:
                  from lumibot.backtesting import YahooDataBacktesting
           
                  # Backtest this strategy
                  backtesting_start = datetime(2023, 1, 1)
                  backtesting_end = datetime(2024, 9, 1)
           
                  results = BuyAndHold.run_backtest(
                      YahooDataBacktesting,
                      backtesting_start,
                      backtesting_end,
                      benchmark_asset="SPY",
                  )
           
                  # Print the results
                  print(results)
              else:
                  ALPACA_CONFIG = {
                      "API_KEY": "YOUR_API_KEY",
                      "API_SECRET": "YOUR_API_SECRET",
                      "PAPER": True,
                  }
           
                  from lumibot.brokers import Alpaca
                  from lumibot.traders import Trader
           
                  trader = Trader()
           
                  broker = Alpaca(ALPACA_CONFIG)
           
                  strategy = BuyAndHold(broker=broker)
           
                  trader.add_strategy(strategy)
                  strategy_executors = trader.run_all()
          
          

          Output Files and Key Metrics Explained: Tearsheet, Indicators, and Trades

          When backtesting a strategy using Lumibot, the results are compiled into several detailed output files. These files help traders analyze the performance of their strategy, both in terms of profitability and risk. Below is an explanation of the key output files—tearsheet.html, indicators.html/csv, and trades.html/csv—along with an in-depth look at important metrics commonly found in the tearsheet.

          1. Tearsheet.html / Tearsheet.csv

          The tearsheet is the primary report generated from a backtest, summarizing the strategy’s overall performance across various dimensions. It contains critical metrics that give insight into returns, risk, and consistency. Let’s break down some of the key metrics:

          Key Performance Metrics in Tearsheet:

          • Risk-Free Rate:
            • The theoretical return on an investment with zero risk, often based on government bond yields. This is used to calculate risk-adjusted metrics like the Sharpe and Sortino Ratios.
          • Time in Market:
            • The percentage of time that the strategy was actively holding a position in the market during the backtest period. This shows how much of the time the strategy had exposure to the market.
          • Total Return:
            • The overall return the strategy achieved over the entire backtest period. It’s a simple percentage that represents how much your portfolio grew from start to finish.
          • CAGR (Compound Annual Growth Rate) %:
            • The average annual growth rate of the investment over the backtesting period, assuming the profits are reinvested each year. It gives a clearer picture of the strategy’s long-term growth potential.
          • ROMAD (Return Over Maximum Drawdown):
            • A performance metric that measures the return earned per unit of maximum drawdown (the worst drop in portfolio value). A higher ROMAD indicates better performance relative to the strategy’s worst downturn.
          • Correlation to Benchmark:
            • This metric shows how closely the strategy’s returns are correlated to the benchmark index (e.g., S&P 500). A correlation closer to 1 means the strategy moves similarly to the benchmark, while a value closer to 0 means the strategy behaves independently.
          • Sortino Ratio:
            • A risk-adjusted return metric that focuses only on downside risk (negative volatility). It is similar to the Sharpe Ratio but penalizes only those returns that fall below a specific threshold (usually the risk-free rate).
          • Max Drawdown:
            • The largest peak-to-trough decline in portfolio value during the backtesting period. It shows the most significant loss the strategy experienced and is crucial for assessing risk tolerance.
          • Maximum Drawdown Days:
            • The number of days it took for the strategy to recover from its maximum drawdown. This shows how long the portfolio was underwater before it reached its previous peak.
          • Minimum Volatility:
            • The lowest level of volatility (price fluctuation) experienced by the strategy during the backtest period. Lower volatility generally indicates a smoother, less risky strategy.

          Win/Loss Metrics:

          These metrics help assess how often the strategy outperforms over different timeframes:

          • Win Days %:
            • The percentage of days where the strategy generated a positive return. This metric helps evaluate the consistency of the strategy on a day-to-day basis.
          • Win Months %:
            • The percentage of months where the strategy was profitable. This gives insight into the strategy’s long-term consistency.
          • Win Quarters %:
            • The percentage of quarters where the strategy showed gains. Winning in most quarters indicates strong and consistent performance.
          • Win Years %:
            • The percentage of years where the strategy was profitable. A high win percentage over years indicates a robust long-term strategy.

          Below is the tearsheet.html  for the above code:

          2. Indicators.html / Indicators.csv

          The indicators file records various technical indicators used in the strategy during the backtest. These indicators help you track how market conditions evolved and how your strategy responded based on predefined rules. The data is typically logged at each time step (daily, weekly, etc.), providing a clear view of the key metrics that influence trading decisions.

          Key Contents of Indicators File:

          • Indicator Values Over Time:
            • This might include moving averages, RSI, MACD, Bollinger Bands, or other indicators the strategy uses. You can see how these values fluctuate over time and correlate to the strategy’s buy/sell actions.
          • Custom Strategy Metrics:
            • If the strategy logs custom values (like portfolio value, cash position, or specific thresholds), they will also appear in this file.

          Below is the indicator.html  file for the above code:

          This file is particularly helpful for understanding how the strategy reacts to changing market conditions and whether the signals generated by indicators are being used effectively.

          3. Trades.html / Trades.csv

          The trades file records every trade executed by the strategy during the backtest. This detailed log helps you analyze each transaction to see if the strategy performed as expected. It provides transparency into when and why trades were made.

          Key Contents of Trades File:

          • Trade Timestamp:
            • The date and time when each trade was executed.
          • Symbol:
            • The asset being traded (e.g., QQQ).
          • Buy/Sell Action:
            • Indicates whether the trade was a buy or sell order.
          • Trade Price:
            • The price at which the asset was bought or sold.
          • Quantity:
            • The number of units (e.g., shares or contracts) involved in the trade.
          • Profit/Loss:
            • The resulting profit or loss from each trade. This helps you identify which trades were successful and which weren’t.
          • Order Type:
            • Whether the order was a market, limit, or other type of order.

          Below is the traders.html file for the above code:

          This file allows you to do a deep dive into the mechanics of the strategy. By reviewing the trades, you can verify that the strategy was executing properly, and identify patterns in profitable and unprofitable trades.

          Conclusion

          This strategy and backtest allow us to evaluate how a simple Buy-and-Hold approach performs over time using Lumibot and Yahoo Finance data. While the strategy only buys one asset and holds it, backtesting on historical data provides crucial insights into its performance against benchmarks like the S&P 500.

          By leveraging Lumibot, you can further customize this strategy or explore more complex trading strategies. Yahoo Finance data works best for daily strategies, making it ideal for long-term investments rather than short-term or high-frequency trades.

          Categories
          Algorithmic Trading

          Technical Analysis Using Bollinger Bands and Lumibot

          Introduction 

          Many technical analysis challenges, such as identifying market trends and determining entry or exit points, can be effectively addressed using Bollinger Bands in a trading bot. Bollinger Bands helps solve these issues by providing a dynamic range around the price that signals overbought or oversold conditions, making it easier for a trading bot to make informed decisions. With the rise of automated trading platforms like Lumibot, traders can seamlessly integrate Bollinger Bands into their bots for real-time technical analysis. Lumibot’s user-friendly interface and robust API make it a perfect tool for implementing Bollinger Bands strategies in an automated trading setup.

          Furthermore, Lumibot empowers traders to customize their Bollinger Bands parameters, such as period length and deviation settings, based on market conditions and trading objectives. This flexibility enhances a bot’s ability to react swiftly to market fluctuations, improving overall trading efficiency. Lumibot’s extensive backtesting capabilities also allow traders to refine their Bollinger Bands strategies by testing them on historical data.

          How Bollinger Bands Can Be Used for Technical Analysis of an Asset: Bollinger Bands provide crucial insights into market volatility and potential price reversals, making them a valuable tool for analyzing any asset. By observing how an asset’s price behaves relative to the upper and lower bands, traders can gauge whether the asset is overbought or oversold. This helps in formulating strategies for when to enter or exit trades based on clear market signals.

          Read on to find the nitty-gritty of how Bollinger Bands can be effectively incorporated into a trading bot for technical analysis of an asset.

          Using Bollinger Bands in a Trading Bot for Technical Analysis

          Bollinger Bands are widely used in trading strategies due to their ability to highlight market volatility, overbought/oversold levels, and potential price reversals. By incorporating Bollinger Bands into a trading bot, traders can automate decisions and capitalize on these insights. Below are five ways Bollinger Bands can be utilized in a trading bot for technical analysis, with a breakdown of what each method is and how it can be applied effectively in an automated trading system.

          1. Identifying Overbought and Oversold Conditions

          What It Is:

          Bollinger Bands help determine whether an asset is overbought (price touching or exceeding the upper band) or oversold (price touching or dropping below the lower band). These conditions often signal a potential price reversal.

          How It Can Be Used in a Trading Bot:

          In a trading bot, Bollinger Bands can be programmed to trigger buy signals when the price hits the lower band (oversold) and sell signals when it hits the upper band (overbought). This allows the bot to make decisions based on predefined criteria without human intervention.

          2. Volatility Breakouts (The Squeeze)

          What It Is:

          When the bands contract (i.e., when they come close together), it indicates reduced volatility, commonly referred to as “the squeeze.” This phase is often followed by a volatility breakout, where the price sharply moves in either direction.

          How It Can Be Used in a Trading Bot:

          Traders can configure a bot to monitor for contractions in Bollinger Bands. Once the bands start expanding, the bot can place orders based on the direction of the breakout (up or down). The bot can also be set to place stop-losses to manage risk in case of a false breakout.

          3. Mean Reversion Strategy

          What It Is:

          Bollinger Bands are centered around a moving average, which acts as a benchmark for the “fair” price of the asset. When the price moves too far from this average (toward either band), it often reverts back to the mean.

          How It Can Be Used in a Trading Bot:

          A trading bot can be programmed to execute trades when the price moves away from the moving average toward the bands and then place opposing trades (sell or buy) when the price reverts back to the mean. This strategy works well in range-bound markets.

          4. Trend Following with Bollinger Bands

          What It Is:

          In a strong trend, the price tends to hug one of the Bollinger Bands. In an uptrend, the price often stays near the upper band, while in a downtrend, it stays near the lower band.

          How It Can Be Used in a Trading Bot:

          A trading bot can be designed to open positions following the trend direction. For example, if the price is consistently hitting the upper band, the bot can place buy orders. Conversely, if the price sticks to the lower band, the bot can open sell positions, adjusting stop-losses and profit-taking points accordingly.

          5. Detecting Double Tops and Bottoms

          What It Is:

          Bollinger Bands can help detect double tops (two peaks near the upper band signaling a potential bearish reversal) or double bottoms (two troughs near the lower band indicating a bullish reversal).

          How It Can Be Used in a Trading Bot:

          Bots can be configured to recognize these patterns and automatically trigger trades when they form. For example, a bot can be set to initiate a sell order after identifying a double top or a buy order after spotting a double bottom. This pattern recognition can be enhanced by combining Bollinger Bands with other indicators for better accuracy.

          By incorporating these strategies, traders can enhance their trading bot’s performance and create systems that are responsive to market conditions, taking full advantage of Bollinger Bands for technical analysis.

          Steps to Get the Bollinger Bands of the Historical Price of an Asset with Lumibot

          Prerequisites

          Must have Python installed in the system(version 3.10 or above)

          1. Install required Python packages.
          
           pip install lumibot
          

          2. Necessary imports for running the Python file.

          
          from lumibot.strategies import Strategy
          import pandas_ta as ta
          
          

          3. Create ALPACA_CONFIG with API KEY and API SECRET by logging in or signing up at https://alpaca.markets/.

          Steps for Using Bollinger Bands of the Historical Price of an Asset

          Step 1: Add ALPACA_CONFIG Details

          Alpaca is a broker, just like the interactive broker. The details below are required to use the Alpaca broker API.

          ALPACA_CONFIG = {
          	"API_KEY": "YOUR_API_KEY_HERE", # Get your API Key from https://alpaca.markets/
          	"API_SECRET": "YOUR_SECRET_HERE", # Get your secret from https://alpaca.markets/
          	"PAPER":True # Set to False for real money
          }
          
          

          Step 2: Create a GetHistoricalPrice Class 

          Once you have added the Alpaca config detail, create a GetHistoricalPrice class, which will inherit the Strategy class as below.

          class GetHistoricalPrice(Strategy):
          

          Step 3: Add  on_trading_iteration() Method 

          Once you have added the initialize method, follow with the creation of  on_trading_iteration() method as below:

          def on_trading_iteration(self):
          
             	# Get historical prices for AAPL
                 bars = self.get_historical_prices("AAPL", 30, "day")
                 df = bars.df
          
             	# Calculate Bollinger Bands
                 bbands = ta.bbands(df['close'], length=20, std=2)
                 df['BB_Middle'] = bbands['BBM_20_2.0']
                 df['BB_Upper'] = bbands['BBU_20_2.0']
                 df['BB_Lower'] = bbands['BBL_20_2.0']
          
             	# Drop any rows with NaN values
                 df.dropna(inplace=True)
          
             	print(df[['close', 'BB_Middle', 'BB_Upper', 'BB_Lower']])
          

          In this code, the function on_trading_iteration retrieves historical price data for Apple Inc. (“AAPL”) over the last 30 days, with each data point representing a day. It then calculates the Bollinger Bands for the closing prices of AAPL using a 20-day moving average and 2 standard deviations. The calculated Bollinger Bands consist of three lines: the middle band (20-day moving average), upper band (2 standard deviations above the middle), and lower band (2 standard deviations below the middle). These values are added to the dataframe df as new columns. Afterward, any rows containing missing values (NaN) are removed, and the relevant columns (close price, middle, upper, and lower Bollinger Bands) are printed to the console for analysis.

          Note 1: Running the Code in the Same File 

          In Python, if __name__ == “__main__”: is a conditional statement, which allows you to control the execution of code depending on whether the script is run directly or imported as a module. This implies that the code will run only if runs as a script and not as a module. 

          if __name__ == "__main__": 
          
          

          Step 4: Import Alpaca and Trader 

          Import Alpaca and Trader classes from Lumibot.brokers and Lumibot.traders modules. While Alpaca is an interface to the Alpaca trading platform, it leverages us with the functionalities to interact with the Alpaca API for implementing things like placing orders, managing positions, and fetching market data like Historical Price Data, which we are doing in this blog.

          The Trader class helps orchestrate the trading process, managing multiple trading strategies, interacting with brokers like Alpaca, Interactive Brokers, and Tradiers, handling order execution and position management, and ensuring a framework for live trading and backtesting. 

          from lumibot.brokers import Alpaca
          from lumibot.traders import Trader
          
          

          Step 5: Create Trader Class Object

          As you import the Alpaca and Trader class, create the trader object of the Trader() class.

           trader = Trader()
          

          Step 6: Create an Object of Alpaca Class

          On creation of the trader class object, create the object of the Alpaca class by passing the Alpaca_Config array created above.

          broker = Alpaca(ALPACA_CONFIG)
          

          Step 7: Create an Object of GetHistoricalPrice Class

          Once we have created the object for the Alpaca class, we will create an object of the GetHistoricalPrice class by passing the Alpaca object (broker) as a parameter to the GetHistoricalPrice class. 

           strategy = GetHistoricalPrice(broker=broker)
          
          

          Step 8: Pass the Strategy to the Trader Class Object

          On creation of the object of the GetHistoricalPrice class, add the strategy to the trader class object using the add_strategy() method.

          trader.add_strategy(strategy)
          

          Step 9: Start the Overall Trading Process

          The code below starts the overall trading process. This typically executes backtesting or a live trading process for a collection of strategies within a trading platform. This command starts the execution engine. It establishes the connection with a broker, which is Alpaca, and starts background tasks like market data ingestion and order management. Briefly, it is the starting point of the trading process.

          trader.run_all()
          


          Complete Code

          from lumibot.strategies import Strategy
          import pandas_ta as ta
          
          # Alpaca API configuration
          ALPACA_CONFIG = {
              "API_KEY": "YOUR_API_KEY_HERE", # Get your API Key from https://alpaca.markets/
              "API_SECRET": "YOUR_SECRET_HERE", # Get your secret from https://alpaca.markets/
              "PAPER":True # Set to False for real money
          }
          class GetHistoricalPrice(Strategy):
          
             def on_trading_iteration(self):
          
             	# Get historical prices for AAPL
                 bars = self.get_historical_prices("AAPL", 30, "day")
                 df = bars.df
          
             	# Calculate Bollinger Bands
                 bbands = ta.bbands(df['close'], length=20, std=2)
                 df['BB_Middle'] = bbands['BBM_20_2.0']
                 df['BB_Upper'] = bbands['BBU_20_2.0']
                 df['BB_Lower'] = bbands['BBL_20_2.0']
          
             	# Drop any rows with NaN values
                 df.dropna(inplace=True)
          
             	print(df[['close', 'BB_Middle', 'BB_Upper', 'BB_Lower']])
          
          
          if __name__ == "__main__":
          
             from lumibot.brokers import Alpaca
             from lumibot.traders import Trader
          
             broker = Alpaca(ALPACA_CONFIG)
             strategy = GetHistoricalPrice(broker=broker)
            
             trader = Trader()
             trader.add_strategy(strategy)
             trader.run_all()
          

          Output

          Conclusion

          Bollinger Bands provides traders with a clear framework for analyzing market trends, detecting overbought and oversold conditions, and identifying potential breakouts. When integrated into a trading bot, Bollinger Bands can automate these insights, helping traders execute strategies with precision and without emotional bias. Tools like Lumibot simplify this automation, enabling traders to fine-tune their Bollinger Bands parameters backtest strategies and set up fully autonomous trading systems based on real-time market conditions.

          By leveraging Lumibot’s advanced features, traders can unlock the full potential of Bollinger Bands, increasing their chances of success in a dynamic market environment.

          Take Your Trading to the Next Level with Lumibot

          Ready to harness the power of Bollinger Bands and automate your trading strategies? Lumibot offers the perfect platform to build, customize, and optimize your trading bot. Whether you’re new to automated trading or a seasoned professional, Lumibot provides intuitive tools to help you stay ahead of the market.

          Sign up for Lumibot today and start integrating Bollinger Bands into your trading bot for a more efficient, data-driven trading experience!

          Categories
          Algorithmic Trading

          Technical Analysis Using MACD and Lumibot

          Introduction

          Are you struggling to optimize your trading bot’s performance? MACD, or Moving Average Convergence Divergence, can be a powerful tool to help you address common challenges in automated trading. By identifying potential trend reversals, overbought or oversold conditions, and generating trading signals, MACD can provide valuable insights for your bot’s decision-making process.
          Lumibot, a leading trading bot platform, offers seamless integration with MACD, allowing you to leverage its capabilities to enhance your trading strategies. With Lumibot, you can easily incorporate MACD into your bot’s logic, making informed decisions based on real-time market data.
          Let’s read on to find out how to effectively use MACD in your trading bot.

          Five Applications of MACD in Trading Bots

          MACD, a versatile technical indicator, offers a wealth of insights for traders. Let’s read on to explore five key applications that can help you make informed decisions and enhance your trading strategies.

          1. Trend Reversal Detection

          • Bullish Crossovers: When the MACD line crosses above the signal line, it suggests a potential uptrend reversal. A trading bot can be programmed to automatically initiate long positions when this occurs.
          • Bearish Crossovers: When the MACD line crosses below the signal line, it suggests a potential downtrend reversal. A trading bot can be programmed to automatically initiate short positions or close existing long positions based on this signal.

          2. Overbought/Oversold Indicators

          • Positive Histogram: A significantly positive MACD histogram indicates that the asset may be overbought, suggesting a potential pullback or correction. A trading bot can be programmed to reduce existing long positions or avoid entering new long positions when the histogram reaches a predetermined threshold.
          • Negative Histogram: A significantly negative MACD histogram indicates that the asset may be oversold, suggesting a potential rebound. A trading bot can be programmed to initiate long positions or reduce existing short positions based on this signal.

          3. Divergence Detection

          • Positive Divergence: When the price makes a new low, but the MACD fails to make a new low (positive divergence), it can signal a potential bullish reversal. A trading bot can be programmed to automatically initiate long positions or reduce existing short positions based on this divergence.
          • Negative Divergence: When the price makes a new high, but the MACD fails to make a new high (negative divergence), it can signal a potential bearish reversal. A trading bot can be programmed to automatically initiate short positions or reduce existing long positions based on this divergence.

          4. Momentum Confirmation

          • Upward Sloping MACD: When the MACD line is above the signal line and moving upward, it confirms an uptrend. A trading bot can be programmed to maintain or increase existing long positions based on this signal.
          • Downward Sloping MACD: When the MACD line is below the signal line and moving downward, it confirms a downtrend. A trading bot can be programmed to maintain or increase existing short positions based on this signal.

          5. Trading Strategy Development

          • Combination with Other Indicators: MACD can be combined with other technical indicators, such as RSI, Bollinger Bands, or support and resistance levels, to develop more complex trading strategies. A trading bot can be programmed to execute trades based on a combination of these indicators.
          • Backtesting and Optimization: Traders can backtest their MACD-based trading strategies using historical data to evaluate their performance and optimize parameters like the fast and slow EMAs and the signal line period. A trading bot can be programmed to continuously backtest and optimize its trading strategy based on historical data.

          Risk Management: To mitigate potential losses, it’s essential to incorporate risk management techniques, such as stop-loss orders and position sizing when using MACD in trading bots. A trading bot can be programmed to automatically implement stop-loss orders and adjust position sizes based on predefined criteria.

          Steps To Get the MACD(Moving Average Convergence Divergence) of the Historical Price of an Asset with Lumibot

          Prerequisites

          Must have Python installed in the system(version 3.10 or above)

          1. Install required Python packages.
          
           pip install lumibot
          


          2. Necessary imports for running the Python file.

          
          from lumibot.strategies import Strategy
          

          3. Create ALPACA_CONFIG with API KEY and API SECRET by logging in or signing up at https://alpaca.markets/

          Steps To Get the MACD (Moving Average Convergence Divergence) of an Asset with Lumibot

          Step 1: Add ALPACA_CONFIG Details

          Alpaca is a broker, just like the interactive broker. The details below are required to use the Alpaca broker API.

          ALPACA_CONFIG = {
          	"API_KEY": "YOUR_API_KEY_HERE", # Get your API Key from https://alpaca.markets/
          	"API_SECRET": "YOUR_SECRET_HERE", # Get your secret from https://alpaca.markets/
          	"PAPER":True # Set to False for real money
          }
          

          Step 2: Create a GetHistoricalPrice Class 

          Once you have added the Alpaca config detail, create a GetHistoricalPrice class, which will inherit the Strategy class as below.

          class GetHistoricalPrice(Strategy):
          

          Step 3: Add on_trading_iteration() Method

          Once you have added the initialize method, follow with the creation of  on_trading_iteration() method as below:

             def on_trading_iteration(self):
          
                  bars = self.get_historical_prices("AAPL", 50, "day")  # Ensure enough data for MACD
                  df = bars.df
          
                  # Calculate MACD values
                  macd = ta.macd(df['close'])
                  df['MACD'] = macd['MACD_12_26_9']
                  df['MACD_Signal'] = macd['MACDs_12_26_9']
                  df['MACD_Hist'] = macd['MACDh_12_26_9']
          
                  # Drop any rows with NaN values that could cause errors
                  df.dropna(inplace=True)
          
                  print(df)
          
          
          
          
          


          The provided code calculates the MACD values for Apple stock based on historical price data. It first fetches the necessary data, creates a DataFrame, and then uses the ta.macd function to calculate the MACD line, signal line, and histogram. Finally, it removes any rows with missing values and prints the resulting DataFrame for inspection. This DataFrame can be used for further analysis or to generate trading signals based on the calculated MACD values.

          Note 1: Running the Code in the Same File 

          In Python, if __name__ == “__main__”: is a conditional statement, which allows you to control the execution of code depending on whether the script is run directly or imported as a module. This implies that the code will run only if runs as a script and not as a module. 

          if __name__ == "__main__": 
          
          

          Step 4: Import Alpaca and Trader 

          Import Alpaca and Trader classes from Lumibot.brokers and Lumibot.traders modules. While Alpaca is an interface to the Alpaca trading platform, it leverages us with the functionalities to interact with the Alpaca API for implementing things like placing orders, managing positions, and fetching market data like Historical Price Data, which we are doing in this blog.

          The Trader class helps orchestrate the trading process, managing multiple trading strategies, interacting with brokers like Alpaca, Interactive Brokers, and Tradiers, handling order execution and position management, and ensuring a framework for live trading and backtesting. 

          from lumibot.brokers import Alpaca
          from lumibot.traders import Trader
          
          

          Step 5: Create Trader Class Object

          As you import the Alpaca and Trader class, create the trader object of the Trader() class.

           trader = Trader()
          

          Step 6: Create an Object of Alpaca Class

          On creation of the trader class object, create the object of the Alpaca class by passing the Alpaca_Config array created above.

          broker = Alpaca(ALPACA_CONFIG)
          

          Step 7: Create an Object of GetHistoricalPrice Class

          Once we have created the object for the Alpaca class, we will create an object of the GetHistoricalPrice class by passing the Alpaca object (broker) as a parameter to the GetHistoricalPrice class. 

           strategy = GetHistoricalPrice(broker=broker)
          

          Step 8: Pass the Strategy to the Trader Class Object

          On creation of the object of the GetHistoricalPrice class, add the strategy to the trader class object using the add_strategy() method.

          trader.add_strategy(strategy)
          

          Step 9: Start the Overall Trading Process

          The code below starts the overall trading process. This typically executes backtesting or a live trading process for a collection of strategies within a trading platform. This command starts the execution engine. It establishes the connection with a broker, which is Alpaca, and starts background tasks like market data ingestion and order management. Briefly, it is the starting point of the trading process.

          trader.run_all()
          

          Complete Code

          Below is the entire code. Simply paste the code below in a gethistprice.py file, add the Alpaca API and secret keys, install the prerequisites, and run the code. However, ensure the market is open, which applies to US markets. The pre-trading hours start at 4 a.m. and end at 9:30 a.m. The regular trading hours begin at 9:30 a.m. and end at 4 p.m., and the after-trading hours last from 4 p.m. to 8 p.m. All timings are in Eastern Time (ET). Run the below code during regular trading hours.

          from lumibot.strategies import Strategy
          import pandas_ta as ta
          
          # Alpaca API configuration
          ALPACA_CONFIG = {
              "API_KEY": "YOUR_API_KEY_HERE", # Get your API Key from
          https://alpaca.markets/
              "API_SECRET": "YOUR_SECRET_HERE", # Get your secret from
          https://alpaca.markets/
              "PAPER":True # Set to False for real money
          }
          class GetHistoricalPrice(Strategy):
          
              def on_trading_iteration(self):
          
                  bars = self.get_historical_prices("AAPL", 50, "day")  # Ensure enough data for MACD
                  df = bars.df
          
                  # Calculate MACD values
                  macd = ta.macd(df['close'])
                  df['MACD'] = macd['MACD_12_26_9']
                  df['MACD_Signal'] = macd['MACDs_12_26_9']
                  df['MACD_Hist'] = macd['MACDh_12_26_9']
          
                  # Drop any rows with NaN values that could cause errors
                  df.dropna(inplace=True)
          
                  print(df)
          
          
          if __name__ == "__main__":
          
              from lumibot.brokers import Alpaca
              from lumibot.traders import Trader
          
              broker = Alpaca(ALPACA_CONFIG)
              strategy = GetHistoricalPrice(broker=broker)
              
              trader = Trader()
              trader.add_strategy(strategy)
              trader.run_all()

          Output

          Conclusion

          By effectively integrating MACD into a trading bot, traders can automate their trading strategies and improve their trading results. However, it’s important to note that no trading strategy is foolproof, and it’s essential to conduct thorough backtesting and risk management to minimize losses. Ready to leverage the power of MACD and other technical indicators in your trading bot? Lumibot offers a robust platform with easy-to-use APIs and comprehensive documentation to help you build and deploy sophisticated trading strategies. Start your journey towards automated trading success with Lumibot today!


          Categories
          Algorithmic Trading

          Technical Analysis of Asset: An RSI Approach Using Lumibot

          Introduction

          In the dynamic world of financial markets, where trends can shift rapidly, trading bots have become an invaluable tool for investors seeking to capitalize on opportunities. To create a truly effective trading bot, it’s essential to equip it with powerful analytical tools. One such tool is the Relative Strength Index (RSI), a popular technical indicator that measures the speed and change of price movements.

          To incorporate RSI into your trading bot’s strategy, consider using a robust tool like Lumibot, a comprehensive Python library. Lumibot offers exceptional capabilities for retrieving historical price data, calculating technical indicators, and integrating them into your trading strategies. By leveraging Lumibot’s powerful features, you can build a smarter, more profitable trading bot that can effectively harness the insights provided by the RSI.

          RSI: A Powerful Tool for Algorithmic Trading

          The Relative Strength Index (RSI) is a versatile technical indicator that can be used to create various trading strategies. Here are five pointers on how to incorporate RSI into your trading bot:

          1. Overbought and Oversold Signals:

          • Identifying Extreme Conditions: When the RSI reaches extreme levels (e.g., above 70 or below 30), it might indicate an overbought or oversold condition.
          • Trading Signals: Your trading bot can use these signals to initiate sell orders when the RSI is overbought and buy orders when the RSI is oversold. For instance, if the RSI reaches 80, the bot might automatically sell the asset, assuming it is overvalued.

          2. Divergence:

          • Identifying Divergence: Divergence occurs when the price and the RSI move in opposite directions. For example, if the price is making new highs, but the RSI is making new lows, it might suggest a potential reversal.

          Trading Signals: Your trading bot can identify potential trend changes with divergence signals. For instance, if the price is making new highs, but the RSI fails to make new highs, it might indicate a bearish divergence, suggesting a potential downtrend.

          3. Combining with Other Indicators:

          • Enhanced Signal Reliability: Combining RSI with other technical indicators can help improve trading signals’ accuracy and reliability.

          Example: Combining RSI with the Simple Moving Average (SMA) can provide a more comprehensive analysis. For instance, if the price is above the SMA and the RSI is near the overbought level, it might suggest a potential sell signal, indicating that the asset is overvalued and the uptrend might be nearing its end.

          4. Backtesting:

          • Evaluating Strategy Performance: Backtesting involves testing your trading strategy on historical data to assess its performance.
          • Identifying Improvements: By backtesting, you can identify potential weaknesses in your strategy and make necessary adjustments.

          Example: You can backtest an RSI-based trading strategy using historical data for a specific asset to determine its profitability and risk exposure.

          5. Risk Management:

          • Protecting Investments: Implementing risk management strategies is essential to protect your investments from excessive losses.
          • Stop-Loss Orders: Set stop-loss orders to automatically sell the asset if the price is predetermined, limiting potential losses.
          • Take-Profit Orders: Set take-profit orders to automatically sell the asset when it reaches a predetermined profit target, securing your gains.
          • Position Sizing: Carefully manage your position size to avoid excessive risk. Consider factors such as your risk tolerance and the asset’s volatility.

          By incorporating these pointers into your trading bot, you can effectively leverage the RSI to make informed trading decisions and improve investment outcomes.

          Steps To Get the Relative Strength Index (RSI) of the Historical Price of an Asset with Lumibot

          Prerequisites

          Must have Python installed in the system(version 3.10 or above)

          1. Install required Python packages.
          pip install lumibot
          

          2. Necessary imports for running the Python file.

          
          from lumibot.strategies import Strategy
          import pandas_ta as ta
          
          

          3. Create ALPACA_CONFIG with API KEY and API SECRET by logging in or signing up at https://alpaca.markets/

          Steps for Using Relative Strength Index (RSI) of the Historical Price of an Asset

          Step 1: Add ALPACA_CONFIG Details

          Alpaca is a broker, just like the interactive broker. The details below are required to use the Alpaca broker API.

          ALPACA_CONFIG = {
              "API_KEY": "YOUR_API_KEY_HERE", # Get your API Key from
          https://alpaca.markets/
              "API_SECRET": "YOUR_SECRET_HERE", # Get your secret from
          https://alpaca.markets/
              "PAPER":True # Set to False for real money
          }

          Step 2: Create a GetHistoricalPrice Class 

          Once you have added the Alpaca config detail, create a GetHistoricalPrice class, which will inherit the Strategy class as below.

          class GetHistoricalPrice(Strategy):
          

          Step 3: Add  on_trading_iteration() Method 

          Once you have added the initialize method, follow with the creation of  on_trading_iteration() method as below:

          def on_trading_iteration(self):
          
                  # Retrieve historical price data for AAPL
                  bars = self.get_historical_prices("AAPL", 10, "day")
                  df = bars.df
          
          
                  df['RSI_10'] = ta.rsi(df['close'], length=9)
          
                  print(df)
          

          The provided code snippet defines a function that retrieves historical price data for AAPL, calculates a 10-day Relative Strength Index (RSI), and prints the resulting data frame. This function could be used in various algorithmic trading scenarios, such as trend following, mean reversion, backtesting, or real-time trading. By incorporating this function into a larger trading system, you can leverage the power of technical analysis to make data-driven trading decisions.

          Note 1: Running the Code in the Same File 

          In Python, if __name__ == “__main__”: is a conditional statement, which allows you to control the execution of code depending on whether the script is run directly or imported as a module. This implies that the code will run only if runs as a script and not as a module. 

          if __name__ == "__main__": 

          Step 4: Import Alpaca and Trader

          Import Alpaca and Trader classes from Lumibot.brokers and Lumibot.traders modules. While Alpaca is an interface to the Alpaca trading platform, it leverages us with the functionalities to interact with the Alpaca API for implementing things like placing orders, managing positions, and fetching market data like Historical Price Data, which we are doing in this blog.

          The Trader class helps orchestrate the trading process, managing multiple trading strategies, interacting with brokers like Alpaca, Interactive Brokers, and Tradiers, handling order execution and position management, and ensuring a framework for live trading and backtesting. 

          from lumibot.brokers import Alpaca
          from lumibot.traders import Trader
          

          Step 5: Create Trader Class Object

          As you import the Alpaca and Trader class, create the trader object of the Trader() class.

            trader = Trader()
          

          Step 6: Create an Object of Alpaca Class

          On creation of the trader class object, create the object of the Alpaca class by passing the Alpaca_Config array created above.

           broker = Alpaca(ALPACA_CONFIG)
          
          

          Step 7: Create an Object of GetHistoricalPrice Class

          Once we have created the object for the Alpaca class, we will create an object of the GetHistoricalPrice class by passing the Alpaca object (broker) as a parameter to the GetHistoricalPrice class. 

           strategy = GetHistoricalPrice(broker=broker)
          


          Step 8: Pass the Strategy to the Trader Class Object

          On creation of the object of the GetHistoricalPrice class, add the strategy to the trader class object using the add_strategy() method.

          trader.add_strategy(strategy)
          
          
          
          

          Step 9: Start the Overall Trading Process

          The code below starts the overall trading process. This typically executes backtesting or a live trading process for a collection of strategies within a trading platform. This command starts the execution engine. It establishes the connection with a broker, which is Alpaca, and starts background tasks like market data ingestion and order management. Briefly, it is the starting point of the trading process.

          trader.run_all()

          Complete Code

          from lumibot.strategies import Strategy
          import pandas_ta as ta
          
          ALPACA_CONFIG = {
              "API_KEY": "",
              "API_SECRET": "",
              "PAPER": True
          }
          
          class GetHistoricalPrice(Strategy):
          
              def on_trading_iteration(self):
          
                  # Retrieve historical price data for AAPL
                  bars = self.get_historical_prices("AAPL", 10, "day")
                  df = bars.df
          
          
                  df['RSI_10'] = ta.rsi(df['close'], length=9)
          
                  print(df)
          
          if __name__ == "__main__":
          
              from lumibot.brokers import Alpaca
              from lumibot.traders import Trader
          
              trader = Trader()
              broker = Alpaca(ALPACA_CONFIG)
              strategy = GetHistoricalPrice(broker=broker)
              trader.add_strategy(strategy)
              trader.run_all()
          
          

          Output

          Conclusion

          The Relative Strength Index (RSI) is a valuable technical indicator that can help you identify overbought or oversold conditions and potential trend reversals. By using Lumibot, you can easily retrieve historical price data, calculate RSI, and develop effective trading strategies. Combine RSI with other indicators and backtest your strategies to optimize their performance. Start your algorithmic trading journey today and unlock the power of RSI with Lumibot. By leveraging Lumibot’s features, you can streamline the process of using RSI in your trading strategies and make data-driven investment decisions.

          Categories
          Algorithmic Trading

          How to Get the Simple Moving Average of the Historical Price of an Asset with Lumibot

          .

          Introduction

          In the realm of algorithmic trading, technical analysis plays a pivotal role in deciphering market trends and making informed investment decisions. One of the fundamental tools employed in technical analysis is the Simple Moving Average (SMA). The SMA calculates the average price of an asset over a specific period, providing a smoothed representation of its price movement.

          Lumibot, a powerful Python library, simplifies the process of retrieving historical price data and applying technical analysis techniques. In this blog post, we will delve into how to effectively use Lumibot’s get_historical_prices function to obtain historical price data for an asset and subsequently calculate its Simple Moving Average.

          Leveraging SMA for Effective Trading Strategies

          The Simple Moving Average (SMA) is a versatile technical indicator that can be used to create various trading strategies. Here are five pointers on how to incorporate SMA into your trading bot:

          1. Trend Following Strategies:

          • Identify Uptrends: When the price consistently trades above the SMA, it suggests an uptrend. This indicates that the asset’s price is likely to continue rising. Your trading bot can use this signal to enter long positions.
          • Identify Downtrends: Conversely, if the price consistently trades below the SMA, it suggests a downtrend, indicating a potential price decline. Your bot can use this signal to enter short positions.

          Example: A trading bot might use a 50-day SMA and a 200-day SMA. If the 50-day SMA consistently trades above the 200-day SMA, it could indicate a bullish trend, prompting the bot to enter a long position.

          2. Crossover Strategies:

          • Golden Cross: A crossover occurs when a shorter-term SMA (e.g., 50-day) crosses above a longer-term SMA (e.g., 200-day). This is often interpreted as a bullish signal, suggesting a potential uptrend.
          • Death Cross: A crossover occurs when a longer-term SMA crosses below a shorter-term SMA. This is often interpreted as a bearish signal, indicating a potential downtrend.

          Example: A trading bot might use a 50-day SMA and a 200-day SMA. If the 50-day SMA crosses above the 200-day SMA (a Golden Cross), the bot could initiate a buy order, and if the 50-day SMA crosses below the 200-day SMA (a Death Cross), the bot could initiate a sell order.

          3. Mean Reversion Strategies:

          • Identify Overbought/Oversold Conditions: When the price deviates significantly from the SMA, it might indicate an overbought or oversold condition. An overbought condition suggests that the price is likely to fall, while an oversold condition suggests a potential price increase.
          • Trading Signals: Your bot can use these signals to sell when the price is overbought and buy when the price is oversold, assuming that prices will eventually return to the average level.

          Example: A trading bot might calculate the percentage difference between the current price and the 200-day SMA. If the difference is significantly positive (e.g., above 3 standard deviations), it might indicate an overbought condition, prompting the bot to sell.

          4. Support and Resistance Strategies:

          • Support Level: The SMA can act as a support level, especially during downtrends. If the price reaches the SMA and then bounces back up, it might signal a potential buying opportunity.
          • Resistance Level: The SMA can also act as a resistance level, especially during uptrends. If the price reaches the SMA and then fails to break through, it might indicate a potential selling opportunity.

          Example: A trading bot might monitor the price’s relationship to the 200-day SMA. If the price consistently tests the 200-day SMA as a support level during a downtrend and then bounces back up, the bot could initiate a buy order.

          5. Combining SMA with Other Indicators:

          • Enhanced Signal Reliability: Combining SMA with other technical indicators can help to improve the accuracy and reliability of trading signals.

          Example: Combining SMA with the Relative Strength Index (RSI) can help to identify overbought and oversold conditions more effectively. If the price is above the SMA and the RSI is near the overbought level, it might suggest a potential sell signal.

          Steps To Get the Simple Moving Average of the Historical Price of an Asset with Lumibot

          Prerequisites

          Must have Python installed in the system(version 3.10 or above)Install required Python packages.

          
           pip install lumibot
          
          

          Necessary imports for running the Python file.

          
          from lumibot.strategies import Strategy
          import pandas_ta as ta
          
          

          Create ALPACA_CONFIG with API KEY and API SECRET by logging in or signing up at https://alpaca.markets/.………..

          Steps for Using Simple Moving Average of the Historical Price of an Asset

          Step 1: Add ALPACA_CONFIG Details

          Alpaca is a broker, just like the interactive broker. The details below are required to use the Alpaca broker API.

          ALPACA_CONFIG = {
              "API_KEY": "YOUR_API_KEY_HERE", # Get your API Key from
          https://alpaca.markets/
              "API_SECRET": "YOUR_SECRET_HERE", # Get your secret from
          https://alpaca.markets/
              "PAPER":True # Set to False for real money
          }
          

          Step 2: Create a GetHistoricalPrice Class 

          Once you have added the Alpaca config detail, create a GetHistoricalPrice class, which will inherit the Strategy class as below.

          class GetHistoricalPrice(Strategy):
          

          Step 3: Add on_trading_iteration() Method

          Once you have added the initialize method, follow with the creation of  on_trading_iteration() method as below:

           def on_trading_iteration(self):
          
                  # Retrieve historical prices
                  bars = self.get_historical_prices("AAPL", 10, "day")
                 
                  df = bars.df
          
                  # Calculate the 10-day Simple Moving Average (SMA)
                  df['SMA_10'] = ta.sma(df['close'], length=10)
              
                  # Log a message if the last SMA value is greater than 20
                  if df['SMA_10'].iloc[-1] > 200:
                      self.log_message("SMA is more than 200")
          
                  else:
                      self.log_message("SMA is less than or equal to 200")
              
                  # Print the DataFrame for debugging
                  print(df)      

          The provided code snippet defines a function that retrieves historical price data for AAPL, calculates a 10-day Simple Moving Average (SMA), and prints the resulting DataFrame. This function could be used to analyze market trends, identify potential trading opportunities, or backtest trading strategies.

          Note 1: Running the Code in the Same File 

          In Python, if __name__ == “__main__”: is a conditional statement, which allows you to control the execution of code depending on whether the script is run directly or imported as a module. This implies that the code will run only if runs as a script and not as a module. 

          if __name__ == "__main__": 
          

          Step 4: Import Alpaca and Trader 

          Import Alpaca and Trader classes from Lumibot.brokers and Lumibot.traders modules. While Alpaca is an interface to the Alpaca trading platform, it leverages us with the functionalities to interact with the Alpaca API for implementing things like placing orders, managing positions, and fetching market data like Historical Price Data, which we are doing in this blog.

          The Trader class helps orchestrate the trading process, managing multiple trading strategies, interacting with brokers like Alpaca, Interactive Brokers, and Tradiers, handling order execution and position management, and ensuring a framework for live trading and backtesting.

          from lumibot.brokers import Alpaca
          from lumibot.traders import Trader
          
          

          Step 5: Create Trader Class Object

          As you import the Alpaca and Trader class, create the trader object of the Trader() class. This object will be responsible for handling trading strategies and interactions with the Alpaca API. It can potentially execute trades, analyze market data, and implement various trading algorithms.

           trader = Trader()
          


          Step 6: Create an Object of Alpaca Class

          On creation of the trader class object, create the object of the Alpaca class by passing the Alpaca_Config array created above.

           broker = Alpaca(ALPACA_CONFIG)
          
          

          Step 7: Create an Object of GetHistoricalPrice Class

          Once we have created the object for the Alpaca class, we will create an object of the GetHistoricalPrice class by passing the Alpaca object (broker) as a parameter to the GetHistoricalPrice class. 

           strategy = GetHistoricalPrice(broker=broker)
          
          

          Step 8: Pass the Strategy to the Trader Class Object

          On creation of the object of the GetHistoricalPrice class, add the strategy to the trader class object using the add_strategy() method.

          trader.add_strategy(strategy)

          Step 9: Start the Overall Trading Process

          The code below starts the overall trading process. This typically executes backtesting or a live trading process for a collection of strategies within a trading platform. This command starts the execution engine. It establishes the connection with a broker, which is Alpaca, and starts background tasks like market data ingestion and order management. Briefly, it is the starting point of the trading process.

          trader.run_all()
          


          Complete Code

          from lumibot.strategies import Strategy
          import pandas_ta as ta
          
          ALPACA_CONFIG = {
              "API_KEY": "",
              "API_SECRET": "",
              "PAPER": True
          }
          
          class GetHistoricalPrice(Strategy):
          
              def on_trading_iteration(self):
          
                  # Retrieve historical prices
                  bars = self.get_historical_prices("AAPL", 10, "day")
                 
                  df = bars.df
          
                  # Calculate the 10-day Simple Moving Average (SMA)
                  df['SMA_10'] = ta.sma(df['close'], length=10)
              
                  # Log a message if the last SMA value is greater than 20
                  if df['SMA_10'].iloc[-1] > 200:
                      self.log_message("SMA is more than 200")
          
                  else:
                      self.log_message("SMA is less than or equal to 200")
              
                  # Print the DataFrame for debugging
                  print(df)      
                      
          
          if __name__ == "__main__":
          
              from lumibot.brokers import Alpaca
              from lumibot.traders import Trader
          
              trader = Trader()
              broker = Alpaca(ALPACA_CONFIG)
              strategy = GetHistoricalPrice(broker=broker)
              trader.add_strategy(strategy)
              trader.run_all()
          
          
          

          Output

          Conclusion

          The Simple Moving Average (SMA) is a versatile technical indicator that can be effectively used in trading strategies. By following the steps outlined in this guide, you can retrieve historical price data using Lumibot, calculate the SMA, and leverage it to identify trends, generate trading signals, and make informed investment decisions. Lumibot simplifies the process of retrieving historical price data and calculating the SMA, making it a valuable tool for traders of all levels. The SMA provides valuable insights into market trends, support and resistance levels, and potential trading opportunities. Combining SMA with other technical indicators can further enhance the accuracy and reliability of trading signals. Start your algorithmic trading journey today by incorporating the SMA into your trading strategies using Lumibot.


          Categories
          Algorithmic Trading

          How to Get Multiple Asset’s Historical Prices Using Lumibot’s get_historical_prices_for_assets

          Introduction

          Crafting effective trading strategies requires a solid foundation – historical price data. This data acts as a window into an asset’s past performance, revealing valuable insights for backtesting, fundamental analysis, and long-term trend identification. Lumibot, a Python library designed for algorithmic trading, simplifies the process of acquiring historical price data for multiple assets simultaneously. This article focuses explicitly on the get_historical_prices_for_assets method, allowing you to efficiently retrieve historical price information for assets like AAPL and MSFT.

          Here’s what Lumibot’s get_historical_prices_for_assets offers:

          • Flexibility: Retrieve data for various asset types, including stocks, ETFs, cryptocurrencies, and more (depending on your data source configuration).
          • Efficiency: Fetch data for multiple assets in one call, saving time and effort compared to individual requests.
          • Customization: Control the data timeframe (e.g., daily, hourly) and granularity (e.g., including after-hours data) to fit your specific needs.

          By leveraging historical price data through Lumibot, you can unlock new possibilities for your trading bot development.

          Advantages of Historical Data in Algorithmic Trading

          Historical data plays a crucial role in algorithmic trading, providing valuable insights for developing and optimizing strategies. Here are some of the key advantages:

          1. Pattern Recognition

          • Identifying Trends: Historical data can help identify trends, such as uptrends, downtrends, and sideways movements, which can be used to inform trading decisions.
          • Recognizing Chart Patterns: Patterns like head and shoulders, double tops/bottoms, and triangles can be detected in historical data, providing potential signals for future price movements.

          2. Statistical Analysis

          • Volatility Analysis: Historical data can be used to calculate volatility metrics, such as standard deviation and beta, which are essential for risk management.
          • Correlation Analysis: By analyzing correlations between different assets, traders can construct diversified portfolios and hedge risk.

          3. Machine Learning

          • Training Models: Historical data can be used to train machine learning models, such as neural networks and support vector machines, to predict future price movements.
          • Time series forecasting: Historical data enables time series forecasting, predicting future market trends and patterns.

          4. Risk Management

          • Stop-Loss and Take-Profit Levels: Historical data can be used to set appropriate stop-loss and take-profit levels based on past price movements and volatility.
          • Position Sizing: By analyzing historical data, traders can determine optimal position sizes to manage risk effectively.

          5. Market Analysis

          • Fundamental Analysis: Historical data on financial statements, economic indicators, and industry trends can be used to assess the underlying value of assets.
          • Sentiment Analysis: Analyzing historical news and social media sentiment can provide insights into market sentiment and potential price movements.

          By effectively utilizing historical data, algorithmic traders can develop robust and profitable strategies that have a solid foundation in past market behavior.practical implementation of get_historical_prices_for_assets in Lumibot.

          Steps To Use get_historical_prices_for_assets() Method in Lumibot

          Prerequisites

          Must have Python installed in the system(version 3.10 or above)Install required Python packages.

          
           pip install lumibot
          

          Necessary imports for running the Python file.

          from lumibot.strategies import Strategy
          import pandas_ta as ta

          Create ALPACA_CONFIG with API KEY and API SECRET by logging in or signing up at https://alpaca.markets/.………..

          Steps for Using get_historical_price()

          Step 1: Add ALPACA_CONFIG Details

          Alpaca is a broker, just like the interactive broker. The details below are required to use the Alpaca broker API.

          ALPACA_CONFIG = {
              "API_KEY": "YOUR_API_KEY_HERE", # Get your API Key from
          https://alpaca.markets/
              "API_SECRET": "YOUR_SECRET_HERE", # Get your secret from
          https://alpaca.markets/
              "PAPER":True # Set to False for real money
          }

          Step 2: Create a GetHistoricalPrice Class 

          Once you have added the Alpaca config detail, create a GetHistoricalPrice class, which will inherit the Strategy class as below.

          class GetHistoricalPrice(Strategy):
          

          Step 3: Add  on_trading_iteration() Method 

          Once you have added the initialize method, follow with the creation of  on_trading_iteration() method as below:

            def on_trading_iteration(self):
                  """Get the historical price for assets"""
          
                  # Fetch historical prices for multiple assets
                  bars = self.get_historical_prices_for_assets(["AAPL", "MSFT"], 10, "day")
          
                  for symbol, asset_data in bars.items():
                      # Log the DataFrame of historical prices
          
                      self.log_message(f"Historical prices for {symbol}:\n{asset_data.df}")
          
          
          

          The code snippet is fetching historical price data for two assets, AAPL and MSFT, over the last 10 days. The data is retrieved in the form of DataFrames. The function then logs these DataFrames, providing a detailed view of the historical prices for each asset. This information can be used for further analysis or trading strategies.

          Note: The bars  variable is assigned its value through:

            bars = self.get_historical_prices_for_assets(["AAPL", "MSFT"], 10, "day")
          

          The data stored in bars is, hence, like open price, close price, etc., as mentioned above. Some others can be volume and dividend.

          Further details

          for symbol, asset_data in bars.items():
                      # Log the DataFrame of historical prices
          
                      self.log_message(f"Historical prices for {symbol}:\n{asset_data.df}")
          

          The above code iterates over the dictionary of asset data, where the keys are symbols, and the values are DataFrames containing historical prices. For each asset, it logs the symbol and its corresponding DataFrame, providing a detailed view of the historical price data.

          Note 1: Running the Code in the Same File 

          In Python, if __name__ == “__main__”: is a conditional statement, which allows you to control the execution of code depending on whether the script is run directly or imported as a module. This implies that the code will run only if runs as a script and not as a module.

          if __name__ == "__main__": 

          Step 4: Import Alpaca and Trader

          Import Alpaca and Trader classes from Lumibot.brokers and Lumibot.traders modules. While Alpaca is an interface to the Alpaca trading platform, it leverages us with the functionalities to interact with the Alpaca API for implementing things like placing orders, managing positions, and fetching market data like Historical Price Data, which we are doing in this blog.

          The Trader class helps orchestrate the trading process, managing multiple trading strategies, interacting with brokers like Alpaca, Interactive Brokers, and Tradiers, handling order execution and position management, and ensuring a framework for live trading and backtesting. 

          from lumibot.brokers import Alpaca
          from lumibot.traders import Trader

          Step 5: Create Trader Class Object

          As you import the Alpaca and Trader class, create the trader object of the Trader() class.

           trader = Trader()

          Step 6: Create an Object of Alpaca Class

          On creation of the trader class object, create the object of the Alpaca class by passing the Alpaca_Config array created above.

          broker = Alpaca(ALPACA_CONFIG)

          Step 7: Create an Object of GetHistoricalPrice Class

          Once we have created the object for the Alpaca class, we will create an object of the GetHistoricalPrice class by passing the Alpaca object (broker) as a parameter to the GetHistoricalPrice class. 

           strategy = GetHistoricalPrice(broker=broker)
          

          Step 8: Pass the Strategy to the Trader Class Object

          On creation of the object of the GetHistoricalPrice class, add the strategy to the trader class object using the add_strategy() method.

          trader.add_strategy(strategy)
          
          

          Step 9: Start the Overall Trading Process

          The code below starts the overall trading process. This typically executes backtesting or a live trading process for a collection of strategies within a trading platform. This command starts the execution engine. It establishes the connection with a broker, which is Alpaca, and starts background tasks like market data ingestion and order management. Briefly, it is the starting point of the trading process.

          trader.run_all()
          

          Complete Code

          from lumibot.strategies import Strategy
          
          ALPACA_CONFIG = {
              "API_KEY": "",
              "API_SECRET": "",
              "PAPER": True
          }
          
          class GetHistoricalPrice(Strategy):
          
              def on_trading_iteration(self):
                  """Get the historical price for assets"""
          
                  # Fetch historical prices for multiple assets
                  bars = self.get_historical_prices_for_assets(["AAPL", "MSFT"], 10, "day")
          
                  for symbol, asset_data in bars.items():
                      # Log the DataFrame of historical prices
          
                      self.log_message(f"Historical prices for {symbol}:\n{asset_data.df}")
          
          if __name__ == "__main__":
          
              from lumibot.brokers import Alpaca
              from lumibot.traders import Trader
          
              trader = Trader()
              broker = Alpaca(ALPACA_CONFIG)
              strategy = GetHistoricalPrice(broker=broker)
              trader.add_strategy(strategy)
              trader.run_all()
          
          
          


          Output

          Conclusion

          Lumibot’s get_historical_prices_for_assets method offers a powerful tool for efficiently retrieving historical price data for multiple assets. By leveraging this data, you can enhance your trading strategies, make informed decisions, and gain a competitive edge in the market.

          Start exploring the possibilities with Lumibot today! Visit the Lumibot documentation and begin building your trading bot strategies:





          Multiple assets historical prices