How to Backtest Multiple Metrics & Parameter Variations in One Code?

To address Jacko's query about backtesting multiple metrics and parameter variations in one code using QuantConnect, here's a structured approach:

Key Concepts and Methods

1. Parameterized Algorithms: You can use parameters to dynamically adjust your algorithm's settings, such as moving average lengths or risk levels. This is done using the get_parameter method. Here's a basic example:

   class ParameterizedAlgorithm(QCAlgorithm):
       def initialize(self) -> None:
           self.set_start_date(2020, 1, 1)
           self.set_cash(100000)
           self.add_equity("SPY")
   
           # Get and store the EMA settings to local parameters.
           fast_period = self.get_parameter("ema-fast", 100)
           slow_period = self.get_parameter("ema-slow", 200)
   
           self.fast = self.ema("SPY", fast_period)
           self.slow = self.ema("SPY", slow_period)
   

2. Backtesting Over Multiple Time Periods: You can set different start and end dates for your backtests using the set_start_date and set_end_date methods. This allows you to test your algorithm over different historical periods like 2012, 2017, and 2019.

3. Calculating Metrics: Within your algorithm, you can calculate various performance metrics such as return, drawdown, and Sharpe ratio. These can be computed using custom logic or by leveraging QuantConnect's built-in statistics services.

4. Iterating Over Parameter Combinations: You can loop through different parameter combinations within your algorithm to test various scenarios. This can be done by setting parameters dynamically and running the algorithm for each set of parameters.

5. Using Alpha and Risk Management Models: Incorporate alpha models and risk management strategies using AddAlpha and AddRiskManagement methods to adapt to different parameter settings.

Example Workflow

• Initialize Parameters: Use get_parameter to fetch and set parameters for your strategy.
• Set Time Periods: Use set_start_date and set_end_date to define the backtest periods.
• Compute Metrics: Implement logic to calculate desired metrics like return, drawdown, and Sharpe ratio.
• Run Backtests: Iterate over parameter combinations and time periods, collecting results for each variation.

By following this approach, you can efficiently backtest multiple metrics and parameter variations within a single codebase on QuantConnect.

Sources: Parameters - QuantConnect.com, Parameterized Algorithm, Overview - QuantConnect.com, Parameters - QuantConnect.com, Getting Started - QuantConnect.com