This workflow is an example for nested decision execution in backtesting. Qlib supports nested decision execution in backtesting. It means that users can use different strategies to make trade decision in different frequencies.

This workflow provides an example that uses a DropoutTopkStrategy (a strategy based on the daily frequency Lightgbm model) in weekly frequency for portfolio generation and uses SBBStrategyEMA (a rule-based strategy that uses EMA for decision-making) to execute orders in daily frequency.

Start backtesting by running the following command:

    python workflow.py backtest

Start collecting data by running the following command:

    python workflow.py collect_data

This workflow also provides a high-frequency example that uses a DropoutTopkStrategy for portfolio generation in daily frequency and uses SBBStrategyEMA to execute orders in minutely frequency.

Start backtesting by running the following command:

    python workflow.py backtest_highfreq