1.4 KiB
Multi-level Trading
This worflow is an example for multi-level trading.
Introduction
Qlib supports backtesting of various strategies, including portfolio management strategies, order split strategies, model-based strategies (such as deep learning models), rule-based strategies, and RL-based strategies.
And, Qlib also supports multi-level trading and backtesting. It means that users can use different strategies to trade at different frequencies.
Weekly Portfolio Generation and Daily Order Execution
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.
Usage
Start backtesting by running the following command:
python workflow.py backtest
Start collecting data by running the following command:
python workflow.py collect_data
Daily Portfolio Generation and Minutely Order Execution
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.
Usage
Start backtesting by running the following command:
python workflow.py backtest_highfreq