qlib/examples/multi_level_trading

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