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37 lines
1.4 KiB
Markdown
37 lines
1.4 KiB
Markdown
# Multi-level Trading
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This worflow is an example for multi-level trading.
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## Introduction
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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.
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And, Qlib also supports multi-level trading and backtesting. It means that users can use different strategies to trade at different frequencies.
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## Weekly Portfolio Generation and Daily Order Execution
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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.
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### Usage
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Start backtesting by running the following command:
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```bash
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python workflow.py backtest
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```
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Start collecting data by running the following command:
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```bash
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python workflow.py collect_data
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```
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## Daily Portfolio Generation and Minutely Order Execution
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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.
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### Usage
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Start backtesting by running the following command:
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```bash
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python workflow.py backtest_highfreq
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``` |