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fix bugs & add highfreq backtest example

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2021-05-28 22:29:21 +08:00
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examples/multi_level_trading/README.md
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@@ -8,9 +8,12 @@ Qlib supports backtesting of various strategies, including portfolio management
And, Qlib also supports multi-level trading and backtesting. It means that users can use different strategies to trade at different frequencies.
This example uses a DropoutTopkStrategy (a strategy based on the daily frequency Lightgbm model) in weekly frequency for portfolio generation. And, at the daily frequency level, this example uses SBBStrategyEMA (a rule-based strategy that uses EMA for decision-making) to split orders.
## Usage
## 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:
```bash
@@ -22,3 +25,13 @@ 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:
```bash
python workflow.py backtest_highfreq
```