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42 lines
1.7 KiB
Markdown
42 lines
1.7 KiB
Markdown
# Introduction
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This folder contains 2 examples
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- A high-frequency dataset example
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- An example of predicting the price trend in high-frequency data
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## High-Frequency Dataset
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This dataset is an example for RL high frequency trading.
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### Get High-Frequency Data
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Get high-frequency data by running the following command:
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```bash
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python workflow.py get_data
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```
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### Dump & Reload & Reinitialize the Dataset
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The High-Frequency Dataset is implemented as `qlib.data.dataset.DatasetH` in the `workflow.py`. `DatatsetH` is the subclass of [`qlib.utils.serial.Serializable`](https://qlib.readthedocs.io/en/latest/advanced/serial.html), whose state can be dumped in or loaded from disk in `pickle` format.
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### About Reinitialization
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After reloading `Dataset` from disk, `Qlib` also support reinitializing the dataset. It means that users can reset some states of `Dataset` or `DataHandler` such as `instruments`, `start_time`, `end_time` and `segments`, etc., and generate new data according to the states.
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The example is given in `workflow.py`, users can run the code as follows.
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### Run the Code
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Run the example by running the following command:
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```bash
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python workflow.py dump_and_load_dataset
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```
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## Benchmarks Performance (predicting the price trend in high-frequency data)
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Here are the results of models for predicting the price trend in high-frequency data. We will keep updating benchmark models in future.
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| Model Name | Dataset | IC | ICIR | Rank IC | Rank ICIR | Long precision| Short Precision | Long-Short Average Return | Long-Short Average Sharpe |
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|---|---|---|---|---|---|---|---|---|---|
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| LightGBM | Alpha158 | 0.0349±0.00 | 0.3805±0.00| 0.0435±0.00 | 0.4724±0.00 | 0.5111±0.00 | 0.5428±0.00 | 0.000074±0.00 | 0.2677±0.00 |
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