be4646b4b7
* Intermediate version * Fix yaml template & Successfully run rolling * Be compatible with benchmark * Get same results with previous linear model * Black formatting * Update black * Update the placeholder mechanism * Update CI * Update CI * Upgrade Black * Fix CI and simplify code * Fix CI * Move the data processing caching mechanism into utils. * Adjusting DDG-DA * Organize import
Introduction
This folder contains 2 examples
- A high-frequency dataset example
- An example of predicting the price trend in high-frequency data
High-Frequency Dataset
This dataset is an example for RL high frequency trading.
Get High-Frequency Data
Get high-frequency data by running the following command:
python workflow.py get_data
Dump & Reload & Reinitialize the Dataset
The High-Frequency Dataset is implemented as qlib.data.dataset.DatasetH in the workflow.py. DatatsetH is the subclass of qlib.utils.serial.Serializable, whose state can be dumped in or loaded from disk in pickle format.
About Reinitialization
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.
The example is given in workflow.py, users can run the code as follows.
Run the Code
Run the example by running the following command:
python workflow.py dump_and_load_dataset
Benchmarks Performance (predicting the price trend in high-frequency data)
Here are the results of models for predicting the price trend in high-frequency data. We will keep updating benchmark models in future.
| Model Name | Dataset | IC | ICIR | Rank IC | Rank ICIR | Long precision | Short Precision | Long-Short Average Return | Long-Short Average Sharpe |
|---|---|---|---|---|---|---|---|---|---|
| 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 |