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41 lines
1.4 KiB
Markdown
41 lines
1.4 KiB
Markdown
# Collect Point-in-Time Data
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> *Please pay **ATTENTION** that the data is collected from [baostock](http://baostock.com) and the data might not be perfect. We recommend users to prepare their own data if they have high-quality dataset. For more information, users can refer to the [related document](https://qlib.readthedocs.io/en/latest/component/data.html#converting-csv-format-into-qlib-format)*
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## Requirements
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```bash
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pip install -r requirements.txt
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```
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## Collector Data
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### Download Quarterly CN Data
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```bash
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cd qlib/scripts/data_collector/pit/
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# download from baostock.com
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python collector.py download_data --source_dir ~/.qlib/stock_data/source/pit --start 2000-01-01 --end 2020-01-01 --interval quarterly
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```
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Downloading all data from the stock is very time-consuming. If you just want to run a quick test on a few stocks, you can run the command below
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```bash
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python collector.py download_data --source_dir ~/.qlib/stock_data/source/pit --start 2000-01-01 --end 2020-01-01 --interval quarterly --symbol_regex "^(600519|000725).*"
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```
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### Normalize Data
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```bash
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python collector.py normalize_data --interval quarterly --source_dir ~/.qlib/stock_data/source/pit --normalize_dir ~/.qlib/stock_data/source/pit_normalized
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```
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### Dump Data into PIT Format
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```bash
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cd qlib/scripts
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python dump_pit.py dump --csv_path ~/.qlib/stock_data/source/pit_normalized --qlib_dir ~/.qlib/qlib_data/cn_data --interval quarterly
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```
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