中文 README:

Chinese blog about this project: 量化系列2 - 众包数据集

• How to use it
• Developement Setup
  • Install dolt
  • Clone data
  • Export to qlib format
  • Run Daily Update
  • Daily update and output
  • Extract tar file to qlib directory
• Initiative
• Project Detail
  • Data Source
  • Initial loading and Validation logic for each table
• Contribution Guide
  • Add more stock index
  • Add more data source or fields

Table of contents generated with markdown-toc

To: dmnsn7 Who provided tushare token and make daily update possible.

To continue maintain the CI/CD pipeline for this project, we need VPS with 30G+ memory, 4 core+ CPU and good connection to dolthub and github.

If you are willing to sponsor the running environment, please contact me directly.

If you are willing to sponsor the VPS fee, you can donate in this page: https://mp.weixin.qq.com/s/Athd5hsiN_hIKKgxIiO_ow

Thanks for everyone's help.

1. Download the canonical archive and its provenance manifest from the same GitHub release.
2. Validate the completed archive bytes and date-bearing members.
3. Extract the canonical archive to the default qlib directory.

wget "https://github.com/chenditc/investment_data/releases/download/<release-tag>/qlib_bin.tar.gz"
wget "https://github.com/chenditc/investment_data/releases/download/<release-tag>/qlib_bin.manifest.json"
python3 qlib/validate_archive.py \
  --archive qlib_bin.tar.gz \
  --manifest qlib_bin.manifest.json \
  --expected-tag "<release-tag>" \
  --require-publishable
tar -zxvf qlib_bin.tar.gz -C ~/.qlib/qlib_data/cn_data --strip-components=1

If you want to contribute to the set of scripts or the data, here is what you should do to set up a dev environment.

Follow https://github.com/dolthub/dolt

Raw data hosted on dolt: https://www.dolthub.com/repositories/chenditc/investment_data

To download as dolt database:

dolt clone chenditc/investment_data

docker run \
  -v /<some output directory>:/output \
  -it --rm chenditc/investment_data bash dump_qlib_bin.sh

The standalone diagnostic grammar is bash dump_qlib_bin.sh [WORKING_DIR [QLIB_REPOSITORY]]. It always creates and validates qlib_bin.tar.gz together with qlib_bin.manifest.json. A standalone manifest has image_digest:null, so it is suitable for local inspection but cannot be published as a release asset.

You can use the following parameter to mount an existing dolt chenditc/investment_data folder to the container.

  -v /<dolt directory>:/dolt 

You will need tushare token to use tushare api. Get tushare token from https://tushare.pro/

export TUSHARE=<Token>
bash daily_update.sh

docker run -v /<some output directory>:/output -it --rm chenditc/investment_data \
  bash -lc 'bash daily_update.sh && bash dump_qlib_bin.sh'

Release publication is authorized only by the digest-pinned workflow on main. Dispatch the normal publisher with:

gh workflow run upload_release.yml --repo chenditc/investment_data --ref main -f operation=publish

upload_release.sh is workflow-internal and rejects direct/local/container publication. The workflow validates the ten-field manifest and the complete archive before any release mutation, then redownloads and validates both canonical assets.

A full repository revert is ordered and fail-closed:

1. Run gh workflow disable upload_release.yml --repo chenditc/investment_data.
2. Query gh workflow view upload_release.yml --repo chenditc/investment_data --json state --jq .state and require the exact result disabled_manually.
3. Query both upload_release.yml and data_update.yml runs and wait until every queued or in-progress job using the shared Dolt volume has drained.
4. Perform the full repository revert.
5. Run gh workflow view upload_release.yml --repo chenditc/investment_data --json state --jq .state again and require disabled_manually.
6. Do not re-enable publication until validator-backed digest pinning, workflow authority, and shared concurrency/filesystem locking are restored and verified end to end.

The revert may move the convenience latest image and therefore affect data update, but it cannot publish while the upload workflow is disabled. Draining is mandatory because a full revert may remove the shared lock and concurrency group. Already accepted release assets are untouched. An interrupted historical repair may complete only through the fixed repair-2026-07-20 operation, and the stale backup is never auto-restored. The deployed monitor is separate external state; roll it back only with the tracked ops/investment-data-project-monitor/deploy.sh rollback, never as part of the repository revert.

tar -zxvf qlib_bin.tar.gz -C ~/.qlib/qlib_data/cn_data --strip-components=1

1. Try to fill in missing data by combining data from multiple data source. For example, delist company's data.
2. Try to correct data by cross validate against multiple data source.

The database table on dolthub is named with prefix of data source, for example ts_a_stock_eod_price. The meaning of the prefix:

• w(wind): high quality static data source. Only available till 2019.
• c(caihui): high quality static data source. Only available till 2019.
• ts: Tushare data source
• ak: Akshare data source
• yahoo: Use Qlib's yahoo collector https://github.com/microsoft/qlib/tree/main/scripts/data_collector/yahoo
• baostock: Baostock
• final: Merged final data with validation and correction

• final_a_stock_eod_price
• final_a_stock_limit

The initial date for each stock might be different, when we calculate the adjusted price, we are using the first date price as adjust factor = 1.0.

In order to merge different data sources, we need to rescale the adjust factor, so that each data source will have the same adjusted price.

Each data source will have a dedicated link table, which is generated by:

1. If the final_a_stock_eod_price already has this stock, adjust_ratio = final_a_stock_eod_price.adjust_price / current_data_source.adjust_price
2. If the stock is new to final_a_stock_eod_price, adjust_ratio = 1.

Data validation needs to run to verify if the adjust factor match between each data source:

• data_source_1.adjust_ratio * data_source_1.adjust_price = final_a_stock_eod_price.adjust_price

To add a new stock index, we need to change:

1. Add index weight download script. Change tushare/dump_index_eod_price.py script to dump the index info. If the index is not available in tushare, write a new script and add to the daily_update.sh script. Example commit
2. Add price download script. Change tushare/dump_index_eod_price.py to add the index price. Eg. Example Commit
3. Modify export script. Change the qlib dump script qlib/dump_index_weight.py#L13, so that index will be dump and renamed to a txt file for use. Example commit

Please raise an issue to discuss the plan, example issue: #11

It should includes:

1. Why do we want this data?
2. How do we do regular update?
   • Which data source would we use?
   • When should we trigger update?
   • How do we validate regular update complete correctly?
3. Which data source should we get historical data?
4. How do we plan to validate the historical data?
   • Is the data source complete? How did we verify this?
   • Is the data source accurate? How did we verify this?
   • If we see error in validation, how will we deal with them?
5. Are we changing exisiting table or adding new table?

If the data is not clean, we might try hard to dig insight from it and find incorrect insight. So we want high quality data instead of just data.