liuyu/qlib
1
0
Fork 0
mirror of https://github.com/microsoft/qlib.git synced 2026-07-10 14:26:56 +08:00
Code Activity

DDG-DA paper code (#743)

Browse Source 
* Merge data selection to main

* Update trainer for reweighter

* Typos fixed.

* update data selection interface

* successfully run exp after refactor some interface

* data selection share handler &  trainer

* fix meta model time series bug

* fix online workflow set_uri bug

* fix set_uri bug

* updawte ds docs and delay trainer bug

* docs

* resume reweighter

* add reweighting result

* fix qlib model import

* make recorder more friendly

* fix experiment workflow bug

* commit for merging master incase of conflictions

* Successful run DDG-DA with a single command

* remove unused code

* asdd more docs

* Update README.md

* Update & fix some bugs.

* Update configuration & remove debug functions

* Update README.md

* Modfify horizon from code rather than yaml

* Update performance in README.md

* fix part comments

* Remove unfinished TCTS.

* Fix some details.

* Update meta docs

* Update README.md of the benchmarks_dynamic

* Update README.md files

* Add README.md to the rolling_benchmark baseline.

* Refine the docs and link

* Rename README.md in benchmarks_dynamic.

* Remove comments.

* auto download data

Co-authored-by: wendili-cs <wendili.academic@qq.com>
Co-authored-by: demon143 <785696300@qq.com>
This commit is contained in:
you-n-g
2022-01-10 16:52:37 +08:00
committed by GitHub
parent 184ce34a34
commit cf35562e84
52 changed files with 2441 additions and 456 deletions
Download Patch File Download Diff File Expand all files Collapse all files

15
examples/benchmarks_dynamic/baseline/README.md Normal file
View File

@@ -0,0 +1,15 @@
# Introduction
This is the framework of periodically Rolling Retrain (RR) forecasting models. RR adapts to market dynamics by utilizing the up-to-date data periodically.
## Run the Code
Users can try RR by running the following command:
```bash
python rolling_benchmark.py run_all
```
The default forecasting models are `Linear`. Users can choose other forecasting models by changing the `model_type` parameter.
For example, users can try `LightGBM` forecasting models by running the following command:
```bash
python rolling_benchmark.py --model_type="gbdt" run_all
```