liuyu/qlib
1
0
Fork 0
mirror of https://github.com/microsoft/qlib.git synced 2026-06-06 05:51:17 +08:00
Code Activity
Files
be4646b4b7ea062095fb702eb3de654107d5bbd5
qlib/examples/benchmarks_dynamic/baseline
History
you-n-g be4646b4b7 Adjust rolling api (#1594) 
* 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
2023-07-14 12:16:12 +08:00
..
README.md
Adjust rolling api (#1594)
2023-07-14 12:16:12 +08:00
rolling_benchmark.py
Adjust rolling api (#1594)
2023-07-14 12:16:12 +08:00
workflow_config_lightgbm_Alpha158.yaml
Adjust rolling api (#1594)
2023-07-14 12:16:12 +08:00
workflow_config_linear_Alpha158.yaml
Adjust rolling api (#1594)
2023-07-14 12:16:12 +08:00

README.md

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:

    python rolling_benchmark.py run

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:

    python rolling_benchmark.py --conf_path=workflow_config_lightgbm_Alpha158.yaml run