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
d149c2b17757047cbe9080c602db775e1b6bb0c9
qlib/examples
History
Lewen Wang d149c2b177 Use average weights in DoubleEnsemble. (#1205) 
* Use average weights in DoubleEnsemble.

* Use average weights in DoubleEnsemble.

Co-authored-by: lwwang1995 <lewenwang@msrawsa02.corp.microsoft.com>
2022-07-17 23:02:46 +08:00
..
benchmarks
Use average weights in DoubleEnsemble. (#1205)
2022-07-17 23:02:46 +08:00
benchmarks_dynamic
Update README.md
2022-06-14 10:53:09 +08:00
data_demo
add ops_warning_log to config (#685)
2021-11-12 22:23:29 +08:00
highfreq
[807] Move the REG_CONSTANT/EPS to constant.py. (#811)
2022-01-09 21:39:46 +08:00
hyperparameter/LightGBM
Bump numpy from 1.17.4 to 1.21.0 in /examples/hyperparameter/LightGBM (#896)
2022-01-27 11:16:48 +08:00
model_interpreter
[807] Move the REG_CONSTANT/EPS to constant.py. (#811)
2022-01-09 21:39:46 +08:00
model_rolling
[807] Move the REG_CONSTANT/EPS to constant.py. (#811)
2022-01-09 21:39:46 +08:00
nested_decision_execution
pylint code refine & Fix nested example (#848)
2022-01-14 09:09:21 +08:00
online_srv
Add backtest example to online simulation (#984)
2022-03-19 01:53:14 +08:00
orderbook_data
Black(new version) Format
2022-02-06 22:33:16 +08:00
portfolio
Update Portfolio README.md (#900)
2022-02-07 16:04:11 +08:00
rolling_process_data
[807] Move the REG_CONSTANT/EPS to constant.py. (#811)
2022-01-09 21:39:46 +08:00
tutorial
Update detailed_workflow.ipynb (#1084)
2022-05-03 15:11:27 +08:00
README.md
Update README.md
2020-12-17 00:35:35 +08:00
run_all_model.py
Csi500 example (#1126)
2022-06-15 10:18:13 +08:00
workflow_by_code.ipynb
Be compatible with Google Colab (#1188)
2022-07-08 14:23:25 +08:00
workflow_by_code.py
Add introduction for workflow_by_code.py (#1186)
2022-07-08 10:16:08 +08:00

README.md

Requirements

Here is the minimal hardware requirements to run the workflow_by_code example.

  • Memory: 16G
  • Free Disk: 5G

NOTE

The results will slightly vary on different OSs(the variance of annualized return will be less than 2%). The evaluation results in the README.md page are from Linux OS.