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qlib/examples
History
Maksim Zayakin 9e22e5168b Remove unused DNNModelPytorch params (#1470) 
* Remove lr_decay and lr_decay_steps params

More flexible way to pass a scheduler (via callable function) is already
supported

* remove lr_decay and lr_decay_steps from mlp workflow configs
2023-04-28 17:48:40 +08:00
..
benchmarks
Remove unused DNNModelPytorch params (#1470)
2023-04-28 17:48:40 +08:00
benchmarks_dynamic
Refine DDG-DA (#1472)
2023-04-07 15:00:21 +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
rl
Remove (#1464)
2023-03-15 15:26:44 +08:00
rl_order_execution
Refine Qlib RL data format (#1480)
2023-04-26 21:14:30 +08:00
rolling_process_data
[807] Move the REG_CONSTANT/EPS to constant.py. (#811)
2022-01-09 21:39:46 +08:00
tutorial
Add ipynb format check (#1439)
2023-02-21 09:23:22 +08:00
README.md
Update README.md
2020-12-17 00:35:35 +08:00
run_all_model.py
Correct errors and typos in doc strings (#1338)
2022-11-07 23:37:18 +08:00
workflow_by_code.ipynb
Add ipynb format check (#1439)
2023-02-21 09:23:22 +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.