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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
..
benchmarks
Adjust rolling api (#1594)
2023-07-14 12:16:12 +08:00
benchmarks_dynamic
Adjust rolling api (#1594)
2023-07-14 12:16:12 +08:00
data_demo
add ops_warning_log to config (#685)
2021-11-12 22:23:29 +08:00
highfreq
Adjust rolling api (#1594)
2023-07-14 12:16:12 +08:00
hyperparameter/LightGBM
Adjust rolling api (#1594)
2023-07-14 12:16:12 +08:00
model_interpreter
Adjust rolling api (#1594)
2023-07-14 12:16:12 +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
Adjust rolling api (#1594)
2023-07-14 12:16:12 +08:00
rl
Remove (#1464)
2023-03-15 15:26:44 +08:00
rl_order_execution
Resolve RL FIXMES (#1503)
2023-05-17 16:57:08 +08:00
rolling_process_data
Adjust rolling api (#1594)
2023-07-14 12:16:12 +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
Adjust rolling api (#1594)
2023-07-14 12:16:12 +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.