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