* Init model for both dataset
* Remove some deprecated code
* Add model template;
* We must align with previous results
* We choose another mode as the initial version
* Almost success to run GRU
* Successfully run training
* Passed general_nn test
* gru test
* Alignment test passed
* comment
* fix readme & minor errors
* general nn updates & benchmarks
* Update examples/benchmarks/GeneralPtNN/workflow_config_gru2mlp.yaml
---------
Co-authored-by: Young <afe.young@gmail.com>
Co-authored-by: you-n-g <you-n-g@users.noreply.github.com>
* download orderbook data
* fix CI error
* fix CI error
* test fix CI error
* test fix CI error
* test fix CI error
* test fix CI error
* test fix CI error
* test fix CI error
* test fix CI error
* test fix CI error
* test fix CI error
* test fix CI error
* test fix CI error
* test fix CI error
* test fix CI error
* test fix CI error
* test fix CI error
* test fix CI error
* test fix CI error
* test fix CI error
* test fix CI error
* optimize get_data code
* optimize get_data code
* optimize get_data code
* optimize README
---------
Co-authored-by: Linlang <v-linlanglv@microsoft.com>
* Add multi pass port ana record
* Add list function
* Add documentation and support <MODEL> tag
* Add drop in replacement example
* reformat
* Change according to comments
* update format
* Update record_temp.py
Fix type hint
* Update record_temp.py
* 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
* move config file to benchmark_dynamic & switch default sim task model to GBDT
* Update benchmark_dynamic results
* Change the default value of alpha of DDG-DA
* transpose dimension 1 and 2 to match nn.Conv1d input
* 1.update TCN benchmarks;
2.Emphasize updating the benchmark table;
* replace specific version with main
---------
Co-authored-by: lijinhui <362237642@qq.com>
* 1.specify group_keys=False to avoid FutureWarning;
2.fix get train_start from dict unexpected problem;
* fix black
* Add comments
* Add make file
---------
Co-authored-by: Young <afe.young@gmail.com>
* 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
* wip
* wip
* wip
* Fix naming errors
* Backtest test passed
* Why training stuck?
* Minor
* Refine train configs
* Use dummy in training
* Remove pickle_dataframe
* CI
* CI
* Add more strict condition to filter orders
* Pass test
* Add TODO in example
---------
Co-authored-by: Young <afe.young@gmail.com>
* Waiting for bin data
* Complete readme
* CI
* Add inst filter by time
* Update qlib/data/dataset/processor.py
* typo
* Fix time filter bug
* Add Filter and set Universe
* Complete data pipeline
* Fix Provider Logger Info Args
* Add DQN; a minor bugfix in ppo reward.
* update readme. modify assertion logic in strategy check.
* Fix Doc issues and fix black
* Fix pylint Error
---------
Co-authored-by: Young <afe.young@gmail.com>
Co-authored-by: you-n-g <you-n-g@users.noreply.github.com>
* Update test_qlib_from_source.yml
* add ipynb format check to workflow
* test ipynb CI
* modify nbqa check path
* add pylint flake8 mypy check to ipynb
* check ipynb with black and pylint
* reformat .ipynb files
* format line length
nbqa black . -l 120
* update nbqa .ipynb format CI
* format old ipynb files
* add nbconvert check to CI
* adjust CI order to avoid repeating download data
* add missing parameters to doc string in order_generate
* fix some typos in doc strings
* reformat base on code style standard
* Update qlib/backtest/__init__.py
* Update examples/run_all_model.py
* Update examples/run_all_model.py
Co-authored-by: you-n-g <you-n-g@users.noreply.github.com>
