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* Simple RL notebook * Add link to the notebook Co-authored-by: Young <afe.young@gmail.com>
61 lines
1.7 KiB
Markdown
61 lines
1.7 KiB
Markdown
This folder contains a simple example of how to run Qlib RL. It contains:
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```
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.
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├── experiment_config
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│ ├── backtest # Backtest config
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│ └── training # Training config
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├── README.md # Readme (the current file)
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└── scripts # Scripts for data pre-processing
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```
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## Data preparation
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Use [AzCopy](https://learn.microsoft.com/en-us/azure/storage/common/storage-use-azcopy-v10) to download data:
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```
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azcopy copy https://qlibpublic.blob.core.windows.net/data/rl/qlib_rl_example_data ./ --recursive
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mv qlib_rl_example_data data
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```
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The downloaded data will be placed at `./data`. The original data are in `data/csv`. To create all data needed by the case, run:
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```
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bash scripts/data_pipeline.sh
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```
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After the execution finishes, the `data/` directory should be like:
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```
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data
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├── backtest_orders.csv
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├── bin
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├── csv
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├── pickle
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├── pickle_dataframe
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└── training_order_split
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```
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## Run training
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Run:
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```
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python -m qlib.rl.contrib.train_onpolicy --config_path ./experiment_config/training/config.yml
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```
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After training, checkpoints will be stored under `checkpoints/`.
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## Run backtest
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```
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python -m qlib.rl.contrib.backtest --config_path ./experiment_config/backtest/config.yml
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```
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The backtest workflow will use the trained model in `checkpoints/`. The backtest summary can be found in `outputs/`.
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## Others
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The RL module is designed in a loosely-coupled way. Currently, RL examples are integrated with concrete business logic.
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But the core part of RL is much simpler than what you see.
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To demonstrate the simple core of RL, [a dedicated notebook](./simple_example.ipynb) for RL without business loss is created.
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