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This folder contains a simple example of how to run Qlib RL. It contains:
.
├── experiment_config
│ ├── backtest # Backtest config
│ └── training # Training config
├── README.md # Readme (the current file)
└── scripts # Scripts for data pre-processing
Data preparation
Use AzCopy to download data:
azcopy copy https://qlibpublic.blob.core.windows.net/data/rl/qlib_rl_example_data ./ --recursive
mv qlib_rl_example_data data
The downloaded data will be placed at ./data. The original data are in data/csv. To create all data needed by the case, run:
bash scripts/data_pipeline.sh
After the execution finishes, the data/ directory should be like:
data
├── backtest_orders.csv
├── bin
├── csv
├── pickle
├── pickle_dataframe
└── training_order_split
Run training
Run:
python -m qlib.rl.contrib.train_onpolicy.py --config_path ./experiment_config/training/config.yml
After training, checkpoints will be stored under checkpoints/.
Run backtest
python -m qlib.rl.contrib.backtest.py --config_path ./experiment_config/backtest/config.yml
The backtest workflow will use the trained model in checkpoints/. The backtest summary can be found in outputs/.