In this project, we mainly focus on the high frequency trading prediction in the scenario of label imbalance, based on machine learning methods. We provide four networks including: MLP, LSTM, BERT, and Mamba as the backbone. We use two simple methods to reduce the influence of label imbalance, including: resampling and class weighting.
Due to copyright restrictions, we do not provide the original data. However, we provide the data structure and class distribution in our experiment. As a result, you can replace the dataset.py file with your own data and change some necessary parameters in train_classification.py to run the code on your dataset.
TODO: data description
To run the model, you can use the following command:
python train_classification.py --input_dim <input dim> --output_dim <class numbers> --model <please select from 'mlp', 'lstm', 'bert', and 'mamba'>To change the model scale or other parameters related to training, please refer to the get_args function.
We provide the following methods for addressing label imbalance:
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--class_weight: You can specify the weight for each class in the loss function. -
--data_balance: We randomly delete 7/8 of the data in the '0' class to balance the dataset.