Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Relation-Aware Transformer for Portfolio Policy Learning
Authors: Ke Xu, Yifan Zhang, Deheng Ye, Peilin Zhao, Mingkui Tan
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on real-world crypto-currency and stock datasets verify the state-of-the-art performance of RAT. |
| Researcher Affiliation | Collaboration | Ke Xu1,2 , Yifan Zhang1,2 , Deheng Ye3 , Peilin Zhao3 , Mingkui Tan1 1South China University of Technology, Guangzhou, China 2Pazhou Lab, Guangzhou, China 3Tencent AI Lab, Shenzhen, China |
| Pseudocode | No | The paper describes the architecture and algorithms in prose and mathematical equations but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | 1The source code is available: https://github.com/Ivsxk/RAT. |
| Open Datasets | Yes | All crypto-currency datasets are accessed with Poloniex2, where data selection is based on the method in [Jiang et al., 2017]. We also evaluate our methods on the S&P500 stock dataset obtained from Kaggle3. |
| Dataset Splits | No | The paper provides statistics for training and test datasets in Table 1, but it does not explicitly mention or describe a separate validation dataset split with specific percentages or sample counts. |
| Hardware Specification | Yes | In the training process, we adopt Adam optimizer on a single NVIDIA Tesla P40 GPU. |
| Software Dependencies | No | The paper states that "RAT is implemented via pytorch" but does not provide specific version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | The number of attention heads is set to H=2, and the dimension of the feature space is set to df=12. In the training process, we adopt Adam optimizer on a single NVIDIA Tesla P40 GPU. The training step is 80000 for crypto-currency data and 20000 for stock data, where the batch size is 128. We set learning rate to 10 4 and weight decay of l2 regularizer to 10 7. The transaction cost rate is 0.25%. The temporal length of the local context is set to l=5, while the length of the price series is k=30. For all RL based methods, results are averaged over 5 runs with random initialization seeds. |