MacMic: Executing Iceberg Orders via Hierarchical Reinforcement Learning
Authors: Hui Niu, Siyuan Li, Jian Li
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experimental results on 200 stocks across the US and China A-share markets validate the effectiveness of the proposed method. |
| Researcher Affiliation | Academia | Hui Niu 1 , Siyuan Li 2 and Jian Li 1 1Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China 2Faculty of Computing, Harbin Institute of Technology, Harbin, China |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link regarding the public availability of its source code. |
| Open Datasets | No | The paper states they collected data from '200 constituent stocks within two real-world stock indexes spanning both the Chinese and US markets' but does not provide concrete access information (link, DOI, repository, or formal citation) to their specific compiled dataset. |
| Dataset Splits | No | The paper states, 'For our experiments, 80% of the trading days are utilized as the training dataset, while the remaining 20% constitute the test dataset,' but does not mention a separate validation dataset split. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or library versions needed to replicate the experiment. |
| Experiment Setup | Yes | The trading task is to sell the shares of 5% of the entire market trading volume of each asset during a 4-hour period following the market s opening every day, i.e., T = 240 minutes. The episode length of the high-level policy is H = 240, and the execution time for the low-level policy is t = 1 minute. Besides, we set np = 7 for the low-level policy. |