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..
MacMic: Executing Iceberg Orders via Hierarchical Reinforcement Learning
Authors: Hui Niu, Siyuan Li, Jian Li
IJCAI 2024 | Venue PDF | 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. |