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..
Spotlight News Driven Quantitative Trading Based on Trajectory Optimization
Authors: Mengyuan Yang, Mengying Zhu, Qianqiao Liang, Xiaolin Zheng, MengHan Wang
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on three realworld datasets demonstrate our proposed model s superiority over the state-of-the-art NQT methods. |
| Researcher Affiliation | Collaboration | Mengyuan Yang1 , Mengying Zhu1 , Qianqiao Liang2 , Xiaolin Zheng1 , Meng Han Wang3 1Zhejiang University, Hangzhou, China 2MYbank, Ant Group, Hangzhou, China 3e Bay Inc., Shanghai, China |
| Pseudocode | Yes | Algorithm 1: Spotlight Trader s pipline |
| Open Source Code | No | The paper states: "We elaborate on the generation of trajectory-level offline data and provide the offline dataset in our Git Hub repository3." The associated footnote 3 points to "https://github.com/Yangmy412/Spotlight Trader Offline Dataset". This explicitly states the repository contains the *dataset*, not the source code for the model's methodology itself. |
| Open Datasets | Yes | We conduct experiments on three real-world datasets1. Twitter-SP500 is a public dataset consist stocks in S&P 500 index from U.S. market and tweets from Twitter. ... 1Data is collected from https://github.com/yumoxu/stocknet-dataset, http://www.51ifind.com.cn/, and https://aylien.com/. ... We elaborate on the generation of trajectory-level offline data and provide the offline dataset in our Git Hub repository3. 3https://github.com/Yangmy412/Spotlight Trader Offline Dataset |
| Dataset Splits | Yes | We divide each dataset into non-overlapping offline and online datasets, and the statistics of the datasets are presented in Table 1. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU models, CPU types, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions software tools like "Fin BERT" and "Chinese-Fin BERT" with a GitHub link to FinBERT, but it does not specify version numbers for these or other key software dependencies (e.g., Python, PyTorch, TensorFlow) required for replication. |
| Experiment Setup | No | The parameter setting and implement details will be presented in a longer version of this paper. |