Data-Driven Market-Making via Model-Free Learning
Authors: Yueyang Zhong, YeeMan Bergstrom, Amy Ward
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our main training dataset is derived from event-by-event data recording the state of the LOB. Our proposed trading strategy has passed both insample and out-of-sample testing in the backtester of the market-making firm with whom we are collaborating, and it also outperforms other benchmark strategies. |
| Researcher Affiliation | Collaboration | Yueyang Zhong1 , Yee Man Bergstrom2 and Amy Ward3 1,3Booth School of Business, University of Chicago 2Proprietary Trading, Chicago yzhong0@chicagobooth.edu, yee.man.bergstrom@gmail.com, Amy.Ward@chicagobooth.edu |
| Pseudocode | Yes | Algorithm 1 Q-learning algorithm pseudocode |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | Our dataset is a common and competitive futures contract traded on the Chicago Mercantile Exchange (CME) s Globex electronic trading platform in 2019. It is Level II order book data. The paper describes the dataset but does not provide any link, citation, or specific access information for it to be considered publicly available. |
| Dataset Splits | No | The paper mentions 'in-sample' and 'out-of-sample' testing for performance evaluation but does not specify exact percentages, sample counts, or detailed methodology for these splits. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers, such as programming languages, libraries, or solvers. |
| Experiment Setup | No | The paper mentions the use of 'f, I, and P thresholds' and parameters for the Q-learning algorithm (e.g., α0, N) but does not provide the specific numerical values for these settings. |