An intelligent hybrid trading system for discovering trading rules for the futures market using rough sets and genetic algorithms
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DRL trading agents build a multi-factor model and provide algorithmic trading strategies, which are difficult for human traders.
“Taking many complex financial factors into account, DRL trading agents build a multi-factor model and provide algorithmic trading strategies, which are difficult for human traders [3, 47, 24, 22].”
From FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance (2020) · cites this paper as [24]
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- Authors
- Kim, Y., Ahn, W., Oh, K. J., & Enke, D.
- Affiliations
- Missouri University of Science and Technology (US)
- Yonsei University (KR)
- Corresponding author
- Enke, David
- Venue
- Applied Soft Computing
- Publisher
- Elsevier BV
- Published
- 2017-06
- Volume
- 55
- Pages
- 127-140
- ISSN
- 1568-4946 (ISSN-L)
- OpenAlex
- W2587152362
- Cited by
- 64
- References
- 47
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 10 · 2023: 5 · 2022: 5 · 2021: 9 · 2020: 17 · 2019: 4 · 2018: 3 · 2017: 4
- FWCI
- 6.79
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Applied Soft Computing (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Rough Sets and Fuzzy Logic
- Social Sciences › Economics, Econometrics and Finance › Finance › Financial Markets and Investment Strategies
- Keywords
- technical trading rules, futures markets, rough set analysis, genetic algorithm, data discretization, reducts, KOSPI 200 futures, trade performance, risk-adjusted returns
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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