![[2602.19919] Janus-Q: End-to-End Event-Driven Trading via Hierarchical-Gated Reward Modeling](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.19919] Janus-Q: End-to-End Event-Driven Trading via Hierarchical-Gated Reward Modeling
Summary
The paper presents Janus-Q, an innovative framework for event-driven trading that leverages financial news events as primary decision-making signals, enhancing trading performance through hierarchical-gated reward modeling.
Why It Matters
This research addresses significant challenges in financial trading by integrating textual data from news events into trading algorithms. By improving the alignment between language model reasoning and trading behavior, it offers a potential breakthrough in achieving more profitable and interpretable trading strategies, which is crucial for investors and financial analysts.
Key Takeaways
- Janus-Q utilizes a two-stage approach for event-driven trading, focusing on data construction and model optimization.
- The framework builds a large-scale dataset of financial news events, enhancing the quality of trading signals.
- Janus-Q significantly improves trading performance metrics, including a 102% increase in the Sharpe Ratio.
- The model combines supervised learning with reinforcement learning to optimize trading decisions.
- Results indicate that Janus-Q outperforms traditional market indices and existing LLM strategies.
Computer Science > Computation and Language arXiv:2602.19919 (cs) [Submitted on 23 Feb 2026] Title:Janus-Q: End-to-End Event-Driven Trading via Hierarchical-Gated Reward Modeling Authors:Xiang Li, Zikai Wei, Yiyan Qi, Wanyun Zhou, Xiang Liu, Penglei Sun, Yongqi Zhang, Xiaowen Chu View a PDF of the paper titled Janus-Q: End-to-End Event-Driven Trading via Hierarchical-Gated Reward Modeling, by Xiang Li and 7 other authors View PDF HTML (experimental) Abstract:Financial market movements are often driven by discrete financial events conveyed through news, whose impacts are heterogeneous, abrupt, and difficult to capture under purely numerical prediction objectives. These limitations have motivated growing interest in using textual information as the primary source of trading signals in learning-based systems. Two key challenges hinder existing approaches: (1) the absence of large-scale, event-centric datasets that jointly model news semantics and statistically grounded market reactions, and (2) the misalignment between language model reasoning and financially valid trading behavior under dynamic market conditions. To address these challenges, we propose Janus-Q, an end-to-end event-driven trading framework that elevates financial news events from auxiliary signals to primary decision units. Janus-Q unifies event-centric data construction and model optimization under a two-stage paradigm. Stage I focuses on event-centric data construction, building a large-scale financial news...
