arXiv：cs.AI· Guanghao Wu, Zhuo Cai, Zhidong Li, Shoujin Wang·· 16 小时前
SOFA：当交易商适应后，订单流干预何时以及如何奏效
When and How Order-Flow Interventions Work After Dealers Adapt
AI 导读
研究提出 SOFA（State-aligned Order-Flow Allocation），一种由 State Scorer、Commitment Scheduler 和 Quote-to-Flow Allocator 三部分组成的公开订单流合约，通过配对再适应协议在 ABIDES 市场的 20 个独立训练交易商群体中测试。
正文
Abstract:Learning dealer agents can settle on persistently wide quotes, raising what customers pay to trade. A venue can route more orders to tighter quotes, but a wide quote may reflect real risk, and once dealers adapt, an end-to-end comparison cannot show why a rule works: because it intervenes more, picks better moments, is announced in advance, or rewards the right quote. We propose SOFA (State-aligned Order-Flow Allocation), a public order-flow contract with three parts: a State Scorer ranks market states from lagged public data, a Commitment Scheduler announces a budgeted intervention strength before dealers quote, and a Quote-to-Flow Allocator shifts a bounded share of fixed customer demand toward tighter quotes. Our paired re-adaptation protocol gives competing rules the same intervention strengths, only re-timed, lets dealers re-adapt to each rule from a shared checkpoint, and breaks one link at a time. Across 20 independently trained dealer populations in an ABIDES market, concentrating the intervention in states that the score ranks as low-risk lowers low-risk execution cost relative to random timing in every population, by 0.040 tick on average, while a stale announcement or a broken quote-to-flow link raises this cost. In the primary setting the gain comes at the expense of high-risk trades, whose costs rise, so state alignment pays off only for a venue that weighs low-risk costs more than 1.38 times as heavily as high-risk costs. Our code is available at this https URL.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.18158 [cs.AI] |
| (or arXiv:2610.18158v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.18158 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Guanghao Wu [view email]
[v1]
Tue, 13 Oct 2026 11:02:13 UTC (1,097 KB)
来源：arXiv：cs.AI · arxiv.org