Search arXiv⌕ Search

arXiv · 2610.26324

The Control Layer Behind Rule-Following Agents Trading Across Market Conditions

Abstract

Trading discipline often fails people. Our study tested whether four AI trading agents followed their strategies and risk rules. Offering traders the evidence and an agent evaluation toolkit to separate luck from following the rules, before giving agents meaningful autonomy or capital. Our study analysed 2,627 closed positions from 12 February to 27 August 2026 across the same asset universe and execution conditions. Most positions were simulated against live prices with a 0.09% round-trip fee; 18% involved real Hyperliquid orders. Each position declared a strategy, stop and target, with a limit of five open positions per agent. The fleet finished net positive after fees. Rule adherence was high across all four agents, including the unprofitable one. Profitability tracked how often agents won relative to their break-even win rates. Institutional-style controls ran by default and contained losses through a 29% market drawdown and recovery, with documented exceptions. The discipline held for all four agents. Agents kept 97.5% of gross loss within declared stops, and typical losses closed at declared levels. Declared risk remained stable after losses and broadly stable across calm and volatile months. Twenty-three of 24 runs respected the position limit; logs identify the exception's size and duration. The same live data produced different results across assigned strategies. The trade-log tests can be rerun from the published trade snapshot; regime tests also require the published Bitcoin prices and regime labels. Section 3.5 specifies the stop-breach tolerance. These trade-level results are not account-level returns or proof of a lasting edge. Our study provides an evaluation toolkit to help traders distinguish rule-following from luck before committing capital. For anyone considering allocating capital to agents to trade, discipline should be the entry test, not the only test.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Saad Naja, Nasser Odtallah, Jake Van-Praag, Sarah May. 2026-10-19. The Control Layer Behind Rule-Following Agents Trading Across Market Conditions. https://arxiv.org/abs/2610.26324

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Gibson Env: Real-World Perception for Embodied Agents

Developing visual perception models for active agents and sensorimotor control are cumbersome to be done in the physical world, as existing algorithms are too slow to efficiently learn in real-time and robots are fragile and costly. This has given rise to learning-in-simulation which consequently casts a question on whether the results transfer to real-world. In this paper, we are concerned with the problem of developing real-world perception for active agents, propose Gibson Virtual Environment for this purpose, and showcase sample perceptual tasks learned therein. Gibson is based on virtualizing real spaces, rather than using artificially designed ones, and currently includes over 1400 floor spaces from 572 full buildings. The main characteristics of Gibson are: I. being from the real-world and reflecting its semantic complexity, II. having an internal synthesis mechanism, "Goggles", enabling deploying the trained models in real-world without needing further domain adaptation, III. embodiment of agents and making them subject to constraints of physics and space.

cs.AI↗

GenoMAS: Guided Multi-Agent Programming for Scientific Discovery from Genomic Data

Gene expression analysis holds the key to many biomedical discoveries, yet extracting insights from raw transcriptomic data remains challenging due to the complexity of multiple large, semi-structured files and the need for extensive domain expertise. Current approaches either rely on inflexible workflows that fail in edge cases or fully autonomous agents that lack the precision required for rigorous scientific inquiry. GenoMAS addresses this by presenting a team of LLM-based agents that integrates structured workflows with autonomous adaptability, orchestrating diverse specialized agents through typed message-passing protocols. At its core lies a guided-planning framework where programming agents decompose task guidelines into Action Units and dynamically choose to advance, revise, bypass, or backtrack, maintaining logical coherence while adapting to the idiosyncrasies of genomic data. On the GenoTEX benchmark, GenoMAS achieves 89.13% CSC for data preprocessing and 60.48% $F_1$ for identifying trait-associated genes, surpassing prior art by 10.61% and 16.85%. Beyond transcriptomics, it outperforms strong agent baselines on ScienceAgentBench and DiscoveryBench, two independent bioinformatics benchmarks. Qualitative analysis shows that GenoMAS identifies gene-phenotype associations that are biologically plausible and corroborated by the literature.

cs.AI↗

How Far Are LLMs from Symbolic Planners? An NLP-Based Perspective

AI planning provides agents the ability to act autonomously towards achieving a goal in a given environment. Recent advances in Large Language Models (LLMs) have introduced the prospect of using unstructured input for planning, making LLMs in planning an active area of research, especially for supporting autonomous agents. However, LLMs remain unreliable as long-horizon planners, since some of the plans they produce contain mistaken or hallucinated actions; executing such plans could lead to unexpected and harmful outcomes. Existing benchmarks for LLMs in planning primarily rely on binary evaluation metrics, providing limited insight into the quality of plans that do not achieve the goal, and how far those are from becoming valid. In this paper, we frame the evaluation of LLMs in planning as a natural language processing (NLP) task, as LLMs are themselves NLP models. We propose a pipeline comprising three NLP-based steps that assess plan quality from complementary perspectives, followed by a symbolic planning step to achieve a valid plan, to provide a finer-grained understanding of the quality of invalid plans. In our experiments, we find no clear evidence of logical derivation or state-tracking during LLM plan generation -- on average, for all non-empty plans, only 3.55 actions per plan are executable, despite symbolically-generated optimal plans having an average length of 9.47 actions, which suggests that LLMs are still far from the reliability of symbolic planners. Our suite of metrics is further used to investigate patterns in LLM plan generation over time, and can provide a useful metric for assessing the quality of individual invalid plans.

cs.AI↗