What LLM Trading Agents Actually Do in Production: A Six-Month, Population-Scale Record from Two Fleets
Six-month record of 7.5M LLM trading agent invocations shows volatility-blind sizing, minimal upside capture, and no directional edge across two fleets.
The study records autonomous LLM trading agents in production across DX Terminal Pro (3,505 user-funded vaults trading real ETH in Base memecoin markets) and the DXAP fleet (500-599 agents on Hyperliquid perpetuals), spanning roughly six months, 7.5M single-model invocations and about 300K onchain actions. A risk slider explains leverage (+0.425 per level), median leverage is 5.0x in every volatility sextile, and one posture-slider cell holds 62% of liquidations. Agents capture little upside: 43.2% of positions saw +300 bps favorable excursion within 24h yet 49.3% of those closed negative, while the DXAP fleet trails a matched retail benchmark (41% vs 50% roundtrip win rate). A paired-replay league of frontier models finds decision quality statistically indistinguishable at this horizon.
- Operating layer (risk sliders, UI boundaries) drives behavior more than strategy text
- Median leverage is 5.0x across all volatility sextiles, one cell holds 62% of liquidations
- 43.2% of positions reached +300 bps excursion but 49.3% closed negative
- DXAP fleet unprofitable: 41% win rate vs 50% matched retail benchmark
- Paired-replay league shows frontier model decision quality statistically indistinguishable
Full article279 words · extracted from huggingface.co · click to collapse
We present a continuous, population-scale measurement record of autonomous language-model trading agents operating in production across two systems with one design lineage: DX Terminal Pro (3,505 user-funded vaults trading real ETH in Base memecoin markets for 21 days, February to March 2026) and the DXAP live alpha fleet (500 to 599 user-created agents all-history, 91 to 117 concurrently active, trading Hyperliquid perpetuals, June to August 2026). The record spans roughly six months, 7.5M single-model invocations with about 300K onchain actions, and a further 231,638 multi-tool turns producing 14,596 fills. Four findings carry the paper. First, the operating layer determines behavior more than anything written in strategy text: a risk slider explains leverage (+0.425 per level), agent fixed effects absorb 60% of variance, and a leaderboard render boundary causally routes selection (regression discontinuity 1.75x at the top-3 cut). Second, sizing is volatility-blind: median leverage is 5.0x in every volatility sextile, and one posture-slider cell (11% of the book) holds 62% of liquidations. Third, agents capture almost none of the upside they reach: 43.2% of positions saw at least +300 bps of favorable excursion within 24h, yet 49.3% of those closed with a negative trade return; a mechanical bracket recovers +39.0 bps per position. Fourth, neither fleet shows a directional edge. The DXAP fleet is not profitable and trails a matched Hyperliquid retail benchmark (41% vs. 50% roundtrip win rate). A paired-replay league of frontier models on 416 captured production scenarios finds decision quality statistically indistinguishable at this horizon, while choice stability differs sharply across model families. Every headline survives day-clustered inference, permutation nulls, and a common-fee restatement; the paper closes with a 17-rule methodology canon bought with our own retractions.
Text extracted automatically; images, tables and formatting may be missing. Original: https://huggingface.co/papers/2609.05663