Multi-Agent Coordination for Portfolio Management: Evidence from Concurrent Perpetual Futures Trading

Multi-Agent Coordination for Portfolio Management: Evidence from Concurrent Perpetual Futures Trading Status: Pilot study | Venue: Hyperliquid perpetuals | Sample: 2,310 orders (1,157 filled), 1.51M funding observations Abstract We investigate whether concurrent trading activity across multiple perpetual futures instruments exhibits coordination patterns consistent with multi-agent portfolio management. Using 2,310 order events (1,157 filled) from a Hyperliquid-based trading system, we analyze entry/exit timing across five actively traded assets (BTC, SOL, FARTCOIN, PUMP, MON). We find that H1 receives moderate support: inter-asset entry timing shows sub-minute clustering (median inter-entry gap: 18 minutes), suggesting coordinated capital deployment. H2 is not supported by this pilot sample: funding rate signals do not statistically predict entry timing at the 5% level. H3 receives exploratory support: per-trade PnL variance is lower for multi-asset sessions vs. single-asset sessions, but the sample is insufficient for robust inference. This is a pilot study with known limitations — results should not be generalized beyond the observed system configuration. 1. Introduction The emergence of autonomous trading agents raises a fundamental question: can multiple specialized agents coordinate portfolio-level decisions more effectively than a single monolithic strategy? This is not a question of individual signal quality, but of portfolio-level orchestration — the timing, sizing, and correlation structure of multi-asset positions. Hypotheses - H1 (Timing Coordination): Concurrent agent positions across correlated assets exhibit coordination patterns detectable via timing analysis. - H2 (Funding-Rate-Driven Entry): Funding rate regimes predict agent entry timing more reliably than price momentum signals. - H3 (Variance Reduction): Multi-asset coordination reduces per-trade variance relative to single-asset strategies. 2. Data Description 2.1 Trading Data | Metric | Value | |--------|-------| | Total orders | 2,310 | | Filled | 1,157 (50.1%) | | Cancelled | 944 (40.9%) | | Expired | 209 (9.0%) | | Date range | Feb–Apr 2026 | | Venue | Hyperliquid (single exchange) | | Asset count | 5 actively traded (BTC, SOL, FARTCOIN, PUMP, MON) | Data provenance: Orders are recorded in real-time via the Hyperliquid API and stored in a local SQLite database. All prices and sizes are execution-level (post-fill). No simulated or backtested data. 2.2 Funding Rate Data | Metric | Value | |--------|-------| | Total observations | 1,513,232 | | Average annualized rate | -1.95% | | Most negative asset | BLUR (-66.25% avg) | | Most positive asset | GRIFFAIN (+5.99% avg) | | Sample per asset | 6,608 observations | Data provenance: Collected via Hyperliquid API at regular intervals. Annualized from 8-hour funding rates. 2.3 Cross-Exchange Spreads Top cross-exchange spreads (annualized): | Asset | Pair | Spread % | Annualized | |-------|------|----------|------------| | POLYX | AsterDex/Binance | 0.97% | 1,059% | | POLYX | Hyperliquid/Binance | 0.91% | 1,001% | | KAT | AsterDex/Binance | 0.53% | 576% | | GAS | Hyperliquid/Binance | 0.49% | 535% | ⚠️ Reliability: UNRELIABLE. Annualized spreads assume persistent, frictionless arbitrage. Real execution costs, slippage, and withdrawal delays would substantially reduce realized yields. These figures represent theoretical upper bounds, not achievable returns. 3. Methodology 3.1 Timing Analysis (H1) We compute inter-entry intervals — the time between consecutive ENTRY-tagged fills across different assets. Under independent agent behavior (null), entry times should follow a Poisson process with exponentially distributed gaps. Coordination predicts excess clustering (sub-exponential gap distribution). 3.2 Funding Rate Entry Model (H2) For each entry trade, we record the prevailing 8-hour funding rate for that asset. Under H2, entries should cluster in favorable funding regimes (positive for shorts, negative for longs). We test via logistic regression: $0.008/trade, std = 0.002/trade, std = 80.64, another enters FARTCOIN at $0.163 within minutes — the capital is recycled rather than idle. Theoretical advantages of multi-agent coordination: Each specialist agent optimizes for its asset class while the portfolio manager handles cross-asset constraints (max drawdown, correlation limits, capital utilization). 6. Limitations This is a pilot study with substantial limitations: 1. Sample size: 1,157 filled trades is insufficient for robust statistical claims about coordination. We need 10x more data for meaningful regime analysis. 2. Single system: We observe one trading system's behavior. We cannot distinguish agent-level coordination from a single strategy executing across multiple assets. 3. No ground truth for "agents": The system may or may not implement true multi-agent architecture. We observe outputs consistent with coordination, not multiple independent agents. 4. No benchmark: Without a single-ag