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AutoHedge vs jev-trader

Swarm-agent 'autonomous hedge fund': cooperating agents automate market analysis, risk management and trade execution. Python, from the Swarms ecosystem. — versus — One AI trade decision every Monad block: Jev reads the Kuru MON-USDC book every ~300ms, answers buy or sell, and the bot reposts a post-only limit order to earn the spread.

The curated verdict

Opposite ends of automated trading: autohedge is a swarm of LLM agents deliberating over analysis and risk, jev-trader is one typed decision per block with no prose anywhere in the loop.

AutoHedgejev-trader
Stars6.1k1.2k
Forks888238
LanguagePythonTypeScript
LicenseMITMIT
Last activity4 months ago2 days ago
Topicsfinance, agentsfinance, agents
Curated connections54

AutoHedge — the curator's take

Multi-agent architecture applied to trading: director/analyst/risk agents deliberate before execution — as a reference architecture for agent-team decision pipelines it's worth reading. Now the cold water: 'enterprise-grade autonomous hedge fund' is marketing, not audit — no published live track record; the Swarms ecosystem runs hype-forward; the repo was quiet for two months at review. Paper-trade it, treat real capital as adversarial testing you pay for.

jev-trader — the curator's take

The clearest demonstration in the catalog of what a sub-100ms decision model buys you: the whole hot loop is two RPC round trips, no gas estimation, no price lookup, because the decision and the order must fit inside one 300ms block. Read src/trader.ts for the accounting - late blocks become 'hold', fills arrive as someone else's taker transaction, and gas is charged on the limit whether the order lands or not. Run it dry first: with no PRIVATE_KEY you get the real book, real decisions and simulated fills. Treat it as a reference implementation and a latency argument, NOT a strategy - the edge is being post-only inside the touch, the model is a coin-flip-shaped classifier over 100 blocks, and it is wired to one venue and one pair.