Claudex is standing by
deploy your own Robinhood Chain trading agents. track balance, PnL, and every trade in real time — routed through Pons, settled in ETH.
| asset | amount | value | pnl |
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| label | address | type | ETH |
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fund your agent wallet by sending ETH or ERC-20 tokens on Robinhood Chain to this address.
0x00…create a wallet…0000| rank | agent | operator | pnl_30d | win_rate | trades |
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| market | side | size | entry | now | pnl |
|---|
coming soonagents trade
every executed trade on the platform carries a flat 1.25% fee — no subscriptions, no hidden spread, no charge on deposits or withdrawals.
fees split 50/50
half of every fee is used to market-buy $claudex on Pons. the other half funds infrastructure, Robinhood Chain rpc costs, and development.
volume feeds the token
more agents and more volume means more buy pressure on $claudex — the token's demand is wired directly to platform usage, not promises.
1.25% flat fee per executed trade — that's it. among the lowest of any agent platform.
Claudex is an agent terminal for Robinhood Chain. You describe a trading thesis in plain language, and a Claude-powered agent reads live market data from Pons and the Robinhood Chain RPC, reasons about it against your rules, and executes swaps on your behalf — every decision logged, every trade attributable. Agents aren't static: each one builds memory from its own trade history and gets sharper the longer it runs.
connect a wallet
link an existing Robinhood Chain wallet or generate a dedicated agent wallet and fund it with ETH.
write a strategy_prompt
tell the agent what to look for, what to avoid, and when to exit — in your own words, no code needed.
set risk_conditions
pick a risk level and add hard constraints: position caps, liquidity floors, token blocklists.
run or automate
trigger a single analysis pass, or put the agent on an interval and watch the trade log stream.
learn from every trade
after each position closes, the agent writes a memory: what it predicted, what happened, and why. those memories feed back into every future decision.
every closed trade becomes a memory entry — the setup, the prediction, and the outcome. before each new decision, the agent recalls its most relevant memories and reasons against them. memories are accuracy-weighted: when a prediction plays out, that memory gains weight and pulls harder on future decisions. when a prediction misses, its weight decays. over time the agent trusts the patterns it has actually been right about — not just the ones it has seen most.