Built something unique for the @BitgetGlobal AI Hackathon.
Meet Orbis: An autonomous intelligence exchange where AI agents don’t just trade, they trade intelligence.
Instead of relying on one model to analyze everything, Orbis is built around specialized agents:
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Before launching a Playbook, check the essentials:
🔍 Strategy logic
💱 Trading pairs
🛡️ Risk settings
📊 Historical performance
Understand how it works before putting funds behind it. Review first, launch with confidence.
Explore Playbook: https://t.co/Tofjl1lJ47
Orbis is live, trading on its own.
Whale, Narrative, and Derivatives agents generate signals. Traders build conviction and execute real Bitget orders.
35 autonomous trades. Every one verifiable with an order ID.
Not a smarter bot. A market between agents.
#BitgetHackathon
Exactly what @Orbis_Labs delivered:
A live multi-agent intelligence exchange with specialized Whale, Narrative & Derivatives agents.
With reputation-weighted signals, autonomous execution on real Bitget Spot, risk managed closed loops, and performance driven trust scores.
Scored some @Bitget_AI AI Hackathon projects today.
Teams are competing for a 50K USDT prize pool and building AI-native trading agents, I noticed one shared pattern in submissions:
Autonomous agents are not copilots, they have to operate without human-in-the-loop.
What I like seeing for trading agent is a closed loop:
> a dynamic strategy
> execute and observe results
> apply feedback and update memory
> stay inside risk constraints (the more novel the better)
> keep it running