Bitget introduced seven AI trading avatars, integrated into its GetAgent platform to offer strategy-specific automated trading and improved transparency. The Bitget AI trading avatars package aims to make advanced algorithmic approaches accessible to a wider range of traders while exposing the rationale behind automated decisions.
GetAgent is presented as a Level 4 AI Agent, a system that can execute multi-step tasks autonomously and refine decisions through closed-loop feedback; in this context, it manages order execution, strategy logic and user interaction. The seven avatars each encapsulate a distinct trading philosophy and are intended for different market conditions and risk appetites: Steady Hedge, Majors Momentum, Altcoin Turbo, CTA Force, Infinite Grid, Dip Sniper and Baseline DeepSeek mode.
Each avatar is described as a purpose-built agent rather than a generic large language model, designed to follow its named strategy while producing traceable actions.
Seven avatars and the GetAgent engine
Bitget emphasized user interaction and transparency as core differentiators. Through GetAgent, traders can query an avatar about why it entered a position, how it sets stop-losses, which signals it prioritizes and how it adapts to market cycles. The platform’s Model Arena provides real-time tracking of entries, exits, drawdowns and adjustments to make algorithmic behavior observable.
The company framed the deployment as a live experiment: DeepSeek serves as the control model to enable direct performance comparisons across agent designs under actual trading conditions. The approach tests whether purpose-built agents, operating transparently in live markets, can meaningfully improve execution or risk outcomes relative to an unmodified baseline.
Gracy Chen, Bitget’s CEO, said the initiative aims to make trading “more personal and more approachable,” signaling an emphasis on user comprehension rather than opaque automation.
The launch places a suite of strategy-focused AI agents into live crypto markets with an explicit control for benchmarking; the immediate implication is a new data source for assessing automated strategies under real conditions.
