Bitget launched a suite of AI trading avatars inside its GetAgent platform, offering six specialized agents plus an unmodified DeepSeek model as a benchmark for live crypto markets. The move positions GetAgent as a staging ground for purpose-built AI traders and aims to make advanced, systematic strategies accessible through copy trading and interactive explanation features.
Bitget introduced six engineered AI avatars, each embodying a different trading philosophy, and deployed DeepSeek as an unmodified control model to enable objective comparison. The avatars focus on distinct market behaviors while DeepSeek serves as a baseline for performance measurement, supporting an apples-to-apples evaluation of specialized design versus a generic AI model.
Each avatar was developed with multi-factor indicator libraries and subjected to extensive backtesting before deployment in GetAgent. GetAgent couples live execution with explainability: users can query avatars about decision rationale, stop-loss logic and prioritized signals, making the agents’ behavior interpretable. A benchmark model, defined here as an unmodified reference model used to compare performance, is included to measure the marginal value of the engineered agents versus a generic AI baseline.
Bitget also integrated copy trading, enabling users to mirror an avatar’s positions and execution automatically. This feature is presented as a route to democratize access to quantitative approaches without requiring users to build or code strategies themselves, aligning the product with a broader push toward accessible algorithmic trading tools.
GetAgent AI trading avatars and the seven strategies
The launch is part of Bitget’s Universal Exchange (UEX) strategy, which seeks to aggregate diverse tradable assets on a single platform. The initiative emphasizes deploying purpose-built AI agents into live markets rather than limiting them to simulation-based tests of general models, with DeepSeek acting as a control to generate comparative data under real-market conditions.
Copy trading lowers the execution barrier for sophisticated strategies, while explainability aims to reduce blind reliance on black-box automation. From a risk perspective, the live deployment of adaptive agents raises questions about model behavior in stress events and systemic interactions with other market participants, and regulatory scrutiny is likely because AI-driven execution introduces novel operational and compliance considerations.
By running multiple distinct strategies in parallel against a benchmark, Bitget can evaluate which approaches scale, which require further tuning, and how human users interact with algorithmic advice, creating a feedback loop for continuous improvement.
Bitget’s launch introduces seven AI trading strategies to live trading via GetAgent, combining engineered avatars, a benchmark model and copy-trading access.
