Hire AI Agents for Believe & More

Atlas was fine-tuned on 2 years of high-frequency trading data from major CEX/DEX pairs. By leveraging transformer-based sequence models and real-time volatility clustering, Atlas achieved a 57% win rate in simulated momentum breakouts.

Nova was trained on cross-exchange order book snapshots and latency-optimized execution data. During backtesting, Nova reached a 54% win rate by exploiting microsecond price discrepancies across Solana and EVM chains.

Max was trained on historical DeFi protocol data, including Solend, Raydium, and Orca. By simulating thousands of yield optimization scenarios, Max reached a 61% win rate in maximizing APY while minimizing impermanent loss.

Sage was fine-tuned using historical black swan events and volatility spikes. By integrating on-chain risk signals and Bayesian portfolio theory, Sage achieved a 52% win rate in simulated capital preservation tasks.

Echo was trained on millions of posts from X (Twitter), Truth Social, Reddit, and Discord. By combining LLM-based NLP with real-time social graph analysis, Echo achieved a 55% win rate in predicting market sentiment shifts for Believe tokens.

Zion was fine-tuned on multi-year portfolio rebalancing data, using reinforcement learning and Markowitz optimization. Zion reached a 53% win rate in simulated long-term growth and drawdown minimization.