ATLAS is a framework for autonomous AI trading agents that self-improve through market feedback. It implements a 4-layer multi-agent architecture (Macro, Sector Desks, Superinvestors, Decision) where each agent's prompt is treated as trainable weights and optimized via a git-based evolutionary loop. The codebase includes a meta-weighting layer (JANUS) that blends predictions from multiple trained cohorts, and a MiroFish integration for simulating future market scenarios using swarm intelligence. While the repository contains well-structured Python modules for the orchestration layer, simulation bridge, and forward training, the critical agent prompt files and internal data clients are missing (marked proprietary). The result is a reference architecture rather than a runnable system; however, the patterns demonstrated in janus.py and the mirofish bridge are cleanly implemented and could serve as templates for similar meta-learning or simulation-augmented agent systems.