ATLAS

Implements an autoresearch loop that modifies the worst-performing agent's prompt based on Sharpe ratio, with a 30% survival rate for modifications leading to continuous self-improvement without manual tuning.

Uses PRISM training to develop separate agent sets for distinct market conditions like bull markets or crises, enabling specialized survival strategies that adapt to different environments.

Runs on a $20/month Azure VM instead of GPUs, with full 18-month backtests costing $50-80, making iterative testing and deployment accessible for small teams.

The JANUS meta-layer algorithmically weights trained cohorts based on recent accuracy, with weight differentials emerging as a detector for novel or historical market regimes without explicit programming.