Algo: Self Improving Autonomous Trading Agent

Algo is an autonomous multi-agent trading system designed to make trading decisions adaptive, explainable, and safe. Instead of relying on a single AI model or fixed strategy, Algo uses specialized agents to observe market conditions, identify market regimes, select appropriate strategies, challenge proposed trades, evaluate risk, and learn from outcomes. The Market Agent builds real-time market context, while the Regime Agent determines whether conditions are trending, volatile, or range-bound. The Strategy Agent selects an appropriate strategy and generates a trade proposal. Before that proposal can proceed, a Critic Agent independently searches for reasons the trade could fail, and a Risk Agent evaluates additional risk factors. A key architectural principle is that AI never has direct execution authority. Every AI-generated proposal must cross a trust boundary and pass Algo's deterministic safety and policy controls before reaching Alpaca. Even a high-confidence AI recommendation can be blocked when a hard risk constraint is violated. Algo's second major differentiator is its self-improvement loop. After completed trades, a Post-Trade Agent compares expected behavior with actual market outcomes, identifies what worked or failed, and stores those lessons in strategy memory. This historical evidence can then influence future strategy selection and confidence. Algo therefore goes beyond asking, “What should I trade?” It continuously asks: What market are we in? Which strategy fits? Why might I be wrong? Is this trade safe? And what can I learn from the outcome?