How I Built an AI Agent That Monitors My Positions

The idea was simple: I wanted something watching my positions 24/7 that could think, not just alert on thresholds.

The Problem

Traditional alerting is dumb. You set a stop-loss, maybe a trailing stop, and you get a notification when price hits a number. But markets don't move in isolation — correlations shift, volatility regimes change, and a -2% move in a low-vol environment is very different from -2% during a VIX spike.

I wanted an agent that could reason about context.

Architecture

The system has three layers:

Data ingestion — WebSocket feeds from exchanges, normalized into a common schema
Context engine — Aggregates position data, market regime signals, and correlation matrices
LLM reasoning — Claude processes the context and generates structured assessments

class PositionMonitor:
    def __init__(self, positions, market_context):
        self.positions = positions
        self.context = market_context
        self.agent = TradingAgent(role="risk_monitor")

    async def assess(self):
        prompt = self.build_context_prompt()
        return await self.agent.reason(prompt)

What I Learned

The biggest insight: confidence scoring matters more than the recommendation itself. When the agent says "consider reducing exposure" with 0.4 confidence vs 0.9 confidence, the appropriate action is completely different.