Building an Agentic AI Trading Heartbeat

The Breakthrough

The author implements a heartbeat-based monitoring system using a sub-agent to compress live websocket trade data into structured JSON, which allows a primary agent to make informed trading decisions at 30-second intervals without overwhelming the main model context window.

What Actually Worked

• Sub-agent Role Separation: The author deployed a dedicated 'trade data reporter' agent running on GPT-4o-mini to perform read-only monitoring of live position data, ensuring high-speed processing and token efficiency.
• Heartbeat Loop: The main agent is configured to sleep for 30-second intervals, waking up to process the JSON output generated by the sub-agent to evaluate current P&L and determine if the existing strategy remains valid.
• Goal-Oriented Risk Adjustment: By setting a specific target (e.g., $1 profit in 30 minutes), the agent dynamically calculates necessary risk parameters, such as hedge ratios or position sizing, based on the gap between the current market state and the defined objective.
• Dynamic Hedge Execution: The system demonstrates the ability to adapt to hedge signals by opening counter-positions (e.g., long Nvidia) or scaling existing positions (e.g., doubling SP500 short margin) based on real-time signal strength.

Context

The author aims to solve the problem of maintaining persistent, autonomous control over live trading positions. Standard LLM agents often struggle with the overhead of processing high-frequency data streams. By offloading data ingestion to a lightweight sub-agent, the system maintains a consistent 'heartbeat' that allows the main agent to react to market changes while keeping token usage and latency within manageable limits.

Content References

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