Let us use the power of LLM to analyze stocks and provide suggestions A Deep Thinking Trading system has many departments, each made up of sub-agents that use logical flows to make smart decisions. For example, an Analyst team gathers data from diverse sources, a Researcher team debates and analyzes this data to form a strategy, and the Execution team refines and approves the trade while working alongside portfolio management, other supporting sub-agents, and more. There is a lot that happens under the hood, a typical flow works like this …
Agentic Trading System (Created by Sumanth Dhanya)
The following LangGraph visualization shows the execution flow of our trading agents, highlighting how each agent interacts within the system:
LangGraph execution flow showing agent interactions and decision points (Created by Sumanth Dhanya)
- First, a team of specialized Analyst agents conducts comprehensive market intelligence gathering, collecting a 360-degree view including technical indicators, news coverage, social media sentiment, and company fundamentals.
- Next, Bull and Bear agents engage in adversarial debate to rigorously stress-test the findings, which a Research Manager synthesizes into a cohesive, balanced investment strategy.
- A specialized Trader agent then transforms this strategy into a concrete, executable proposal, which undergoes immediate scrutiny from a multi-perspective Risk Management team (representing Risky, Conservative, and Balanced viewpoints).
- The Portfolio Manager agent makes the final, binding decision, carefully weighing the Trader's proposal against the risk assessment debate before issuing final approval.
- Upon approval, the system extracts a clean, machine-readable signal (BUY, SELL, or HOLD) from the manager's natural language decision, optimized for seamless execution and comprehensive auditing.
- The process completes with an integrated feedback loop, where agents systematically reflect on trade outcomes to generate actionable insights, which are stored in long-term memory to continuously enhance future decision-making performance.
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