The "Wall Street" Sentiment Analyzer System Design

Resolving Multi-Agent Conflict in Real-Time Financial Trading Systems
Author: AgileWoW Team
Category: Financial Intelligence / Event-Driven Architecture
Read Time: 12 Minutes
Parent Guide: The Agentic AI Engineering Handbook

1. The Design Challenge: The "Buy vs. Sell" Deadlock

Most multi-agent tutorials show agents agreeing with each other. In finance, value is found in disagreement.

The Failure Mode:

• Chart Agent: "RSI is 30. Oversold. Recommendation: Strong Buy."
• News Agent: "SEC just opened an investigation. Recommendation: Strong Sell."

Result: Without a conflict resolution layer, the system either crashes, hallucinates a "Hold," or flips a coin.

The Architectural Goal: Implement "Weighted Consensus." We need a hierarchical structure where a "Manager Agent" weighs the confidence score and time horizon of each sub-agent to render a final verdict.

2. The Tech Stack Selection

To build a system that reacts to the market in real-time, we need a stack that handles streaming data and hierarchical delegation.

3. Architecture Deep Dive: The "Swarm" Topology

3.1 The Agent Roster

We deploy a "Wolf Pack" of four distinct agents, each with a narrow instruction set to prevent overlap.

1. The "Technical" Analyst (Quant):
   • Tools: AlphaVantage TIME_SERIES_INTRADAY, RSI, MACD.
   • Role: Looks only at charts. Ignores news.
2. The "Fundamental" Analyst (Warren):
   • Tools: OVERVIEW (P/E Ratio, EPS), INCOME_STATEMENT.
   • Role: Looks only at balance sheets. Ignores price action.
3. The "Sentiment" Analyst (Hype):
   • Tools: NEWS_SENTIMENT, Twitter/X API Scraper.
   • Role: Scans for fear, uncertainty, and doubt (FUD).
4. The Portfolio Manager (Boss):
   • Tools: None.
   • Role: Reads the three reports. Resolves conflicts. Issues the trade.

3.2 The Conflict Resolution Protocol

How does the Manager decide when the Quant says "Buy" and the Fundamental says "Sell"?

The "Confidence-Weighted" Algorithm:
The Manager is prompted to assign a weight to each agent based on current market conditions (Volatility Regime).

• Low Volatility: Weight Fundamental Analysis (40%) > Technical (30%) > Sentiment (30%).
• High Volatility (News Spike): Weight Sentiment (60%) > Technical (20%) > Fundamental (20%).

Why this matters: A cheap stock (Fundamental Buy) can drop 20% in a day if bad news hits (Sentiment Sell). The Manager must dynamically shift trust to the Sentiment agent during crises.

4. Implementation Guide (CrewAI)

Phase 1: Defining the Manager

In CrewAI, you enable hierarchy by defining a manager_llm and setting process=Process.hierarchical.

from crewai import Crew, Process, Agent

# The Boss
manager = Agent(
    role="Portfolio Manager",
    goal="Maximize alpha while minimizing drawdown. Resolve conflicting signals.",
    backstory="You are a veteran hedge fund manager. You act on evidence, not hype.",
    allow_delegation=True,
    llm=gpt4_o
)

# The Crew
finance_crew = Crew(
    agents=[quant_agent, fundamental_agent, sentiment_agent],
    tasks=[analyze_task],
    process=Process.hierarchical,  # CRITICAL: Enables the Manager Logic
    manager_agent=manager
)

Phase 2: Connecting the Data (MCP)

Instead of writing Python requests, we configure the AlphaVantage MCP server.

• Config: Add mcp.alphavantage.co to your agent's tool config.
• Usage: The agents can now naturally ask: "Get me the RSI for NVDA on a 15-min interval."

Phase 3: The "Circuit Breaker" Logic

Real-time agents can go rogue during a flash crash. Implement a "Human-in-the-Loop" stop. If Confidence Score < 70%, the Manager output is not "Execute Trade," but "Request Human Review."

5. Use Cases for Financial Swarms

1. Earnings Call Sentinel:
Trigger: A company releases its Q3 earnings PDF.
Action: The swarm instantly parses the PDF (Fundamental), checks the after-hours price drop (Technical), and scans Twitter for reaction (Sentiment) to decide if the drop is an overreaction.

2. Crypto "Whale" Watcher:
Trigger: A massive wallet transfer is detected on-chain.
Action: The Manager weighs "On-chain data" vs "Current Market Depth" to predict if a dump is imminent.

3. Macro-Economic Pivot Prediction:
Trigger: Fed Chairman Powell gives a speech.
Action: Sentiment agent analyzes tone (Hawkish/Dovish) while Quant agent watches bond yield spikes.

6. Frequently Asked Questions (FAQ)

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