TradingAgents/docs/components/analyst-team.md

Analyst Team

The analyst layer ingests raw market signals and transforms them into structured reports that seed downstream debates. Each analyst node is a LangGraph-compatible callable created in tradingagents/agents/analysts/ and shares a common shape:

1. Build a role-specific system prompt.
2. Bind the permitted tools from the shared Toolkit.
3. Invoke the LLM with the conversation history stored in AgentState.messages.
4. Append the generated report back into AgentState.

Roles at a Glance

Role	Factory	Primary Data Sources	Output Field
Market Analyst	create_market_analyst	Yahoo Finance OHLCV, Stockstats indicators	market_report
Social Media Analyst	create_social_media_analyst	Reddit company threads, OpenAI-powered sentiment feeds	sentiment_report
News Analyst	create_news_analyst	Google News, Finnhub archives, Reddit global news	news_report
Fundamentals Analyst	create_fundamentals_analyst	Finnhub insider data, SimFin financial statements, custom OpenAI tool	fundamentals_report

All analysts conclude with a Markdown summary that downstream roles reuse verbatim.

Tool Selection and Control Flow

Each analyst chooses tool variants based on Toolkit.config['online_tools']. When set to True, network-bound functions collect fresh data; otherwise, they fall back to cached datasets under tradingagents/dataflows/.

flowchart LR
    subgraph Analyst Node
        Prompt[LLM Prompt]
        ToolCall{{Tool Decision}}
    end
    Toolkit[(Toolkit)]
    Cache[(Offline Cache)]
    APIs[(External APIs)]

    Prompt --> ToolCall
    ToolCall -- online --> APIs
    ToolCall -- offline --> Cache
    APIs --> Toolkit
    Cache --> Toolkit
    Toolkit --> ToolCall
    ToolCall --> Prompt
    Prompt --> Report[Report -> AgentState]

Tool invocations are mediated by LangGraph ToolNodes created in _create_tool_nodes() inside tradingagents/graph/trading_graph.py. ConditionalLogic.should_continue_* inspects the last LLM message: if a tool call is present, execution loops through ToolNode to fetch data; otherwise, the node emits its final report and the workflow advances.

Extending the Analyst Layer

• Add a new tool to Toolkit with the @tool decorator so LangGraph can route invocations.
• Create a node factory mirroring the existing analysts: bind the tool subset, craft role prompts, and update the returned state dict with a new report key.
• Register the node in GraphSetup.setup_graph() by adding it to analyst_nodes and conditional_logic.
• Update AgentState (tradingagents/agents/utils/agent_states.py) with the new report field so downstream roles can consume it.