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Research manager

research_manager

Functions:

Name Description
create_research_manager

Creates a research manager node for the trading graph.

create_research_manager 

create_research_manager(
    llm: ChatModel, memory: FinancialSituationMemory
) -> Callable[[AgentState], dict[str, Any]]

Creates a research manager node for the trading graph.

Parameters:

Name Type Description Default

llm

ChatModel

The language model to use for generating responses.

 required

memory

FinancialSituationMemory

The memory module for retrieving past financial situations.

 required

Returns:

Type Description
Callable[[AgentState], dict[str, Any]]

Callable[[AgentState], dict[str, Any]]: A function representing the research manager node.
Source code in src/tradingagents/agents/managers/research_manager.py 
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def create_research_manager(
    llm: ChatModel, memory: FinancialSituationMemory
) -> Callable[[AgentState], dict[str, Any]]:
    """Creates a research manager node for the trading graph.

    Args:
        llm (ChatModel): The language model to use for generating responses.
        memory (FinancialSituationMemory): The memory module for retrieving past financial situations.

    Returns:
        Callable[[AgentState], dict[str, Any]]: A function representing the research manager node.
    """

    def research_manager_node(state: AgentState) -> dict[str, Any]:
        """Executes the research manager logic to evaluate research and formulate an investment plan.

        Args:
            state (AgentState): The current state of the agent, including various reports and debate state.

        Returns:
            dict[str, Any]: A dictionary containing the updated investment_debate_state and the investment_plan.
        """
        debate = state.investment_debate_state

        curr_situation = (
            f"{state.market_report}\n\n"
            f"{state.sentiment_report}\n\n"
            f"{state.news_report}\n\n"
            f"{state.fundamentals_report}"
        )
        past_memories = memory.get_memories(curr_situation, n_matches=2)
        past_memory_str = "".join(rec["recommendation"] + "\n\n" for rec in past_memories)

        prompt = load_prompt("research_manager").format(
            market_research_report=state.market_report,
            sentiment_report=state.sentiment_report,
            news_report=state.news_report,
            fundamentals_report=state.fundamentals_report,
            past_memory_str=past_memory_str,
            history=debate.history,
        )
        response = llm.invoke(prompt)

        new_debate_state = InvestDebateState(
            judge_decision=response.content,
            history=debate.history,
            bear_history=debate.bear_history,
            bull_history=debate.bull_history,
            current_response=response.content,
            count=debate.count,
        )

        return {"investment_debate_state": new_debate_state, "investment_plan": response.content}

    return research_manager_node