Implement a supervisor agent

A skeleton code for the suggested agents utilizing the LangGraph framework for agentic workflows:
from typing import Annotated, TypedDict
from langgraph.graph import Graph
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage

# Define the state structure
class AgentState(TypedDict):
    messages: list[str]
    current_step: str
    portfolio: dict
    market_data: dict

# Define agent nodes
def fundamental_analysis(state: AgentState):
    # Implement fundamental analysis logic
    return state

def mpt_optimization(state: AgentState):
    # Implement Modern Portfolio Theory optimization
    return state

def risk_management(state: AgentState):
    # Implement risk management logic
    return state

def margin_of_safety(state: AgentState):
    # Implement margin of safety calculation
    return state

def portfolio_rebalancing(state: AgentState):
    # Implement portfolio rebalancing logic
    return state

def market_sentiment(state: AgentState):
    # Implement market sentiment analysis
    return state

# Define the graph
workflow = Graph()

# Add nodes to the graph
workflow.add_node("fundamental_analysis", fundamental_analysis)
workflow.add_node("mpt_optimization", mpt_optimization)
workflow.add_node("risk_management", risk_management)
workflow.add_node("margin_of_safety", margin_of_safety)
workflow.add_node("portfolio_rebalancing", portfolio_rebalancing)
workflow.add_node("market_sentiment", market_sentiment)

# Define edges (connections between nodes)
workflow.add_edge("fundamental_analysis", "mpt_optimization")
workflow.add_edge("mpt_optimization", "risk_management")
workflow.add_edge("risk_management", "margin_of_safety")
workflow.add_edge("margin_of_safety", "portfolio_rebalancing")
workflow.add_edge("portfolio_rebalancing", "market_sentiment")
workflow.add_edge("market_sentiment", "fundamental_analysis")  # Create a cycle

# Set the entrypoint
workflow.set_entry_point("fundamental_analysis")

# Compile the graph
app = workflow.compile()

# Initialize the state
initial_state = AgentState(
    messages=[],
    current_step="fundamental_analysis",
    portfolio={},
    market_data={}
)

# Run the workflow
for output in app.stream(initial_state):
    print(f"Step: {output['current_step']}")
    # Process or display the output as needed

# Optionally, you can use a ChatOpenAI model for natural language interactions
llm = ChatOpenAI()

def process_user_input(user_input: str, state: AgentState):
    response = llm.invoke([HumanMessage(content=user_input)])
    state["messages"].append(response.content)
    return state

# Add user input processing to the graph
workflow.add_node("process_user_input", process_user_input)
workflow.add_edge("market_sentiment", "process_user_input")
workflow.add_edge("process_user_input", "fundamental_analysis")

# Recompile the graph with the new node
app = workflow.compile()

# Run the updated workflow
for output in app.stream(initial_state):
    print(f"Step: {output['current_step']}")
    if output['current_step'] == "process_user_input":
        user_input = input("Enter your query: ")
        output = process_user_input(user_input, output)
    # Process or display the output as needed
SaudAltamimi / value-mpt

Implement a supervisor agent #10
