from typing import Annotated, Dict, List, Sequence
from datetime import date, timedelta, datetime
from typing_extensions import TypedDict, Optional
from langchain_openai import ChatOpenAI
from tradingagents.agents import *
from langgraph.prebuilt import ToolNode
from langgraph.graph import END, StateGraph, START, MessagesState


# Researcher team state
class InvestDebateState(TypedDict):
    bull_history: Annotated[
        str, "Bullish Conversation history"
    ]  # Bullish Conversation history
    bear_history: Annotated[
        str, "Bearish Conversation history"
    ]  # Bullish Conversation history
    history: Annotated[str, "Conversation history"]  # Conversation history
    current_response: Annotated[str, "Latest response"]  # Last response
    judge_decision: Annotated[str, "Final judge decision"]  # Last response
    count: Annotated[int, "Length of the current conversation"]  # Conversation length


# Risk management team state
class RiskDebateState(TypedDict):
    risky_history: Annotated[
        str, "Risky Agent's Conversation history"
    ]  # Conversation history
    safe_history: Annotated[
        str, "Safe Agent's Conversation history"
    ]  # Conversation history
    neutral_history: Annotated[
        str, "Neutral Agent's Conversation history"
    ]  # Conversation history
    history: Annotated[str, "Conversation history"]  # Conversation history
    latest_speaker: Annotated[str, "Analyst that spoke last"]
    current_risky_response: Annotated[
        str, "Latest response by the risky analyst"
    ]  # Last response
    current_safe_response: Annotated[
        str, "Latest response by the safe analyst"
    ]  # Last response
    current_neutral_response: Annotated[
        str, "Latest response by the neutral analyst"
    ]  # Last response
    judge_decision: Annotated[str, "Judge's decision"]
    count: Annotated[int, "Length of the current conversation"]  # Conversation length


class AgentState(MessagesState):
    company_of_interest: Annotated[str, "Company that we are interested in trading"]
    trade_date: Annotated[str, "What date we are trading at"]
    target_ticker: Annotated[str, "Ticker selected by the orchestrator for deep analysis"]

    sender: Annotated[str, "Agent that sent this message"]

    # research step
    market_report: Annotated[str, "Report from the Market Analyst"]
    sentiment_report: Annotated[str, "Report from the Social Media Analyst"]
    news_report: Annotated[
        str, "Report from the News Researcher of current world affairs"
    ]
    fundamentals_report: Annotated[str, "Report from the Fundamentals Researcher"]

    # researcher team discussion step
    investment_debate_state: Annotated[
        InvestDebateState, "Current state of the debate on if to invest or not"
    ]
    investment_plan: Annotated[str, "Plan generated by the Analyst"]

    trader_investment_plan: Annotated[str, "Plan generated by the Trader"]

    # risk management team discussion step
    risk_debate_state: Annotated[
        RiskDebateState, "Current state of the debate on evaluating risk"
    ]
    final_trade_decision: Annotated[str, "Final decision made by the Risk Analysts"]
    portfolio_profile: Annotated[Dict[str, object], "Static portfolio configuration"]
    portfolio_summary: Annotated[str, "Orchestrator briefing for the run"]
    orchestrator_status: Annotated[str, "Status message produced by the orchestrator"]
    alpaca_account_text: Annotated[str, "Raw Alpaca account summary text"]
    alpaca_positions_text: Annotated[str, "Raw Alpaca positions text"]
    alpaca_orders_text: Annotated[str, "Raw Alpaca recent orders text"]
    orchestrator_hypotheses: Annotated[List[Dict[str, object]], "List of active hypotheses evaluated by the orchestrator"]
    active_hypothesis: Annotated[Optional[Dict[str, object]], "The hypothesis currently under deep analysis"]
    scheduled_analysts: Annotated[List[str], "Analyst roles the orchestrator requested to run for the active hypothesis"]
    scheduled_analysts_plan: Annotated[List[str], "Full list of orchestrator-requested analysts for this cycle"]
    orchestrator_action: Annotated[str, "Immediate action directive from the orchestrator for the active hypothesis"]
    action_queue: Annotated[List[str], "Actions queued by the orchestrator for execution"]
    next_directive: Annotated[str, "Directive for the scheduler when the queue is empty"]
    next_node: Annotated[str, "Next node selected by the action scheduler"]
    portfolio_account_summary: Annotated[Dict[str, object], "Parsed Alpaca account summary used by the orchestrator"]
    portfolio_positions_summary: Annotated[List[Dict[str, object]], "Parsed Alpaca positions summary used by the orchestrator"]
    planner_plan: Annotated[Dict[str, object], "Raw plan response produced by the sequential planner"]
    planner_notes: Annotated[str, "Notes or commentary returned by the sequential planner"]
    orchestrator_focus_override: Annotated[List[str], "Optional override forcing orchestrator to focus on specific tickers"]