97 lines
4.9 KiB
Python
97 lines
4.9 KiB
Python
from typing import Annotated, Dict, List, Sequence
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from datetime import date, timedelta, datetime
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from typing_extensions import TypedDict, Optional
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from langchain_openai import ChatOpenAI
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from tradingagents.agents import *
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from langgraph.prebuilt import ToolNode
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from langgraph.graph import END, StateGraph, START, MessagesState
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# Researcher team state
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class InvestDebateState(TypedDict):
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bull_history: Annotated[
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str, "Bullish Conversation history"
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] # Bullish Conversation history
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bear_history: Annotated[
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str, "Bearish Conversation history"
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] # Bullish Conversation history
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history: Annotated[str, "Conversation history"] # Conversation history
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current_response: Annotated[str, "Latest response"] # Last response
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judge_decision: Annotated[str, "Final judge decision"] # Last response
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count: Annotated[int, "Length of the current conversation"] # Conversation length
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# Risk management team state
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class RiskDebateState(TypedDict):
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risky_history: Annotated[
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str, "Risky Agent's Conversation history"
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] # Conversation history
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safe_history: Annotated[
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str, "Safe Agent's Conversation history"
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] # Conversation history
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neutral_history: Annotated[
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str, "Neutral Agent's Conversation history"
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] # Conversation history
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history: Annotated[str, "Conversation history"] # Conversation history
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latest_speaker: Annotated[str, "Analyst that spoke last"]
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current_risky_response: Annotated[
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str, "Latest response by the risky analyst"
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] # Last response
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current_safe_response: Annotated[
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str, "Latest response by the safe analyst"
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] # Last response
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current_neutral_response: Annotated[
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str, "Latest response by the neutral analyst"
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] # Last response
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judge_decision: Annotated[str, "Judge's decision"]
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count: Annotated[int, "Length of the current conversation"] # Conversation length
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class AgentState(MessagesState):
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company_of_interest: Annotated[str, "Company that we are interested in trading"]
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trade_date: Annotated[str, "What date we are trading at"]
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target_ticker: Annotated[str, "Ticker selected by the orchestrator for deep analysis"]
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sender: Annotated[str, "Agent that sent this message"]
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# research step
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market_report: Annotated[str, "Report from the Market Analyst"]
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sentiment_report: Annotated[str, "Report from the Social Media Analyst"]
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news_report: Annotated[
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str, "Report from the News Researcher of current world affairs"
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]
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fundamentals_report: Annotated[str, "Report from the Fundamentals Researcher"]
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# researcher team discussion step
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investment_debate_state: Annotated[
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InvestDebateState, "Current state of the debate on if to invest or not"
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]
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investment_plan: Annotated[str, "Plan generated by the Analyst"]
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trader_investment_plan: Annotated[str, "Plan generated by the Trader"]
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# risk management team discussion step
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risk_debate_state: Annotated[
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RiskDebateState, "Current state of the debate on evaluating risk"
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]
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final_trade_decision: Annotated[str, "Final decision made by the Risk Analysts"]
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portfolio_profile: Annotated[Dict[str, object], "Static portfolio configuration"]
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portfolio_summary: Annotated[str, "Orchestrator briefing for the run"]
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orchestrator_status: Annotated[str, "Status message produced by the orchestrator"]
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alpaca_account_text: Annotated[str, "Raw Alpaca account summary text"]
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alpaca_positions_text: Annotated[str, "Raw Alpaca positions text"]
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alpaca_orders_text: Annotated[str, "Raw Alpaca recent orders text"]
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orchestrator_hypotheses: Annotated[List[Dict[str, object]], "List of active hypotheses evaluated by the orchestrator"]
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active_hypothesis: Annotated[Optional[Dict[str, object]], "The hypothesis currently under deep analysis"]
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scheduled_analysts: Annotated[List[str], "Analyst roles the orchestrator requested to run for the active hypothesis"]
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scheduled_analysts_plan: Annotated[List[str], "Full list of orchestrator-requested analysts for this cycle"]
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orchestrator_action: Annotated[str, "Immediate action directive from the orchestrator for the active hypothesis"]
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action_queue: Annotated[List[str], "Actions queued by the orchestrator for execution"]
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next_directive: Annotated[str, "Directive for the scheduler when the queue is empty"]
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next_node: Annotated[str, "Next node selected by the action scheduler"]
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portfolio_account_summary: Annotated[Dict[str, object], "Parsed Alpaca account summary used by the orchestrator"]
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portfolio_positions_summary: Annotated[List[Dict[str, object]], "Parsed Alpaca positions summary used by the orchestrator"]
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planner_plan: Annotated[Dict[str, object], "Raw plan response produced by the sequential planner"]
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planner_notes: Annotated[str, "Notes or commentary returned by the sequential planner"]
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orchestrator_focus_override: Annotated[List[str], "Optional override forcing orchestrator to focus on specific tickers"]
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