crew_groq.py

import os
import warnings
from crewai import Agent, Task, Crew
from crewai_tools import ScrapeWebsiteTool, SerperDevTool
from crewai import Crew, Process
from langchain_groq import ChatGroq
from dotenv import load_dotenv
load_dotenv()
warnings.filterwarnings('ignore')

SERPER_API_KEY = os.getenv('SERPER_API_KEY')

# Model Selection
# def initialize_llm(model_option, groq_api_key):
#     if model_option == 'Llama 3 8B':
#         return ChatGroq(groq_api_key=groq_api_key, model='llama3-8b-8192', temperature=0.1)
#     elif model_option == 'Llama 3.1 70B':
#         return ChatGroq(groq_api_key=groq_api_key, model='llama-3.1-70b-versatile', temperature=0.1)
#     elif model_option == 'Llama 3.1 8B':
#         return ChatGroq(groq_api_key=groq_api_key, model='llama-3.1-8b-instant', temperature=0.1)
#     else:
#         raise ValueError("Invalid model option selected")

llm = ChatGroq(groq_api_key=os.getenv('GROQ_API_KEY'), model='llama-3.1-70b-versatile', temperature=0.1)

search_tool = SerperDevTool()
scrape_tool = ScrapeWebsiteTool()

def crew_creator(stock_selection, 
                #  model_option, groq_api_key
                 ):

    # llm = initialize_llm(model_option, groq_api_key)

    data_analyst_agent = Agent(
        role="Data Analyst",
        goal="Monitor and analyze market data in real-time "
            "to identify trends and predict market movements.",
        backstory="Specializing in financial markets, this agent "
                "uses statistical modeling and machine learning "
                "to provide crucial insights. With a knack for data, "
                "the Data Analyst Agent is the cornerstone for "
                "informing trading decisions.",
        verbose=True,
        allow_delegation=True,
        tools = [scrape_tool, search_tool],
        llm=llm,
    )

    trading_strategy_agent = Agent(
        role="Trading Strategy Developer",
        goal="Develop and test various trading strategies based "
            "on insights from the Data Analyst Agent.",
        backstory="Equipped with a deep understanding of financial "
                "markets and quantitative analysis, this agent "
                "devises and refines trading strategies. It evaluates "
                "the performance of different approaches to determine "
                "the most profitable and risk-averse options.",
        verbose=True,
        allow_delegation=True,
        tools = [scrape_tool, search_tool],
        llm=llm,
    )

    # execution_agent = Agent(
    #     role="Trade Advisor",
    #     goal="Suggest optimal trade execution strategies "
    #         "based on approved trading strategies.",
    #     backstory="This agent specializes in analyzing the timing, price, "
    #             "and logistical details of potential trades. By evaluating "
    #             "these factors, it provides well-founded suggestions for "
    #             "when and how trades should be executed to maximize "
    #             "efficiency and adherence to strategy.",
    #     verbose=True,
    #     allow_delegation=True,
    #     tools = [scrape_tool, search_tool],
    #     llm=llm,
    # )

    # risk_management_agent = Agent(
    #     role="Risk Advisor",
    #     goal="Evaluate and provide insights on the risks "
    #         "associated with potential trading activities.",
    #     backstory="Armed with a deep understanding of risk assessment models "
    #             "and market dynamics, this agent scrutinizes the potential "
    #             "risks of proposed trades. It offers a detailed analysis of "
    #             "risk exposure and suggests safeguards to ensure that "
    #             "trading activities align with the firm’s risk tolerance.",
    #     verbose=True,
    #     allow_delegation=True,
    #     tools = [scrape_tool, search_tool],
    #     llm=llm,
    # )

    # Task for Data Analyst Agent: Analyze Market Data
    data_analysis_task = Task(
        description=(
            "Continuously monitor and analyze market data for "
            "the selected stock ({stock_selection}). "
            "Use statistical modeling and machine learning to "
            "identify trends and predict market movements."
        ),
        expected_output=(
            "Insights and alerts about significant market "
            "opportunities or threats for {stock_selection}."
        ),
        agent=data_analyst_agent,    
    )

    # Task for Trading Strategy Agent: Develop Trading Strategies
    strategy_development_task = Task(
        description=(
            "Develop and refine trading strategies based on "
            "the insights from the Data Analyst and "
            # "user-defined risk tolerance ({risk_tolerance}). "
            # "Consider trading preferences ({trading_strategy_preference})."
        ),
        expected_output=(
            "A set of potential trading strategies for {stock_selection} "
            "that align with the user's risk tolerance."
        ),
        agent=trading_strategy_agent,
    )

    # Task for Trade Advisor Agent: Plan Trade Execution
    # execution_planning_task = Task(
    #     description=(
    #         "Analyze approved trading strategies to determine the "
    #         "best execution methods for {stock_selection}, "
    #         "considering current market conditions and optimal pricing."
    #     ),
    #     expected_output=(
    #         "Detailed execution plans suggesting how and when to "
    #         "execute trades for {stock_selection}."
    #     ),
    #     agent=execution_agent,
    # )

    # Task for Risk Advisor Agent: Assess Trading Risks
    # risk_assessment_task = Task(
    #     description=(
    #         "Evaluate the risks associated with the proposed trading "
    #         "strategies and execution plans for {stock_selection}. "
    #         "Provide a detailed analysis of potential risks "
    #         "and suggest mitigation strategies."
    #     ),
    #     expected_output=(
    #         "A comprehensive risk analysis report detailing potential "
    #         "risks and mitigation recommendations for {stock_selection}."
    #     ),
    #     agent=risk_management_agent,
    # )

    # Define the crew with agents and tasks
    financial_trading_crew = Crew(
        agents=[
                data_analyst_agent, 
                trading_strategy_agent, 
                # execution_agent, 
                # risk_management_agent
            ],
        
        tasks=[
                data_analysis_task, 
                strategy_development_task, 
                # execution_planning_task, 
                # risk_assessment_task
            ],

        manager_llm = llm,
        process=Process.sequential,
        verbose=True,
    )

    result = financial_trading_crew.kickoff(inputs={
        'stock_selection': stock_selection,
        # 'initial_capital': initial_capital,
        # 'risk_tolerance': risk_tolerance,
        # 'trading_strategy_preference': trading_strategy_preference,
        # 'news_impact_consideration': news_impact_consideration
    })
    return str(result)

# print(result)