app.py

import warnings
warnings.filterwarnings('ignore')

from crewai import Agent, Task, Crew
from crewai import Crew, Process
from langchain_openai import ChatOpenAI
from crewai_tools import ScrapeWebsiteTool, SerperDevTool
import gradio as gr
import os

os.environ["SERPER_API_KEY"] = os.getenv('SERPER_API_KEY')

search_tool = SerperDevTool()
scrape_tool = ScrapeWebsiteTool()
llm=ChatOpenAI(model="gpt-4o-mini", openai_api_key =os.getenv('OPENAI_API_KEY'), temperature=0.7)

data_analyst_agent = Agent(
    role="Data Analyst",
    goal="First step is to monitor markets in the given country to identify companies with "
         "highest investments or contribution to "
         "socially responsible causes like CSR (corporate social responsibility), "
         "ESG (environment, social and governance), chaity trust etc.,. "
         "Main goal is to monitor and analyze market data of only these stock trading codes "
         "identified in first step in real-time to identify trends and predict market movements.",
    backstory="Specializing in social responsible activities and 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, portfolio analysis and quantitative analysis, "
              "this agent devises and refines trading strategies. "
              "Given a set of stock code options (for example a set of 5 codes) along with "
              "the number of top stocks to be shortlisted from the same set of stock code options (for example 3 out of the given 5) "
              "and the total initial capital to be invested in the shortlisted stocks "
              "in order to decide on portfolio of stocks to invest-in. "
              "It evaluates the performance of different approaches to determine "
              "the most profitable and risk-averse options "
              "before recommending the portfolio of stocks, their quantities and investment amount allocation.",
    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=(
        "Must consider the country of interest ({country}). "
        "Continuously monitor and analyze market data for "
        "5 or more companies in the given country ({country}) with highest investments or contribution to "
        "socially responsible causes like CSR (corporate social responsibility), "
        "ESG (environment, social and governance), chaity trust etc.,. "
        "Obtain stock trading codes for at least 5 companies and "
        "assign them as the potential optional stocks list for investment "
        "to an input variable >>> 'stock_set', which will be used for further processing and "
        "generating the variable >>> 'stock_selecton'. "
        "Use market research, statistical modeling and machine learning to "
        "identify trends and predict market movements."
    ),
    expected_output=(
        "The identified list of company trading codes with highest investments "
        "in CSR activities >>> ({stock_set}) in the country of interest ({country}) "
        "with rationale why they were selected must appear in the report. "
        "Insights and alerts about significant market "
        "opportunities or threats for each of the stocks in {stock_set}."
    ),
    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}). "
        "Must consider the country of interest ({country}) and total initial capital ({initial_capital}), "
        "trading preferences ({trading_strategy_preference}), "
        "and how many stock options to be selected ({n_stock_options}) "
        "from the given potential optional stocks list for investment ({stock_set}) identified by data_analyst_agent "
        "and arrive at a short list of selected stocks for investment."
        "Assign this shortlist as values to the input variable >>> 'stock_selection' for further processing."
    ),
    expected_output=(
        "The shortlist of selected stocks for investment >>> ({stock_selection}) must appear in the output. "
        "A brief on why the stocks in ({stock_selection}) were selected and why not others "
        "must appear in the output "
        "A set of potential trading strategies for ({stock_selection}) that align with the user's risk tolerance."
        "An estimation of quantities and investment amount for each of the selected stocks in ({stock_selection}) "
        "to appear in the output. "
        "Under each trading strategy, briefly explain why certain stocks from ({stock_selection}) are cosidered "
        "and why not others. This to appear in the output. "
    ),
    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} that was recommended by trading_strategy_agent, "
        "considering current market conditions and optimal pricing."
    ),
    expected_output=(
        "Detailed execution plans suggesting how and when to "
        "execute trades for {stock_selection} that was recommended by trading_strategy_agent."
    ),
    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} that was recommended by trading_strategy_agent. "
        "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} that was recommended by trading_strategy_agent."
    ),
    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=ChatOpenAI(model="gpt-4o-mini", openai_api_key =os.getenv('OPENAI_API_KEY'), temperature=0.7),
    full_output =True,
    process=Process.hierarchical,
    verbose=True
)

# Function to handle the inputs and display the results
def process_input(country, n_stocks, capital, risk_label, strategy_label, news_impact):
    financial_trading_inputs = {
        'country' : country.strip().capitalize(),
        'stock_set': [],
        'stock_selection': [],
        'n_stock_options': int(n_stocks),
        'initial_capital': int(capital),
        'risk_tolerance': risk_label.strip(),
        'trading_strategy_preference': strategy_label.strip(),
        'news_impact_consideration': news_impact
    }

    result = financial_trading_crew.kickoff(inputs=financial_trading_inputs)
    # global result
    output1 = result['tasks_outputs'][0].exported_output+'\n\n=================================\n=================================\n'
    output2 = result['tasks_outputs'][1].exported_output
    output3 = result['tasks_outputs'][2].exported_output+'\n\n=================================\n=================================\n'
    output4 = result['tasks_outputs'][3].exported_output
    return output1, output2, output3, output4

# Create the input fields
country = gr.Textbox(label="Country Name", placeholder="Enter country name", value="USA")
n_stocks = gr.Slider(label="How Many Different Stocks You Want to Invest-in?", minimum=1, maximum=10, step=1, value=5)
initial_capital = gr.Number(label="How much capital you would like to invest", minimum=50000, maximum=100000000, step=10000,value=500000)
risk_label = gr.Dropdown(label="Your Risk Appetite Level", choices=["high", "medium", "low"], value="medium")
trading_strategy = gr.Dropdown(
    label="Select Trading Strategy",
    choices=["day time trading", "swing trading", "scalping", "position trading", "algorithmic trading", "arbitrage", "news-based trading"],
    value="swing trading"
)
news_impact = gr.Radio(label="Select True or False", choices=[True, False], value=True)

# Create markdown output fields
output1 = gr.Markdown(label="Companies Identified with Significant Investments in Social, Charitable, Environmental Activities")
output2 = gr.Markdown(label="Stocks Identified for Investment")
output3 = gr.Markdown(label="Investment Execution Startegies")
output4 = gr.Markdown(label="Risks & Mitigation strategies")

# Create submit and clear buttons
submit_button = gr.Button("Submit")
clear_button = gr.Button("Clear")

# Create the layout and interface
with gr.Blocks() as app:
    gr.Markdown("""# A Platform for Investing in Socially Responsible Companies in Your Country of Choice
    ## After entering the details and hit Submit button, wait for few minutes for the results to appear. You will be pleasantly surprised with the results""")

    with gr.Row():
        with gr.Column():
            country.render()
            n_stocks.render()
            initial_capital.render()
        with gr.Column():
            risk_label.render()
            trading_strategy.render()
            news_impact.render()

    with gr.Row():
        with gr.Column():
            output1.render()
            output2.render()
        with gr.Column():
            output3.render()
            output4.render()

    with gr.Row():  # Add the submit and clear buttons
        submit_button.render()
        clear_button.render()

    # Link inputs and outputs to the submit button
    submit_button.click(
        fn=process_input,
        inputs=[country, n_stocks, initial_capital, risk_label, trading_strategy, news_impact],
        outputs=[output1, output2, output3, output4]
    )

    # Link the clear button to reset inputs and outputs
    clear_button.click(
        fn=lambda: ("", "", "", ""),  # Clear all outputs
        inputs=[],
        outputs=[output1, output2, output3, output4]
    )
    # Link inputs and outputs to function
    submit_button.click(
        fn=process_input,
        inputs=[country, n_stocks, initial_capital, risk_label, trading_strategy, news_impact],
        outputs=[output1, output2, output3, output4]
    )

# Launch the app
app.launch(debug=True)