Create app.py

app.py

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+import warnings
+warnings.filterwarnings('ignore')
+
+from crewai import Agent, Task, Crew
+from crewai import Crew, Process
+from langchain_openai import ChatOpen
+from crewai_tools import ScrapeWebsiteTool, SerperDevTool
+import gradio as gr
+import os
+
+os.environ["SERPER_API_KEY"] = os.envget('SERPER_API_KEY
+
+search_tool = SerperDevTool()
+scrape_tool = ScrapeWebsiteTool()
+llm=ChatOpenAI(model="gpt-4o-mini", openai_api_key =os.envget('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.envget('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:
+    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)