Inicial

app.py

@@ -0,0 +1,259 @@
+# ANALÍTICA BOUTIQUE, SC (https://www.visoresanalitica.com.mx/)
+# DEMO
+# 
+# PROBLEM: 
+
+# Multi-agent Collaboration for Financial Analysis
+# Demostrate ways for making agents collaborate with each other.
+# 
+
+# python app.py
+
+# Dependencies:
+import gradio as gr
+from crewai import Agent, Task, Crew, Process
+from langchain_openai import ChatOpenAI
+#from langchain_community.chat_message_histories import GradioChatMessageHistory
+from langchain_community.chat_message_histories.in_memory import ChatMessageHistory
+import os
+import sys
+import io
+import pandas as pd
+from crewai_tools import SerperDevTool, ScrapeWebsiteTool
+#from scrape_website_tool import ScrapeWebsiteTool
+#from serper_dev_tool import SerperDevTool
+
+#
+# Obtener las API keys desde .env
+openai_api_key = os.getenv("OPENAI_API_KEY")
+serper_api_key = os.getenv("SERPER_API_KEY")
+
+# Obtener las API keys desde SECRETS
+#openai_api_key = st.secrets["OPENAI_API_KEY"]
+#serper_api_key = st.secrets["SERPER_API_KEY"]
+
+# Define the list of models and initialize your agents and tasks as per the earlier code
+MODEL_LIST = ['gpt-4o-mini', 'gpt-4o']
+
+# crewAI Tools
+search_tool = SerperDevTool()
+scrape_tool = ScrapeWebsiteTool()
+
+# Define Gradio interface components
+def execute_financial_analysis( stock_selection, model_option, initial_capital, risk_tolerance, trading_strategy_preference ):
+    sys.stdout = io.StringIO()  # Capture output to display in the interface
+    
+    # Setup the environment based on model selection
+    os.environ["OPENAI_MODEL_NAME"] = model_option
+    
+    # Creating Agents
+    # Agent: Data Analyst
+    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]
+    )
+    # Agent: Trading Strategy Developer 
+    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]
+    )
+    # Agent: Trade Advisor
+    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]
+    )
+    # Agent: Risk Advisor
+    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]
+    )
+
+    # Creating Tasks
+    # 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}."
+            "Provide your final answer in Spanish."
+        ),
+        agent=risk_management_agent,
+    )
+    
+    # Creating the Crew
+    # Note: The Process class helps to delegate the workflow to the Agents (kind of like a Manager at work)
+    #       In this example, it will run this hierarchically.
+    #       manager_llm lets you choose the "manager" LLM you want to use.
+
+    # 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=model_option,
+                            temperature=0.7),
+        process=Process.hierarchical,
+        verbose=True
+    )
+    
+    # Define your inputs
+    financial_trading_inputs = {
+        'stock_selection': stock_selection,
+        'initial_capital': initial_capital,
+        'risk_tolerance': risk_tolerance,
+        'trading_strategy_preference': trading_strategy_preference,
+        'news_impact_consideration': True
+    }
+
+    # Execute your Crew process
+    result = financial_trading_crew.kickoff(inputs=financial_trading_inputs)
+    verbose_output = sys.stdout.getvalue()
+    sys.stdout = sys.__stdout__  # Restaurar la salida estándar
+
+    # Convertir el objeto 'result' a cadena para que Gradio pueda procesarlo
+    return str(result), verbose_output
+
+#
+#execute_financial_analysis( 'VOD', 'gpt-4o-mini', '100000', 'Medium', 'Day Trading' )
+
+#
+with gr.Blocks() as demo:
+    gr.Markdown("# Colaboración Multiagente para el Análisis Financiero")
+    with gr.Sidebar():
+        gr.Markdown("# DEMO")
+        gr.Image("images/Logo_AB.png")
+        gr.Markdown("Contact: [email protected] [email protected] [email protected]")
+        gr.Markdown("## Configuración del Modelo")
+        model_option = gr.Dropdown(MODEL_LIST, label="Choose OpenAI model", value='gpt-4o-mini')
+
+    gr.Markdown("## Configuración")
+    gr.Markdown("""
+        ## 📊 AI Agents para Análisis de Trading
+        
+        **Este DEMO crea un sistema de agentes de inteligencia artificial (AI Agents) para analizar datos del mercado financiero y sugerir estrategias de trading para un activo.**
+        La tripulación de agentes incluye a:
+        * Data Analyst
+        * Trading Strategy Developer
+        * Trade Advisor
+        * Risk Advisor
+        
+        Todos, con herramientas de acceso a información en tiempo real que sea disponible en Internet.
+        Por favor, selecciona el **Ticker** respecto del cual quieras el análisis financiero.
+        
+        Algunos ejemplos son:
+        - VOD - Nasdaq Index
+        - AAPL - Apple
+        - GOOG - Alphabet (Google)
+        - INTC - Intel
+        - BTC-USD - Bitcoin USD
+        """)
+    ticker = gr.Textbox(label="Introduce el Ticker del activo financiero (Ej: AAPL):", value="AAPL")
+    initial_capital = gr.Textbox(label="Initial Capital", value="100000")
+    risk_tolerance = gr.Radio(label="Risk Tolerance", choices=["Low", "Medium", "High"], value="Medium")
+    trading_strategy_preference = gr.Radio(label="Trading Strategy Preference", choices=["Day Trading", "Swing Trading", "Long Term"], value="Day Trading")
+    execute_button = gr.Button("🚀 Iniciar Análisis")
+
+    gr.Markdown("## 📌 Resultado Final")
+    result_display = gr.Markdown()
+    verbose_output = gr.Textbox(label="🔎 Detalle del Proceso de Razonamiento", lines=10)
+
+    execute_button.click(
+        execute_financial_analysis,
+        inputs=[ticker, model_option, initial_capital, risk_tolerance, trading_strategy_preference],
+        outputs=[result_display, verbose_output]
+    )
+#
+demo.launch()
+

requirements.txt

@@ -0,0 +1,7 @@
+pandas
+gradio
+crewai
+crewai_tools
+langchain_openai
+langchain_community
+