pip install openai ibm-watsonx-ai python-dotenv

IBM_API_KEY=your_ibm_api_key
IBM_PROJECT_ID=your_ibm_project_id

OPENTOOLS_API_KEY=your_opentools_api_key

import os
from openai import OpenAI
from dotenv import load_dotenv

load_dotenv()

def gather_stock_data(symbol):
    """Gather recent news and market data for a stock symbol"""

    client = OpenAI(
        api_key=os.getenv("OPENTOOLS_API_KEY"),
        base_url="https://api.opentools.com/v1"
    )

    response = client.chat.completions.create(
        model="anthropic/claude-3.5-sonnet",
        messages=[
            {
                "role": "system",
                "content": "You are a financial research assistant. Search for and summarize recent news, earnings reports, analyst opinions, and market data about the requested stock. Focus on factual information and recent developments."
            },
            {
                "role": "user",
                "content": f"Find recent news, earnings data, and analyst opinions about {symbol} stock. Include any significant market movements or company developments from the past month."
            }
        ],
        tools=[{"type": "mcp", "ref": "exa"}]
    )

    return response.choices[0].message.content

if __name__ == "__main__":
    market_data = gather_stock_data("NVDA")
    print("Market Data Gathered:")
    print(market_data)

from ibm_watsonx_ai import APIClient, Credentials
from ibm_watsonx_ai.foundation_models import ModelInference

def analyze_with_watsonx(symbol, market_data):
    """Analyze market data using IBM watsonx.ai"""

    # Set up IBM watsonx.ai client
    credentials = Credentials(
        url="https://us-south.ml.cloud.ibm.com",  # Use your region's URL
        api_key=os.getenv("IBM_API_KEY")
    )

    client = APIClient(credentials)
    project_id = os.getenv("IBM_PROJECT_ID")

    print(f"Using IBM watsonx.ai project ID: {project_id}")

    # Initialize the model
    model = ModelInference(
        model_id="ibm/granite-3-8b-instruct",
        api_client=client,
        project_id=project_id,
        params={
            "max_new_tokens": 1000,
            "temperature": 0.3
        }
    )

    # Create analysis prompt
    analysis_prompt = f"""
    As a senior financial analyst, analyze the following market data for {symbol} and provide:

    1. Key market developments and their potential impact
    2. Risk assessment (Low/Medium/High)
    3. Investment recommendation (Buy/Hold/Sell) with reasoning
    4. Price target considerations
    5. Key factors to monitor going forward

    Market Data:
    {market_data}

    Provide a clear, structured analysis suitable for investment decision-making.
    """

    # Generate analysis
    print(f"Analyzing {symbol} with IBM watsonx.ai...")
    response = model.generate_text(
        prompt=analysis_prompt,
        params={
            "input_type": "text"
        }
    )

    return response

if __name__ == "__main__":
    # Example usage
    market_data = "Sample market data would go here..."
    analysis = analyze_with_watsonx("NVDA", market_data)
    print("Investment Analysis:")
    print(analysis)

from datetime import datetime

def create_research_report(symbol):
    """Generate a complete research report for a stock symbol"""

    print(f"🔍 Generating research report for {symbol}...")
    print("=" * 50)

    # Step 1: Gather market data
    print("Step 1: Gathering real-time market data...")
    market_data = gather_stock_data(symbol)

    # Step 2: Analyze with IBM watsonx
    print("\nStep 2: Analyzing with IBM watsonx.ai...")
    analysis = analyze_with_watsonx(symbol, market_data)

    # Step 3: Format the report
    report = f"""
📊 INVESTMENT RESEARCH REPORT
{'=' * 50}
Stock Symbol: {symbol}
Report Date: {datetime.now().strftime('%Y-%m-%d %H:%M')}
Generated by: IBM watsonx.ai + OpenTools API

📈 MARKET DATA SUMMARY
{'-' * 30}
{market_data}

🧠 AI INVESTMENT ANALYSIS
{'-' * 30}
{analysis}

📋 REPORT METHODOLOGY
{'-' * 30}
• Real-time data gathered via OpenTools API web search
• Analysis powered by IBM watsonx.ai foundation models
• Report generated: {datetime.now().strftime('%Y-%m-%d %H:%M UTC')}
    """

    return report

def main():
    """Main function to run the research assistant"""
    symbol = "NVDA"  # You can change this to any stock symbol

    try:
        report = create_research_report(symbol)
        print(report)

        # Optionally save to file
        filename = f"{symbol}_research_report_{datetime.now().strftime('%Y%m%d_%H%M')}.txt"
        with open(filename, 'w') as f:
            f.write(report)
        print(f"\n💾 Report saved as: {filename}")

    except Exception as e:
        print(f"❌ Error generating report: {str(e)}")

if __name__ == "__main__":
    main()

🔍 Generating research report for NVDA...
==================================================
Step 1: Gathering real-time market data...
Step 2: Analyzing with IBM watsonx.ai...

📊 INVESTMENT RESEARCH REPORT
==================================================
Stock Symbol: NVDA
Report Date: 2024-12-19 14:30
Generated by: IBM watsonx.ai + OpenTools API

📈 MARKET DATA SUMMARY
------------------------------
Based on recent search results, NVIDIA (NVDA) has shown strong performance
driven by AI chip demand. Recent earnings exceeded expectations with revenue
growth of 206% year-over-year. Key developments include new data center
partnerships and expanded AI infrastructure investments...

🧠 AI INVESTMENT ANALYSIS
------------------------------
Key Market Developments:
- Strong AI chip demand continues to drive revenue growth
- Data center segment showing exceptional performance
- New partnerships expanding market reach

Risk Assessment: MEDIUM
- Market volatility in tech sector
- Potential regulatory changes in AI space
- Competition from other chip manufacturers

Investment Recommendation: BUY
The fundamental strength in AI infrastructure demand, combined with NVIDIA's
market leadership position, supports a positive outlook despite short-term
market volatility...

💾 Report saved as: NVDA_research_report_20241219_1430.txt