Financial Analysis System

from swarms import Agent, MixtureOfAgents
import os

# Configure your LLM
api_key = os.getenv("OPENAI_API_KEY")

# Define specialized financial analyst agents
fundamental_analyst = Agent(
    agent_name="Fundamental-Analyst",
    system_prompt="""
    You are a fundamental analysis expert.
    Your role is to:
    - Analyze financial statements (P&L, balance sheet, cash flow)
    - Calculate key financial ratios (P/E, ROE, debt-to-equity, etc.)
    - Assess company financial health and profitability
    - Evaluate management quality and corporate governance
    - Determine intrinsic value using DCF and comparable analysis
    
    Provide thorough fundamental analysis with specific metrics.
    """,
    model_name="claude-sonnet-4-6",
    max_loops=1,
    dynamic_temperature_enabled=True,
)

technical_analyst = Agent(
    agent_name="Technical-Analyst",
    system_prompt="""
    You are a technical analysis specialist.
    Your role is to:
    - Analyze price trends, support/resistance levels
    - Identify chart patterns and technical indicators
    - Assess momentum using RSI, MACD, moving averages
    - Evaluate volume patterns and market sentiment
    - Provide entry/exit points based on technical signals
    
    Focus on price action and market dynamics.
    """,
    model_name="claude-sonnet-4-6",
    max_loops=1,
    dynamic_temperature_enabled=True,
)

risk_analyst = Agent(
    agent_name="Risk-Analyst",
    system_prompt="""
    You are a risk management expert.
    Your role is to:
    - Assess market risk, credit risk, and operational risk
    - Calculate Value at Risk (VaR) and stress test scenarios
    - Analyze volatility and beta
    - Evaluate concentration risk and correlations
    - Recommend risk mitigation strategies
    
    Identify and quantify all material risks.
    """,
    model_name="claude-sonnet-4-6",
    max_loops=1,
    dynamic_temperature_enabled=True,
)

macro_analyst = Agent(
    agent_name="Macro-Analyst",
    system_prompt="""
    You are a macroeconomic analysis expert.
    Your role is to:
    - Analyze economic indicators (GDP, inflation, employment)
    - Assess monetary and fiscal policy impacts
    - Evaluate sector and industry trends
    - Identify macroeconomic risks and opportunities
    - Provide economic context for investment decisions
    
    Connect macro trends to investment implications.
    """,
    model_name="claude-sonnet-4-6",
    max_loops=1,
    dynamic_temperature_enabled=True,
)

quant_analyst = Agent(
    agent_name="Quantitative-Analyst",
    system_prompt="""
    You are a quantitative analyst specializing in statistical models.
    Your role is to:
    - Build statistical and mathematical models
    - Perform regression analysis and factor modeling
    - Calculate Sharpe ratio, alpha, and other performance metrics
    - Analyze historical patterns and correlations
    - Backtest investment strategies
    
    Provide data-driven quantitative insights.
    """,
    model_name="claude-sonnet-4-6",
    max_loops=1,
    dynamic_temperature_enabled=True,
)

sentiment_analyst = Agent(
    agent_name="Sentiment-Analyst",
    system_prompt="""
    You are a market sentiment and behavioral finance expert.
    Your role is to:
    - Analyze news sentiment and social media trends
    - Assess investor sentiment and positioning
    - Identify behavioral biases in market pricing
    - Monitor insider trading and institutional flows
    - Evaluate market psychology and crowd behavior
    
    Gauge market sentiment and contrarian opportunities.
    """,
    model_name="claude-sonnet-4-6",
    max_loops=1,
    dynamic_temperature_enabled=True,
)

aggregator_agent = Agent(
    agent_name="Chief-Investment-Officer",
    system_prompt="""
    You are the Chief Investment Officer who synthesizes all analyst inputs.
    Your role is to:
    - Integrate insights from all analytical perspectives
    - Identify consensus views and disagreements
    - Weigh different analytical approaches appropriately
    - Assess confidence level based on analyst agreement
    - Provide clear investment recommendations with rationale
    - Assign conviction levels (High/Medium/Low)
    
    Make final investment decisions based on collective intelligence.
    """,
    model_name="claude-sonnet-4-6",
    max_loops=1,
    dynamic_temperature_enabled=True,
)

# Create the Mixture of Agents financial analysis system
financial_analysis_system = MixtureOfAgents(
    name="Financial-Analysis-System",
    agents=[
        fundamental_analyst,
        technical_analyst,
        risk_analyst,
        macro_analyst,
        quant_analyst,
        sentiment_analyst,
    ],
    aggregator_agent=aggregator_agent,
    aggregator_system_prompt="""
    Synthesize all analyst perspectives into a comprehensive investment recommendation.
    
    Structure your output as:
    1. Executive Summary
    2. Analyst Consensus & Disagreements
    3. Investment Thesis
    4. Risk Assessment
    5. Recommendation (Buy/Hold/Sell with conviction level)
    6. Price Targets and Timeline
    7. Key Risks and Mitigants
    """,
    layers=2,  # Two rounds of analysis for deeper insights
)

# Execute financial analysis
if __name__ == "__main__":
    analysis_request = """
    Provide comprehensive investment analysis for:
    
    Company: Tesla Inc. (TSLA)
    
    Analysis scope:
    - Current valuation and financial health
    - Technical price trends and momentum
    - Risk factors (market, business, regulatory)
    - Macroeconomic tailwinds/headwinds for EV sector
    - Quantitative performance metrics vs peers
    - Market sentiment and investor positioning
    
    Context:
    - Investment horizon: 12-18 months
    - Portfolio context: Growth-oriented equity portfolio
    - Risk tolerance: Moderate-aggressive
    
    Provide actionable buy/hold/sell recommendation with price targets.
    """
    
    # Run the financial analysis
    print("Running multi-agent financial analysis...")
    investment_report = financial_analysis_system.run(analysis_request)
    
    # Save the investment report
    with open("tesla_investment_analysis.md", "w") as f:
        f.write(investment_report)
    
    print("Investment analysis complete! Report saved to tesla_investment_analysis.md")

esg_analyst = Agent(
    agent_name="ESG-Analyst",
    system_prompt="""
    You are an ESG (Environmental, Social, Governance) analyst.
    Your role is to:
    - Assess environmental impact and sustainability
    - Evaluate social responsibility and labor practices
    - Analyze governance structure and ethics
    - Identify ESG risks and opportunities
    - Rate ESG performance vs peers
    """,
    model_name="claude-sonnet-4-6",
    max_loops=1,
)

# Add to the agent list
financial_analysis_system = MixtureOfAgents(
    agents=[
        fundamental_analyst,
        technical_analyst,
        risk_analyst,
        macro_analyst,
        quant_analyst,
        sentiment_analyst,
        esg_analyst,  # Add ESG perspective
    ],
    aggregator_agent=aggregator_agent,
)

portfolio_manager = Agent(
    agent_name="Portfolio-Manager",
    system_prompt="""
    You manage portfolio construction and optimization.
    Your role is to:
    - Assess how new position fits existing portfolio
    - Analyze correlation and diversification benefits
    - Optimize position sizing
    - Evaluate portfolio-level risk metrics
    - Recommend rebalancing actions
    """,
    model_name="claude-sonnet-4-6",
    max_loops=1,
)

# For technology stocks
tech_specialist = Agent(
    agent_name="Tech-Sector-Specialist",
    system_prompt="""
    You are a technology sector specialist.
    Expertise in:
    - SaaS metrics (ARR, LTV/CAC, net retention)
    - Platform economics and network effects
    - Technology moats and competitive dynamics
    - Regulatory risks (antitrust, privacy)
    - Innovation cycles and disruption risks
    """,
    model_name="claude-sonnet-4-6",
    max_loops=1,
)

# For financial services
financials_specialist = Agent(
    agent_name="Financials-Specialist",
    system_prompt="""
    You specialize in financial services analysis.
    Expertise in:
    - Net interest margin and efficiency ratios
    - Loan quality and credit metrics
    - Capital adequacy and regulatory compliance
    - Interest rate sensitivity
    - Fintech disruption risks
    """,
    model_name="claude-sonnet-4-6",
    max_loops=1,
)

# For quick screening
quick_screen = MixtureOfAgents(
    agents=[fundamental_analyst, technical_analyst, risk_analyst],
    aggregator_agent=aggregator_agent,
    layers=1,  # Single layer for speed
)

# For deep due diligence
deep_analysis = MixtureOfAgents(
    agents=[all_analysts],
    aggregator_agent=aggregator_agent,
    layers=3,  # Three layers for thorough analysis
)

competitive_analyst = Agent(
    agent_name="Competitive-Analyst",
    system_prompt="""
    You analyze competitive positioning and market share.
    Your role is to:
    - Map competitive landscape and key players
    - Assess competitive advantages and moats
    - Analyze market share trends
    - Evaluate pricing power and unit economics
    - Identify competitive threats and opportunities
    """,
    model_name="claude-sonnet-4-6",
    max_loops=1,
)

aggregator_agent = Agent(
    agent_name="Chief-Investment-Officer",
    system_prompt="""
    Provide confidence score based on analyst agreement:
    
    High Confidence (85-100%):
    - 5+ analysts agree on direction
    - Strong fundamental and technical alignment
    
    Medium Confidence (60-84%):
    - 3-4 analysts agree
    - Some conflicting signals
    
    Low Confidence (<60%):
    - Significant analyst disagreement
    - Mixed or unclear signals
    - Recommend further analysis or wait
    
    Always disclose confidence level in recommendation.
    """,
    # ... other params
)

analysis_request = """
Analyze TSLA under three scenarios:

1. Bull Case: Strong EV adoption, margin expansion
2. Base Case: Moderate growth, competitive pressure
3. Bear Case: Recession, increased competition

Provide price targets and probabilities for each scenario.
"""

result = financial_analysis_system.run(analysis_request)

market_monitor = Agent(
    agent_name="Market-Monitor",
    system_prompt="""
    Monitor real-time market developments:
    - Breaking news and events
    - Price movements and volume spikes
    - Earnings announcements
    - Regulatory filings
    
    Alert on material changes requiring re-analysis.
    """,
    max_loops="auto",
    stopping_condition="market_close",
)

aggregator_agent = Agent(
    agent_name="Chief-Investment-Officer",
    system_prompt="""
    Include performance metrics in recommendation:
    
    - Analyst Agreement Score: X/6 analysts agree
    - Confidence Level: High/Medium/Low
    - Risk-Reward Ratio: X:1
    - Conviction Level: 1-5 stars
    - Expected Return: X% (base case)
    - Downside Risk: -X% (worst case)
    - Time Horizon: X months
    """,
    # ... other params
)

# Pseudo-code for data integration
from financial_data_api import get_financial_data

# Gather data
data = get_financial_data("TSLA")

# Enhance request with data
enhanced_request = f"""
{analysis_request}

Current financial data:
{data}
"""

result = financial_analysis_system.run(enhanced_request)

def send_alert(report, threshold="High"):
    if threshold in report:
        # Send email/Slack notification
        notify_investors(report)

result = financial_analysis_system.run(analysis_request)
send_alert(result)