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name: financial-analysis-agent description: Create agents for financial analysis, investment research, and portfolio management. Covers financial data processing, risk analysis, and recommendation generation. Use when building investment analysis tools, robo-advisors, portfolio trackers, or financial intelligence systems.

Financial Analysis Agent

Build intelligent financial analysis agents that evaluate investments, assess risks, and generate data-driven recommendations.

Financial Data Integration

See examples/financial_data_collector.py for the FinancialDataCollector class that:

• Integrates with yfinance for stock data
• Retrieves financial statements (income, balance sheet, cash flow)
• Fetches key metrics (market cap, PE ratio, dividend yield, etc.)

Financial Analysis Techniques

Technical Analysis

See examples/technical_analyzer.py for TechnicalAnalyzer:

• Moving averages calculation
• Relative Strength Index (RSI)
• Support and resistance level identification

Fundamental Analysis

See examples/fundamental_analyzer.py for FundamentalAnalyzer:

• Profitability ratios (gross margin, operating margin, net margin, ROA, ROE)
• Valuation ratios (PE, PB, PEG, price-to-sales)
• Liquidity ratios (current ratio, quick ratio, debt-to-equity)

Risk Assessment

See examples/risk_analyzer.py for RiskAnalyzer:

• Volatility calculation
• Value at Risk (VaR) assessment
• Sharpe Ratio calculation
• Company risk assessment

Investment Recommendations

See examples/investment_recommender.py for InvestmentRecommender:

• Generates recommendations (Strong Buy, Buy, Hold, Sell, Strong Sell)
• Calculates investment scores based on technical and fundamental signals
• Provides confidence levels and risk assessments

Portfolio Management

See examples/portfolio_manager.py for PortfolioManager:

• Calculate portfolio total value
• Rebalance portfolio based on target allocations
• Assess portfolio risk and volatility

Market Intelligence

Build market intelligence capabilities by:

• Analyzing overall market trends and sector performance
• Calculating market volatility indices
• Fetching economic indicators
• Identifying undervalued, growth, and dividend opportunities

Best Practices

Analysis Quality

• ✓ Use multiple data sources
• ✓ Cross-validate findings
• ✓ Document assumptions
• ✓ Consider time horizons
• ✓ Account for fees and taxes

Risk Management

• ✓ Assess downside risk
• ✓ Implement stop losses
• ✓ Diversify appropriately
• ✓ Position size accordingly
• ✓ Review regularly

Ethical Considerations

• ✓ Disclose conflicts of interest
• ✓ Avoid market manipulation
• ✓ Base recommendations on analysis
• ✓ Update recommendations regularly
• ✓ Acknowledge limitations

Tools & Data Sources

Data APIs

• yfinance
• Alpha Vantage
• IEX Cloud
• Polygon.io
• Yahoo Finance

Analysis Libraries

• pandas
• NumPy
• scikit-learn
• TA-Lib
• statsmodels

Getting Started

1. Collect financial data
2. Perform technical analysis
3. Analyze fundamentals
4. Assess risks
5. Generate recommendations
6. Monitor positions
7. Rebalance periodically