financial-analysis-agent
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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.
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Summary
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.
Raw SKILL.md
3,380 bytes--- 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](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](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](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](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](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](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
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