ALwrity/ToBeMigrated/ai_writers/ai_finance_report_generator

AI Finance Report Generator

An advanced AI-powered financial analysis and report generation system that combines data collection, technical analysis, visualization, and automated report generation.

Project Structure

ai_finance_report_generator/
├── ai_financial_dashboard.py    # Main dashboard interface
├── utils/                       # Utility functions
│   ├── __init__.py
│   └── storage.py              # Data persistence
├── reports/                     # Report generation modules
│   ├── technical_analysis/     # Technical analysis reports
│   ├── fundamental_analysis/   # Fundamental analysis reports
│   ├── options_analysis/      # Options analysis reports
│   ├── portfolio_analysis/    # Portfolio analysis reports
│   ├── market_research/       # Market research reports
│   └── news_analysis/         # News analysis reports
└── README.md                   # This file

Features

Current Features

• Unified dashboard interface for all financial analysis tools
• Technical Analysis report generation
• Options analysis report generation
• User preferences management
• Recent reports tracking
• Data persistence with JSON storage
• Financial data collection from various sources
• Integration with LLM for report generation

Planned Features

1. Data Collection Module

• Web scraping for financial news and data
• API integrations (Yahoo Finance, Alpha Vantage, Financial Modeling Prep)
• Real-time market data collection
• Historical data retrieval
• Company financial statements
• Market sentiment data
• Economic indicators
• Sector analysis data

2. Technical Analysis Module

• Moving averages (SMA, EMA, WMA)
• RSI, MACD, Bollinger Bands
• Volume analysis
• Support/Resistance levels
• Trend analysis
• Pattern recognition
• Fibonacci retracements
• Momentum indicators

3. Fundamental Analysis Module

• Financial ratios calculation
• Company valuation metrics
• Growth analysis
• Profitability analysis
• Debt analysis
• Cash flow analysis
• Industry comparison
• Peer analysis

4. Data Visualization Module

• Candlestick charts
• Technical indicator overlays
• Volume charts
• Price action patterns
• Correlation matrices
• Heat maps
• Interactive charts
• Custom chart templates

5. Report Generation Module

• Technical analysis reports
• Fundamental analysis reports
• Market research reports
• Investment recommendations
• Risk assessment reports
• Sector analysis reports
• News impact analysis
• Custom report templates

6. News and Sentiment Analysis Module

• News aggregation
• Sentiment scoring
• Social media analysis
• Market sentiment indicators
• News impact analysis
• Event correlation
• Trend detection
• Sentiment visualization

7. Portfolio Analysis Module

• Portfolio performance analysis
• Risk assessment
• Asset allocation
• Correlation analysis
• Diversification metrics
• Performance attribution
• Portfolio optimization
• Rebalancing suggestions

Usage

Basic Usage

from lib.ai_writers.ai_finance_report_generator.ai_financial_dashboard import get_dashboard

# Get dashboard instance
dashboard = get_dashboard()

# Generate technical analysis report
ta_report = dashboard.generate_technical_analysis("AAPL")

# Generate options analysis report
options_report = dashboard.generate_options_analysis("AAPL")

# Get recent reports
recent_reports = dashboard.get_recent_reports()

User Preferences

# Update user preferences
dashboard.update_preferences({
    "report_format": "markdown",
    "include_charts": True,
    "chart_style": "dark",
    "language": "en"
})

# Get current preferences
preferences = dashboard.get_preferences()

Portfolio Analysis

# Create portfolio
portfolio = [
    {"symbol": "AAPL", "shares": 100},
    {"symbol": "GOOGL", "shares": 50}
]

# Generate portfolio report
portfolio_report = dashboard.generate_portfolio_analysis(portfolio)

Installation

pip install -r requirements.txt

Dependencies

1. Data Collection

   • finance_data_researcher
   • web_scraping_tools

2. Analysis Tools

   • pandas_ta
   • numpy
   • scipy

3. Visualization

   • matplotlib
   • plotly

4. Text Generation

   • llm_text_gen
   • gpt_providers

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.