SEC SWOT Analysis Dashboard

A comprehensive Python application for analyzing SEC 10-K filings and generating automated SWOT (Strengths, Weaknesses, Opportunities, Threats) reports using natural language processing and machine learning techniques.

🌟 Features

• Automated SEC Filing Analysis: Download and process 10-K filings directly from the SEC EDGAR database
• AI-Powered SWOT Classification: Uses machine learning to classify text into SWOT categories
• Interactive Dashboard: Modern Streamlit web interface with dark theme and professional styling
• Comprehensive Visualizations: Interactive charts and graphs powered by Plotly
• Export Options: Download results in CSV, JSON, and PDF formats
• Multi-Company Support: Analyze multiple companies and time periods
• Real-Time Processing: Live progress tracking during analysis

📊 Dashboard Preview

The application features three main modes:

• 📈 Quick Analysis: Select a ticker and date range for automated analysis
• 📋 Upload Documents: Process custom SEC filings (coming soon)
• 📊 View Results: Browse and visualize previously generated reports

🛠️ Installation

1. Clone the repository:

   git clone <repository-url>
   cd nlp-project

2. Install dependencies:

   pip install -r requirements.txt

3. Run the dashboard:

   streamlit run dashboard.py

📋 Requirements

streamlit>=1.28.0
plotly>=5.15.0
pandas>=1.5.0
datamule
tqdm
pathlib

🏗️ Project Structure

├── dashboard.py              # Main Streamlit dashboard
├── swot_analysis.ipynb      # Jupyter notebook for SWOT analysis
├── requirements.txt         # Python dependencies
├── sec_10k_sentences.csv   # Raw SEC filing sentences
├── sec_10k_sentences_clean.csv # Cleaned sentences
├── sec_portfolio/          # Downloaded SEC filings
│   ├── 000032019323000106.tar
│   └── 000032019324000123.tar
└── sec_swot_output/        # Analysis results
    ├── index.json          # Master index of reports
    ├── swot_AAPL_*.csv    # Individual SWOT data
    └── swot_report_AAPL_*.json # Structured reports

🚀 Usage

Quick Analysis Mode

1. Launch the dashboard: streamlit run dashboard.py
2. Select 📈 Quick Analysis from the sidebar
3. Choose a company ticker (e.g., AAPL, MSFT, GOOGL)
4. Set your desired date range
5. Click 🚀 Run Analysis
6. View results in the 📊 View Results section

Dashboard Navigation

📈 Quick Analysis

• Company Selection: Choose from popular tickers (AAPL, MSFT, GOOGL, AMZN, TSLA, META, NVDA) or enter custom ticker
• Date Range: Set start and end dates for filing analysis (2020-2025)
• One-Click Analysis: Automated processing with real-time progress tracking

📋 Upload Documents (Coming Soon)

• Support for .txt, .pdf, and .html files
• Custom document processing capabilities
• Batch upload functionality

📊 View Results

• Interactive report selector
• Professional SWOT visualizations
• Detailed category breakdowns with key themes and insights
• Export options (CSV, JSON, PDF)

Jupyter Notebook Analysis

For advanced users and development:

1. Open swot_analysis.ipynb
2. Configure parameters:

   TICKERS = ["AAPL"]  # Companies to analyze
   FORMS = ["10-K"]    # SEC form types
   DATE_RANGE = ("2023-01-01", "2024-12-31")

3. Run all cells to perform analysis

📈 Output Files

The analysis generates several output files:

• CSV Files: Raw SWOT classifications with confidence scores
• JSON Reports: Structured reports with key themes and insights
• Index File: Master catalog of all generated reports

Sample JSON Report Structure

{
  "meta": {
    "ticker": "AAPL",
    "accession": "000032019324000123",
    "filing_date": "2024-11-01"
  },
  "report": {
    "Strength": {
      "count": 25,
      "top_bullets": ["Key strength indicators..."],
      "key_themes": ["company", "growth", "innovation"],
      "key_insights": ["Strategic advantages identified..."],
      "summary": "25 strength indicators found"
    },
    "Weakness": { ... },
    "Opportunity": { ... },
    "Threat": { ... }
  }
}

🎨 Dashboard Features

Professional Dark Theme

• Gradient backgrounds and modern styling
• Color-coded SWOT categories:
  • 💪 Strengths: Green gradient
  • ⚠️ Weaknesses: Red gradient
  • 🎯 Opportunities: Blue gradient
  • ⚡ Threats: Orange gradient

Interactive Visualizations

• Pie Charts: SWOT category distribution with pull-out effects
• Key Themes: Highlighted tag-style display of common topics
• Sample Evidence: Expandable sections with filing excerpts
• Metrics Cards: Professional summary statistics

Export Capabilities

• CSV Export: Raw classification data for further analysis
• JSON Export: Complete structured reports
• PDF Generation: Professional report formatting (coming soon)

🔧 Key Components

dashboard.py

Main Streamlit application featuring:

• Modern dark theme with professional styling
• Interactive visualizations with Plotly
• Real-time analysis progress tracking
• Multi-format export capabilities
• Responsive design with custom CSS

swot_analysis.ipynb

Core analysis engine providing:

• SEC filing download via datamule
• Text preprocessing and sentence extraction
• ML-based SWOT classification
• Report generation and export

🎯 SWOT Classification

The system uses keyword-based weak supervision to classify sentences:

• Strengths: Competitive advantages, strong performance metrics, market leadership
• Weaknesses: Risk factors, operational challenges, regulatory concerns
• Opportunities: Growth potential, market expansion, new technologies
• Threats: External risks, competitive pressures, economic factors

📊 Visualization Features

Interactive Charts

• Distribution Analysis: Pie charts showing SWOT category proportions
• Theme Analysis: Most frequent topics per category
• Evidence Display: Representative sentences for each category
• Executive Metrics: Key performance indicators and summaries

Professional Styling

• Dark Theme: Modern gradient backgrounds
• Color Coding: Intuitive category identification
• Responsive Layout: Adapts to different screen sizes
• Professional Typography: Clean, readable font choices

🔍 Data Sources

• SEC EDGAR Database: Official 10-K filings
• Supported Companies: All publicly traded US companies
• Time Range: 2020-present (configurable)
• Filing Types: 10-K annual reports (expandable)

🚧 Roadmap

Near Term

• PDF document upload support
• Enhanced text preprocessing
• Batch analysis capabilities
• PDF report generation

Medium Term

• Advanced NLP models (BERT, GPT)
• Comparative analysis across companies
• Trend analysis over time
• Email notification system

Long Term

• Multi-language support
• Real-time market integration
• Automated report scheduling
• API development

🎮 Getting Started

1. First Time Setup:

   git clone <repository-url>
   cd nlp-project
   pip install -r requirements.txt

2. Launch Dashboard:

   streamlit run dashboard.py

3. Run Your First Analysis:

   • Navigate to "Quick Analysis" mode
   • Select "AAPL" from the ticker dropdown
   • Set date range to last year
   • Click "Run Analysis"
   • View results in "View Results" mode

🙏 Acknowledgments

• datamule: For SEC filing download and processing
• Streamlit: For the interactive web interface framework
• Plotly: For advanced data visualizations
• SEC EDGAR: For providing public access to corporate filings

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