📊 Fundamental Indicators - Stock Analysis Toolkit

A professional-grade toolkit for comprehensive fundamental analysis of Indian stocks with automated scoring, pattern detection, statistical analysis, and report generation.

Features • Quick Start • Documentation • Examples • Contributing

🎯 Overview

Fundamental Indicators is a comprehensive Python-based stock analysis toolkit designed specifically for Indian equity markets. It provides automated analysis across multiple dimensions:

📈 Fundamental Scoring - 14-metric weighted scoring system (0-100 scale)
🔍 Pattern Detection - Cyclical patterns (weekday, monthly, quarterly)
📊 Statistical Analysis - 26+ technical indicators (MA, RSI, MACD, Bollinger Bands)
💰 Fundamental Metrics - MCAP growth, liquidity analysis, valuation ratios
📑 Automated Reporting - Excel reports, visualizations, and markdown summaries
⚡ Bulk Processing - Analyze NIFTY50, NIFTY500, or custom stock lists

✨ Key Features

🎯 Core Fundamental Scoring

14 fundamental metrics with customizable weights
Intelligent threshold-based scoring (0-100 scale)
Excel reports with charts and visualizations
Sample implementation with Eternal Ltd data

🔬 Generic Stock Analyzer

Universal compatibility - Works with any stock CSV
Pattern analysis - 35+ cyclical and special patterns
Technical indicators - Moving averages, RSI, MACD, Bollinger Bands, ATR
Fundamental metrics - Market cap trends, liquidity, price-to-book
Rich visualizations - 5+ charts per stock
Comprehensive reports - Detailed markdown documentation

🚀 Bulk Analysis Tools

Batch process multiple stocks simultaneously
NIFTY50 and NIFTY500 support
ACE Equity CSV compatibility
Interactive data collection
Retry mechanism for failed analyses

📦 Pre-analyzed Data

50 NIFTY50 stocks - Complete analyses available
500 NIFTY500 stocks - Individual CSVs ready
Historical data from 2000-2025
10 columns per stock (OHLC, Volume, MCAP, etc.)

🚀 Quick Start

Prerequisites

# Python 3.8 or higher
python --version

# Install dependencies
pip install -r requirements.txt

Installation

# Clone the repository
git clone https://github.com/Mohit1053/Fundamental_Indicators.git
cd Fundamental_Indicators

# Install required packages
pip install -r requirements.txt

Basic Usage

1️⃣ Core Fundamental Scoring

cd 1_Core_Fundamental_Scoring
python main.py

Output: Excel report with scoring, visualizations, and metrics

2️⃣ Analyze Any Stock

cd 2_Generic_Stock_Analyzer
python analyze_stock.py path/to/your/stock.csv

Output: Complete analysis with patterns, statistics, fundamentals, charts, and reports

3️⃣ Bulk Analysis (Multiple Stocks)

cd 5_Bulk_Tools
python bulk_market_analyzer.py

Output: Batch analysis of multiple stocks with consolidated results

📂 Project Structure

Fundamental_Indicators/
│
├── 1_Core_Fundamental_Scoring/        # Original scoring system (Eternal Ltd example)
│   ├── main.py                        # Main execution script
│   ├── eternal_ltd_real_data.py       # Sample data (Eternal Ltd/Zomato)
│   ├── metric_calculator.py           # 14 fundamental metrics calculator
│   ├── scoring_engine.py              # Weighted scoring engine (0-100 scale)
│   ├── report_generator.py            # Excel & visualization generator
│   ├── eternal_analysis.png           # Sample output visualization
│   └── eternal_fundamental_analysis.xlsx  # Sample Excel report
│
├── 2_Generic_Stock_Analyzer/          # Universal stock analysis toolkit
│   ├── analyze_stock.py               # Master pipeline for any stock
│   ├── universal_pattern_analyzer.py  # Cyclical pattern detection
│   ├── universal_statistical_analyzer.py  # Technical indicators & statistics
│   ├── universal_visualization_generator.py  # Chart generation
│   ├── universal_report_generator.py  # Comprehensive markdown reports
│   ├── README.md                      # Toolkit documentation
│   ├── EXAMPLES.md                    # Usage examples
│   └── requirements.txt               # Python dependencies
│
├── 3_Example_Analyses/                # Sample analysis outputs
│   └── Reliance_Analysis_Complete/    # Complete Reliance Industries analysis
│       ├── 00_Master_Data/            # Original CSV + filled data
│       ├── 01_Supporting_Raw_Data/    # All statistical calculations
│       ├── 02_Median_Statistics/      # Median values by metric
│       ├── 03_Cyclical_Patterns/      # Weekday/monthly/quarterly patterns
│       ├── 04_Fundamental_Analysis/   # Scoring and fundamental metrics
│       ├── 05_Visualizations/         # Charts and graphs
│       ├── 06_Comprehensive_Reports/  # Detailed markdown reports
│       ├── 07_Verification/           # Audit trails and verification
│       ├── 08_Phase1_Original_Analysis/  # Historical workflow
│       └── 09_Reports/                # Master reports
│
├── 4_Legacy_Scripts/                  # Archived analysis workflows
│   └── Reliance_Workflow/             # Original Reliance analysis scripts
│       ├── analyze_and_fill_csv.py    # CSV data filler
│       ├── create_visualizations.py   # Visualization generator
│       ├── verify_calculations.py     # Calculation verifier
│       ├── weekday_monthly_analysis.py  # Cyclical analysis
│       ├── create_pattern_visualizations.py  # Pattern charts
│       ├── extract_supporting_data.py  # Raw data extractor
│       └── pattern_visualizations.png  # Sample output
│
├── 5_Bulk_Tools/                      # Multi-stock analysis tools
│   ├── bulk_market_analyzer.py        # Analyze multiple stocks
│   ├── quick_start_bulk.py            # Bulk analysis quick start
│   ├── interactive_data_collector.py  # Interactive data collection
│   ├── ace_equity_template.csv        # ACE Equity CSV template
│   ├── ACE_EQUITY_COLUMN_MAPPING.md   # Column mapping guide
│   ├── ACE_EQUITY_GUIDE.md            # ACE Equity usage guide
│   └── INTERACTIVE_TOOL_GUIDE.md      # Interactive tool documentation
│
├── 6_Documentation/                   # Project documentation
│   ├── ANALYSIS_RESULTS.md            # Analysis results summaries
│   ├── CLEANUP_SUMMARY.md             # Cleanup and refactoring notes
│   ├── COMPLETE_AUDIT_AND_TOOLKIT.md  # Comprehensive audit report
│   ├── DATA_REQUIREMENTS.md           # Data format requirements
│   ├── DATA_VERIFICATION_REPORT.md    # Verification methodology
│   ├── PROJECT_SUMMARY.md             # Project overview
│   ├── QUICK_START_GUIDE.md           # Quick start instructions
│   ├── ROADMAP.md                     # Future development roadmap
│   ├── SCORE_CALCULATION_EXPLAINED.md  # Scoring methodology
│   ├── SCORING_LOGIC_EXPLAINED.md     # Detailed scoring logic
│   ├── SCORING_THRESHOLDS_VISUAL.md   # Threshold visualization
│   └── USAGE_GUIDE.md                 # Comprehensive usage guide
│
├── 7_Configuration_Data/              # Configuration files
│   ├── scoring_parameters.csv         # Metric weights and thresholds
│   ├── detailed_scoring_parameters.csv  # Extended parameters
│   ├── trading_calendar.csv           # Trading calendar data
│   ├── monthly_analysis.csv           # Monthly pattern data
│   ├── quarter_analysis.csv           # Quarterly pattern data
│   └── weekday_analysis.csv           # Weekday pattern data
│
├── requirements.txt                   # Python package dependencies
└── index.html                         # Web interface (if applicable)

🚀 Quick Start

1. Core Fundamental Scoring (Eternal Ltd Example)

Demonstrates the original 14-metric scoring system:

cd 1_Core_Fundamental_Scoring
python main.py

Output:

Console: Detailed metric scores and final rating
eternal_analysis.png: Visual dashboard
eternal_fundamental_analysis.xlsx: Excel report

Key Features:

14 fundamental indicators across 5 categories
Weighted scoring (0-100 scale)
Automatic rating: Excellent (80-100), Good (60-79), Average (40-59), Below Average (20-39), Poor (0-19)

2. Generic Stock Analyzer (Any Stock)

Universal toolkit for analyzing any stock from CSV data:

cd 2_Generic_Stock_Analyzer
python analyze_stock.py your_stock_data.csv "Company Name"

Capabilities:

✅ Cyclical pattern detection (weekday, monthly, quarterly)
✅ Statistical analysis (mean, median, percentiles, skewness, kurtosis)
✅ Technical indicators (moving averages, volatility, momentum)
✅ ANOVA testing for cyclical significance
✅ Automated chart generation
✅ Comprehensive markdown reports

See: 2_Generic_Stock_Analyzer/README.md for detailed usage

3. View Example Analysis (Reliance Industries)

Complete reference analysis with 6,433 trading days (2000-2025):

3_Example_Analyses/Reliance_Analysis_Complete/
├── 00_Master_Data/Reliance_Industries_Updated.csv  (Filled data)
├── 01_Supporting_Raw_Data/  (50+ statistical files)
├── 02_Median_Statistics/  (Median values by metric)
├── 06_Comprehensive_Reports/COMPLETE_ANALYSIS_REPORT.md  (Master report)
└── 09_Reports/VERIFICATION_CHECKLIST.md  (Audit trail)

Key Insights from Reliance Example:

Complete fill-in of missing data using statistical methods
All raw supporting data preserved for verification
Median statistics for every metric
Cyclical pattern analysis with ANOVA testing
Pattern visualizations and statistical charts

🛠️ Tool Selection Guide

Use Case	Tool	Location
Analyze one stock (CSV)	Generic Stock Analyzer	`2_Generic_Stock_Analyzer/`
Score fundamentals (manual data)	Core Scoring System	`1_Core_Fundamental_Scoring/`
Analyze multiple stocks	Bulk Market Analyzer	`5_Bulk_Tools/`
Interactive data collection	Interactive Collector	`5_Bulk_Tools/`
View example workflow	Reliance Analysis	`3_Example_Analyses/`
Study legacy methods	Legacy Scripts	`4_Legacy_Scripts/`

📊 Key Metrics & Categories

1. Profitability (30%)

Return on Equity (ROE)
Return on Assets (ROA)
Operating Profit Margin
Net Profit Margin

2. Liquidity (20%)

Current Ratio
Quick Ratio
Cash Ratio

3. Efficiency (15%)

Asset Turnover
Inventory Turnover

4. Valuation (20%)

Price-to-Earnings (P/E)
Price-to-Book (P/B)

5. Solvency (15%)

Debt-to-Equity
Interest Coverage
Equity Ratio

Total Score: 0-100 (weighted average)

📋 Requirements

Python Version

Python 3.8+

Dependencies

pip install -r requirements.txt

Core Libraries:

pandas >= 2.0.0 (Data manipulation)
numpy >= 1.24.0 (Numerical computing)
matplotlib >= 3.7.0 (Plotting)
seaborn >= 0.12.0 (Statistical visualization)
scipy >= 1.10.0 (Statistical analysis)
openpyxl >= 3.1.0 (Excel export)

🎯 Analysis Workflow

For Individual Stocks (Generic Analyzer)

Prepare CSV: Ensure columns match required format (see 2_Generic_Stock_Analyzer/README.md)
Run Analysis: python analyze_stock.py your_data.csv "Company Name"
Review Outputs:
- CompanyName_Outputs/analysis_report.md (Comprehensive report)
- CompanyName_Outputs/visualizations/ (Charts)
- CompanyName_Outputs/pattern_analysis/ (Cyclical patterns)

For Bulk Analysis

Collect Data: Use interactive_data_collector.py or ACE Equity template
Configure: Edit scoring_parameters.csv if needed
Execute: python bulk_market_analyzer.py
Compare: Review cross-stock comparisons and rankings

📖 Documentation Guide

Document	Purpose
`QUICK_START_GUIDE.md`	Fast setup and basic usage
`USAGE_GUIDE.md`	Comprehensive user manual
`SCORE_CALCULATION_EXPLAINED.md`	Scoring methodology deep-dive
`DATA_REQUIREMENTS.md`	CSV format specifications
`COMPLETE_AUDIT_AND_TOOLKIT.md`	Full project audit report
`ROADMAP.md`	Future development plans

All documentation: 6_Documentation/

🔍 Verification & Transparency

Every calculation in this toolkit is:

✅ Traceable: All raw data preserved in supporting files
✅ Reproducible: Step-by-step calculation logs
✅ Verifiable: Cross-referenced against original data
✅ Documented: Methodology explained in reports

Example: 3_Example_Analyses/Reliance_Analysis_Complete/07_Verification/

🧪 Testing & Validation

Validated On:

Reliance Industries: 6,433 trading days (2000-2025)
Eternal Ltd (Zomato): Fundamental scoring demo
Multiple Stocks: Bulk analysis testing

Statistical Methods:

ANOVA for cyclical pattern significance
T-tests for group comparisons
Percentile analysis (10th, 25th, 50th, 75th, 90th)
Skewness & kurtosis for distribution analysis

🤝 Contributing & Extensions

Add New Metrics

Edit 1_Core_Fundamental_Scoring/metric_calculator.py
Update 7_Configuration_Data/scoring_parameters.csv
Modify scoring_engine.py with new thresholds

Customize Scoring

Adjust weights in scoring_parameters.csv
Modify thresholds for your market context
Add industry-specific benchmarks

Extend Analyzers

Add new patterns to universal_pattern_analyzer.py
Create custom visualizations in universal_visualization_generator.py
Build new reports in universal_report_generator.py

⚠️ Disclaimer

This toolkit is for educational and research purposes only.

❌ Not financial advice
❌ Not investment recommendations
✅ Academic analysis tool
✅ Learning resource for fundamental analysis

Always consult qualified financial advisors before making investment decisions.

📧 Support & Issues

For detailed usage questions, refer to:

6_Documentation/USAGE_GUIDE.md
2_Generic_Stock_Analyzer/README.md
2_Generic_Stock_Analyzer/EXAMPLES.md

For specific tools:

ACE Equity: 5_Bulk_Tools/ACE_EQUITY_GUIDE.md
Interactive Collector: 5_Bulk_Tools/INTERACTIVE_TOOL_GUIDE.md

📝 License

This project is provided as-is for educational purposes. Refer to individual files for specific licenses if applicable.

🎓 Learning Path

Beginner: Start with 1_Core_Fundamental_Scoring/main.py to understand metrics
Intermediate: Run 2_Generic_Stock_Analyzer/ on your own CSV data
Advanced: Study 3_Example_Analyses/Reliance_Analysis_Complete/ for comprehensive methodology
Expert: Use 5_Bulk_Tools/ for portfolio-level analysis

🔄 Version History

v3.0 - Full reorganization into modular structure (Current)
v2.0 - Generic Stock Analyzer toolkit created
v1.5 - Reliance Industries complete analysis
v1.0 - Core fundamental scoring system (Eternal Ltd)

📊 Supported Metrics

Fundamental Metrics (14)

Market Cap, Volume, Number of Trades
Price-to-Book Value Ratio
Revenue, Net Profit, Operating Profit
Total Assets, Equity, Total Liabilities
Current Assets, Current Liabilities
Quick Assets, Inventory

Technical Indicators (26+)

Moving Averages (SMA, EMA)
RSI, MACD, Bollinger Bands
ATR, Standard Deviation
Volume trends and analysis
And more...

Pattern Analysis (35+)

Weekday patterns
Monthly patterns
Quarterly patterns
Special event patterns
Seasonal trends

📖 Documentation

Comprehensive documentation is available in the 6_Documentation folder:

Quick Start Guide - Get started in 5 minutes
Usage Guide - Detailed usage instructions
Scoring Logic - How scores are calculated
Data Requirements - CSV format specifications
ACE Equity Guide - Using ACE Equity data
API Reference - Complete toolkit audit

🎨 Examples

Example 1: Reliance Industries Analysis

python 2_Generic_Stock_Analyzer/analyze_stock.py 3_Example_Analyses/Reliance_Analysis_Complete/00_Master_Data/Reliance.csv

Example 2: NIFTY50 Bulk Analysis

cd 5_Bulk_Tools
python bulk_market_analyzer.py --input 4_NIFTY50_Individual_Stocks/ --output analysis_results/

Example 3: Custom Stock Scoring

cd 1_Core_Fundamental_Scoring
# Edit eternal_ltd_real_data.py with your data
python main.py

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

How to Contribute

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

📋 Requirements

Python 3.8+
pandas
numpy
matplotlib
openpyxl
seaborn
scipy
And more (see requirements.txt)

📝 License

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

🙏 Acknowledgments

Data source compatibility: ACE Equity, BSE, NSE
Built for Indian equity market analysis
Inspired by fundamental analysis principles

📧 Contact

Mohit - @Mohit1053

Project Link: https://github.com/Mohit1053/Fundamental_Indicators

⚠️ Disclaimer

This toolkit is for educational and research purposes only.

❌ Not financial advice
❌ Not investment recommendations
✅ Academic analysis tool
✅ Learning resource for fundamental analysis

Always consult qualified financial advisors before making investment decisions.

🌟 Star History

If you find this project helpful, please consider giving it a ⭐!

Made with ❤️ for Indian Stock Market Analysis

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Name		Name	Last commit message	Last commit date
Latest commit History 3 Commits
.github		.github
1_Core_Fundamental_Scoring		1_Core_Fundamental_Scoring
2_Generic_Stock_Analyzer		2_Generic_Stock_Analyzer
3_Example_Analyses		3_Example_Analyses
5_Bulk_Tools		5_Bulk_Tools
6_Documentation		6_Documentation
7_Configuration_Data		7_Configuration_Data
.gitignore		.gitignore
CHANGELOG.md		CHANGELOG.md
CONTRIBUTING.md		CONTRIBUTING.md
GIT_GUIDE.md		GIT_GUIDE.md
LICENSE		LICENSE
README.md		README.md
REPOSITORY_SETUP_COMPLETE.md		REPOSITORY_SETUP_COMPLETE.md
index.html		index.html
requirements.txt		requirements.txt
update_repo.ps1		update_repo.ps1

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📊 Fundamental Indicators - Stock Analysis Toolkit

🎯 Overview

✨ Key Features

🎯 Core Fundamental Scoring

🔬 Generic Stock Analyzer

🚀 Bulk Analysis Tools

📦 Pre-analyzed Data

🚀 Quick Start

Prerequisites

Installation

Basic Usage

1️⃣ Core Fundamental Scoring

2️⃣ Analyze Any Stock

3️⃣ Bulk Analysis (Multiple Stocks)

📂 Project Structure

🚀 Quick Start

1. Core Fundamental Scoring (Eternal Ltd Example)

2. Generic Stock Analyzer (Any Stock)

3. View Example Analysis (Reliance Industries)

🛠️ Tool Selection Guide

📊 Key Metrics & Categories

1. Profitability (30%)

2. Liquidity (20%)

3. Efficiency (15%)

4. Valuation (20%)

5. Solvency (15%)

📋 Requirements

Python Version

Dependencies

🎯 Analysis Workflow

For Individual Stocks (Generic Analyzer)

For Bulk Analysis

📖 Documentation Guide

🔍 Verification & Transparency

🧪 Testing & Validation

Validated On:

Statistical Methods:

🤝 Contributing & Extensions

Add New Metrics

Customize Scoring

Extend Analyzers

⚠️ Disclaimer

📧 Support & Issues

📝 License

🎓 Learning Path

🔄 Version History

📊 Supported Metrics

Fundamental Metrics (14)

Technical Indicators (26+)

Pattern Analysis (35+)

📖 Documentation

🎨 Examples

Example 1: Reliance Industries Analysis

Example 2: NIFTY50 Bulk Analysis

Example 3: Custom Stock Scoring

🤝 Contributing

How to Contribute

📋 Requirements

📝 License

🙏 Acknowledgments

📧 Contact

⚠️ Disclaimer

🌟 Star History

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