Gamma Exposure (GEX) Analytics 📊

Real-time options market gamma exposure analysis for major indices - identify dealer hedging flows and key support/resistance levels

🚀 Quick Start

# Clone the repository
git clone https://github.com/pikki622/gex-analytics.git
cd gex-analytics

# Install dependencies with uv
uv sync

# Run gamma analysis for S&P 500
uv run python gammaProfileCommandLine10bps.py SPX

📊 What This Tool Does

Analyzes options market gamma exposure to reveal:

• 🎯 Gamma flip points - Where dealer hedging behavior reverses
• 📈 Support/Resistance levels - Based on options positioning
• ⚡ Volatility forecasts - Whether dealers amplify or dampen moves
• 💰 Hedging flow estimates - Dollar amounts dealers must trade

📋 Table of Contents

• Features
• How It Works
• Installation
• Usage
• Available Tickers
• Output Charts
• Interpretation Guide
• 📚 Terminology Guide
• Development Roadmap
• Contributing

✨ Features

Current Capabilities

• ✅ 10 Major Indices - SPX, NDX, RUT, VIX, and more
• ✅ Real-time Data - 15-minute delayed quotes from CBOE
• ✅ Two Analysis Modes - 1% moves or precise 10bps moves
• ✅ 5-Panel Visualization - Comprehensive gamma analysis charts
• ✅ ADTV Context - Hedging flows relative to daily volume
• ✅ Batch Processing - Generate reports for all indices at once

Coming Soon (See Roadmap)

• 🔄 Visual hierarchy with risk indicators
• 🔄 Trade setup recommendations
• 🔄 Historical gamma tracking
• 🔄 Cross-index correlation analysis

How It Works

This tool fetches real-time options data from CBOE (Chicago Board Options Exchange) and calculates the gamma exposure of market makers at different price levels. This helps identify:

• Where market makers need to hedge (buy/sell underlying)
• Potential support and resistance levels
• The "gamma flip" point where hedging behavior changes

Workflow

1. Data Fetching (lines 32-34)

   • Connects to CBOE's delayed quotes API
   • Downloads complete options chain for the specified index
   • Extracts current spot price

2. Data Processing (lines 46-87)

   • Parses option symbols to extract strike prices and expiration dates
   • Separates calls and puts
   • Merges data into a unified DataFrame
   • Converts data types for calculations

3. Gamma Calculations (lines 90-98)

   • Calculates spot gamma exposure for each strike
   • Formula: GEX = Unit Gamma × Open Interest × 100 × Spot² × 0.01
   • Puts have negative gamma exposure (market makers are short puts)

4. Gamma Profile Generation (lines 138-173)

   • Simulates gamma at different spot prices
   • Uses Black-Scholes model for theoretical gamma values
   • Identifies the "gamma flip" point (zero gamma crossing)

5. Visualization (lines 100-206)

   • Generates 4 interactive charts showing different aspects of gamma exposure

Key Concepts

Gamma Exposure (GEX)

Gamma exposure represents the amount of shares market makers must buy or sell to maintain delta neutrality when the underlying price moves 1%.

• Positive Gamma: Market makers dampen volatility (buy dips, sell rallies)
• Negative Gamma: Market makers amplify volatility (sell dips, buy rallies)
• Zero Gamma (Flip Point): Transition between stabilizing and destabilizing hedging flows

Black-Scholes Gamma Calculation

The calcGammaEx function (lines 13-25) implements the Black-Scholes gamma formula:

gamma = exp(-q*T) * norm.pdf(d1) / (S * vol * sqrt(T))

Where:

• S: Spot price
• K: Strike price
• vol: Implied volatility
• T: Time to expiration
• r: Risk-free rate (set to 0)
• q: Dividend yield (set to 0)

Days to Expiration Handling

• Business days are calculated excluding weekends (line 142-143)
• 0DTE options are set to 1/262 year to avoid division by zero
• Monthly options are identified by third Friday detection (lines 27-29, 147-149)

Code Architecture

Main Components

1. Option Greeks Calculator (calcGammaEx)

   • Pure Black-Scholes implementation
   • Handles edge cases (zero time/volatility)
   • Returns dollar gamma exposure

2. Data Parser

   • Extracts components from CBOE option symbols
   • Format: INDEX YYMMDD C/P STRIKE
   • Example: SPX241108C05500 = SPX Nov 8, 2024 Call at 5500

3. Aggregation Engine

   • Groups by strike price
   • Sums total gamma across all expirations
   • Handles "ex-next expiry" calculations

4. Visualization Module

   • 4 distinct chart types
   • Interactive matplotlib plots
   • Color-coded regions (red = negative gamma, green = positive)

Available Tickers

The tool works with CBOE-listed index options through their free API. ETF options require a paid subscription.

✅ Working Tickers (Free Access)

Ticker	Description	Current Spot Price
SPX	S&P 500 Index	~$6,728
NDX	NASDAQ-100 Index	~$25,059
DJX	Dow Jones Index (1/100th scale)	~$469
RUT	Russell 2000 Index	~$2,432
VIX	CBOE Volatility Index	~$19
MXEA	MSCI EAFE Index	~$2,774
MXEF	MSCI Emerging Markets Index	~$1,381
XSP	Mini-SPX Index (1/10th SPX)	~$672
XND	Mini-NDX Index (1/100th NDX)	~$251
MRUT	Mini-Russell 2000 Index (1/10th RUT)	~$243

❌ Unavailable Without Subscription

ETF options (SPY, QQQ, IWM, etc.) return HTTP 403 errors and require a CBOE DataShop subscription ($380-2000+/month).

📦 Installation

Prerequisites

• Python 3.8 or higher
• uv package manager (recommended) or pip

Option 1: Using uv (Recommended)

# Install uv if you haven't already
curl -LsSf https://astral.sh/uv/install.sh | sh

# Clone and setup
git clone https://github.com/pikki622/gex-analytics.git
cd gex-analytics
uv sync

Option 2: Using pip

# Clone repository
git clone https://github.com/pikki622/gex-analytics.git
cd gex-analytics

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install pandas numpy scipy matplotlib requests

🎯 Usage

Quick Examples

# Analyze S&P 500 with 10bps precision (recommended)
uv run python gammaProfileCommandLine10bps.py SPX

# Generate comprehensive report for all indices
uv run python generate_all_charts.py

# Test which tickers are available
uv run python test_tickers.py

Available Scripts

Script	Description	Use Case
gammaProfileCommandLine10bps.py	Recommended - 10bps precision with grid layout	Daily analysis
gammaProfileCommandLine.py	Original 1% move analysis	Quick checks
generate_all_charts.py	Batch process all indices	Full market scan
test_tickers.py	Check ticker availability	Troubleshooting

Example Commands

# Analyze S&P 500 gamma (1% moves)
uv run python gammaProfileCommandLine.py SPX

# Analyze NASDAQ-100 gamma (10bps moves, grid view)
uv run python gammaProfileCommandLine10bps.py NDX

# Test all available tickers
uv run python test_tickers.py

📊 Example Output

The tool generates a comprehensive 5-panel analysis (10bps version includes ADTV table):

Example output for S&P 500 showing gamma exposure, open interest distribution, and flip points

Output Charts

The tool generates 5 comprehensive visualizations:

Chart 1: Total Gamma Exposure

• Bar chart showing net gamma at each strike price
• Helps identify key support/resistance levels
• Positive bars = net long gamma, Negative = net short gamma

Chart 2: Open Interest Distribution

• Shows call OI above zero, put OI below zero
• Identifies where most options positions are concentrated
• Large OI strikes often act as "magnets" for price

Chart 3: Gamma by Type (Calls vs Puts)

• Separates call gamma (positive) from put gamma (negative)
• Shows which side dominates at each strike
• Useful for understanding directional hedging flows

Chart 4: Gamma Exposure Profile

• Line chart showing gamma across different spot prices
• Identifies the "gamma flip" point (zero crossing)
• Shows ex-next expiry and ex-monthly expiry profiles
• Red shading = negative gamma zone (volatility amplifying)
• Green shading = positive gamma zone (volatility dampening)

Chart 5: Gamma Impact Table (10bps version)

• Shows gamma exposure for different move sizes (1-500bps)
• Contextualizes hedging flows vs 20-day ADTV
• Highlights when flows exceed 10% of daily volume
• Critical for understanding market impact potential

Interpretation Guide

🆕 New to Options?

Check out our comprehensive guides:

• Terminology Guide - Plain English explanations of all terms
• FAQ - Quick answers to common questions
• Real-world examples and decision trees
• Trading strategies based on gamma state

Key Metrics

• Total Gamma: Net $ exposure market makers must hedge per 1% or 0.1% move
• Gamma Flip: Price where market maker hedging behavior reverses
• Negative Gamma: Below flip point, dealers sell into weakness, buy into strength
• Positive Gamma: Above flip point, dealers buy into weakness, sell into strength

Trading Implications

• Large positive gamma = Lower volatility expected (dampening effect)
• Large negative gamma = Higher volatility expected (amplifying effect)
• Gamma flip point often acts as pivot level for intraday price action
• High OI strikes can act as support/resistance due to pinning effects

Requirements

• Python 3.8+
• pandas, numpy, scipy, matplotlib, requests
• uv package manager (recommended) or pip

Data Source

Options data is sourced from CBOE's delayed quotes API (15-minute delay). Real-time data requires CBOE subscription.

🗺️ Development Roadmap

See IMPLEMENTATION_PLAN.md for the detailed roadmap. Key upcoming features:

Phase 1 (In Progress)

• ✅ Documentation improvements
• 🔄 Visual hierarchy with risk indicators
• 🔄 Glossary and terminology guide

Phase 2 (Next Week)

• Trade setup recommendations
• Scenario analysis engine
• Risk management guidelines

Phase 3 (Future)

• Historical gamma tracking
• Enhanced visualizations
• Cross-index correlation analysis

🤝 Contributing

We welcome contributions! Here's how to get started:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes
4. Run tests (when available)
5. Commit with descriptive message (git commit -m 'Add amazing feature')
6. Push to your branch (git push origin feature/amazing-feature)
7. Open a Pull Request

Please use the PR template and follow the coding standards in IMPLEMENTATION_PLAN.md.

📄 License

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

🙏 Acknowledgments

• CBOE for providing options data API
• The options trading community for gamma exposure concepts
• Contributors and users providing feedback

📧 Contact

• GitHub Issues: Report bugs or request features
• Repository: github.com/pikki622/gex-analytics

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