📈 Enhanced Crypto Volatility Analysis

A comprehensive analysis tool to identify the best cryptocurrency for scalping/day trading based on multiple factors including volatility, liquidity, spread impact, and stability metrics.

🎯 Features

• Multi-Factor Analysis: Considers volatility, liquidity, spread impact, and stability
• Real-Time Data: Fetches live market data via CCXT library
• Multi-Timeframe: Analyzes 5min, 10min, 15min, and 30min intervals
• Comprehensive Scoring: Weighted composite score for optimal trading pair selection
• Spread-Adjusted Metrics: Calculates net volatility after bid-ask spread
• Volume Analysis: Incorporates trading volume and liquidity metrics
• Stability Assessment: Evaluates volatility consistency over time

🚀 Quick Start

Prerequisites

• Python 3.8 or higher
• Internet connection for fetching market data

Installation

1. Clone the repository

   git clone https://github.com/imanhavangi/crypto-volatility-analysis.git
   cd crypto-volatility-analysis

2. Create virtual environment

   python -m venv env
   source env/bin/activate  # On Windows: env\Scripts\activate

3. Install dependencies

   pip install -r requirements.txt

Basic Usage

Default Analysis (Kraken Exchange)

python main.py

Custom Exchange

export EXCHANGE_ID=binance  # or coinbase, bitfinex, bybit, okx
python main.py

📊 Sample Output

🏆 RANKING SUMMARY (Exchange: KRAKEN)
------------------------------------------------------------
 rank   symbol composite_score raw_volatility net_volatility spread_pct volume_percentile
    1 DOGE/USD           0.744         0.503%         0.493%     0.010%      90%
    2  ADA/USD           0.708         0.529%         0.511%     0.017%      70%
    3  XRP/USD           0.697         0.410%         0.398%     0.012%      80%

🔍 TOP 3 DETAILED ANALYSIS
------------------------------------------------------------
#1 DOGE/USD
   Composite Score: 0.744
   Raw Volatility: 0.503%
   Net Volatility (after spread): 0.493%
   Efficiency Ratio: 0.98
   Volume Percentile: 90%
   Volatility Stability: 0.594

💡 RECOMMENDATION
------------------------------------------------------------
🚀 DOGE/USD appears to be the best choice for scalping on KRAKEN

🔧 How It Works

Analysis Components

Component	Weight	Description
Raw Volatility	25%	Price range volatility across timeframes
Efficiency	25%	Net volatility after spread deduction
Stability	25%	Consistency of volatility patterns
Liquidity	25%	Trading volume and market depth

Methodology

1. Data Collection: Fetches 30 days of 1-minute OHLCV data
2. Spread Analysis: Estimates bid-ask spread from current market data
3. Multi-Timeframe Resampling: Aggregates data into 5, 10, 15, 30-minute intervals
4. Metric Calculation:
   • Raw volatility: (High - Low) / Mid Price
   • Net volatility: Raw Volatility - Spread
   • Stability: 1 / (1 + Coefficient of Variation)
   • Volume percentile ranking across all symbols
5. Composite Scoring: Weighted combination of all metrics

📋 Supported Exchanges

The tool supports all exchanges available in the CCXT library:

• Major Exchanges: Binance, Coinbase, Kraken, Bitfinex, Bybit, OKX, Huobi
• Regional Exchanges: Bitstamp, Gemini, KuCoin, Gate.io
• Full List: CCXT Supported Exchanges

⚙️ Configuration

Custom Symbol Lists

Edit the SYMBOLS list in main.py:

SYMBOLS = [
    "BTC/USD", "ETH/USD", "BNB/USD", "SOL/USD", "XRP/USD",
    "ADA/USD", "DOGE/USD", "SHIB/USD", "DOT/USD", "AVAX/USD",
    # Add your preferred trading pairs
]

Adjust Scoring Weights

Modify the WEIGHTS dictionary to prioritize different factors:

WEIGHTS = {
    'volatility': 0.25,   # Raw price volatility
    'liquidity': 0.25,    # Trading volume impact  
    'stability': 0.25,    # Volatility consistency
    'efficiency': 0.25,   # Net volatility after spread
}

Custom Timeframes

Update the TIMEFRAMES list for different analysis intervals:

TIMEFRAMES = [5, 10, 15, 30]  # Minutes

📈 Understanding the Metrics

Composite Score (0.0 - 1.0)

Higher scores indicate better suitability for scalping/day trading.

Raw Volatility

Average percentage price range per timeframe window.

Net Volatility

Volatility remaining after accounting for bid-ask spread costs.

Efficiency Ratio

Net Volatility / Raw Volatility - measures how much volatility is "usable" after spread.

Volume Percentile

Ranking of trading volume compared to other analyzed symbols (0-100%).

Stability Score

Measures consistency of volatility patterns. Higher = more predictable.

🛠️ Development

Running Tests

python -m pytest tests/

Code Formatting

black main.py

Type Checking

mypy main.py

📚 Dependencies

• ccxt: Cryptocurrency exchange trading library
• pandas: Data manipulation and analysis
• numpy: Numerical computing
• typing: Type hints (Python 3.8+)

⚠️ Disclaimers

• Not Financial Advice: This tool is for educational and research purposes only
• Market Risk: Cryptocurrency trading involves substantial risk of loss
• Data Accuracy: Market data accuracy depends on exchange API reliability
• Past Performance: Historical volatility doesn't guarantee future results

🤝 Contributing

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

📄 License

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

🙏 Acknowledgments

• CCXT for the excellent exchange integration library
• Pandas for powerful data analysis capabilities
• Cryptocurrency exchanges for providing market data APIs

📞 Support

If you encounter any issues or have questions:

1. Check the Issues section
2. Create a new issue with detailed description
3. Include error messages and system information

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