Built a Python-based system to classify crypto market conditions into volatility regimes.
Key Features
- Historical price data processing using Pandas
- Log return calculation for stable volatility measurement
- Rolling volatility computation (standard deviation)
- Regime classification:
- Low volatility (consolidation)
- Medium volatility (normal trend)
- High volatility (breakout / unstable phase)
- Dynamic thresholding based on distribution instead of fixed values
- Regime labeling for each time window
How It Works
- Convert price series → log returns
- Apply rolling window (e.g., 20 periods)
- Compute volatility = standard deviation of returns
- Define percentile-based thresholds:
- Lower percentile → low regime
- Middle → normal regime
- Upper → high volatility regime
Why It Matters
- Helps identify breakout zones early
- Avoids trading in low-volatility chop
- Useful for position sizing and risk management
Tech Stack
- Python
- Pandas
- NumPy
- Time-Series Analysis
Output
- Labeled dataset with volatility regimes
- Ready for visualization or trading models
Next Steps
- Combine with liquidity signals
- Add regime-based strategy logic
- Integrate with real-time data pipeline