PROJECT-001: Volatility & Risk Analytics Toolkit

📊 Project Overview

Comprehensive Python toolkit for volatility forecasting, risk analytics, and portfolio monitoring. Built to support systematic trading workflow with emphasis on options portfolio management and risk factor decomposition.

        Core Objective: Provide real-time risk monitoring and volatility forecasting
        capabilities to inform position sizing, option structure selection, and portfolio hedging decisions.
      

✨ Key Features

1. ARCH/GARCH Volatility Forecasting

Implementation of GARCH(1,1) and EGARCH models for volatility forecasting with out-of-sample validation:

# GARCH(1,1) Model Implementation
from arch import arch_model

# Fit GARCH model to returns
model = arch_model(returns, vol='Garch', p=1, q=1)
results = model.fit(disp='off')

# Generate volatility forecast
forecast = results.forecast(horizon=5)
volatility_forecast = np.sqrt(forecast.variance.values[-1, :])
      

Automated parameter optimization via maximum likelihood
Rolling window backtesting for model validation
Comparison vs. realized volatility (historical accuracy tracking)

2. Real-Time Correlation Matrix Monitoring

Dynamic correlation tracking to identify regime shifts and diversification breakdown:

Exponentially weighted moving average (EWMA) correlations
Heat map visualization with threshold alerts
Principal Component Analysis for factor extraction
Regime detection algorithm (stable vs. crisis correlations)

3. Portfolio Risk Dashboards

Comprehensive risk metrics for portfolio monitoring:

VaR (Value at Risk): Historical and parametric methods at 95%/99% confidence
CVaR (Conditional VaR): Expected shortfall beyond VaR threshold
Stress Testing: Scenario analysis for market shocks (2008, 2020 analogs)
Factor Exposures: Beta decomposition by sector, size, momentum

4. Greeks Tracking & Delta-Hedging

Options portfolio analytics and automated hedging signals:

Real-time Greeks calculation (Delta, Gamma, Vega, Theta)
Portfolio-level Greeks aggregation
Delta-neutral hedging recommendations (# shares to hedge)
Gamma scalping P&L attribution

🔧 Technical Implementation

Architecture

Data Layer: PostgreSQL for historical data storage, Redis for real-time caching
Computation Layer: NumPy/Pandas for vectorized calculations
Visualization: Plotly Dash for interactive dashboards
API Integration: Yahoo Finance, Alpha Vantage for market data

Performance Optimization

Numba JIT compilation for compute-intensive loops (10x speedup)
Multiprocessing for parallel correlation calculations across assets
Efficient data structures (arrays vs. DataFrames where appropriate)

Code Example: VaR Calculation

import numpy as np
from scipy import stats

def calculate_var(returns, confidence=0.95, method='historical'):
    """
    Calculate Value at Risk using historical or parametric method
    """
    if method == 'historical':
        return np.percentile(returns, (1-confidence)*100)
    
    elif method == 'parametric':
        mu = returns.mean()
        sigma = returns.std()
        z_score = stats.norm.ppf(1-confidence)
        return mu + sigma * z_score
    
# Usage
daily_returns = portfolio_df['returns']
var_95 = calculate_var(daily_returns, confidence=0.95)
print(f"1-Day VaR (95%): ${var_95 * portfolio_value:,.0f}")
      

💡 Use Cases & Applications

Position Sizing

Use volatility forecasts to adjust position sizes based on expected risk:

Target volatility approach: size = (target_vol / forecast_vol) × base_size
Reduce exposure ahead of known vol events (earnings, FOMC)

Options Strategy Selection

Compare realized vs. implied volatility to identify mispricing:

GARCH forecast < IV → sell options (iron condors, credit spreads)
GARCH forecast > IV → buy options (long straddles, verticals)

Portfolio Hedging

Monitor portfolio Greeks and implement dynamic hedging:

Delta hedge when net delta exceeds ±0.25
Vega hedge with opposite-sign options when vega > threshold

🚀 Future Enhancements

Machine learning models (LSTM) for volatility forecasting
Integration with IB API for automated hedge execution
Multi-asset correlation regime detection using HMM
Real-time alerts via email/SMS for risk threshold breaches
Backtesting framework for vol-based trading strategies

🔗 Related Projects

Integration with Trading Logs: This toolkit directly supports the risk analysis in TL-LOG-012 (NVDA Call Spread) and TL-LOG-008 (Iron Condor Strategy).

Research Application: Volatility regime detection feeds into NOTE-003 (Volatility Regime Analysis).

Titan Li

Volatility & Risk Analytics Toolkit