Nifty Options Volatility Trading Model

🎯 Overview

Model Versions

📊 v5 Backtest Results (Nov 2023 – Apr 2026)

v5 Key Metrics

• Overall Sharpe: 0.428
• Bear Market Sharpe: -3.515 (vs -4.141 for baseline)
• Max Drawdown: -48.5 bps (12% less than always-short)
• Win Rate: 73.7%
• Long vol positions: ~3% (only during confirmed crashes)

🐻 v5: Bear Market Corrections

Problem with v3 (Bull-Biased)

1. Asymmetric position range [−0.5, 1.5] — couldn't go aggressively long vol
2. No regime detection — relied only on spike probability, missed sustained bear trends
3. VRP turns negative in bear markets — short vol bleeds when RV > IV consistently
4. No drawdown protection — held full short vol position through crashes

v5 Fixes

1. Bear Regime Detection Head — 4th auxiliary task predicting forward 22-day bear regime
2. 15 New Bear Market Features:
   • Drawdown from 252d high, drawdown velocity
   • Death cross (SMA50/SMA200 ratio)
   • Risk-adjusted trend strength (22d, 63d)
   • Down-day streak frequency
   • VRP negative fraction (22d, 63d)
   • Realized vol acceleration
   • Composite bear score
   • VIX velocity (5d, 10d)
   • Tail risk frequency
   • VIX return skewness
3. Multi-Confirmation Long Vol — requires bear_prob > 0.8 AND spike > 0.6 AND drawdown < −10% AND VRP < 0 (prevents false signals)
4. Gradual Position Reduction — scales down smoothly in bear regime instead of binary switching
5. Deep Drawdown Circuit Breaker — flattens position at >15% drawdown with negative VRP
6. Bear Data Oversampling (2×) during training

🧠 Architecture

Model: Bear-Aware Multi-Task Attention-LSTM

Input (57 features × 22 timesteps)
    ↓
LSTM (2 layers, 96 hidden, dropout=0.25)
    ↓
Self-Attention (temporal weighting)
    ↓
BatchNorm
    ↓
┌─────────────────────┬──────────────────┬────────────────────┬──────────────────┐
│  P&L Prediction     │  Spike Detection │  Profit Classifier │  Bear Regime     │
│  (regression)       │  (binary)        │  (binary)          │  (binary) [NEW]  │
│  → position sizing  │  → risk mgmt     │  → entry signal    │  → regime aware  │
└─────────────────────┴──────────────────┴────────────────────┴──────────────────┘

Signal Generation (v5 Bear-Aware)

🔬 Features (57 inputs — 42 original + 15 new)

📦 Data Sources

• ^NSEI — Nifty 50 Index (OHLCV, 2009-present)
• ^INDIAVIX — India VIX (2009-present)
• ^VIX — CBOE VIX (cross-market feature)

🚀 Usage

📁 Files

v5 (Bear-Corrected) — Current

• nifty_vol_model_v5_bear.pt — v5 trained PyTorch model checkpoint
• nifty_vol_trading_v5_bear.py — v5 complete training + backtest pipeline
• backtest_results_v5.csv — v5 backtest results
• backtest_results_v5.png — v5 comprehensive visualization
• recent_performance_v5.png — v5 recent 6-month detail
• day_by_day_backtest_v5.png — v5 vs always-short comparison
• metrics_v5.json — v5 metrics (includes Bear Sharpe)

v3 (Original Bull-Biased)

• nifty_vol_model_final.pt — v3 trained model
• nifty_vol_trading_v3.py — v3 training pipeline
• backtest_results.csv / backtest_results.png — v3 results
• metrics.json — v3 metrics

⚠️ Disclaimer

📚 References

1. Corsi, F. (2009). "A Simple Approximate Long Memory Model of Realized Volatility." J. Financial Econometrics
2. Lim et al. (2023). "Constructing Time-Series Momentum Portfolios with Deep Multi-Task Learning." arXiv:2306.13661
3. Qin et al. (2024). "Dynamic Graph Neural Networks for Enhanced Volatility Prediction." arXiv:2410.16858
4. Michańków et al. (2025). "Forecasting Probability Distributions of Financial Returns with Deep Neural Networks." arXiv:2508.18921
5. Hofmann et al. (2021). "A Two-Step Framework for Arbitrage-Free Prediction of the Implied Volatility Surface." arXiv:2106.07177
6. Chen & Zhang (2019). "Forecasting Implied Volatility Smile Surface via Deep Learning and Attention Mechanism." arXiv:1912.11059
7. Vallarino (2025). "Adaptive Market Intelligence: A Mixture of Experts Framework for Volatility-Sensitive Stock Forecasting." arXiv:2508.02686