Regime-Adaptive Equity Model
Regime-adaptive equity strategy research on Indian markets (NIFTY 200), as much about disciplined negative-result reporting as the final strategy.
Key metrics
13.5%
Strategy CAGR
Net of friction, 2024–2026
7.7%
Buy-and-hold CAGR
NIFTY baseline
0.83
Sharpe
vs 0.65 buy-and-hold
64%
Win rate
vs 52% baseline
9.4 pp
2008 drawdown cut
HMM regime sizing ablation
Research question
Most retail-adjacent quant research falls for its own backtest: a dashboard shows gross returns, looks great, and the friction, regime risk, and realistic execution constraints get added later, if at all. Building a strategy that survives contact with real costs and real regime shifts requires being willing to kill your own architecture when the evidence says so.
Method
Phase 1 built an XGBoost baseline and found that daily-return prediction is mostly noise, the real signal is monthly. Phase 2 built a custom multimodal transformer (regime cross-attention, mixture-of-experts, ranking loss) to exploit that signal directly; a 60-line diagnostic showed it performing worse than plain 21-day momentum with zero ML.
Phase 3 pivoted: production became a 21-day momentum ranking with 3-state HMM regime-based position sizing, sector caps, per-stock stops, and a portfolio drawdown killswitch, benchmarked with 0.22%/rebalance friction. Not a high-frequency system, rebalances every 21 trading days.
Architecture
- 01
Phase 1, XGBoost baseline; daily-return prediction mostly noise, monthly signal holds
- 02
Phase 2, Custom multimodal transformer (RAMT); diagnostic showed it underperformed plain 21-day momentum
- 03
Phase 3, Production: 21-day momentum ranking + 3-state HMM regime sizing (100%/50%/20%)
- 04
Risk controls, sector caps, per-stock stops, portfolio drawdown killswitch, 0.22%/rebalance friction
- 05
Validation, 4-window historical ablation (2008, 2010, 2013, 2024)
Findings
- Production strategy backtest (2024–2026, net of friction): 13.5% CAGR vs 7.7% NIFTY buy-and-hold, Sharpe 0.83 vs 0.65, 64% win rate vs 52%
- Diagnostic script exposed the custom transformer underperforming a zero-ML momentum sort on every metric, published, then rebuilt around the finding
- Regime-based position sizing cut historical 2008-style drawdown by 9.4 percentage points in ablation testing, at the documented cost of capping upside in bull markets
Method details
- Universe
- NIFTY 200 equities
- Strategy
- 21-day momentum ranking + 3-state HMM regime sizing (100%/50%/20% allocation)
- Also tested
- Custom transformer (RAMT), Chronos-T5 + LoRA foundation-model fine-tune
- Backtest rigor
- Realistic friction, per-stock stops, portfolio drawdown killswitch, 4-window historical ablation (2008, 2010, 2013, 2024)
Stack
References
- NIFTY 200 equities universe (yfinance)
- Chronos-T5 + LoRA foundation-model fine-tune (ablated)
- IEEE-format research writeup + Streamlit dashboard