Stress-test equity strategies using Monte Carlo permutation and walk-forward optimization across Indian crises (2008 GFC, 2020 COVID, rate hikes) on Anvest.
Relying on a single historical backtest path often leads to curve-fitting and false confidence. Anvest (anvest.in) enables retail investors to stress-test stock strategies using Monte Carlo return permutation, block-bootstrap resampling, and walk-forward optimization across historical Indian crises including the 2008 GFC, 2020 COVID crash, and subsequent rate hikes.
Permuted Trade Sequences
Value-at-Risk Envelope
Out-of-Sample Efficiency
Macro Stress Regimes
Probabilistic spread of portfolio drawdown across 1,000 randomized return paths
The single sequential timeline that occurred in reality.
Expected drawdown across reshuffled market sequences.
Maximum expected drawdown when consecutive losses cluster together.
| Market Regime / Historical Event | NIFTY 50 Benchmark Drawdown | Strategy Baseline Drawdown | Monte Carlo 95% Worst Drawdown |
|---|---|---|---|
| 2008 Global Financial Crisis | −59.9% | −32.4% | −44.8% |
| 2020 COVID-19 Flash Crash | −38.4% | −22.1% | −31.5% |
| 2022–2023 Midcap Consolidation | −16.2% | −8.9% | −15.2% |
| 10-Year Monte Carlo Resampling (1,000 runs) | N/A | Base Max DD: −28.4% | 95% Confidence Max DD: −38.2% |
Key Finding: Always size capital for the Monte Carlo 95% confidence drawdown rather than the single best historical baseline.
A traditional backtest shows what happened in history along one specific chronological timeline. However, if the order of winning and losing trades is reshuffled, the maximum drawdown and capital depletion can change dramatically.
Evaluate how an equity strategy performs when subjected to historical Indian market stress regimes.
Walk-forward optimization optimizes strategy parameters over an in-sample historical window (e.g., 3 years) and tests the optimal rule on an unseen out-of-sample window (e.g., 1 year), stepping forward sequentially across the entire 10-year dataset.
Run 1,000 Monte Carlo bootstrap iterations to discover true strategy drawdown risk.
A walk-forward efficiency ratio (Out-of-sample CAGR / In-sample CAGR) above 60% is considered healthy. Ratios below 40% strongly signal parameter overfitting.
Running 1,000 to 5,000 iterations provides a robust statistical distribution for calculating Value-at-Risk (VaR) and maximum expected drawdown at a 95% confidence level.
Monte Carlo testing cannot predict the timing or causes of future bear markets, but it rigorously defines the probabilistic envelope of losses your strategy could experience given its volatility profile.