Intermediate

Case Study: Comparing Systematic Strategy Performance Pre and Post Covid

March 2020 split the Indian market into two worlds. Volatility, correlation, liquidity and the retail investor base all changed within weeks, and every systematic strategy that had been tuned on the calm years before it was suddenly running on unfamiliar ground. This case study walks through how momentum, mean reversion, trend following and low volatility strategies behaved across the pre Covid, crash, recovery and post Covid windows on NSE, how to measure that behaviour honestly after costs, and how to diagnose why the numbers moved. Built for aspiring quant analysts, prop desk applicants and discretionary traders who are systematising their process and need to show they can evaluate a strategy across regimes rather than on one lucky backtest.

Regime AnalysisStrategy BacktestingPerformance MetricsMomentumMean ReversionTrend FollowingLow VolatilityTransaction CostsMarket Microstructure

MODULES

DURATION

~2.5 hrs

TRACK

Quantitative Finance

What You'll Master

Split a market history into defensible regime windows and justify the boundaries with data rather than hindsight

Compute and interpret CAGR, Sharpe, Sortino, maximum drawdown, rolling returns and time under water for a systematic strategy

Explain why momentum, mean reversion, trend following and low volatility behaved so differently across the Covid crash and recovery on NSE

Separate gross and net performance using realistic NSE brokerage, STT, impact cost and slippage assumptions

Use India VIX, market breadth and cross sectional correlation as regime signals

Test whether a strategy's optimal parameters are stable across regimes and spot overfitting

Present strategy findings the way a prop trading desk or quant research team expects to see them

Access Level

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Regime Aware Allocation Across Strategies11 min read

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Writing the Case Study Report the Way a Prop Desk Expects It10 min read

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What to Test Next: Extending the Case Study8 min read