Investezee — Research NotePersonal share, not for distribution

What ₹10,00,000 could have become

A backtest of a systematic, rules-based India equities strategy, Jan 2020 – Sep 2026. This shows the return and risk profile only — the selection method itself isn't disclosed here.

Strategy final value
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CAGR
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Worst drawdown
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Growth of ₹10,00,000

Log scale, so early and late gains are equally readable. Compared against the Nifty 50 over the identical dates, costs and no gate applied on Nifty's side.

Strategy Nifty 50

Year by year

₹10,00,000 invested at the start, tracked at each calendar year-end.

Year-endStrategyNifty 50

The risk side

Same period, shown as % below the running peak — the part a headline CAGR number hides.

What the drawdown chart doesn't say loudly enough

  • The worst peak-to-trough fall was −14.67%, in Feb 2022 — mild by equity standards, but it followed a huge prior run-up, so it still meant giving back several months of gains.
  • Growth was not smooth. 2020–21 and 2023–24 did almost all of the compounding; 2022 and the stretch from early 2025 to mid-2026 were largely flat to choppy sideways, with real dips along the way.
  • A backtest's worst historical drawdown is not a ceiling — a live strategy can, and eventually will, see something worse than anything in its own backtest.

What this is

  • A backtest — simulated on historical prices, not a live or paper-traded track record.
  • A systematic, rules-based approach: holds a rotating basket of roughly 20 Indian stocks, moves fully to cash during market downtrends, rebalances monthly. Costs and cash-drag on idle capital are included.
  • Benchmarked against the Nifty 50 buy-and-hold over the identical dates.

What this isn't

  • Not investment advice and not a recommendation to buy, sell or hold anything.
  • Not a guarantee — past performance, especially one backtested window that includes a COVID crash-and-recovery, does not predict future results.
  • Not sized for real capital yet — at larger amounts, trading costs and liquidity constraints would likely reduce these numbers; this hasn't been validated at scale.