Rules‑based India results
A rules‑based screening backtest in Indian equities (2020–2026) showed an 18% CAGR with a maximum drawdown of 22%; trend‑following had a 64% win rate and a 1.8:1 reward:risk ratio, suggesting systematic rules beat intuition in that stretch Rules-based screening in Indian equities (2020-2026) results. That’s a concrete data point if you’re sizing tactical, quant or trend allocations into emerging markets exposure.
The results were published as a single post on X at the linked status (status ID 2032857672761085995), where the author shared the backtest output and performance chart. (x.com) Public systematic engines built for Indian equities commonly backtest across the NIFTY‑500 or NSE500 universe with monthly rebalancing and trade rules encoded in code repositories such as the diamond_stock_engine on GitHub. (github.com) For context, the NIFTY 50’s 5‑year CAGR was reported at 9.74% as of March 9, 2026, providing a contemporaneous benchmark for equity performance in the same window. (stockinsideout.com) Independent long‑run momentum studies on Indian equities have produced annual returns near 17.39% over 1990–2026 while recording maximum drawdowns above 45%, illustrating that high gross returns in rules‑based tests can coexist with deep stress periods. (paperswithbacktest.com) Realistic backtests in India increasingly bake in slippage, brokerage and market impact, and commercial platforms advertise simulated execution and rebalancing features to reflect those costs. (portfoliovisualizer.com) Regulatory and deployment context changed recently: AlgoTest documented SEBI’s 2025 algo‑trading framework as enabling wider retail automated strategy deployment, which affects how a backtest moves to live execution. (algotest.in) Codebases and platform guides for Indian strategies recommend walk‑forward and out‑of‑sample validation as standard next steps for robustness testing, and several public projects include walk‑forward modules to implement that process. (github.com)
