Quant Strategy
A fully systematic, equity-first strategy. Quantitative models drive stock selection across Indian equities, with shifts into foreign ETFs, gold/silver, and liquid funds when market conditions warrant it.
Emotion-free by design
Every allocation decision is determined by a quantitative model. When a signal crosses a defined threshold, the model generates an instruction. We execute that instruction. Fear, greed, hope, and narrative play no role — structurally, not as a promise.
Data over opinion
Investment decisions are outputs of statistical models, not market views. The model reads the regime and responds — no human judgment required.
Built for full cycles
Designed to participate meaningfully when markets run, and limit damage when they don’t. Not optimised for any single phase.
18 years of validation
The framework was stress-tested on rolling 3-year periods across 18 years of Indian market history before deployment with real capital.
What the model invests in
Four asset classes, each with a defined role. The model allocates across them based on quantitative signals — not forecasts.
Indian Equities
20–30 multicap stocks, selected and sized by quantitative rules. This is where the majority of the portfolio sits.
Gold & Silver ETFs
Allocated when precious metal scores strengthen or equity scores weaken. Responds to its own triggers independently of equity conditions.
Foreign Equity ETFs
Activated when domestic equity scores are weak and global scores exceed their entry point. Not a permanent allocation.
Cash / Liquid Funds
The model moves to cash when no asset class scores above its minimum entry point. Not a view — a rule.
How the model evolves
The model executes without intervention during a trade. But the framework itself is researched, tested, and refined continuously — in a structured, out-of-sample manner.
What if the model is wrong?
We don’t panic. No ad hoc changes based on a bad quarter.
We don’t rewrite rules to explain away recent losses.
Changes are validated out-of-sample. New factors and parameters are tested on data the model has never trained on. If a change doesn’t hold up on unseen data, it doesn’t ship — regardless of how good it looks in-sample.
Improvements are forward-looking, not backward-fitting. Every refinement must demonstrate robustness across multiple market conditions before deployment. This prevents overfitting to recent conditions.
Performance
Net of fees. TWRR. The strategy has been live as a PMS for 2 years. This period has coincided with a consolidation/sideways equity phase with no sustained bull market run. The strategy is designed to take quick small losses on failed signals and hold for large gains when trends materialise.
| Period | Strategy | BSE 500 TRI |
|---|---|---|
| 3 Months | 11.0% | -1.7% |
| 6 Months | 16.6% | -4.3% |
| 1 Year | 33.2% | -3.6% |
| Since Inception* | 9.1% | 4.8% |
Historically, 86% of 1-year rolling lags were followed by outperformance in the subsequent 12 months. Backtested performance is hypothetical, has inherent limitations, and should not be the sole basis for investment decisions.
Is this strategy right for you?
Who manages this strategy
B.Tech Computer Science (NIT), MBA (FMS Delhi). 7+ years in investment management including SEBI-registered investment advisory. A few years of prior experience in tech. Designs and maintains all quantitative models, factor systems, and the systematic trading infrastructure that powers this strategy.
Interested in the Quant Strategy?
Walk through the model, the data, and whether systematic investing fits your goals.
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