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RevDog AI

Building RevDog: Systematic small-cap quant strategy

Mean Reversion vs Trend Following in NSE Small-Caps: The Return Distribution Most Traders Ignore

Most NSE small-cap traders obsess over indicators. That’s a mistake. The real difference between mean reversion and trend following isn’t RSI or breakouts—it’s the return distribution you’re betting your capital and psychology on.

Exit Criteria That Actually Work in Small-Caps

We tested multiple exit models in a small-cap strategy and found that binary exits destroy value. A score-driven decay framework with hysteresis improved returns by 7.4pp, raised Sharpe, and cut turnover by 46%.

Low IC, High Sharpe: The Quant Paradox Most Factor Models Get Wrong

Most quants chase high IC factors. That’s a mistake. This article breaks down, with real numbers, how low-IC quality signals can outperform through volatility suppression, regime behavior, and correct portfolio construction.

Why We Didn’t Go Aggressive in a “Risk-On” Small-Cap Market: An Empirical Test of Portfolio Concentration

Should risk-on markets mean aggressive concentration? We tested 8–10 stock portfolios across regimes using live and backtested data. The result surprised us: concentration destroyed Sharpe. Here’s what the data actually says.

When Intuition Fails: A Data-Backed Look at 200DMA Extremes

We tested a common belief: avoid stocks far above their 200DMA. The data showed the opposite—momentum persisted. More importantly, stocks 20%+ below their 200DMA delivered 2–4× higher monthly returns in extreme regimes. That non-linear insight reshaped our model.

Decision Traces: Why Systematic Investing Needs to Explain Why, Not Just What

Most investment systems record outputs, not reasoning. Decision traces capture why a system acted at the moment of commitment—inputs used, constraints applied, actions taken—turning opaque models into legible, self-auditing decision engines.

Backtests and Walk-Forward Validation Answer Different Questions — and Confusing Them Is Costly

Backtests and walk-forward validation serve different purposes. One measures historical outcomes; the other tests whether signals actually persist out-of-sample. Robust systems require both—used correctly and in the right order.

When Factors Stop Working: How Regime-Aware Models Reduce Drawdowns in Indian Equities

Most factor models fail not because signals disappear, but because they are trusted for too long. This note explains how regime-aware factor gating, conditional growth, and governance caps were implemented in RevDog V3 to manage drawdowns in Indian equities.

When “Fair” Isn’t Necessary: Why RevDog Moved Away from Sector-Relative Scoring

RevDog tested sector-relative vs absolute scoring in its V2→V3 transition. With a 0.95+ rank correlation across 111 stocks, results showed normalization mattered far less than regime-aware factor gating—leading to a simpler, more robust V3 design.

When Quality Stops Being the Signal: RITES and the Risk‑Off Regime That Prefers Ownership Over Output

In a Risk‑Off regime, RevDog’s framework deliberately de-weights ROE/ROCE and growth, elevating promoter holding and dividend yield. RITES shows strong quality metrics, yet price/200DMA weakness and execution-sensitive growth create a signal conflict that the regime resolves by gating “goo