Systematic Investing in India: the complete guide

A systematic equity strategy defines five things up front. First, the universe: which pool of stocks are we picking from, say Nifty 50 or Nifty 500. Second, the selection rule: which stocks get picked from that universe, say top 10 by 6-month momentum. Third, the weighting: equal weight or market-cap weight or momentum-score weight. Fourth, the rebalance frequency: weekly, fortnightly, monthly. Fifth, the exit rule: when do we stop running this and sit in cash.

Once those five are written down, the strategy runs itself. You are no longer the decision maker. You are the execution operator.

• 2.1 Why rules beat gut feeling: The behavioural and statistical case for systematic execution over discretionary judgement.
• 2.2 Introduction to factors: Value, Momentum, Quality, Size, Low Volatility. The five academically validated factors.
• 2.4 How backtesting works: How to validate a rule set on historical data. Survivorship bias, look ahead bias, transaction costs.
• 2.3 Risk metrics that matter: Sharpe, Sortino, max drawdown, alpha, beta. What each reveals and what it hides.
• 2.5 Building your first strategy: Universe, signal, portfolio construction, rebalancing. End to end walkthrough.

A written rule cannot panic when Nifty drops 8 percent in a week. It cannot get greedy when a stock runs 40 percent in two months. It cannot fall in love with a company. It just executes. That consistency is where the edge comes from, not any secret formula.

• 3.1 Momentum factor deep dive: Why momentum works. Short term, medium term, and long term momentum. How to measure it.
• 3.2 Value factor deep dive: P/B, P/E, EV/EBITDA. Why value underperformed 2015 to 2020 and what that means going forward.
• 3.3 Quality factor deep dive: ROE, ROCE, earnings stability. Why quality compounds relative to junk over decades.
• 3.4 Low volatility factor deep dive: The counterintuitive finding that lower-volatility stocks often outperform on risk-adjusted basis.
• 3.5 Size factor deep dive: The small-cap premium in India. When it works, when it fails, and the liquidity cost.
• 3.6 Combining factors: multi factor construction: How to blend momentum, quality, and low volatility to improve Sharpe ratio.
• 3.7 Factor timing and cycles: When each factor tends to work. Why you should not try to time them.
• 3.8 Factor investing: India evidence: What happens when too much capital chases a factor. Capacity constraints in Indian mid and small cap.

In Indian markets, momentum has historically been the strongest factor. Stocks that have outperformed over the previous 6 to 12 months tend to continue outperforming in the near future, until the trend exhausts. Several RupeeCase indices, including Allcap, Large Midcap and Allcap Multi Asset, use momentum as the main selection rule.

Value works too but on a longer horizon and with more patience required. Quality blends well with momentum to reduce drawdowns. Low volatility is a powerful defensive factor during bear markets. The art of factor investing is not picking one factor but combining them in a way that complements.

• 5.1 Statistical foundations: Probability distributions, hypothesis testing, sampling. The math under every backtest metric.
• 5.2 Time series analysis: Stationarity, autocorrelation, cointegration. How to analyse financial time series.
• 5.3 Machine learning for alpha: Practical applications of ML in Indian equity strategies. Where it helps, where it overfits.
• 5.5 Out of sample strategies: Walk forward testing, holdout periods, avoiding data snooping. The hardest part of research.
• 5.4 Alternative data in India: Non traditional data sources available to Indian quants. Satellite, NLP, credit card data.

The five modules in Path 5 cover the practical versions of these methods: what they need in data, how they overfit, and how to test them honestly out of sample.

Each index publishes its selection rule, its weighting method, its rebalance schedule and its full historical equity curve. That transparency is the point. If you do not know what a rule-based portfolio is actually doing, it might as well be discretionary.

Today the lineup has 18 index algos. Their rebalance cadences run from one week for the sector baskets to four weeks for the largecap and equal weight ones, and every past rebalance is published. The fee is one line: 0.2 percent of the value traded at a rebalance, nothing else. Every return figure on those pages is labelled model, back tested, with its date, because that is what it is.