You’ve covered a lot of ground in Path 2. You know why rules beat gut feeling (2.1). You understand the five major factors (2.2). You can read risk metrics (2.3) and evaluate a backtest honestly (2.4). Now it all comes together.
This module walks through the complete process of building a systematic strategy from scratch, every decision you need to make, the tradeoffs involved, and what it looks like in practice on RupeeCase. By the end, you’ll have a clear mental model for strategy design that applies to everything from a simple momentum screen to a sophisticated multi-factor portfolio.
The five decisions in every systematic strategy
Every systematic equity strategy, no matter how simple or complex, requires five design decisions. Get these right and you have a strategy worth backtesting.
RupeeCase default: Nifty 500, 500 stocks across large, mid, and small cap, all with sufficient daily liquidity for retail portfolio sizes. More factor opportunities than Nifty 50, more manageable than the full NSE universe.
The signal must be objective (same calculation for all 500 stocks), historically documented (not invented for this backtest), and available at decision time (no look-ahead bias).
Equal weight (1/N each) is simplest and most robust. Volatility-weighted (inverse volatility) typically improves Sharpe. Market-cap weighting defeats the purpose of factor selection.
A complete strategy specification
Here’s what a properly documented strategy specification looks like. Every decision is explicit. Nothing is ambiguous.
This specification is complete. Any programmer given this document would build exactly the same strategy. That’s what systematic means, no ambiguity at execution time.
Evaluating your backtest honestly
| What to check | What you want to see | Red flag |
|---|---|---|
| CAGR vs benchmark | Consistent 8 to 15% alpha over full period | Alpha concentrated in 1 to 2 exceptional years only |
| Max drawdown | < 40%, recovery within 18 months | Deeper drawdown than benchmark; multi-year recovery |
| Sharpe ratio | > 1.0 over 5+ years | Sharpe > 2.5 on long-only equity |
| Annual performance | Positive alpha in most years | Alpha only in 1 exceptional year |
| Parameter sensitivity | Similar results at 9M, 10M, 11M, 12M lookback | Only 12M works; 11M collapses |
| Cost impact | Net returns still attractive vs benchmark | Gross attractive; net mediocre |
The most important thing: stick to it
I’ve spent most of Path 2 on the intellectual work of strategy design. But in 17 years of trading, the biggest source of return destruction I’ve observed isn’t bad strategy design. It’s good strategy design abandoned at exactly the wrong moment.
Every momentum strategy has stretches, sometimes 6 to 12 consecutive months, where it underperforms the index. The portfolio is doing everything right: buying the highest-momentum stocks, rebalancing consistently. But momentum is in a “crash phase” and the strategy is down 20% while the market is flat.
This is when most investors abandon the strategy. And it’s almost always the worst time to do so, the subsequent recovery is often sharp. The investors who stick with the process through the bad periods capture the good ones.
The process is the product. A systematic strategy only delivers its stated returns if you follow it consistently. If you override it every time it underperforms, you don’t have a systematic strategy, you have a discretionary strategy with systematic window dressing. Before deploying real money, answer honestly: what is the maximum drawdown I can actually hold through without overriding the system? Choose a strategy whose historical max drawdown is below that number. That is your real constraint.
- Universe
- The set of stocks eligible for selection by the strategy. Typically a liquidity-filtered index like Nifty 500.
- Signal
- The factor metric used to rank stocks | e.g., 12M-1M momentum, P/B for value, ROE composite for quality.
- Equal weighting
- Allocating the same capital to each stock in the portfolio (1/N). Simplest and most robust weighting for most factor strategies.
- Volatility weighting
- Allocating inversely proportional to each stock's volatility | giving higher weight to less volatile stocks. Typically improves Sharpe ratio.
- Strategy spec
- A complete, unambiguous written definition covering universe, signal, portfolio size, weighting, rebalance frequency, and cost assumptions.
- Parameter sensitivity
- Testing how strategy performance changes across a range of parameter values. Robust strategies produce similar results across reasonable adjacent values.
Sources & further reading
- → NSE India, Transaction Charges Schedule
- → NSE Indices, Nifty 500 (universe and benchmark)
- → SEBI, Portfolio Management Regulations
- → Ilmanen, A. (2011). Expected Returns. Wiley.
- → Asness, C., Moskowitz, T. & Pedersen, L. (2013). Value and Momentum Everywhere. Journal of Finance.
🎓 Path 2 Test, Systematic Investing Fundamentals
30 questions across all 5 modules. Pass 21/30 to unlock your certificate.
This test covers everything in Path 2: why rules outperform discretion, the five major factors, six risk metrics, backtest evaluation, and strategy design. You’ve read the modules, now prove it.
Questions are drawn from all five modules. You need 21 correct to pass. No timer.
Quick check, Module 2.5
Backtest Sufficiency Check
A backtest is honest only if its sample includes enough independent regime cycles. This sanity check tells you whether the statistical claim is credible.