Everything from Path 2 in one place. Universe, signal, sizing, costs, backtest, going live. A complete walkthrough of how a systematic strategy is actually built from scratch.
TK
Tanmay Kurtkoti
Founder & CEO, RupeeCase · QC Alpha
⏱ 16 min read⟳ Updated 1 May 2026◆ Intermediate
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.
1
Universe: which stocks are eligible?
The universe defines your opportunity set, liquidity profile, and benchmark. Most systematic strategies use a liquidity-filtered index universe.
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.
2
Signal: how do you rank stocks?
The signal is the factor, the measurable characteristic that ranks stocks from most to least attractive. Momentum: rank by 12M-1M return. Value: rank by P/B or P/E. Quality: composite of ROE, D/E, earnings stability.
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).
3
Portfolio: how many stocks, how weighted?
Tradeoff: fewer stocks = higher potential alpha, higher volatility. More stocks = smoother ride, lower tracking error. For most retail investors, 20 to 30 stocks is the right balance.
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.
4
Rebalance: when and how often?
Tradeoff: more frequent = fresher signals, more trading costs. For most retail investors: monthly for momentum (signals decay quickly), quarterly for value and quality (signals are slower-moving). Weekly adds significant cost for small portfolios without proportional benefit.
5
Cost model: what are realistic costs?
Every trade costs brokerage + STT (0.1% both sides) + exchange charges + GST + stamp duty + slippage. RupeeCase uses 0.5% round-trip, this covers all charges plus estimated slippage. A backtest that uses 0.05% round-trip is hiding 90% of real costs.
A complete strategy specification
Here’s what a properly documented strategy specification looks like. Every decision is explicit. Nothing is ambiguous.
Strategy Specification
Simple Momentum, Nifty 500
UniverseNifty 500 (point-in-time constituents)
Signal12M-1M trailing price return
SelectionTop 30 stocks by momentum rank
WeightingEqual weight (1/30 each)
RebalanceFirst trading day of each month
Cost assumption0.5% round-trip per trade
BenchmarkNifty 500 TRI
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.
You now have the complete intellectual foundation for systematic investing. You know why rules work (2.1), what factors drive returns (2.2), how to measure risk honestly (2.3), how to read a backtest without being deceived (2.4), and how to design a strategy from scratch (2.5). That’s more than most active fund managers formally learn. Path 3 goes deeper on each individual factor with Indian market evidence. The terminal is where you apply it with real data.
Apply everything you’ve learned
Build, backtest, and deploy systematic strategies on NSE data
The real edge isn’t the strategy, it’s the discipline
I’ve built dozens of systematic strategies over the years. The honest truth is that many well-designed strategies produce similar returns over long periods. The factors are known. The evidence is public. What separates investors who actually capture factor premia from those who don’t isn’t strategy sophistication.
It’s discipline during the months when the strategy feels broken. It’s resisting the urge to “just this once” override the signal because conditions feel different. It’s staying in a momentum strategy through a momentum crash. This is genuinely hard, much harder than understanding the academic research. RupeeCase is designed to make the process as friction-free as possible, because the easier it is to follow your strategy, the more likely you are to actually do it.
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.
All content on this page is original work by Tanmay Kurtkoti and QC Alpha Technologies Pvt Ltd. Protected under Indian copyright law. tanmay@rupeecase.com
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A backtest is honest only if its sample includes enough independent regime cycles. This sanity check tells you whether the statistical claim is credible.
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