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Intermediate

Case Study: Building and Testing a Volatility Breakout Strategy on Nifty Options

A hands-on case study that follows a single idea, volatility compression followed by expansion on the Nifty 50, all the way from a written hypothesis to a costed backtest and a paper-trading plan. You will source NSE bhavcopy and India VIX data, build the squeeze and breakout signals in Python, express the view with Nifty options (straddles, directional buys, debit spreads), account for STT, brokerage and slippage, and then stress the results with walk-forward tests and regime splits across the 2020 crash, the 2021 rally and the 2024 election week. Built for aspiring quant analysts, prop trading applicants and discretionary traders who want to systematise.

Volatility BreakoutNifty OptionsBacktestingPythonIndia VIXWalk-Forward TestingZerodha Kite Connect

MODULES

5

DURATION

~3 hrs

TRACK

Algorithmic Trading

What You'll Master

Write a falsifiable trading hypothesis before touching data or code

Source and clean Nifty spot, India VIX and options chain data from NSE bhavcopies

Build ATR and Bollinger Band Width squeeze signals and a VIX regime filter in Python

Choose between a straddle, a directional option buy and a debit spread for the same signal

Backtest with realistic fills, STT, brokerage and slippage, and read CAGR, drawdown and expectancy honestly

Stress a strategy with walk-forward tests, parameter sensitivity and regime splits

Take a backtested idea to paper trading through a broker API with SEBI margin rules in mind

Access Level

LEARNER

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Everything included

Full Text Playbooks

Actionable Exercises

Mobile Reading Mode

Lifetime Updates

Curriculum Breakdown

Chapter 1: Framing the Case

3 Lessons

The Trading Idea in One Page: Volatility Compression, Expansion, and Why Options10 min read

Nifty Options as the Instrument: Lot Size, Weekly Expiry, Strikes, and What SEBI Changed11 min read

Writing the Hypothesis Before Any Code: Entry, Exit, and What Would Prove It Wrong10 min read

Chapter 2: Building the Dataset

3 Lessons

Sourcing Nifty Spot, India VIX, and Options Data: NSE Bhavcopy, Free Feeds, and Paid Feeds11 min read

Cleaning Options Data in Python: Contract Identifiers, Expiry Mapping, and Survivorship Traps12 min read

Constructing the Signals: ATR, Bollinger Band Width, and the VIX Filter12 min read

Chapter 3: Coding the Strategy

3 Lessons

From Rules to Code: Detecting the Squeeze and the Breakout Bar12 min read

Expressing the View With Options: Long Straddle, Directional Buy, or Debit Spread13 min read

Position Sizing, Stops, and Exits: Premium Stop, Time Stop, and Expiry Handling11 min read

Chapter 4: Backtesting and Reading the Results

3 Lessons

The Backtest Engine: Event Loop, Bid-Ask Fills, STT, Brokerage, and Slippage13 min read

Reading the First Results: CAGR, Drawdown, Win Rate, Expectancy, and the Equity Curve12 min read

Robustness: Walk-Forward Tests, Parameter Sensitivity, and Regime Splits13 min read

Chapter 5: From Backtest to Live

3 Lessons

Paper Trading Through a Broker API: Zerodha Kite Connect, Order Types, and Latency Reality12 min read

Margin, SEBI Rules, and Risk Limits for Option Buyers and Sellers11 min read

The Post-Mortem: What Worked, What Did Not, and How to Iterate Without Curve-Fitting11 min read