Intermediate
Building a Pairs Trading Strategy Using Python and Statsmodels
A build course for quant analyst aspirants, prop desk applicants and traders who already know what a pairs trade is and now want to code one properly. You will use statsmodels as your statistics engine: sm.OLS and RollingOLS for hedge ratios, adfuller and kpss for stationarity, coint for Engle-Granger, coint_johansen for multi-stock baskets, an AR(1) regression for half-life, a state space model for a time-varying hedge ratio, and VECM for the adjustment dynamics of both legs. Each tool is taught by reading its actual output, not just calling it. You then scan sector universes from the Nifty 200 for candidates without falling into the multiple testing trap, code a z-score signal engine with no look-ahead bias, backtest it in pandas with the full Indian cost stack, walk it forward, and package the whole thing as a reusable, tested Python module.
Pairs TradingstatsmodelsCointegration TestingOLS and RollingOLSADF and KPSS TestsJohansen TestHalf-Life of Mean ReversionKalman Filter Hedge RatioVECMBacktesting in pandasWalk-Forward TestingNSE Market Data
MODULES
6
DURATION
~5 hrs
TRACK
Python for Finance
What You'll Master
Set up a clean pairs trading project and pull, adjust and align NSE price data for two or more stocks
Estimate a hedge ratio with sm.OLS and read every line of the regression summary, including the ones that mislead you on price data
Track hedge ratio drift with RollingOLS and model it directly with a Kalman filter built on statsmodels state space
Run adfuller, kpss, coint and coint_johansen correctly, choose their parameters deliberately, and interpret their critical values
Measure the half-life of mean reversion and use it to set lookback windows and maximum holding periods
Scan a sector universe for candidate pairs while controlling for multiple testing and ranking by stability and liquidity
Code a look-ahead-free z-score signal engine and a vectorised backtest with STT, exchange charges, slippage and futures rollover
Walk the strategy forward, package it as a tested module, and understand what live execution on the NSE demands
Access Level
LEARNER
Everything included
Full Text Playbooks
Actionable Exercises
Mobile Reading Mode
Lifetime Updates
Curriculum Breakdown
Chapter 1: The Build Setup
4 Lessons▶
What This Course Builds: A Pairs Strategy From Raw NSE Prices to a Tested Module9 min read
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Setting Up the Stack: pandas, statsmodels, yfinance and a Pairs Project Layout11 min read
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Pulling and Aligning NSE Prices for a Pair: Adjustments, Holidays and Missing Bars12 min read
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statsmodels in Ten Minutes: Array vs Formula API and the Results Object11 min read
Chapter 2: The Hedge Ratio With OLS
4 Lessons▶
Estimating the Hedge Ratio With OLS in statsmodels: Reading the Summary Line by Line13 min read
▶
Log Prices vs Raw Prices and Which Stock Goes on the Left-Hand Side11 min read
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Residual Diagnostics for a Pairs Regression: Durbin-Watson, ACF Plots and Spurious Fits12 min read
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RollingOLS: Watching a Pair's Hedge Ratio Drift Over Time11 min read
Chapter 3: Stationarity and Cointegration Tests in statsmodels
5 Lessons▶
adfuller Line by Line: Lag Selection, Regression Type and autolag12 min read
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Pairing ADF With KPSS: What to Do When the Two Tests Disagree11 min read
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The statsmodels coint Function: Engle-Granger With the Right Critical Values12 min read
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The Johansen Test for Baskets: Trace and Max-Eigenvalue Statistics in statsmodels13 min read
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Half-Life of Mean Reversion From an AR(1) Regression on the Spread11 min read
Chapter 4: Scanning a Universe for Pairs
4 Lessons▶
Building Sector-Wise Candidate Universes From the Nifty 20010 min read
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Looping coint Over Every Pair Without Fooling Yourself: The Multiple Testing Problem12 min read
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Ranking Pair Candidates: p-Value, Half-Life, Hedge Ratio Stability and Liquidity11 min read
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Case Study: Scanning Nifty IT and Nifty Metal Stocks for Tradeable Pairs13 min read
Chapter 5: Signals and a Vectorised Backtest
5 Lessons▶
From Spread to Z-Score With Rolling Windows That Never Peek Ahead11 min read
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Coding Entry, Exit and Stop Rules as a Position State Machine12 min read
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A Vectorised Pairs Backtest in pandas: Leg Returns, Rupee Neutrality and Daily Returns13 min read
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Adding the Indian Cost Stack: STT, Exchange Charges, Brokerage, Slippage and Rollover12 min read
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Scoring the Pair: Sharpe, Maximum Drawdown, Hit Rate and Holding Period11 min read
Chapter 6: Advanced Models, Robustness and Shipping
5 Lessons▶
A Time-Varying Hedge Ratio With a Kalman Filter in statsmodels State Space14 min read
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VECM in statsmodels: Which Leg Does the Correcting12 min read
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Walk-Forward Testing: Re-Estimating and Re-Qualifying the Pair Every Quarter12 min read
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Packaging the Strategy as a Reusable pairs Module With pytest12 min read
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From Backtest to NSE Execution: Two-Leg Orders, SEBI Algo Rules and Retiring a Pair12 min read