Intermediate
Introduction to Statistical Arbitrage
A rigorous, from-first-principles path into statistical arbitrage for quant analyst aspirants, prop trading applicants, and traders looking to systematize their trading. Covers market-neutral thinking, correlation and spread construction, the z-score as a trading signal, formal cointegration testing (stationarity, the Augmented Dickey-Fuller test, Engle-Granger), building and running a real pairs trade, dynamic hedge ratios with the Kalman filter, the risk and cost realities that make backtests lie, and a first look at factor-based stat arb with orthogonalization. Built entirely on real NSE and BSE data, with Python throughout.
Statistical ArbitragePairs TradingCointegration TestingKalman FiltersFactor ModelsMarket-Neutral StrategiesAlgorithmic Trading Basics
MODULES
7
DURATION
~5.1 hrs
TRACK
Quantitative Finance
What You'll Master
How statistical arbitrage isolates alpha from a relationship between two assets instead of a directional market call
How to build a spread and turn it into a z-score trading signal
How to formally test whether a pair is cointegrated using the Augmented Dickey-Fuller and Engle-Granger tests
How to size, hedge, and manage a real pairs trade from entry to exit
How to use a Kalman filter to keep a hedge ratio adaptive instead of static
Why transaction costs, slippage, and backtesting bias quietly destroy stat arb returns
How to combine multiple signals into a factor and remove overlap between them with orthogonalization
Access Level
LEARNER
Everything included
Full Text Playbooks
Actionable Exercises
Mobile Reading Mode
Lifetime Updates
Curriculum Breakdown
Chapter 1: What Statistical Arbitrage Actually Is
4 Lessons▶
What Is Statistical Arbitrage? Market Neutrality and Alpha From Relationships, Not Direction10 min read
▶
Relative Value Thinking: Trading the Spread, Not the Stock9 min read
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Mean Reversion: The Statistical Idea Stat Arb Is Built On11 min read
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Case Study: Why Two Similar Indian Stocks Can Still Offer a Trade11 min read
Chapter 2: The Statistical Toolkit — Correlation, Spread, and Z-Score
4 LessonsChapter 3: Cointegration — Testing Whether a Pair Really Mean-Reverts
4 LessonsChapter 4: Building a Simple Pairs Trade
4 Lessons▶
Screening for Pairs: What Makes Two Stocks Good Candidates10 min read
▶
Entry and Exit Rules: Turning a Z-Score Into Buy/Sell Decisions10 min read
▶
Position Sizing and Hedge Ratios: Making the Trade Actually Market-Neutral11 min read
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Case Study: Walking Through a Full Pairs Trade From Signal to Exit12 min read
Chapter 5: The Kalman Filter for Dynamic Hedge Ratios
4 LessonsChapter 6: Risk, Costs, and Why Backtests Lie
4 Lessons▶
Stop-Losses and Structural Breaks: When a Pair Stops Working10 min read
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Transaction Costs and Slippage: The Silent Killer of Stat Arb Returns10 min read
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Common Backtesting Mistakes: Look-Ahead Bias and Survivorship Bias10 min read
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Setting Up to Trade Stat Arb in India: Brokers, Algo Rules, and SEBI Basics11 min read
Chapter 7: From Pairs to Factors
5 Lessons▶
What Is a Factor? From Single Pairs to Systematic Signals9 min read
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Common Alpha Factors: Value, Momentum, and Mean-Reversion Signals10 min read
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Factor Orthogonalization: Removing Overlap Between Signals11 min read
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Combining Factors: Building Your First Multi-Signal Stat Arb Sleeve11 min read
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Key Takeaways and Where to Go Next8 min read