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RSK

Statistical Arbitrage Quant

High Demand$140K – $450K+
Statistical Arbitrage (StatArb) Quants specialize in pairs and basket trading — finding instruments that should move together and profiting when they temporarily diverge. This is one of the oldest and most enduring quantitative strategies, dating back to the 1980s at Morgan Stanley. StatArb requires deep understanding of cointegration, mean reversion, and the delicate balance between signal strength and transaction costs. The V7 engine's S18 (Pairs Trading) module implements Engle-Granger cointegration testing with Kalman filter hedge ratio estimation — and this track teaches you exactly how and why it works.
8
Core Skills
7
Key Tools
10
Lessons
5
Career Stages

A Day in the Life

Your universe consists of 200 equity pairs screened for cointegration. The morning scan shows 3 pairs with z-scores above 2.5 — potential mean reversion trades. You verify the Kalman-filtered hedge ratios are stable, check that the half-life is under 15 days, and size the positions to maintain dollar neutrality. One pair triggers: MSFT/AAPL spread has widened 2.8σ. You enter the pairs trade with a 1.5σ take-profit target.

Core Skills

1
Cointegration Analysis
2
Kalman Filtering
3
Mean Reversion Models
4
Hedge Ratio Estimation
5
Spread Modeling
6
Transaction Cost Analysis
7
Portfolio Neutrality
8
Regime Switching Models

Tools & Technologies

PythonstatsmodelspykalmanpandasnumpyscipyBloomberg

Prerequisites

Econometrics
Time Series Analysis
Multivariate Statistics
Pairs Trading Basics

Career Progression

Junior StatArb Analyst
StatArb Quant
Senior StatArb Quant
StatArb Portfolio Manager
Head of StatArb

Curriculum

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