A practical guide to a career in quant finance.
Structured training in the mathematics, programming, and market intuition that quantitative roles demand, built for people who want to do the work, not just read about it.
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What's covered
Six core areas that take you from data and code to strategies you can defend.
Python for Finance
Work with market data using pandas and NumPy, and build clean, reproducible research pipelines.
Probability & Statistics
Distributions, hypothesis testing, and statistical inference, the language of quantitative edge.
Time-Series Analysis
Stationarity, autocorrelation, and forecasting techniques applied to real financial series.
Strategy Backtesting
Build, validate, and stress-test trading strategies while learning to avoid overfitting.
Portfolio & Risk
Allocation, position sizing, drawdown, and the risk metrics that keep a strategy alive.
Interview Preparation
The probability, brainteaser, and coding questions quant firms actually ask, and how to think through them.
Who it's for
The material assumes curiosity and a willingness to code, not a finance degree.
Aspiring quants
Students and career-changers who want a rigorous, practical foundation for quant roles.
Software engineers
Developers moving into finance who want to apply their skills to markets and data.
Self-directed traders
Discretionary traders who want to make their process systematic, testable, and repeatable.
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