Quantitative Finance for Developers: Where to Start

Making the transition from software developer to quant developer requires bridging two worlds: software engineering and financial mathematics. This guide maps the most important quantitative finance concepts to practical algorithmic trading applications.

Time Series Analysis

Financial prices are time series. Understanding autocorrelation, stationarity, and seasonal decomposition is foundational. Most python algo trading research starts with a time series analysis of the target asset to understand its properties.

Statistical Distributions

Asset returns are not normally distributed. They have fat tails—extreme events happen far more often than a Gaussian model predicts. Model your returns with a Student's t-distribution or use bootstrapping for risk simulations. This directly impacts how you size positions in your crypto algo trading system.

Hypothesis Testing

Every strategy idea is a hypothesis: "momentum exists in BTC hourly returns." Test it rigorously. Calculate a p-value. Report the effect size. Be suspicious of any result with p < 0.05 on the first try—multiple testing bias is rampant in algo trading strategy research.