Godzilla.dev AI Quant Trader Series Day 4: Key Python Libraries for Finance

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The fourth installment of the godzilla.dev AI x Quant Trader Series focuses on widely used Python libraries for quantitative finance, with an emphasis on SciPy. The article covers SciPy's statistics module, explaining how to work with continuous and discrete probability distributions such as normal, uniform, beta, and Poisson. It introduces SciPy's 'freezing' feature, which allows traders to fix distribution parameters for repeated use without retyping them each time. The tutorial also walks through hypothesis testing using the Kolmogorov–Smirnov test to determine whether a dataset, such as stock daily returns, follows a normal distribution. Future installments are expected to cover additional SciPy modules as they become relevant to quantitative analysis workflows.

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