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Python for Quantitative Finance / Python Foundations

Overview

Every model in this vault — a discounted cash flow, a Monte Carlo option price, a covariance matrix — is eventually a sequence of typed values passed through operations that a computer executes exactly as written, with no room for the reader's usual "you know what I mean." This topic is the vault's on-ramp into that discipline: what a program actually is, how a computer runs one, and the handful of primitive objects (int, float, bool, str, None) and operators every later note assumes without re-explaining. It is deliberately short — two lessons — because the goal here is orientation, not depth: get comfortable naming values, evaluating expressions, and reading Python's error categories before Stage 2 introduces the first branch or loop.

By the end of this topic you should be able to:

  • distinguish an algorithm from a program and explain why the Church–Turing thesis makes Python's expressive power equivalent (not superior) to any other general-purpose language;
  • classify a Python error as a syntax error, a static semantic error, or a runtime/logic error, and say which is caught when;
  • name the four scalar object types and predict the type an arithmetic expression produces;
  • read and write a correct variable-assignment statement, including multiple assignment, and explain why "a variable is just a name" matters for aliasing later in the unit.

In this lesson

  1. Contents
  2. The big picture

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