Skip to content
Python for Quantitative Finance / Data access and delivery

Applied Python Project

The programme's claim was never "know Python syntax" — it was "build, test and distribute a quantitative model." This capstone stitches the stack into one reproducible workflow: construct a seeded price panel, compute returns, estimate μ^\hat{\mu} and Σ^\hat{\Sigma}, price a zero-coupon bond with a DiscountCurve class, and assert put–call parity on a Black–Scholes side check. The exercise asks you to change one input and regenerate the numbers.
Learning objectives. By the end of this note you should be able to:
  • run an end-to-end research script with a single seed and named config block;
  • connect a pandas panel to NumPy estimators without losing the date contract;
  • price a cash-flow instrument through a composed curve object;
  • lock one algebraic identity with a tolerance assert;
  • recompute after a controlled one-parameter change.

In this lesson

  1. Config and seeded panel
  2. Returns, mean, covariance
  3. Curve + zero bond (composed classes)
  4. Identity test (Black–Scholes parity)
  5. One-parameter change drill
  6. End-to-end checklist

Before you start

The full lesson, with worked examples and exercise solutions, is for members.

Read the full lesson

New to SelfMadeQuant.com? See membership options or browse the Python for Quantitative Finance syllabus.