Finance & Banking · Quantitative Finance

CodeFin: Python Financial Analysis Lab

Interactive lab where learners calibrate financial model parameters across six tasks: portfolio optimization, correlation estimation, moving-average backtesting, Black-Scholes pricing, GARCH volatility fitting, and Monte Carlo VaR.

6 rounds Executive English, Español

Preview

About this simulation

Interactive lab where learners calibrate financial model parameters across six tasks: portfolio optimization, correlation estimation, moving-average backtesting, Black-Scholes pricing, GARCH volatility fitting, and Monte Carlo VaR. Each task scores parameter choices against an optimal benchmark, building computational finance intuition.

Who it is for

An advanced simulation for participants used to working with the main frameworks and trade-offs of the subject, designed for executive education and experienced professionals.

How a session runs

  1. The instructor creates a session from the Eureka dashboard and invites the participants.
  2. Participants play 6 rounds. In each one they submit their decisions and the simulation calculates the results.
  3. The simulation can be run in English, Español.
  4. The instructor follows each participant's progress and results from the dashboard, and uses the class results for the debrief.

What participants track

What participants follow on screen as the rounds go by:

  • Your Parameters

Subjects covered

Designed for courses in Quantitative Finance.

  • Programming for Finance (Python, MATLAB, R)

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