Explore a demo-driven talk from the EuroPython 2019 conference that introduces the Probo package for teaching Python programming and computational finance concepts. Dive into derivative pricing and hedging using the Black-Scholes model, Monte Carlo simulation, and binomial trees. Learn how Jupyter notebooks, NumPy, and Pandas create an ideal learning environment for developing deeper quantitative reasoning. Discover how the Probo package enables students to operationalize their understanding by implementing derivative pricing theories in clean, simple code. Gain insights into dynamic hedging, a crucial concept in modern financial derivatives theory, through Monte Carlo simulation of delta-hedging. Witness how Python's power and simplicity, combined with Jupyter notebooks, make Probo an ideal learning platform for computational finance students.
Overview
Syllabus
Introduction
My experience
Simple example
Verify in Python
Simulation
Sample Sizes
Law of Large Numbers
New Course
Delmar
Computational and Inferential Thinking
Python is an excellent tool
Kennedys sampling distribution
Learning to program
Module Introduction
Option Facade
Option Definition
Option Interface
Vanilla Option
Option Pricing Models
Monte Carlo Engine
Mathematical Review
Market Data
Whats Next
Taught by
EuroPython Conference
