Python in Finance Curriculum

Curriculum

1 – Basics

• Jupyter Notebook Introduction

• Variables

• Python Functions

• Data Types

• Operators (arithmetic, comparison, Boolean)

2 – Data Structures

• Tuples

• Lists

• Sets

• Indexing and Slicing

3 – Language Structures

• Range Function

• For Loop

• While Loop

• If Statement

• User-defined Functions

4 – Advanced Python Features

• List Comprehension

• Lambdas

5 – Pandas for Data Analysis

• Dataframe Basics

• Data Import/Export

• Indexing, and Slicing Data

• Access Methods

• Date Columns, and Arithmetic

• Data Manipulation

• Handling Missing Values

• Delimited Data

• Merging Dataframes

• Dataframe Arithmetic Operations

• Data Aggregation

6 – Visualisation Using Matplotlib

• Line Chart

• Bar Chart

• Histogram

• 3D Plotting

7 – Basic Financial Calculations with Pandas

• Moneyness Computation

• Forward Filling, Backward Filling

• Linear Interpolation Using Specific Index

8 – Financial Computing with NumPy, and SciPy

• Arrays

• Arrays Mathematics

• Array Operations

• Indexing, and Slicing

• Black Scholes Option Pricer

• Monte Carlo Pricing (Pure Python)

• Monte Carlo Pricing (NumPy)

• Sensitivities (Greeks) Computation

• Vectorised Black Scholes Option Pricer

• Implied Volatility Surface Generation

• Value at Risk (VaR) estimation

• Newton Iteration

• Altman Iteration

9 – Python Projects (Financial Applications)

• Residual Risk Add On capital

• PnL Attribution Test (PLAT) – Spearman correlation, and Kolmogorov-Smirnov test

• Scenario Generation

• Sensitivities Based Method sensitivities

• Risk Factor Eligibility Test

• Value at Risk
• Expected Shortfall

• Timeseries imputation

• Vanilla option pricing

• Exotic option pricing

• Stress testing

• Back testing
• Monte Carlo stock price simulation (geometric brownian motion)

10 – Project (for FinBA students only)

At the end of the cohort, students will build Python programs with financial applications, using the skills acquired during the course.