Coding for Finance

Python in Finance

Python in Finance

Designed to meet the enormous rise in demand for individuals with knowledge of Python in the financial industry, students are taught the practical coding skills now required in many roles.
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Python in Finance is a unique, easy-to-follow course which requires no prior programming knowledge or experience. Designed to meet the enormous rise in demand for individuals with knowledge of Python in the financial industry, students are taught the practical coding skills now required in many roles.

The material contains multiple examples of practical applications in finance with a focus on quantitative risk/pricing analytics (taught in the Market Risk (Finance) course), giving you an opportunity for valuable practical experience. The course contents provide you with the must-have coding skills needed to excel in the modern finance sector. The course material has been developed in partnership with industry veteran and renowned practitioner, Dr Simon Clift.

After this course, candidates will possess the knowledge to write their own code from scratch in the Python programming language to, for example price options with the Black Scholes model, derive greeks, perform Monte Carlo simulation, generate an implied vol surface and many more (see course curriculum for additional details).

The Python programs developed by the student during the training program, and for the project completed at the end of the cohort (in the full program only) become an important part of the student’s portfolio. The portfolio is a valuable addition to a student’s CV/resume, and is a real asset that is evidence of the student’s practical experience and knowledge. This is particularly useful during a job search to set the student apart from other applicants.

The Python in Finance course is not offered on a standalone basis. It is included in our Certificate in Finance Business Analysis (FinBA), and Coding (Python, SQL) in Finance Certificate training programs.

You will learn how to use the following and more, to write Python code for financial applications.

  • Jupyter Notebook
  • Pandas (for data analysis)
  • NumPy, and SciPy (for quantitative computing)
  • Matplotlib (for data visualisation)

We will cover:
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

All students are to work on the following Python projects:

  • 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
  • Volatility surface construction
  • Monte Carlo stock price simulation (geometric brownian motion)
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