Comprehensive course analysis
Who should attend
This course is ideal for financial analysts, business analysts, portfolio analysts, quantitative analysts, risk managers, model validators, quantitative developers and information systems professionals. There are no pre-requisites to attend this course. We expect participants to have a basic knowledge of finance and basic notions of programming.
About the course
This two-day course offers a short but intensive introduction to the use of Python in finance. In particular, it explores the key characteristics of this powerful and modern programming language to solve problems in finance and risk management.
Key Learning Outcomes:
- Learn a structured method to programming via the Bento Box Method
- Explore the benefits of using Python in practical day-to-day business activities
- Have a hands-on experience of programming in Python to solve financial problems
- Explore in detail how Python is used in modern Finance, Portfolio Management, Financial Derivatives and Risk Management
Introduction to Python
- Programming in 3 Easy Steps
- The Bento Box Method
- Why learning a new programming language?
- From Excel to Python
- From VBA to Python
- Installing Python Packages
- Representing and working with data: tuples, lists, dictionaries and sets.
- Designing functions and organizing larger programs into functions.
- Array Operations, Random Numbers, Plotting
- Data Visualization via Matplotlib.
Applications of Python in Finance
- On Investments
- Example 1: Discount factors and cashflows
- Example 2: Net Present Value (NPV) and Internal Rate of Return (IRR)
- Example 3: Bonds: Zero-coupon and Coupon
Applications of Python in Portfolio Management
- On Portfolio Management
- Example 1: Modern Portfolio Theory (MPT) and the The Efficient Frontier
- Example 2: The Capital Asset Pricing Model (CAPM)
- Example 3: Asset Pricing Theory (APT)
Extending Python: the NumPy, SciPy and Pandas Packages
- Why we need packages?
- Description of NumPy.
- Description of SciPy.
- Description of Pandas.
Using the Packages
- NumPy Examples: interpolation functions, matrix decompositions, computing eigenvalues, solving systems of equations and matrix inversion.
- SciPy Examples: statistical functions, how to generate different distributions and perform
- statistical computations.
- Pandas Examples: working with tabular data in Python (including missing data and data alignment).
Applications of Python in Financial Derivatives
- On Financial Derivatives
- Example 1: Classic Black-Scholes-Merton formula
- Example 2: Monte Carlo Simulation
- Example 3: Binomial Trees
Applications of Python in Quantitative Risk Management
- On Risk Management
- Example 1: Classic Value at Risk (VaR)
- Example 2: Mixing Statistical Distributions
- Example 3: Principal Component Analysis
Because of COVID-19, many providers are cancelling or postponing in-person programs or providing online participation options.
We are happy to help you find a suitable online alternative.