Course description
Fundamentals of Python Modeling in Finance
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.
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 although we expect participants to have a basic knowledge of finance and basic notions of programming.
Training Content
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
Python Fundamentals
- 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
About supplier
Fitch Learning
Part of the Fitch Group, Fitch Learning partners with clients to enhance knowledge, skills and conduct. With centers in London, New York, Singapore, Dubai and Hong Kong, we are committed to questioning and understanding client needs across the globe and...
