Python Programming for Finance

• An understanding of finance (securities, derivatives, etc.)
• A general understanding of probability and statistics
• Elementary differential and integral calculus

Python is a programming language that has gained huge popularity in the financial industry. Adopted by the largest investment banks and hedge funds, it is being used to build a wide range of financial applications ranging from core trading programs to risk management systems.

In this instructor-led, live training, participants will learn how to use Python to develop practical applications for solving a number of specific finance related problems.

By the end of this training, participants will be able to:

• Understand the fundamentals of the Python programming language
• Download, install and maintain the best development tools for creating financial applications in Python
• Select and utilize the most suitable Python packages and programming techniques to organize, visualize, and analyze financial data from various sources (CSV, Excel, databases, web, etc.)
• Build applications that solve problems related to asset allocation, risk analysis, investment performance and more
• Troubleshoot, integrate, deploy, and optimize a Python application

Audience

• Developers
• Analysts
• Quants

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Note

• This training aims to provide solutions for some of the principle problems faced by finance professionals. However, if you have a particular topic, tool or technique that you wish to append or elaborate further on, please please contact us to arrange.

Introduction

Setting up the Development Environment

• Programming locally vs online: Anaconda and Jupyter

Python Programming Fundamentals

• Control structures, data types, functions, data structures and operators

Extending Python's Capabilities

• Modules and Packages

Your first Python Application

• Estimating beginning and ending dates and times

Accessing External Data with Python

• Importing and exporting, reading and writing CSV data
• Accessing data in an SQL database

Organizing Data Using Arrays and Vectors in Python

• NumPy and vectorized functions

Visualizing Data with Python

• Matplotlib for 2D and 3D plotting, pyplot, and SciPy

Analyzing Data with Python

• Data analysis with scipy.stats and pandas
• Importing and exporting financial data (Excel, website data, etc.)

Simulating Asset Price Trajectories

• Monte Carlo simulation

Asset Allocation and Portfolio Optimization

• Performing capital allocation, asset allocation, and risk assessment

Risk Analysis and Investment Performance

• Defining and solving portfolio optimization problems

Fixed-Income Analysis and Option Pricing

• Performing fixed-income analysis and option pricing

Financial Time Series Analysis

• Analyzing time series data in financial markets

Taking Your Python Application into Production

• Integrating your application with Excel and other web applications

Application Performance

• Optimizing your application
• Parallel Computing and Multiprocessing

Troubleshooting

Closing Remarks