Python for Finance, Investing and Trading
Python for Finance, Investing and Trading is an introductory quantitative finance, intensive practical two day optional module.
Python is an essential, fast growing and highly in demand programming language in the financial industry for financial analysis, systematic algorithmic trading, back testing strategies, portfolio construction, optimisation and portfolio management because Python is free, has excellent libraries and documentation and can integrate seamlessly into trading environments.
Day one introduces you to the basics of the Python programming language, statistical analysis, how to run OLS regressions and how to obtain free financial data from a wide variety of sources.
Day two focuses on specific quantitative finance applications, trading and backtesting strategies and how to send your first algorithmic trades in Interactive Brokers Trading Platform (TWS and Gateway).
This is a hands on practical programming course with step by step source code, in class exercises and full solutions provided. The course is catered for students with no previous knowledge of programming required and will start from the absolute basics. However, it is recommended to at least to have some previous knowledge / contact with the language.
If you are interested in automated trading, computer science and computational quantitative finance this course would be well suited for you and can only enhance your personal career objectives and quantitative skillsets for future job applications.
You will learn:
- Basic Python Programming Introduction
- Spyder and Anaconda IDE Programs
- How to import/export Financial Data from a wide variety of sources
- Main Financial Data Analysis Python Libraries
- How to graph customised charts
- Statistical analysis and regressions
- Introduction to SQL Databases
- Back-testing trading strategies
- Analysing strategy portfolio statistics
- Interactive Brokers systematic trading
Python for Finance, Investing and Trading is one of several optional modules offered to postgraduate students by the School of Economics and Finance at Queen Mary University of London.