Our society aims to enhance the algorithmic knowledge of students interested in financial markets, boosting both their employability and coding skill.
The Algorithmic Trading Society will teach members python and design trading programs. We will backtest these on historical data and analyse their performance to optimise them.
Backtesting is the most crucial way to evaluate a trading strategy, without backtesting one might find themselves in the deep red wondering where they went wrong.
The most important advantage is that its performance can be ascertained on historical data which (hopefully) represents future market data.
Download our Powerpoint to learn more about Backtesting here.
In this project we used twitter data and a naive bayes classifier to evaluate the correlation between public sentiment and stock price. Knowing how to utilise sentiment analysis when it comes to stocks is very important, as public backlash or support heavily influences the pricing of a stock. Find out more.
Q&A session with Macquire
In the past we have had guest speakers from industry professionals to academics.
Here we had quantitative developers from the Australian bank Macquire come in for a talk and a Q&A session regarding the importance of low latency development when it comes to algorithmic trading.
