Last semester, the Quant division conducted a series of lectures focused on various key aspects of data preparation and analysis using Python. The sessions covered topics such as data cleaning, feature engineering, and the use of specific financial libraries like Backtesting and TA-Lib. Additionally, foundational techniques and best practices in trading and risk management were taught.

 

The primary goal of these lectures was to equip the members with the necessary skills to develop trading and risk management strategies using different historical datasets of assets, which we provided. These datasets included indices and commodities, with a time frame of five minutes. By the end of the semester, each group had developed their own unique strategies, backtested them, and presented the results to the division.