01. Project Description

Project 8: Backtesting

In this project, you will build a fairly realistic backtester that uses the Barra data. The backtester will perform portfolio optimization that includes transaction costs, and you'll implement it with computational efficiency in mind, to allow for a reasonably fast backtest. You'll also use performance attribution to identify the major drivers of your portfolio's profit-and-loss (PnL). You will have the option to modify and customize the backtest as well.

Suggestion to customize your project

• Try backtesting on different time periods and interpret the final results.
• Try different factors to be their alphas.
• Try different weights for each alpha, based on some metric that tells us how confident we are in that alpha, such as a rolling average of the sharpe ratio for each alpha factor.
• Try different transaction cost models. Read the paper "Crossover from Linear to Square-Root Market Impact”. It has a good overview of the transaction cost models, and it also references other papers that are useful in studying transaction cost models.
• Note about testing previous alphas: To test the alphas that you've created using the QuoteMedia data source, we would need a mapping file that identifies which cusip is associated with which barra ID. We currently aren't able to provide this in the classroom.