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Backtesting

Udacity - AI for Trading Nanodegree Program

Project Overview

  • Build a fairly realistic backtester that uses the Barra data.
  • The backtester will perform portfolio optimization that includes transaction costs, with computational efficiency in mind, to allow for a reasonably fast backtest.
  • Use performance attribution to identify the major drivers of the portfolio's profit-and-loss (PnL).
  • Modify and customize the backtest.