The Sharpe ratio is a commonly used measure in finance for evaluating the performance of an investment strategy, by comparing its returns to a risk-free asset and adjusting for the risk taken. However, with the advent of advanced computing and machine learning techniques, it has become increasingly easy to backtest a large number of alternative investment strategies. This process, known as backtest optimization, often leads to an inflated Sharpe ratio due to the selection of parameter combinations that maximize the simulated historical performance. To address this issue, the Deflated Sharpe Ratio (DSR) was developed as an estimator of the Sharpe Ratio that corrects for both selection bias and non-normal returns. The DSR adjusts the rejection threshold to account for the number of trials, making it more robust to the multiple testing problem. In this project, we will be examining the use of the DSR in evaluating the performance of investment strategies.