Sharpe Ratio distribution

Four Problems with the Sharpe Ratio

If you are an algorithmic trader, developer, or data scientists they you have already heard of the Sharpe Ratio. Many of you use this measurement as your score card for how well your algo performs.

The Sharpe Ratio, named after William Forsyth Sharpe,  measures the excess return per unit of deviation in an investment asset or a trading strategy. There are the four potential problems in using the Sharpe Ratio to measure trading performance. The first two problems are relevant if trading results in different intervals are correlated, while the latter two problems are relevant even if trading results are uncorrelated.

Problem 1. Failure to distinguish between intermittent and consecutive losses

Problem 2. Dependency on time interval

Problem 3. Failure to distinguish between upside and downside fluctuations

Problem 4. Failure to distinguish between retracements in unrealized profits versus retracements from “Trade Entry Date” equity.

Read the four problems with more detail and graphs on Oxford Capital Strategies

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