We use maximum drawdown as one of the key statistics for evaluating our quantitative investment strategies and for deciding on the introduction of new variables in our models. Most investors would strongly prefer the first strategy, because it has a much lower maximum drawdown than the second strategy! Furthermore, the length of the drawdown period is shorter. in my pair trading with copulas strategy, in this case im taking 2 correlated etfs, the results shows good of returns but very low maximum drawdown. However, the maximum drawdown can also be calculated based on returns relative to a benchmark index, for identifying strategies that show steady outperformance over time.įor example: two strategies can have the same average outperformance, tracking error, information ratio and volatility, but their maximum drawdowns compared to the benchmark can be very different.įor instance, suppose that the first one achieves a monthly performance of 1%, -0.5%, 1%, -0.5% and so on versus the benchmark, while the second strategy achieve an outperformance of 1% each month during the first half of the sample, but an underperformance of 0.5% each month during the second half of the sample. In order to calculate the Profit to Drawdown Ratio, the Net Profit has to be divided by the Maximum Drawdown for a certain period of time. The maximum drawdown can be calculated based on absolute returns, in order to identify strategies that suffer less during market downturns, such as low-volatility strategies. It is usually quoted as a percentage of the peak value. By setting a 20 maximum drawdown level, investors can trade with peace of mind and always make meaningful decisions in the market that will, in the long run. This article introduces three algorithms for trade sizing with the objective of controlling tail risk or maximum drawdown when applied to a trading strategy. Maximum drawdown is defined as the peak-to-trough decline of an investment during a specific period.
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