Thus you need to set periods to $252*6.5 = 1638$, which is the number of US trading hours within a year. However, if your strategy trades within the hour you need to adjust the Sharpe to correctly annualise it. Usually this value is set to 252, which is the number of trading days in the US per year. It has a single parameter, that of the number of periods to adjust for when scaling up to the annualised value. Note that the Sharpe ratio is a measure of risk to reward (in fact it is one of many!). As with most of our calculation-heavy classes we need to import NumPy and pandas: # performance.py The first task is to create a new file performance.py, which stores the functions to calculate the Sharpe ratio and drawdown information. In this article we will implement the Sharpe ratio, maximum drawdown and drawdown duration as measures of portfolio performance for use in the Python-based Event-Driven Backtesting suite. The former quantities the highest peak-to-trough decline in an equity curve performance, while the latter is defined as the number of trading periods over which it occurs.
The maximum drawdown and drawdown duration are two additional measures that investors often uses to assess the risk in a portfolio. Where $R_a$ is the returns stream of the equity curve and $R_b$ is a benchmark, such as an appropriate interest rate or equity index. In that article I outline that the (annualised) Sharpe ratio is calculated via: We've already considered the Sharpe Ratio in a previous article.
Max drawdown duration matlab how to#
In this article we are going to discuss how to assess the performance of a strategy post-backtest using the previously constructed equity curve DataFrame in the Portfolio object.
Max drawdown duration matlab series#
In the last article on the Event-Driven Backtester series we considered a basic ExecutionHandler hierarchy.
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