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.
#Portfolio drawdown pandas 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.
#Portfolio drawdown pandas series#
Mdd, sd, ed = max_draw_down_relative(p, b)į, a = plt.subplots(2, 1, figsize=)Ĭum].plot(title='Cumulative Absolute', ax=a)Ī.axvspan(sd, ed, alpha=0.1, color='r')Ĭum].plot(title='Cumulative Active', ax=a)Ī.axvspan(sd, ed, alpha=0.In the last article on the Event-Driven Backtester series we considered a basic ExecutionHandler hierarchy. The drawdown caclulation can now be made analogously using the formula above: dd = (p * b0 - b * p0) / (p0 * b0) p0 = pd.Series(p.ix.values, index=p.index)ī0 = pd.Series(b.ix.values, index=b.index) So, we generate a series of ' whens' captured in cam ( cumulative arg max) and subsequent series of portfolio and benchmark values at those ' whens'. The difference is that we want to keep track of what the p and b were at this time and not the difference itself.
We get this series of cumulative active returns with p - b. Similar to the absolute case, at each point in time, we want to know what the maximum cumulative active return has been up to that point. P0 = pd.Series(p.iloc.values, index=p.index)ī0 = pd.Series(b.iloc.values, index=b.index) This is how we can extend the absolute solution: def max_draw_down_relative(p, b):Ĭam = pmb.expanding(min_periods=1).apply(lambda x: x.argmax()) The active return from period j to period i is: the variables below are assumed to already be in cumulative return space. Starting with a series of portfolio returns and benchmark returns, we build cumulative returns for both. I wanted to follow up by asking how others are calculating maximumĪssumes that the solution will extend on the solution above.
The max drawdown is then just the minimum of all the calculated drawdowns. r = returns.add(1).cumprod()Īt each point in time, the current drawdown is calcualted by comparing the current level of the return index with the maximum return index for all periods prior. We start by generating a series of cumulative returns to act as a return index. It takes a return series and gives back the max_drawdown along with the indices for which the drawdown occured. This is what I implemented for max drawdown based on Alexander's answer to question linked above: def max_drawdown_absolute(returns): I wanted to follow up by asking how others are calculating maximum active drawdown? This calculates Max Drawdown. I recently asked a question about calculating maximum drawdown where Alexander gave a very succinct and efficient way of calculating it with DataFrame methods in pandas.
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