![]() This will be shown in the following sections. In the next section we bring it all together by running and analysing a single backtest, which sets the scene for running parameter sweeps with hundreds or thousands of parameters sets for this strategy. Now we have three metrics to characterise our strategies: PnL, Sharpe ratio and drawdown. We note a drawdown of approximately -46 dollars, which is confirmed by the graph. Running this on our random price data we get the following plot: This could be build into a Pandas data frame but in this case I leave you with the looped form for clarity. If we were to vectorise this we would need a rolling maximum function. Looking at the code you will note that it contains a loop, unlike the vectorised examples for Sharpe and PnL presented earlier. ![]() In the example below we calculate it in absolute terms, in order to be consistent with further sections. Drawdown can be calculated in relative or absolute terms. It denotes our loss after our strategy has reached another high point in PnL (high watermark). The example above shows clearly that it is absolutely necessary to have additional metrics that will help us to characterise a strategy. It is very important to be aware of this. This is, of course, a very artificial example but it shows very graphically that this metric has its limitations. However, in the sorted case the strategy would blow up very quickly. The result looks like this where the blue line is the shuffled case and the green the sorted case:īoth of them have the same Sharpe ratio of 1.95 and the same final PnL. Let’s do this and compare the normal (shuffled) and the sorted case with the code below: However, what happens if this is not the case? In the extreme case our returns would be sorted from the smallest to the largest. Normally, we assume that our price data are shuffled thoroughly, i.e. For strategies that are different, especially if they work on a different time granularity, the Sharpe ratio can give very skewed results.Īnother important fact to take into account when using the Sharpe is entropy. Note, that the Sharpe ration is only a good metric to compare similar strategies. This is chosen over relative returns to avoid inflation of the Sharpe due to initial (very small) PnL values. In this code snippet we calculate the Sharpe based on the absolute returns. Sharpe = np.nanmean(retsx)/np.nanstd(retsx)*np.sqrt(252) We have already produced a PnL-curve in the previous section and this is what we are going to use now. Let’s have a look at the code for the calculation. If this big jump does not happen or even goes in the other direction, we are in trouble. What this means is that we get the same return for much less risk since we have lots of little events contributing to the overall PnL whereas in the first example we have one big step. So, if we have two PnL series with the same final PnL and in one all the money is made in one single time step whereas in the other we have a consistently upward trending PnL curve, the latter will give us a much better Sharpe because the standard deviation of the returns would be much lower. Basically, we divide the mean of our returns by the standard deviation of the returns (risk). Let’s start with the Sharpe ratio which is also called the risk-adjusted return. In this section we will add two more metrics that are very important for strategy evaluation: Sharpe ratio and drawdown. ![]() We also calculated our first metric – PnL and tested its functionality. Putting drawdowns and the Maximum Drawdown (MDD) together in a dataframe so you can compare the result: > df_dd = pd.In the previous sections of Quant Basics we looked at producing data sources and how to write a vectorised backtest. > drawdown = 1 - final.div(final.cummax()) Pandas.expanding will apply a function in the manner pandas.rolling does, but with a window that starts at the beginning of the dataframe and expands up to the current row (more info about the Window Functions here and pandas.expanding): > cum_returns = (1 + final).cumprod() You can get a dataframe with the maximum drawdown up to the date using pandas.expanding()( doc) and then applying max to the window. ReturnsĬode: cum_returns = (1 + final).cumprod()Ĭan anyone help me in solving this. Is there any way possible to calculate the maximum draw down using returns of the portfolio. I have tried the below code and did see many stackexchange questions. ![]() I am trying to calculate the MaxDrawdown using the returns. I have DataFrame final with returns of my portfolio.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |