Max drawdown geekforgeek6/29/2023 ![]() ![]() Plt.table(cellText=np.round(btstats. If you would like to see these ratios applied to a more realistic backtest you can take a look at this crypto-algo trading example ![]() As with the Sharpe and Sortino, higher values are preferable. It appears that Microsoft performs the best according to this ratio. The Sharpe ratio also provides a useful metric to compare investments. You want to maximize your profit by choosing a single day to buy one stock and choosing a different day in the future to sell that stock. This allows us to adjust the returns on an investment by the amount of risk that was taken in order to achieve it. The Sharpe ratio is the most common ratio for comparing reward (return on investment) to risk (standard deviation). For every $1 you invested in Apple in 2013 you would now have approximately $7 and so-forth. The plot shows the growth of $1 invested on 1st Jan 2013 until 10th Oct 2020. df = stocks.pct_change().dropna()ĭf = df.mean(axis=1) # 20% apple. Plot the normalized stock prices for comparison. Execute the following code block in your editor: import pandas_datareader.data as web In order to get the data necessary to complete this analysis we will make use of Pandas Datareader, which allows us to directly download stock data into Python. Port: Equally weighted portfolio of the securities above.Since the statistics in question are usually calculated on a portfolio, we will add an equal weighted portfolio to the analysis also. In order to provide examples on real data we will use the following stocks to illustrate the concepts shown. As with lookback options, the first instinct is to price them using Monte Carlo techniques, but one can actually do so much more quickly using a multi-level PDE solver, at least for sufficiently simple SDEs.In this article we will calculate the a number of well know statistics related to risk and reward in equities. So far as efficient computation goes, we can regard this as a payoff very similar to a lookback option (much as in the PDF you linked). Therefore you should play with a variety of parameterizations to estimate your model error. (After the clarification, this answer is no longer relevant)Įxpected maximum drawdown is going to be highly sensitive to your choice of SDE, and to your calibration of it. def maxdrawdown(returnseries): compret (returnseries+1).cumprod() peak compret.expanding(minperiods1).max() dd (compret/peak)-1 return dd.min() maxdrawdowns df.apply(maxdrawdown,axis0) () plt. Rd.DrawDown, rd.DrawDownPeak, rd.DrawDownTrough, rd.PeakIndex, rd.TroughIndex)) Python code to calculate max drawdown for the stocks listed above. Int _startIndex, _endIndex, _troughIndex Here is the code of the simple drawdown class used for the comparisons: public class SimpleDrawDownĪnd here is the code for the full efficient implementation. We are achieving about a 20:1 improvement in calculation time. Test10 - running drawdown test with 500 period rolling window. Test9 - running drawdown test with 360 period rolling window. Test8 - running drawdown test with 180 period rolling window. Test7 - running drawdown test with 60 period rolling window. Here we compare to the results generated from my efficient rolling window algorithm where only the latest observation is added and then it does it's magic test6 - running drawdown test with 30 period rolling window. Test5 - simple drawdown test with 500 period rolling window. Test4 - simple drawdown test with 360 period rolling window. ![]() Test3 - simple drawdown test with 180 period rolling window. Test2 - simple drawdown test with 60 period rolling window. Here we take a simple drawdown implementation and re-calculate for the full window each time test1 - simple drawdown test with 30 period rolling window. I want to share this as the effort required to replicate this work is quite high. I have gone ahead and written a solution to this in C#. This is quite a complex problem if you want to solve this in a computationally efficient way for a rolling window. ![]()
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