Forward testing is used to assess stability of the trading system in the changing market behavior. The system produced a CAR of In the example below, N was chosen to be Basics of Technical Analysis. The trader would be able to input or change the lengths of the two moving averages used in the system. This entails entering a range for the specified input and letting the computer "do the math" to figure out what input would have performed the best. A trader's next step is to apply the system to historical data that has not been used in the initial backtesting phase.
Stress Testing for Trading Strategy Robustness by Michael R. Bryant In the article on multi-market trading strategies, I discussed the concept of robustness, which I described as insensitivity to variations in the data on which the strategy is based.
Since this trading system trades relatively cheap stocks it is necessary to add slippage since we will not always be able to trade on the open price as recorded by the data. Now we can see what happens if we move the start dates slightly to make sure the results are not a fluke.
By dropping the start date back by two months we get a slightly lower return of Another thing we can do to stress-test the system is to try a different market or watch-list. For this test I will use the Russell Index. The system produced a CAR of In this test I will take the trade results and I use Monte Carlo to randomly alter the sequence of trades.
This will be done using the software from Equity Monaco. I have now performed a number of stress-tests in order to decide whether this trading system works. Personally, I am pretty happy with the results. Reducing or turning off short positions completely. Introducing a market timing filter and fundamental elements.
Increasing the total number of stocks held in the portfolio. Trading is a personal affair and some traders would find this system difficult to follow. Furthermore, markets are dynamic and the future is never known. This system is now in the public domain and it could easily stop working.
It may well perform poorly over the next couple of years. As always, traders should prefer to build their own trading systems in line with their own trading styles and risk preferences. Doing otherwise would be foolish. I know from experience how difficult it is to produce a profitable, technical trading system.
Trading systems were back-tested using Amibroker and historical data was obtained from Premium Data. Monte Carlo was conducted with Equity Monaco.
Generally speaking, varying the input variables randomly over a large number of iterations in order to generate a statistical distribution of results for the function that depends on those inputs is called Monte Carlo analysis. By repeating the stress test many times, we end up with multiple sets of trading results. To understand how the Monte Carlo process works, consider the example shown in Fig.
Original equity curve for a forex trading strategy. The equity curve depicted in Fig. This is one of the bonus strategies included with Adaptrade Builder. It was developed in March The last trades or so have been since release, which shows that it has held up well in real-time out-of-sample tracking. To illustrate how stress testing results can be analyzed using a Monte Carlo approach, consider the results of stress testing the forex strategy on the price data, as shown in Fig.
Along with the original curve, shown as the thicker green line, there are a total of 20 sets of results. The total number was kept as small as possible for illustrative purposes; more iterations will be used below in the remaining examples. Stress testing the forex strategy by varying the price data 19 times. The total net profit corresponding to each equity curve in Fig. In a Monte Carlo analysis, we can ask what the net profit is likely to be with a specified degree of confidence given the variation in the results.
We can interpret this result as follows: The same approach can be applied to any performance metric we might want to track. Now consider a more representative example, in which a total of samples were generated for the Monte Carlo analysis. Stress testing the forex strategy by varying the price data 99 times, for a total of equity curves.
Stress testing the forex strategy by varying the price data. As expected, the Monte Carlo results from modifying the price data show a reduction in performance compared to the results for the original price data.
However, the stress test results are still positive, indicating that the strategy is at least moderately robust. All of the inputs were modified by at least the minimum amount for each evaluation. The original equity curve is shown near the top of the chart as the thicker, green line.
Compared to the results for the price modifications, modifying the strategy inputs had a stronger effect on performance. Stress testing the forex strategy by varying the strategy inputs 99 times, for a total of equity curves. The Monte Carlo results for the same sample of performance metrics as above are shown in Table 2 below, which includes the results for the original input values.
Stress testing the forex strategy by varying the strategy inputs. The results from varying the starting bar for the same forex strategy are shown below in Fig. Compared to the results from the other two tests, relatively little effect is seen from varying the starting bar, suggesting that the strategy is mostly insensitive to this variable.
Stress testing the forex strategy by varying the starting bar 99 times, for a total of equity curves. The Monte Carlo results from this test are shown in Table 3 below, where they're compared to the results for the original starting bar. Stress testing the forex strategy by varying the starting bar. It's also possible to modify everything together or to modify combinations of variables, such as modifying the strategy inputs at the same time as the price data.
This means the strategy inputs, price data, and starting bar were randomly modified at the same time prior to evaluating the strategy. Clearly, this combination of stress tests is a severe test of the strategy's robustness.
One or two of the equity curves shown in Fig. Only one equity curve approaches the original one. The Monte Carlo results based on this test are shown below in Table 4. Stress testing the forex strategy by varying the price data, strategy inputs, and starting bar. Over-fitting is always a concern when developing a trading strategy. So-called stress tests measure how robust a trading strategy is, which is an indication of whether or not the strategy is over-fit.
This entails entering a range for the specified input and letting the computer "do the math" to figure out what input would have performed the best. For example, traders can tell the program which inputs they would like to add into their strategy; these would then be optimized to their ideal weights given the tested historical data.
Backtesting can be exciting in that an unprofitable system can often be magically transformed into a money-making machine with a few optimizations. Unfortunately, tweaking a system to achieve the greatest level of past profitability often leads to a system that will perform poorly in real trading.
This over-optimization creates systems that look good on paper only. Curve fitting is the use of optimization analytics to create the highest number of winning trades at the greatest profit on the historical data used in the testing period.
Backtesting and optimizing provide many benefits to a trader, but this is only part of the process when evaluating a potential trading system.
A trader's next step is to apply the system to historical data that has not been used in the initial backtesting phase. How to Start Trading: Testing Your Trading Plan.
When testing an idea on historical data, it is beneficial to reserve a time period of historical data for testing purposes. The initial historical data on which the idea is tested and optimized is referred to as the in-sample data. The data set that has been reserved is known as out-of-sample data. This setup is an important part of the evaluation process because it provides a way to test the idea on data that has not been a component in the optimization model.
As a result, the idea will not have been influenced in any way by the out-of-sample data, and traders will be able to determine how well the system might perform on new data, i. Prior to initiating any backtesting or optimizing, traders can set aside a percentage of the historical data to be reserved for out-of-sample testing. One method is to divide the historical data into thirds and segregate one-third for use in the out-of-sample testing. Only the in-sample data should be used for the initial testing and any optimization.
Figure 1 shows a time line in which one-third of the historical data is reserved for out-of-sample testing, and two-thirds are used for the in-sample testing. Although Figure 1 depicts the out-of-sample data in the beginning of the test, typical procedures would have the out-of-sample portion immediately preceding the forward performance. Correlation refers to similarities between the performances and the overall trends of the two data sets. Correlation metrics can be used in evaluating strategy performance reports created during the testing period a feature that most trading platforms provide.
The stronger the correlation between the two, the better the probability that a system will perform well in forward performance testing and live trading. Figure 2 illustrates two different systems that were tested and optimized on in-sample data, then applied to out-of-sample data. The chart on the left shows a system that was clearly curve-fit to work well on the in-sample data and completely failed on the out-of-sample data.
Trading systems that work: stress-testing the systems However, as mentioned already, the systems in the book are ideas so it pays to analyse them further so I shall now stress-test the system by taking different factors into account. Jul 09, · Trade system stress test - reloaded I will try the new trade system tomorrow. If the list jumps once, I'm out. If you want me to do stress tests on the system, make sure it's usable first. apologies, i didn't realise you wouldn't update the trading system to the newest internal build before testing it. Backtesting is a way to evaluate the effectiveness of a trading strategy by running the strategy against historical data to see how it would have fared.