Walk forward analysis forex malaysia

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Generally, when these changes occur, they arrive at a fast pace, heavily degrading the trading performance. The WFA may extend its study in a wide range of time; however, analyses and evaluates the trading performance by separate windows. The broad range of results obtained by the study could provide the developer with a piece of useful information about the market changes impact the trading strategy performance.

Parameters Selection As a robust optimization system, the WFA can provide the most appropriate parameters for real-time trading. Simultaneously, the walk-forward analysis provides the strategy developer with the duration of the optimal period of time in which the set of parameters will consistently produce real-time benefits before the deterioration in trading system performance occurs. This concept is also valid in the walk-forward analysis.

In this context, the walk-forward analysis must be large enough to produce several trades such that a large amount of data can be generated for the study. According to Pardo, in his work, he says that a WFA must be as long as possible, usually at least 10 to 20 years, whenever possible. Finally, he adds that these multiple walk-forwards combined performance will often be sufficient to produce the statistical rigor required in the analysis.

Developing the Walk-Forward Analysis A walk-forward analysis consists of two stages. In the first section, traditional backtest optimization is developed. The second stage, which is the one that characterizes a walk-forward analysis, evaluates the performance of the parameters using an additional sample that was not used during the previous optimization stage.

The walk-forward analysis process requires the following elements to be set: Scan range for variables to optimize. The developer should consider that the scan range uses a computational resources level that it will use to evaluate and weight the parameter to be optimized. In this regard, an exploration in a small number of historical simulations will consume less computational resources than a more extensive optimization.

Identify a target or a search function. The developer must define what the purpose of the optimization study is. These three factors will define the quality of any trading system. A fourth key factor is the number of monthly trades delivered by the system. Size of the optimization window. Generally, this optimization range can vary from 3 to 6 years.

This duration depends on different factors such as, for example, the market, the type of trading strategy, the confidence level required by the developer in the optimization results, among other factors determined by the developer. Size of the walk-forward out-of-sample window. This period is defined based on the optimization window. The length of the optimization window is determined by: Availability of data. It is known as well as Walk Forward optimization or Walk Forward validation.

But the base word is Walk Forward, and I will show you what that means. We have the optimizer which works pretty similar to the optimizer in Meta Trader but works much faster, and I will show it to you right now. So the optimizer finds better parameters for the strategy, and you can normally see on the balance chart the old balance line in a grey color, and you can see how the parameters are changing. We have the range, the minimum and the maximum, and we have the step.

Now, when we are optimizing a strategy, there is a risk to over-optimize the strategy. This means that the Expert Advisor Studio might find better parameters for the indicators, but they will be precisely for this period. Walk Forward is a tool to prove over-optimization If we run a strategy after that on Meta Trader, it might show losses.

So this is why we either use round steps for optimization like 5 or 10 or 20, or we use the other robust tools like the Walk Forward validation, the Monte Carlo, and the Multi Market. The Expert Advisor Studio can work on different tabs simultaneously.

So you can open it in another tab and run the generator there. Or if you want to run the generator for a couple of currencies, you can do this simultaneously but on different browsers, on separate tabs. Out of Sample is what the Walk Forward analysis uses. If you go to the Walk Forward in the EA Studio , you will see its quite different from the optimizer.

So here, what we see are different segments. We have five segments. And on the right side, you have the Out of Sample Net profit. And you can see that the primary outcome is; in the first segment it makes a profit, in the second one it loses, then a small profit in the third one. The fourth one, the last one lose.

Now, what is the idea of the Walk Forward analysis? This is the whole back-tested period that we have. So we can say the first segment is from this point over here, from the beginning till the end of this green line or to the second vertical line. This is precisely what I have explained about the Out of Sample. After that, it takes the parameters that result from this optimization, and including this period where we had simulated trading, it performs new optimization.

This is the optimization period for the second segment. And then it will simulate trading for the next zone, for this period, for the second green line. After that same thing repeats. It will add this period, and it will do optimization for this period over here, and it will simulate trading for the next segment.

And this repeats till the end. Now, what is the basic idea of having the Walk Forward analysis? It is to recognize the over-optimized strategy. So this strategy is the initial strategy that was generated. The over-optimized strategy is what we need to be scared of. And we want to know if this strategy is over-optimized for this period or not. If it is robust enough to use it in the future.

Now, the Walk Forward analysis performs optimization for one period then it simulates trading. It includes the next period to the previous one, performs optimization, and simulates trading for the next one. Now, if the Walk Forward fails to find better parameters, this gives us a sign that the initial strategy might be over-optimized. Because it shows the result for the complete backtest. If the Walk Forward analysis fails to show a better result, this means that the parameters of the initial strategy were over-optimized.

So let me run now the Walk Forward, and you will see how it works. It will start to optimize the strategy for the first segment, and then it will simulate trading for this zone over here between the first vertical and the second vertical line. And if I go to the parameters, you can see that I have here the original settings.

The parameters. What green and red symbolize. I can see the parameters of the first segment, and you can see that the RVI signal changed to And then, the commodity channel index or the CCI changed the period and as well the level. And when it is ready with the first segment, it shows the second one, and it is optimizing again. Going back to the equity chart, I can see here that the simulated trading shows worse results from the initial strategy.

And then, for the second segment, it shows a positive result of 9. And this is better because the initial strategy during this period has a negative of

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The second stage, which is the one that characterizes a walk-forward analysis, evaluates the performance of the parameters using an additional sample that was not used during the previous optimization stage. The walk-forward analysis process requires the following elements to be set: Scan range for variables to optimize. The developer should consider that the scan range uses a computational resources level that it will use to evaluate and weight the parameter to be optimized. In this regard, an exploration in a small number of historical simulations will consume less computational resources than a more extensive optimization.

Identify a target or a search function. The developer must define what the purpose of the optimization study is. These three factors will define the quality of any trading system. A fourth key factor is the number of monthly trades delivered by the system. Size of the optimization window. Generally, this optimization range can vary from 3 to 6 years.

This duration depends on different factors such as, for example, the market, the type of trading strategy, the confidence level required by the developer in the optimization results, among other factors determined by the developer. Size of the walk-forward out-of-sample window. This period is defined based on the optimization window. The length of the optimization window is determined by: Availability of data.

Depending on the type of market and accessibility of the data to perform the analysis, the developer might find a restriction on the amount of historical data needed to perform strategy optimization. Trading strategy style. A short-term trading strategy should require a smaller optimization window than a long-term strategy.

The pace of trading strategy. The pace of strategy is highly variable and tends to vary from strategy to strategy. For example, a long-term strategy, such as swing trading, will slower than a short-term trading strategy, which will require less time to produce the same number of trades. The relevance of data. The relevant data depends on a large number of factors that could empirically be determined. However, Pardo proposes a basic guideline stating that a short-term strategy can be tested using one or two years of data, whereas an intermediate-term strategy would require two to four years, and a long-term strategy four to eight years of data.

The developer expects the parameters employed will generate benefits in the historical simulation. Furthermore, it also requires a trading strategy to produce profits in real-time market trading. If you go to the Walk Forward in the EA Studio , you will see its quite different from the optimizer.

So here, what we see are different segments. We have five segments. And on the right side, you have the Out of Sample Net profit. And you can see that the primary outcome is; in the first segment it makes a profit, in the second one it loses, then a small profit in the third one.

The fourth one, the last one lose. Now, what is the idea of the Walk Forward analysis? This is the whole back-tested period that we have. So we can say the first segment is from this point over here, from the beginning till the end of this green line or to the second vertical line.

This is precisely what I have explained about the Out of Sample. After that, it takes the parameters that result from this optimization, and including this period where we had simulated trading, it performs new optimization. This is the optimization period for the second segment. And then it will simulate trading for the next zone, for this period, for the second green line. After that same thing repeats. It will add this period, and it will do optimization for this period over here, and it will simulate trading for the next segment.

And this repeats till the end. Now, what is the basic idea of having the Walk Forward analysis? It is to recognize the over-optimized strategy. So this strategy is the initial strategy that was generated. The over-optimized strategy is what we need to be scared of. And we want to know if this strategy is over-optimized for this period or not.

If it is robust enough to use it in the future. Now, the Walk Forward analysis performs optimization for one period then it simulates trading. It includes the next period to the previous one, performs optimization, and simulates trading for the next one. Now, if the Walk Forward fails to find better parameters, this gives us a sign that the initial strategy might be over-optimized. Because it shows the result for the complete backtest.

If the Walk Forward analysis fails to show a better result, this means that the parameters of the initial strategy were over-optimized. So let me run now the Walk Forward, and you will see how it works. It will start to optimize the strategy for the first segment, and then it will simulate trading for this zone over here between the first vertical and the second vertical line. And if I go to the parameters, you can see that I have here the original settings.

The parameters. What green and red symbolize. I can see the parameters of the first segment, and you can see that the RVI signal changed to And then, the commodity channel index or the CCI changed the period and as well the level. And when it is ready with the first segment, it shows the second one, and it is optimizing again. Going back to the equity chart, I can see here that the simulated trading shows worse results from the initial strategy.

And then, for the second segment, it shows a positive result of 9. And this is better because the initial strategy during this period has a negative of Walk Forward analysis update. This way, we will see at the end if we will have a better strategy or not. Also, in the end, the Walk Forward analysis will show us what is the result of the complete backtest with the new parameters. The parameters from the last segment.

It was updated in the middle of This is why I decided to include it in this course. So if I continue with the test, it will reach the fourth segment, and here, only the period of CCI changed 1 point lower than the previous one. So one more time, to summarize it, what it does: Walk Forward analysis optimizes one period and then simulates trading for the next one. Then it includes this period to the previous and not to the whole last period.

These are the parameters for the last period and is just completing the backtest. So now, we have different values from the beginning. These are the original values that we have. The RVI signal was changed.