John Peter

Backtesting Expert Advisor

What is the best way to test forex trading strategies? Rapid research on the subject suggests that the focus is on software testing. Despite the importance of the program, the program is only part of the whole picture. For comparison, little attention is paid to the problem of the test method. However, it is important to obtain objective assessments of the quality of the trading strategy.

In the classical approach, the purpose of the test is to manage risk and provide numerical quality estimates. This is achieved by assessing the probability of failure based on the results of carefully selected test scenarios that will run over the available time. We apply these classic principles to the testing of foreign exchange strategies aimed at risk management by defining an appropriate testing strategy and developing test scenarios.

risk management. The purpose of the test is to reduce the risk of failure and the potential impact. More testing usually means less risk. The risk can be practically eliminated for simpler systems, when all possible raw materials can be tested fundamentally. The situation in the foreign exchange market is significantly different. As markets are unpredictable, an infinite number of "unused raw materials" will remain after any number of trials. Therefore, no successful test guarantees further action.

Does this mean that testing a Forex Tester 4 strategy is completely pointless? In our opinion, this question is very theoretical - regardless of the correct answer.

Of course, the risk reduction target must remain through scrutiny. However, we cannot make it clear that testing the strategy will manage business risks in all situations. We therefore need to review the definitions of failure and, more importantly, test the criterion of success. For example, we can measure success by identifying specific circumstances in which a trading strategy can operate with certain opportunities.

The testing strategy is another essential component of classical testing. The right strategy strikes a balance between the level of risk and the effort required to achieve it. The unpredictability of the foreign exchange market makes the test strategy even more important: as we remember, no test still gives us 100% test coverage. On the other hand, testing a number of strategy parameters against a large amount of market data is a complex and truly time-consuming task. Therefore, the test strategy is responsible for selecting the basic test scenarios in order to achieve a reliable level of quality within a reasonable time.

To facilitate risk assessment, we need to simplify. One approach is to assess the risk of the strategy independently of market risk. This is in line with the observation that trading strategies often fail when market behavior changes (high market risk) rather than "stable" market conditions (low risk). The advantage of this approach is that in case of low market uncertainty, we can use all known classical testing methods to obtain quantifiable quality assessments.

Test scenario. Classical test scenarios include the most common (positive) and most likely use (negative) failures. Our approach is to use market models as scenarios. Although the foreign exchange market is unpredictable, the patterns are clearly visible. Elliott waves are one of the well-known examples of market models. We try to determine the most appropriate model for our test scenarios by analyzing the performance of trading strategies under real market conditions.

For example, general markets are a positive scenario. All trends are similar to a clear general market trend, so market risk is low. Negative scenarios are those in which market risks are high, such as when market conditions change or trading strategies fail. The main criteria for assessing adverse scenarios are to identify them as early as possible and to reduce losses.

In the case of positive scenarios, the main purpose of the test is to improve the parameters of the trading strategy. In the past, the most common market is, for example, a combination of trends of different shapes and sizes in a test category and at different times. Each direction is unique - for example, dragging is different in shape and size. In this case, the quality of the strategy is essentially the quality of the algorithm and its implementation. As a result, this test essentially yields more valuable results than a strategy that harmonizes markets at random.

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