Working on a forecasting model can be a unique experience. But besides using a defect tracking tool to ensure it’s free of bugs, you must also make sure that the model is as accurate and reliable as possible. Perhaps you’re not familiar with how to do this if you’ve just joined this field, but once you figure it out and learn the different steps, you will become more confident. Model back-testing is what developer teams use to ensure the reliability of their projects.
But how does back-testing work? This article will tell you everything about it so you can use it alongside a test management tool.
Defining Back-Testing
Back-testing represents a strategy where a model is tested to figure out how accurate it is. It tests the way your project would have performed in the past. The predictive model is applied to historical information. What is great about it is that it allows traders to apply various trading strategies without risking their capital.
The idea behind this type of testing is that if the strategy didn’t show favorable results in the past, chances are it will also have a negative experience in the future. Experts who back-test models look at their risk level and profitability. Teams generally take data and split it into two separate parts, respectively: the validation set and the training set. The value set analyzes how a model works on unseen data, while the training one operates to train a model.
If a strategy had good results in the past, then the back-test is successful and proves to the team that the project will show positive results in the future as well.
Possible Complications
While spreading the data across two different areas, respectively validation sets and train sets, is a very common practice, it has its own drawbacks. If you keep doing this to analyze the test set again, the winning model may only coincidentally end up being the best. This may be due to a specific factor of the information that shows up in the test set.
This is why experienced teams usually make a validation set that one can only use when the process comes to an end. This way, they can see if the selected technique is accurate in a real-world scenario. Creating unbiased datasets is necessary to make sure there are no flaws in your model.
Multiple Measures for Back-testing
As you keep doing back-tests and getting more experienced, you will notice that there are multiple measures that apply. These involve:
- Volatility
- Market Exposure
- Risk-adjusted return
- Return
The Bottom Line
Mixing your test management software methods with back-testing will tell you more about how good and accurate your model is. Back-testing helps reveal the accuracy of a certain model, and this is commonly done by splitting the data across two separate areas. However, if the data sets are biased, you will end up with a flawed project. So, you should always ensure the data is representative and unbiased before testing.
