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Despite its many advantages, analysis can be a challenge to master. In the process, errors can lead to inaccurate results with serious consequences. Recognizing these mistakes and avoiding them is essential for harnessing the full potential of data-driven decision-making. The majority of these errors are caused by omissions or misinterpretations, which are easily rectified if you set specific goals and promote accuracy over speed.
Another mistake that is common is to think that an individual variable is in a normal distribution when it does not. This can result in models being either overor under-fitted, which can compromise confidence levels and prediction intervals. Additionally, it can cause leakage between the test and the training set.
It is essential to select an MA method that fits your trading style. A SMA is ideal for markets that are trending, while an EMA will be more reactive. (It removes the lag in the SMA because it assigns priority to the most recent data.) The MA parameter should be carefully considered based on whether you are seeking an ongoing trend or a short-term one. (The 200 EMA is a good choice for a longer period of time).
In the end, it is essential to ensure that you double-check your work prior to you submit it for review. This is particularly important when dealing with large amounts data, as errors are more likely to occur. You could also ask an employee or supervisor look over your work to identify any errors you might have missed.