Despite its many benefits, analysis can be a challenge to master. In the process, errors could lead to incorrect results with serious consequences. Recognizing these errors and avoiding them is essential to fully harness the power of data-driven decision-making. Most of these mistakes result from omissions, or misinterpretations that can be easily corrected by setting clear goals and promoting accuracy over speed.

Another common error is to think that a variable is typically distributed when it’s not. This can lead to over-/under-fitting their models, resulting in lower prediction intervals and confidence levels. Additionally, it can cause leakage between the test and the training set.

When choosing the MA method, it is essential to select one that fits the needs of your trading style. For example, a SMA will be best for trending markets while an EMA is more receptive (it removes the lag that is present in the SMA by putting a priority on the most recent data). The MA parameter should be carefully chosen depending on if you are looking for the long-term or short-term trend. (The 200 EMA would be suitable for a long-term timeframe).

It is essential to double-check your work before you submit it for review. This is particularly important when dealing with large quantities of data as errors could be more likely to occur. You can also have your supervisor or a colleague review your work to discover any errors you may have missed.

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