Despite its many benefits, analysis can be a challenge to master. Mistakes often arise in the process, resulting in incorrect results which can have grave consequences. Recognizing these errors and avoiding them is crucial in maximizing the potential of data-driven decision-making. The majority of these errors result from omissions or misinterpretations that can be easily rectified by setting clearly defined goals and promoting accuracy over speed.
Another common error is to believe that a variable is generally distributed, when it isn’t. This can lead to over- or under-fitting their models, compromising the confidence levels and intervals of prediction. In addition, it could result in leakage between the test and the training set.
When selecting when choosing an MA method it is important to choose one that suits the needs of your trading style. For example, a SMA is ideal for markets that are trending, while an EMA is more reactive (it removes the lag that is present in the SMA by putting a priority on the most recent data). Additionally, the parameter of the MA should be chosen carefully based on whether you are seeking either a long-term or short-term trend (the special info 200 EMA will be more suitable for a longer-term timeframe).
Finally, it’s vital to always double check your work before submitting it for review. This is especially true when dealing with large amounts of data, as mistakes could be more likely to occur. It is helpful to have a manager or colleague take a look at your work may help you catch any mistakes you may have missed.
