Ma analysis isn’t simple to master despite its many advantages. When it comes to the process, mistakes can result in incorrect results that can have serious consequences. Recognizing these errors and avoiding them is crucial to unlock the full potential of data-driven decision making. The majority of these errors are due to omissions or misinterpretations which can be easily rectified by setting clear objectives and promoting accuracy over speed.
Another common error is to believe that the variable has normal distribution even though it does not. This can result in models being overor under-fitted, which can compromise confidence levels and prediction intervals. Furthermore, it could cause leakage between the test and the training set.
It is essential to select the MA method that fits your trading style. For instance, an SMA will be best for markets that are trending while an EMA is more reactive (it eliminates the lag that occurs in the SMA by placing priority on the most recent data). Additionally, the parameter of the MA should be chosen with care depending on whether you are looking for the trend to be long-term or short-term (the 200 EMA would suit a longer timeframe).
It’s important to double-check your work prior to submitting it to be reviewed. This is especially important when dealing with large amounts of data, as errors are more likely to occur. You can also have your supervisor or a colleague review your work to assist you identify any errors you might have missed.