# Is it ok if i post process my ML model's output used to predict stock movement?

So I made a machine learning model which predicts stock movements which returns 1 for the price going up or 0 for the price going down. without the post processing the training accuracy and testing accuracy is in the range of 50% - 54% but if I add the postprocessing the testing and training accuracy goes above 75% depending on how long I train the model and the data i use to train and test it like RSI and MACD.

Here are the logs in the form of the three different post processing techniques:
On Training data:

[0.5000609533097647, 0.8257954406924296, 0.532244300865537]
8203
8202
[0.4964037547238815, 0.8203096428136047, 0.5323662074850665]
8203
8202
[0.49676947458246984, 0.8121418993051323, 0.5323662074850665]
8203
8202
[0.49737900768011706, 0.7832500304766549, 0.5323662074850665]


On testing data:

2021
[0.5121227115289461, 0.8090054428500743, 0.5314200890648194]
2020
[0.5096486887679367, 0.8169223156853043, 0.5319148936170213]
2021
2020
[0.5091538842157348, 0.8213755566551212, 0.5329045027214251]
2021
2020
[0.5091538842157348, 0.830282038594755, 0.5338941118258288]


As you can see the second technique works best. I just want to make sure that it is ok to do this and this and note that it worked repeatedly on multiple runs with different window sizes and data.

EDIT:
Just to be clear this works on different data and windows unless the validation accuracy stagnates at the start of training which results in an accuracy less than 50.

• This completely depends on the what type of post-processing you are applying and how you are applying it, and since this it not mentioned in your question we cannot answer if what you're doing is fine or not. Jan 1 at 13:41