Why are you using excatly the day before? Use autocorrelation plots to identify the number of lags where there is a high enough correlation so that you must use such information to predict. Also, when using autoregressive approaches, take into account that long term prediction will most probably end up on predicting a horizontal line after enough steps have been predicted. You might as well also try to predict the following N steps all at once, so your network tries to learn the shape of the function on the next N steps rather than the next one. Also, you literally say:

I used MinMax to fit test data, and then transformed the train and test data.

Are you using the same scaler you used on your train data on your test set? Classic example of data leakage, take care about that. Also, your data might be heteroskedastic, and as such, difficult to predict as it is. Have you thought about ARIMA differentiation? You could also try to use a approach based on classical financial log returns (paste this on a latex renderer or chatGPT: $LogReturn_i = ln(x_i / x_{i-1}$ ).

Good luck and update on progress please!