Query about Loss Functions for LSTM models (Binary Classification)

Question

I'm working on building an LSTM model to binary classify price movements.

My training data is data I simulated, it's a 2,000 rows * 3,780 columns dataframe of price movements. I have a separate labels file that classifies price movements as either 1 or 2 (due to memory).

From what I've read, it appears as though two loss functions are the most appropriate for binary classification:

1. Binary Cross-Entropy
2. Hinge Loss

I've implemented two separate LSTM models in google colab which run as expected.

I have the same code for both models, with just the loss function being changed from a Squared Hinge loss in the former to a Binary Cross Entropy in the latter.

My issue is deciding which is the better model, as the model outputs give conflicting outputs.

Hinge Loss Output:

Training Output:

1. The loss starts at 0.3, then goes to 0.20 after and stays pretty much constant for the remaining 98 epochs.
2. The MSE does decrease marginally across the epochs from 2.8 to 1.68 at the end. Average MSE = 1.72.
3. The accuracy is 0.00 on every epoch (which I don't understand).

Validation Output:

1. The Validation loss starts at 0.0117 and goes to 9.8264e-06 by the end.
2. The Validation MSE starts at 2.4 and ends at 1.54. Average Validation MSE = 1.31.
3. The Validation accuracy is 0.00 on every epoch (which again I don't understand).

Binary Cross Entropy Loss Output:

Training Output:

1. The loss starts at 8.3095, then goes to 3.83 after and stays pretty much constant for the remaining 97 epochs.
2. The MSE does decrease marginally across the epochs from 2.8 to 1.68 at the end. Average MSE = 1.69.
3. The accuracy starts at 0.00 and increased to roughly 0.8 by end.

Validation Output:

1. The Validation loss starts at -0.82 and goes to -.89 by the end.
2. The Validation MSE starts at 1.56 and ends at 1.53. Average Validation MSE = 1.30.
3. The Validation accuracy starts at 0.00 and increased to roughly 0.997 by end.

So, I have a question now:

Why is the accuracy of the SHL model 0.00? Is there an error in my model?

My code is saved here: https://nbviewer.jupyter.org/github/Ianfm94/Financial_Analysis/blob/master/LSTM_Workings/LSTM_Model.ipynb

The training data* and labels data are saved at the below location: https://github.com/Ianfm94/Financial_Analysis/tree/master/LSTM_Workings

*Training data here is split into two separate files due to Github limiting file size to 25 mb's.

Any help would be greatly appreciated.

Thanks.