Train Accuracy : 95% - Test Accuracy : 35%

Model_v2 is the code used to train a time series forecasting model. Actually, the training accuracy is quite good (i.e. 95%) and the testing accuracy is quite bad (i.e. 35). There's clearly the presence of overfitting. The purpose here is to improve the testing accuracy. I tried many things, but none of them worked well. I tried to increase the dropout ratio, increase the learning rate, increase the number of epochs, changed the number of input features. Many people think it is going to work with the right recipe. Suggestions : 1- Use weight decay (L2 regularization on the weights) 2- Try enforcing that all batches have the same number of -1, 0, 1 (i.e. classes) 3- "Setting a confidence threshold before accepting a prediction...so, unless buy or sell > 0.8, then do nothing"