# Do we have anything like accuracy and loss in RNN models?

I have a paper about trading which has been implemented with RNN on Tensorflow. We have about 2 years of data from trading. Here are some samples :

Date, Open, High, Low, Last, Close, Total Trade Quantity, Turnover (Lacs)

2004-08-25 , 1198.7, 1198.7, 979.0, 985.0, 987.95, 17116372.0, 172587.61

2004-08-26 , 992.0, 997.0, 975.3, 976.85, 979.0, 5055400.0, 49828.65

I need to predict the the future of trading (for example, the latest 10 days ). So, how can I make sure that my model is working correctly. Do we have any "accuracy" or "loss" like what we have in Deep Learning?

There has to be a loss since you're dealing with supervised learning and the typical loss metrics used are the same as you would see in feedforward networks (usually binary cross-entropy), the main difference being loss would be calculated between the true label at a particular time stamp $$(t)$$ and the prediction made from the subset of the network until time-stamp $$(t-1)$$. This leads the loss to act on all timestamps.