Hi all. I am fairly new to this area and just joined the forum. I hope I can learn a lot from the people in this forum and also try to share my knowledge to newbies like myself.
That being said, I have gone through a couple of finance and trading courses (autotrading) and am working on my own code for that. I was hoping people here could help me with my problem. I am using MLP (4 layer, 50 neuron each) for making prediction on forex data. I have been working for a long time on the model now with no luck. I totally understand that prediction accuracy in forex is fairly low, and I am not expecting much. But I am almost %1 above/below random which I think could be better. Now I can provide you with all the details but it will be a pretty long post. I will mention the ones that are essential and ask my question. I am using 20min period for prediction, EUR_USD. I work on Oanda, and use 180 days data to train my model on. I download 5min data and resample to 20min. Then I use some features (my main question will be about these) and use their lagged versions up to 5 lags (5*20 min=1H) but the features contain, for example, moving averages with window of 50 (so I am not limited to 1H). I train my MLP without any regularization and am able to overfit. But no matter what kind of regularizer I add, the end result is that my model cannot generalize. More precisely, the training and validation loss both decrease, the training accuracy increases but the validation accuracy is either almost same value (about %50) or goes down. Right now I have a comprehensive set of features but I am using MA, EMA, real price and volume as features for testing (any feature I have been adding actually makes everything worse).
Now my question is, is MLP a good model for prediction in this case? What kind of regularization works better? and finally what features do you suggest I use?
That being said, I have gone through a couple of finance and trading courses (autotrading) and am working on my own code for that. I was hoping people here could help me with my problem. I am using MLP (4 layer, 50 neuron each) for making prediction on forex data. I have been working for a long time on the model now with no luck. I totally understand that prediction accuracy in forex is fairly low, and I am not expecting much. But I am almost %1 above/below random which I think could be better. Now I can provide you with all the details but it will be a pretty long post. I will mention the ones that are essential and ask my question. I am using 20min period for prediction, EUR_USD. I work on Oanda, and use 180 days data to train my model on. I download 5min data and resample to 20min. Then I use some features (my main question will be about these) and use their lagged versions up to 5 lags (5*20 min=1H) but the features contain, for example, moving averages with window of 50 (so I am not limited to 1H). I train my MLP without any regularization and am able to overfit. But no matter what kind of regularizer I add, the end result is that my model cannot generalize. More precisely, the training and validation loss both decrease, the training accuracy increases but the validation accuracy is either almost same value (about %50) or goes down. Right now I have a comprehensive set of features but I am using MA, EMA, real price and volume as features for testing (any feature I have been adding actually makes everything worse).
Now my question is, is MLP a good model for prediction in this case? What kind of regularization works better? and finally what features do you suggest I use?
