How do I create & train a neural net to predict a stock closing price at T+5min using OHLC and other data?

Question

I'm trying to create a neural network and train it to test predictability of short term stock price movements.

I've collated a 1-min open, high, low, closing and volume dataset for a particular stock. The idea is to train a network to crunch the data for times T - 1, T - 2, T - 3, T - 4, T - 5 to predict T + 5 closing price. Ideally, the network takes 7 input vectors

{{open}, {high}, {low}, {close}, {volume}, {dayofweek}, {minutes_since_open}}

over the past 5 minutes, i.e., 7 inputs x 5 time-steps, to produce a single output: {close} at T + 5.

I'm still a bit rough, dusting off my Mathematica skills, as the last time I used it was with V7, before all the new features :)

Would really love some help here...

Here's a link to the data set (csv) :)

Answer 1

This is for example

data = Import@
  "C:\\Users\\myAccount\\Downloads\\fb_1min_1q2019_ohlcvmd (2).csv";
trainset = 
 Table[data[[i]] -> data[[i, 4]], {i, 2, 
   Length@data - .3 Length@data}];
predictor=Predict[trainset, Method -> "NeuralNetwork"]
List @@ predictor // "Model" /. # & // "Network" /. # &

Next,For Answer.

First Setting Up the Network. I used eLU as activation layer.

trainet=NetGraph[
{BatchNormalizationLayer[],
LinearLayer[30],
ElementwiseLayer[LogisticSigmoid[-500#]*(1*Exp[#]-1)+LogisticSigmoid[500#]*#&],LinearLayer[1]},
    {NetPort["Input"]->1,
    1->2,
    2->3,
    3->4},"Input"->7,"Output"->1]

Train it.

trainedNet = 
  NetTrain[trainet, <|"Input" -> Keys@trainset, 
    "Output" -> Evaluate@({#} & /@ Values@trainset)|>, 
   LossFunction -> MeanAbsoluteLossLayer[]];

Make ValidationSet And Check the error.

validationset = 
  Table[data[[i]] -> data[[i, 4]], {i, 
    IntegerPart[Length@data - .3 Length@data + 1], Length@data}];
errors = (trainedNet[Keys@#1] - Values@#1) & /@ validationset;
ListLinePlot@Flatten@errors

---

When Predict receives a matrix as an input, it is internally vectorized, so it seems that there is no problem in this way.