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) :)

  • $\begingroup$ Have you looked into the Predict function to get started? $\endgroup$
    – kickert
    Commented Jul 8, 2019 at 1:53
  • $\begingroup$ My understanding is that Predict is more of a statistical function not a NN per se.... am I correct? $\endgroup$ Commented Jul 8, 2019 at 10:16
  • $\begingroup$ @CuriousDudeFromEgypt, Predict is considered supervised machine learning. You put labeled data in and it builds a model you can use for future data. I recommend you start there before you move into the NetTrain family of functions. $\endgroup$
    – kickert
    Commented Jul 8, 2019 at 11:33
  • $\begingroup$ @CuriousDudeFromEgypt Predict[data, Method -> "NeuralNetwork"] does use NeuralNetwork. $\endgroup$
    – Xminer
    Commented Jul 8, 2019 at 11:40
  • $\begingroup$ I retracted the flag because I found the person in the wolfram community dealing with the topic. $\endgroup$
    – Xminer
    Commented Jul 8, 2019 at 11:48

1 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" /. # &

Mathematica graphics

Next,For Answer.

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


Mathematica graphics

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;

Mathematica graphics

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

  • $\begingroup$ I spent some time trying to get my head around the example above...some stuff I don't understand... could you break down List @@ % // "Model" /. # & // "Network" /. # & to me please? Also the training set is a {7} -> {1} set....what I'm looking for is a {5x7}->{1} set to use for training, is there a way to use Predict with that? $\endgroup$ Commented Jul 25, 2019 at 17:26
  • $\begingroup$ updated my code. check the structure of List@@predictor,then you'll notice something like <|"Model"-> ....<|"Network"->...|>|> I used 7-dimensional input in the answer because it took time, or as an example it was cumbersome. Try replacing the input part with Flatten@data [[i-4;;i]]-> data[[i,4]] and "Input"->7 with "Input"->35 $\endgroup$
    – Xminer
    Commented Jul 25, 2019 at 17:35
  • $\begingroup$ Hmmm...so the idea is to flatten the 5x7 input matrix into 35? Also, the 5 = T-1, T-2... T-5 vectors , each vector is o,h,l,c,vol,minuteindex,dayoftheweek. The o,h,l,c numbers are similar but vol is different, min runs from 1 to 390 and day runs from 1 to 5... wouldn’t flattening make a mess of all of that? Also does normalization also make its own mess? Is it better to normalize each vector individually before feeding the network? $\endgroup$ Commented Jul 25, 2019 at 20:01
  • $\begingroup$ please check my update. $\endgroup$
    – Xminer
    Commented Jul 25, 2019 at 22:03

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