Q:

Identifying machine learning data to make predictions

As a learning exercise I plan to implement a machine learning algorithm (probably neural network) to predict what users earn trading stocks based on shares bought , sold and transaction times.  Below datasets are test data I've formulated.  
acronym's : 
tab=millisecond time apple bought
asb=apple shares bought
tas=millisecond apple sold
ass=apple shares sold
tgb=millisecond time google bought
gsb=google shares bought
tgs=millisecond google sold
gss=google shares sold

training data : 
username,tab,asb,tas,ass,tgb,gsb,tgs,gss
a,234234,212,456789,412,234894,42,459289,0
b,234634,24,426789,2,234274,3,458189,22
c,239234,12,156489,67,271274,782,459120,3
d,234334,32,346789,90,234254,2,454919,2

classifications : 
a earned $45
b earned $60
c earned ? 
d earned ? 

Aim : predict earnings of users c & d based on training data 

Is there any data points I should add to this data set?  I should use alternative data perhaps ?  As this is just a learning exercise of my own creation can add any feature that may be useful. 
This data will need to be normalised, is there any other concept I should be aware of ? 
Perhaps should not use time as a feature parameter as shares can bounce up and down depending on time. 

A:

You might want to solve your problem in below order:

Prediction for an individual stock's future value based on all stock's  historical data. 
Prediction for a combination of stocks' total future value based on a portfolio and all stocks' historical data. 
A buy-sell short-term strategy for managing a portfolio. ( when and what amount to buy/sell on which stock(s) )

If you can do 1) well for a particular stock, probably it's a good starting point for 2).  3) might be your goal but I put it in the last because it's even more complicated.  
I would make some assumptions below and focus on how to solve 1) hopefully. :)  
I assume at each timestamp, you have a vector of all possible features, e.g.:

stock price of company A  (this is the target value)
stock price of other companies B, C, ..., Z (other companies might affect company A directly or indirectly)
52 week lowest price of A, B, C, ..., Z   (long-term features begin)
52 week highest price of A, B, C, ..., Z
monthly highest/lowest price of A, B, C, ..., Z
weekly highest/lowest price of A, B, C, ..., Z (short-term features begin)
daily highest/lowest price of A, B, C, ..., Z
is revenue report day of A, B, C, ..., Z   (really important features begin) 
change of revenue of A, B, C, ..., Z
change of profit of of A, B, C, ..., Z
semantic score of company profile from social networks of A, ..., Z
...  (imagination helps here)

And I assume you have almost all above features at every fixed time interval.  
I think a lstm-like neural network is very relevant here. 