I recently found this script that you can find here, and I have tried it out, but it says [TICKER] has not been predicted. So, I dug into the code, and found a little code that helped me:
for i in stock_list:
print("Number: " + str(number))
try:
predictData(i, 5)
except:
print("Stock: " + i + " was not predicted")
number += 1
I then commented that out to see what was causing it to not predict it. This is what I got out of it:
Number: 0
AKS
Traceback (most recent call last):
File "finance.py", line 104, in <module>
getStocks(200)
File "finance.py", line 34, in getStocks
predictData(i, 5)
File "finance.py", line 86, in predictData
X, Y, test_size=0.5)
ValueError: too many values to unpack (expected 3)
Aha! So, the little piece of code that was being a b*tch was this:
X = np.array(df.drop(['prediction'], 1))
Y = np.array(df['prediction'])
X = preprocessing.scale(X)
X_prediction = X[-forecast_time:]
X_train, Y_train, Y_test = model_selection.train_test_split(
X, Y, test_size=0.5)
I think the reason that this was not working is because
X_train, Y_train, Y_test = model_selection.train_test_split(
X, Y, test_size=0.5)
was giving 4 outputs, but there were only 3 variables. I don't know what I should do here, because I tried to add another variable to that, but got this: TypeError: only size-1 arrays can be converted to Python scalars...
sigh
model_selection.train_test_split(...) and cross_validation.train_test_split(...) return an even number of arrays (in fact, it is 2 * the original number of arrays). With that said, you're inputting an X and Y array so you should expect the output to contain 4 arrays. Try changing your code to read
X_train, X_test, Y_train, Y_test = model_selection.train_test_split(X, Y, test_size = 0.5)
If you said you get an error when you put in the 4th variable, can we see the traceback? I suspect it's not on the same line but I have no way to run the code right now so I can't do it myself