I changed lstm.py code , so I can test it on Python 3.6:
change xraneg to range.
change range(len(data)/prediction_len): to range(len(data)//prediction_len):
I got this error:
TypeError: %i format: a number is required, not list

any help please

Google blocks me from that link because i might be sending automated requests... While I'm clearly not.
Could you direct me to the dataset?

Hi,

I run this demo code in my ubuntu 16.04.2 installed with tensorflow 1.0, keras 2.0.2 and python 2.7.12. Everything runs fine without error and warning, all console printout are similar to yours. However, the output prediction diagram is quite different from yours.

All those prediction lines are just pointing upwards of nearly the same slope.

What's wrong with it ? Thanks.

Hi, Is there any release for python 3.6 or 3.7?

Hi

I noticed you shuffled the data, but doing would't you lose information ?

Thanks for your sharing!
However, the prediction result looks not so good as shown in the last figure. how do you think about this prediction result?

Besides, could you share your thoughts that why you design 2 LSTM layers with the output length of 50 and 100 for this price prediction problem? What's your detailed consideration behind this design ?

Thank you for the example. I get the following error:

Using Theano backend.
WARNING (theano.configdefaults): g++ not detected ! Theano will be unable to execute optimized C-implementations (for both CPU and GPU) and will default to Python implementations. Performance will be severely degraded. To remove this warning, set Theano flags cxx to an empty string.
File "C:\BrainPorn\How-to-Predict-Stock-Prices-Easily-Demo-master\lstm.py", line 17
print 'yo'
^
SyntaxError: Missing parentheses in call to 'print'

I'm more then confused.

I have Anaconda with a TensorFlow Env and a Keras lib.
But I can't start a notebook on the TensorFlow shell, so I start the notebook on the Anaconda shell, where the above output appears in the browser.
Who can provide a descent "how to start the example" (cross-platform or Win7)?

Update Day Two:

The environment of the example is not install able on Windows, so trying other ways.

Install native on Windows: Keras install failed, probably because of Scipy
Install Anaconda way: Keras install failed, Notebook showing up in Browser (incl. failed dependency Keras)
Install Docker way: Jupyter install failed...

Trying the docker way. Would be nice to have a container, so more people could follow.

I got a functional container with TensorFlow + Keras from here:

https://blog.thoughtram.io/machine-learning/2016/09/23/beginning-ml-with-keras-and-tensorflow.html

Now I have a running TensorFlow with Keras but no jupyter notebook option.

Unfortunately 'pip install jupyter' in the environment leads to errors too.

So still no success.

Does someone has a Docker container with TensorFlow + Keras + Jupitor Notebook?

Update: Found an all in one docker image here:

https://github.com/floydhub/dl-docker (Python2 and iTouch Kernel)

docker run -it -p 192.168.99.100:8888:8888 -p 192.168.99.100:6006:6006 -v /sharedfolder:/root/sharedfolder floydhub/dl-docker:cpu jupyter notebook

...does not show the example (empty notebook)...sadly giving up.

Update:

Turns out Docker on Win needs a special syntax....

docker run -it -p 192.168.99.100:8888:8888 -p 192.168.99.100:6006:6006 -v //c/Users//sharedfolder:/root/sharedfolder floydhub/dl-docker:cpu jupyter notebook

...finally does the trick....but after a while in the browser I get this:

The kernel has died, and the automatic restart has failed. It is possible the kernel cannot be restarted. If you are not able to restart the kernel, you will still be able to save the notebook, but running code will no longer work until the notebook is reopened.

What a disaster after such a long journey!!!

Which Kernel do we need?

Testing this on python3 and jupyter notebook, I get:

NameError Traceback (most recent call last)
in ()
1 #Step 4 - Plot the predictions!
----> 2 predictions = lstm.predict_sequences_multiple(model, X_test, 50, 50)
3 lstm.plot_results_multiple(predictions, y_test, 50)

~\git\How-to-Predict-Stock-Prices-Easily-Demo\lstm.py in predict_sequences_multiple(model, data, window_size, prediction_len)
99 #Predict sequence of 50 steps before shifting prediction run forward by 50 steps
100 prediction_seqs = []
--> 101 for i in xrange(len(data)/prediction_len):
102 curr_frame = data[i*prediction_len]
103 predicted = []

NameError: name 'xrange' is not defined

I just copy the code from stockdemo.ipynb into a .py file. Then, put all other 3 files into a folder and run under my ubuntu. Everything runs fine except the output prediction plot looks quite different from yours.

Must it be run under jupyter ?

When calling lstm.load_data('sp500.csv', 50, True) in ipython notebooks I get:

TypeError Traceback (most recent call last)
in ()
1 #Step 1 Load Data
----> 2 X_train, y_train, X_test, y_test = lstm.load_data('sp500.csv', 50, True)

How-to-Predict-Stock-Prices-Easily-Demo/lstm.py in load_data(filename, seq_len, normalise_window)
25 def load_data(filename, seq_len, normalise_window):
26 f = open(filename, 'rb').read()
---> 27 data = f.split('\n')
28
29 sequence_length = seq_len + 1

TypeError: a bytes-like object is required, not 'str'