While trying to run the code: https://github.com/ShuaiW/teach-machine-to-trade/blob/master/run.py
with: python run.py --mode train

I get the error:

Traceback (most recent call last):
File "C:\Users\i\Anaconda3\lib\site-packages\tensorflow\python\eager\execute.py", line 141, in make_shape
shape = tensor_shape.as_shape(v)
File "C:\Users\i\Anaconda3\lib\site-packages\tensorflow\python\framework\tensor_shape.py", line 946, in as_shape
return TensorShape(shape)
File "C:\Users\i\Anaconda3\lib\site-packages\tensorflow\python\framework\tensor_shape.py", line 541, in init
self._dims = [as_dimension(d) for d in dims_iter]
File "C:\Users\i\Anaconda3\lib\site-packages\tensorflow\python\framework\tensor_shape.py", line 541, in
self._dims = [as_dimension(d) for d in dims_iter]
File "C:\Users\i\Anaconda3\lib\site-packages\tensorflow\python\framework\tensor_shape.py", line 482, in as_dimension
return Dimension(value)
File "C:\Users\i\Anaconda3\lib\site-packages\tensorflow\python\framework\tensor_shape.py", line 37, in init
self._value = int(value)
TypeError: int() argument must be a string, a bytes-like object or a number, not 'tuple'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "run.py", line 38, in
agent = DQNAgent(state_size, action_size)
File "C:\Users\i\Q-Learning-for-Trading-master\agent.py", line 18, in init
self.model = mlp(state_size, action_size)
File "C:\Users\i\Q-Learning-for-Trading-master\model.py", line 11, in mlp
model.add(Dense(n_neuron_per_layer, input_dim=n_obs, activation=activation))
File "C:\Users\i\Anaconda3\lib\site-packages\keras\engine\sequential.py", line 161, in add
name=layer.name + '_input')
File "C:\Users\i\Anaconda3\lib\site-packages\keras\engine\input_layer.py", line 178, in Input
input_tensor=tensor)
File "C:\Users\i\Anaconda3\lib\site-packages\keras\legacy\interfaces.py", line 91, in wrapper
return func(*args, **kwargs)
File "C:\Users\i\Anaconda3\lib\site-packages\keras\engine\input_layer.py", line 87, in init
name=self.name)
File "C:\Users\i\Anaconda3\lib\site-packages\keras\backend\tensorflow_backend.py", line 517, in placeholder
x = tf.placeholder(dtype, shape=shape, name=name)
File "C:\Users\i\Anaconda3\lib\site-packages\tensorflow\python\ops\array_ops.py", line 1734, in placeholder
return gen_array_ops.placeholder(dtype=dtype, shape=shape, name=name)
File "C:\Users\i\Anaconda3\lib\site-packages\tensorflow\python\ops\gen_array_ops.py", line 5925, in placeholder
shape = _execute.make_shape(shape, "shape")
File "C:\Users\i\Anaconda3\lib\site-packages\tensorflow\python\eager\execute.py", line 143, in make_shape
raise TypeError("Error converting %s to a TensorShape: %s." % (arg_name, e))
TypeError: Error converting shape to a TensorShape: int() argument must be a string, a bytes-like object or a number, not 'tuple'.

I am using python 3.7 instead of python 2.7

Thanks in advance

I have met an error as follows:

File "C:\Users\User\Desktop\research_project\run.py", line 63, in
next_state, reward, done, info = env._step(action)
File "C:\Users\User\Desktop\research_project\env.py", line 73, in _step
self._trade(action)
File "C:\Users\User\Desktop\research_project\env.py", line 97, in _trade
action_vec = action_combo[action]
IndexError: tuple index out of range

May I know how to fix it?

Hi Shuai,

thanks for your time preparing and sharing this nice example of RL applied to finance. I'm learning a lot by exploring the code.

I've noticed that during data pre-processing you normalize the data using StandardScaler. Two questions come to my mind:

1. Are you aware that by creating a StandardScaler based on min-max over all the test data you are introducing "look-ahead", meaning your model will include information that in production mode would not be available to you?

2. It feels quite strange that you normalize the number of stocks owned by some "magic number" as you write in the code. Is there any rationale behind this number?

Thanks a lot!

It doesn't work with python 3.X