I’m looking for **Stock Price Time Series Forecasting using Deep Convolutional/fully connected Networks/RNN** *code* for forecasting the prices on stock markets *using Python*. The simplest possible code for this task with `.ipynb`

extension will do.

I cannot find one with google, any hints for downloading or creating it on my own ?

**EDIT** More specifically, I need from data with one or *more* times series-a daily data with prices, and my *input how many days from now
on* and my

*probability input*I need

**lower and upper bound**how much will the asset cost after this period: estimated min and max value.

*Or, in other words*

I need to compose the optimal

*portfolio*

for these assets, leaving some money without buying anything: how much fraction of the initial amount of money should be intact on my account, among how much % shopuld I dedicate to n-th asset.

**EDIT**

Example:

say, I have this 2 time series:

[1,2,4,3,2,2,2]

[3,3,3,3,4,4,8]

and 100$ with initial amount

should yield this (by now just random, in reality that should make a sense and be derived from the 2 assets by statistical means and Deep Learning) probabilities:

31$ of asset 1,

49$ of asset 2,

20$ leaving on my account *after 4 days*, all happening with probability 8%.

Is there such a code .ipynb at best already available ?