irhum

# build your encoder upto here. It can simply be a series of dense layers, a convolutional network
# or even an LSTM decoder. Once made, flatten out the final layer of the encoder, call it hidden.

# we use Keras to build the graph

latent_size = 5
mean = Dense(latent_size)(hidden)

# we usually don't directly compute the stddev σ 
# but the log of the stddev instead, which is log(σ)

StuartGordonReid

import math
import numpy
import numpy.random as nrand

"""
Note - for some of the metrics the absolute value is returns. This is because if the risk (loss) is higher we want to
discount the expected excess return from the portfolio by a higher amount. Therefore risk should be positive.
"""