The document discusses using Python and TensorFlow for deep learning applications in stock exchange trading and asset management. It covers foundational concepts in machine learning, including neural networks, training models, and performance evaluation, along with practical examples. Additionally, it explores the use of neural networks in portfolio management to maximize profits while incorporating various constraints.

1. PYTHON + TENSORFLOW:
how to earn money
in the Stock Exchange
with Deep Learning
Jose M. Leiva
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2. Summary 1. A primer on Machine Learning
2. Neural Networks and Deep
Learning
3. Asset Management and
Quantitative Finance

3. Imagine we want to build an
automatic digit recognizer.
A primer on
Machine Learning
REMEMBER!
A digital image is
nothing but a matrix
of numbers!
Source: MNIST Dataset
Source: https://medium.com/@ageitgey/machine-learning-is-fun

4. Example: Automatic Digit Recognition
2 DIFFERENT ALTERNATIVES:
1. Solve the problem in the original domain
with 784 values (28 x 28 images)
2. Extract features (for example, height and width)
4
Linear classifiers:
perform the
transformation ⟨ ,x⟩
= T
x
Width
Height
Width
Height
Examples of “1”
Examples
of “8”

5. ● We are usually interested in providing a degree of confidence
about the prediction rather than yes/no answers.
● How to transform the unbounded value T
x into a probability?
● Sigmoid function
5
Obtaining Probabilities
p(y=’8’)
p(y=’1’) = 1-p(y=’8’)

6. Fundamental problem in machine learning:
How to choose the best ?
We must evaluate the cost of the errors!
ALTERNATIVES:
● Counting the errors
● Log-loss or cross-entropy loss: the one usually
employed in neural networks
● Maximum margin
● Etc.
6
The cost of the errors
Width
Height
Example: Maximum-margin classification
(Support Vector Machines, SVM)
When the sigmoid function is combined with the log-loss, we have
a logistic regression classifier.

7. ● scikit-learn (aka sklearn) has become the Python library for Machine Learning.
● It is object-oriented, very comfortable to work with.
● It offers submodules for data encoding, preprocessing, dimensionality reduction,
regression, classification, error metrics, model selection, etc., etc.
7
Machine Learning with Python
BUILD A MODEL AND PERFORM PREDICTION:
from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.fit(X,y)
yt = model.predict(Xtest) # predicts the labels
pt = model.predict_proba(Xtest)# obtain the probs
EVALUATE ACCURACY:
from sklearn.metrics import accuracy_score
acc = accuracy_score(yt, Ytest)
OR DIRECTLY:
acc = model.score(Xtest,Ytest)

8. We can build non-linear models by
concatenating several layers of
non-linear units.
We can think of each unit as a logistic
regressor as the one we saw before.
Neural Networks
and Deep Learning
Source: http://cs231n.github.io/neural-networks-1/

9. Fully connected Neural Networks
It is important that the units in the
network are followed by a non-linear
transformation:
This way we can draw non-linear
separation functions!
y = ( T
x+b)
9
A non-linear activation
function can replicate
the spiking mechanism
of biological neurons.
Source: Bruce Blause, from en.wikipedia.org/wiki/neuron
Example of activation
function: Rectified
Linear Unit (ReLU)

10. CONVOLUTIONAL NEURAL NETWORKS
10
Deep Learning: other architectures
RECURRENT NEURAL NETWORKS
They are used in Natural Language
Processing (NLP) and Machine Translation
Source: http://colah.github.io/posts/2015-08-Understanding-LSTMs/Sources:
http://cs231n.github.io/convolutional-networks/
http://web.eecs.umich.edu/~honglak/icml09-ConvolutionalDeepBeliefNetworks.pdf

11. Fully connected Neural Networks
All layers can use units with ReLU activations, but we need something else in the last one.
We would like the output to give us a set of probability values
To this end, we apply a softmax transformation in the last layer:
● It preserves the order of the outputs
● The new outputs sum up 1.
11

12. ● Example: evaluate f= x2
+ y2
. First define the graph.
import tensorflow as tf
x = tf.Variable( 3)
y = tf.Variable( 4)
f = x*x + y*y
● It’s separated from its execution:
>>> sess = tf.Session()
>>> sess.run(x.initializer)
>>> sess.run(y.initializer)
>>> result = sess.run() # Result equals 25
● Or more compactly:
init = tf.global_variables_initializer()
with tf.Session() as sess:
init.run()
result = f.eval
12
Deep Learning with TensorFlow
● Example:Graph for Logistic Regression on the MNIST database.
# Data input
x = tf.placeholder(tf.float32, [ None, 784])
y = tf.placeholder(tf.float32, [ None, 10])
# Set model weights
W = tf.Variable(tf.zeros([ 784, 10]))
b = tf.Variable(tf.zeros([ 10]))
# Construct model
pred = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax
# Minimize error using cross entropy
cost = tf.reduce_mean( -tf.reduce_sum(y *tf.log(pred), axis=1))
# Gradient Descent
optimizer = tf.train.GradientDescentOptimizer(learning_rate)
.minimize(cost)
● Running and optimising:
with tf.Session() as sess:
sess.run(init)
for i in range(total_batch):
batch_xs, batch_ys = mnist.train.next_batch(batch_size)
_, c = sess.run([optimizer, cost], feed_dict ={x: batch_xs,
y: batch_ys})
# Compute average loss
avg_cost += c / total_batch
TensorFlow is a open source software library for numerical computation.

13. ● In Portfolio Management, we aim at
invest a given wealth in a set of
assets.
● The strategy can be either static or
dynamic: we focus on the latter.
● The proportion of the wealth
investing in the i-th asset is wi
, with
● We focus on the case where wi
≥ 0
(long only) and wi
< 1 (no leverage).
Asset Management
and Quantitative
Finance

14. A neural network with softmax
output is perfect for the job!
14
Neural Networks in Portfolio Management
PROBLEM DEFINITION:
We would like to maximize the profit:
● t is the time instant
● n is the index of each asset in the portfolio
● rn
t+1
is the return at t+1
● wn
t
is the weight of asset n in the portfolio at t

15. We train the network every year.
Usually, we would use the following procedure:
...but we cannot afford to skip the validation set!
Instead, we use a constant number of iterations
(100.000) and incorporate the validation set in
the train one.
15
Neural Networks in Portfolio Management
We use the following set of assets:
We use the following variables as inputs to the
network:
● T last daily price movements.
● H last weekly price movements
● Day of the week (5 binary variables)

16. RESULTS
● Liquidity constraint: 5%
● Management fee: 1%
● Transaction fee: 0.05%
16
Neural Networks in Portfolio Management
There are three recommendations a week (on
Monday, Wednesday and Friday), each one
incorporating the output of the NN in one third of the
portfolio.

17. Is it really possible
to consistently beat
the market?
“Beating the stock market requires
outpredicting teams of investors in
fancy suits with MBAs from Ivy League
schools who are paid seven-figure
salaries and who have state-of-the-art
computer systems at their disposal.”
Nate Silver, The Signal and the Noise

18. ETS - @EtsFactory
José M. Leiva Murillo - @JLeivaMurillo
ETS Asset Management Factory
José M. Leiva Murillo
Machine Learning, Artificial Intelligence,
Asset Management, Python and more in
our blog:
www.quantdare.com
Thanks
@ETSFactory
#PyConES2017