Ags AIforTrading

New Techniques, New Tools
Milan, 12 April 2018
Cristiana Corno – Director A.I.4Trading
Nicola Marino – Solution Architect A.I.4Trading

Agenda
Confidential - all right reserved2
Introduction to A.I.4Trading Project
New Techniques, New Tools
From Technical Analysis to Pattern Recognition
From Factor Analysis to Autoencoders

About Artificial Intelligence in AGS
Advanced Global Solution AGS S.p.A. is an Italian company that provides IT and technology services
and solutions: cutting-edge projects, technologies and algorithms.
Founded in 1998, AGS currently offers a team of over 400 employees with deep expertise and strong
technical skills. Consolidated partnerships with market-leading firms have also contributed to the
company's solid record of success in Italy.
Starting 2015 AGS has been making considerable R&D investments in new areas, among which Machine
and Deep Learning, to turn our experience in Artificial Intelligence into Business solutions.
We were the first in
Italy to organise a
course on Machine
Learning
KNOW-HOW
We have an
international team
of Data scientists
specialised in Deep
Learning
TEAM
Our Data Center has a
capacity of trillions of
operations per second
PROCESSING
POWER

A.I.4Trading Project
“A.I.4Trading” is the Fintech division of AGS: it joins the industrial experience of “Machine and Deep
learning” with Capital Markets expertise, to bring Artificial Neural Networks into Finance.
The team is composed by Economists, electronical engineers, computer scientists and collaborates
actively with the ML Team.
Trading Experience Deep Learning

Why Artificial Intelligence?
Excess profit from “early” and “confidential” adoption?
Recent version of the relative efficient market hypothesis states that "it is not possible to systematically earn
excess profits without some sort of competitive advantage”. (“Perspective in neural computing”)
Sophisticated modelling techniques, speed of elaboration are probably the “COMPETITIVE ADVANTAGE”
and have the potential to generate excess profits….
….as long as they are early adopted and kept confidential!

The 3 Laws (also for A.I.4Trading)
1. A robot may not injure a human being or, through inaction, allow a human being to come to harm.
2. A robot must obey any orders given to it by human beings, except where such orders would conflict
with the First Law.
3. A robot must protect its own existence as long as such protection does not conflict with the First or
Second Law.
I. Asimov

1st A.I.4Trading “Mantra”:
IMPROVE THE INFORMATION SET
 Extract as much information as possible from data or the “screen”
 “Context” analysis + coding can make the difference (-)
Rogue Trader Quants Machine & Deep Learning

AI and Machine Learning
Eurekahedge AI Hedge Fund Index

Example: Eureka
As an example, using context (other assets) and few lines of code ( constrained least square minimization), we
can derive the “ex-post” performance’s drivers of the Eureka AI and HFR hedge fund index.
A.I. allocation seems more conservative (in appendix the possible rolling asset allocations)
Rf=rt(:,2) % fund return
Ra=rt(:,4:end) % return of the
universe of assets
x=zeros(size(Ra,2),1) % Investment
percentage
I=ones(1,size(Ra,2)) % Ones matrix
Constrained Least square
minimization problem
Rf-Ra*x sub sum(x)=1 or X’I=1
[x]=lsqlin(Ra,Rf,[],[],I,1)
Optimization problem: AVERAGE solution Eureka vs HFR
Matlab code
A.I. on average is more conservative

Eureka allocations : total dataset and rolling HFR allocations : total dataset and rolling
Rolling the problem (300 obs), AI asset allocation (change of weights in time) solution seems more active.
In conclusion with the AI fund index, we have:
• Overperformance
• Active asset allocation
• and a conservative approach!
Example: Eureka

HFR return distribution Eureka return distribution
• Less volatile and higher average return for the AI.
• We will see again this picture later on
Example: Eureka

2nd A.I.4Trading “Mantra”:
ENLIGHTEN THE BLACK BOX
 We start from simple model of the phenomena (statistics) and evolve them in more flexible neural network
 We try to maintain in this process the “economic interpretability” of the results.
 We run in parallel statistical and black box models and check relative performance and signals

3rd A.I.4Trading “Mantra”:
TRUST THE MACHINE
Confidential - all right reserved
13
“THERE is an old joke among pilots that says the ideal flight crew is a computer, a pilot and a dog.
The computer’s job is to fly the plane. The pilot is there to feed the dog. And the dog’s job is to bite
the pilot if he tries to touch the computer”. Cit. Economist
How can you
buy this… market

A.I.4Trading Platform
CURVE VIEW
Relative value
between similar
assets
CROSS ASSET
Relative value
between asset
classes
FORECAST
Inter daily forecast
MARKET CLOCK
Intraday forecast
Platform
Four modules where «typical»
issues in finance can be
mapped into econometric
models and in their «machine
and deep learning alter ego»

A.I.4Trading Platform
Correlated assets
same risk factors
Multivariate
approach
Correlated assets
with different risk
factors
Multivariate
approach
Forecasting
Univariate and
multivariate
approach (Error
Correction Models)
Intraday
Univariate
Platform
Pattern recognition
clustering, NN for
classification
Recurrent
Networks,
Recursive
Autoencoder
Autoencoders,
Factor analysis

A.I.4Trading: from raw data to automatic trading
Raw data
analysis
•Database production (BBG)
•Maintenance: importance of
autonomous anomaly detection
Algorithm
•Problem definition
•Research
•Algo design
•Back testing on historical data
Production
•Code production
•Online Algo testing
•Automatic trading with expert
system
Machine Learning
Team
collaboration
TEAM
Our workflow

Agenda
New Techniques, New tools

Market Clock module: Looking for rhymes
“History doesn't repeat itself,
but it does rhyme.”
M. Twain
Market Clock dashboard
Market Clock:
Ensemble of pattern
recognition algorithms for
forecasting.
The objective is to give
the trader a probability
distribution of the possible
day outcome.

From Technical Analysis to Market Clock
Technical analysis
• Known pattern recognition (finite
selection of pattern)
• Trader identifies formation and
behaves based on past market
behaviour
Market Clock
• Known and unknown pattern
recognition
• Algorithm identifies most probable
«trajectory of the market»
• Provide a probability distribution for end
of day return.
Candlestick scanner Market Clock output
Very important point: probability distribution can be used to build
cash+option (univariate) strategy or spread trades (multivariate)

Our approach to Pattern Recognition
Confidential - all right reserved
The intraday time series is a “trajectory” with a starting point (an eventual bias), an arrival destination, and
intermediate features exactly as a taxi ride
Exactly as a TAXI RIDE the trajectory destination can be influenced by:
• the hour of the day,
• day of the week,
• season of the year,
• events that can catalyse traffic (bias)
This comparison enable us to use the tools and results of the analytics
of movement.
“Artificial Neural Networks Applied to Taxi Destination Prediction”. de Brébisson, Alexandre & Simon, Étienne & Auvolat, Alex & Vincent,
Pascal & Bengio, Y. ,2015

Our approach to Pattern Recognition
 We assume that there is a finite set of typical or “archetypal” trajectories (“market states”) that represent
intraday market behaviour.
 These trajectories are created by the interactions of different players and different speed-moving capital
 We design algorithms that can predict the “most probable” destination, looking at the partial trajectory at
subsequent point in time.
“Intraday patterns in FX returns and order flow”,
https://www.snb.ch/n/mmr/reference/working_paper_2011_04/source/working_paper_2011_04.n.pdf

Online forecasting
In the framework we have created, the challenge is to:
Find a good rappresentation
of the dataset in terms of
«typical» trajectories
Find a performing algorithm
to «learn» how to connect the
online observation with the «most
probable» trajectory for the day
Unsupervised methods for
clustering
Supervised methods for
forecasting (NN for classification,
SVM)
Better if done together.
And A.I. can do it.

Market Clock algorithms
In Market Clock, we currently use 5 different algorithms:
 a dynamic K-means clustering
 a K-means modified, proprietary version
 a multilevel SVM classifier
 a Deep Tapped Delay Line with enhanced clustering
 a MLP for classification
Algorithms performances are
continuosly evaluated
New algorithms are added
if backtesting yields good results or
viceversa.

Market Clock main takeaways:
I. Time Series clustering
II. Deep Learning contribution
III. The Out-Performance shape

I. Time Series clustering
The nucleo of Market Clock is the general theme of time series clustering aimed at forecasting.
The problem arises whenever we observe a sample of time series and we want to group them into different
categories. This is particularly useful in finance. Generally, the notion of similarity used relies on two criteria:
 Proximity between raw time (standard partitioning clustering)
 Proximity between underlying processes (model based clustering)
We use proximity between raw time series after proper normalization in the machine learning algorithms
and model based clustering in the neural network framework.
“Time-series clustering – A decade review” Saeed Aghabozorgi Ali Seyed Shirkhorshidi TehYing Wah, 2015

II. Deep Learning contribution
Improve the clustering (optimized inside the neural network)
Weight the features based on their importance in forecasting daily evolution of returns
Gain more flexibility

III. Out-Performance Shape
Performances varies with the asset features; for a set of assets, realized performances in
backtesting are outstanding.
-2
0
2
4
6
8
10
12
14
0,25 0,5 0,75 1 1,25 1,5 0,25 0,5 0,75 1 1,25 1,5 0,25 0,5 0,75 1 1,25 1,5
PL_sum_stop_1:1 PL_sum PL_sum_stop_1:0.5
Backtesting EURUSD P&L for different trading strategies
(see appendix)
Number of Trades
0
1000
2000
3000
4000
5000
6000
0,25 0,5 0,75 1 1,25 1,5 0,25 0,5 0,75 1 1,25 1,5 0,25 0,5 0,75 1 1,25 1,5
N_trades

Is the Algorithm the «New Asset Allocation»?
• The combination trading strategy + algorithm works as a P&L regularizer in back testing, avoiding large
swing with a positive average return (figure below)
• Algo strategy could serve as an asset for portfolio allocation
Volatility of daily return: strategy vs underlying asset
These return distributions are exactly the same as the ones we saw before for
HFR and Eureka indexes
Distribution of asset daily return and strategy P&L

Agenda
New Techniques, New tools

Autoencoders
• Autoencoders are a comprehensive category of Neural Network
• In the process of rebuilding the input, they try to find an “informative representation” of the
original data.
• They represent the natural choice for modelling relative value in finance.
• They contain as special (linear) cases:
• Independent component analysis
• Clustering analysis
• Principal component analysis
• Archetypal analysis
They can be further generalized to take in account non linearities and probalistic
structure of the “hidden variables”

Python
# process file.txt and print
# all the words ending in ing
for line in open("file.txt"):
for word in line.split():
if word.endswith('ing'):
print word
• Simple yet powerful programming language
• Object-oriented: line is a string object so has a “method” split(),
word is a string object so has a “method” ends with with the
argument 'ing'
• Highly readable
• Shallow learning curve, facilitates interactive exploration, permits
data and methods to be encapsulated and re-used easily,
permits attributes to be added to objects on the fly, and permits
variables to be typed dynamically, facilitating rapid
development.
• Extensive standard library
• Heavily used in industry, scientific research, and education

PCA
• Dimensionality reduction technique: mapping
multidimensional data into a lower dimensional space
with minimal loss of information.
• First principal component: direction in space, along
which, projections have the largest variance
• Second principal component: direction, which maximizes
variance among all directions orthogonal to the first, and
so on…
𝑌 = 𝑊 ⋅ 𝑋 𝑋 = 𝑊 𝑇 ⋅ 𝑌
𝑉𝑎𝑟 𝑋 = 𝑊Λ𝑊 𝑇
𝑉𝑎𝑟 𝑌 = Λ
• In Python, just few lines of code
# imports the PCA class and finds a
# projection over 3 components
from sklearn.decomposition import PCA
n_comp = 3
pca = PCA(n_comp, whiten=False)
proj = pca.fit_transform(x_train)
print pca.components_.T

Autoencoder
• Neural Network trained to give as output the input itself
• Two networks: Encoder, Decoder
• Encoder extracts few features from the data
• Decoder reconstructs the data from the features
𝑌 = 𝜎1(𝑊1 ⋅ 𝑋 + 𝑏1)
𝑋 = 𝜎2 𝑊2 ⋅ 𝑌 + 𝑏2
• Can use nonlinearities to give better results

Implementation
• Encoder, no bias, tied weights
𝑌 = 𝑊 𝑇
⋅ 𝑋
• Decoder, simple matrix multiplication
𝑋 = 𝑊 ⋅ 𝑌
• Model, just call encoder inside
decoder
• Loss: MSE of the estimate
𝐸 ∥ 𝑋 − 𝑋 ∥2
= 𝐸[∥ 𝑋 − 𝑊 𝑇
(W ⋅ 𝑋) ∥2
]
# Building the encoder
def encoder(x):
layer_1 = x
layer_2 = tf.matmul(layer_1, tf.transpose(weights['decoder_h1']))
return layer_2
# Building the decoder
def decoder(x):
layer_1 = tf.matmul(x, weights['decoder_h1'])
layer_2 = layer_1
return layer_2
# Construct model
encoder_op = encoder(X)
decoder_op = decoder(encoder_op)
y_pred = decoder_op
# Define loss and optimizer, minimize the squared error
loss = tf.reduce_mean(tf.pow(y_true - y_pred, 2))
optimizer = tf.train.AdamOptimizer(learning_rate_v).minimize(loss)

Shape of hidden factors (PCA vs AE)
-1
-0,8
-0,6
-0,4
-0,2
0
0,2
0,4
0,6
0,8
Second AE component
-1
-0,8
-0,6
-0,4
-0,2
0
0,2
0,4
0,6
0,8
First PCA component
-1
-0,8
-0,6
-0,4
-0,2
0
0,2
0,4
0,6
0,8
First AE component
-1
-0,8
-0,6
-0,4
-0,2
0
0,2
0,4
0,6
0,8
Second PCA component
-1
-0,8
-0,6
-0,4
-0,2
0
0,2
0,4
0,6
0,8
Third AE Component
-1
-0,8
-0,6
-0,4
-0,2
0
0,2
0,4
0,6
0,8
Third PCA component
See our paper “PCA vs AE: an application to CDS market” https://goo.gl/jhEB7w

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We are officially certified by NVIDIA as “Deep Learning Instructor”
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exploit their respective specificities
• Prerequisites
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For more info, email us at DLI@ags-it.com

THANK YOU
www.ai4trading.com
ai4trading@ags-it.com
cristiana.corno@ags-it.com

DISCLAIMER
The information in this document has been prepared by Advanced Global Solution AGS S.p.A. (VAT
IT01692340035, also as AGS S.p.A.) and is general background information about AGS activities current as at the
date of this presentation. This information is given in summary form and does not purport to be complete.
Information in this presentation, including forecast financial information, should not be considered as advice or a
recommendation to investors or potential investors in relation to holding, purchasing or selling securities or other
financial products or instruments. All securities and financial product or instrument transactions involve risks, which
include (among others) the risk of adverse or unanticipated market, financial or political developments and, in
international transactions, currency risk.
All industrial and intellectual property rights on any information that AGS S.p.A. communicate or make available
in this document, as well as on the results of the experimental activities carried out are and remain of AGS S.p.A.
The communication or making available of such Confidential Information or the results achieved cannot be
considered an assignment or license of industrial or intellectual property rights by AGS S.p.A.

Appendix 1
Deep Tapped Delay Line
training
predict
Input
Output
Hidden
layer
Layers
Layers
Layers
Layers
y[t*+1]
y[t*+2]
y[t*+3]
y[t*+4]
x[start:t*] x[t*+1] x[t*+2] x[t*+3]

Appendix 2
Market Clock: performances assumptions
The P&L of the strategies are calculated as a passive strategy re-evaluated at each 30m time stamp.
The trading strategy consists of starting the position on the signal above/below a certain threshold,
keepening the position until the end of day and reverting it if signal changes.
The threshold is defined as a function of the forecast and its standard deviation.
Three zeta version have been tested, based on the forecasting horizon (next 30 minute or End of the
day):
Zeta_eod= (last_obs-forecast_eod)/std(forecast_eod)
Zeta_next= (last_obs-forecast_next)/std(forecast_next)
Zeta_star= (last_obs-forecast_eod)/std(forecast_next)

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