Qopius has developed an artificial intelligence asset allocation model and financial engine based on years of research in artificial intelligence and deep learning techniques. The model analyzes data from global financial markets to adjust exposures across asset classes and achieve absolute returns. It uses evolutionary algorithms and deep learning to generate predictions and combines numerous predictions to determine optimal outcomes. The model and engine demonstrate strong performance in backtests and paper trading, with returns that are uncorrelated to traditional asset classes in the long term. Qopius also provides an automated trading system to implement the model's strategies.

1. FROM CUTTING EDGE A.I. RESEARCH
TO INNOVATIVE FINANCIAL MODELS
Qopius - Private & Confidential 1

2. CONTENT
Qopius - Private & Confidential
1. QOPIUS ARTIFICIAL INTELLIGENCE ASSET
ALLOCATION MODEL
2. QOPIUS FINANCE
2

3. QOPIUS A.I ASSET ALLOCATION
MODEL

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ASSET ALLOCATION FOR AN EVOLVING WORLD
A global and flexible Artificial Intelligence multi asset model
 Goal: Absolute return performance, uncorrelated in the long term to traditional
asset classes.
 Approach: A multi-strategy model, with flexibility to adapt to different market
environments over time, increasing efficiency and robustness.
 Tool: An Artificial Intelligence, quantitative and systematic system inspired by
neurosciences designed to behave optimally in highly complex and unstable
environments.
 Universe: The model daily adjust the exposure of the 40 most liquids financial
instruments across all asset classes and geographic areas, depending upon
deep price analysis, correlations, risks and market sentiment.
Flexibility is key in today’s rapidly transforming global economy.

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INVESTMENT UNIVERSE
The universe contains the 36 most liquid instruments available,
representing all asset classes and geographic areas. All the Asset are
UCITS IV Compliants.
 24 stock index futures.
 6 long term (10 years) government bond futures.
 3 short term (2/3 years) government bond futures.
 A Gold future.
 A commodities index (DJP ETF).
 A Volatility index future (VIX Index).

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RISK MANAGEMENT
Two layers of risk control rules:
1- A Value-at-Risk (VaR) approach monitors and measures risk exposures
at all times. The Fund is subjected to an absolute VaR limitation of 15%
over a 1 month holding period with a confidence interval of 99%.
2- A maximum exposure constraint for each asset class.
Asset class constraints
Min Exposure Max Exposure
Stocks indexes futures -40% 80%
Sovereign Debt AAA futures 0% 60%
Gold future 0% 8%
DJP Commodities ETF 0% 8%
Volatility futures -4% 4%
Sum of the exposures 0% 100%

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DEVELOPMENT
7 years of asset management quantitative research combined with a
cumulated 9 years of research in Artificial Intelligence and deep
learning techniques.
 Backtest
9 Years of statistical backtest (2005-2013)
The backtest period is not accounting the predictions of the Qopius
Financial Engine which was in the learning and validation processes.
 Paper Trading
2 years (2014-2015)
The period is a pure test with the full model.
Any optimization neither over-fitting was possible during the period.

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QOPIUS FINANCIAL ENGINE PREDICTIONS
For each instrument Qopius Financial Engine use deep learning techniques
mutated by evolutionary algorithms and generates and combines numerous
predictions in order to result in the desired prediction of the outcome.
Predictions are verified and the performance of the predictive model is
adjusted accordingly by refining its attentional control over the data
acquisition layer and assigning high confidence on the most successful paths
taken.
The model adjusts progressively the exposure to global risk (beta)
across asset classes depending on a ‘state matrix’. The matrix axis are
the averages predictions of Equities and Government bonds
components.
Qopius I.A scoring
Debt 88.8%
Equities 37.5%

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ALLOCATION MATRIX
A global state of the market is defined by the momentum of global
equities and Long term Government debt :
1 : Flight to quality
2 : Inverse Flight to quality
Cash is King
Offensive
Mode
Defensive
Mode
Transition
Mode
Risk
Appetite
Global momentum Equities
GlobalmomentumGovbonds
2
1

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BEHAVIORAL COMPONENTS
Qopius Financial Engine use many sentiment based indicators to predict markets
direction, like Twitter and news sentiment. Indeed, the model shifts fully from
technical to behavioral analysis when the market is emotionally driven. The model
use a Highly filtered Put/Call ratio to measure the excess in market sentiment and
defines a Risk on/Risk off mode.
 When market sentiment is too high, risk is off to anticipate bull market reversal.
 When market sentiment is too low, risk is on to anticipate rebound after big sell off.
 Else the indicator is inactive.
Measure of excess
in market Sentiment
Pessimism excess :
Risk on
No excess :
Measure of trend
excess
Normal Condition:
Qopius Financial
Engine predictions
Unusual trend :
Long/Short market
Neutral mode
Optimism excess :
Risk off

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2016 DAILY ASSET CLASS ALLOCATION

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BACKTEST PERFORMANCE

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PAPER TRADING PERFORMANCE

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MONTHLY PERFORMANCES
Paper trading including trading fees
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Year
2016 0,81% 13,27% 11,65% 1,71% 0,95% 5,79% 41,53%
2015 7,10% 2,45% 1,69% 4,78% 0,02% 6,01% 4,86% 9,84% 7,04% 5,31% 3,54% 9,16% 81,91%
2014 2,13% 3,17% -3,81% 2,73% -0,23% 2,30% 0,25% 3,80% 0,97% 11,54% 2,51% 7,78% 37,58%
Backtest including trading fees
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Year
2013 4,20% 5,55% 2,78% 3,25% -0,12% 7,97% 0,36% -2,14% 3,27% -0,71% -0,16% 1,13% 27,98%
2012 7,08% 2,07% -1,98% 0,45% 2,07% -1,09% -0,10% 1,92% 4,61% -1,75% 6,00% 1,95% 22,87%
2011 -1,05% -0,79% 0,98% 0,71% 0,25% 0,81% 3,08% 13,33% 6,59% -0,69% 2,57% -0,28% 27,62%
2010 -0,99% 0,76% 6,93% 0,07% 3,61% 0,30% 0,90% 0,75% 4,76% 4,48% -0,74% 3,04% 26,24%
2009 4,64% 1,71% -1,03% 8,52% 13,38% -0,22% 0,39% 7,91% 5,63% 0,03% 4,56% 6,14% 64,30%
2008 7,24% -0,43% 4,75% 1,26% 7,24% 4,99% 0,07% 0,69% 11,85% 38,06% 20,36% 3,24% 146,59%
2007 -0,62% 2,07% 10,67% 5,47% 5,97% -1,96% 3,11% 8,88% 4,27% 1,92% -1,35% -0,98% 43,37%
2006 5,88% 0,68% 0,92% 3,64% 3,36% 6,03% 2,30% 3,90% 1,05% 4,80% 3,08% 2,87% 45,86%
2005 -0,62% 4,62% -0,98% -0,79% 2,54% 3,66% 2,67% 1,11% 4,91% -0,94% 5,02% 1,02% 24,28%

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A NON CORRELATED MODEL TO TRADITIONAL
ASSET CLASSES
Good resilience when market is
down and no correlation in the
long term to traditional asset
classes :
Comparison to traditional asset classes
S&P 500 US 10 YR
Alpha 2.95% 2.84%
Beta 0.09 0.15
Correlation 2.0% 7.5%
Outperformance w/Benchmark is
Positive (Monthly) 60.3% 54.0%
Outperformance w/Benchmark is
Negative (Monthly) 91.5% 92.5%
These statistics include the backtest period(2004-2013)
Statistics
Compound Annual Return 51.3%
Average Monthly Return 3.4%
Largest Monthly Gain 38.1%
Largest Monthly Loss (3.8%)
% Positive Months 80%
Average Positive Return 4.5%
Average Negative Return (1.0%)
% Negative Months 20%
Worst 12 Months 5.6%
Best 12 Months 150.6%
Annualized Standard dev. 12.6%
Sharpe Ratio (0,0%) 4.02
Sortino Ratio (0,0%) 10.78
Downside Deviation (0,0%) 1.30%
Max Drawdown (daily) (7.8%)
Months in Max Drawdown 3
Months To Recover 2

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QOPIUS FINANCIAL TRADING SYSTEM
Qopius is offering a fully systematic and automated system. The data loading from
Bloomberg, the computation via IBM cloud, the internal risk control and the trading
via Bloomberg EMSX are fully integrated.
The client will run all the process once a day via Excel in less than 15 minutes.
Qopius Team will update the model once a month to insure that the predictive model
is adjusted to the latest market conditions and correlations.

17. QOPIUS FINANCE

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OUR MISSION
Qopius proposes a new generation of Artificial Intelligence inspired by neurosciences designed to
behave optimally in highly complex and unstable environments.
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Like human beings, Artificial Intelligence learns by experience and instructions. Collecting data from
its environment, it evolves to progressively achieve objectives through autonomous learning to
perform better from one day to the other.

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QOPIUS FINANCE TEAM
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Antonin Bertin - CEO Qopius Technology / Fondateur
▪ Qopius Research, cognitive sciences and artificial intelligence - Paris (1 year)
▪ Ecole Normale Supérieure (ULM) de Paris, Master in artificial intelligence.
▪ HEC Paris, Master in Management of new technologies
▪ Engineer - Telecom Paristech
Amine Bennis - CEO Qopius Finance / Fondateur
▪ Chahine Capital : Fund Manager, portfolio combining European equity and Alternative Global Macro
strategies - €550M AUM, Luxembourg (3 years)
▪ Financière Arbevel : Quantitative global fund Manager, Wealth management advisor- Paris (3 years)
▪ OTC Financial : Asset Management Consultant - New York (1 year)
▪ AXA France : Quantitative Analyst - Paris (1 year)
▪ Engineer - Ecole Normale Supérieure Telecom Bretagne
Nicolas Barral - Business Développement - US
▪ Columbia Business School, MBA, New York City.
▪ Schlumberger, senior mechanical engineer and team leader - Oslo (6 years)
▪ UC Berkeley, Master in Mechanical Engineering
▪ Engineer - Arts et Métiers ParisTech
Roy Moussa - Business Développement - Europe
▪ Schlumberger, team lead - Oslo (3 years)
▪ Best Buy, Commercial - Montreal (2 years)
▪ Engineer - Concordia University

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QOPIUS TECHNOLOGY TEAM
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Alexandre De Larrard - Research – Statistical modeling
▪ AXA, Pricing and modelisation- Paris (1 year)
▪ Mazars. Audit - Paris (1 an)
▪ ENSAE, actuary certification
▪ Engineer - Telecom Paristech
Benoit Boyadjis - Research – Image recognition
▪ Expert in deep learning techniques of language and image processing, parallel computing, algorithms on
attentional processing of information.
▪ Thales, PHD in artificial intelligence (2 years)
▪ Engineer - Telecom Paristech
Vincent Lostanlen - Research – Signal processing
▪ Expert deep wavelet representations and signal processing.
▪ Ecole Normale Supérieure (ULM) Paris, PHD in artificial intelligence. (3 years)
▪ Engineer – Telecom Paristech
Léopold CRESTEL - Research - Signal processing
▪ ATIAM – IRCAM, PHD in artificial intelligence applied to acoustic and speech processing. (3 years)
▪ Expert in deep wavelet representations and signal processing.
▪ Engineer - Telecom Paristech

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ADVANTAGES
The research team of a big company with the agility of a start up
Common ressources for development
A Holistic approach A multidisciplinary approach
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Qopius is using a unique cognitive architecture for a variety of
challenges, the research team combines and mixes inside a common
artificial intelligence engine the latest techniques of speech, image, and
time series processing.

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QOPIUS FINANCIAL ENGINE
Similar to the human brain, the Q-Engine is powered by components that are continuously learning,
communicating and adapting to form a single unit that's robust in the face of surprise. The engine is
constantly making prediction about future events and comparing that with the actual outcome to
improve on its predictions. After the system has accomplished a task through the "Intelligent
Behavior" module, it evaluates it's accuracy and transmits back the need (when necessary) for more
relevant data sources.
Qopius - Private & Confidential
 Selective Data Acquisition
It powers the exploration of the system's
environment
 Artificial Reality
It builds its own representation of the environment
 Intelligent Behavior
Takes decisions, acts and adapts to achieve
various objectives or tasks.
Although the Q-Engine develops autonomously, we
have constructed a structure that can be unveiled
and understood by the people it works with.

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QOPIUS ENGINE TECHNIQUES
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A few of the techniques behind the engine include deep learning, evolutionary algorithms
and decision tree learning :
Deep Learning Evolutionary Algorithms
Q-Engine aims at :
 Searching Optimal Parameters in high dimensional space
 Integrating and fusing multimodal sources
 Combining collection of generated and external models

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CHARACTERISTICS
 PLASTICITY : The integration of new knowledge is done
so that it does not disturb the existing one. The importance
of each informational stream is constantly influences and
weighted depending on their likelihood and usefulness. The
Qopius engine has the ability to select modalities that give
high performances at a time and inhibit the others.
 ARTIFICIAL MOTIVATION AND CURIOSITY : The Qopius
Engine has the ability to discover by itself the objectives
that will accelerate its development.
 VIGILANCE : The Engine always tries to predict the next
information it will receive. Mis-predicted information receive
high attention from the engine and lead to curiosity and
learning.
 TRANSPARENCY : The structure of the engine has been
imagined so that we can follow (and potentially influence)
the development of the artificial intelligence.

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SUMMARY

26. FROM CUTTING EDGE A.I. RESEARCH
TO INNOVATIVE SOLUTIONS
Qopius - Private & Confidential 26