cube

A Machine-Learning Based Systematic Fund

Systematic Alpha Fund

Table of contents

Rcube Asset Management

Rcube Systematic Alpha Fund Overview

Track record & Conclusion

Appendix: The case for using Machine Learning in systematic trading

2

Investment Process

Rcube Asset Management

R C U B E

Rcube Asset Management

• Rcube stands for Research, Returns and Risk management

• Started in 2011 as an investment research provider

• Team of 6 experienced professionals

• Currently managing two funds totaling $55 m AUM

• Rcube Global Macro Fund UCITS (launched in February 2014)

• Rcube Systematic Alpha Fund (launched in May 2015)

4

Founding PartnersRcube Team

Systematicfunds

Paul Buigues

Remi Takase

Global Macro funds

Cyril Castelli

Paul Buigues

Risk management, middle office, compliance

Morgan Rossi

Agama Conseil

Business Development

Kati Kukkasniemi

Max Kamir

Asset Management

Partners: Cyril Castelli (CEO), Paul Buigues

Systematic Alpha Investment Team

Paul is a founding partner and CIO of systematic strategies at Rcube Asset Management. Paul has 17

years of experience in systematic trading (in credit, equities and global macro). For the past few years

Paul has focused on applying machine learning to systematic trading. Previously, Paul founded

Fimaxis, a quantitative investment research firm. Formerly, Paul was a fund manager at ADI, where he

managed up to $1Bn of assets in quantitative long‐only high yield and credit arbitrage funds.

Paul holds an MSc in Engineering and an MSc in International Finance from HEC, Paris.

In addition to co-managing the strategy, Remi is responsible for coding the machine learning

algorithms used in the Systematic Alpha Fund. Before joining Rcube, Remi had gained experience in

machine learning and semantic analysis at Proxem SAS.

Remi holds an MSc in Engineering from ENSIIE, an MSc in Financial Engineering from the University

of Evry, and is certified by the AMF.

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Remi Takase, PM

Paul Buigues, CIO Systematic Strategies, PM

Investment Process

Rcube Systematic Alpha Fund Overview

R C U B E

Quick facts

Systematic trading based on Machine Learning

10% per year over the risk-free rate

10-15% annualized

Highly liquid instruments (in equities, rates, forex, commodities)

Management fees: 1.5% / performance fees: 15%

Cayman fund

May 1st, 2015

3 months since launch (plus 14 months as a sub-portfolio)

Investment Strategy

Target return

Target volatility range

Investment Universe

Fees (A shares)

Fund type

Fund inception

Strategy track record

8

Trading Systems

9

• The fund is a portfolio of thousands of trading systems involving 9 assets.

• For a given asset, trading systems are based on:

• By design, a trading system has a neutral long-term average exposure to the assets it trades (no structural beta => pure alpha)

Economic data: 80%

Other assets’ prices / returns:

20%

Prices/returns

of the asset

itself:<0.1%

Investment process

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Step 1

Trading system

generation &

selection

Step 2

Trading system

allocation

Step 3

Execution

For each of the assets

we trade, thousands of

trading systems are

selected

Allocation to trading

systems, subject to the

portfolio’s aggregate

volatility target

Daily execution of the

aggregate portfolio of

trading systems

S&P 500 Estoxx 50 MSCI EM US 10Y DE 10Y Dollar Index Gold Copper Crude Oil

11

For each of the 9

assets we trade,

machine learning

algos generate

and select

thousands of

trading systems.

...

Examples of trading systems generated by machine learning algorithms:

Asset: Gold

Input: Consumer Sentiment

Exposure

to the asset

In-sample

performance

and Sharpe

ratio

Asset: Eurostoxx 50

Input: German 10Y yields

Asset: S&P 500

Input: SLOS Survey

Sharpe = 0.44 Sharpe = 0.64

Step 1: Trading system generation & selection

Sharpe = 0.67

LONG

SHORT

Step 2: Trading system allocation

S&P 500 Estoxx 50 MSCI EM US 10Y DE 10Y Dollar Index Gold Copper Crude Oil

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9 assets

Thousands of

trading systems per

asset

Trading systems are aggregated according to:

- their past risk-adjusted performance

- their expected persistenceS

One aggregate

trading system for

each asset (9).

Trading system

aggregation

The allocation to aggregate trading systems is subject to the portfolio’s volatility target.

Step 3: Execution

• Every night, trading systems are selected and aggregated.

• At 7am CET, the system generates new target positions for each asset.

• After checking for potential errors, we trade the delta between current fund positions and target positions (using execution algos).

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Asset codeCurrent # of

contracts

Target # of

contractsDelta

ES1 Index 56 52 -4

VG1 Index 193 189 -4

MES1 Index -10 -10 0

TY1 Comdty -63 -53 10

RX1 Comdty 79 86 7

DX1 Curncy 88 88 0

GC1 Comdty -22 -22 0

CL1 Comdty -21 -21 0

HG1 Comdty -23 -25 -2

• We trade on average around 10% of the portfolio’s nominal size every morning.

Note: Trading the aggregation of thousands of trading systems vs. trading individual systems dramatically lowers trading costs

Typical daily trading activity (for $ 25m):

Forward test procedure

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Step 1

Trading system

generation &

selection

Step 2

Trading system

allocation

Rerun the process at times t, t+1, t+2…, T

Step 3

Execution

Computing power needed to forward test 200 assets since 2000: around 300 Trillion arithmetic operations

Risk management

• Risk management is embedded in the investment process (maximum volatility target: 15% annualized)

• Risk is also managed independently by our risk manager (using HedgeGuard Financial Software)

• If the NAV experiences a 10% drawdown, we cut the portfolio for a minimum period of one week.

• Discretionary interventions: When we believe that an asset’s former drivers are not going to be relevant for a certain time, we can discretionarily adjust (or eliminate) the strategy’s target position for the asset. This can take place in the event of:

• Major geopolitical events

• Changes in the nature of an asset (e.g.: pegging)

• …

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The importance of trading system diversification

• Correlations between beta-neutral trading systems are considerably lower than correlations between assets.

• Running a diversified set of trading systems vastly increases the Sharpe ratio of the portfolio.

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r=0

r=0.01

r=0.05

r=0.20

r=0.50

Investment Process

Track record & Conclusion

R C U B E

Live track record - Sharpe ratio: 1.13 (net of fees)

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Performance before May 2015 corresponds to the Systematic Alpha Program levered by 1.5x (12% volatility target).

Management fees (1.5%) and performance fees (15%) are taken into account.

Before May 2015, the Systematic Alpha Program was run as a sub-portfolio of the Rcube Global Macro Fund.

Annualized rate of return: 11.64% Annualized volatility: 10.26%

Forward test backfill (01/2000-03/2014)

• Forward test of 90,000 trading systems on 9 assets (no selection nor look-ahead bias)

• Target volatility:12%

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Rcube Systematic Alpha Fund: Conclusion

• An innovative investment process

• Harnessing the collective intelligence of tens of thousands of “virtual” traders

• 17 month live track record

• 10% target excess return with 10-15% volatility target range

• Investor relations: Kati Kukkasniemi kk@rcube.com

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General Fund Information

Fund name: Rcube Systematic Alpha Fund

Launch Date: May 1st, 2015

Prime Broker: Credit Suisse

Administrator: Bank of New York

Legal Advisor: Walkers

Minimum Investment: 150 000 $ (or equivalent in €)

Liquidity: Monthly

Subscription / Redemption Notice: 10 Business Days

Domicile: Cayman Islands

Fees: 1.5% Management fees / 15% Performance fees

High-Water mark

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Investment Process

Appendix: The case for Machine Learning-based trading

R C U B E

What is Machine Learning-based trading ?

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Machine Learning

Giving computers

the ability to learn

Systematic Trading

Giving computers

the ability to trade

Machine Learning-based Trading

Giving computers the ability to learn how to trade

(and to trade accordingly)

+

=

The discretionary / systematic trading continuum

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Pure

discretionary

trading

Discretionary

trading with

quantitative

inputs

Systematic with

discretionary

overrides (or

vice versa)

Traditional

systematic

trading

Machine

Learning-based

trading

0% systematic

Machine Learning: the most extreme systematic trading approach.

25% systematic 50% systematic 75% systematic 95% systematic

Traditional systematic trading

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Backtest Execution

Not validated

System abandoned due to weak live performances

Trading systems are the “assets” of traditional systematic trading. Trading those assets can be as emotional as discretionary trading.

ValidatedTrading

system

75% systematic

Trading System designer

Machine learning-based trading

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Automated TS

discovery & selectionTS

execution

New market observations=> learning

When ML is used, trading systems are discovered and selected by algorithms. Humans only provide computer code and data sets.

Data universe

ML algorithms

Coder / data scientist

Selected TS

95% systematic

Man vs. Machine: Trading System discovery speed

Manual discovery

≈ 1

trading system / hour

Automated discovery

≈ 1 000 000

trading systems / hour

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TS

Manual validation

Code

Man vs. Machine: validation accuracy

• Due to selection bias, a group of manually selected trading systems will underperform when traded live vs. backtests.

• Validation is therefore impossible (or at least misleading)

• A continuous and automated selection of trading systems enables forward testing (no selection bias)

• Forward test results are close to live performances (WYSIWYG)

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Selection point

Selection points

Manual trading system selection Automated trading system selection

Time Time

Per

form

ance

Per

form

ance

×

Machine Learning: two alternative approaches

This approach is valid in fields where rules do not change over time.

It generally does not work in finance, which is a complex adaptive system.

Simple models are structurally much more persistent than complex ones.

=> We opted for this approach

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A small number of complex models A large number of simple models

Input 1

Input 2Input 3

Input 4

Input 5

- Each model has many inputs

- Models are based on non-parametric techniques such as Neural Networks:

- Each model only has a few inputs

- Models are based on simple and robust statistical techniques

Input Output

Recap: Successful human traders vs. Machine Learning

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Successful traders Machine Learning

Quick-witted

Disciplined

Unemotional

Patient

Thinks probabilistically

Able to adapt

Have a critical mind Under continuous

improvement

Machine Learning algos outperforms human pros in:

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• Chess LearningLemming (2680 ELO)

• Quiz competitions IBM Watson (Jeopardy)

• Reading facial expressions MIT Affectiva

• Criminal recidivism prediction (Maloof, 1999)

• Short-term trading (Kirilenko, 2012)

Why not in medium-term systematic trading?