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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.
6
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
10
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
12
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).
13
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
14
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)
• …
15
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.
16
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)
18
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%
19
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
20
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
21
Investment Process
Appendix: The case for Machine Learning-based trading
R C U B E
What is Machine Learning-based trading ?
23
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
24
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
25
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
26
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
27
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)
28
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
29
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
30
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:
31
• 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?
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