📄 Extracted Text (5,638 words)
4
June 2012
Wit II
[email protected] Confidential
EFTA00556664
Table of Contents
3 Introduction
4 Executive Summary
5 Competitive Advantages
6 Investment Process
7 Strategies Description
11 Performance
13 Appendix
33
4ARMIN
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Introduction
Founded in October 2010, QARMIN is a small Paris-based Prop Trading House
that specializes in quantitative systematic fully automatized strategies with a
focus on listed and highly liquid instruments (Europe and US) and medium
frequency strategies.
First stage of development was dedicated to development and live-testing of our
proprietary trading platform including automated strategies, backtesting tools,
risk management engine and execution algorithms.
We have gone live with the founders' private funds and are now looking for a
business partner to operate on a larger scale.
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Executive Summary
J Primary objective is to achieve consistent risk-adjusted returns throughout different market
cycles, taking advantage of a flexible dynamic allocation process relying on 3 largely
uncorrelated investment axes: multi-asset directional, market neutral and volatility
arbitrage.
We typically target a minimum Sharpe Ratio of 2, with an annualized Return to Max
Drawdown of 3.
J Custom trading platform: Research and Development, backtesting and deployment run on a
unified platform, hence making possible process streamlining.
We're maintaining an extensive historical database of prices and fundamentals for 6000+
instruments listed on US and European exchanges, going back more than 15 years.
QARMIN is a team of experienced prop traders and developers who have committed a
significant amount of their wealth in the project, and have worked extensively together in the
past. Each team member has a strong academic background in mathematical finance and has
extensive experience in the derivatives market.
A diversified strategy portfolio with high pre-leverage target returns
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Competitive Advantages
❑ Strong methodology and flexible platform allow industrialization of idea generation and
testing: time to market from R&D to production is low
❑ Capacity to create and manage several uncorrelated bets by creating nonstandard coherent
underlyings (basket, synthetic instruments)
❑ Large existing (and growing) library of uncorrelated single models/strategies displaying low
pairwise correlation (-5% average correlation over 10yr period) within each investment axis
Extensive menu of allotment between each of those models benefiting from our proprietary
allocation model therefore increasing Sharpe Ratio compared to any given single strategy's
Sharpe
Total liberty to switch off strategies with low prospective returns
❑ Active hedging method for tail risks via quantitative process involving long only cheap OTM
long-term options; focus on macro picture, top down approach and exogenous stimuli
analysis therefore departing from the historical bias and enhancing the capital preservation
capacity of the portfolio
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Investment Process Strategy Development (cf. Appendix p15)
• Underlying idea stemming from market experience
77 Strategy
•
(risk premium location)
Historical validation (backtesting, choice of relevant
i Development parameters, observed returns)
I Alpha Discovery • Data-mining bias correction (reality check, expected
Historical Validation
Expected Returns returns, coherence with market fundamentals — is the
edge still in place?)
Portfolio Allocation (cf. Appendix p16)
• Bottom-up approach using customized allocation
algorithms at each allocation step
• Top-Down approach adjusting marginal weights of single
models and investment axes via our own proprietary risk
Portfolio
index
Allocation
Bottom-Up
Top•Down
Risk Management (cf. Appendix p18)
• Single model dedicated risk management (stop-loss,
volatility/VaR adjusted size by instrument)
• Specific risk manager for each investment style (directional,
market neutral, volatility arbitrage)
• Global aggregation for ultimate capital preservation
constraint
Execution Management (cf. Appendix p21)
• Specific algorithms for each investment style
• Transaction costs analysis (brokerage, slippage,
rebates) for best execution algorithm selection and
trades/performance reconciliation
Identification of alpha at each step of the process
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Strategies Description
Overview
• 3 Investment axes whose relative weights are controlled via a proprietary portfolio
allocation model; directional, market neutral and volatility arbitrage are effectively
uncorrelated through different market cycles
Investment Axis Description Asset Class Horizon
Equities, Equity Index, Fixed Income, Commodity,
Technical and fundamental short/medium
Currency Futures and ETFs. Options on these asset
term pattern
From
hours
behavioural
coherent
2
to
with
Directional classes.
finance, market macro/micro structure and several weeks
Geographic positioning: Europe, US and Emerging
directional events
Markets (via ETF)
Adaptive statistical analysis to capitalize on
Equities, Equity Index, Fixed Income Futures From 10 minutes
Market Neutral local divergence and mean reversion nature
Geographic positioning: Europe, US to 2 weeks
of risk neutral baskets
Equities: main indices and their stock components, VIX,
Arbitrage of the volatility curve mispricing
Volatility V2X, Main Currencies, Commodities, ETF Volatilities
(local vol, skew, kurtosis, term structure) of From 1 week to
VXX, VXZ, Listed vanilla options
Arbitrage an underlying compared to its peers, both 1 month
Geographic positioning: Europe, US and Emerging
using fundamental and statistical approach
Markets (via ETF)
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Strategies Description
Directional
Directional
Strategy Sample* Strategy Description Underlyings Horizon
Take advantage of short term negative Large Cap Equities 1 to 5 days
Mean Reversion autocorrelation using specific volatility analysis Equity Futures
Exploit long term trend following nature of capital Equity Index Futures 5 days to
markets; overlay via tactical options writing Commodity Futures several weeks
Trend Following FX Futures
Fixed Income Futures
Emerging Market ETFs
Capitalize on over/under-reaction of various indices Equity Index Futures 2 to 10 hours
Macro Events around macroeconomic announcements Fixed Income Futures
Exploit statistical patterns of gaps at open Equity Index Futures 2 to 10 hours
Gap
Benefit from intraday and extraday seasonality due Equities 2 hours to 5
Seasonality to structural imbalances Equity Index Futures days
non-exhaustive list of currently available live strategies
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Strategies Description
Market Neutral
Market Neutral
Strategy Sample* Strategy Description Underlyings Horizon
Exploit local relative mispricing of equities and Volatility Index Futures 10 minutes to
Equity vs. Volatility
volatility as an asset class Equity Index Futures 1 day
Arbitrage
Take advantage of temporary divergence within a Equities 1 day to 2
bespoke basket constructed via various statistical weeks
Equity Statistical Arbitrage methods
Take advantage of temporary divergence within a Equity Index Futures 2 hours to 3
Cross Asset Futures bespoke basket of instruments from different asset Commodity Futures days
Arbitrage classes constructed via adequate statistical methods FX Futures
Fixed Income Futures
' non-exhaustive list of currently available live strategies
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Strategies Description
Volatility Arbitrage
Volatility Arbitrage
Strategy Sample* Strategy Description Underlyings Horizon
Benefit from implied volatility curve mispricing of an Equity Options 1 week to 1
Single Stocks Relative underlying compared to its peers (L/S volatility, Equity Index Options month
Value Dispersion)
Take advantage of large time decay effect for short Equity Options 1 day to 1
Short term options term options around expiration; identify behaviours of week
expiration effect underlyings on expiration date deriving from large
options positions hedging
Exploit volatility curve dislocation around idiosyncratic Equity Options 1 week to 1
Event Driven events month
• non-exhaustive list of currently available live strategies
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Performance
1/2
Consistent risk-adjusted returns over a 10yr backtest period with no sign of abating in the recent past
Directional/Market Neutral Historical Drawdown
e e
Sharpe 3.92
Yearly Return to Max DD 5.34
Avg Yearly Return 22.8%
Profitable Days % 59%
Avg Daily Return 0.08%
StDev Daily Return 0.37%
Best Daily Return 2.91%
Worst Daily Return -2.24%
Directional/Market Neutral Equity Curve
Worst DD -4.27%
Max DD Duration 136 days
Avg Worst 10 DD Duration 62.9 days
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Performance
2/2
Badctested Yearly Returns (Post-Transaction Costs, Unleyeraged) • 1 Margin of safety built into
target return expectations
45% moving forward; 15% yearly
40% target performance has
been reached 9 years out of
35%
the last 10; it is a large 7.8%
30% (or 0.85 std dev) below
25% average over the entire period
20%
15% 15% target return is coherent
with 4.27% max backtested
drawdown, hence allowing us
to construct a portfolio with
2007 2008 2009 2010 2011 2012
an expected Return to max
drawdown higher than 3
Yearly Returns — — — Average Backtested Returns 23.5% — — — Target Return 15%
Strategy can be leveraged up
Y2012 performance has been frozen as of May 31,1 and extrapolated for the remainder to 5 times (margin to equity <
of the calendar year
100%) to achieve an iso
The backtest is presented with an equal-weighted allocation method; live allocation will
return-to-max-drawdown
tweak the relative weights of each strategies and each investment style, hence
improving the risk profile of the portfolio; weight of any given axis will never represent couple with no significant
more than 50% at any given time with the exception of Volatility Arbitrage which will be foreseeable loss of
limited to 25% relative weight performance (besides
Volatility Arbitrage investment axis is not represented on this chart as backtesting marginal funding costs)
remains difficult to achieve; however the expected performance is in line with the
above-presented result and should add an extra layer of diversification, therefore
improving the overall profile of the portfolio
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Appendix
14 Team Bio
15 Strategy Development
16 Portfolio Allocation
17 Risk Management
20 Execution Management
21 Technical Platform
28 Performance Details
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Team Bio
Li Bertrand LOUVARD, Founding Partner
Bertrand joined Societe Generale in 2000 as a trader on the US Indices Derivatives Trading Book in NY. In 2003, he became
Head of the US Indices Flow Business Trading Book before being appointed Head of the US Equity Flow Business Trading Book
in 2005. He expanded the Hedge Fund Client Business in the US for vanilla derivatives products. He then joined the Lyxor
Structured Product Solutions team in 2009 to develop the structured products active management offer within the Quantitative
Asset Management team of Lyxor.
Bertrand Louvard is a graduate of the tcole Centrale Paris and has a Master's degree in Mathematics, Probability and Statistics
from the University of Jussieu (Paris VI).
Li Francois-Charles SCAPULA, Founding Partner
Francois-Charles was a fellow associate at the CEREG between 2001 and 2004 and a professor of Econometrics and Quantitative Techniques at
University Paris IX Dauphine. He has spent the last 7 years as a prop trader for Fortis and Societe Generale focusing on quant equity derivatives
strategies, where he traded listed and OTC stock and index options, variance swaps, futures from all asset classes. He was also responsible for
setting up the trading platform at Lyxor for the quant asset management department.
Francois-Charles is a graduate of the Ecole Normale Superieure and ESSEC. He has an « Agregation » in Economics, a Master's degree in Applied
Mathematics and a Master's degree in Finance from Paris IX Dauphine. He was a PhD candidate in Mathematical Finance at the University of
Paris IX Dauphine.
Sylvain Rey, Founding Partner
As a graduate of Telecom SudParis with a specialty in parallel and distributed system, Sylvain has been a Software Consultant in the San Francisco
Bay Area from 2000, then in Paris, France from 2003. As a seasoned Application and Systems Architect, he has designed and developed many
solutions for various banking and financial institutions such as BNP Paribas, AXA Investment Managers, Banque de France and the European
System of Central Banks.
Each Partner has committed a substantial amount of his personal wealth to the venture.
Sylvain and Bertrand have known each other for more than 15 years. Bertrand and Francois Charles were working together at Societe Generale.
Sylvain, Bertrand and Francois Charles have founded QARMIN in October 2010 and have worked extensively since then to build
a cutting edge systematic platform.
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IDEA GENERATION
Strategy Risk Premium Intuited
Asset Class/Universe/Instruments defined
Development Systematic Rule Formulated
1. Entry / Exit
2. Specific Risk Management (Size, Stop Loss, Overlay)
Yes
Are observed results
Config
coherent with
market experience?
Top-down process
Database
Selection Criterion
Bottom-up process
1. Prices
2. Volumes 8acktesting Data-Mining 1 Average Return
2 Sharpe
3. Fundamentals
Engine Engine 3 Ret on drawdown
4. Derivatives
5. Proprietary Data 4. Ret. on VaR
Yes Observed
uputrun parameters set dnu sysemauc rule ITOM
weights
performance set observed data sets.
satisfactory?
Data
Mining
Correction
Yes
SINGLE STRATEGY READY
Integrate Strategy In Library
°IQARMIN
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STEPWISE
BOTTOM UP CONTROL PORTFOLIO ALLOCATION TOP DOWN CONTROL
PROCESS
DIRECTIONAL LIBRARY MARKET NEUTRAL LIBRARY VOL ARB UBRARY
Single Strategy Generation Process Single Strategy Generation Process
INITIAL STEP 0 Direct. I Direct. Direct. Neut.
CI Strategy Development 1 n 1 q. Strategy Development
i
STATISTICAL EQUITY CURVES ANALYSIS EXOGENEOUS CRITERIA ANALYSIS
Including: Including:
1. Mean Variance Optimization over 1. Strength of Risk Premia underlying
various horizons each of the single strategies (e.g. IV
2. Maximum diversification under Alloc. Alloc. Alloc. %Ile vs. RV, frequency of channel
STEP 1
performance constraint borders crossing, high volume in bear
3. Maximization of various performance market, etc.)
measures in walk forward process 2. Liquidity of traded instruments
INTERMEDIATE
RESULT 1 Directional Market Neutral
STATISTICAL EQUITY CURVES ANALYSIS EXOGENEOUS CRITERIA ANALYSIS
Kelly Criterion, Markowitz Portfolio Including:
Allocation 1. Macro/Flow Environment (e.g.
Economic Indicators)
2. Volatility/Credit Conditions (VIX,
iTraxx)
Allocation 3. Fear/Greed Index, Deal Index
STEP 2
(Proprietary index)
4. Crowding out trades (recent HF
performance vs. LT mean)
Decision to tilt allocation towards better
historically performing strategies in
identified conditions
virec:Lionai volatility mroitrage
INTERMEDIATE
RESULT 2 aggregation aggregation aggregation
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Risk Management
1/2 Ex-ante and ex-post approaches
Ex-ante approach
❑ Portfolio allocation taking decisions based on past performance analysis under risk constraints
• Variance minimization of the portfolio for a given performance target via Var-Covar matrix for strategies equity
curves and underlyings historical return
• Minimum ex-ante diversification required for inclusion of a given strategy in the meta-portfolio
❑ Sentiment Index adjusting notional at risk based on fundamental and technical data keeping the
margin to equity relatively constant (and targeting stable risk return profile)
❑ Hedging macro picture with "long-only" cheap long term OTM options via fundamental and
quantitative screening of underlyings with available derivative markets (see focus)
Ex-post approach
❑ Single position aggregation and real-time dedicated risk metrics for each business line...
• Multi Directional: Tenor notional exposure (via Var-Covar Matrix for underlying return)
• Market Neutral: Notional Replication, Spread Risks
• Volatility Arbitrage: Volatility, Kurtosis/Skew Exposure (Tail Risks)
❑ ...and at the global level for ultimate risk control and capital preservation
• Greeks, beta-adjusted notional exposure, dispersion risks
❑ Stress-testing of all positions via adverse relevant / historical
scenarios, VaR, etc.
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Risk Management
2/2 Macro Picture - Options
❑ « Buy only» long dated out-of-the-money options
❑ For a wide variety of asset classes and instruments, identify cheap options in terms of current implied
volatility vs. universe (universe-relative cheapness at a given time) and long term average (self-relative
cheapness over time)
❑ Analyze technicals, fundamentals and sentiments, leveraging our platform and proprietary database via a
quantitative process that allows us to screen hundreds of assets
Come up with a list of instruments in a position to capitalize on high potential global macro situations
❑ « Be fearful when others are greedy and greedy when others are fearful » (W. Buffet)
• Market psychology is the motor of the performance: we try and benefit from fear, greed, hysteria and mania
• Options are often mispriced during periods of irrationality hence providing great risk/reward opportunities
❑ Actively manage options
• Long term only options in order to reduce adverse time decay impact: we do not keep options with time to maturity lower
than 1 year
• Profit-taking/Stop-loss methodology: we seek to return 5/10 times the original investment on any given bet; as soon as
intermediary targets are reached, we deleverage part of the bet
❑ Risk management/performance enhancement process
• Our general investment philosophy is to look for statistical anomalies and capitalize on it by designing systematic strategies;
although an adaptive process, it is bound to be historically biased
• These strategies are often - not always- based on cashing in risk premia (implicit or explicit)
• Buying cheap OTM options allows us to hedge both biases (historical and short risk/premium)
via a deductive approach that covers any unpredictable events that June fall out
of historical scope
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STEPWISE
BOTTOM UP CONTROL RISK MANAGEMENT TOP DOWN CONTROL
PROCESS
Market Neutral Volatility Arbitrage
INTERMEDIATE
RESULT 2 aggregation aggregation
NO CONTROL RISK CONTROL BASED ON CALCULATED
Risk ex-ante has been measured and MEASURES
calibrated at STEP 2 Including:
1. VaR, Capital at risk under adverse
Directional Market Neutral Volatility Arbitrage
scenarios
Risk Engine Risk Engine Risk Engine 2. Greeks
STEP 3 3. Notional replication, spread risks
4. Tenor Notional on uncorrelated
instruments
Each axis is managed separately as they
are incurring different types of risk
INTERMEDIATE
RESULT 3 Global Aggregation
NO CONTROL RISK CONTROL BASED ON CALCULATED
Risk ex-ante has been measured and MEASURES
allocated at STEP 2 Risks are aggregated at the top level to
Global Risk Engine ensure global capital preservation
STEP 4
INTERMEDIATE Throat Dacitinne
RESULT 4
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STEPWISE
BOTTOM UP CONTROL EXECUTION MANAGEMENT TOP DOWN CONTROL
PROCESS
INTERMEDIATE
RESULT 4 Target Positions
SLIPPAGE & TCA FOR ALL EXECUTIONS LIQUIDITY, LEVEL 2, BOOK ANALYSIS
METHODS Execution Algorithm Library
1. Simulated Analysis of book order, market
2. Real microstructure, number of trades at the
Directional Market Neutral Volatility Arbitrage bid/offer over last relevant bar
We allocate more to the best recent library library library
execution method while keeping
minimum diversification.
STEP 5
Those measures give us a hint as to what
the street is doing in regards to our
trading signals.
Combination of algorithmic orders for single instruments
Pair trading and basket algorithms
Aggressive vs. passive algorithm for directional trades
FINAL ORDERS
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Technical Platform
Architecture and flows overview
Prime Broker
Custom API / FIX FIX
Monitoring and Reporting UHF Trader Engine
tools
Q Strategy Packages
Reporting Database
L-
I IIIICLIdSt QARMIN Trading
Framework
fi
11
Pricing, Corporate Actions and Real-time Data Aggregator
Fundamental Data Aggregator
I
QARMIN
I I
Bloomberg Activ Financials Additional Providers
Custom indicators Pricing Real-time data Real-time data
Fundamental data
Corporate Actions
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Technical Platform
Features 1/2
❑ Custom integrated Platform
• Fully mastered internally
• Modern software methodologies
• Every team member is a developer
• Code base is managed, factorized and peer reviewed
• Fast development cycles using agile methodologies
❑ Based on QuantServer / QuantOffice from Deltix
La
• Well renown software, powering various Institutionals, Funds and Proprietary Trading Houses C
• Complex Event Processing architecture a)
• Heavily parallelized, high performance system a
• Modern foundation with highly optimized, managed processes
• Tight technical partnership with Deltix
❑ Modern tools for business development
• Instrument / Universe / Calendar Managers
• Strategy Manager
• Integrated Development Environment based on Microsoft Visual Studio
• Visual Alpha tool for fast prototyping
• Strategy Runner for backtesting
• Database administration tools
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Technical Platform
Features 2/2
❑ QARMIN interoperability layer
• Flexible architecture allowing us to plug-in within various environments
• Real-time data connectors
• Pricing, corporate actions and fundamental data connectors
• Proprietary engine for index and scoring indicators generation
❑ QARMIN Trading Framework
• Extensive financial and mathematical library La
• Integrated with well established Econometry, Statistical, Financial and Solver libraries. C
• QARMIN own library with custom indicators a)
Signal Instrument facility, providing directly usable meta instruments for signal processing (chain
a
•
management, corporate action adjustment, pairs, baskets)
a
• State-of-the-art Meta Models facility for static and dynamic discovery/aggregation/allocation of strategies
• Real-time Risk Engines, enabling proactive decisions
• Order Processors, with custom execution algorithms and operational costs management
❑ Monitoring / Reporting tools
• Integrated within Hyperic HQ monitoring suite / alert center
• Real-time Trading Console
• Reporting engine with realtime charts
• Audit trails / logs
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Technical Platform
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ℹ️ Document Details
SHA-256
98a68238193bc3f308c73095873368598d119488d58fe1145685fd68e611fd31
Bates Number
EFTA00556664
Dataset
DataSet-9
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