Algorithmic
Trading 2008
The definitive conference
on quantitative trading, models and technology
Monday
7th April 2008
The
Cumberland Hotel, Marble Arch, London
This conference is aimed at attracting individuals
from Investment banks, Asset managers and Hedge funds who are
considering on utilising algorithmic trading solutions for increasing
investment returns.
It is focused at Fund managers, traders, investment analysts,
risk managers and other investment professionals.
Algorithmic or rule based trading strategies
are fast becoming the standard across a number of financial
institutions. This has been in reaction to low returns on traditional
structured and index based products.
Institutional and high net worth individuals
are being targeted with algorithm-based strategies that are
constantly gaining in sophistication.
Algorithmic Trading 2008 will be a focal
point for research and discussion on new strategies within algorithmic
trading and a forum for existing vendors to display their models
and supporting technology.
The conference will allow for sell-side financial
institutions to market their algorithmic trading strategies
and for buy-side financial institutions to determine how these
can be complementary to their existing trade process.
In an environment where gaining investor
confidence is becoming more and more difficult; the demands
set by potential investors rest more on finding asset classes
that provide diversification and stable positive returns. It
has become increasingly important to start exploiting new algorithm
based investment strategies.
The conference also aims to be the definitive
breeding ground for a new type of investment professional. One
that uses the power of mathematical inference to generate alpha
and exploit anomalies found in global financial markets
Conference
Programme
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9.00am - 9.40am
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REGISTRATION,
COFFEE, EXHIBITION AND NETWORKING
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9.40am - 9.50am
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Opening remarks
from chair
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9.50am - 10.30am
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KEYNOTE LECTURE
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Algorithmic Alpha Generation: New Methods
for Today's Markets
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Quantitative
and statistically driven strategies are increasingly operating
in highly competitive markets, challenging conventional
methods consistently capture alpha over the long term. We
propose that asset managers committed to these investment
approaches must expand their ability to profit from transient
opportunities, as well as greatly reduce their overall time
to market from discovery to production. This requires changes
in business process, as well as an expanded set of building
blocks. We aim to demonstrate in our presentation that by
combining a number of different techniques in an alpha discovery
environment these objectives can be achieved. We demonstrate
identification of potentially persistent non-random price
behaviors with interesting statistical properties that can
be found in unexpected places through exploratory R&D and
discuss how to exploit this information using traditional
and non-traditional methods. We further demonstrate and
discuss the use of heterogeneous optimization methods within
the same signal processing network leading to multi-level
adaptive algorithms for alpha generation. We introduce a
novel machine learning algorithm that is useful for short-term
real-time optimization of multiple inputs streams based
upon singular or multiple weighted evaluation metrics and
demonstrate its use as well as compare its performance to
Linear Regression. |
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Mr
Rosario M. Ingargiola, Chief Technology Officer |
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Alphacet,
USA |
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10.30am - 11.00am
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Building an Algorithmic Trading System
and the Application of Manifold Learning in the FX-market
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This talk will
give a brief overview of the work of introducing machine
learning intelligence in the Nordea e-markets system, to
facilitate auto-hedging, smart price engine algorithms and
proprietary automatic positioning within the foreign exchange
market. Since the work began one year ago, steady progress
has been made and the system is ready to be employed in
a "semi"-live setting this spring. In this talk we will
give a brief overview of the steps taken so far in the project.
A number of quantitative techniques have been implemented
in the system and evaluated. As of late we have investigated
the use of manifold learning; a class of geometrically motivated
nonlinear data mining methods, to predict movements in the
foreign exchange market. Financial time series are often
correlated over time; and may contain valuable customer
specific proprietary information. In principle, such relationships
may be exploited for forecasting. However, they may be noisy,
nonlinear and changing over time, making this a challenging
task. Hence, robust methods for detection and exploitation
of such correlations are of high interest for model trading
and quantitative strategies. To this end, we study the application
of a recently proposed method for nonlinear regression on
manifolds. The approach involves dimensionality reduction
through Laplacian Eigenmaps and optimization of cross-covariance
operators in the kernel feature space induced by the normalized
graph Laplacian. |
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Dr
Erik Alpkvist, Analyst |
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Mr Jens Nilsson
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Nordea Markets
Division
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11.00am - 11.30am
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TEA, EXHIBITION
AND NETWORKING
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11.30am - 12.00pm
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Short Horizon Covariance Forecasting
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Asset
return covariances at intra-day horizons are known to bebiased
towards zero due to market microstructure effects. Thus,
traders who simply scale their daily covariance forecast
to match their trading horizon, are likely to over-estimate
the actual experienced asset dependence. In this paper we
discuss some of the key challengesencountered in intra-day
covariance forecasting. Based on extensive empirical analysis,
we make specific recommendations regarding model design
and data sampling. |
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Mr Roel Oomen, Quantitative Analyst
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Deutsche Bank AG
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| 12.00pm - 12.40pm |
KEYNOTE
LECTURE |
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Adaptive Arrival
Price
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Electronic trading
of equities and other securities makes heavy use of "arrival
price" algorithms, that balance the market impact cost of
rapid execution against the volatility risk of slow execution.
In the standard formulation, mean-variance optimal trading
strategies are static: they donot modify the execution speed
in response to price motions observed during trading. We
show that with a more realistic formulation of the mean-variance
tradeoff, with no momentum or mean reversion in the price
process, substantial improvements are possible by using
dynamic trading strategies. We develop a technique for computing
optimal dynamic strategies to any desired degree of precision.
The asset price process is observed on a discrete tree with
a arbitrary number of levels. We introduce a novel dynamic
programming technique in which the control variables are
not only the shares traded at each time step, but also the
maximum expected cost for the remainder of the program;
the value function is the variance ofthe remaining program.
The resulting adaptive strategies are"aggressive-in-the-money":
they accelerate the execution when the price moves in the
trader's favor, spending parts of the trading gains to reduce
risk. The improvement is larger for large initial positions. |
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Mr Julian Lorenz
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ETH Zurich
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12.40pm - 12.50pm
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Open Forum
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12.50pm - 2.00pm
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LUNCH, EXHIBITION
AND NETWORKING
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2.00pm - 2.30pm
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The next Generation in Algorithmic
Trading
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Liquidity fragmentation resulting from
regulatory changes such as Reg. NMS (in the US) or MiFID
(in Europe), combined with the aggressive competition
between traditional exchanges and alternative trading
venues, is making current execution algorithms “obsolete”.
Therefore, are financial institutions spending millions
on antiquated technology? This discussion paper provides
Quod Financial’s view on today’s algorithms, as well as
how a new generation of algorithms are addressing the
two crucial market phenomena, first, fragmentation of
liquidity and second, multi-asset trading.
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Mr Ali Pichvai
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Quod Financial |
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2.30pm - 3.00pm
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Calibrating Market
Impact Model with Exponential Decay
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The market impact
model of Almgren-Thum-Hauptmann-Li (ATHL) has to some extent
become an industry standard in the area of transaction cost
analysis. We improve upon the baseline specification in
several aspects. Firstly, by calibrating the decay speed
of the temporary impact (after Obizhaeva-Wang), we can both
ensure finite (as opposed to infinite in case of ATHL) cost
of instantaneous execution and the property that temporary
impact depends not just on the speed of trading but also
on the time it takes to trade (maintaining a 30% trading
rate for 1 minute is substantially cheaper than doing the
same for several hours). Secondly, we introduce spreads
into the model. Thirdly, we argue that temporary market
impact is a linear function of a properly modelled expected
trading rate. Our model provides a substantially better
fit to the available transactions data and exhibits consistent
behavior if scaled to smaller and/or faster traded orders. |
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Dr Alexander
Gerko, Quantitative Analyst
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Deutsche Bank
AG
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3.00pm - 3.30pm
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CEP Based FX
Algorithmic Trading
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This presentation
will demonstrate a real world FX trading applicationhighlighting
how both technical analysis, signal generation andorderplacement
is implemented within a Complex Event Processing Frameworks |
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Mr Colin Clark |
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Streambase
Systems, Inc |
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3.30pm - 4.00pm
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TEA, EXHIBITION
AND NETWORKING
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4.00pm - 4.30pm
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Algorithmic Trading:
Market Impact Models and Trade Scheduling
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In general, trading
means implementation of a portfolio decision, which in
turn incorporates transaction cost. This may lead to potentially
high costs of execution. So a proper quantitative framework
to manage such transaction costs can make the trading
process more efficient. Trade scheduling models can provide
the appropriate order slicing scheme to improve portfolio
returns during and after rebalancing. Market impact, price
appreciation, timing and opportunity risks contribute
towards overall trade cost and need to be taken into consideration
to derive optimal trading decisions. There exist various
trade algorithms and an investor can choose those that
are consistent with his investment objectives.
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Ms Ekaterina
Kochieva
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Brunel
University |
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4.30pm - 5.00pm
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Market Analytics: A New Front in the
Algo Wars
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In the early days of electronic trading, electronic execution
channels were made available to save brokers the trouble
of dealing with annoying small orders, so they could focus
on their primary task of executing large orders on the
floor.
Now with the proliferation of algorithms, and markets
of makers and takers, the electronic channels are rapidly
becoming the primary venue for execution of ever larger
orders. 10,000 shares looks increasingly like 100 shares,
100 times. Algorithm designers are fighting a multi-front
war for an edge in speed, and real-time analysis. The
task is further complicated by an ever growing population
of execution venues.
Exchanges are moving aggressively to provide a friendly
environment for this order flow. They build ever faster
trading systems, whose performance is measured in milliseconds,
and offer collocated hosting for major participants, to
further shave delays so small that the speed of light
enters into their calculation.
Executions are not the only type of electronic traffic
needed to support leading edge algo trading. Analytics,
calculations that convey information about markets, are
of growing importance. All of the major global stock exchanges
have added real time analytics to their data offerings.
These analytics convey information derived from much
higher volume data flows. The raw information used includes
all order book events, all order flows, including cancels
and replacements, and partial fills. Using this data directly
is generally not possible or practical. It is not possible,
since markets do not disclose the micro details of their
customers' order working process, and not practical, since
there is far too much data.
Order traffic rates go as high as millions per minute,
and are still rising. There is only so much that can be
conveyed over even today's broadband channels, and not
every market participant wants to deal with the computational
task of analyzing that torrent in real time.
This creates a need for a new class of high speed analytics,
sent via the same low latency systems developed for rapid
order execution. These analytics seek to convey information
needed by algo participants, without conveying information
that those participants want to hide from each other.
They replace the "market color" and "sense of the floor"
familiar to traders of the past.
The customers for these market analytics fall into two
groups: human traders and computers running algos. Humans
read, but the computers act, as rapidly as they can, on
the information in these analytics. These forces result
in a new set of low latency computing machinery being
deployed in this latest front in the Algo Wars.
This paper gives a quick overview of the state of real
time market analytics offered today, using examples from
US and European Exchanges. An example showing the application
of low latency analytics to order data shows a significant
and potentially exploitable speed advantage.
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Dale Stevens
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Aleri
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5.00pm - 5.10pm
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Concluding
remarks from chair
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5.10pm
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End of Conference
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Conference
Documents
Aims
and Objectives
Conference
Programme
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++Stop
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send your registration forms
to
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Productions
Suite
370, 8 Shepherd Market, Mayfair, London W1J 7JY
or
fax through your completed registration form to:
(0044)
0208-351 0943
or
call our mobile registration hotline on:
(0044)
07956-594 472
or
email to:
info@chameleonproductions.co.uk
Please
send all enquiries to Seromanie Bernard
at
info@chameleonproductions.co.uk
This
Years Sponsors:
| |
Automated Trader is the first global magazine
dedicated to automated and algorithmic trading, and offers
in-depth business and technical coverage through comprehensive
news, features, in-depth articles on best practice/techniques
and detailed user case studies. In addition to thousands
of CTAs, hedge funds, proprietary trading operations and
conventional asset managers globally, Automated Trader
is also read by all major sellside participants in automated
and algorithmic trading.
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Aleri specializes in enterprise-level event
processing technology. The Aleri Streaming Platform is
the leading event processing technology in terms of performance
and versatility. Designed from the ground up for high
throughput with minimal latency, it provides an industrial
strength engine that can address a wide variety of event
processing needs in financial services and beyond.
The Aleri Liquidity Management System is
the first enterprise-class application built on event
processing technology. Designed for the demanding environment
of global bank treasuries, it aggregates transaction information
across multiple disparate systems to provide a consolidated
real-time dashboard for managing intraday liquidity.
Aleri Global Banking, a division of Aleri,
provides integrated banking systems including the popular
Atlas system and its successor the Aleri Global Banking
Solution (GBS).
At Aleri we've spent over 20 years supporting
mission critical banking applications and have world class
clients who depend on Aleri software. Aleri is based in
Chicago with additional offices in New York and London.
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Last
Years Sponsors:
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