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Valdi Algorithms e-Book
Algorithmic Trading for Busy People
Volume 1: Fundamentals & Execution algorithms
by Benjamin Becar
©2011 SunGard. First published, April 2011. Second edition, June 2011.
Trademark information: SunGard and the SunGard logo are trademarks or registered trademarks of SunGard Data Systems Inc. or its subsidiaries in the
U.S. and other countries. All other trade names are trademarks or registered trademarks of their respective holders.
2
Table of contents
What does algorithmic trading really mean?
Global usage of algorithms in 2011
Why do people use algorithms?
What are the most popular algos?
Providers and naming conventions
Glossary and Resources
2
3
What does it really mean ?
3
Too many words…
Over the last five years or so, we have
been inundated with documents and
articles about algorithmic trading: often
these have created confusion for many
people working in the market: traders,
technologists and regulators….
It‟s not uncommon to hear “I want to do
algorithmic trading” when the real objective
is market making, High Frequency Trading,
index arbitrage, or a dozen other things.
Automated
Blackbox HFT Flashcrash
Algorithmic Arbitrage
Low latency VWAP quantitative
Percentage Volume Micro-Second
Stat Arbitrage CEP regulation
PTA
4
Automated Trading Strategies
4
Algorithmic Trading
High Frequency Trading
Market Making
Program Trading
Index Arbitrage
Smart Order Routing
Decision-making Algorithms
The main reason for confusion is that for some people, Algorithmic Trading is a
specific trading strategy, while for others, it‟s just a broad category involving any
application of software to support trading. The term AUTOMATED TRADING is more
correctly used for this broad category, to include all strategies that rely on computers,
algorithms or automation of some kind.
The most common types of automated
trading strategies are briefly described
in the following slides:
5
Automated Trading Strategies
5
Algorithmic Trading
Automated execution of a block trade, in search of optimization. Also referred as
“execution algorithms”, this trading strategy is usually applied to single instruments.
Example:
VWAP, Percentage Volume, Iceberg
High Frequency Trading (HFT)
Framework aimed at profit by trading quickly, based on low-latency technology. A
HFT strategy involves optimizing all aspects of the trade flow to be as fast as
possible, including hardware, network, and distance from the trading engine to the
exchange servers.
Example:
A HFT strategy could be to quote thousands of times per second the same
instrument, hoping to be hit at a competitive price, and subsequently resell it
higher. In a typical HFT strategy, less than 5% of the quotes sent to market result
in actual trades.
6
HFT controversy
6
There is controversy around HFT: some market participants, politicians and
regulators view it as unfair.
The charts below indicate how a small number of High Frequency Trading firms
dominate US equities trading.
High Frequency
56%High Frequency
1%
Traditional
99% Traditional
44%
Percentage of US trading firms
with a HF strategy
Percentage of US equity volume
driven by HF strategies
Source: TABB Group, Aug 2009
7
Automated Trading Strategies
7
Market Making
A market maker is a company, or an individual, that quotes both bid and offer prices
in a range of financial instruments or commodities held in inventory, with the
objective of making a profit on the bid/offer spread.
Smart Order Routing (SOR)
Looking for best/improved execution by making use of the price discrepancies that
can occur between venues. As the number and specificities of venues expands in
certain markets (60 venues in US equity markets, including dark pools,
exchanges, systematic internalizers…), smart routing strategies need to be more
and more sophisticated. The associated algorithms are sometimes referred to as
Liquidity Seeking Algorithms
What is a
quant?
A quant, or “quantitative analyst”, is a
mathematical and financial expert who
analyses the market and defines
trading algorithms to benefit from it.
8
Automated Trading Strategies
8
Program Trading
A program trader trades multiple instruments at the same time, in a „basket‟.
Some program traders focus on a single instrument type (e.g. equities-only
baskets), while others trade derivatives of these instruments at the same time
(options or futures). Cf. Index Arbitrage, described below.
Index Arbitrage
Index arbitrage is a specific type of Program Trading, aimed at profiting from
temporary discrepancies between the value of an index and the net asset value
of the underlying components. Opportunities for index arbitrage tend to
decrease over time in a given market, as the market becomes more
competitive and the technology more sophisticated. On newer markets (like
CFFEX at the present time, for example) there can be significant scope for
index arbitrage.
9
Automated Trading Strategies
9
Decision-making Algorithms
While all of the strategies described before take some sort of decision, “decision-
making algorithms” describes a category of algorithms that encode a complete
trading strategy, and then operate entirely automatically. In an ideal world, you
could turn on the strategy in the morning, turn it off in the afternoon, and just cash
in your profit. In reality, because most markets are complex and changeable, you
have to monitor the algorithm‟s progress, and modify it regularly. Decision-making
algorithms rely on charts, statistics, historical and real-time data as the basis for
each trading decision.
Because of the fierce competition in the automated world,
those strategies evolve very quickly. More and more, you can
see bridges between them, creating “super algorithms”:
market makers add execution algorithms, index arbitrageurs
use Smart Order Routers for improved efficiency, and so on.
And these moves can influence how the markets behave for
other participants, so vigilance is necessary!
10
Automated Trading Strategies
10
Algorithmic Trading now accounts for a large share of
the daily volume, as you can see in the below table.
Following the 2010 flash crash (see glossary), a slight
decline in algorithmic trading usage has been noticed
in the US and in Europe. However if we compare to
2005 figures, we can see that this is just a minor
trend in the overall growth pattern.
If we look back to 2001, at Chicago Mercantile
Exchange 90% of trades were handled… manually!
In just five years, meanwhile, Europe has multiplied
its algorithmic trading usage by more than 30 times.
2005 2009 2010 2011
US 21% 61% 56% 54%
Europe 1% 29% 38% 35%
Source: TABB Group, Apr 2011
11
Usage in Asia
11
Algorithmic
Trading
%
70%
60%
50%
40%
30%
20%
10%
0%
Singapore Hong Kong Japan Australia India
Source: Celent, October 2010
* Note: 2011 & 2012 figures are estimations based on market analysis.
12
Usage in Asia
12
In Asia, Hong Kong is clearly leading the
pack. Estimated usage is expected to reach
US levels within two years.
Apart from Singapore, which will be following
closely, and Japan, for which the chart
seems to understate the reality, the other
Asian markets are still well behind in terms of
usage, leading to big opportunities for new
entrants in this area.
However, there are some hurdles, mostly
regulatory, which continue to put a brake on
the rate of adoption.
13
Hurdles
13
All around the world, the economic crisis, followed by the flash
crash in May 2010, created a skeptical approach towards
algorithms at various levels.
After some concerns raised by legislators and general public
opinion, regulators worldwide have begun to move. In
European and US markets, this is the #1 source of concern for
2011.
In Asia, some of the hurdles include:
Requirements for pre-approval of algorithms (India,
Indonesia),
Ensuring “fair trading”, i.e. providing the same price for orders
received within the same time frame. (China)
Exchanges resisting the rise of alternative trading venues:
potential competitors transform into new entities such as the
Chi-East joint venture between SGX and Chi-X. In other
countries, alternative venues may have to wait a long time to
have their approval (eg, Australia), or enjoy the facilities of the
main exchange, like a central clearing (eg, Japan).
14
Why use algorithms?
14
Most businesses can profit from algorithmic
trading, with a variety of objectives:
Sell-side Improve performance of executions,
provide additional services to clients
Buy-side Route orders to the most effective
sell-side, spread the risk, obtain better prices
Market makers Improve strategies
Hedge funds React immediately to trend
changes, defining proprietary strategies quickly
Most businesses Access multiple markets
with the same or similar strategies
15
Impact at the trading desk
15
Respondents to a recent survey*
were asked to consider which
factors affecting their trading
desks were important.
Ease of use and trader
productivity were seen as being
almost equally important
considerations.
This reflects the role of algorithms
in changing workflows in the
trading environment.
*SunGard conducted a survey in 2011 with 150 buy-side firms. There were 500 individual responses: 45%
of respondents are heads of trading desks.
Source: SunGard Business Intelligence, Mar 2011
16
Impact on Execution
16
Survey respondents clearly consider that reduced market impact is a key
benefit. This received 37% of all mentions, well ahead of Execution consistency
(27%). Reducing latency was the area of least perceived importance , gaining
only 17% of mentions. What Is clear from responses is that different firms are
looking to achieve quite different things via algorithmic trading. Providers need
to be sensitive to these differences, as they impact the views that different
customers have of services.
Source: SunGard Business Intelligence, Mar 2011
17
Other factors
17
Among other considerations in the use of algorithms, anonymity and access to
internal crossing received the highest proportion of mentions. These scores are
consistent with the importance of reduced market impact noted earlier.
Customization received somewhat fewer mentions. Given the amount of
resources devoted to creating „custom‟ solutions for clients, this may be
regarded as disappointing.
Source: SunGard Business Intelligence, Mar 2011
18
Client satisfaction
18
George and his demanding client
George is the head of trading for a famous broker. He‟s calling one of his software
vendors: it seems something is wrong.
George Hello Jack, I need to do algo trading!
Jack Well sure, what do you want to do with it? Improve your executions?
George No!
Jack I see - it’s to be more productive then?
George No, it’s not.
Jack Then what?
George I have this client, a very large insurance firm and they told me: “George, if you
don’t have these simple algorithms within three months, I’m going with another
broker!”
Jack Let’s see what we can do!
With the buy-side becoming more and more educated in electronic
trading techniques, this situation is now very common.
19
What are the most popular algorithms?
19
Dozens of algorithms are created and advertised by brokers
and software vendors. Their names can range from descriptive
to technical, not forgetting strange names involving wildlife…
There are two major categories of popular algorithms:
Benchmark Seeking
Liquidity Seeking
20
The 2 majors
20
Liquidity Seeking AlgorithmsDark only
Aggressive inside a price (all venues)
Benchmark Seeking AlgorithmsVWAP
TWAP
Percentage of Volume
Implementation shortfall
21
Execution Trading Algorithms
21
Execution Algorithms provide various benefits to dealers when they have to
work their orders. Benchmark seeking algorithms, as described previously,
are a kind of execution algorithm.
Hide your interest from the market
Send your order at the right time
Cover your position
Be sure to be executed
Follow a trend
Meet key benchmarks
22
Providers and Naming Conventions
22
Provider Liquidity Seeking Percentage
Volume
Hidden Dark only
UBS Tap, Tap Now Volume Inline Hidden
Credit Suisse Pathfinder,
Crossfinder
Workit, Workit
IW, Inline
Guerrilla, Sniper,
Reserve
Deutsche Bank Stealth
Barclays Work & Pounce
Hydra
With Volume Ghost
BNP Killer, Grab pvol, volscaler Dark Peg,
Dark IOC
Citi Dagger Participate
Nomura Float & Pounce, Hide
& Pounce
With Volume Dark Cross
Others Hunt, Snake, Eclipse VOLINL Dark
23
Providers and Naming Conventions
Similar algorithms tend to be given the same names by different
providers, but variations are not uncommon. For example:
BWAP (Bloomberg)
DWAP (Bloomberg),
ALPHA (Barclays)
IS
Arrival Price
Aqua/Arrid (JPMorgan)
Tex (Credit Suisse)
MOC
At Close
Tclose
Target Close
VWAP
Implementation
Shortfall
Market On Close
24
Glossary
24
Algorithmic Trading: Automated execution of a block
trade, in search of optimization. Also referred as “execution
algorithms”, this trading strategy is usually applied to single
instruments.
Arbitrage: Any trading strategy whose purpose is to profit
from market discrepancy.
> Index Arbitrage is when you trade the index against
its underlying components.
> Statistical Arbitrage‟s goal is to identify correlated
instruments, and buy/sell them at appropriate time for a
profit.
ATS – Alternative Trading System: A trading platform
which is not considered as a stock exchange.
Automated Trading: Any automated order manipulation
(order creation, modification, cancellation). Algorithmic
Trading, High Frequency Trading, Pairs Trading, Basket
trading are all various kinds of Automated Trading.
CEP - Complex Event Processing:
Dark Pool: Crossing venues where some or all information
is hidden (bid/ask prices, volume, participants). Used by
traders with large order to avoid showing their interest.
Decision Making Algorithms: Complex strategies, usually
used by hedge funds and prop desk, to trade based on
various information, including real-time and historical data.
Index Arbitrage, Pairs Trading, MACD-based strategies are
all decision making algorithms.
ECN – Electronic Clearing Network: See ATS.
Execution Algorithms: Check “Algorithmic Trading”
Flashcrash: Event that happened on 6th May 2010, in
NYSE. In just a couple of minutes, the price of some stocks
went crazy (from 0.01$ to 100,000$) before returning to
normal.
HFT - High Frequency Trading: Framework aimed at
profit by trading quickly, based on low-latency technology.
Latency: Usually defined as the time taken from the
trading system to the exchange. Latency can be impacted
by various factors including software performance,
hardware, network, location, etc.
MTF – Multi-lateral Trading Facility: See ATS.
PTA – Post Trade Analysis: Analyze the performance of
your order.
PTS – Proprietary Trading System: See ATS.
25
Resources
25
There are many online information resources
relevant to algorithmic trading. Some useful
starting points are given below.
Automated Trader
www.automatedtrader.com
Advanced Trading
www.advancedtrading.com
To find out how SunGard can help you
implement algorithmic trading solutions with
Valdi Algorithms, please contact
Or visit
www.sungard.com/globaltrading
Valdi Algorithms
About the author
Benjamin Becar heads
product management for
algorithmic trading
solutions at SunGard.
With a strong background
in mathematics, and
knowledge of international
markets, he drives
product strategy.
About SunGard
SunGard is one of the world‟s leading
software and technology services
companies. Our four businesses serve
25,000 customers in over 70 countries.
SunGard‟s global trading business
provides multi-asset, front- to back-
office trading solutions for equities,
fixed income, derivatives and
commodities on exchanges worldwide.
These solutions support full-lifecycle
trading and trade processing activities
including information services, market
connectivity and order management
that help improve trade efficiency and
risk monitoring.
Learn more at sungard.com/globaltrading
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