algo_Blank

IT
IS
ILLEG
A
L
TO
R
EPRO
D
U
C
E
TH
IS
A
RTIC
LE
IN
A
N
Y
FO
R
M
AT
SPRING 2006 ALGORHYTHMICS GUIDE 1
EXECUTIVE SUMMARY
Defining Trade Algorithms
A
n algorithm has been defined as any
predefined step-by-step method used to
accomplish a task. To further refine
this concept, trade algorithms can be
defined in both a narrow and a broad manner.
Narrowly, a trade algorithm is considered to
be any automated, computer-based execution
of orders via direct market access channels,
attempting to meet a particular goal. Broadly
defined, trade algorithms include any rules-
based, mechanized trading model.
Trade algorithms form a crucial step in
an automated trading system, which can be
organized into a series of formal steps. In the
first step, the trading system acquires and fil-
ters proper data feeds. In the second step, the
automated system implements a formal deci-
sion model to select profitable trades. In the
third step, the automated system manages order
execution. This is the step in which trade algo-
rithms are applied most often. Finally, the fourth
step routes orders and feeds back information
to the first step.
Order management algorithms are mar-
keted by vendors in a few popular forms. The
VWAP order management algorithm breaks
up an order and seeks a fill equal to, or better
than, the day’s “volume weighted average
price” (abbreviated VWAP). “Time weighted
average price” (TWAP) orders are entered
evenly over time without reference to volume.
“Implementation Shortfall” algorithms seek
improvement on the prevailing price. “Volume
participation” algorithms adjust order size with
the day’s volume pattern. Additionally, there
are a number of investment strategies and
“smart router” algorithms.
Demand Drivers
The first, and most important, factor dri-
ving growth in trade algorithms has been the
sideways trading of equities—trading when
price moves are very small, either up or down.
The buy-side looked at trade transaction costs
when returns became scarce. The second factor
was NYSE decimal stock trading, which made
it difficult to hide large orders in the equity
markets. The third factor was electronic com-
munication networks (ECNs) and FIX proto-
cols, which made it easier to apply trade
algorithms. The final set of factors was new
National Market System (NMS) regulations
and SEC best execution guidelines. Compli-
ance with these regulations created greater
interest in trade algorithms, particularly order
routing algorithms.
Trade algorithms are most effective when
they operate in a deep and liquid market. The
most popular VWAP algorithms start to break
down when the processed trade represents more
than 10 percent of average daily volume. A
number of other problems also limit the use of
trade algorithms. Widespread use of similar
algorithms cancels out their advantages, and
algorithms limit experienced traders in using
the scope of their skills. Finally, most inde-
pendent trade algorithm vendors can’t answer
the question: “Why is your VWAP better?”
Trade Algorithms 2005
Evaluating the Rapid Growth
of Automated Order Execution
JOHN J. BLANK
JOHN J. BLANK
is Associate Director,
Corporate Development,
CME, Chicago, IL.
jblank@cme.com
algo_Blank.qxp 3/14/06 10:28 PM Page 1

IT
IS
ILLEG
A
L
TO
R
EPRO
D
U
C
E
TH
IS
A
RTIC
LE
IN
A
N
Y
FO
R
M
AT
Algorithms in Futures Trading
Banks trading currency futures were the first to
import/develop trade algorithms for the futures markets,
followed by hedge funds, commodity trading advisors
(CTAs), and proprietary trading groups. Within the futures
space, order management algorithms execute well in deep
and liquid equity futures markets. They are difficult to
use for interest rate futures markets. With lots of unfore-
seen events, volume patterns are not stable enough. Going
forward, automated futures traders look for self-evolving
algorithms to run thousands of permutations, simulate the
profit and loss of each, and then swap in the most prof-
itable algorithm.
At CME, we need to be aware that internal fill
algorithms run up against front-end trade algorithms
more and more. In currencies, if the exchange is sought
to improve the utility of front-end trade algorithms, half-
ticks would be an appropriate decision. CME equity and
Eurodollar fill algorithms can also be examined from the
automated trader’s perspective. In filling equity orders,
CME uses a first-in first-out (FIFO) mechanism. CME
Eurodollar futures use a pro-rata fill. Facing pro-rata fills,
CME Eurodollar traders often put in bigger orders than
they want, then cancel orders that exceed what they
really want.
Supply Chain Dynamics
Suppliers of trade execution services offer unique
sets of service bundles to segment the order execution
market. At the high price end, sell-side firms offer full
service. By offering a pared down set of services, boutique
execution brokers then seek to undercut full service exe-
cution. Large blocks of securities traded within estab-
lished programs/portfolios receive a discounted rate.
Finally, the low-service bundle of algorithms and Direct
Market Access (DMA) offers a no service bundle at the
lowest cost. DMA is the front-end software, connectivity,
and order routing technology used to bypass human inter-
vention. It has grown hand in hand with trade algorithms.
Algorithms and DMA execute trades for 1.0Ϫ4.0
cents per share, versus 3.0Ϫ6.0 cents per share for the full
service bundle. But explicit trade commissions ignore the
full reality of trade execution costs as they represent just
11 percent of the total execution costs, indirect costs
accounting for the remaining 89 percent.
Goldman Sachs’ recent experience offers a powerful
example of the force of trade algorithms in the current
equity market. By mid-2005, 50 percent of Goldman
Sachs equity clients’ trading was being done via auto-
mated transactions, having grown from just a small frac-
tion of trades just a few years earlier. This growth reflects
a preference for “low-touch” electronic trading for rou-
tine large volume orders. Perceived conflicts of interest
helped to drive uptake too.
Finally, independent vendors are seeking to differ-
entiate product sets and pressure trade execution costs
further. Consolidation may come, but not at the moment,
as vendor growth strategies look to non-equity and cross-
asset applications, and to Europe and Asia.
Looking Forward
It bears repeating that buy-side shops increasingly
look for higher net returns through lower trade transac-
tion costs. These customers also want better advice on
choosing algorithms, real-time feedback loops, and greater
randomness in orders. Demand looks to grow for pre-
and post-trade analytics too. In 2004, buy-side algorithms
equaled 7 percent of all trades. By 2006, according to the
Tower Group, it could be 21 percent.
Hedge funds are likely to continue development of
new proprietary algorithms and apply advanced trading
strategies in more scenarios. The smaller “Mom-and-
Pop” hedge fund groups are likely to continue using full
service sell-side trade execution.
In concluding this overview, it is important to call
attention to a set of specific effects that automated exe-
cution has upon securities exchanges. First, matching
algorithms (FIFO in equities, pro-rata in CME Euro-
dollars) may impact the order management algorithms.
Further, a reduced tick size in currencies would encourage
algorithms. Would this be a sound decision? Trade algo-
rithms also fragment orders resulting in more transac-
tions. More generally, do securities exchanges have
enough bandwidth/processing capabilities? Should secu-
rities exchanges incorporate algorithms into their elec-
tronic trading systems? This may reduce messages from
the proliferating algorithms. Finally, what about closing
auctions?
***
This article is organized as follows:
The first section opens with definitions of algorithm
and trade algorithms. A subsection presents popular order
management, investment strategy, and order routing algo-
rithms. Another subsection addresses the main determinants
2 TRADE ALGORITHMS 2005 SPRING 2006

IT
IS
ILLEG
A
L
TO
R
EPRO
D
U
C
E
TH
IS
A
RTIC
LE
IN
A
N
Y
FO
R
M
AT
of trade algorithms’ rapid growth over the last 5 years,
and some limitations.
The second section specifically addresses trade algo-
rithms in futures. It reviews the front-end trading system
effects upon exchange fill mechanisms for currencies, equi-
ties, and interest rates. The case study of Apama is presented.
The third section lays out the key parts of the supply
chain. This includes assessing trade execution costs, the
sell-side trader’s situation, and the ongoing evolution of
independent algorithm vendors.
The fourth, and final, section finishes with demand
trends for both the buy-side and hedge funds, and demand
is projected forward. A final comment adds thoughts on
what securities exchanges could do to better accommo-
date trade algorithms.
OVERVIEW
Defining Trade Algorithms
Defining and understanding the roots of the word
algorithm is a first step toward understanding the meaning
of a trade algorithm. Defined simply, an algorithm is any
predefined step-by-step method used to accomplish a task.
The term derives from the surname of the famed eighth-
century mathematician Abu Abdullah Muhammad ibn
Musa al-Khwarizmi. Al-Khwarizmi (whose name mor-
phed into “algorithm” over the ensuing twelve centuries)
introduced the concept of algebra and algorithms into
European mathematics.
A narrow-based definition of trade algo-
rithm has been provided and is widely adopted
by the marketplace. It comes from the inde-
pendent algorithm vendor ITG. ITG states
that a trade algorithm is an automated, com-
puter-based execution of orders via direct
market access channels, attempting to meet a
particular goal. Under this narrow-based def-
inition, trade algorithms determine the
timing, size, and destination of a large insti-
tutional equity trade. The algorithm seeks to
meet a benchmark goal, such as a volume-
weighted average price (VWAP) for a given
security over a given trading day.
In a broad definition of trade algorithm
we would include any rules-based, mecha-
nized trading model. This broad definition
would include index and pairs arbitrage, pro-
gram trading, and order routing.
Trade Algorithms Can Automate One
Step in a Four-Step Trading Process
The first step toward automated trading is acquiring
the proper data feeds. Data feeds represent a crucial input.
The data may be historical data or real-time data, or both.
Data may incorporate fundamental news items of sub-
jective importance to the trading process. For example, a
news item could be the monthly non-farm payrolls
announcement from the U.S. Labor Department. Any
news items would supplement streaming empirical data
from the exchanges on bids, offers, and firm quotes (see
Exhibit 1).
An algorithm built to manage this first step in trade
automation has been labeled event stream processing (by
one vendor). In a predefined step-by-step process, a com-
puter software algorithm selectively reviews all the streams
of historical, real-time, and event-driven data to select
the most useful data to inform a given trade model.
The second automated step implements a decision
model to select profitable trades. A formal decision model,
under a broad-based definition, would be considered a
trade algorithm. The model could refer to any rules-based
or mechanized means of making a trade decision. This
model might be based on technical analysis, on funda-
mental factors, on arbitrage relationships, or on a com-
bination of these factors. For example, a model could
select a trade based on the moving average of a security’s
E X H I B I T 1

IT
IS
ILLEG
A
L
TO
R
EPRO
D
U
C
E
TH
IS
A
RTIC
LE
IN
A
N
Y
FO
R
M
AT
price. Other models guide trades by following a time
chart of a security’s price movement. Still other models
find successful trades by looking at a basis point spread
relationship between two sets of related securities. Models
can advocate trades for an intra-day period, follow a long-
term trend over a period of months or years, or do any-
thing in between.
The third automated step manages orders. Once a
trading decision has been made, algorithms can deter-
mine how best to work or finesse an order to get a better
fill. These order management algorithms mechanize the
traditional function of floor brokers. For example, algo-
rithms can execute orders to achieve a volume-weighted
average price (VWAP), or a time-weighted average price
(TWAP). VWAP order management algorithms repre-
sent the bulk of the trade algorithms applied in the equity
market today. In still other versions, algorithms can exe-
cute trades by implementing dynamic scaling, adjust up
or down based on pegs, or implement a customized algo-
rithm to execute trades uniquely for an individual shop.
The fourth and final automated step routes orders
and provides a feedback mechanism. In this step, an algo-
rithm may seek “Smart Routing” to get the order exe-
cuted at the exchange that offers the best price. Smart
routing algorithms search for the best value when items
are listed at multiple venues. Then, information is fed
back to the front-end trading program on the status of
both filled and unfilled orders.
Well-Known Order Management
Algorithms
VWAP techniques break up an order to ensure a fill
equivalent to, or better than, the day’s volume weighted
average price. A VWAP order management
algorithm references historic intra-day volume
patterns over the past (N) days. VWAP trades
are often successful in the presence of histor-
ically stable volume patterns that generally
peak on open and close in a characteristic
“U” pattern. The algorithm may incorpo-
rate mean reversion or momentum indica-
tors in an attempt to beat the VWAP
benchmark goal too. The trader can also
impose a volume constraint based on the per-
centage of average daily volume (ADV) to
avoid impacting the market price.
In one example of a VWAP algorithm,
a trader submits an order into an algorithm
to buy 500,000 shares of General Electric (GE), under a
25 percent volume constraint, at a limit price of $37.00.
The VWAP algorithm would break up the trade and
submit small orders all day into the equity market,
attempting to beat the historic daily VWAP of General
Electric shares over a given (N ϭ 50?) day period. The
algorithm would not execute at any price over $37.00, and
would not make up any more than 25 percent of ADV at
any point. The GE order would be selectively filled from
market on open to market on close on a given day (see
Exhibit 2).
Using the “TWAP” technique, a trade algorithm
would break up and enter the order to ensure a fill that
is at least equivalent to a historic TWAP. The TWAP is
similar to the VWAP, but the order is entered evenly over
time without reference to volume. For example, the order
to buy GE might seek 500,000 shares in small trade sizes,
under a 10 percent volume constraint at any time, from
1:00 p.m. to 3:00 p.m.
In an “implementation shortfall” technique, a trade
algorithm attempts to improve the fill relative to the pre-
vailing price when order was originally entered (known
as the “arrival price”). This algorithm balances the need
to get trade executed with the need to reduce the trade’s
impact on the market price. These algorithms often track
price momentum and filter out “noise” in the market
price. In the GE example, a trader could seek to buy the
500,000 GE shares with a lower limit price of $33.70.
The implementation shortfall algorithm would execute the
order as soon as possible without causing market impact.
It would post the trade on bid but lift the offer if the
market for GE shares was advancing.
E X H I B I T 2
General Electric (GE) on 8/16/05
0
100,000
200,000
300,000
400,000
500,000
600,000
700,000
800,000
900,000
1,000,000
0:00
8:45
9:00
9:15
9:30
9:45
10:00
10:15
10:30
10:45
11:00
11:15
11:30
11:45
12:00
12:15
12:30
12:45
13:00
13:15
13:30
13:45
14:00
14:15
14:30
14:45
14:55
Volume
$33.85
$33.90
$33.95
$34.00
$34.05
$34.10
$34.15
$34.20
Price
Volume
Last Price

IT
IS
ILLEG
A
L
TO
R
EPRO
D
U
C
E
TH
IS
A
RTIC
LE
IN
A
N
Y
FO
R
M
AT
A “volume participation” technique uses an algo-
rithm similar to a VWAP algorithm. However, it dynam-
ically adjusts an order size in accordance with today’s
volume pattern, not to historic patterns like the VWAP
algorithm. Participation algorithms consider the likeli-
hood of order execution at the current price while aiming
for price improvement. In the GE example, a participa-
tion algorithm would seek to buy 500,000 GE shares with
a limit price of $34.00, but there would be a low 5 per-
cent volume constraint imposed at all times, between
12:00 noon and 2:00 p.m.
Well-Known Investment
Strategy Algorithms
Arbitrage strategies are very sensitive to how an order
is filled. Thus, many vendors that offer order management
algorithms also offer investment strategy modules.
In a “Risk Arbitrage” investment strategy algorithm,
a model arbitrages between the shares of two firms
involved in an M&A deal. For example, the algorithm
could buy/sell stock A and buy/sell stock B when the
exchange-determined ratio diverges even slightly from
the stated merger ratio.
More broadly, these algorithms can arbitrage across
“contingent pairs.” It may arbitrage single pairs or baskets
and can often adjust for FX rates when the pairs are traded
in different currencies. In one example, the algorithm
could compare buying two shares of stock X against selling
one share of stock Y as long as stock A’s value is 50 per-
cent or less than B’s.
Well-Known Routing Algorithms
Order fills may be affected by the market to which
it is routed. Thus, many vendors that offer order man-
agement algorithms also offer order routing modules.
When using a smart router algorithm, the trader attempts
to discover “hidden liquidity” across exchanges and elec-
tronic communication networks (ECNs). For example, an
algorithm would process an order to sell 500,000 shares
of GE with a limit price of $33.50. This means the routing
algorithm would sell at $33.50 or better on a given default
exchange. If the algorithm was unable to do a trade at
$33.50 on the default exchange, it would then post limit
offers at $33.50 on multiple exchanges/ECNs.
In a block peg algorithm, the computer would enter
a bid or offer at multiple exchanges/ECNs and then adjust
the order as the bid/offer spread fluctuated. For example,
consider an order to sell 500,000 shares of GE with a limit
price of $33.50. The block peg algorithm would post
offers on designated exchanges/ECNs at the midpoint of
the prevailing bid/offer spread and then adjust the offer
upward or downward until the order was filled without
violating the $33.50 price limit. There are several varia-
tions on the theme, all based on where the order is placed
relative to prevailing bid/offers.
Trade Algorithm Drivers
Trade algorithms have been a fixture on sell-side pro-
prietary trading desks for 15 years. In the last 5 years or so,
several changes in the U.S. equity market combined to
either force, or provide, incentives to encourage more
investors to adopt trade algorithms. Some factors were more
important than others in driving the rapid growth that has
emerged. Below, we discuss the more important forces first.
The first, and most important, change to the trading
landscape probably began with the sideways trading state
of the equity market. Weak equity portfolio returns began
to appear in 2001, after years of 20 percent plus returns
in the 1990s. Lower returns focused the buy-side on
trading costs and led to the rapid expansion of hedge
funds. Trade algorithms offered a cost advantage over the
full service trading costs at most sell-side firms. Hedge
funds advertised that they could match the 20 percent
plus annual returns by applying strategies that did not
follow long-only equity investing.
The second major force was the adoption of dec-
imal stock trading by the NYSE. Decimalization forced
a breakup of big trades. With 100 price points replacing
the 8 eighths price points, depth of book at each new
price point collapsed. It made the equity markets better
suited for retail traders with smaller trade sizes than insti-
tutional traders with large block size trades. Algorithms
could break up large block orders and submit them into
the market in smaller increments. The average trade at
the NYSE dropped from around 1200 shares per trans-
action in 2000 to approximately 400 shares in 2005. Also
important to algorithms, decimalization made it easier
and cheaper to increment the bid/offer by one price point
to get ahead of another order. The “penneying” problem
had surfaced. Algorithms offered a way to break up a trade
into hundreds of small transactions. Small waves of orders
have more of a chance of being filled by the end of a
trading session in a decimal market (see Exhibit 3).
Representing a third set of forces, the rise of ECNs
spread liquidity across multiple exchanges. This made it

IT
IS
ILLEG
A
L
TO
R
EPRO
D
U
C
E
TH
IS
A
RTIC
LE
IN
A
N
Y
FO
R
M
AT
more difficult to find liquidity in a single market. Smart
order routing algorithms provided a solution to the
problem. ECNs and crossing networks also increased com-
petition for orders. This resulted in a reduction in com-
missions on simple trades. So, cheaper trade algorithms
increased their share of the overall trade transactions
market. Fierce competition among independent tech-
nology suppliers continues to generate new algorithms
and keep downward pressure on commissions. Finally,
financial information eXchange (FIX) protocols allowed
smooth computer links amongst the front-end electronic
trading systems and the ECNs and crossing networks.
These FIX protocols encouraged further growth in auto-
mated trade algorithms.
A final set of forces favoring trade algorithms came
from regulatory considerations. The National Market
System (NMS) regulations put fast electronic markets on
par with the NYSE with respect to order filling. This pro-
vided incentives for traders to break up orders to seek a
better fill. Thus, order routing algorithm demand increased.
SEC guidance on best execution responsibilities also
focused many buy-side firms on algorithmic trading. The
cheaper trade algorithm transaction costs and greater trans-
parency of the transaction costs offered compliance with
the SEC guidance.
Appropriate Use of Algorithms
Trade algorithms are most effective when the instru-
ment is traded into a deep and liquid market. For example,
a trader might wish to sell 250,000 shares of Microsoft
(MSFT). Note that MSFT is a large liquid security. The
250,000 shares represent 0.4 percent of the 70 million
shares that make up Microsoft’s average daily
volume. It is unlikely that such a trade will
have a noticeable effect on the market price
of the security. When average daily volume
follows a stable volume pattern (like Microsoft
stock), a VWAP algorithm can mimic the his-
toric volume pattern with little variability.
Trade algorithms are less effective where liq-
uidity is limited. For example, imagine that a
trader attempts to sell 250,000 shares, but for
a less-liquid mid-cap stock. An algorithm to
beat the historic VWAP would likely drive
this large order into a small amount of average
daily trading volume. Such a trade may have
a negative effect on the market price. Studies
have found that VWAP trade algorithms break
down when the trade offered to the market is more than
10 percent of the average daily volume.
Problems with Trade Algorithms
There are a number of other identifiable problems
limiting the use of trade algorithms. Commoditization,
that is, the widespread use of the same algorithm, can
cancel out competitive advantages. The most profitable
algorithms aren’t shown to the market. Instead, they are
kept in-house. As a result, the marketplace may become
full of low value-added, easily duplicated algorithms.
Algorithms also limit access to an experienced trader’s
skills. In some cases, a trader’s skills bring about a more
valuable outcome. Further, algorithmic trades are often
tied to a broker. This means that crucial trade informa-
tion can leak into the sell-side and be manipulated. Finally,
most independent algorithm vendors still can’t answer the
question: Why is your VWAP better? VWAPs have come
to dominate the equity algorithm market, but measuring
the performance of one VWAP algorithm against another
has not been fully developed.
TRADE ALGORITHMS IN FUTURES
Background
Banks trading currencies were probably the first
futures market participants to port automated trade algo-
rithms. The big banks were followed by hedge funds,
commodity trading advisors (CTAs), and proprietary
trading groups. Order management algorithms execute
well for equity index futures but they are difficult to use
E X H I B I T 3
Average Trade Size on NYSE
0
200
400
600
800
1,000
1,200
1,400
1,600
1995 1996 1997 1998 1999 2000 2001 2002 2003

IT
IS
ILLEG
A
L
TO
R
EPRO
D
U
C
E
TH
IS
A
RTIC
LE
IN
A
N
Y
FO
R
M
AT
for interest rate futures. The volume patterns are not stable
enough in the interest rate marketplace due to activity
surrounding the release of information (employment
reports, etc.). With respect to individual algorithms, the
TWAP—or time slicing—works easily for futures. Par-
ticipation—or trying to be a percentage of the market—
also works well for futures. An implementation shortfall
algorithm—to successfully put on a hedge—may be the
most useful.
Future self-evolving algorithms are soon to be intro-
duced for equity trading. They are likely to be well suited
for futures trading. These self-evolving algorithms run
through thousands of permutations, simulate the profit
and loss of each, and then swap-in the most profitable
algorithm.
CASE STUDY: APAMA DERIVATIVES
ALGORITHMS
Apama was founded in late 1999. It is a spin-off
from Cambridge University. The founders, Dr. John Bates
and Dr. Giles Nelson, are experts in distributed systems
and event processing. Each brought to Apama over 10
years of research in this area. Venture capital came from
a number of sources, including the Carlyle Group. Cus-
tomers such as JP Morgan, Deutsche Bank, and ABN
Amro run Apama’s real-time, algorithmic trading models,
both for proprietary use and for the buy-side. Progress
Software acquired Apama in April 2005 for $30 million.
Progress wants to leverage the Apama Event Stream Pro-
cessing platform into other areas of finance as well as into
defense, oil and gas, and telecommunications—anywhere
streaming data analysis is required.1
Apama futures algorithms are among the best avail-
able. They provide control over all steps and asset classes
(equities, derivatives, FX). FX prices can be exploited
across currencies and countries. Trade scenarios can
operate across direct sources (quotes) or derived sources
(e.g., a Black-Scholes’ implied price calculation). Each
algorithm can use a range of parameters, modified by the
trader on-the-fly. With “Iceberg” algorithms such as
Apama provides, large orders are broken into slices. The
real size of the order is hidden. What is shown to the
market is just the tip of the iceberg. Liquidity is held
within the firm, or in a broker’s algorithmic staging area,
rather than submitted to the exchange and resting in the
order book. Institutional investors say greater fragmenta-
tion and less transparency came about because of this
phenomenon.
Apama Futures Algorithms
1. VWAP trading using historic volume. Historical data
(provided by user database) is used to build a “volume
profile” for a trading interval from one or more his-
torical periods. Trades are then executed against this
profile at the defined time intervals. VWAP price
monitoring—the latest VWAP for an instrument is
calculated and used, in combination with a user
parameter, to define a price with which to issue
orders. Orders are issued at defined intervals within
a defined trading window. If orders are not filled
they are repriced to adjust for market movements.
Orders are then converted into market orders if they
are still not fully filled following a second timeout.
2. Simple iceberg. Between a defined time window, orders
are submitted regularly into the market. Each order
is a quantity that is a defined percentage of the total
volume. Iceberg with delay—as above, with the addi-
tion of a small delay before the next clip. Random dis-
tribution iceberg—as in the simple iceberg, but quantities
issued to market are not regular. They are random
values between two limits. Random distribution iceberg
with delay—as above, but with the delay feature.
3. “Off-market” Limit/Stop order—Monitor the
market and only issue an order when the price and
market depth is available.
4. Advanced pairs trading. Monitor the bid/ask ratio of
a pair or instruments. If these ratios fall under an
arbitrage limit and the market depth is available,
then execute trades. Straight pairs trading—monitor
the price ratio between two instruments. If this ratio
exceeds a defined limit, execute the two trades, then
wait for a configurable period of time. The algorithm
then continues looking for the next opportunity.
5. One-cancels-the-other (OCO). A standard OCO strategy.
One-triggers-the-other (OTO)—a standard OTO strategy.
6. Statistical arbitrage. Use of Bollinger bands to ana-
lyze a spread. Repetitively buy/sell when thresholds
are breached. Index arbitrage—trade constituents
against an exchange-traded index (ETF).
How Should CME Accommodate Trade
Algorithms in Currency Futures?
Algorithms used to trade futures on currencies, in
terms of volume, will grow in use with more CME Globex

IT
IS
ILLEG
A
L
TO
R
EPRO
D
U
C
E
TH
IS
A
RTIC
LE
IN
A
N
Y
FO
R
M
AT
system speed, increased bandwidth, and reliability. To avoid
reliability problems at peak trading times, according to one
source, “[a]ny exchange bandwidth you look at, multiply
by ten!” In addition, a move to half-ticks on the bid and
ask sides of the currency futures market, along with
showing a 10-deep book in half-tick increments, would
increase the use of trade algorithms. Half-ticks would allow
more opportunities for getting in and getting out of posi-
tions, and create an incentive to increase trade volumes by
offering smaller losses with the smaller tick sizes. The result
would be smaller orders at tighter prices in the currency
space. Market makers won’t like it, but it is where com-
petitive dynamics will take CME.
In Interest Rate Futures, Traders
“Game” Fill Mechanisms
In filling CME equity index futures orders, the
exchange uses a first-in first-out (FIFO) mechanism. The
first order offering the market the best price is served first.
This system mimics the actions of a floor broker. In con-
trast to this, the Eurodollar bank deposit futures market
uses a pro-rata fill mechanism. Trade orders are filled based
on how much of the volume they make up at a given
price. Eurodollar prices are static, so orders accumulate at
the stable price. This generates a game. Facing pro-rata
fill mechanisms, Eurodollar traders put in bigger orders
than they actually want. Then, traders cancel any orders
that exceed what they really want, after the desired number
of contracts is reached. With growing algorithm use in
futures, does the exchange have the right fill mechanism?
Nothing is really clear from the traders’ perspective. A
hybrid order fulfillment system probably
works best.
CHANGES IN THE SUPPLY CHAIN
Stock Trading Cost Comparison
Comparing stock trading costs demon-
strates the microeconomics of order execu-
tion. Exhibit 4 rank-orders trade execution.
It starts with the high-service/high-cost
bundle of full service trade execution. These
sell-side brokers offer customized support
from experienced traders, research support,
and prime brokerage in addition to trade algo-
rithms and DMA. The next bundle is a lower
service/lower cost boutique execution broker.
These are often experienced traders located at smaller
independent trade algorithm/order execution vendors.
Next is the program or portfolio trading service bundle.
Program trading is defined by the NYSE as a linked set
of 15 securities or more of greater than $1 million in
value. Executing linked trades typically delivers a volume
discount. Finally, Exhibit 5 lists a bundle with no ser-
vice/lowest cost. It uses standalone trade algorithms
combined with DMA.
The trade algorithm/DMA bundle offers 1.0Ϫ4.0
cents per share, versus the 3.0Ϫ6.0 cents a share for bulge
bracket execution services. So 100 shares, when executed
by trade algorithms/DMA, cost $1.00Ϫ4.00, versus
$3.00Ϫ6.00 executed by a full service broker.
Suppliers have segmented demand for trade execu-
tion for good reasons. Clients such as hedge funds may
desire the full service aspects of custom trading, research,
prime brokerage, and algorithmic/DMA opportunities.
They are willing to pay extra for these services. Other
customers such as mutual funds and exchange-traded funds
may seek a discounted price for executing their bundled
trades. Still other customers at buy-side shops may seek
the anonymity, control, and the cheaper execution costs
offered by the trade algorithm/DMA bundle.
Direct Market Access
Direct Market Access (DMA) is offered by a wide
range of full service brokers and by an array of indepen-
dent vendors. It is defined as a routing technology used
to bypass human intervention in the order placement
E X H I B I T 4
Average CME E-mini Futures Trade Size
0
1
2
3
4
5
6
2000 2001 2002 2003 2004
Contracts

IT
IS
ILLEG
A
L
TO
R
EPRO
D
U
C
E
TH
IS
A
RTIC
LE
IN
A
N
Y
FO
R
M
AT
process. DMA has grown hand in hand with trade algo-
rithms. Seeing the market shift in this lower cost direc-
tion, sell-side brokers have recently acquired DMA
suppliers. They have augmented their presence at the
high-service/high-cost segment with a low-service/low-
cost offering. In 2004, Bank of America acquired Direct
Market Access Corp. Citigroup acquired Lava. The Bank
of New York acquired Sonic. In addition, Goldman Sachs
had already developed REDIPlus, Morgan Stanley has
Passport, and CSFB offers Pathfinder.
DMA users often demand direct market feeds from
exchanges rather than use consolidated feeds. Why? Because
data latency—the time delay experienced during data trans-
mission—can inhibit an advanced algorithmic trade. Think
about the issue in terms of investment management algo-
rithms like index or pairs arbitrage. In an execution mar-
ketplace rapidly expanding the use of advanced trade
algorithms, execution quality is being measured in finer
and finer increments of time. The greater use of DMA
continues to pressure commission charges, and it focuses
attention on the true cost of trading . . . slippage.
Focusing on commissions alone ignores the full
reality of trade execution costs. Indirect trade execution
costs are 89 percent of the total trading costs, and 49 per-
cent of the cost in trade execution comes from the cost
of delayed transactions. A price sought often changes
while the trade is being routed and displayed to the market,
with 22 percent of the change coming from the effect of
the trade’s impact on the market price. The trade itself
often forces the market price to change. About 18 per-
cent comes from the opportunity cost of a missed trade.
Orders are often not filled at the desired price. This means
explicit direct trade commissions represent just 11 per-
cent of total execution cost. Thus, 17 basis points (bp) go
directly to a commission on an average equity trade
whereas 130 bp accrue indirectly from the trade slippage
issues (see Exhibit 6 ).
Sell-Side Operations
By mid-2005, 50 percent of trades and trading deci-
sions by Goldman Sachs’ equity clients were done as auto-
mated transactions. In June 2005, in response to this shift
toward automation, Goldman Sachs eliminated 30 equi-
ties traders. In August 2005, Swiss investment bank UBS
also eliminated the positions of 30 equities traders.
According to a Reuters report, the UBS job cuts were
around 10 percent of equities sales and trading staff. UBS
was responding to reduced broker commissions, due in
part to their clients’ rapid take-up of electronic trading.
The growth in automated trade execution reflects
the equity market’s evolving preference for “low-touch”
electronic trading for routine large volume order flow.
Human traders are only needed for “high-touch” large
value trading. There is going to be continued pressure on
full service brokerage commissions. Buy-side clients are
looking to take control of “easy” trades. The remaining
sell-side traders will focus more on “harder”
trades. However, some customers will con-
tinue to demand enhanced automated service
and the traditional broker/client relationship.
Conflicts of interest within sell-side bro-
kerage firms help to drive the uptake of auto-
mated trading by buy-side firms. Order flow
information from in-house trade algo-
rithms/DMA may be viewed and exploited
by sell-side traders. The SEC has been inves-
tigating these activities. In addition, buy-side
trade execution orders typically pass through
the sell-side broker’s internal matching systems
before reaching the market. This increases the
delay cost for the buy-side customer.
Trade Algorithm Vendors
Strong demand continues pushing
growth of independent trade algorithm
E X H I B I T 5
Execution Service Examples Cost/Share
Full-Service Brokers Morgan Stanley
Merrill Lynch, UBS
3 - 6¢
Boutique Execution
Brokers
BNY Brokerage, ITG,
Miletus
2 - 4¢
Program/Portfolio
Trading
CSFB, Morgan Stanley 1 - 3 ¢
Algorithmic Trading ITG, Miletus, Edgetrade,
Algorithmic Trading
Solutions
0.5 - 2¢
Broker-sponsored
(Goldman’s REDIPlus,
Citi’s Lava)
ECNs: Inet, Tradebook
Direct market access
(DMA)
ATSs: POSIT, Liquidnet
0.5 - 2¢

IT
IS
ILLEG
A
L
TO
R
EPRO
D
U
C
E
TH
IS
A
RTIC
LE
IN
A
N
Y
FO
R
M
AT
vendors. These vendors are differentiating their product
sets with more effective algorithms and rapidly pushing
the state of the art. There is greater emphasis on measur-
able performance. Ongoing competition among these
vendors is expected eventually to push down costs further
at the low-cost/low-service end of the market. Consol-
idation amongst these suppliers may come, but not at the
moment. The automated trade execution market is on
the steep side of its growth curve. Vendor growth strate-
gies are looking to target non-equity asset classes and
cross-asset applications. Two non-equity expansions are
exchange-traded equity derivatives and FX markets.
Market share battles are likely to spill into European and
Asian institutional trade executions.
DEMAND TRENDS AND CONCLUSIONS
Buy-Side Demand Trends
With a sideways equity market, the buy-
side looks to continue pursuit of higher net
returns through lower transaction costs. The
buy-side looks to seek more and better advice
on choosing an appropriate algorithm for a
given trading situation. VWAP trades now
constitute 50 percent of all algorithmic
trading. But the buy-side is beginning to
understand that good algorithmic trading
means advancing beyond VWAP. They are
looking to develop real-time feedback loops
in analysis. They want strategies that introduce
greater randomness to limit information
arising from trade orders. Demand continues
to grow for pre- and post-trade analytics, as
trading functions are increasingly brought into
buy-side shops. Buy-side shops are reluctant
to use broker-dealer analytic tools due to their
concerns about confidential data leaks.
Instead, many buy-side shops are using pro-
prietary or third party software.
Hedge Fund Demand Trends
Going forward, quantitative hedge funds
look to develop more proﬁtable proprietary
algorithms and apply advanced trading strate-
gies in more scenarios. The small mom-and-
pop hedge funds (a large percentage of the
hedge fund industry) look to continue to rely
on sell-side trade execution. An integrated suite of trading
tools will remain available from prime brokers. Many
hedge funds use the leverage. They are likely to continue
to use this channel.
Projecting Use of Algorithms
The Tower Group collected data on the growth of
trade algorithm use. It is presented in Exhibit 6, which
shows an early base of sell-side proprietary trading shops
and hedge funds (80 percent of the total in 2000) using
trade algorithms. However, growth has decisively shifted
with greater adoption of automated techniques by the
buy-side. In 2004, the buy-side may execute trade trans-
actions with algorithms equal to 7 percent of all trades.
By 2006, Tower Group believes it will grow to be 21 per-
cent of all trades (see Exhibit 7).
E X H I B I T 7
Projected Growth of Algorithmic Trading
5%
7%
10%
13%
17%
20%
27%
1%
3%
3%
4%
7%
21%
13%
0%
5%
10%
15%
20%
25%
30%
2000 2001 2002 2003 2004e 2005f 2006f
Total Algorithmic Trading
Buy-Side Algorithmic Trading
E X H I B I T 6
Trade Transaction Costs
34
29
17
77
0
10
20
30
40
50
60
70
80
90
Delay
Trade
Impact
Missed
Trades
Commissions
Cost(basispoints)

IT
IS
ILLEG
A
L
TO
R
EPRO
D
U
C
E
TH
IS
A
RTIC
LE
IN
A
N
Y
FO
R
M
AT
Outstanding Questions
With the strong growth in trade algorithms sweeping
the execution marketplace, we finish this article by looking
at the effects upon CME. First, we believe that the appli-
cation of matching algorithms (FIFO in CME equities,
pro-rata in CME Eurodollars) may have an impact upon
the performance of order management algorithms. More
study is required to assess the compatibility of the two
interfacing algorithms and customer demand for any
changes.
The second issue is whether securities exchanges in
general have enough bandwidth. We expect the use of
trade algorithm techniques to fragment orders further
resulting in more transactions. Furthermore, a reduced
tick size, particularly in currency futures trading, would
encourage algorithmic applications.
The third issue is whether securities exchanges
should incorporate algorithms into their electronic market
systems. Customers may demand the integration of algo-
rithmic order management systems. It is important to
explore whether trade algorithms are an independent soft-
ware vendor (ISV) or an exchange host function. Could
securities exchanges develop a business evaluating algo-
rithms? This may be a means of reducing messaging traffic
from the proliferating trade algorithms.
Finally, there are closing auctions to consider. Many
European exchanges offer market-on-close auctions.
There are specific algorithms built for this environment.
Trade algorithm back-testing is done more easily when
compared with the market-on-close benchmark.
ENDNOTES
1
Jim Feingold, Senior Sales Manager, Financial Services,
Progress Software Company, Apama Algorithmic Trading
Platform.
To order reprints of this article, please contact Dewey Palmieri at
dpalmieri@iijournals.com or 212-224-3675.

algo_Blank

Recommended

Recommended

More Related Content

Similar to algo_Blank

Similar to algo_Blank (20)

algo_Blank