simulating hft for low-latency investors - the investor’s view (robert almgren)

Robert Almgren

Copenhagen Business School Symposium on High-Frequency Trading

Oct 5, 2015

Electronic Bond Trading

Main points

1.Bond trading is big, electronic, and of very active interest

2.Algorithmic execution is important

3.Techniques for achieving good execution

2

Outline

1. Rates trading is becoming electronicfutures first, then cash

2. Business of algo executionnew in futures and fixed income

3. How to get good resultsunderstanding of market microstructure

4. How do we improve toward that result?use a market simulator

5. How do we build and use a simulator?

3

4

501.5 Treasury201.2 Agency MBS 27.3 Corporate 9.5 Muni 3.4 Non-agency MBS 1.5 ABS 4,6 Agency748.9 Total

193 Total

US average daily traded volume in $Billion 2015 Jan-Aug

Fixed Income Equity

Source: SIFMA

Fixed Income markets are bigger than equities

Pit trading → electronic

5

Pit

position traders

floor brokers

Electronic

HFT & Market Makers

algorithmic brokers (QB)

CME rates futures shift to electronic

6Produced by QB from CME data

Euro

dolla

rTr

easu

ry

Futures Options

Dai

ly tr

aded

vol

ume

(mill

ions

)

2008 2009 2010 2011 2012 2013 2014 2015

0.0

0.5

1.0

1.5

0

10

20

30

40

50

60

70

80

90

100

Floor

Electronic

5-day 2-sided moving exponential average

Mon 06 A

pr 2015

Percent electronicEurodollar Options

Dai

ly tr

aded

vol

ume

(mill

ions

)

2000 2002 2004 2006 2008 2010 2012 2014

0

1

2

3

4

0

10

20

30

40

50

60

70

80

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Floor

Electronic


Mon 06 A

pr 2015

Percent electronicEurodollar Futures

Dai

ly tr

aded

vol

ume

(mill

ions

)

2008 2009 2010 2011 2012 2013 2014 2015

0.0

0.2

0.4

0.6

0.8

0

10

20

30

40

50

60

70

80

90

100

Floor

Electronic


Mon 06 A

pr 2015

Percent electronicTreasury Options

Dai

ly tr

aded

vol

ume

(mill

ions

)

2001 2003 2005 2007 2009 2011 2013 2015

0

1

2

3

4

5

0

10

20

30

40

50

60

70

80

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FloorElectronic


Mon 06 A

pr 2015

Percent electronicTreasury Futures

All CME futures pitsclosed July 2 2015

7

April 9, 2015

Cash Treasury products shift to electronic

8TPMG White Paper, April 9, 2015

TMPG CONSULTATIVE PAPER

7 | P a g e

volumes and internalization of flows to achieve target returns on equity.11

These trends

support the high concentration of trading activity among large market makers, with the top five

dealers now accounting for more than 55 percent of DtC volume.12

These dealers play the role

of both market makers and providers of algorithmic trading solutions to investors. As a result,

cutting-edge trading technology increasingly resides not only with dealers but also with their

clients who deploy algorithmic trading strategies to minimize trading cost. In the inter-dealer

market, market making in on-the-run Treasuries now appears to be dominated by dedicated

trading firms and dealers with the know-how to develop the cutting-edge algorithms required

to compete.

Several important initiatives to accelerate the automation of trading in Treasury markets mirror

key innovations from the foreign exchange (FX) market.13

Recent trends with FX antecedents

include the creation of new liquidity pools in the form of single-dealer platforms streaming

executable quotes, the emergence of aggregators that provide a single interface with streaming

quotes from multiple underlying markets, and the increased “internalization” of orders by

banks.

The evolution of automated trading in the Treasury securities market is also likely to be

influenced by innovation in the Treasury futures market and the interest rate swaps market, in

which execution is becoming increasingly automated. The Treasury futures and interest rate

11

For example, while Treasury securities are exempted from important aspects of the Volker Rule, leverage ratios

make holding Treasuries as expensive as corporate bonds. As a result, dealers are incentivized to shift balance

sheet to assets with higher yield and/or wider bid-ask spreads. 12

Source: Greenwich Associates 2014 North American Fixed Income Study. 13

The over-the-counter spot FX market is in many ways similar to the cash Treasury market. Liquidity in both

markets is concentrated in a few venues, with trading in the Treasury market focused in a few on-the-run

securities, and trading in the spot FX market concentrated in a few major currency pairs (though arguably across

more trading venues). In both markets, the vast majority of trading occurs with or between market makers, with

end-investors almost never trading bilaterally and intermediation facilitating flows between buyers and sellers.

Historically, the natural market makers in both FX and Treasuries were large banks with significant internal flows,

giving them an advantage in liquidity provision. As with Treasury markets, the drive to reduce FX trading costs

through electronic trading and the decision to allow automated trading on the inter-dealer FX platforms has led to

an increase in automated trading and a growing presence of HFT firms. Automated and HFT trading now account

for an estimated 70 percent and 40 percent, respectively, of trading volume in the three major currency pairs.

TMPG CONSULTATIVE PAPER

8 | P a g e

swap markets are closely linked to cash Treasury securities markets by active spread trading

between the markets.14

14 A key difference between Treasury futures, Treasury securities, and interest rate swaps is their individual market infrastructures. Trading in Treasury futures is limited to the Chicago Mercantile Exchange (CME), with all trading activity subject to the rules of the CME and the Commodity Futures Trading Commission (CFTC). Over-the-counter trading in the Treasury securities market is not centralized in one execution venue, and each electronic trading platform has a different set of rules. However, since each major electronic trading platform is operated by a broker-dealer registered with and subject to the rules and regulations of the Securities and Exchange Commission (SEC) and the Financial Industry Regulatory Authority (FINRA), there are currently certain risk management standards and supervisory procedures applicable to all platforms (see Box 2). As a result of recent regulatory reforms, more interest rate swap trading has shifted onto electronic trading venues. Similar to the Treasury securities market, there are multiple execution venues in the interest rate swaps markets. While most DtC swap execution facilities (SEFs) employ an RFQ protocol, inter-dealer SEFs are more likely to use central limit order books. Compared to the maturity of automated trading in Treasury futures, automated trading in the inter-dealer SEF market remains nascent. As data feeds improve and more activity migrates to SEFs using central limit order books, automated trading will likely gain traction in the interest rate swap market as well. Given the size of the futures and interest rate swap markets and the active market for spread trading between them, changes in liquidity in the futures and interest rate swap markets have the potential to also meaningfully impact liquidity in the Treasury securities market.

BOX 1: TMPG Review of October 15, 2014 On October 15, 2014, U.S. Treasury securities experienced record-high trading volumes and significant intraday volatility. The yield on the 10-year Treasury note traded in the fourth-largest intraday range since the 2008 financial crisis, and saw a 15-basis-point round trip during a short 15-minute window. This price action was outsized relative to the fundamental economic news of that day and, given available data, market participants have generally been unable to attribute the price action to any single factor. Many factors have been suggested, such as bearish sentiment related to the global macroeconomic outlook, a significant capitulation in crowded short interest rate and volatility positions, and the evolving structure of the Treasury cash and futures market. The evolving market structure is reflected in broad changes in market participation and the risk-taking capacity of liquidity providers, changing regulation, increased use of electronic and automated trading, and other factors. Prices remained highly correlated across related products despite the substantial volatility witnessed, and the continuous and rapid price adjustments amid record volumes, especially in the 15-minute window, were only possible with automated trading. The speed and size of price movements may have led some proprietary trading firms to limit participation and some broker-dealers to reduce their market-making activity to customers. The events of October 15 suggest that it is worthwhile to continue to evaluate issues related to the evolving structure and liquidity characteristics of the Treasury market, including with respect to the role of automated trading.

9

High-frequency trading now accounts for roughly 40% to 50% of the daily volume in Treasuries, compared to a "negligible amount" just five years ago, according to research firm Tabb Group.

The enormous size of the Treasuries market makes it appealing to firms that make money from huge volumes of trades.

As with stocks, the fear is that flash crashes could cause sharp plunges in Treasury prices. That would lead to soaring yields, from all-time lows below 1.5%, to as high as 4%.

Some of the biggest players in high-frequency trading, DRW Holdings, Sun Trading, Virtu Financial, Hudson River Trading, Jump Trading and GSA Capital Partners, have all been expanding their reach to Treasuries, according to numerous trading sources.

The Treasury Department wasn't able to provide recent breakdowns on trading in government debt.

"It's been less profitable for high-frequency firms to trade equities, so these firms are looking at other asset classes," said Justin Schack, a managing director at the institutional brokerage Rosenblatt Securities. "Treasuries are one of the most liquid markets in the world, so it's very fertile ground for high-frequency market making."

That liquidity has attracted hedge funds that use computer driven formulas to capture profits from tiny price movements.

12

Oct 15, 2014, NY time

Treasury "flash crash"?

Produced by QB from CME and cash market data

09:33 09:40

Ret

ail S

ales

08:00 08:20 08:40 09:00 09:20 09:40 10:00 10:20 10:40 11:00

Sca

led

pric

e

5-year note futures10-year note futures30-year bond futuresUltra bond futures5-year note cash7-year note cash10-year note cash30-year bond cash

08:30: Retail sales dropped more than forecast in September on a broad pullback in spending that indicates American consumers provided less of a boost for the economy in the third quarter. Victoria Stillwell, Bloomberg

13

Merger arb + short gamma

15

U.S. Department of the Treasury

Board of Governors of the Federal Reserve System

Federal Reserve Bank of New York

U.S. Securities and Exchange Commission

U.S. Commodity Futures Trading Commission

Joint Staff Report:

The U.S. Treasury Market on October 15, 2014

THE

DEP

ARTMENT OF THE TREA

SURY

1789

July 13, 2015






Joint Staff Report:


THE

DEP

ARTMENT OF THE TREA

SURY

1789

July 13, 2015






Joint Staff Report:


THE

DEP

AR

TMENT OF THE TREASU

RY

1789

July 13, 2015

The Evolving Structure of the U.S. Treasury Market Oct 20-21, NY Fed

The U.S. Treasury market is the deepest and most liquid government securities market in the world. However, the events of October 15 of last year, when yields experienced an unusually high level of volatility and rapid round-trip in prices without a clear cause, highlight the need to better understand the factors that impact the liquidity of the Treasury market, especially during stressed market conditions. Because of the Treasury market’s unique role in the global economy, its liquidity and functioning have implications for the cost of financing the government, the market’s role as a risk-free benchmark for pricing financial instruments, the costs borne by investors transacting in the Treasury market, and the implementation of monetary policy.

The U.S. Department of the Treasury, Board of Governors of the Federal Reserve System, Federal Reserve Bank of New York, Securities and Exchange Commission, and Commodity Futures Trading Commission invite you to a conference to explore the key factors underlying the evolution of the Treasury market's structure and liquidity. The conference will consist of a variety of panels on subjects ranging from the October 15th Joint Staff Report, automated and algorithmic trading, market making, liquidity, end investor perspectives on market structure, operational risks, repo markets, academic and practitioner perspectives on potential improvements to market structure, and regulatory requirements applicable to the government securities market. It will include prominent industry and academic experts and will feature remarks by several authorities in the official sector.

report

conference

16

...

...

17

Dealers claim they are victims of predatory behaviour in US Treasury markets dominated by high-frequency traders

The interdealer US Treasury markets have long been a sanctuary for primary dealers – a safe place to lay off the risks of their client-facing activities among themselves. But an influx of high-frequency traders has changed all that. Non-banks armed with speedy algorithms and superior technology are now the dominant players on the main interbank platforms, Icap's BrokerTec and Nasdaq-owned eSpeed. Dealers claim they are being pushed out of what has always been one of their core markets.

"BrokerTec and eSpeed have become markets where the fastest player wins and everyone else comes last," says a senior rates trader at a primary dealer in New York. "The banks are just not set up to compete in that scenario. We've been crowded out of those markets, and we're not actively making markets there anymore."

19

32

100. The bottom line is that, even after controlling for movements in secondary market

yields for previously issued Treasuries, almost every Treasury maturity exhibits a consistent

pattern of negative post-auction yield movements (i.e., in Defendants’ favor), as seen by the

consistency of the below-zero purple bars in the following chart.

Case 1:15-cv-06782 Document 1 Filed 08/26/15 Page 36 of 85

32

100. The bottom line is that, even after controlling for movements in secondary market

yields for previously issued Treasuries, almost every Treasury maturity exhibits a consistent

pattern of negative post-auction yield movements (i.e., in Defendants’ favor), as seen by the

consistency of the below-zero purple bars in the following chart.


UNITED STATES DISTRICT COURT SOUTHERN DISTRICT OF NEW YORK

CLEVELAND BAKERS AND TEAMSTERS PENSION FUND, CLEVELAND BAKERS AND TEAMSTERS HEALTH AND WELFARE FUND, AND MASTERINVEST KAPITALANLAGE GMBH, on behalf of themselves and all others similarly situated,

Plaintiffs, vs. BANK OF NOVA SCOTIA, NEW YORK AGENCY; BMO CAPITAL MARKETS CORP.; BNP PARIBAS SECURITIES CORP.; BARCLAYS CAPITAL INC.; CANTOR FITZGERALD & CO.; CITIGROUP GLOBAL MARKETS INC.; COMMERZ MARKETS LLC; COUNTRYWIDE SECURITIES CORP.; CREDIT SUISSE SECURITIES (USA) LLC; DAIWA CAPITAL MARKETS AMERICA INC.; DEUTSCHE BANK SECURITIES INC.; GOLDMAN, SACHS & CO.; HSBC SECURITIES (USA) INC.; JEFFERIES LLC; J.P. MORGAN SECURITIES LLC; MERRILL LYNCH, PIERCE, FENNER & SMITH INCORPORATED; MERRILL LYNCH GOVERNMENT SECURITIES INC.; MIZUHO SECURITIES USA INC.; MORGAN STANLEY & CO. LLC; NOMURA SECURITIES INTERNATIONAL, INC.; RBC CAPITAL MARKETS, LLC; RBS SECURITIES INC.; SG AMERICAS SECURITIES, LLC; TD SECURITIES (USA) LLC; and UBS SECURITIES LLC,

Defendants.

Case No.

CLASS ACTION COMPLAINT JURY TRIAL DEMANDED





Defendants.

Case No.






Defendants.

Case No.



...

4

when the Department of Justice’s investigation was announced and Defendants curtailed their

improper conduct.

8. To take one example (many others are described in the body of this Complaint),

Plaintiffs examined what are known as “reissued” Treasuries. In this scenario, the United States

Treasury sells a security at auction, and then later sells the exact same security (i.e., a Treasury

Security with the same principal amount and the same maturity date) in a later auction.4 By the

time of the later auction, the Treasury Securities that were sold in the earlier auction are already

trading in the secondary market. Accordingly, it is possible to compare the yield/price of the

identical security at the same point in time at the auction in which Defendants had an “insider”

position. If the yields at the auction were repeatedly higher than the yields in the secondary

market, it would demonstrate that Defendants were artificially increasing the auction yields

(correspondingly suppressing prices). Conversely, if both the auction and the secondary markets

were truly competitive, the yield for the security would be substantially similar in both markets,

or at least vary in a random fashion.

9. The data shows that the yields for these identical securities indeed repeatedly

diverged as between the auction and secondary markets, almost always in the direction of a

higher yield (lower price) in the auction relative to the lower yield (higher price) in the secondary

market. Across all tenors (i.e., lengths of time to maturity) of Treasuries, the yields of reissued

Treasuries in the primary market were inflated in 69% of the auctions, by 0.91 basis points, a

clearly significant disparity. This repeated bias cannot be explained as a result of random

4 Reissued Treasuries also have the same coupon rate as the previously-issued security.

For reissued securities, price is thus the variable that is determined at the auction, and which was directly manipulated by Defendants.


5

chance; instead, the only plausible explanation is that Defendants coordinated artificially to

influence the results of the auctions in the primary market.

10. This is just one example of anomalous economic data. Indeed, these and the other

data analyses discussed herein confirm that Defendants took full advantage of their privileged

position as primary dealers at the Treasury auctions. As Duke Law School Professor James Cox,

an expert on financial markets, has observed, “[i]n the Treasury market, where you have a small

number of participants and the sales volume is very high, it is a fertile area for harmful collusive

behavior.”5 Defendants should be held to account under United States antitrust laws for the

injuries they have caused.

JURISDICTION AND VENUE

11. This Court has subject matter jurisdiction over this action pursuant to Sections 4

and 16 of the Clayton Act (15 U.S.C. §§ 15(a) and 26), Section 22 of the Commodity Exchange

Act (7 U.S.C. § 25), and pursuant to 28 U.S.C. §§ 1331 and 1337(a). This Court also has

jurisdiction over the state law claims under 28 U.S.C. § 1367 because those claims are so related

to the federal claims that they form part of the same case or controversy, and under 28 U.S.C. §

1332, because the amount in controversy for the Class exceeds $5,000,000 and there are

members of the Class who are citizens of a different state than Defendants.

12. Venue is proper in this District pursuant to 15 U.S.C. §§ 15(a), 22 and 28 U.S.C.

§ 1391(b), (c), (d) because during the Class Period all Defendants resided, transacted business,

were found, or had agents in this District; a substantial part of the events or omissions giving rise

to these claims occurred in this District; and a substantial portion of the affected interstate trade

and commerce discussed herein has been carried out in this District.

5 See Scaggs, Kruger & Geiger, supra.


20

May 2014http://www.euromoney.com/Article/3338325/Algorithmic-trading-set-to-transform-the-bond-market.html

Quantitative Brokers

Algorithmic execution and cost measurementNo prop trading or market making

Interest rate products, starting with futuresEquities, FX already well servedLive on 5 futures exchanges (all productsCash Treasuries: eSpeed, BrokerTec, dealer poolsbasis trading futures vs cash

Good execution depends on microstructure expertise

21

22

20,000 lots20,000 lots

EIA

Petro

leu

m S

tatu

s Rep

ort

BML 1000 lots

98.795

98.800

98.805

98.810

98.815

98.820

98.825

09:22 09:24 09:26 09:28 09:30 09:32 09:34

CDT on Wed 14 May 2014

BUY 129 GEH6 BOLT

09

:23

:31

Don

e a

t 09

:32

:52

Exec 98.806

VWAP 98.812

Strike 98.8075

Sweep 98.8100

GE

H6

Midpoint fillsPassive fillsCumulative execMarket tradesLimit ordersCumulative VWAPCointegrationMicropriceBid-ask

Exec = 98.806 Cost to strike = -0.31 tick = -$3.92 per lot

09:22 09:24 09:26 09:28 09:30 09:32 09:34

0

20

40

60

80

100

120

Exe

cute

d a

nd

wo

rkin

g q

ua

nti

ty

500

1000

1500

2000

2500

2742

Cu

mu

lati

ve

ma

rke

t vo

lum

e

09

:23

:31

Don

e a

t 09

:32

:52

48 @ 98.808

5 @ 98.805

15 @ 98.805

6 @ 98.805

12 @ 98.805

43 @ 98.805

4.7% mkt

81 passv 48 midpt 0 aggrMidpoint fillsPassive fillsWorking quantityCumulative Market VolumeFilled quantityAggressive quantity

Our limit orders

Cointegration signal (indicating down move)

Benchmark =Arrival price

Order book(direct + implied)Midpoint liquidity

Our fills

buy sell

Produced by QB from CME and internal data

Chicago time

23

125−05+

125−06

125−06+

125−07

125−07+

125−08

125−08+

125−09

125−09+

125−10

10:11 10:13 10:15 10:17 10:19 10:21 10:23 10:25

CDT on Thu 29 Aug 2013

2000 lots

SELL 362 ZNU3 BOLT

10:12:31

Done at 10:23:21

Exec 125−07.71

VWAP 125−07.27Strike 125−07¼Sweep 125−07 ●●●●●●

●●●● ●●

●

●●●●●●●●●●

●

●●●●

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●●●●●●●●●●●●

●

●●●●● ●●● ●

ZNU

3●

Aggressive fillsPassive fillsCumulative execMarket tradesLimit ordersCumulative VWAPMicropriceBid−ask

Exec = 125−07.71 Cost to strike = −0.92 tick = −$14.33 per lot

10:11 10:13 10:15 10:17 10:19 10:21 10:23 10:25

0

50

100

150

200

250

300

350

Exec

uted

and

wor

king

qua

ntity

2k

4k

6k

8k

10k

12k

Cum

ulat

ive m

arke

t vol

ume

10:12:31

Done at 10:23:21

111 @ 125−07+

68 @ 125−08 12 @ 125−07+

70 @ 125−08

50 @ 125−08

22 @ 125−08

20 @ 125−06+5 @ 125−06+2 @ 125−06+1 @ 125−06+

2.9% mkt

311 passv 51 aggrAggressive fillsPassive fillsWorking quantityFilled quantityAggressive quantity

passive fills aggressive crossbased on signal


24

9955

9955½

9956

9956½

12:56 12:58 13:00 13:02 13:04 13:06 13:08

CDT on Fri 30 Aug 2013

20000 lots

BML 500 lots

BUY 165 GEM4 BOLT

12:57:07

13:07:07D

one at 13:07:01

Exec 9955.88VWAP 9955.82

Strike 9955¾

Sweep 9956 ●● ●

●

●

●

●

●

●

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●

●

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●

●

● ●

GEM

4

●

Aggressive fillsMidpoint fillsCumulative execMarket tradesLimit ordersCumulative VWAPCointegrationMicropriceBid−ask

Exec = 9955.88 Cost to strike = 0.25 tick = $3.14 per lot

12:56 12:58 13:00 13:02 13:04 13:06 13:08

0

50

100

150

Exec

uted

and

wor

king

qua

ntity

50

100

150

200

259

Cum

ulat

ive m

arke

t vol

ume

12:57:07

13:07:07 D

one at 13:07:01

82 @ 9956

12 @ 9955¾

18 @ 9955¾

52 @ 9955¾ 1 @ 9956

64% mkt

0 passv 82 midpt 83 aggrAggressive fillsMidpoint fillsWorking quantityFilled quantityAggressive quantity

Butterfly middle legmidpoint liquidity


Cointegration signal indicates up move:

aggressive buy

Example bond execution

25

50 lots

Baker Hughes U

.S. Rig C

ount

100−22+

100−23

100−23+

100−24

100−24+

100−25

100−25+

100−26

16:51 16:53 16:55 16:57 16:59 17:01 17:03

UTC on Fri 25 Apr 2014

BUY 67 CT10 BOLT

16:52:48

17:02:48D

one at 17:02:42

Exec 100−24.90VWAP 100−24.89

Strike 100−25¼

Sweep 100−25.66

● ●●●●●● ●●●●●●●● ●●●● ● ●● ●●●●●● ●●●● ●●

●●●●●●●●●●●●●●●●●●

● ● ●●●● ●●● ●●

●

CT1

0

●



16:51 16:53 16:55 16:57 16:59 17:01 17:03

0

10

20

30

40

50

60

Exec

uted

and

wor

king

qua

ntity

20

40

60

80

100

120

140147

Cum

ulat

ive m

arke

t vol

ume

16:52:48

17:02:48 D

one at 17:02:42 2 @ 100−25

8 @ 100−25 2 @ 100−25

14 @ 100−24+

41 @ 100−25

46% mkt

26 passv 41 aggrAggressive fillsPassive fillsWorking quantityCumulative Market VolumeFilled quantityAggressive quantity


Multi-asset trades

26

−23.38

−23.36

−23.34

−23.32

14:31 14:32 14:33 14:34 14:35 14:36 14:37 14:38 14:39

CDT on Fri 25 Apr 2014

BUY 10 CT10SELL 100 ZN

−$50

−$40

−$30

−$20

−$10

$10

Slippage per lot to target

MISSHIT

14:32:08

Done at 14:38:35

Exec −23.359

Target −23.340Strike −23.344

Exec = −23.359 Cost to target = −0.019 = −$17.61 per lot, −$1,937.50 total

0

20

40

60

80

100

110

14:31 14:32 14:33 14:34 14:35 14:36 14:37 14:38 14:39


14:32:08

Done at 14:38:35

LegRiskTolerance: 0%

66 ZNM4 @ 124−01+

6 CT10 @ 100−22

24 ZNM4 @ 124−01+

10 ZNM4 @ 124−01+1 CT10 @ 100−22

5,000 lots

124−00+

124−01

124−01+

124−02

124−02+

14:31:00 14:32:00 14:33:00 14:34:00 14:35:00 14:36:00 14:37:00 14:38:00 14:39:00


SELL 100 ZNM4 LEGGER

14:32:08

Done at 14:38:08

Exec 124−01.50VWAP 124−01.41

Strike 124−01¼

● ● ● ●

●●

● ● ● ● ●●● ● ●●●●●●●● ● ● ●●● ● ●●●●●●●●●●

●

●●●●● ●

●

ZNM

4

●

Passive fillsCumulative execMarket tradesLimit ordersCumulative VWAPMicropriceBid−ask


14:31:00 14:32:00 14:33:00 14:34:00 14:35:00 14:36:00 14:37:00 14:38:00 14:39:00

0

20

40

60

80

100

Exec

uted

and

wor

king

qua

ntity

200

400

600

800

1006

Cum

ulat

ive m

arke

t vol

ume

14:32:08

Done at 14:38:08

66 @ 124−01+

34 @ 124−01+

3.6% mkt

100 passv 0 aggrPassive fillsWorking quantityCumulative Market VolumeFilled quantityAggressive quantity

50 lots

100−21

100−21+

100−22

100−22+

100−23

19:31 19:32 19:33 19:34 19:35 19:36 19:37 19:38 19:39

UTC on Fri 25 Apr 2014

BUY 10 CT10 LEGGER

19:32:08

Done at 19:38:35

Exec 100−22.00VWAP 100−22.02

Strike 100−22¼

Sweep 100−22+

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●

●●●●●●●●●

CT1

0

●



19:31 19:32 19:33 19:34 19:35 19:36 19:37 19:38 19:39

0

2

4

6

8

10

Exec

uted

and

wor

king

qua

ntity

20

40

60

80

101

Cum

ulat

ive m

arke

t vol

ume

19:32:08

Done at 19:38:35

6 @ 100−22

3 @ 100−22

1 @ 100−22

9.4% mkt

4 passv 6 aggrAggressive fillsPassive fillsWorking quantityCumulative Market VolumeFilled quantityAggressive quantity


10 msec

Treasury futurestrading on CME

in Chicago

Cash Treasury notetrading on eSpeed

in New Jersey

28

http://www.ctaservicesawards.com/

http://marketsmedia.com/the-2015-markets-choice-awards

Differences between rates futures and equities

•No market fragmentation (futures)simple routing, good market data

•Trading rules more complicatedmatch algorithmsimplied quoting

•Large tick size (bid-ask spread)•High degree of interrelation

cointegrationmultidimensional algorithms

basis trading, substitutions, etc

•Round the clock tradingInformation events

29

Match algorithms

•Pro rata (short-term rates products)•Mixed time / pro rata•Workups (BrokerTec)

30

Pro rata order matching

31

1 2 3 4 5 6 7Best bid price

Incoming volume dividedamong all resting orders

at best price

Incoming market sell order

Versions of pro rata matchingare common in short-term

interest rate products, because of large tick size

and low volatility.

32

7

1. Orders placed during the “pre-opening” or at the indicative opening price (IOP) will be matched on

a price and time priority basis. Note that implied orders are not taken into consideration, as

they are only active during the continuous trading session.

2. Priority is assigned to an order that betters the market, i.e. a new buy order at 36 betters a 35 bid.

Only one order per side of the market (buy side and sell side) can have this TOP order priority.

There will be situations where a TOP order doesn’t exist for one or both sides of the market (for

example, an order betters the market, but is then canceled). There will never be a situation that

results in two orders on the same side of the market having TOP order status.

3. Only outright orders can be TOP orders, however the TOP orders of underlying orders that are

creating implied orders will be taken into consideration during the matching process so as not to

violate the TOP order rule in any market.

4. TOP orders are matched first, regardless of size.

5. After a TOP order is filled, Pro Rata Allocation is applied to the remainder of the resting orders at

the applicable price levels until the incoming order is filled.

6. The Pro Rata algorithm allocates fills based upon each resting order’s percentage representation

of total volume at a given price level. For example, an order that makes up 30% of the total

volume resting at a price will receive approximately 30% of all executions that occur at that price.

Approximate fill percentages may occur because allocated decimal quantities are always rounded

down (i.e. a 10-lot order that receives an allocation of 7.89- lots will be rounded down to 7-lots).

7. The Pro Rata algorithm will only allocate to resting orders that will receive 2 or more contracts.

8. After percentage allocation, all remaining contracts not previously allocated due to rounding

considerations are allocated to the remaining orders on a FIFO basis.

! Outright orders will have priority over implied orders and will be allocated the remaining

quantity according to their timestamps.

! Implied orders will be then allocated by maturity, with the earliest expiration receiving the allocation

before the later expiring contracts. If spread contracts have the same expiration (i.e., CONTRACT A-

CONTRACT B and CONTRACT A-CONTRACT C), then the quantity will be allocated to the earliest

maturing contracts making up that spread (i.e., the CONTRACT A-CONTRACT B will get the allocation

before the CONTRACT A-CONTRACT C because the CONTRACT B expires before the CONTRACT

C).

Example: Pro Rata Allocation Matching

Note: For this example, any of the orders involved could be either outright or implied. As timestamp is

not taken into account, the outcome is the same.

Orders in the market:

Order No. Bid Qty Bid Price Offer Price Offer Qty Order No.

INCOMING 250 9711 9711 200 1 (Top Order)

9711 25 2

9711 50 3

9711 10 4

CME Eurodollar order matching

Mixed time priority / pro rata

33

Robert Almgren and Eugene Krel May 31, 2013 2

0 10

LIFFE

q k = 1 (pro rata)

2qk = 2 (Euribor)

4q

k = 4 (Short Sterling)

0 10

1 CME

p q

(1−p) q

Figure 1: Mixed allocation algorithms, for small market order sizes; q denotes thesize of the incoming market order as a fraction of total resting size. Horizontal axisis the position of a limit order in the queue as a fraction of total resting volume: 0denotes the earliest order, 1 the last order entered. Vertical axis is the fraction ofeach order that is filled, neglecting rounding and assuming that q is small.

and FIFO algorithms, giving priority to early orders while still allowing late entrantsto trade; McPartland [2013] and others suggest that this should be universal.

LIFFE matching algorithm

For its STIR products, LIFFE takes

f

j

= (V � Pj�1)

k � (V � Pj

)

k

V

k

.

Pro rata matching is obtained for k = 1, and FIFO in the limit k ! 1. Intermediatevalues of k interpolate between the two. LIFFE sets k = 2 for Euribor, and k = 4 forShort Sterling and Euroswiss (effective May 29, 2013, [LIFFE, 2013]).

To understand this algorithm, note that by the Mean Value Theorem of calculus,

f

j

= V

j

V

g(x

j

)

where

g(x) = d

dx

(1� x)k = k(1� x)k�1 andP

j�1

V

< x

j

<

P

j

V

.

If all limit orders are small fractions of the total, then xj

⇡ Pj�1/V ⇡ Pj/V and the

fraction of each limit order that is filled depends only on its position in the queue.The fraction of order j that is filled is

Q

j

V

j

= min�

1, q g(xj

)

.

Pj = volume preceding order jRobert Almgren and Eugene Krel May 31, 2013 2

0 10

LIFFE

q k = 1 (pro rata)

2qk = 2 (Euribor)

4q

k = 4 (Short Sterling)

0 10

1 CME

p q

(1−p) q

Figure 1: Mixed allocation algorithms, for small market order sizes; q denotes thesize of the incoming market order as a fraction of total resting size. Horizontal axisis the position of a limit order in the queue as a fraction of total resting volume: 0denotes the earliest order, 1 the last order entered. Vertical axis is the fraction ofeach order that is filled, neglecting rounding and assuming that q is small.

and FIFO algorithms, giving priority to early orders while still allowing late entrantsto trade; McPartland [2013] and others suggest that this should be universal.

LIFFE matching algorithm

For its STIR products, LIFFE takes

f

j

= (V � Pj�1)

k � (V � Pj

)

k

V

k

.

Pro rata matching is obtained for k = 1, and FIFO in the limit k ! 1. Intermediatevalues of k interpolate between the two. LIFFE sets k = 2 for Euribor, and k = 4 forShort Sterling and Euroswiss (effective May 29, 2013, [LIFFE, 2013]).

To understand this algorithm, note that by the Mean Value Theorem of calculus,

f

j

= V

j

V

g(x

j

)

where

g(x) = d

dx

(1� x)k = k(1� x)k�1 andP

j�1

V

< x

j

<

P

j

V

.

If all limit orders are small fractions of the total, then xj

⇡ Pj�1/V ⇡ Pj/V and the

fraction of each limit order that is filled depends only on its position in the queue.The fraction of order j that is filled is

Q

j

V

j

= min�

1, q g(xj

)

.

LIFFE

2-year Treasury& some Treasury calendar spreads

Market order size as fraction of resting limit orders

k=1: pro ratak=∞: time priority

BrokerTec workup

34

(Workups are now largely inactive)

Events cause price jumps

35

Need to locate and characterize these event reactionssystematically from historical market data

138.25

138.30

138.35

138.40

138.45

138.50

138.55

138.60

138.65

138.70

138.75

07:20 07:22 07:24 07:26 07:28 07:30 07:32 07:34 07:36 07:38 07:40

1000 lots

Change in N

onfarm Payrolls

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CST on Fri 06 Jan 2012

March BundUS Change in Nonfarm Payrolls

139.59139.60139.61139.62139.63139.64139.65139.66139.67139.68139.69139.70139.71139.72139.73139.74

11:50 11:52 11:54 11:56 11:58 12:00 12:02 12:04 12:06 12:08 12:10

500 lots

30−Year Bonds

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CDT on Thu 12 Apr 2012

June BundUS 30−Year Bond Auction

Goal is to forecast “something” happening

Produced by QB from Eurex market data

Trading through information events

Intraday forecast curves

36

time date NYtime UKtime GEtime src region event -------------------------------------------------------------------------------------------2015.04.07T08:00:00.000 2015.04.07 04:00 09:00 10:00 econ EC PMI Composite 2015.04.07T08:30:00.000 2015.04.07 04:30 09:30 10:30 econ UK CIPS/PMI Services Index2015.04.07T09:00:00.000 2015.04.07 05:00 10:00 11:00 econ EC PPI 2015.04.07T12:30:00.000 2015.04.07 08:30 13:30 14:30 econ US Gallup US ECI 2015.04.07T12:55:00.000 2015.04.07 08:55 13:55 14:55 econ US Redbook 2015.04.07T14:00:00.000 2015.04.07 10:00 15:00 16:00 econ US JOLTS 2015.04.07T15:30:00.000 2015.04.07 11:30 16:30 17:30 auct US 4-Week Bill Auction 2015.04.07T17:00:00.000 2015.04.07 13:00 18:00 19:00 auct US 3-Yr Note Auction 2015.04.07T19:00:00.000 2015.04.07 15:00 20:00 21:00 econ US Consumer Credit

cont

ract

s tra

ded

in 1

5 m

inut

es

17:00 19:00 21:00 23:00 01:00 03:00 05:00 07:00 09:00 11:00 13:00 15:00

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Volume − 2015.04.07 2−Year Note

America/Chicago on Tue Apr 07 2015

quantitativebrokers

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17:00 19:00 21:00 23:00 01:00 03:00 05:00 07:00 09:00 11:00 13:00 15:00

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17:00 19:00 21:00 23:00 01:00 03:00 05:00 07:00 09:00 11:00 13:00 15:00

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Volume − 2015.04.07 T−Bond


quantitativebrokers

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Volume − 2015.04.07 U−Bond


quantitativebrokers

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0

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Volatility − 2015.04.07 2−Year Note


quantitativebrokers ticks

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quantitativebrokers

ticks

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Volatility − 2015.04.07 U−Bond


quantitativebrokersProduced by QB from CME market data

Slippage measurement

Live or die by transaction costsLook at main determinants of good slippage

37

38

$/lot

BEST EXECUTION FOR FIXED INCOME AND FUTURES

+1 646 293-1800 [email protected] www.quantitativebrokers.com

Page 1

May 5, 2014

Algorithm Performance Comparison

Algorithmic futures trading has recently become very popular among the world’s largestcommodity trading advisors (CTAs), global macro hedge funds, pension funds and assetmanagers. Buy side traders use algorithms to efficiently, anonymously, and cost effectivelyexecute their futures trades across global markets. Above all, these traders value consistentlygood performance in line with benchmarks and expectations. In this environment, it hasbecome utterly essential for traders to analyze and compare alternative sell side algorithmproviders in order to select the best broker to achieve investment goals.

Quantitative Brokers (QB) is a fully independent, agency-only broker that services fixed-income, equity index, and energy futures traders with a suite of algorithms designed tominimize market impact on outright, spread, and multi-leg inter-commodity transactions. QBoffers existing and prospective clients free access to both real-time and historical simulatorsfor back testing and algorithm performance comparison analysis. Beyond simulations, QBstrongly encourages its current and prospective clients to simultaneously route similaractual orders through alternative algorithms to conduct the most realistic performancecomparison analysis.

Revolution Capital Management (RCM) is a CTA based in Broomfield, Colorado. RCMdirects trading for approximately $680 million in its Mosaic Institutional Program, a diver-sified program that trades over 50 markets with an average holding period of just over3 days. Revolution’s research approach is statistically rigorous and can be summarizedas short-term pattern recognition. RCM traders executed similarly sized orders using thearrival price algorithms at Quantitative Brokers and at three of the world’s largest bulgebracket banks. RCM was gracious enough to share the results of this comparative analysis.

This analysis covers over 1.56MM futures contracts traded in Revolution’s Mosaic Institu-tional Program from April 10, 2013 to April 17, 2014. Markets included are the E-mini S&P500 and NASDAQ, Eurex Bund and Bobl, LIFFE Long Gilt, US 5-, 10-, and 30-year Treasuryfutures, and NYMEX Heating Oil, Crude Oil, and Natural Gas contracts. In total, over 866,000contracts were routed at a wide variety of trading times to the three bulge bracket banks’arrival price algorithms and over 814,000 contracts were executed through QuantitativeBrokers’ Bolt algorithm. Table 1 summarizes the comparative order flow by market.

Bulge Bracket Banks Quantitative Brokers

Market No. Trades Avg. Size Total lots No. Trades Avg. Size Total lotsUS 5-yr Note 1,121 167.1 187,319 1,100 165.2 181,720

US 10-yr Note 1,142 132.9 151,771 1,068 131.3 140,228US 30-yr Bond 1,036 49.4 51,178 990 47.9 47,421

Eurex Bobl 785 66.0 51,810 841 65.4 55,001Eurex Bund 1,055 87.0 91,785 1,161 80.7 93,692

LIFFE Long Gilt 981 61.2 60,037 1,100 60.5 66,550E-mini S&P 1,263 88.6 111,901 1,053 89.5 94,243

E-mini NASDAQ 1,049 24.0 25,176 919 24.3 22,331NYMEX Heating Oil 799 12.8 10,227 696 13.1 9,117NYMEX Crude Oil 1,043 51.3 53,505 765 47.7 36,490NYMEX Nat. Gas 1,014 71.1 72,095 941 71.7 67,469

Totals 11,288 77.0 866,807 10,634 77.0 814,266

Table 1: Order flow characteristics. Over 1.68MM total lots were executed by Revolutionthrough bulge bracket bank algorithms and QB algorithms. The order samples were broadlysimilar, although orders were not executed simultaneously through all platforms.

$8.5MM in 21 months: 88 bp annual improvement in return

April 2013 through Jan 2015

1.4MM orders 1.2MM orders

−2 −1 0 1 2

0.00

0.25

0.50

0.75

1.00

TakePost

Cum

ulat

ive v

olum

e fra

ctio

n

Slippage to arrival price in ticks

CC

P: m

ean

= 0.

53

QB:

mea

n =

0.37

Thu 15 Aug 2013 to Fri 31 Jan 2014

All Eurodollar outrights

39

clie

nt

Produced by QB from internal data

$2MM savings in 6 months

1 2 5 10 20 50 100 200 500 1000 2000 5000 10000

-2

0

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Slip

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rice

incr

emen

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Take

Post

ZN -- Fri 04 Apr 2014 to Thu 02 Oct 2014

Size is not most important variable for rates

40

10-year Treasury futures


For rates products, order size is not

most important factorin slippage

Execution interval VWAP - AP in units of min px incr

Aver

age

exec

utio

n pr

ice

- AP

(bid

-ask

mid

poin

t) in

uni

ts o

f min

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incr

Fri 04 Apr 2014 to Wed 01 Oct 2014

-6

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-8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11

ZB 0.47

ZB = -0.02 + 0.69 x

ZF 0.63

ZF =

-0.00

+ 0.8

7 x

ZN 0.30

ZN = -0.04 + 0.64 x

ZT 0.13

ZT = 0.03 + 0.70 xT-Bond (589 ZB)5-year (618 ZF)10-year (632 ZN)2-year (120 ZT)

0

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-8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11

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ZTZNZFZB

41Produced by QB from CME and internal data

For rates, slippage is largelycontrolled by ability

to forecast price motion(and passive fills).

Price change during execution

Slippage to

ArrivalPrice

1 2 5 10 20 50 100 200 500 1000 2000

-10

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Slip

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TakePost

NG -- Fri 04 Apr 2014 to Thu 02 Oct 2014

Size matters more for non-rates

42

Natural gas futures


For non-rates products, slippage depends on

order size (market impact)

Impact cost model

43

1 5 10 50 100 500 1000

-1

0

1

2

3

Executed size in lots

Slip

page

as

fract

ion

of m

in p

x in

cr

mean = 0.423

50 1000.250 0.295

ES from Thu 02 Jan 2014 to Thu 02 Oct 2014SP500 futures


What does performance depend on?

Passive fillsShort term price signalsReliable performance in different mkt conds

44

Futures vs cash

45

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19:00 21:00 23:00 01:00 03:00 05:00 07:00 09:00 11:00 13:00 15:00 17:00 19:00

real time basis10 min exponential moving average

CT30 intraday basis on 2015.01.29

EST

18

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19:00 21:00 23:00 01:00 03:00 05:00 07:00 09:00 11:00 13:00 15:00 17:00 19:00

real time basis10 min exponential moving average

Cheapest−to−deliver intraday basis on 2015.01.29

EST

Basis = CF x Bond - Futures

Strongly mean-reverting: price signal

Produced by QB from CME and cash market data

How to develop and improve algos?

1. Pure theory and quant modeling

2. Experiment with real client orders

3. Simulator

46

47

“Skate to where the puck is going to be, not where it has been,” Wayne

Gretzky once told an interviewer. As the Great One described it, what cuts certain players a level above isn’t native instinct alone, so much as end-less practice seeing the ice and, frankly, the hard work of getting to where a scoring opportunity will be, before it reveals itself.

Gretzky’s advice is one of Robert Almgren’s favorite lines—but not because the co-founder of Quantitative Brokers (QB) is a hockey fan. Instead, he says a similar idea applies to the business of algorithmic futures execu-tion: the more you see, the more you

test, the more instinctual an algo-based strategy can become. Changing expectations at systematic hedge funds and commodity trading advisors (CTAs) have put increasing emphasis on an active and dogged approach to execution, with recognition that in correlated markets like interest rates, the extra step is crucial, even if it is also more expensive.

Shops both big and small now argue it’s worth it, including AHL—Man Group’s managed futures fund—and Revolution Capital Management, a Broomfield, Colo.-based CTA. And much of the value, they say, derives from what comes before any trades are even made.

A potent mixture of in-house, futures commission merchant, and boutique brokerage-provided algorithms now play a part in commodity trading advisors’ and managed futures funds’ trading activities. Tim Bourgaize Murray examines why a new cadre of simulation tools is helping to organize—and perhaps re-mold—these buy-side specialists’ order flow.

April 2014 waterstechnology.com

development of our own execution algos has allowed traders to move up the value chain and focus on more complex markets and strategies. Currently, we

rithms designed to handle multi-legged

Indeed, complex strategies like legging are where the two streams of QB’s information—post-trade total

ker takes on projects like constructing a es the target

price on a synthetic instrument using

SALIENT POINTS• Managed futures specialists are increasingly

taking advantage of boutique agency brokers’ algorithms, citing their ability to be opportunistic and adjust to markets’ behavior, as well as faster speed to implementation and greater alpha realized through price slippage.

• Rates futures, particularly, are ripe for these applications given their correlation and the char-acteristics of the complexes within which they’re traded, and are well-serviced by Quantitative Brokers (QB), among other independent shops. Hedge fund AHL and CTA Revolution Capital Management are among QB’s users for rates.

• Another value-added feature at smaller shops like QB is their simulation environments, which mimic the matching engine logic of relevant futures exchange venues and can test new adjustments to algorithms with real-time market data before putting the algos into production.

• Sources expect a greater variety of such brokers to crop up in coming years, while sell-side futures commission merchants (FCMs), sensing greater competition, are also expected to mature their offerings and continue bundling futures algos with other execution and clearing services.

trading technologies for financial-market professionals ALGO CHASEManaged Futures’

Waters TechnologyTim Murray April 2014

The algorithmic “palette” for futures is diversifying for two reasons. The

rst is that the style of trading is in transition, somewhat belatedly, just as

oading more of

ing algo development—built in the

ers—many of them, including QB and Pragma, run by former quants like Almgren—put a premium on quality rather than price. Sure, the uptake

“We do perform reviews on all algos internally using our own simulator, and are always keen to compare these results with those of the provider. If they cannot provide a simulator, it takes a lot longer to see if we believe their story.” Murray Steel, AHL

Computational fluid dynamics

48

Examples of CFD applications

CFD simulations by Lohner et al.

Examples of CFD applications

Smoke plume from an oil fire in Baghdad CFD simulation by Patnaik et al.

Introduction to Computational Fluid Dynamics

Instructor: Dmitri Kuzmin

Institute of Applied Mathematics

University of Dortmund

[email protected]

http://www.featflow.de

Fluid (gas and liquid) flows are governed by partial differential equations which

represent conservation laws for the mass, momentum, and energy.

Computational Fluid Dynamics (CFD) is the art of replacing such PDE systems

by a set of algebraic equations which can be solved using digital computers.

http://www.mathematik.uni-dortmund.de/∼kuzmin/cfdintro/cfd.html

Computational fluid dynamics

Complete simulation is impossibleDiscretize to capture key features:• Conservation of mass, momentum, etc• Positivity of density, etc• Vortex dynamics• Chemical reactions• 2-D, 3-D, axisymmetric, etc• Nonlocal effects (incompressible flow)

49

Computational market simulationComplete simulation is impossible(Human reaction is very complicated)

Key features to include:queue position and match algorithmsprice movement

Features to neglect for simplicity:market impact

(Literature on agent-based markets)

50

Market simulatorTool for developing and testing execution algorithms for interest rate products.Capture essential features of main markets:• matching algorithms and passive fill probabilities• short term pricing signals

Will have limitations -- useful anywayDoes not embody model of market impactThe one most natural way to build a simulator

51

What did not work

52

CME TestEnvironment

Algo being tested

Dummy algos

Dummy algos

Dummy algos

ZI

ZI

ZI

ZI (Santa Fe zero-intelligence

market algorithm)

Random ordersizes and times

No real market data:no price signals

53

Simulator (artificial market)

Algorithm

OrdersFills

Marketdata

Market data(real-time or historical)

Merge real market data

Quote volume at each level and number of orders(CME does not givedetailed order info)

Criteria for simulator

• If no algo orders, reproduce market data

• If no market data, reproduce match engine

• Challenge: combine market data with orders

54

55

Project(Report(!

Combining(historical(data(with(a(market(simulator(for(testing(algorithmic(trading(

!Huang,!Wensheng!

Su,!Li!Zhu,!Yuanfeng!

!Advisor!

Dr.!Robert!Almgren!!

Abstract(!In! algorithmic! trading! field,! it! is! very! important! to! have! a! good!market! simulator! to! for! back!testing!trading!algorithms!or! trading!strategies.!Before!trading!algorithms!or! trading!strategies!are!used!in!production!environment,!they!are!often!required!to!be!tested!against!historical!data!in!a!market!simulator.!One!of!the!challenges!is!to!merge!the!orders!generated!from!algorithms!or!strategies!into!market!quotes!and!trades.!This!project!develops!an!algorithm!to!merge!orders!into!historical!data!so!that!people!can!pragmatically!back!testing!trading!algorithms!or!strategies.!This!algorithm!is!applied!to!US!Treasury!Futures!on!CME!and!results!are!proved!to!be!promising.!!1( Introduction(!With!the!growing!popularity!of!electronic!trading,!algorithmic!trading!has!become!the!dominant!force! in! many! markets.! There! are! two! types! of! algorithmic! trading:! agency! execution! and!proprietary! trading.! Agency! execution! algorithms! typically! are! developed! by! brokers! to! serve!their!clients!to!execute!clients’!orders!in!an!efficient!way.!Proprietary!trading!algorithms!employ!various!quantitative!and!fundamental!tools!to!trade!in!the!hope!of!achieving!returns!in!excess!of!the! risk! borne.! In! either! case,! it! is! generally! too! expensive! for! traders! to! put! real! money! in!before!they!back!test!their!algorithms.!!!For!medium!to!low!frequency!trading!strategies,!practitioners!usually!assume!that!their!orders!are!filled!with!benchmark!prices!plus!estimated!transaction!costs.!!This!method!is!usually!good!enough! for! medium! to! low! frequency! strategies.! For! high! frequency! and! intraday! trading!algorithms,!however,!this!approach!usually!is!not!enough.!For!one!thing,!benchmarks!are!often!the!targets!these!algorithms!are!designed!to!beat.!Secondly,!specific!trading!algorithms!usually!have! a! different! risk! profile! from! benchmarks! so! their! execution! performances! are! different!from!benchmarks!even!if!they!are!designed!properly.!!!There!are!some!challenges!to!realistically!back!test!new!trading!algorithms.!Since!we!can!not!go!back!in!time!to!execute!our!orders!into!historical!markets,!it!is!impossible!to!have!a!perfect!back!testing.!Therefore!we!need!to!make!some!assumptions!which!should!not!deviate!too!much!from!reality.!Furthermore,!with!the!assumptions,!we!need!to!reinsert!our!orders!into!historical!trades!and!quotes!and!make! them! interact!with!historical! flows! in!a! realistic!way.!Depending!on! the!goals! of! the! algorithms! and! dynamics! of! markets,! the! assumptions! and! simulation! processes!need!to!be!adapted.!

NYU MS Students, May 2012

How to use simulator

Historicalrerun scenarios for algo improvementbacktests for potential clients

Real-timeclients can connect to “test-drive” algos

Algorithm developmenttest new signals on historical ordersmulti-market legging trades

Real-time splitting for testingcompare simulator executions with real

56

Signal development

1. Propose ideaplausibility tests

2. Statistical tests on historical market datanonzero correlation with future price movement

3. Rerun actual orders executedshow improvement in slippage

57

58

99mqw

99mqy

99mq%

99my-

99my$

99myw

99myy

99my%

99m@-

99m@$

99m@w

99m@y

99m@%

99m%-

99m%$

99m%w

99m%y

99m%%

-@:ww -@:wy -@:w% -@:q- -@:q$ -@:qw -@:qy -@:q% -%:-- -%:-$ -%:-w

CST on Mon =- Feb $-=w

=-- lots

BUY 500 CLH4 BOLT

-@:wq:w$

Done at -%:-=:w-

Exec 99myy

VWAP 99m@=Strike 99m@$q

CLH

w

Aggressive fillsPassive fillsIntended passiveCumulative execMarket tradesLimit ordersCumulative VWAPMicropriceBid-ask

Exec = 99myy Cost to strike = -ymw@ tick = -kywm@$ per lot

-@:ww -@:wy -@:w% -@:q- -@:q$ -@:qw -@:qy -@:q% -%:-- -%:-$ -%:-w

-

=--

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3--

w--

q--

Exe

cute

d an

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orki

ng q

uant

ity

=k

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qk

Cum

ulat

ive

mar

ket v

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-@:wq:w$

Done at -%:-=:w- 3 @ 99m@= = @ 99m@=

= @ 99my@= @ 99myq

$ @ 99my$

@3 @ 99myw3 @ 99my%

@ @ 99my@$ @ 99my@ = @ 99my%

$ @ 99m@- = @ 99m%=$ @ 99m@9= @ 99m@q3 @ 99m@=

= @ 99my9@ @ 99my9% @ 99my9

= @ 99myy@ @ 99myw

@9 @ 99my@q @ 99myq= @ 99my$

@9 @ 99my3=9 @ 99my=

9myL mkt

=@$ passv 3$% aggrAggressive fillsPassive fillsWorking quantityFilled quantityAggressive quantity


Signal evaluation

99.5499.5699.5899.6099.6299.6499.6699.6899.7099.7299.7499.7699.7899.8099.8299.8499.8699.88

07:44 07:46 07:48 07:50 07:52 07:54 07:56 07:58 08:00 08:02 08:04

CST on Mon 10 Feb 2014

100 lots100 lots

07:45:42

Strike 99.725●

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CLH

4

Buy/sell signalsbased on short-term

mean reversionand trading ranges

59

50 lots43.1843.1943.2043.2143.2243.2343.2443.2543.2643.2743.2843.2943.3043.3143.3243.3343.3443.3543.3643.3743.3843.3943.40

08:30 08:31 08:32 08:33 08:34 08:35 08:36 08:37 08:38 08:39 08:40 08:41 08:42 08:43 08:44

CDT on Mon 28 Apr 2014

BUY 13 ZLN4 BOLT

08:34:54

Done at 08:38:50

Exec 43.34

VWAP 43.31

Strike 43.255

Sweep 43.266

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ZLN

4

●



08:30 08:31 08:32 08:33 08:34 08:35 08:36 08:37 08:38 08:39 08:40 08:41 08:42 08:43 08:44

0

2

4

6

8

10

12

Exec

uted

and

wor

king

qua

ntity

100

200

300

400

500

583

Cum

ulat

ive m

arke

t vol

ume

08:34:54

Done at 08:38:50

1 @ 43.25

1 @ 43.31

1 @ 43.32

1 @ 43.33

2 @ 43.37

1 @ 43.36

1 @ 43.36

1 @ 43.35

2 @ 43.35

1 @ 43.34

1 @ 43.33

2.2% mkt


50 lots

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43.1843.1943.2043.2143.2243.2343.2443.2543.2643.2743.2843.2943.3043.3143.3243.3343.3443.3543.3643.3743.3843.3943.40

08:30 08:32 08:34 08:36 08:38 08:40 08:42 08:44 08:46

CDT on Mon 28 Apr 2014

BUY 13 ZLN4 BOLT

08:34:54

Done at 08:42:06

Exec 43.30

VWAP 43.31

Strike 43.255

Sweep 43.266

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ZLN

4

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08:30 08:32 08:34 08:36 08:38 08:40 08:42 08:44 08:46

0

2

4

6

8

10

12

Exec

uted

and

wor

king

qua

ntity

100

200

300

400

500

600

700

800

888

Cum

ulat

ive m

arke

t vol

ume

08:34:54

Done at 08:42:06

1 @ 43.25

1 @ 43.32

1 @ 43.31

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1 @ 43.33

1 @ 43.32

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1 @ 43.30

1 @ 43.29

1 @ 43.28

1 @ 43.27

1 @ 43.29

1.5% mkt


without signal with signal


60

Client

QBAlgo’s quotes

trades

child orders

quotes

trades

child orders QBsimulationmatchingengine

quotes

trades

exchangematchingengine

Splitting of actual orders in real time

QBAlgo’s

parent orders

61Produced by QB from CME and internal data

Comparison of simulator with real execution

−8 −7 −6 −5 −4 −3 −2 −1 0 1 2 3 4 5 6 7 8

−8

−7

−6

−5

−4

−3

−2

−1

0

1

2

3

4

5

6

7

8

Production slippage

Sim

ulat

or s

lippa

ge

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● EurodollarUltraT−Bond5−year10−year2−year

Wed 01 Oct 2014 to Thu 01 Oct 2015

Simulator slippage

(min px incr)

Production slippage (min px incr)

62

125−30

125−30+

125−31

125−31+

126−00

126−00+

126−01

126−01+

126−02

126−02+

126−03

126−03+

126−04

08:56:40 08:57:20 08:58:00 08:58:40 08:59:20 09:00:00

2,000 lots2,000 lots

ISM N

on−Manf. C

omposite

08:57:16

Strike 126−02Â¼Sweep 126−02+● ● ●

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ZNH

4

Simulator

125−30

125−30+

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126−00

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126−01

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126−04

08:56:40 08:57:20 08:58:00 08:58:40 08:59:20 09:00:00

2,000 lots2,000 lots

ISM N

on−Manf. C

omposite

08:57:16

Strike 126−02Â¼Sweep 126−02+● ● ●

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ZNH

4

Production

Buy 148 ZNH4, 2014-02-05


Simulator crosses spreadbecause of extra volume on bid side

22 lots 46+51 lots

Remaining size at much lower

price level following event

Pessimistic fill assumptions

63

98.075

98.080

98.085

98.090

98.095

98.100

98.105

10:30:00 10:31:00 10:32:00 10:33:00 10:34:00 10:35:00 10:36:00 10:37:00 10:38:00

10,000 lots10,000 lots3&6−Month Bills

BML 100 lots

10:31:21

Strike 98.0875

Sweep 98.0900●● ● ● ●

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GEU

6

Production

98.075

98.080

98.085

98.090

98.095

98.100

98.105

10:30:00 10:31:00 10:32:00 10:33:00 10:34:00 10:35:00 10:36:00 10:37:00 10:38:00

10,000 lots10,000 lots3&6−M

onth Bills

BML 100 lots

10:31:22

Strike 98.0875

Sweep 98.0934

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GEU

6

Simulator

2014-01-27: Buy 56 GEU6

Production receivespassive fills at 10:37, simulator does not


Main differences simulator/production:• quote imbalance• timing and latency• random number sequences• pessimistic fill model

64

Summary

Fixed income trading is becoming electronic

Need full range of algo execution tools:Transaction Cost Analysis (TCA) reportingMarket microstructure analysisAlgorithm optimizationMarket simulator

65

Disclaimer

This document contains examples of hypothetical performance. Hypothetical performance results have many inherent limitations, some of which are described below. No representation is being made that any account will or is likely to achieve profits or losses similar to those shown. In fact, there are frequently sharp differences between hypothetical performance results and the actual results subsequently achieved by any particular trading program. One of the limitations of hypothetical performance results is that they are generally prepared with the benefit of hindsight. In addition, hypothetical trading does not involve financial risk, and no hypothetical trading record can completely account for the impact of financial risk in actual trading. For example, the ability to withstand losses or to adhere to a particular trading program in spite of trading losses are material points which can also adversely affect actual trading results. There are numerous other factors related to the markets in general or to the implementation of any specific trading program which cannot be fully accounted for in the preparation of hypothetical performance results and all of which can adversely affect actual trading results.The reader is advised that futures are speculative products and the risk of loss can be substantial. Futures spreads are not necessarily less risky than short or long futures positions. Consequently, only risk capital should be used to trade futures. The information contained herein is based on sources that we believe to be reliable, but we do not represent that it is accurate or complete. Nothing contained herein should be considered as an offer to sell or a solicitation of an offer to buy any financial instruments discussed herein. All references to prices and yields are subject to change without notice. Past performance/profits are not necessarily indicative of future results. Any opinions expressed herein are solely those of the author. As such, they may differ in material respects from those of, or expressed or published by or on behalf of, Quantitative Brokers or its officers, directors, employees or affiliates. Quantitative Brokers, LLC, 2010.

66

simulating hft for low-latency investors - the investor’s view (robert almgren)

Economy & Finance